Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 342 appraisals
Bulletin of the World Health Organization
Digital epidemiology investments, Saudi Arabia
PROBLEM: Traditional epidemiological surveillance methods are often limited by delays in reporting and fragmented data systems. Saudi Arabia faces additional public health challenges from mass gatherings during Hajj and Umrah, an increasing burden of noncommunicable diseases and rapid urbanization, highlighting the need for investing in digital epidemiology. APPROACH: Saudi Arabia has accelerated digital transformation in health care through Vision 2030 initiatives. The strategies include health information exchange platforms, analytics driven by artificial intelligence, telemedicine services and digital monitoring systems used during Hajj. We review current initiatives to invest in digital epidemiology in Saudi Arabia, implementation challenges and policy priorities. LOCAL SETTING: Saudi Arabia's health system operates under a predominantly public model. The health ministry is the main provider, regulator and finance provider of most health-care services. Health-care coverage is nearly universal, with citizens receiving services free of charge through the public system. Ongoing reforms aim to gradually decentralize certain functions. RELEVANT CHANGES: The initiatives under Vision 2030 have supported disease surveillance, data integration and public health response capacities. Existing digital health reforms have created a foundation for integrating digital epidemiology into routine public health practice. However, challenges remain, including fragmented interoperability between institutions, workforce shortages, unequal digital access, and concerns about data governance, privacy and algorithmic bias. LESSONS LEARNT: Saudi Arabia's experience suggests that digital epidemiology is more effective when integrated within broader digital health reforms. Successful implementation requires not only digital infrastructure, but also workforce development, ethical governance, transparency and mechanisms for integrating digital data into public health decision-making. PROBLÈME: Les méthodes traditionnelles de surveillance épidémiologique sont souvent limitées par des retards dans la transmission des données et par la fragmentation des systèmes de données. L’Arabie saoudite est confrontée à des défis supplémentaires en matière de santé publique liés aux rassemblements de masse lors du Hajj et de la Omra, au fardeau croissant des maladies non transmissibles et à l’urbanisation rapide, ce qui souligne la nécessité d’investir dans l’épidémiologie numérique. APPROCHE: L’Arabie saoudite a accéléré la transformation numérique dans le domaine des soins de santé grâce aux initiatives de la Vision 2030. Ces stratégies comprennent des plateformes d’échange d’informations de santé, des analyses alimentées par l’intelligence artificielle, des services de télémédecine et des systèmes de surveillance numérique employés pendant le Hajj. La présente étude passe en revue les initiatives actuelles visant à investir dans l’épidémiologie numérique en Arabie saoudite, les défis liés à leur mise en œuvre et les priorités politiques. ENVIRONNEMENT LOCAL: Le système de santé saoudien fonctionne selon un modèle majoritairement public. Le ministère de la Santé est le principal prestataire, régulateur et bailleur de fonds de la plupart des services de santé. La couverture des soins de santé est pratiquement universelle, les citoyens bénéficiant de services gratuits au travers du système public. Les réformes en cours visent à décentraliser progressivement certaines fonctions. CHANGEMENTS SIGNIFICATIFS: Les initiatives menées dans le cadre de la Vision 2030 ont favorisé la surveillance des maladies, l’intégration des données et le renforcement des capacités d’intervention de santé publique. Les réformes existantes en matière de santé numérique ont jeté les bases nécessaires à l’intégration de l’épidémiologie numérique dans les pratiques courantes de santé publique. Toutefois, des défis subsistent, notamment le manque d’interopérabilité entre les établissements, la pénurie de main-d’œuvre, les inégalités d’accès au numérique, ainsi que des préoccupations en matière de gouvernance des données, de confidentialité et de biais dû aux algorithmes. LEÇONS TIRÉES: L’expérience de l’Arabie saoudite suggère une plus grande efficacité de l’épidémiologie numérique lorsque celle-ci s’inscrit dans le cadre de réformes plus larges en matière de santé numérique. La réussite de la mise en œuvre nécessite non seulement une infrastructure numérique, mais aussi le développement des ressources humaines, une gouvernance éthique, la transparence ainsi que des mécanismes d’intégration des données numériques dans la prise de décision en matière de santé publique. SITUACIÓN: Los métodos tradicionales de vigilancia epidemiológica suelen verse limitados por retrasos en la notificación y por sistemas de datos fragmentados. Arabia Saudí se enfrenta además a desafíos de salud pública derivados de las concentraciones masivas de personas durante el Hach y la Umrah, del aumento de la carga de las enfermedades no transmisibles y de la rápida urbanización, lo que destaca la necesidad de invertir en epidemiología digital. ENFOQUE: Arabia Saudí ha acelerado la transformación digital de la atención sanitaria mediante las iniciativas de la Visión 2030. Las estrategias incluyen plataformas de intercambio de información sanitaria, análisis basados en inteligencia artificial, servicios de telemedicina y sistemas digitales de vigilancia utilizados durante el Hach. Se examinan las iniciativas actuales de inversión en epidemiología digital en Arabia Saudí, los desafíos para su implementación y las prioridades en materia de políticas. MARCO REGIONAL: El sistema sanitario de Arabia Saudí funciona bajo un modelo predominantemente público. El ministerio de salud es el principal proveedor, regulador y financiador de la mayoría de los servicios sanitarios. La cobertura sanitaria es prácticamente universal y la ciudadanía recibe servicios gratuitos a través del sistema público. Las reformas en curso tienen por objeto descentralizar gradualmente determinadas funciones. CAMBIOS IMPORTANTES: Las iniciativas de la Visión 2030 han fortalecido la vigilancia de enfermedades, la integración de datos y la capacidad de respuesta en salud pública. Las reformas existentes en materia de salud digital han creado una base para integrar la epidemiología digital en la práctica habitual de la salud pública. No obstante, persisten desafíos, entre ellos la interoperabilidad fragmentada entre instituciones, la escasez de personal, las desigualdades en el acceso digital y las preocupaciones relacionadas con la gobernanza de los datos, la privacidad y los sesgos algorítmicos. LECCIONES APRENDIDAS: La experiencia de Arabia Saudí sugiere que la epidemiología digital es más eficaz cuando se integra en reformas más amplias de salud digital. Su implementación satisfactoria requiere no solo infraestructura digital, sino también desarrollo de capacidades del personal, gobernanza ética, transparencia y mecanismos para incorporar los datos digitales a la toma de decisiones en salud pública. المشكلة: غالبًا ما تكون طرق الرصد الوبائي التقليدية محدودة بسبب التأخيرات في الإبلاغ وأنظمة البيانات المشتتة. وتواجه المملكة العربية السعودية تحديات إضافية في مجال الصحة العامة نتيجة للتجمعات الكبيرة خلال موسمي الحج والعمرة، وتزايد عبء الأمراض غير المعدية، والتوسع الحضري السريع، مما يُبرز الحاجة إلى الاستثمار في علم الأوبئة الرقمي. الأسلوب: سارعت المملكة العربية السعودية في التحول الرقمي في مجال الرعاية الصحية من خلال مبادرات رؤية 2030. وتشمل هذه الاستراتيجيات منصات تبادل المعلومات الصحية، والتحليلات المدعومة بالذكاء الاصطناعي، وخدمات العلاج الطبي عن بُعد، وأنظمة المراقبة الرقمية المستخدمة خلال موسم الحج. نستعرض في هذا البحث المبادرات الحالية للاستثمار في علم الأوبئة الرقمي في المملكة العربية السعودية، وتحديات التنفيذ، وأولويات السياسة الحكومية. المواقع المحلية: يعمل النظام الصحي في المملكة العربية السعودية بموجب نموذج حكومي بشكل رئيسي. وتُعد وزارة الصحة هي المزود الرئيسي، والجهة المنظمة، والممول لمعظم خدمات الرعاية الصحية. وتعد تغطية الرعاية الصحية شاملة تقريبًا، حيث يحصل المواطنون على الخدمات مجانًا من خلال النظام الحكومي. وتهدف الإصلاحات الجارية إلى تطبيق اللامركزية تدريجيًا على وظائف محددة. التغيّرات ذات الصلة: إن المبادرات بموجب رؤية 2030 تدعم رصد الأمراض، وتكامل البيانات، وقدرات الاستجابة في مجال الصحة العامة. لقد أدت إصلاحات الصحة الرقمية إلى إنشاء أساس لدمج علم الأوبئة الرقمي في ممارسات الصحة العامة الروتينية. ومع ذلك، لا تزال هناك تحديات، تشمل التشغيل المشترك المشتت بين المؤسسات، ونقص الكوادر، والوصول غير العادل إلى البيانات الرقمية، والمخاوف المتعلقة بحوكمة البيانات، والخصوصية، والتحيز الخوارزمي. الدروس المستفادة: تشير تجربة المملكة العربية السعودية إلى أن علم الأوبئة الرقمي يكون أكثر فعالية عند دمجه ضمن إصلاحات أوسع نطاقًا في مجال الصحة الرقمية. ويتطلب التنفيذ الناجح ليس فقط بنية تحتية رقمية، بل أيضًا تطوير القوى العاملة، والحوكمة الأخلاقية، والشفافية، وآليات دمج البيانات الرقمية في عملية صنع القرار في مجال الصحة العامة. 问题: 传统流行病学监测手段常受制于报告滞后与数据系统碎片化的问题。沙特阿拉伯还面临朝觐与副朝期间大型聚众活动、非传染性疾病负担攀升以及快速城市化带来的额外公共卫生难题，凸显出投资发展数字流行病学的必要性。. 方法: 沙特阿拉伯依托《2030 愿景》(Vision 2030) 相关举措，加快推进医疗健康领域数字化转型。相关举措包括医疗信息交互平台、人工智能驱动的数据分析、远程医疗服务以及朝觐期间启用的数字化监测系统。本文梳理了沙特阿拉伯的当前数字流行病学投资项目、项目实施方面的挑战及政策优先发展方向。. 当地状况: 沙特阿拉伯的医疗卫生体系以公立运营模式为主。卫生部是大多数医疗护理服务的主要提供方、监管机构和出资主体。医疗卫生保障基本实现全民覆盖，本国公民可通过公立医疗体系免费就医。现行改革旨在逐步下放部分职能。. 相关变化: 《2030 愿景》框架下的各项举措，助力完善疾病监测、数据整合与公共卫生应急处置能力建设。现有的数字医疗改革为将数字流行病学融入常规公共卫生工作奠定了基础。然而，仍存在一些挑战：医疗机构间数据互通割裂、人才短缺、数字资源获取不均衡，以及数据治理、隐私安全与算法偏见等隐患问题。. 经验教训: 沙特阿拉伯的实践经验表明，将数字流行病学融入大范围数字医疗改革体系中，其实施成效更佳。实施成功不仅需要数字化基础设施，还需人才队伍建设、合规伦理治理、信息公开制度，以及把数字化数据纳入公共卫生决策的配套机制。. ПРОБЛЕМА: Традиционные методы эпидемиологического надзора часто ограничены задержками в предоставлении отчетности и фрагментированностью систем данных. Саудовская Аравия сталкивается с дополнительными проблемами общественного здравоохранения, связанными с массовыми скоплениями людей во время хаджа и умры, растущим бременем неинфекционных заболеваний и стремительной урбанизацией. Все это подчеркивает необходимость инвестиций в цифровую эпидемиологию. ПОДХОД: Саудовская Аравия ускорила цифровую трансформацию здравоохранения в рамках инициатив программы Vision 2030. Среди ключевых направлений – платформы для обмена медицинской информацией, аналитические системы на основе искусственного интеллекта, телемедицинские сервисы и цифровые системы мониторинга, используемые во время хаджа. Авторы рассматривают текущие инвестиционные инициативы в области цифровой эпидемиологии в Саудовской Аравии, проблемы внедрения и приоритетные направления политики. МЕСТНЫЕ УСЛОВИЯ: Система здравоохранения Саудовской Аравии опирается преимущественно на государственную модель. Министерство здравоохранения является основным поставщиком медицинских услуг, регулятором и главным источником финансирования большинства медико-санитарных услуг. Охват медицинской помощью практически всеобщий: граждане получают услуги бесплатно через государственную систему. Текущие реформы направлены на постепенную децентрализацию отдельных функций. ОСУЩЕСТВЛЕННЫЕ ПЕРЕМЕНЫ: Инициативы в рамках программы Vision 2030 способствовали развитию эпидемиологического надзора, интеграции данных и укреплению потенциала реагирования системы общественного здравоохранения. Проводимые реформы в области цифрового здравоохранения заложили фундамент для внедрения цифровой эпидемиологии в повседневную практику общественного здравоохранения. Однако сохраняются проблемы, включая недостаточную совместимость информационных систем разных учреждений, нехватку кадров, неравномерный доступ к цифровым технологиям, а также вопросы управления данными, конфиденциальности и алгоритмической предвзятости. ВЫВОДЫ: Опыт Саудовской Аравии показывает, что цифровая эпидемиология наиболее эффективна, когда интегрирована в более широкие реформы цифрового здравоохранения. Для успешного внедрения необходима не только цифровая инфраструктура, но и развитие кадрового потенциала, этическое регулирование, прозрачность и механизмы интеграции цифровых данных в процессы принятия решений в сфере общественного здравоохранения.
2 Aug 2026
Read appraisal →JMIR mHealth and uHealth
Machine Learning Frameworks for Wearable-Based Stress Modeling in Naturalistic Settings: Scoping Review
BACKGROUND: Stress, as commonly recognized, is an integral part of modern life and can significantly affect both mental and physical health. While substantial advancements have been made in measuring physical fitness through wearable devices, the detection and assessment of mental stress remain in their early stages. OBJECTIVE: The objective of this paper is to review recent studies of wearable-based stress detection in naturalistic settings, with a specific focus on characterizing machine learning frameworks inspired by the model card approach. METHODS: This review was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. A total of 353 articles were identified through searches in databases such as PubMed, MEDLINE, ScienceDirect, IEEE, ACM Digital Library, Web of Science, and Embase. Studies were considered eligible if they collected data from healthy adults in naturalistic settings using wearable devices and used machine learning models for stress detection. RESULTS: A total of 34 articles met the eligibility criteria, including 11 conference papers, 22 journal articles, and 1 preprint published between 2017 and 2024. From these studies, we analyzed key machine learning modeling decisions such as problem formulation, ground truth determination, and machine learning algorithms. Additionally, we examined the major contributions of each study, focusing on the challenges they addressed and the solutions they proposed. Based on these findings, we proposed a model card framework for reporting machine learning-based, wearable-based stress detection. CONCLUSIONS: This scoping review highlights recent trends in machine learning models for stress detection and measurement using wearable signals. It underscores the need for improved standardization in reporting practices for datasets and key machine learning decisions, as well as the importance of addressing critical challenges associated with data collection in real-world settings. We hope this review will support and strengthen ongoing research efforts, promote knowledge sharing, and promote collaboration among researchers-ultimately advancing the field as a community.
1 Aug 2026
Read appraisal →PloS one
Artificial intelligence for early detection of diabetic retinopathy: A vision transformer-based approach
BACKGROUND: Early identification of diabetic retinopathy (DR), which is a primary cause of vision impairment globally, is a crucial phasis for effective intervention and treatment. Traditional screening workflows rely on manual diagnosis by ophthalmologists, which remains the gold standard but can be time-consuming and subject to variability due to human factors. To support and enhance the screening process, artificial intelligence (AI)-based tools have shown promise in automating DR detection, particularly with recent advances in deep learning. However, medical images with long-range dependencies and spatial linkages can be challenging for CNN-based algorithms to handle. METHODS: This paper proposes a Vision Transformer (ViT)-based model, specifically using a Compact Convolutional Transformer (CCT), for early automated detection of DR. The model uses self-attention techniques to improve feature extraction and classification performance; combining three main stages: the CCT tokenizer, transformer encoder, and sequence pooling. The proposed approach was trained on public datasets (EyePACS and APTOS 2019) and evaluated against state-of-the-art deep learning architectures. RESULTS: Our experimental findings demonstrate that ViT performs among the best in the current state of the art with an overall accuracy of 97% and F1-scores above 0.95 across all DR severity levels. Our system is primarily designed for the pre-screening stage of diabetic retinopathy workflows, enabling rapid and reliable identification of potential DR cases for further clinical evaluation. CONCLUSION: These results highlight the potential of transformer-based designs in medical picture analysis, as well as the implications for telemedicine and e-health solutions in real-time, especially in cases of low-resource settings.
28 July 2026
Read appraisal →Biomedical physics & engineering express
BMSNet: a boundary-guided multi-scale polyp segmentation network
Accurate polyp segmentation in colonoscopy images is essential for computer-aided diagnosis and early colorectal cancer screening. However, this task remains challenging due to large variations in polyp size, shape, and texture, as well as blurred boundaries and low contrast between polyps and surrounding mucosa. To address these challenges, we propose boundary-guided multi-scale polyp segmentation network (BMSNet), a boundary-guided multi-scale network for accurate polyp segmentation. First, an Scale-Aware Modulation Meets Transformer-T backbone initialized with officially released pretrained weights is adopted to extract multi-level features efficiently. Then, a boundary prediction module is introduced to generate preliminary boundary priors through bidirectional feature interaction and differentiable Canny-based auxiliary supervision. Based on the generated boundary information, we design a boundary-guided feature enhancement module to refine feature representations. This module combines frequency-domain enhancement, multi-scale feature interaction, and boundary-guided modulation. Finally, a feature fusion unit is introduced to progressively integrate multi-scale features and generate the final segmentation prediction. Extensive experiments on five benchmark datasets demonstrate the effectiveness of the proposed method. On ClinicDB, Kvasir, ColonDB, ETIS, and CVC-300, BMSNet achieves Dice scores of 0.943, 0.920, 0.828, 0.825, and 0.913, respectively. These results indicate that BMSNet achieves competitive segmentation performance with moderate computational complexity under the evaluated experimental setting.
28 July 2026
Read appraisal →Environmental science & technology
Bridging the Gap between External and Internal Exposure: Challenges and a Machine Learning Approach Validated Using Benzene Ring-Containing Pollutants
Establishing quantitative linkages between external and internal exposures is essential for accurate exposure and health risk assessment, yet this goal remains hindered by multiple challenges. This Perspective systematically reviews major challenges limiting the quantitative linkage across three dimensions: (i) External exposure: complex multimedia and multipathway exposure, environmental factor variability, and substantial data gaps; (ii) Internal exposure: difficulties in biological sample collection, large-scale biomonitoring, and appropriate biomarker selection; and (iii) External-internal exposure linkage: interindividual variability, scarce toxicokinetic parameters, and methodological limitations. In this context, the Perspective proposes ML-EIExpLink (machine learning-based external-internal exposure linkage), a framework integrating multisource, multidimensional variables, including pollutant physicochemical properties, ADME parameters, exposure factors, environmental conditions, and socioeconomic indicators, to model complex external-internal exposure relationships. An exploratory case study on benzene ring-containing pollutants demonstrates the framework's feasibility and reliability. Using gradient boosting decision trees as the core predictive engine, the model achieved robust predictive performance, with an external validation Qext2 of 0.784. Most key variables, including external air concentration, molecular planarity, and temperature, exhibited nonlinear and threshold effects on predicted blood concentrations. This Perspective introduces a novel ML-based framework for establishing robust, interpretable external-internal exposure linkages, thereby providing a useful basis for improved exposure assessment and health risk evaluation.
22 July 2026
Read appraisal →Scientific reports
Integrating skeleton based representations for robust yoga pose classification using deep learning models
Yoga is a popular form of exercise worldwide due to its spiritual and physical health benefits, but incorrect postures can lead to injuries. Automated yoga pose classification has therefore gained importance to reduce reliance on expert practitioners. While human pose keypoint extraction models have shown high potential in action recognition, systematic benchmarking for yoga pose recognition remains limited, as prior works often focus solely on raw images or a single pose extraction model. In this study, we introduce a curated dataset, "Yoga-16", which addresses limitations of existing datasets, and systematically evaluate three deep learning architectures-VGG16, ResNet50, and Xception-using three input modalities: direct images, MediaPipe Pose skeleton images, and YOLOv8 Pose skeleton images. Our experiments demonstrate that skeleton-based representations outperform raw image inputs, with the highest accuracy of 96.09% achieved by VGG16 with MediaPipe Pose skeleton input. Additionally, we provide interpretability analysis using Grad-CAM, offering insights into model decision-making for yoga pose classification with cross validation analysis.
17 July 2026
Read appraisal →Current psychiatry reports
Sleep Interventions and the Prevention of Pediatric Anxiety: A Systematic Review
PURPOSE OF REVIEW: Anxiety disorders are prevalent in children, adolescents, and young adults. As many youth either do not receive adequate intervention or do not achieve remission, prevention of anxiety disorders is a public health priority. Sleep problems are a modifiable risk factor that precedes anxiety. This systematic review includes recent pediatric sleep interventions and anxiety outcomes, and introduces anxiety sensitivity as an understudied mechanism. RECENT FINDINGS: In 21 intervention and experimental studies, sleep intervention reduced anxiety symptoms/disorders and the onset of anxiety disorders. Three additional recent studies examined anxiety sensitivity in the relationship between sleep problems and anxiety, supporting the inclusion of anxiety sensitivity in future research studies. Sleep health is a scalable, accessible target for reducing anxiety and is an important part of pediatric anxiety prevention. Sleep treatments can be brief and implemented in psychiatric practice. Future research studies should consider anxiety sensitivity as a potential mechanism.
14 July 2026
Read appraisal →Metabolomics : Official journal of the Metabolomic Society
A metabolomic signatures in hyperuricemia: a systematic review
BACKGROUND: Hyperuricemia (HUA) is traditionally viewed as a disorder of purine metabolism. However, its broader metabolic alterations remain incompletely understood. Metabolomics provides a useful approach for exploring metabolite changes associated with HUA, but a comprehensive synthesis of existing findings is still lacking. AIM OF REVIEW: This systematic review and meta-analysis aimed to characterize the systemic metabolic signature of HUA beyond purine pathways. By synthesizing data from 27 metabolomics studies involving 12,335 participants, the study sought to identify consistent metabolite biomarkers and key dysregulated pathways to provide new insights for diagnosis and therapeutic targeting. KEY SCIENTIFIC CONCEPTS OF REVIEW: This review included 27 metabolomics studies involving 12,335 participants and identified 1,187 metabolites reported in association with HUA. Qualitative synthesis showed 54 consistently elevated and 20 consistently decreased blood metabolites, mainly involving amino acids, lipid-related metabolites, energy-related compounds, vitamins and their derivatives, and purine nucleoside metabolites. The meta-analysis was limited to two eligible studies, with one study contributing most of the statistical weight; it suggested higher levels of Alanine, Leucine, Phenylalanine, and Tyrosine and lower Histidine levels in HUA. Pathway enrichment analysis highlighted "One carbon pool by folate," "Arginine biosynthesis," "Glutathione metabolism," and related amino acid and energy metabolism pathways. Overall, these findings suggest that HUA may be associated with metabolic perturbations beyond purine metabolism alone, but the candidate metabolites and pathways require further validation in longitudinal, standardized, and mechanistic studies.
13 July 2026
Read appraisal →BMC medical informatics and decision making
A systematic review of patient decision aids for hypertension
BACKGROUND: Hypertension is a major cause of premature death and a modifiable risk factor for cardiovascular disease. Effective management includes both pharmacological treatment strategies, including antihypertensive medications, and non-pharmacological treatment strategies, such as lifestyle changes. Shared decision making (SDM) between patients and healthcare professionals is essential to determine the most appropriate treatment for hypertension. Patient decision aids (PDAs) facilitate SDM by providing information on treatment options and helping patients to clarify their values and preferences. However, there is no comprehensive comparison of PDAs for hypertension. OBJECTIVE: This systematic review aimed to identify and evaluate PDAs for arterial hypertension using the International Patient Decision Aid Standards (IPDAS) criteria. METHODS: A comprehensive search of bibliographic databases (PubMed, Embase) and gray literature (Google Scholar, Google) was performed in October 2023 and updated in April 2026. Studies and tools were included if they presented a PDA for patients with arterial hypertension, met the IPDAS definition of a PDA and were in German or English. Two researchers screened the literature, extracted data and assessed the quality of the PDAs using the IPDAS Minimal Criteria. Data were synthesized narratively. RESULTS: Of 1,874 records screened, five PDAs met the inclusion criteria. These PDAs were developed between 2019 and 2024 and originated from the United States, United Kingdom, and Germany. All PDAs addressed the two option areas of medication and lifestyle but differed in the number of individual treatment options presented (range: 2-12 options) and the level of detail in which the options were addressed. All five PDAs were accessible online. Common design features of the PDAs included an option grid format for presenting treatment options with associated benefits and adverse effects, visuals and graphics to enhance comprehension, and value clarification. Quality assessments showed varying levels of compliance with the IPDAS criteria, with overall scores ranging from 49/116 to 87/116. CONCLUSIONS: Few PDAs exist despite the prevalence of hypertension. PDAs varied in quality and format, highlighting gaps in the development process. Further research is needed to evaluate the effectiveness of PDAs for hypertension and to determine which formats are most effective in supporting SDM in different patient populations. CLINICAL TRIAL NUMBER: Not applicable.
13 July 2026
Read appraisal →BMC oral health
The association between oral hygiene and metabolic dysfunction-associated steatotic liver disease - a systematic review
BACKGROUND: Given the increasingly evident links between oral health and systemic health, the aim of this review was to investigate whether oral hygiene practices influence the risk or progression of metabolic dysfunction-associated steatotic liver disease (MASLD). The findings support the potential importance of good oral hygiene and regular dental visits, especially for patients with MASLD. METHODS: This systematic review was registered with PROSPERO (CRD420251041530), and PubMed, Scopus, Embase and Web of Science were used for literature research up to June 2026. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, screening, study selection and quality check were performed using Rayyan and the Joanna Briggs Institute (JBI) critical appraisal checklists. Studies assessing the potential link between the frequency of oral hygiene procedures and the incidence, prevalence or progression of MASLD were selected for a systematic synthesis. The old terms non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) were considered as well. RESULTS: A total of 570 results were identified of which six studies met the inclusion criteria. Five of them reported a correlation between higher tooth brushing frequency and a lower incidence, prevalence or progression of NAFLD/NASH compared to individuals with lower tooth brushing frequency. Notably, one study revealed significantly worse liver parameters among NASH patients without regular dental visits than among those who visited a dentist more than once a year. CONCLUSIONS: An association between oral hygiene habits and NAFLD/NASH was found in all included studies. A greater frequency of daily tooth brushing as well as regular dental visits might have beneficial effects on both the development and progression of fatty liver disease. However, there is not enough evidence to establish causality yet and the impact of confounding factors must be further investigated. CLINICAL TRIAL REGISTRATION: Not applicable.
11 July 2026
Read appraisal →PloS one
Air quality index prediction using machine learning regression models: A comparative analysis
Air plays a vital role in human life, and poor air quality can lead to respiratory infections. Given the significant impact of air quality on people's health, monitoring and assessing air quality is crucial. With advancements in machine learning (ML) and artificial intelligence (AI), we now have extensive tools to measure the Air Quality Index (AQI). Air quality is influenced by various pollutants, including carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2), which are prevalent in highly polluted areas and contribute to a wide range of illnesses. Particulate matter, such as PM2.5 (particles with an aerodynamic diameter of less than 2.5 µm) and PM10, poses additional health risks. To address these concerns, this study focuses on predicting AQI values for major cities in Pakistan, specifically Karachi and Peshawar, using four prominent ML algorithms: Random Forest (RF), Gradient Boosting (GB), Linear Regression (LR), and Ridge Regression (RR). The results indicate that the models effectively predicted AQI using evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R2). This research's novelty lies in using the latest AQI datasets for Karachi and Peshawar and applying standard scaling for AQI normalization. Additionally, the study compares evaluation metric results across different cities, highlighting the importance of using standard scalers to achieve optimal model performance. This research underscores the value of advanced ML techniques for accurate AQI prediction and analysis.
10 July 2026
Read appraisal →Scientific reports
Differentially private federated learning for localized control of infectious disease dynamics
In times of epidemics, swift reaction is necessary to mitigate epidemic spreading. For this reaction, localized approaches have several advantages, limiting necessary resources and reducing the impact of interventions on a larger scale. However, training a separate machine learning (ML) model on a local scale is often not feasible due to limited available data. Centralizing the data is also challenging because of its high sensitivity and privacy constraints. In this study, we consider a localized strategy based on the German counties and communities managed by the related local health authorities (LHA). For the preservation of privacy to not oppose the availability of detailed situational data, we propose a privacy-preserving forecasting method that can assist public health experts and decision makers. ML methods with federated learning (FL) train a shared model without centralizing raw data. Considering the counties, communities or LHAs as clients and finding a balance between utility and privacy, we study a FL framework with client-level differential privacy (DP). We train a shared multilayer perceptron on sliding windows of recent case counts to forecast the number of cases in the future, while clients exchange only norm-clipped updates and the server aggregates updates with DP noise. We evaluate the approach on COVID-19 data on county-level during two phases: November 2020 and March 2022 (Omicron). As expected, very strict privacy ([Formula: see text]) yields unstable, unusable forecasts. At a moderately strong but still privacy-preserving level ([Formula: see text]), the DP model closely approaches the non-DP model: [Formula: see text] (vs. 0.96) and mean absolute percentage error (MAPE) [Formula: see text] in November 2020; [Formula: see text] (vs. 0.90) and MAPE [Formula: see text] in March 2022. Overall, our results support the feasibility of privacy-preserving collaboration among health authorities for local forecasting. In the evaluated COVID-19 phases, client-level DP-FL delivered useful county-level predictions with formal privacy guarantees under the stated threat model. The appropriate privacy budget should nevertheless be re-evaluated for other epidemic phases and applications.
8 July 2026
Read appraisal →Journal of epidemiology
Comparison of Influenza Epidemic Trends Based on a Large-scale Claims Database and National Infectious Disease Surveillance in Japan
BACKGROUND: Seasonal influenza is a recurrent respiratory infection, and timely detection is essential for public health. In Japan, surveillance is conducted through sentinel medical institutions under the National Epidemiological Surveillance of Infectious Diseases (NESID). Recently, access to large claims databases, such as the JMDC claims database (JMDCdb), has increased. While both are sample-based systems, JMDCdb covers a much larger population. We aimed to assess consistency between these sources in estimating influenza cases and the effective reproduction number (Rt) and to explore their utility in epidemic analysis. METHODS: We analyzed data from week 36 of 2016 to week 35 of 2019. Influenza cases were estimated from NESID (reported cases and cases per sentinel) and JMDCdb (cases with influenza-related diagnoses and antiviral prescriptions). Daily infection counts were derived to estimate Rt. RESULTS: Although minor differences appeared at epidemic peaks, estimates from NESID reports aligned well with JMDCdb. Estimates based on cases per sentinel were lower. Rt values were consistent across data sources. Rt exceeded 1.0 when cases per sentinel surpassed 0.2-0.3. Using a threshold of 0.25 cases per sentinel enabled detection of epidemic onset 4-5 weeks earlier than current standards. CONCLUSION: Claims data, such as those from JMDCdb, may be useful for retrospective examination of influenza trends. Moreover, a detailed analysis of the number of cases reported per sentinel suggested the potential to propose threshold values that enable earlier prediction of epidemics than conventional criteria.
7 July 2026
Read appraisal →Food & function
Apples and apple-based products in the modulation of cardiometabolic and functional markers: a systematic review of human intervention studies.
Apple is one of the most widely consumed fruits worldwide, yet its effects on human health remain the subject of ongoing debate. The aim of the present review was to examine evidence from human intervention studies evaluating the impact of apples and apple-based products on functional and metabolic health markers. A total of 38 studies were included: 13 postprandial interventions, 22 medium- or long-term interventions, and 3 assessing both postprandial and chronic effects. Postprandial studies predominantly investigated the effects of apple consumption on blood glucose levels and plasma antioxidant capacity, whereas medium- or long-term interventions assessed a broader range of biomarkers related to cardiometabolic health, oxidative stress, vascular function, inflammation, and gut function. Overall, the findings suggest that apples and apple-based products may beneficially modulate glycaemia, antioxidant capacity, and vascular endothelial function mainly in short-term interventions, while medium/long-term studies reported an apparent improvement in gut microbiota composition. However, the current evidence remains insufficient to draw definitive conclusions. Additionally, substantial heterogeneity in study design, populations, and products tested limits the ability to generalize results. Nonetheless, apple consumption, consistent with fruit intake in general, represents an important component of a healthy and balanced diet, providing valuable nutrients and bioactive compounds whose intake should be encouraged. Therefore, further well-designed intervention studies, particularly in populations with cardiometabolic risk factors, are warranted to better clarify the role of apples and apple-derived products in human health. Future research should also aim to identify the effective amounts and specific bioactive components responsible for the observed effects, as well as to determine whether these benefits may vary according to the health status of the target population.
6 July 2026
Read appraisal →Food & function
Application of artificial intelligence in synbiotic and functional food development for precision nutrition
Synbiotic and functional food formulations modulate gut microbiota, SCFA production, immune signalling and metabolic pathways, yet current development pipelines remain largely empirical and constrained by nonlinear trade-offs among probiotic viability, prebiotic functionality and sensory acceptance. Artificial Intelligence (AI) and machine learning (ML) approaches offer data-driven strategies to support formulation, multi-objective optimization and functional assessment. This review systematically identified and critically synthesized literature published between 2020 and 2025 across five thematic domains: (i) formulation and ingredient selection; (ii) viability and shelf-life modelling; (iii) functional and antioxidant bioactivity assessment; (iv) sensory and consumer prediction; and (v) personalization and precision nutrition. Studies were identified through database searches and screened for relevance to synbiotics, functional foods, microbiome modulation and nutrition outcomes, following PRISMA 2020 guidelines. Current evidence indicates that AI can assist ingredient pairing, viability forecasting, sensory modelling and functional property prediction, often complementing conventional statistical tools such as Response Surface Methodology in multivariate design spaces. However, most implementations remain computational or pilot-scale, with minimal integration of microbiome-informed personalization, clinical endpoints or adherence outcomes. Major translational gaps include data heterogeneity, model interpretability, regulatory substantiation and scarcity of longitudinal evidence. AI should therefore be considered a complementary decision-support tool that can accelerate hypothesis generation and formulation refinement rather than substitute mechanistic validation or human trials. Bridging computational modelling with microbiome science and nutritional evidence may enable precision synbiotic strategies and next-generation functional food innovation.
6 July 2026
Read appraisal →BMJ health & care informatics
Predicting health and disease: a conceptual framework for AI in preventive and precision medicine
The conventional medical approach of treating symptoms as they appear with restricted screening often limits intervention to slowing disease progression rather than fully reversing it. A new approach leveraging artificial intelligence (AI) and computational technologies across expanding multimodal biomedical datasets holds the promise to enable predicting actionable future changes in health before symptom onset. This article presents a conceptual framework for a preventive paradigm in precision medicine and healthcare, integrating recent advancements in AI and biomedical datasets. Key remaining challenges facing computational systems, real-world clinical validation and implementation, and preventive interventions together with recommendations and prioritised future directions are highlighted. Such an approach could pave the way for more proactive and preventive medical interventions to effectively address the growing burden of chronic disease.
4 July 2026
Read appraisal →PloS one
Forecasting COVID-19 new cases using NBEATS deep learning and mobility data
COVID-19 is a highly contagious disease transmitted primarily through human contact. Therefore, understanding population mobility is essential for predicting COVID-19 case trends. In this paper, we propose a novel deep learning approach for forecasting new COVID-19 cases using a neural architecture called Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS). The N-BEATS model effectively handles long input sequences and large output horizons without information loss or increased computational complexity. We compare the performance of N-BEATS with a state-of-the-art benchmark model, LSTM-Markov, across four major countries: the United States, the United Kingdom, Russia, and Brazil. Three distinct COVID-19 datasets from Google, Apple, and Our World in Data (OWID) were used in this study. Incorporating Google and Apple mobility data as covariates enhances both the accuracy and interpretability of the N-BEATS model. Our results show that N-BEATS consistently outperforms LSTM-Markov across all datasets and countries, consistently yielding lower Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Furthermore, the N-BEATS model with covariates outperforms its counterpart without covariates, indicating that mobility data provide substantial value for forecasting new COVID-19 cases. Overall, this study demonstrates the effectiveness of the N-BEATS architecture in capturing pandemic dynamics and offers valuable insights for policymakers and public health officials in managing future outbreaks.
30 June 2026
Read appraisal →European journal of nutrition
The effectiveness of a plant-based milk with fermented brown rice on constipation symptoms via gut microbiota modulation: a double-blind randomized controlled trial
PURPOSE: To evaluate the effects of a plant-based milk with fermented brown rice on constipation symptoms in patients with functional constipation and to identify post-intervention gut microbial alterations that may underlie potential mechanisms. METHODS: This is a randomized controlled trial among 100 participants with functional constipation. Participants were randomly assigned to the intervention group (plant-based milk with fermented brown rice, 2 bottles/day, 500 ml in total), or the control group (an isocaloric plant protein milk, equivalent dose) for 3 weeks. The primary outcome is complete spontaneous bowel movement (CSBM) rate, while secondary outcomes include score of individual symptoms assessment of constipation, bowel movement frequency (BMF), and gut microbial changes (metagenomics). RESULTS: A total of 99 participants completed the intervention. CSBM and BMF increased, and GSRS scores decreased over time in both groups, with no significant between-group differences. The plant-based milk with fermented brown rice relieved constipation symptoms more than the control group did, with significant between-group differences in straining, bloating and abdominal pain (all P < 0.05). The intervention group showed increases in 8 species, including three beneficial species in the genus Blautia, associated with relief of abdominal pain after the intervention. Meanwhile, machine learning models identified gut microbiota features predicting intervention responders. CONCLUSION: Our study did not find between-group difference in CSBM, while the plant-based milk with fermented brown rice showed greater effectiveness in relieving constipation symptoms and optimizing gut microbiota. Functional species benefiting intestinal health in response to the intervention were also identified. CLINICAL TRIAL REGISTRY: This study has been registered in the Chinese Clinical Trial Registry (https://www.chictr.org.cn/, ChiCTR2400088688).
30 June 2026
Read appraisal →Journal of gastrointestinal and liver diseases : JGLD
Colon Cleaning after Inadequate Bowel Preparation: A Pooled-data Analysis
BACKGROUND AND AIMS: An adequate bowel preparation is essential for performing high-quality ccolonoscopy; however, it remains ineffective for a certain percentage of patients. We conducted a literature search to identify evidence on salvage strategies following inadequate bowel preparation. METHODS: The search of medical literature was conducted using Medline and Google Scholar, restricted to studies published in English as a full paper. From retrieved studies, the success rate was calculated according to the rescue approaches used, including different oral- or device-assisted procedures, and pooling data of similar approaches. RESULTS: By considering data of 17 studies with 2,167 patients, an adequate colon cleaning was overall achieved in 79.3% (95%CI: 77.5-80.1). By grouping data of similar approaches, the success rate was 78.1% with oral high-volume polyethylene glycol (PEG), 75.3% with low-volume PEG, 82.5% with oral sennosides, and 84.6% following colonoscope or device-assisted preparation. CONCLUSIONS: Effective bowel cleansing is not achieved in nearly 20% of patients due to inadequate bowel preparation and although multiple approaches have been investigated, none has consistently provided adequate bowel preparation for all patients.
29 June 2026
Read appraisal →Science advances
A battery-free wireless intelligent aligner for spatially resolved, closed-loop theranostics of chronic oral diseases
Intelligent wearable theranostic systems are advancing personalized health care by enabling continuous monitoring and proactive therapy. However, managing chronic oral diseases such as caries and periodontitis is challenging due to the oral cavity's complex, spatially heterogeneous microenvironments and limited space for powered intraoral electronics. To address this, we developed a wireless, battery-free, and wearable intelligent therapeutic aligner (WiB-ITA) that integrates a butterfly-inspired dual-sided flexible integrated theranostic electrode array (iTEA) to simultaneously monitor compartment-matched pH and nitrite and electrochemically gate on-demand antimicrobial release. The system is powered and commanded via a relay-assisted near-field communication (NFC)-Bluetooth low energy (BLE) dual-hop link, enabling stable battery-free operation across tooth positions and full-arch usability. Validated from in vitro to preliminary human studies, WiB-ITA establishes a scalable, compartment-matched intraoral closed-loop theranostic framework. This integrated theranostic platform represents a breakthrough in personalized oral health care and provides a paradigm for managing other chronic conditions.
27 June 2026
Read appraisal →Placenta
Deep learning-based early prediction of gestational diabetes mellitus through first-trimester placental texture analysis
INTRODUCTION: This study aimed to develop a multi-parameter fusion model for early GDM risk prediction and validate its performance through external multicenter testing. METHODS: A total of 628 pregnant women at 11+0-13+6 weeks were enrolled from two medical centers. The Center I cohort was divided into training (n = 356) and testing sets (n = 153). Radiomic features (1,289) and deep learning features (2,048) were extracted from placental ultrasound images. Feature-level fusion resulted in 3337 features, which were selected using Spearman correlation, mRMR, and LASSO. Five models were built: Rad Model, DTL Model, DLR Model, Clinic Model, and Combined Model. Performance was assessed using ROC analysis, DCA, and calibration curves. RESULTS: The Combined Model achieved the best overall performance, with an area under the ROC curve (AUC) of 0.879 in the internal validation, significantly outperforming any single-modality model (P < 0.05). DCA demonstrated that the fusion-based model provided higher net clinical benefit across a wide range of threshold probabilities compared with both "treat-all" and "treat-none" strategies. The calibration curve showed excellent agreement between predicted and observed probabilities (Hosmer-Lemeshow test, P > 0.05). DISCUSSION: The multimodal fusion model enhanced early GDM prediction by detecting subtle placental changes in first-trimester, enabling timely intervention and personalized decision-making.
27 June 2026
Read appraisal →Familial cancer
When screentime fails: initiative to improve completion of hereditary cancer genetic testing after telemedicine counseling.
Telemedicine has broadened access to genetic counseling while maintaining high levels of patient satisfaction. However, emerging evidence suggests lower completion rates of genetic testing following virtual visits compared with in-person encounters. This study evaluated genetic testing completion rates among patients who underwent cancer risk assessment via telemedicine and assessed the effect of a structured follow-up telephone reminder on genetic testing completion. We conducted a retrospective review of a quality improvement initiative - Genetic Engagement via Navigation and Encouragement (GENE CALL) - designed to address incomplete genetic testing following telemedicine counseling. The study included all patients who underwent virtual cancer risk assessment between September 2023 and July 2024 at a single urban academic cancer genetics program. Patients who expressed interest in genetic testing but had not scheduled specimen collection were contacted by telephone as a reminder. During the call, patients were asked about ongoing interest in testing and barriers to scheduling; they were also offered assistance completing specimen collection. Among 647 patients evaluated via telemedicine, 469 (72.5%) expressed interest in genetic testing. Of these, 290 (61.8%) had scheduled or completed testing, while 179 (38.2%) had not. Among those who had not scheduled testing, 120 (67.0%) were successfully reached by telephone, and 98 (81.7%) confirmed ongoing interest in completing testing. At 3-month follow-up, 48 (40.0%) of the 120 contacted patients had completed testing, compared with 13 (22.0%) of 59 patients who were not reached (p = 0.017). Telephone follow-up is an effective strategy to improve genetic testing completion after virtual counseling.
27 June 2026
Read appraisal →La Medicina del lavoro
Cadmium Exposure and Risk of Pancreatic Cancer: A Systematic Review and Meta-Analysis
INTRODUCTION: Pancreatic cancer is a highly lethal malignancy with poor prognosis and limited treatment options. Environmental exposures, particularly to heavy metals such as cadmium, may contribute to its etiology. This systematic review and meta-analysis evaluated the association between cadmium exposure from different sources and pancreatic cancer incidence or mortality. METHODS: Following PRISMA guidelines, we searched PubMed, EMBASE, and Scopus. Eighteen studies comprising 20 risk estimates were included. Random-effects meta-analyses were conducted overall and stratified by exposure source, gender, region, study design, and outcome. Dose-response relationships were assessed using meta-regression of cadmium exposure measures. Publication bias was evaluated using funnel plots and Egger's test. RESULTS: Overall, cadmium exposure was associated with an increased risk of pancreatic cancer [relative risk (RR) = 1.69, 95% confidence interval (CI): 1.28-2.22]. Occupational exposure showed the most consistent association (RR = 1.38, 95% CI: 1.19-1.61), followed by urinary and blood/serum biomarkers. Risk was higher in men than in women, and in case-control than in cohort studies. Dose-response analysis did not reveal a linear trend. There was limited evidence of publication bias overall, though some asymmetry was observed for urinary cadmium studies (P = 0.045). CONCLUSION: Cadmium exposure was associated with pancreatic cancer risk, particularly in occupational and biomarker-based studies. While findings support a potential causal link, heterogeneity, residual confounding, and limited dose-response data necessitate cautious interpretation.
26 June 2026
Read appraisal →Journal of medical Internet research
Digital Health Intervention to Promote Lifelong Specialized Care in Adults With Congenital Heart Disease: Theory-Driven Community Co-Designed Study
BACKGROUND: Adults with congenital heart disease frequently experience gaps in lifelong adult congenital heart disease (ACHD) specialty care, leading to preventable complications, hospitalizations, and premature mortality. However, effective, scalable, accessible, and sustainable strategies to reduce these gaps are lacking. Digital health offers potential solutions but requires a rigorous scientific approach in its design to address the needs of the target population. OBJECTIVE: This study aims to describe the development of a theory-based, community co-designed digital health intervention to improve lifelong ACHD care. METHODS: We integrated theory-based behavioral frameworks, semistructured qualitative interviews with patients and clinicians, and a community-engaged approach to develop a digital health intervention for ACHD. The primary behavioral target was completing an ACHD specialist appointment. We conducted Capability, Opportunity, Motivation, and Behavior (COM-B)-guided semistructured interviews with patients with ACHD and clinicians to identify barriers to specialized care amenable to a digital intervention, and patient-centered goals for the digital tool. The community partners helped develop key intervention objectives, create a theory-driven framework, and specify how each intervention component targets specific COM-B barriers. RESULTS: We interviewed 54 participants (n=37 patients with ACHD, and n=17 clinicians) and engaged 21 community partners representing 4 advocacy organizations. Design objectives emphasized addressing patient loneliness, ensuring accessibility and credibility, and enabling scalability while centering patient perspectives. Participants identified 4 priorities: providing credible resources, uplifting patient voices, customizing to patient needs, and centering positivity and joy. The digital tool, named by community partners as Empower My Congenital Heart (EMCH), was designed within the web- and mobile-based, Apple- and Android-compatible, Eureka Digital Research platform (University of California, San Francisco). Key intervention components included educational modules, peer support, appointment planning nudges, and a digital medical passport. The EMCH's theory-driven framework specifies how each intervention component targets specific COM-B barriers to specialized ACHD care. CONCLUSIONS: The theory-driven, community co-designed EMCH digital tool provides a scalable approach to promote lifelong ACHD specialist care. Ongoing process evaluation and a planned randomized controlled trial will assess acceptability, engagement, and effectiveness in reducing care gaps. If proven effective, EMCH has the potential to prevent complications, emergency hospitalizations, and mortality affecting the majority of adults with congenital heart disease.
25 June 2026
Read appraisal →Food & function
Impact of tomatoes and tomato-derived products on obesity and cardiometabolic health: a systematic review and meta-analysis
Tomatoes and tomato-based products are central components of the Mediterranean diet and have been associated with improved cardiometabolic health, but their effects on anthropometric parameters remain unclear. This review aimed to assess the potential effects of tomato-based interventions on obesity-related and cardiometabolic outcomes in individuals with overweight or obesity. We conducted a PRISMA-compliant systematic review and meta-analysis. Eligible studies included populations with a mean BMI ≥ 25 kg m-2, evaluated tomatoes, tomato-based products, or tomato extracts (excluding isolated lycopene) and reported at least one anthropometric outcome. The risk of bias was assessed using RoB 2 for randomized controlled trials (RCTs). Epidemiological and preclinical evidence was synthesized qualitatively. Forty-seven studies met the inclusion criteria: 11 clinical trials (RCTs), 5 epidemiological studies, and 31 preclinical studies. In the RCTs, tomato-based interventions produced a small but significant reduction in waist circumference (MD: -1.153 cm, 95% CI: -2.27 to -0.04, p = 0.0432), with no consistent effects on body weight or BMI. Meta-regression analyses indicated that supplementation type influenced blood pressure-related outcomes. Epidemiological studies consistently linked higher tomato intake to more favorable cardiometabolic profiles, whereas preclinical models showed reduced visceral adiposity, inflammation, and oxidative stress, together with improved glucose and lipid homeostasis. Tomato-based interventions confer modest but biologically consistent benefits, targeting visceral adiposity and cardiometabolic pathways rather than overall weight loss. The absence of a lycopene dose response and the efficacy of lycopene-free matrices support a food-matrix synergy rather than lycopene as the primary bioactive compound. Combined clinical, epidemiological, and preclinical evidence suggests potential beneficial effects of tomatoes and tomato-based products on cardiometabolic health, which deserve further investigation. This review underscores the need to standardize tomato-derived interventions to improve comparability and strengthen the evidence base.
24 June 2026
Read appraisal →Oral health & preventive dentistry
Natural Agents for the Improvement of Gingival Health: Systematic Review and Meta-Analysis of Randomized Clinical Trials
PURPOSE: This systematic review and meta-analysis evaluated the efficacy of natural ingredients used in toothpastes and gels in improving gingival health. MATERIALS AND METHODS: A comprehensive search strategy using different databases was conducted in accordance with PRISMA guidelines to identify randomized clinical trials (RCTs) on the use of natural ingredients for improving gingival health. Included studies focussed on outcomes measuring gingival health parameters, such as gingival index (GI), or bleeding on probing (BoP). Out of 249 identified studies, 10 RCTs met the inclusion criteria. Formulations included natural ingredients from plant extracts such as Aloe vera or Pudilan, and hydroxyapatite (as tooth-like calcium phosphate). RESULTS: The meta-analysis demonstrated a statistically significant improvement in gingival health when natural ingredients were used compared to control toothpastes and gels (p=0.0051). These findings suggest that natural ingredients may offer comparable or superior efficacy to conventional oral care products in terms of gingival health. Notably, Aloe vera and hydroxyapatite consistently demonstrated clinical benefits. CONCLUSIONS: Natural ingredients used in oral care products represent a promising strategy in gingivitis prevention and management. Their use in toothpastes and gels may therefore provide clinicians and patients with evidence-based, well-tolerated alternatives or adjuncts to conventional formulations for maintaining and improving gingival health.
20 June 2026
Read appraisal →The American journal of cardiology
Efficacy and Safety of Olezarsen for the Management of Dyslipidemia: An Updated Systematic Review and Meta-Analysis of Randomized Controlled Trials
This updated systematic review and meta-analysis evaluated the efficacy and safety of olezarsen in patients with dyslipidemia. PubMed, Embase, Cochrane CENTRAL, and Scopus were searched through September 2025 for randomized controlled trials comparing olezarsen with placebo. Seven trials (n = 2,121) were included. Olezarsen significantly reduced triglycerides (mean difference [MD] -59.20 mg/dL), very-low-density lipoprotein cholesterol (MD -52.68 mg/dL), apolipoprotein B (MD -10.74 mg/dL), non-HDL cholesterol (MD -17.06 mg/dL), and apolipoprotein C-III (MD -64.16 mg/dL), and increased HDL cholesterol (MD 30.97 mg/dL), with no significant effect on LDL cholesterol. Injection site reactions (risk ratio [RR] 3.93) and platelet counts <140,000/µL (RR 4.76) were increased, whereas serious adverse events and significant liver enzyme elevations were comparable to placebo. Certainty of evidence ranged from moderate to low. In conclusion, olezarsen produces substantial improvements in triglyceride-rich lipoproteins with an acceptable safety profile; long-term cardiovascular outcome trials are warranted.
16 June 2026
Read appraisal →Journal of affective disorders
Heart rate variability as a predictor of future depressive symptoms: A systematic review and meta-analysis
Depression has limited early detection strategies. Heart rate variability (HRV), an indicator of autonomic regulation, has been linked to depression cross-sectionally, but its prospective value remains uncertain. We performed a meta-analysis and systematic review examining whether HRV is prospectively associated with later depressive symptom levels. PubMed, Embase, Cochrane, and PsycINFO (inception November 3, 2024) were searched for longitudinal studies of initially non-depressed baseline HRV and at least one-month follow-up assessing depressive or internalizing symptoms. Meta-analyses used multilevel random-effects models; prediction models were synthesized by HRV's statistical role. From 12,023 records, 56 studies entered the meta-analysis, and 62 the qualitative synthesis. HRV showed small but significant negative correlations with later depressive symptoms for frequency-domain indices (HF-HRV (r = -0.16, 95% CI -0.22 to -0.11), LF-HRV (r = -0.19, 95% CI -0.27 to -0.10), and VLF-HRV (r = -0.19, 95% CI -0.28 to -0.11)), and time-domain indices (RMSSD (r = -0.17, 95% CI -0.28 to -0.05) and SDNN (r = -0.15, 95% CI -0.26 to -0.03)). Across prediction-models, HRV showed direct effects in 43% and conditional effects in 57%, involving RSA and nonlinear patterns. HRV reflects psychophysiological mechanisms of depression vulnerability, but its prospective effects are small and context-dependent. Clinically, HRV may function as an adjunctive tool for identifying individuals who are more vulnerable to adversity and for informing preventive strategies, including the personalization of biofeedback and other regulation-focused interventions.
16 June 2026
Read appraisal →Journal of hazardous materials
Biomarker-anchored screening of dietary microplastics via a gut microbiota-informed machine learning model
Microplastics (MPs) are increasingly recognized as emerging contaminants in the human diet, yet the absence of unified biomarker-anchored screening thresholds hampers quantitative risk assessment. This study establishes a gut microbiota-anchored machine learning framework that links Firmicutes/Bacteroidetes (F/B) ratio thresholds with real-world dietary MP exposure data, enabling a biomarker-anchored screening of dysbiosis-related signals associated with disease risk. Across the compiled disease cohorts, obesity provided the largest and most data-sufficient contrast in this dataset and was therefore selected as the anchor condition for threshold derivation. Receiver operating characteristic (ROC) analysis determined a model-derived screening cutoff for the F/B ratio (cutoff = 1.08), which was further used to define a three-tier risk classification system. A dataset comprising 558 dietary MP records from 129 studies across 25 countries was incorporated to predict food- and region-specific risk levels. Results reveal that seafood represents the dominant global dietary MP risk source, with mollusca contributing the highest predicted F/B-related risk signal. Spatial stratification indicates that the United States and Ecuador were classified into the highest model-derived tier based on national mean predicted F/B values, followed by high-risk regions such as Vietnam, Turkey, and Ireland, while countries in the Middle East and North Africa remain at moderate risk. These geographic contrasts suggest the combined influence of dietary structure and regional exposure intensity. Overall, we present a biomarker-anchored, machine-learning-enabled screening framework that links dietary MP exposure to predicted changes in a dysbiosis-associated indicator (F/B). This approach can support monitoring prioritization, identification of data gaps, and hypothesis generation for future validation studies.
15 June 2026
Read appraisal →Life sciences
Intermittent fasting and cardiometabolic responses under cardiac stress: a systematic review of preclinical studies
Intermittent fasting (IF) has emerged as a dietary strategy with benefits that extend beyond weight management and glycemic control. Although growing evidence supports its favorable effects on cardiometabolic health, the impact of IF under conditions of cardiac stress remains poorly characterized. Since clinical trials cannot ethically expose patients to acute cardiac stress during IF protocols, preclinical models are essential to elucidate its potential cardioprotective mechanisms. This systematic review aimed to synthesize the preclinical evidence on the effects of IF in models of cardiac stress. Following a registered protocol (PROSPERO:#CRD42023466215), we searched databases for preclinical studies evaluating IF under cardiac stress. Models included atherosclerosis induction, diet modulation, hypercholesterolemic mice, doxorubicin-induced cardiotoxicity, pulmonary arterial hypertension, myocardial infarction, ischemia-reperfusion injury, among others. All IF regimens, encompassing variations in timing, frequency, and type, were considered. Clinical trials, observational studies, reviews, meta-analyses, updates, guidelines were excluded. Thirty-two studies involving 451 predominantly male animals were included. Approximately 80% reported that IF improved cardiovascular outcomes, reducing blood pressure, cardiac hypertrophy, fibrosis, and ischemic injury while enhancing vascular and metabolic function. Mechanistically, IF protects the heart under stress by downregulating NF-κB, IL-1β, and TNF, and activating SIRT3 and AMPK pathways, improving mitochondrial function, lipid metabolism, and cardiac energy homeostasis. Cardioprotective benefits were dependent on fasting duration and regimen, emphasizing the importance of metabolic context. In conclusion, IF appears to enhance cardiovascular resilience under stress through multiple metabolic and molecular pathways. These findings support further clinical investigation of IF as a potential adjunct strategy for cardiometabolic protection under stress.
15 June 2026
Read appraisal →Cancer
Alcohol consumers' receptivity to artificial intelligence-generated alcohol-cancer risk messages: An experimental study.
BACKGROUND: Alcohol is a group 1 carcinogen linked to seven cancers, yet awareness of this risk remains low in the United State. Identifying effective alcohol-cancer communication strategies is a public health priority. The objective of this study was to test the effects of message specificity (general vs. cancer-specific) and visual intensity (text-only, neutral pictorial, graphic pictorial) on message receptivity (attention, emotional reactions) and precursors of behavior (harm perception; intentions to reduce, limit, or stop drinking) among moderate and heavy alcohol consumers. METHODS: Eight evidence-based text messages were created across two topics: general and cancer-specific harm. Artificial intelligence was used to generate pictorial versions at two intensities: neutral (symbolic, no visible disease) and graphic (explicit health consequences). In a 2025 online within-subject/between-subject crossover experiment, 639 US adult consumers (aged 21 years and older; 49.3% female) each viewed two randomly selected messages (one general, one cancer-specific) presented in three formats (text-only, neutral, and graphic; for six total formats), with counterbalanced order. Linear mixed-effects models were used to estimate differences by intensity, specificity, and drinking levels, reporting regression coefficients (β, 95% confidence intervals). RESULTS: Cancer-specific messages produced greater attention, emotional reactions, and intentions to reduce drinking than general messages (β = 0.10-0.19; p < .05). Graphic pictorials outperformed neutral images on emotional and behavioral outcomes (β = 0.19-0.22; p < .05). Moderate consumers showed stronger perceived harm and message responsiveness than heavy consumers (β = 0.29-0.77; p < .05). CONCLUSIONS: Artificial intelligence-generated alcohol-cancer messages are feasible and effective in strengthening precursors to behavior change. Cancer-specific content and higher visual intensity enhance impact, particularly among moderate consumers, highlighting the importance of tailoring alcohol-cancer communication strategies to different audience characteristics. Many people in the United States are unaware that drinking alcohol increases the risk of several cancers. This study tested different ways of communicating the alcohol–cancer link, including text‐only messages, messages with artificial intelligence–generated images (neutral or graphic), and general versus cancer‐specific content; in addition, the authors examined whether responses differed between moderate drinkers and heavy drinkers. The authors observed that messages with images were more effective than those with text alone, especially when the images clearly showed health harm. Messages that named specific cancers (such as breast or colon cancer) were also more effective than general statements about cancer; however, heavy drinkers, who face the highest cancer risk, were less responsive overall. The findings suggest that effective alcohol–cancer communication should carefully consider what the message says, how it is presented, and who the audience is. Nonstigmatizing, harm‐reduction messages that encourage small, realistic changes and are delivered in supportive settings, such as conversations with health care providers, may work better for heavy drinkers. The study also highlights the promise of artificial intelligence as a tool for developing and tailoring health messages. Future research should explore how different messages influence behavior over time.
15 June 2026
Read appraisal →Brain research
BDNF relates to prefrontal cortex activity in the context of physical exercise
Exercise has been shown to support brain health, cognitive function, and increase levels of brain-derived neurotrophic factor (BDNF). While BDNF is known to support the central nervous system through improved brain metabolism, vasculature, neurotransmission and synaptic plasticity, the association between exercise-induced changes in BDNF concentrations and exercise-related cognitive improvements is still unclear. This study investigated the relationship between exercise-induced changes in plasma BDNF (pBDNF) and serum BDNF (sBDNF), and haemodynamic indicators of prefrontal cortex function in sedentary adults. Participants (n = 23, female = 7) were randomized into intervention (12-week cycling programme) and control groups (no intervention). Participants completed V̇O2max tests to assess changes in fitness. pBDNF and sBDNF were measured pre- and post-V̇O2max testing, and a battery of executive function and memory tasks were also conducted, alongside functional near-infrared spectroscopy (fNIRS) to assess haemodynamic changes in the prefrontal cortex activity. Changes were assessed using the correlation-based signal improvement (CBSI) method. Results indicated that participants in the exercise intervention group did not show increased levels of resting-state s/pBDNF levels at the end of the intervention; however, they did exhibit a significant exercise-induced increase in sBDNF at week 12. This increase was correlated with changes in V̇O2max. Higher pBDNF levels and exercise-induced sBDNF were associated with a decrease in CBSI values in the frontopolar, dorsolateral and orbitofrontal prefrontal cortex during attention and inhibition tasks, but not during the memory task. These results suggest that increasing physical fitness can enhance BDNF transcription in response to acute bouts of exercise. This might, in turn, play a part in the modulation of neural function during executive tasks after acute exercise.
15 June 2026
Read appraisal →Journal of global health
Plasma proteomic signatures predict incident benign prostatic hyperplasia: a prospective cohort study of 20 996 men
BACKGROUND: Benign prostatic hyperplasia (BPH) is a prevalent disease in elderly men. However, the plasma proteomic signatures for incident BPH are absent, hindering early prediction and risk stratification. We aimed to identify plasma proteins associated with incident BPH and the implicated biological pathways. METHODS: This prospective cohort was constructed based on the UK Biobank, enrolling 20 996 BPH-free males at baseline. We used the Olink Explore platform to determine the abundances of 2920 plasma proteins. We used Cox regression models to evaluate associations and the Extreme Gradient Boosting model to perform feature selection and predictive modelling. We performed pathway enrichment and Mendelian randomisation analyses to elucidate the molecular pathways involved and assess causality. RESULTS: During the median follow-up period of 13.38 years, 2405 incident BPH cases were recorded. Cox regression identified 92 plasma proteins significantly associated with BPH risk (false discovery rate <0.05). Extreme Gradient Boosting model prioritised a three-protein signature: TSPAN1 (hazard ratio (HR) = 1.24) and KLK3 (HR = 1.34) as risk factors, and EDA2R (HR = 0.83) as a protective factor. This three-protein panel achieved an AUC of 0.71 (95% confidence interval = 0.69-0.73) for predicting BPH onset. Enrichment analysis revealed the associated proteins were mainly involved in immune-inflammatory pathways and stromal remodelling. As revealed by Mendelian randomisation, TSPAN1 and KLK3 were causally associated with incident BPH. CONCLUSIONS: We identified a three-protein panel (TSPAN1, KLK3, EDA2R) which can predict incident BPH. The findings highlight potential targets for non-invasive risk assessment and therapy.
15 June 2026
Read appraisal →Scandinavian journal of primary health care
The diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care: a prospective validation study
OBJECTIVES: This study investigated the diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care, using ophthalmologist-led screening as the reference standard. METHODS: Patients with type 2 diabetes attending routine appointments at 10 primary care clinics underwent AI-assisted screening, followed by re-screening at an ophthalmology clinic. The quality of fundus images captured in primary care was independently assessed, and diagnostic accuracy was evaluated by comparing AI-assisted results with ophthalmologist results, including sensitivity, specificity, PPV, NPV, and AUC. Two analyses were conducted: one including all images and one excluding those of poor quality. RESULTS: Among 183 patients (336 images), 18.6% of images were classified as poor quality. When all images were included, the AI-assisted screening achieved a sensitivity of 73.7%, specificity of 90.2%, PPV of 31.1%, NPV of 98.3%, and AUC of 0.82. Excluding poor-quality images improved sensitivity to 80.0%, NPV to 98.7%, and AUC to 0.84. Additional ocular findings unrelated to diabetic retinopathy were observed in 96 patients, including confirmed or non-specific signs of glaucoma, cataract, age-macular degeneration, benign nevus and reduced visual acuity. CONCLUSION: AI-assisted screening in primary care shows potential for clinical application, but further validation in larger populations and improvements in image quality are needed before clinical implementation.
14 June 2026
Read appraisal →Journal of neurology, neurosurgery, and psychiatry
Clinically reported covert cerebrovascular disease and risk of neurological disease: a whole-population cohort of 367 988 people using natural language processing
BACKGROUND: The relevance of covert cerebrovascular disease (CCD) in practice is uncertain, partly because estimation of risk in whole clinical populations is difficult. Studies have had success extracting CCD from clinical text using natural language processing (NLP), though they have been limited to specific CCD phenotypes. Here, we used NLP to measure multiple clinically-reported CCD phenotypes in a large clinical cohort and estimated subsequent disease risk in health record data. METHODS: From all people with brain imaging in Scotland (2010-2018), we selected people with no prior hospitalisation for neurological disease (n=367 988). NLP of imaging reports identified: white matter hypoattenuation or hyperintensities (WMH), lacunes, cortical infarcts and cerebral atrophy. Adjusted HRs (aHRs) were estimated between each phenotype and stroke, dementia and Parkinson's disease (conditions previously associated with CCD), epilepsy and colorectal cancer (control conditions). RESULTS: For each phenotype, the aHR of stroke was WMH 1.4 (95% CI 1.3-1.4), lacunes 1.6 (1.5-1.6), cortical infarct 1.8 (1.7-1.9) and cerebral atrophy 1.1 (1.0-1.1). The aHR of dementia was WMH 1.3 (1.3-1.3), lacunes 1.0 (0.9-1.0), cortical infarct 1.1 (1.1-1.2) and cerebral atrophy 1.7 (1.7-1.8). The aHR of Parkinson's disease was WMH 1.1 (1.0-1.2), lacunes 1.1 (0.9-1.2), cortical infarct 0.7 (0.6-0.9) and cerebral atrophy 1.4 (1.3-1.5). The aHRs between CCD phenotypes and epilepsy and colorectal cancer were around the null. CONCLUSION: CCD and atrophy have implications for future disease risk and can be identified at scale using NLP of clinical reports. Prevention of neurological disease in people with CCD should be a priority for healthcare policy makers.
14 June 2026
Read appraisal →JMIR mental health
Prevalence and Predictors of Self-Reported Adverse Experiences in Digital Meditation Training: 2 Randomized Controlled Trials
BACKGROUND: Digital meditation-based interventions (MBIs) reach vast global audiences with millions of active users, yet concerns persist about the frequency and nature of adverse experiences (ie, AExs) occurring during meditation training. Some researchers have argued that AExs are substantially underdetected and reflect iatrogenic harm caused by meditation (ie, adverse effects [AEfs]). Others contend that these experiences largely reflect common stressors that would be experienced without meditation. These competing perspectives underscore the need for further research, particularly in the context of digital MBIs, the most widely used form of meditation training. OBJECTIVE: This study examined the prevalence, predictors, and subjective evaluations of AExs during a digital MBI and tested whether reported experiences may be caused by meditation practice via comparisons between meditation-exposed and nonexposed participants. METHODS: Data were drawn from 2 trials of the Healthy Minds Program. Exploratory study 1 (n=315) consisted of a sample of distressed US undergraduate students to estimate the prevalence of AExs and identify baseline predictors. Preregistered confirmatory study 2 (n=594) sampled distressed US adults from all 50 states to replicate findings from study 1 and to examine participants' subjective evaluations of AExs. Study 2 additionally compared AEx rates between participants who did and did not complete guided meditations to assess whether AExs could be caused by meditation exposure. Study 3 (n=87) used qualitative methods to analyze study 1 participants' responses to an open-ended question regarding their strategies for coping with AExs. RESULTS: In studies 1 and 2, 27.9% (88/315) and 10.1% (40/396) of participants, respectively, reported at least one AEx during the study period, with 6.7% (21/315) and 3% (12/396) reporting functional impairment, largely aligning with previous research. Critically, in study 2, rates of AExs did not significantly differ between participants who did and did not complete guided meditations, suggesting that these experiences were not caused by meditation practice. Higher baseline depression, anxiety, loneliness, experiential avoidance, and perceived barriers to meditation predicted more frequent AExs. In studies 1 and 2, 89.8% (79/88) and 90% (36/40) of participants who reported AExs, respectively, indicated that they were glad to have learned to meditate. Qualitative analyses showed that participants used diverse coping strategies, often using skills learned through the Healthy Minds Program. CONCLUSIONS: AExs were relatively common but occurred at comparable rates among participants who did and did not meditate, challenging claims that such experiences were caused by meditation practice in distressed individuals. Although a small subset of participants reported some degree of functional impairment, most evaluated their AExs as tolerable and described their overall MBI experience as positive. Together, these findings highlight the importance of distinguishing AExs that likely reflect epiphenomena of preexisting distress or symptoms from iatrogenic harm attributable to MBIs.
14 June 2026
Read appraisal →Current atherosclerosis reports
Comparative Efficacy of Nutraceuticals on Endothelial Function: A Systematic Review and Network Meta-Analysis
PURPOSE OF REVIEW: This network meta-analysis (NMA) compares the efficacy of dietary interventions in improving flow mediated dilation (FMD) as a measure of endothelial function among individuals with cardiovascular diseases (CVDs) and hypertension. This work can be a basis for the design of future clinical trials and for the development of evidence-based nutritional guidelines to improve cardiovascular outcomes. RECENT FINDINGS: The most substantial improvements in FMD were observed with magnesium (MD: 8.17; CoE: High), vitamin D3 (MD: 7.84; CoE: High), flaxseed (MD: 7.39; CoE: Low), barberry (MD: 6.64; CoE: Moderate), folic acid (MD: 3.36; CoE: Low), and omega-3 (MD: 1.83; CoE: Very low). Sensitivity analyses confirmed the robustness of these findings. Several dietary interventions, particularly magnesium and vitamin D₃, have been shown to improve endothelial function in individuals with CVDs and hypertension. These findings highlight the potential role of nutritional strategies as adjunct therapies for preserving vascular health. TRIAL REGISTRATION: Systematic review registered with PROSPERO, registration number CRD420251041093.
14 June 2026
Read appraisal →BMJ open
Effect of a culturally adapted heart-healthy diet with phytosterols and/or krill oil on lipid-related outcomes in familial hypercholesterolaemia: protocol for a multicentre randomised controlled trial in Brazil
INTRODUCTION: An affordable heart-healthy dietary approach is essential for the management of familial hypercholesterolaemia (FH); however, the optimal dietary pattern and the role of adjunctive nutrient supplementation remain uncertain. This study aims to evaluate the effects of the Brazilian Cardioprotective Diet (DICA Br), adapted from the Portfolio Diet, with or without phytosterol and/or krill oil supplementation in individuals with probable or definite FH according to the Dutch Lipid Clinic Network (Dutch MEDPED) criteria. METHODS AND ANALYSIS: The DICA-FH study is a national, multicentre, randomised, factorial, parallel-group, superiority, placebo-controlled clinical trial with a 1:1:1:1 allocation ratio. Participants aged ≥16 years receiving age-appropriate lipid-lowering therapy will be randomised into four groups: (1) adapted cardioprotective diet (DICA-FH) plus phytosterol placebo and krill oil placebo; (2) DICA-FH plus phytosterol 2 g/day and krill oil placebo; (3) DICA-FH plus phytosterol placebo and krill oil 2 g/day or (4) DICA-FH plus phytosterol 2 g/day and krill oil 2 g/day. All participants will undergo whole-genome sequencing and receive appropriate genetic counselling. Primary outcomes will be means of low-density lipoprotein cholesterol and lipoprotein(a) levels after 120 days. Secondary outcomes will include additional lipid biomarkers, adherence to protocol and adverse events. The planned sample size is 300 participants. Follow-up is expected to conclude in July 2026. ETHICS AND DISSEMINATION: This study was registered under CAAE 65549622.2.1001.0060 and received ethical approval from the Hcor Research Ethics Committee (approval number 5.805.072) and the Brazilian National Research Ethics Commission (CONEP; approval number 6.864.951). Written informed consent will be obtained from all participants prior to enrolment. The study findings will be disseminated through peer-reviewed publications, scientific conferences and channels aimed at the general public. TRIAL REGISTRATION NUMBER: NCT06331195.
14 June 2026
Read appraisal →European journal of pediatrics
Ambulatory hypertension and left ventricular hypertrophy in pediatric chronic kidney disease: a systematic review and meta-analysis
UNLABELLED: Cardiovascular disease is the leading cause of mortality in pediatric patients with chronic kidney disease (CKD). In the general pediatric population, hypertension is associated with an increased risk of left ventricular hypertrophy (LVH); however, the strength of this association varies in the pediatric CKD literature. A lack of consensus on optimal blood pressure (BP) targets for this population contributes to variability in clinical practice. This study aims to determine the association between ambulatory hypertension and LVH in pediatric CKD. A systematic literature search was conducted on five databases from 1974 to 2025. The mean left ventricular mass index (LVMI) and odds of LVH was compared between normotensive and hypertensive groups. The association between LVMI and 24-h systolic/diastolic blood pressure, day/night systolic/diastolic blood pressure, and 24-h mean arterial pressure (MAP), was determined. Fifty-one full text studies (n = 6509 children) were eligible for inclusion in the meta-analysis. Pediatric CKD patients with ambulatory hypertension demonstrated increased LVMI (mean difference 5.15 g/m2.7 (95% CI 3.16, 7.15) and increased risk of LVH odds ratio 2.82 (95% CI 2.18, 3.65) to normotensive controls. LVMI was significantly lower among children with a 24-h MAP < 50th percentile (- 8.2 g/m2.7, 95% CI - 16.0, - 0.4) and 24-h SBP between the 50 and 90th percentiles (- 10.8 g/m2.7, 95% CI - 20.8, - 0.8) compared to those with 24-h MAP or 24 h SBP > 90th percentile, respectively. CONCLUSION: In children with CKD, ambulatory hypertension is associated with elevated risk of LVH and increased LVMI. However, there was significant heterogeneity among the included studies with respect to study design and definition of outcomes. Timely detection of hypertensive target organ injury is imperative to mitigate future cardiovascular disease in pediatric CKD patients.Prospero Registration: CRD 42023426891. WHAT IS KNOWN: • Hypertension is a critical predictor of adverse cardiovascular outcomes in children with CKD. Hypertension-induced target organ damage such as LVH may predict future cardiovascular outcomes in high-risk populations. Despite this, there remains considerable variability in clinical blood pressure targets and monitoring practices. Our findings highlight the importance of ambulatory blood pressure monitoring (ABPM) in detecting subclinical cardiovascular injury, supporting its integration into routine CKD care to improve risk stratification and treatment decisions. WHAT IS NEW: • This meta-analysis establishes a robust association between ambulatory hypertension and left ventricular hypertrophy (LVH) in children with CKD, demonstrating that higher blood pressure burden is linked to increased left ventricular mass index (LVMI). Notably, children with 24-h mean arterial pressure (MAP) and systolic blood pressure (SBP) above the 90th percentile exhibited significantly greater LVMI, reinforcing the impact of sustained hypertension on cardiac remodelling in pediatric CKD. • The strong association between ambulatory hypertension and LVH underscores the urgent need for evidence-based BP thresholds that minimize cardiovascular risk in pediatric CKD. Early identification of hypertensive target organ damage may enable timely therapeutic interventions to prevent long-term cardiovascular complications. Future studies should focus on defining optimal BP targets and evaluating the efficacy of antihypertensive strategies tailored to pediatric CKD. Standardizing BP measurement approaches may further refine hypertension management and improve cardiovascular outcomes in this vulnerable population.
14 June 2026
Read appraisal →BMJ open
Barriers and facilitators to digital health tool adoption for hypertension management: systematic review of qualitative studies
OBJECTIVES: Digital health interventions (DHIs) show considerable promise in supporting hypertension self-management by promoting preventative care and self-monitoring. While their efficacy is increasingly evident, the long-term uptake, acceptance and sustained engagement with these tools are frequently challenged by issues such as usability, trust and varying user experiences. This review aims to synthesise qualitative evidence to identify barriers and facilitators and the key factors that impact the adoption, acceptance and engagement with DHIs for hypertension self-management. DESIGN: Systematic review of qualitative literature using thematic analysis following Cochrane's qualitative and implementation methods guidance. DATA SOURCES: PubMed, PsycInfo, Web of Science and the Cochrane Library were searched in February 2025. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: The searches included relevant qualitative and mixed-methods studies on the use of digital devices for hypertension management, which described the barriers and facilitators associated with these tools. We included studies published from 2015 to 2025 to capture relevant evidence. Only studies published in English with a qualitative approach were included. RESULTS: From an initial 10 943 identified publications, 15 met our inclusion criteria, primarily originating from Europe and the USA, exploring diverse racial and ethnic group experiences. Our thematic synthesis revealed 7 analytical and 22 descriptive themes detailing barriers and facilitators encountered by patients with hypertension, healthcare providers (HCPs) and caregivers. These themes covered technology utilisation, design components, linguistic and cultural relevance, healthcare factors, trust and credibility and interpersonal interactions. CONCLUSION: Our analysis underscores that factors such as the usability, design and relevance of social support profoundly influence the uptake and acceptance of DHIs in hypertension self-management among patients, caregivers and HCPs. PROSPERO REGISTRATION NUMBER: CRD42023480389.
14 June 2026
Read appraisal →Journal of global health
Residential green space mitigates genetic susceptibility to incident irritable bowel syndrome in a prospective cohort of 376 749 individuals
BACKGROUND: Environmental exposures may influence irritable bowel syndrome (IBS) pathogenesis, but the interplay between genetic risk, residential green/blue space, and IBS incidence remains unexplored. METHODS: We aimed to study the association between green/blue space and incident IBS, and to assess effect modification by genetic susceptibility. The study included 376 749 UK Biobank participants at baseline. Residential green space was measured using the normalised difference vegetation index (NDVI), and blue space was defined by distance to the nearest water bodies. For time-to-event analyses, we used Cox proportional hazards models, polygenic risk scores (PRS), and mediation analysis to test gene-environment interactions. Results are presented as hazard ratios (HRs) with 95% confidence intervals (95% CIs). RESULTS: During a follow-up of 11.73 years, 7091 incident IBS cases were documented. Higher residential green space exposure was associated with reduced risk of incident IBS. Specifically, within a 1000 m buffer, each interquartile range (IQR) increase in NDVI corresponded to a 5% lower IBS risk (HR = 0.95; 95% CI = 0.91-0.98), with consistent effects observed at 800 m and 500 m buffers. Conversely, a greater distance to water bodies was associated with reduced IBS risk. Stratification by PRS revealed a significant interaction: increased green space (1000 m buffer) reduced IBS risk by 8% exclusively among high-genetic-risk individuals (HR = 0.92; 95% CI = 0.88-0.96), whereas no interaction was observed for blue space. Moreover, physical activity mediated the association between green space and IBS. CONCLUSIONS: Increased residential green space exposure is associated with reduced IBS incidence, particularly among individuals with high genetic susceptibility. These findings underscore the importance of urban greening as a public health strategy to mitigate IBS risk, highlighting the potential for targeted environmental interventions to offset genetic predispositions.
13 June 2026
Read appraisal →BMJ (Clinical research ed.)
Low concentration atropine eye drops and progression of myopia in children: multicentre placebo controlled, double masked, randomised trial in the UK (CHAMP-UK)
OBJECTIVES: To evaluate the efficacy and safety of low concentration atropine eye drops for reducing progression of myopia in children in the UK. DESIGN: Multicentre, double masked, superiority, placebo controlled, randomised trial. SETTING: National Health Service hospital eye services and academic institutions at five UK centres. PARTICIPANTS: 289 children aged 6-12 years with myopia between -0.50 and -10.0 dioptres (D). Participants were allocated in ratio of 2:1 to atropine or placebo. INTERVENTIONS: One eye drop of preserved atropine 0.01% or placebo daily for two years. MAIN OUTCOME MEASURES: The primary outcome was spherical equivalent refractive error of both eyes measured by autorefractor under cycloplegia after two years. Secondary outcomes included change in axial length, best corrected distance and near visual acuity, reading speed, pupil diameter, spectacle correction, adverse event rates, quality of life, and tolerability. Outcomes were collected every six months. An electronic monitoring system was used to assess adherence. RESULTS: 192 participants were included in the atropine group and 97 in the placebo group, with an average age of 9.3 years (standard deviation (SD) 1.7 years). 207 (72%) reported white ethnicity, 161 (56%) were girls, and the mean level of myopia was -2.87 D (SD 1.71 D). A total of 235 (81%) participants completed the study, with primary outcome data available for 230 (80%) participants: 151 (79%) in the atropine group and 79 (81%) in the placebo group. Atropine eye drops were more effective than placebo in reducing myopia progression (mean difference 0.33 D, 95% confidence interval (CI) 0.17 to 0.49 D, P<0.001). Prespecified subgroup analyses did not show differences according to age, ethnicity, sex, or severity of myopia. Changes in central axial length were significantly less in the atropine group versus placebo group: mean difference 0.14 mm (95% CI 0.07 to 0.21, P<0.001). There were no differences in other secondary outcomes, except pupil diameter, which was greater in the atropine group (0.36 mm, 95% CI 0.17, 0.55, P<0.001), and no differences in frequency of adverse events or in tolerability measures. No serious adverse events were related to the trial drugs. CONCLUSIONS: Low concentration atropine (0.01%) eye drops significantly reduced progression of myopia and were well tolerated compared with placebo in children in the UK. TRIAL REGISTRATION: ISRCTN registry ISRCTN99883695, ClinicalTrials.gov NCT03690089.
13 June 2026
Read appraisal →Journal of epidemiology and community health
Incidence of physical non-communicable disease in people who have experienced imprisonment compared with the general population in high-income countries: a systematic review and meta-analysis
BACKGROUND: Non-communicable diseases (NCDs) represent a significant cause of morbidity and mortality globally. Despite a strong evidence base outlining increased risk of communicable diseases and poor mental health in people who have experienced imprisonment, the risk of NCDs has not been quantified in previous reviews. This study aimed to describe the incidence of morbidity and mortality of physical NCDs in people who have experienced imprisonment compared with the general population in high-income countries. METHODS: We searched MEDLINE, Embase, CINAHL and SocINDEX (PROSPERO CRD42024540173) for comparative studies of morbidity or mortality from NCDs. Articles were assessed against predefined criteria to select studies that included people with experience of imprisonment; measured NCD incidence; compared incidence to a general population; and were conducted in a high-income country. Two reviewers conducted independent risk of bias assessment using an adapted version of the Newcastle-Ottawa Scale. Data were synthesised using random-effects meta-analyses and vote counting based on effect direction. Meta-regression and subgroup analysis investigated heterogeneity. RESULTS: Of 3085 articles screened, 32 were included providing 341 datapoints. Meta-analysis showed a higher risk of mortality from several diseases, including respiratory disease (risk ratio (RR) 2.38 (95% CI 1.18 to 4.80), I2=97%), cardiovascular disease (RR 1.80 (95% CI 1.32 to 2.46), I2=99%), liver disease and cirrhosis (RR 2.50 (95% CI 1.08 to 5.77), I2=99%), digestive disease (RR 2.92 (95% CI 1.09 to 7.78), I2=99%), neurological disease (RR 1.94 (95% CI 1.09 to 3.44), I2=92%), head and neck cancer (RR 21.31 (95% CI 4.32 to 105.14), I2=97%), liver cancer (RR 4.07 (95% CI 2.34 to 7.08), I2=94%), cervical cancer (RR 3.95 (95% CI 3.11 to 5.01), I2=0%) and lung cancer (RR 1.95 (95% CI 1.34 to 2.85), I2=0%). Synthesis without meta-analysis also indicated imprisonment was associated with increased morbidity from multiple NCDs. CONCLUSIONS: Several NCDs, including multiple cancers, cardiovascular disease, respiratory disease and liver disease, are substantially more common in people who have experienced imprisonment compared with the general population, highlighting a need for primary prevention and NCD management.
13 June 2026
Read appraisal →Journal of medical Internet research
Text Messaging Support for Patients Diagnosed With Impaired Glucose Tolerance During Pregnancy: Nonrandomized Pre-Post Implementation Study Assessing Impact on Postpartum Transitions of Care
BACKGROUND: Patients with impaired glucose tolerance (IGT) identified during pregnancy who do not develop gestational diabetes mellitus (GDM) often do not receive additional interventions for their long-term metabolic risks. OBJECTIVE: This nonrandomized pre-post implementation study reports the design process and initial program evaluation for Better Follow-up of Impaired Glucose Tolerance (BRIDGE), a 12-week text-based postpartum support program promoting hemoglobin A1c (HbA1c) completion and primary care provider (PCP) visit scheduling for patients diagnosed with IGT during pregnancy, assessing improvement in desired postpartum transition milestones. The 19-month program was divided into 2 arms lasting 9.5 months each, BRIDGE- (SMS text messaging support alone; October 2021-July 2022) and BRIDGE+ (SMS text messaging and IGT-focused postpartum visit; July 2022-April 2023). We aimed to assess whether BRIDGE improved desired postpartum transition milestones. METHODS: Patients were eligible for BRIDGE if they received prenatal care at the study site (a northeastern US academic tertiary care center), were diagnosed with IGT during pregnancy, never developed GDM, and could receive English text messages. We performed a program evaluation using a pre/postimplementation design, comparing outcomes for the BRIDGE population to a 19-month historical population. Primary outcomes were (1) completion of HbA1c testing by 1 year postpartum and (2) PCP visit scheduling by 12 weeks postpartum. A comparative analysis between BRIDGE- and BRIDGE+ was performed. Multivariable logistic regressions controlled for the history of IGT after stepwise backward elimination. RESULTS: In the program evaluation, 503 individuals were included (n=342 in historical population, n=82 in BRIDGE- population, and n=79 in BRIDGE+ population), with similar demographic and clinical characteristics across populations. A total of 212 individuals were screened for eligibility in BRIDGE, and 161 individuals participated in the program. BRIDGE participants had increased odds of HbA1c completion by 1 year postpartum (39.8% vs 12.5%; adjusted odds ratio [aOR] 4.28, 95% CI 2.71-6.78) and PCP visit scheduling (31.0% vs 12.0%; aOR 9.58, 95% CI 4.39-20.9) compared to the historical population. BRIDGE+ patients were more likely to complete HbA1c testing by 12 weeks postpartum than BRIDGE- participants. Most patients attended scheduled PCP visits, but rates of IGT counseling at PCP visits were low. CONCLUSIONS: Individuals with IGT rarely receive targeted interventions during pregnancy or delivery hospitalization. This innovative study demonstrates that individuals with IGT have high rates of uptake for postpartum SMS text messaging support, which tripled completion rates of HbA1c screening within 1 year postpartum and doubled the scheduling rate for PCP visits by 12 weeks postpartum. While attendance at scheduled PCP visits was very high, <60% of PCP visits included IGT counseling, highlighting key improvement areas in the quality of postpartum transitions to primary care. While a randomized trial is needed to ascertain definitive impact, SMS text messaging support may be an effective tool to improve postpartum transitions of care for this underserved population.
12 June 2026
Read appraisal →Vaccine
Can a DNA vaccine protect against Chagas disease? A systematic review of preclinical studies.
Chagas disease, caused by Trypanosoma cruzi, remains a major neglected tropical disease with limited treatment options. DNA-based vaccination has been explored as a preventive strategy, but its overall efficacy and translational value remain uncertain. Despite encouraging preclinical findings, the evidence on DNA-based vaccines against T. cruzi remains heterogeneous and has not been systematically integrated across outcomes. A comprehensive review is therefore needed to contextualize the current knowledge and define the translational relevance of this platform. To systematically review preclinical in vivo evidence on the efficacy of DNA-based vaccination against T. cruzi, focusing on parasitological, immunological, histopathological, and cardiac outcomes. PubMed/MEDLINE, Scopus, Embase, and Web of Science were searched without date restrictions. Eligible studies were controlled in vivo experiments evaluating DNA vaccines against T. cruzi in animal models. Two reviewers performed study selection, data extraction, risk-of-bias (SYRCLE) and certainty of evidence (GRADE) assessment independently. Thirty preclinical studies were included. Murine models predominated, especially BALB/c, C57BL/6, and C3H/He mice, although canine studies added translational relevance. Naked plasmid DNA, Salmonella-delivered constructs, and adjuvanted DNA vaccines were the most common platforms. The principal antigens belonged to the trans-sialidase family and amastigote surface proteins. Overall, vaccination was associated with reduced parasitemia, improved survival in several murine models, Th1 immune responses characterized by increased IFN-γ, IgG2a, and CD8+ T-cell activation, as well as attenuation of inflammatory, histopathological, and cardiac damage. In canine models, vaccination also reduced parasite burden and myocardial inflammation, although protection remained incomplete. Confidence in the evidence was limited by substantial heterogeneity in animal background, antigen selection, parasite strain, inoculation route, dosing regimens, and follow-up duration. Frequent unclear risk of bias and incomplete reporting prevented meta-analysis and constrain causal inference. DNA-based vaccination against T. cruzi can induce Th1 immunity and reduce parasitological and tissue injury outcomes, but protection is often partial and rarely sterilizing.
11 June 2026
Read appraisal →Vaccine
Immunogenicity and safety of Sabin-strain inactivated poliovirus vaccine in infants: a systematic review and meta-analysis
BACKGROUND: Poliomyelitis remains a threat to children under five despite global vaccination efforts. The Sabin-strain inactivated poliovirus vaccine (sIPV) has emerged as a safer alternative to conventional Salk-strain IPV (cIPV) due to lower biosafety requirements. This systematic review and meta-analysis evaluated the immunogenicity and safety of sIPV compared with cIPV in healthy infants completing the primary IPV series. METHODS: PubMed, Embase, Scopus, and Cochrane Library were searched for RCTs. Outcomes included seroconversion rates, seroprotection rates, geometric mean titers (GMTs), and adverse events. Data were pooled using random-effects models, and evidence certainty was assessed using RoB 2.0 and GRADE. RESULTS: Seven RCTs with 4646 infants were included. Seroconversion rates were comparable: PV1 100% (RR 1.00, 95% CI 1.00-1.01), PV2 102% (RR 1.02, 95% CI 0.98-1.07), PV3 100% (RR 1.00, 95% CI 0.99-1.01). GMTs were higher for sIPV after PV1 (MD 1.50, 95% CI 0.98-2.02) and PV2 (MD 0.52, 95% CI 0.06-0.98). Local adverse events were similar (OR 1.27, 95% CI 0.95-1.71), while systemic adverse events were higher with cIPV (OR 1.36, 95% CI 1.15-1.60). Fever (OR 1.50, 95% CI 1.20-1.87) and allergic reactions (OR 2.19, 95% CI 1.02-4.69) were slightly more frequent with sIPV. CONCLUSIONS: sIPV provides immunogenicity comparable to cIPV with an acceptable safety profile in infants. Its lower biosafety requirements support its use in global polio eradication programs.
11 June 2026
Read appraisal →Translational stroke research
Transcarotid Artery Revascularization for Asymptomatic Carotid Stenosis: A Systematic Review and Meta-Analysis
Management of asymptomatic carotid stenosis remains controversial despite advances in contemporary best medical therapy. Transcarotid artery revascularization (TCAR) has been introduced as an alternative revascularization option alongside carotid endarterectomy (CEA) and transfemoral carotid artery stenting (TF-CAS). Evidence focused specifically on asymptomatic carotid disease, however, remains limited and is derived mainly from observational analyses of clinical databases, with only a small amount of single-center retrospective evidence. The present study therefore examined the available comparative evidence on TCAR for asymptomatic carotid stenosis. A systematic review and meta-analysis was performed of studies reporting outcomes of TCAR in asymptomatic carotid stenosis. Eligible studies included asymptomatic cohorts or mixed populations with extractable asymptomatic subgroup data and reported outcomes after TCAR with comparison against CEA and/or TF-CAS. The primary pooled outcomes were stroke, death, stroke or death, and myocardial infarction at 30 days, together with the corresponding stroke, death, and stroke or death outcomes at approximately 1 year. For TCAR versus CEA, no significant between-group differences were identified in the pooled 30-day risks of stroke (risk ratio (RR), 1.25; 95% CI, 0.98-1.59; I²=82%), death (RR, 0.94; 95% CI, 0.64-1.38; I²=57%), or stroke or death (RR, 1.10; 95% CI, 0.95-1.26; I²=75%). In contrast, myocardial infarction within 30 days occurred less frequently after TCAR (RR, 0.68; 95% CI, 0.48-0.96; I²=71%). At approximately 1 year, pooled estimates likewise showed no significant difference in stroke (RR, 1.27; 95% CI, 0.70-2.29; I²=0%) or death (RR, 1.14; 95% CI, 0.97-1.35; I²=0%), whereas the pooled estimate for stroke or death favored CEA (RR, 1.25; 95% CI, 1.15-1.36; I²=31%). A different pattern emerged in the TCAR versus TF-CAS comparison. TCAR was associated with significantly lower pooled 30-day risks of stroke (RR, 0.60; 95% CI, 0.48-0.73; I²=0%), death (RR, 0.55; 95% CI, 0.38-0.81; I²=20%), and stroke or death (RR, 0.60; 95% CI, 0.52-0.70; I²=6%), while no significant difference was observed for 30-day myocardial infarction (RR, 0.91; 95% CI, 0.65-1.29; I²=44%). At approximately 1 year, stroke remained comparable between the 2 groups (RR, 0.75; 95% CI, 0.46-1.23; I²=26%), whereas death (RR, 1.09; 95% CI, 1.00-1.20; I²=66%) and stroke or death (RR, 0.79; 95% CI, 0.71-0.87; I²=45%) differed, with the pooled stroke-or-death estimate favoring TCAR. Current observational evidence suggests that TCAR compares favorably with TF-CAS in asymptomatic carotid stenosis. When compared with CEA, TCAR does not appear to provide a clear benefit in pooled perioperative neurologic outcomes, although myocardial infarction may occur less frequently after TCAR.
11 June 2026
Read appraisal →International journal of biometeorology
Integrating google trends and hybrid statistical-machine learning models for dengue surveillance in an inland vietnamese province: a 94-month evaluation (2013-2021) with search-signal anomaly screening
Dengue transmission in inland Southeast Asia shows strong seasonality and short-term surges that challenge timely public-health response. We assessed whether province-level Google search activity can support short-horizon dengue monitoring in Dong Nai, Vietnam. Monthly reported dengue cases from July 2013 to April 2021 were aligned with the Google Trends index (GTI) for "Sốt xuất huyết". Models were evaluated using leakage-free rolling-origin expanding-window cross-validation with 3-month validation blocks. We compared a negative binomial (NB) autoregressive baseline, NB models incorporating GTI, tree-based machine-learning models using the same covariates, and a simple NB-random-forest ensemble. Predictive performance was assessed using root mean squared error (RMSE), mean absolute error (MAE), R2, and discrimination for 95th-percentile outbreak exceedance. Cross-correlation peaked at lag 0, indicating that GTI functioned primarily as a contemporaneous nowcasting signal rather than a long-lead predictor. NB models incorporating GTI showed competitive calibration, while boosted tree models achieved the lowest point-estimate errors; XGBoost achieved RMSE ≈ 40.95 and R2 ≈ 0.861 compared with RMSE ≈ 45.23 and R2 ≈ 0.823 for NB with GTI. Adding GTI to the autoregressive NB baseline reduced RMSE by 11.77%, but the 95% confidence interval crossed zero, indicating modest and statistically non-significant incremental gain. Search-signal anomaly screening suggested that GTI spikes largely coincided with epidemic peaks and did not clearly inflate out-of-sample errors. Overall, GTI provides operational value for dengue nowcasting and alert triage in inland provinces, while future systems should integrate climate, vector, mobility, and media-monitoring data.
9 June 2026
Read appraisal →Environmental monitoring and assessment
YOLO-geochemical integration for real-time aeolian dust monitoring: a critical review of signatures, health risks, and policy implications
Aeolian dust is one of the most pervasive natural air pollutants, strongly influencing atmospheric chemistry, visibility, climate feedback, and human health. This review critically evaluates the integration of geochemical fingerprinting techniques and YOLO-based deep learning frameworks for real-time aeolian dust monitoring, with the overarching objective of advancing source attribution, health risk assessment, and evidence-based policy formulation in dust-prone regions. The Middle East, particularly the Qatar Peninsula, experiences recurrent dust storms that transport mineral particles and trace metals, contributing to degraded air quality, reduced solar energy efficiency, and elevated risks of respiratory and cardiovascular diseases. Traditional monitoring approaches including satellite observations, ground-based measurements, and geochemical analyses such as inductively coupled plasma mass spectrometry (ICP-MS), scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS), and X-ray diffraction (XRD) have improved source apportionment and compositional understanding, yet they often lack real-time detection and high-resolution spatiotemporal coverage. Emerging artificial intelligence (AI) tools, particularly the You Only Look Once (YOLO) deep learning framework, offer transformative opportunities for atmospheric dust monitoring by enabling rapid, automated plume detection across multispectral and hyperspectral datasets. This review demonstrates that ICP-MS, SEM-EDS, and XRD, when applied in combination, provide robust multi-dimensional source fingerprinting: ICP-MS isotopic ratios discriminate crystal from anthropogenic contributions, SEM-EDS resolves particle-level morphology and elemental composition, and XRD quantifies mineralogical assemblages that vary systematically with source region and transport pathway. Concurrently, successive YOLO generations (YOLOv5 through YOLOv10) have achieved real-time dust plume detection with mAP values exceeding 88% on satellite and UAV imagery. The proposed multimodal YOLO-geochemical integration framework, in which YOLO-guided plume detection triggers targeted field sampling and geochemical labels iteratively retrain detection models, enables compositionally resolved, real-time exposure mapping that directly supports inhalation risk assessment against WHO PM2.5 and PM10 thresholds and metal-specific reference concentrations. Collectively, these findings establish a scalable, evidence-based monitoring architecture that strengthens early-warning systems, informs air quality regulation, and advances global strategies to mitigate the environmental and public health burdens of dust storms.
7 June 2026
Read appraisal →Annals of medicine
A body roundness index (BRI)-based predictive model for metabolic syndrome in perimenopausal and postmenopausal women-from a cross-sectional machine learning study to a longitudinal dynamic assessment
BACKGROUND AND AIMS: Metabolic syndrome (MetS) is highly prevalent among perimenopausal and postmenopausal women and poses a major public health challenge because of its association with cardiovascular disease, type 2 diabetes, and premature mortality. However, prediction tools for this population remain limited. Therefore, this study aimed to develop a Body Roundness Index (BRI)-based prediction model for MetS by integrating cross-sectional machine learning and longitudinal assessment. METHODS AND RESULTS: Cross-sectional models were trained using NHANES 2007-2020 and validated in the Affiliated Hospital of Dalian University (2023-2024). Sixteen predictors were selected via LASSO and Boruta, and eight models were evaluated using AUC, calibration, and decision curve analysis. SHAP ranked high-contribution factors. Longitudinal analysis used a 10-year cohort. Annualized change rates and cumulative exposure metrics of five key predictors were combined with baseline values to build Cox models, compared by C-index and time-dependent ROC.The artificial neural network (ANN) demonstrated optimal cross-sectional performance (internal AUC: 0.854; external AUC: 0.878) with good calibration and clinical benefit. SHAP identified BRI, WBC, ALT, MCV, and AST as top contributors, with BRI showing the strongest impact. Longitudinal analysis revealed that integrating annual change rates and annual cumulative exposure of these five predictors achieved optimal discriminative ability (C-index: 0.847), with time-dependent AUCs of 0.853, 0.859, and 0.847 at 1, 3, and 5 years, respectively. CONCLUSION: BRI is significantly associated with MetS in perimenopausal and postmenopausal women. The ANN model provides an efficient cross-sectional screening tool, while incorporating longitudinal trajectories of BRI and key laboratory indicators enhances long-term MetS risk prediction.
7 June 2026
Read appraisal →Journal of medical Internet research
Daily Stress and Heart Rate Variability Among Mindfulness Meditation Practitioners: mHealth Observational Study
BACKGROUND: Mindfulness meditation has been reported to reduce stress and enhance well-being. However, its effects on heart rate variability (HRV)-a physiological marker of stress-remain underexplored. OBJECTIVE: This study aimed to examine how meditation practice is associated with subjective stress, HRV, and their interaction, using mobile health technologies. METHODS: This 3-week observational study included 90 participants-19 meditation practitioners (meditation group), 32 recreational runners as an active control group characterized by lower stress and higher HRV (running group), and 39 individuals without regular meditation or exercise habits (control group). HRV was continuously recorded using Garmin smartwatches. Subjective stress levels and activity states were assessed 3 times daily through a smartphone-based experience sampling method, yielding a total of 4557 responses (mean 50.6, SD 22.8 per participant). From the meditation group, start and end times of 632 daily meditation sessions (mean 33.3, SD 18.3 per participant) were also collected via the app. Standardized questionnaires on stress and related measures were administered at the end of the study period. RESULTS: The questionnaire survey confirmed that stress levels were significantly lower in both the meditation and running groups compared with controls (median Perceived Stress Scale scores: meditation 21, IQR 17-24; running 22, IQR 19-25; control 25, IQR 21-30; Kruskal-Wallis P=.02; adjusted Wilcoxon P=.05 and .04, respectively). Smartwatch-derived HRV (root-mean-square of successive differences [RMSSD]) was elevated in the running group relative to controls (median 47.0, IQR 44.0-54.2 and median 42.0, IQR 34.2-47.8, respectively; P<.001), whereas no significant difference was observed between the meditation and control groups (median 40.8, IQR 35.5-44.4 and median 42.0, IQR 34.2-47.8, respectively). Bayesian analysis of the experience sampling method data indicated that higher subjective stress levels were associated with a concurrent RMSSD reduction of -2.24 (95% credible interval -3.97 to -0.26) milliseconds. Although this pattern was consistent across groups, the steeper decline of -3.94 (95% CI -7.04 to -0.74) milliseconds was observed only in the running group, likely reflecting their elevated baseline HRV. Additionally, Bayesian modeling of 632 logged meditation sessions revealed an acute RMSSD increase of +4.68 (95% CI 2.96 to 6.38) milliseconds during meditation, with effects maintained for at least 30 minutes post practice. CONCLUSIONS: Although HRV among meditation practitioners did not appear elevated in overall daily life, the ability to increase HRV at arbitrary timings, along with the prolonged residual effect, may correlate with stress reduction. This hypothesis requires further exploration with adequate controls. Despite the preliminary nature of this study due to its limited sample size, our findings highlight the potential of mobile health-based methodologies to capture stress and HRV dynamics in real-world settings.
1 June 2026
Read appraisal →Neurology
Associations of Baseline Clinical Phenotypes With White Matter Hyperintensity Volume Change: A Study of 4,329 UK Biobank Participants
BACKGROUND AND OBJECTIVES: White matter hyperintensities (WMHs) relate to cognitive and physical impairment. Although WMHs typically progress over time, regression has also been observed. Our aim was to explore baseline clinical phenotypes associated with subsequent WMH volume change in the UK Biobank (UKB). METHODS: We included participants with total volume of WMHs at first and follow-up brain MRI. We assessed 107 preselected clinical phenotypes measured at baseline and preprocessed using the Phenome Scan Analysis Tool (PHESANT). We derived a measure of WMH change as the residual from a linear regression of follow-up WMH volumes on baseline WMH volumes to account for baseline WMH burden. We ran (1) linear regressions for continuous WMH change as the outcome and (2) multinomial logistic regressions comparing progression, regression, and stable groups after categorizing WMH volume change using an established percentile-based approach. Models were unadjusted, partially adjusted (age, sex, total brain tissue volume, follow-up time), or fully adjusted (further adding blood pressure; BP). We corrected p values using false discovery rate (FDR) correction. RESULTS: We included 4,329 participants (median age 52; interquartile range 12), 54.6% female, with a median follow-up of 2.3 years (range 1-7 years). Across follow-up, total brain volume decreased (mean decrease 16,058 mm3) and WMH volume increased (median increase 293 mm3; both p < 0.001). WMHs progressed in 53.9% of participants, regressed in 26.01%, and remained stable in 20%. In fully adjusted linear models, higher diastolic BP was the only baseline phenotype associated with greater WMH progression (0.10 SD higher WMH change per 1 SD higher inverse-rank normal transformed diastolic BP; 95% CI 0.05-0.15, FDR p < 0.05). In multinomial models, hip pain lasting more than 3 months (odds ratio [OR] 0.25, 95% CI 0.11-0.58) and faster walking pace (OR 0.79, 95% CI 0.66-0.95) were inversely associated with WMH progression, whereas hip pain was also inversely associated with WMH regression vs stability (OR 0.31, 95% CI 0.12-0.79; all FDR p < 0.05). DISCUSSION: We established vascular risk factors associated with subsequent WMH volume change in the studied cohort. Distinct clinical and demographic profiles characterized WMH progression, regression, and stable groups. Results suggest that vascular factors relate to WMH change but are sensitive to covariate control. Further studies should establish factors differentially and causally related to WMH progression, regression, and stability.
1 June 2026
Read appraisal →Human vaccines & immunotherapeutics
Safety and durability of influenza and SARS-CoV-2 antibody responses through 6 months after a single dose of mRNA-1083, a multicomponent influenza and COVID-19 vaccine, in adults ≥50 years.
Seasonal influenza and SARS-CoV-2 continue to contribute substantially to global morbidity and mortality. mRNA-1083 is an investigational multicomponent mRNA vaccine targeting seasonal influenza and SARS-CoV-2. In this phase 3, observer-blind, active-controlled trial (NCT06097273), adults (aged ≥50 y) were randomly assigned (1:1) to receive mRNA-1083 (40 µg; plus placebo) or age-appropriate licensed comparator vaccines for influenza and COVID-19. Two substudies were included: Cohort A (≥65 y) and Cohort B (50-64 y). Primary objectives were to demonstrate noninferiority of mRNA-1083 versus comparators for influenza hemagglutination inhibition (HAI) responses and SARS-CoV-2 neutralizing antibody (nAb; PsVNA) responses at Day 29 after vaccination and to evaluate safety and reactogenicity of study vaccines. Here, we report safety, reactogenicity, and immunogenicity of mRNA-1083 through Day 181 in adults aged ≥50 y. mRNA-1083 induced HAI titers against all vaccine-matched influenza strains that were maintained through 6 months and were generally comparable to, or higher than, comparator responses. SARS-CoV-2 nAb responses remained higher for mRNA-1083 than comparators through 6 months. Reactogenicity was predominantly grade 1-2 in severity, and vaccination-related unsolicited adverse events were similar between groups. No serious adverse events or deaths related to vaccination were reported through 6 months. In adults aged ≥50 y, mRNA-1083 elicited durable influenza and SARS-CoV-2 humoral immune responses with an acceptable safety profile through 6 months. These findings support the use of mRNA-1083 as a multicomponent vaccine.ClinicalTrials.gov identifier: NCT06097273 (https://clinicaltrials.gov/study/NCT06097273).
1 June 2026
Read appraisal →The New Zealand medical journal
Cancer risk and screening in transgender and gender-diverse individuals on gender-affirming hormone therapy: a review article for the Australasian context
AIM: We aimed to synthesise current evidence on the association between gender-affirming hormone therapy (GAHT) and cancer risk in transgender and gender-diverse (TGD) individuals and to provide evidence-based cancer screening recommendations for Australasian general practitioners. METHODS: A systematic review of epidemiological studies, case reports and clinical guidelines was conducted in accordance with PRISMA 2020 guidelines. Searches were performed across PubMed/MEDLINE, Embase, Scopus and the Cochrane Library for literature published up to December 2025. Key evidence sources were narratively synthesised to develop practical recommendations tailored to the Australasian context. RESULTS: The available evidence, although limited by short follow-up durations and small cohort sizes, does not demonstrate a consistent, statistically significant increase in common cancers among TGD individuals receiving GAHT. However, emerging case reports highlight novel clinical concerns, including malignancies in surgically constructed tissues (neophallus and neovagina). Breast cancer risk in trans women appears to increase with the duration of oestrogen therapy, though it remains lower than in cisgender women. Prostate cancer may present at a higher grade in trans women receiving oestrogen. Screening participation is consistently lower in TGD populations, often resulting in later-stage diagnoses. CONCLUSION: Cancer screening for TGD individuals should be guided by an organ-inventory approach, based on the organs a person has rather than their gender identity. Clinicians should maintain a low threshold for investigating suspicious lesions, particularly in surgically altered tissues. The development of culturally safe and inclusive primary care environments is paramount to improving screening uptake and ensuring equitable health outcomes for TGD communities in Australia and New Zealand.
31 May 2026
Read appraisal →European journal of nutrition
Microencapsulated docosahexaenoic acid increases the Omega-3 Index and attenuates the physiological impact of eccentric exercise in physically trained adults: a 12-week double-blind placebo-controlled trial
PURPOSE: This study investigated the effects of docosahexaenoic acid (DHA) supplementation on delayed onset muscle soreness (DOMS), physical function, and inflammation following eccentric exercise-induced muscle damage in physically trained male and female adults (training ≥ 5d/wk). METHODS: Thirty-eight participants (12 Control, 26 DHA) completed a 12-week double-blind, placebo-controlled matched-pair trial. The Control group received high-oleic acid tablets. The DHA group received 715 mg/d of microencapsulated DHA tablets. Participants performed eccentric cycling at weeks 0 and 12, with assessments conducted pre-exercise, 0-h, 24-h, and 48-h post-exercise. The primary outcomes were DOMS (visual analogue scale) and the Omega-3 Index (O3I) (estimated by finger-stick dry blood spot). Secondary outcomes included neuromuscular function and inflammatory cytokines. RESULTS: O3I was not different between groups at week 0 but was elevated in the DHA group (∆2.43%, [95% CI; 2.10, 2.77], P < 0.001) and between Control at week 12 (P < 0.001). DOMS was lower at 24-h and 48-h post-exercise in the DHA group and between Control at week 12 (P < 0.01). At week 12, jump height and peak vertical force improved at 48-h post-exercise in the DHA group (P < 0.05), resulting in moderate (d = 0.60) and small (d = 0.32) effect sizes for 48-h post-exercise area under the curve (AUC) between groups, respectively. There were interaction effects for 48-h post-exercise AUC of IL-6 (P = 0.049), TNF-α (P = 0.010), and IL-10 (P = 0.026). CONCLUSION: A dietary achievable dose of DHA elevated the O3I and reduced DOMS in physically trained adults. These outcomes support protracted intake of DHA to attenuate the physiological impact of eccentric exercise and promote recovery.
31 May 2026
Read appraisal →Human vaccines & immunotherapeutics
Strategies implemented to increase HPV vaccine uptake and confidence among men who have sex with men and transgender people: A systematic review
Men who have sex with men (MSM) and transgender persons are disproportionately affected by HPV-related disease. The HPV vaccine effectively prevents infection, but vaccine uptake remains suboptimal in this group. Thus, this systematic review aimed to synthesize the literature on interventions and strategies implemented to increase HPV vaccine confidence and uptake among MSM and transgender persons. Six databases were searched in May 2025. We assessed risk of bias and extracted and descriptively summarized key data through narrative synthesis and content analysis. Sixteen articles reporting on eight interventions were identified, all implemented in high-income settings. The interventions can be broadly categorized into two types: strategies to integrate HPV vaccination into existing health services and digital interventions aimed at generating vaccine demand. Overall, evidence remains limited on interventions to increase HPV vaccine uptake in these groups, particularly studies focusing on the needs of transgender persons.
31 May 2026
Read appraisal →Journal of global health
Global, regional, and national trends in blindness and vision loss, 1990-2021: a secondary ecological trend analysis based on modelled population estimates.
BACKGROUND: We aimed to provide global, regional, and national estimates of the burden of blindness and vision loss from 1990 to 2021, stratified by cause, age, and sociodemographic index (SDI). METHODS: This population-based study used data from the 2021 Global Burden of Diseases, Injuries, and Risk Factors Study 2021 (GBD) study, covering 1990 to 2021. It evaluates the burden of blindness and vision loss based on prevalence, age-standardised prevalence rate (ASPR), disability-adjusted life years (DALYs), age-standardised DALY rate (ASR of DALY), and average annual percent changes (AAPCs). RESULTS: Globally, the ASPR of blindness and vision loss increased from 12 453.52 (95% uncertainty interval (UI) = 10 287.58, 15 226.09) in 1990 to 15 784.33 (95% UI = 12 761.44, 19 502.32) in 2021, with an AAPC of 0.85 (95% confidence interval (CI) = 0.7, 1.11, P < 0.001). In contrast, the ASR of DALYs remained stable, changing from 342.01 (95% UI = 237.87, 482.17) in 1990 to 342.78 (95% UI = 224.21, 503.61) in 2021, with an AAPC of 0.05 (95% CI = -0.04, 0.2, P = 0.298). Notably, near vision loss and cataract showed consistent increases, with AAPCs of 1.14 (95% CI = 0.94, 1.51, P < 0.001) and 0.25 (95% CI = 0.16, 0.33, P < 0.001), respectively. Blindness and vision loss prevalence was significantly higher in low and low-middle SDI regions compared to other SDI regions. CONCLUSIONS: Global blindness and vision loss increased from 1990 to 2021, while DALYs stayed stable. Prevention should be tailored by region, country, gender, cause, and age.
31 May 2026
Read appraisal →BMJ open
The association of the Healthy Lifestyle Index (HLI) and HLI trajectories with risk of all-cause and cause-specific mortality: a GRADE-assessed dose-response systematic review and meta-analysis.
BACKGROUND: The Healthy Lifestyle Index (HLI) integrates key behaviours to assess their cumulative impact on health. While higher HLI adherence is linked to lower disease and mortality risk, its long-term trajectory association remains understudied. This study aims to examine the dose-response relationship and long-term association of HLI on mortality risks. DESIGN: Systematic review and dose-response meta-analysis using the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) approach. DATA SOURCES: PubMed, Scopus and Web of Science were searched until June 2024. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included observational cohort studies that assessed the relationship of HLI or its trajectories with all-cause, cardiovascular disease (CVD)-cause or cancer-cause of mortality. RESULT: Analysis of 13.7 million participants demonstrated that higher adherence to the HLI is linked to lower risk of all-cause (HR: 0.48; 95% CI 0.46 to 0.53; GRADE: moderate), CVD-cause (HR: 0.49; 95% CI 0.44 to 0.51; GRADE: moderate) and cancer-cause mortality (HR: 0.55; 95% CI 0.49 to 0.61; GRADE: low). These associations were further confirmed in a dose-response manner. Moreover, compared with maintaining an unhealthy lifestyle, a decline in HLI adherence was associated with a 14% higher risk of all-cause and a 19% higher risk of cancer-related mortality. In contrast, an improvement in HLI adherence was linked to a 20% lower risk of all-cause and a 13% lower risk of cancer-related mortality. CONCLUSION: Adherence to HLI and its long-term patterns are associated with lower mortality risk. These findings emphasise the importance of lifestyle-based prevention and intervention strategies in reducing mortality. PROSPERO REGISTRATION NUMBER: CRD42024500538.
31 May 2026
Read appraisal →Medicine
Associations of TyG-related indices with NAFLD-related outcomes: A systematic review and meta-analysis
BACKGROUND: The triglyceride-glucose (TyG) index and related composite indices have been increasingly studied as accessible metabolic markers for fatty liver disease. However, evidence remains difficult to interpret because different TyG-related indices have been evaluated across heterogeneous populations and nonalcoholic fatty liver disease (NAFLD)-related outcomes. We conducted a systematic review and meta-analysis to clarify the pooled associations that could be formally synthesized and to summarize the broader descriptive evidence. METHODS: We systematically searched PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, Wanfang Data, VIP Database, and China Biology Medicine Database from inception to December 2025. Observational studies evaluating the TyG index or related composite indices, including TyG-body mass index, TyG-waist circumference, and TyG-waist-to-height ratio, in relation to NAFLD-related outcomes were eligible. Random-effects meta-analyses were performed in R. Only studies with sufficient comparability in exposure operationalization, outcome definition, and effect-size reporting were quantitatively pooled; the remaining studies were retained in the descriptive synthesis. RESULTS: Nineteen studies involving 212,618 participants were included in the systematic review, including 16 cross-sectional studies and 3 cohort studies. Of these, 8 studies contributed to at least 1 quantitative synthesis. In continuous-scale analyses, higher TyG levels were associated with increased odds of NAFLD/steatosis (5 studies; pooled odds ratio [OR], 2.72; 95% confidence interval [CI], 2.08-3.55; I2 = 85.7%). In highest-versus-lowest analyses, the association remained significant (4 studies; pooled OR, 4.36; 95% CI, 3.39-5.61; I2 = 92.8%). TyG-body mass index (2 studies; pooled OR, 1.04; 95% CI, 1.03-1.05; I2 = 0.0%) and TyG-waist circumference (2 studies; pooled OR, 1.01; 95% CI, 1.01-1.01; I2 = 15.1%) were also positively associated with NAFLD/steatosis. TyG-waist-to-height ratio showed a positive association in supportive synthesis, but evidence for liver fibrosis and nonalcoholic steatohepatitis remained limited and was mainly descriptive. CONCLUSION: TyG-related indices were consistently associated with NAFLD/steatosis across the available literature. The basic TyG index had the broadest evidence base, whereas composite indices suggested possible additional value in selected settings. These markers may be useful for metabolic fatty liver risk stratification, but further studies with standardized outcome definitions and clearer exposure parameterization are needed.
31 May 2026
Read appraisal →Journal of medical Internet research
Effects of Multicomponent Digital Health Interventions on Multidimensional Physical Activity in Older Adults: Systematic Review, Meta-Analysis, and Meta-Regression of Randomized Controlled Trials
BACKGROUND: The comprehensive effects of multicomponent digital health interventions (DHIs) on multidimensional physical activity indicators and sedentary behavior (SB) remain controversial. OBJECTIVE: This systematic review aimed to evaluate the impact of multicomponent DHIs on daily steps, moderate-to-vigorous physical activity (MVPA), light physical activity, total physical activity, and SB in older adults. METHODS: PubMed, Web of Science, Embase, The Cochrane Library, and CINAHL were searched up to February 20, 2026. Randomized controlled trials concerning multicomponent DHIs for promoting exercise behavior in older adults were included. RoB 2.0 was used to evaluate study quality. Meta-analyses were performed using the Hartung-Knapp-Sidik-Jonkman random-effects model, and 95% prediction intervals (PIs) were calculated via Nagashima adjustment to evaluate effect dispersion. The GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) system was used to evaluate evidence certainty. RESULTS: A total of 26 randomized controlled trials (n=4129) were included. The results showed that multicomponent DHIs significantly improved daily steps (mean difference [MD] 822.8, 95% CI 198.3 to 1447.3 steps/d; 95% PI -1452.4 to 3098.0) and MVPA (MD 45.9, 95% CI 23.9 to 67.9 min/wk; 95% PI -9.4 to 101.2). However, the improvements in SB (MD -283.7, 95% CI -610.8 to 43.5 min/wk; 95% PI -984.5 to 417.1), total physical activity (MD 104.4; 95% CI -109.2 to 318.0 min/wk; 95% PI -444.4 to 653.2), and light physical activity (MD 39.3, 95% CI -96.2 to 174.7 min/wk; 95% PI -227.6 to 306.2) did not reach statistical significance. As some included studies combined digital tools with human support, the independent contribution of digital technology remains uncertain. PIs indicated a certain degree of dispersion across different clinical contexts. Subgroup analysis showed higher effect sizes for standalone wearables, human-assisted interventions, and populations with chronic disease risks. Meta-regression showed that effect sizes remained stable across different ages and durations. The trim-and-fill method confirmed the robustness of MVPA results. GRADE assessment indicated "moderate" certainty for MVPA and "low" for daily steps and other indicators. CONCLUSIONS: This systematic review suggests that multicomponent DHIs may serve as an effective means for enhancing daily steps and MVPA in older adults. The innovation lies in evaluating the true effect distribution of multicomponent DHIs through Hartung-Knapp-Sidik-Jonkman random-effects models and Nagashima PIs. Compared with previous studies, this review identified the impact of population characteristics and control group differences on effect estimates using PI and subgroup models, confirming that advanced age did not significantly diminish the good adaptability of older adults to DHIs. Evidence limitations include high heterogeneity, lack of long-term follow-up, and differences between objective and subjective measurement tools. In practice, priority should be given to hardware carriers with simplified interaction and integrated human support, with tailored strategies developed for different risk subgroups.
30 May 2026
Read appraisal →PloS one
Burden and patterns of dyslipidaemia among adult Ghanaians: A systematic review
BACKGROUND: Dyslipidaemia is a major modifiable risk factor for cardiovascular disease and has become an increasing public health concern in sub-Saharan Africa. Rapid urbanisation, dietary transitions, and sedentary lifestyles have contributed to the rising incidence of cardiometabolic diseases in Ghana. However, evidence on the overall burden and patterns of dyslipidaemia in adults remains unclear. This systematic review aimed to synthesise the evidence on the prevalence and lipid profile patterns of adult Ghanaians. METHODS: A comprehensive search of PubMed, Scopus, Web of Science, African Journals Online, Africa-Wide Information and African Index Medicus databases was conducted for studies published between January 1980 and November 2025. Eligible studies reported the prevalence or distribution of dyslipidaemia in Ghanaian adults (≥ 18 years). Two reviewers independently screened, extracted, and assessed the quality of included studies. Findings were synthesised narratively and summarised in descriptive tables and figures, due to heterogeneity in study designs and data reporting. RESULTS: Twenty-four studies published between 2003 and 2023, comprising approximately 11,400 participants, met the inclusion criteria. Across the included studies, the reported prevalence of dyslipidaemia ranged from 3.0% to 72.4%, reflecting differences in study populations, diagnostic criteria and study settings. Low high-density lipoprotein cholesterol (HDL-C) was the most frequent abnormality, followed by elevated total and low-density lipoprotein cholesterol (LDL-C), whereas hypertriglyceridaemia was the least common. Studies from southern and middle regions-particularly Ashanti, Brong-Ahafo, and Greater Accra-reported a higher dyslipidaemia prevalence than those from northern Ghana. Considerable variation in diagnostic criteria, study populations and sampling strategies limited comparability and precluded meta-analysis. CONCLUSION: Dyslipidaemia is common among adult Ghanaians, with low HDL-C emerging as the predominant abnormality. Methodological differences and sampling biases limit the precise estimation of the national burden, but the available evidence indicates a growing cardiometabolic risk. Future studies should prioritise community-based sampling and context-appropriate lipid thresholds to support evidence-based cardiovascular risk-reduction strategies.
30 May 2026
Read appraisal →Current atherosclerosis reports
Patient Engagement Strategies for Implementing Evidence-Based Care in Atherosclerosis
PURPOSE OF THE REVIEW: Despite continuing therapeutic advances in cardiovascular disease, it remains a leading cause of morbidity and mortality in the US. The current challenge is not the development of therapeutics but rather patient engagement and compliance with medical recommendations. Here in this narrative expert review we discuss strategies to improve patient engagement in cardiovascular care. RECENT FINDINGS: There is a varying level of data surrounding different patient education strategies in the care of atherosclerosis. There are three main categories of interventions reviewed with evidence of efficacy: personalized and accessible patient education, digital health interventions, and implementation of personalized medicine via genetic testing. Implementing strategies in the clinic such as personalizededucation at an accessible health literacy level, digital health interventions,and even genetic testing may improve patient engagement and outcomes.
30 May 2026
Read appraisal →JMIR cardio
Barriers and Facilitators to Maintaining Physical Activity After an mHealth Intervention for People Poststroke or Transient Ischemic Attack: Reflexive Thematic Analysis
BACKGROUND: Regular physical activity is critical for preventing secondary stroke following a stroke or transient ischemic attack (TIA). Although mobile health (mHealth) interventions have shown promise for promoting short-term increases in physical activity, evidence on their long-term effects and the mechanisms that support sustained behavior change remains limited. In particular, little is known about how people poststroke or TIA integrate the skills, knowledge, and habits gained through mHealth interventions into their daily lives once structured intervention support ends. OBJECTIVE: This study aimed to explore the perceived barriers and facilitators of maintaining physical activity among individuals poststroke or TIA after completing an mHealth intervention. METHODS: A qualitative approach was used, involving a strategic sample of 12 participants recruited after they had completed a 6-month mHealth intervention for people poststroke or TIA. The intervention included supervised physical therapy with mHealth support for physical exercises and behavior change (eg, counseling, goal-setting, and self-monitoring), followed by a 6-month postintervention period with access to self-managed mHealth support. To enable richness and depth in participants' accounts, the dataset consisted of 2 semistructured interviews conducted 3 months and 6 months after completing the intervention, along with participant-generated photographs. Between the interviews, participants took photos to reflect their experiences of maintaining physical activity after the intervention. These images served as prompts for dialog and reflection during the second interview. Data were analyzed using reflexive thematic analysis. RESULTS: We generated 3 themes: building experience and knowledge to maintain physical activity, staying physically active in a complex life situation, and the meaning of context for maintaining physical activity after the intervention. Barriers and facilitators were conceptually integrated into a comprehensive understanding of maintaining physical activity, symbolized by the metaphor of a soap bubble, which requires persistence and a supportive environment to stay afloat. The complexity of participants' health and life situations created barriers to maintaining physical activity. To overcome these barriers, developing personal strategies within a supportive social and physical context was crucial. This development was facilitated by the mHealth intervention, which enabled knowledge acquisition of the principles of physical activity after stroke or TIA, along with increased awareness of its health benefits. CONCLUSIONS: mHealth interventions for people poststroke and TIA can serve as a catalyst for physical activity engagement and an enhancer of the knowledge and experience necessary to maintain physical activity after the intervention. Despite health-related and contextual barriers, participants may use personalized strategies, supported by their social and environmental context, to navigate these challenges. These insights highlight opportunities for future mHealth interventions to strengthen the interaction between individuals and their environment and empower tailored strategies for behavior change and long-term physical activity maintenance.
30 May 2026
Read appraisal →Human vaccines & immunotherapeutics
Comparison of safety and immunogenicity between high-dose and low-dose quadrivalent influenza vaccines in children aged 6-35 months in China: A randomized, double-blind clinical study
Influenza vaccines for children aged 6-35 months are typically low-dose (0.25 mL, 7.5 μg hemagglutinin per strain). However, studies have shown that high-dose influenza vaccines (0.5 mL, 15 μg hemagglutinin per strain) can enhance protective efficacy without increasing the risk of adverse reactions. We conducted a randomized, double-blind clinical study in children aged 6-35 months in China. A total of 3,000 participants were randomized (1:1:1) to receive two doses of either an investigational high-dose quadrivalent influenza vaccine (H-QIV), an investigational low-dose quadrivalent influenza vaccine (LQIV-01) or a commercially available low-dose quadrivalent influenza vaccine (LQIV-02). Hemagglutination inhibition (HI) antibody titers 30 d post-vaccination were measured and vaccine safety and tolerability within 6 months post-vaccination were evaluated in these participants. The primary objectives were to demonstrate the non-inferiority of investigational vaccines to the commercially available vaccine in immunogenicity and to compare the H-QIV to L-QIVs. Both investigational vaccines demonstrated non-inferiority, with their seroprotection rates (SPRs), seroconversion rates (SCRs) and geometric mean titers (GMTs) meeting immunogenicity criteria. The H-QIV exhibited >11-fold increases in GMTs against H1N1, H3N2, B/Yamagata and B/Victoria strains compared to pre-vaccination levels, with SPRs exceeding 95% and SCRs exceeding 89%. The H-QIV was superior to both L-QIVs as it induced a stronger immune response to influenza viruses. All vaccines showed comparable safety profiles. The H-QIV can provide greater protection and benefits to this high-risk population.
29 May 2026
Read appraisal →Journal of medical Internet research
Digital Literacy and Interpersonal Trust as Predictors of Willingness to Share Patient-Generated Health Data Among Korean Internet Users: Cross-Sectional Study Using Privacy Calculus and Communication Privacy Management Theories
BACKGROUND: The proliferation of wearable devices and advances in data analytics are accelerating the adoption of personalized digital health care, relying heavily on patient-generated health data (PGHD). However, the sensitive nature of this data creates significant privacy boundaries. While previous research has focused on rational cost-benefit trade-offs, there is a limited understanding of how social and cognitive factors-specifically interpersonal trust and digital literacy (DL)-shape the data-sharing decisions of the general public. OBJECTIVE: This study aims to identify the factors predicting individuals' willingness to share health data (WS) by integrating privacy calculus and communication privacy management theories. It specifically examines the comparative influence of DL, interpersonal trust, and moral motivation on data-sharing decisions. METHODS: We analyzed data from the 2023 Korea Panel Survey on the Digital Society (n=4518), a nationwide representative sample of internet users. Survey-weighted structural equation modeling with weighted least squares mean and variance adjusted estimation was used to examine the relationships among perceived risk (PR), perceived benefit (PB), DL, interpersonal trust, and WS. RESULTS: PR was the strongest negative predictor of WS (standardized coefficient [std β]=-0.189, P<.001), whereas PB was the strongest positive predictor (std β=0.076, P=.009), followed by moral motivation (std β=0.062, P=.03) and interpersonal trust. DL showed a significant negative direct association with willingness to share (std β = -0.060, P<.001). However, subdimension analysis revealed heterogeneous mechanisms: the "understanding" dimension was associated with lower PR and indirectly promoted sharing, whereas use and engagement were associated with higher PR. Age-stratified analyses suggested potential heterogeneity in these relationships, although the overall interaction was not statistically significant. CONCLUSIONS: PBs and risks were the strongest determinants of PGHD sharing, with benefits increasing and risks decreasing willingness to share. Beyond this risk-benefit balance, DL (particularly understanding) and interpersonal trust also played important roles, highlighting the need for trust-based and user-centric strategies to promote PGHD sharing and support the expansion of digital health care.
29 May 2026
Read appraisal →BMJ open
Role of community pharmacy in the prevention of cardiovascular disease in minority ethnic groups in the UK including barriers and facilitators: a mixed-methods systematic review
OBJECTIVES: To evaluate the role of community pharmacy in the prevention of cardiovascular disease (CVD) among minority ethnic groups and to identify key barriers and facilitators influencing engagement with pharmacy-based CVD prevention services. DESIGN: Systematic review using thematic synthesis to analyse both qualitative data and narratively synthesised quantitative data taking a convergent data synthesis approach. Searches were conducted from inception to 16 October 2024 across CINAHL Ultimate (EBSCOhost), EMBASE (Ovid), PubMed (NLM), Scopus (Elsevier) and Web of Science (Clarivate), supplemented by Google Scholar for grey literature and backward and forward citation tracking to identify additional studies. SETTING: Community pharmacy settings across multiple countries, including the UK and international contexts. PARTICIPANTS: Minority ethnic groups accessing community pharmacy services and community pharmacy professionals involved in delivering CVD prevention interventions. INTERVENTIONS: Community pharmacy-based CVD prevention interventions, specifically services addressing CVD risk factors. OUTCOME MEASURES: Outcomes comprised qualitative insights, including barriers and facilitators to engagement with community pharmacy-led CVD prevention services. RESULTS: Twenty-three studies met the eligibility criteria. Five overarching themes were identified: trust, systemic barriers, culturally tailored language, cultural and religious influences and family and social networks. Community pharmacies undertook varied CVD-related roles, most commonly CVD screening and diabetes management with additional contributions to smoking cessation, weight and lipid management, health promotion and medication adherence. Community pharmacies showed potential to support CVD prevention among minority ethnic groups. However, engagement was limited by language barriers, cultural beliefs, lack of trust in healthcare providers and organisational constraints such as time pressures and limited training. Facilitators included culturally tailored interventions, language concordance and strong pharmacist-patient relationships. CONCLUSIONS: Evidence from the UK remains limited, highlighting a need for further research into the role of community pharmacy in reducing CVD disparities among minority ethnic groups. International evidence suggests significant potential for pharmacy-based interventions, but improved cultural competence, targeted service design and greater awareness of determinants of equitable healthcare are essential. Future research should focus on optimising culturally responsive pharmacy-led CVD prevention strategies. PROSPERO REGISTRATION NUMBER: CRD42024579766.
29 May 2026
Read appraisal →BMJ open
Voice-assisted artificial intelligence in cardiovascular disease management: a systematic review and meta-analysis protocol
INTRODUCTION: Cardiovascular disease (CVD) remains a leading cause of global morbidity and mortality, with self-management playing a pivotal role in improving outcomes. Voice-assisted artificial intelligence (AI) technologies such as virtual assistants and voice-controlled applications have emerged as innovative tools for healthcare delivery. While the technologies show promise in areas like primary prevention and chronic disease management, their effectiveness in supporting self-management for patients with CVD remains underexplored. This study aims to evaluate the impact of voice-assisted AI technologies on CVD self-management, specifically focusing on cardiovascular-related mortality, health-related quality of life (HRQoL) and adherence to lifestyle modifications. METHODS AND ANALYSIS: A systematic review and meta-analysis will be conducted following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. A comprehensive search will be performed across databases such as MEDLINE, Scopus, Embase and Cochrane Central Register of Controlled Trials (CENTRAL), from 2010 to 2025. The review will include randomised controlled trials (RCTs), non-RCTs and observational studies that evaluate voice-assisted AI interventions (eg, voice-controlled fitness apps, smart health assistants) aimed at CVD self-management. The primary outcome will be cardiovascular-related mortality. Secondary outcomes will include HRQoL, clinical outcomes (eg, high blood pressure), lipid profiles (eg, cholesterol and glucose levels) and lifestyle modifications (eg, dietary habits and levels of physical activity). Data management and analysis will be conducted using Comprehensive Meta-Analysis software V.2.0. The methodological quality of the included studies will be assessed using the Cochrane Risk of Bias tool for RCTs and the Newcastle-Ottawa Scale for observational studies. The meta-analysis will use random-effects models, with heterogeneity assessed using Q and I² statistics. Subgroup analyses and meta-regression will be conducted to explore potential sources of heterogeneity. ETHICS AND DISSEMINATION: No formal ethical assessment is required, as this study involves analysis of publicly available secondary data. Findings will be disseminated through publications in peer-reviewed scientific journals, conference presentations and media coverage to inform healthcare providers, policymakers and patients. PROSPERO REGISTRATION NUMBER: CRD42024568702.
29 May 2026
Read appraisal →JMIR mHealth and uHealth
Effects, Acceptability, and Use of a Dynamically Tailored Mobile What Do You Drink Intervention to Reduce Excessive Drinking Among Adolescents and Young Adults in the Netherlands: Randomized Controlled Trial
BACKGROUND: Excessive alcohol consumption among adolescents and young adults is a serious health problem. Dynamically tailored interventions could reduce their excessive drinking. We therefore developed "What Do You Drink" (WDYD), a 17-week dynamically tailored mHealth (mobile health) intervention providing personalized support on alcohol consumption. OBJECTIVE: We aim to evaluate the effectiveness, acceptability, and use of WDYD in reducing alcohol consumption of adolescents and young adults at risk. METHODS: We conducted a 2-arm, parallel-group randomized controlled trial using ecological momentary assessments. Recruitment was via an educational alcohol program, an online lifestyle monitor, social media advertisements, or news items on websites. Participants downloaded the standalone WDYD app, and when having given active informed consent, were randomized to the intervention or control group. Participants in the intervention group received dynamically tailored feedback sessions on alcohol consumption (wk 0-5, 7, 9, 13, and 17) and goal-monitoring reminders. Both groups completed an online baseline survey, 2 follow-up surveys (wk 9 and 33), and various ecological momentary assessments (7 daily assessments during wk 1, 7, 13, 19, 25, 31, and 33). Participants provided consent before randomization, in which they were informed that 2 study groups existed. After randomization, no disclosure of group assignment was provided, although participants could potentially infer it from receiving tailored sessions vs no tailored sessions. Primary outcomes were excessive drinking, binge drinking, and weekly alcohol consumption. Secondary outcomes were intrinsic motivation, self-confidence, and mood. Acceptability of WDYD was measured by survey questions; use was tracked via app data logs. RESULTS: Analyses were based on data from 1767 participants; 720 in the intervention group and 1047 participants in the control group. Almost half of them were female (2276/4795, 47.5%), and most (3471/4595, 72.4%) participants were aged 18-24 (median 19.40, IQR 2.92) years. The dropout rate was high, up to 96% (4603/4795) in the final 33rd week. No significant effect of WDYD was found on primary outcomes and mood, except for week 1 (excessive drinking: standardized β=-0.35, SE 0.15; 95% CI -0.64 to -0.05; binge drinking: standardized β=-0.36, SE 0.16; 95% CI -0.68 to -0.04; mood: standardized β=0.20, SE 0.06, 95% CI 0.08 to 0.32). Both groups reduced their alcohol consumption. Significant positive effects were found for intrinsic motivation and self-confidence up to 25 weeks (wk 25: standardized β=0.54, SE 0.24; 95% CI 0.06 to 1.02 for motivation; standardized β=0.72, SE 0.26; 95% CI 0.22 to 1.23 for self-confidence). Participants evaluated WDYD as acceptable and usable. CONCLUSIONS: WDYD did not significantly reduce excessive drinking compared to control, but improved motivation and self-confidence. High dropout rates highlight challenges in sustaining engagement in long-term mHealth interventions. Future research should explore strategies to enhance retention and optimize dynamic tailoring.
28 May 2026
Read appraisal →Journal of the American College of Cardiology
Opportunistic Cardiovascular Risk Assessment Using Routine Head CT in the Emergency Department
BACKGROUND: Routine noncardiac computed tomography (CT) imaging may contain information about cardiovascular risk. Head computed tomography (CTH) is among the most common imaging studies, conducted annually in millions of patients. Its utility for cardiovascular risk assessment has not been studied. OBJECTIVES: The purpose of this study was to develop and validate deep learning models for predicting incident cardiovascular disease (CVD) and estimating coronary artery calcium (CAC) scores from CTH, and assess performance against clinical risk factors. METHODS: This retrospective cohort study used data from the Stanford Health Care Emergency Department from August 2020 to August 2024. The CVD cohort comprised 27,990 adult patients without known CVD who underwent CTH. The CAC cohort included 2,313 patients who underwent both CTH and coronary CT angiography. Imaging features were extracted from CTH using pretrained deep learning models. Other risk factors were extracted from electronic health records. Outcomes were incident CVD complications (myocardial infarction, stroke, heart failure) and CAC scores (0, 1-10, 11-100, 101-400, >400 AU). Performance was evaluated using the concordance index (C-index) and area under the receiver operating characteristic curve, compared against the baseline model using the variables of the American Heart Association PREVENT (Predicting Risk of cardiovascular disease EVENTs) risk model. RESULTS: Four percent (1,110 of 27,990) of patients (median age 63.0 years, 51.7% female) experienced cardiovascular events. The CTH model achieved a C-index of 0.82 (95% CI: 0.78-0.85) compared with PREVENT (0.75; 95% CI: 0.70-0.79) with difference of 0.07 (95% CI: 0.04-0.10). For CAC estimation (n = 2,313, median age 65.0 years, 53.5% female), the CTH+PREVENT model achieved a C-index of 0.76 (95% CI: 0.72-0.80) and area under the receiver operating characteristic curve of 0.80 (95% CI: 0.73-0.85) for CAC >100. Of the patients, 15.7% were reclassified; higher-risk patients were younger but with higher prevalence of vascular calcifications (30.2% vs 24.8%, P = 0.001) and brain infarcts (20.1% vs 5.8%, P < 0.001). CONCLUSIONS: Routine CTH scans complement traditional risk factors for cardiovascular risk stratification, identifying subclinical disease in younger patients with favorable risk profiles. Clinical integration could improve CVD detection and prevention without additional costs or radiation.
28 May 2026
Read appraisal →Journal of the American College of Cardiology
Combining Genomics With Lipid and Inflammatory Biomarkers to Predict Coronary Artery Disease Risk: UK Biobank Study
BACKGROUND: Coronary artery disease (CAD) polygenic risk score (PRS), low-density-lipoprotein cholesterol (LDL-C), lipoprotein(a) (Lp(a)), and high-sensitivity C-reactive protein (hsCRP) are biomarkers that predict CAD. It is unclear whether integrating genomics with lipid and inflammatory biomarkers could complement traditional risk scores in identifying people at risk of CAD. OBJECTIVES: This study assesses the predictive value of CAD PRS, LDL-C, Lp(a), and hsCRP for incident CAD across different age and sex groups. METHODS: Participants (n = 215,695) from the UK Biobank aged 40 to 69 years with baseline CAD PRS, LDL-C, Lp(a), and hsCRP values were followed for 12 years to assess the incidence of CAD. We evaluated a multivariable-adjusted Cox model that included all 4 biomarkers, net reclassification index, C-statistics, and population attributable risk across different age and sex groups. RESULTS: Over a 12-year follow-up, 4,721 men and 2,425 women developed CAD. The HRs for incident CAD associated with each biomarker elevation were 1.79 (95% CI: 1.70-1.89) for CAD PRS, 1.60 (95% CI: 1.48-1.66) for LDL-C, 1.20 (95% CI: 1.12-1.29) for Lp(a), and 1.64 (95% CI: 1.57-1.72) for hsCRP. CAD PRS demonstrated a stronger association in men (HR per SD: 1.49; 95% CI: 1.45-1.54) than women (HR per SD: 1.37; 95% CI: 1.31-1.44; P-interaction ≤ 0.001). All biomarkers conferred greater HRs at younger ages (P < 0.0001). Individuals with all biomarkers elevated had a 4.65-fold increased risk of CAD compared with those with no elevated biomarkers. A combined 4-biomarker model had a higher C-statistic of 0.753 compared with the pooled cohort equations (C-statistic of 0.740). The C-statistic of the combined 4-biomarker model was also higher in younger individuals in both sexes and yielded a 32.0% continuous net reclassification index when compared with the pooled cohort equations. CONCLUSIONS: CAD PRS, LDL-C, hsCRP, and Lp(a) show independent age- and sex-specific associations with CAD. Measuring all 4 biomarkers may improve midlife CAD risk prediction for both male and female patients.
28 May 2026
Read appraisal →European journal of clinical pharmacology
Efficacy and safety of Ongericimab in patients with hypercholesterolemia: a systematic review and meta-analysis
BACKGROUND: Hypercholesterolemia represents abnormally elevated cholesterol levels and constitutes a major modifiable risk factor for cardiovascular diseases (CVDs). Low-density lipoprotein cholesterol (LDL-C) reduction remains the primary therapeutic target. However, many high-risk patients fail to achieve recommended LDL-C targets with conventional lipid-lowering therapies. Ongericimab is a new humanized recombinant monoclonal antibody targeting proprotein convertase subtilisin/kexin type 9 (PCSK9). We aimed to evaluate the efficacy and safety of Ongericimab compared to placebo in patients with hypercholesterolemia. METHODS: Following PRISMA guidelines, we searched PubMed, Scopus, Cochrane Library, Web of Science and Google Scholar through February 2025 for randomized controlled trials (RCTs) comparing Ongericimab versus placebo. Outcomes were pooled as mean difference (MD) or odds ratio (OR) with 95% confidence intervals (CIs) using random-effects models. RESULTS: Five RCTs comprising 1421 patients were included. Ongericimab significantly reduced LDL-C across all dosing regimens: 150 mg every 2 weeks (MD -69.48%, 95% CI: -73.02 to -65.94), 300 mg every 4 weeks (MD -61.82%, 95% CI: -66.66 to -56.98), and 450 mg every 4 weeks (MD -73.11%, 95% CI: -89.56 to -56.65); all p < 0.00001. Significant reductions were also observed in lipoprotein(a) (MD range: -44.81% to -50.06%), apolipoprotein B, non-HDL cholesterol, triglycerides, and total cholesterol, along with significant increases in HDL cholesterol and apolipoprotein A1. Adverse event rates were comparable between groups, with a pooled reduction OR of 0.85 (95% CI: 0.73-0.99) for overall treatment-emergent adverse events, favoring Ongericimab. CONCLUSION: This meta-analysis demonstrates that Ongericimab is effective and safe for improving lipid parameters in patients with hypercholesterolemia and dyslipidemia. These findings support Ongericimab as a valuable therapeutic option for patients requiring additional LDL-C reduction beyond conventional therapy. Further long-term studies are recommended to confirm cardiovascular outcomes.
28 May 2026
Read appraisal →Journal of breast imaging
Breast Arterial Calcification as a Predictor for Future Cardiovascular Events and Mortality: A Systematic Review and Meta-analysis
OBJECTIVE: Breast arterial calcification (BAC) is a surrogate marker of cardiovascular disease (CVD); however, its associations with incident mortality and major adverse cardiovascular events (MACEs) have not been systematically evaluated. The association between BAC and incident MACE and whether this is influenced by the method of BAC assessment was examined. METHODS: PubMed, MEDLINE, and EMBASE were reviewed to June 2025 for studies comparing BAC status with incident MACE (composite of death, myocardial infarction [MI], stroke, heart failure). The primary outcome was all-cause mortality. Random-effects meta-analysis was performed using hazard ratios (HRs) with 95% CIs. Subgroup analysis was conducted by method of BAC measurement and restricted to screening mammography cohorts, PROSPERO registration (CRD42020202612). RESULTS: Of 1118 studies screened, 24 were included (236 475 participants; mean age 58.5 years). Breast arterial calcification presence associated with all-cause mortality (HR, 1.32; 95% CI, 1.20-1.45; P <.0001) (follow-up 0.55-26 years). Increased risk was observed for cardiovascular mortality (HR, 1.38; 95% CI, 1.14-1.66; P = .0009), heart failure (HR, 1.29; 95% CI, 1.20-1.37; P <.0001), stroke (HR, 1.2; 95% CI, 1.11-1.30; P <.0001), and MI (HR, 1.2; 95% CI, 1.15-1.26; P <.0001). When restricted to screening mammography cohorts without concurrent indications for cardiovascular investigation (5 studies, n = 85 647), associations remained significant for all-cause mortality (HR, 1.34; 95% CI, 1.14-1.58; P = .0004), cardiovascular mortality (HR, 1.38; 95% CI, 1.14-1.66; P = .0009), heart failure (HR, 1.48; 95% CI, 1.19-1.86; P = .0005), and stroke (HR, 1.34; 95% CI, 1.08-1.67; P = .008). Associations with incident outcomes were observed only in studies using quantitative BAC assessment (HR, 1.31; 95% CI, 1.19-1.44; P = .002). CONCLUSION: Breast arterial calcification is a promising prognostic marker of mortality and CVD risk. Recognition of this incidental finding may enable timely identification of at-risk individuals.
27 May 2026
Read appraisal →Journal of perinatal medicine
Plant-based antioxidant strategies with potential for preeclampsia prevention: clinical and mechanistic insights
BACKGROUND: Preeclampsia is a leading cause of maternal and perinatal morbidity and mortality, driven by oxidative stress, endothelial dysfunction, and systemic inflammation. Current preventive strategies, such as low-dose aspirin, offer modest benefit, highlighting the need for alternative approaches. Medicinal plants with antioxidant and anti-inflammatory properties, long used in maternal health traditions, may provide biological pathways relevant to preeclampsia prevention. SUMMARY: This review synthesizes human clinical and mechanistic evidence on four medicinal plants with documented antioxidant activity - Curcuma longa, Moringa oleifera, Orthosiphon aristatus, and Centella asiatica. These botanicals demonstrate potential mechanisms of action including redox modulation, preservation of endothelial function, and suppression of pro-inflammatory pathways, all of which are implicated in preeclampsia pathophysiology. CONTENT: A PRISMA-guided systematic search of PubMed, Scopus, Web of Science, and the Cochrane Library (2000-2025) identified human studies evaluating these plants' effects on oxidative stress and vascular health. Evidence was synthesized narratively due to heterogeneous study designs and outcomes. Most studies were small, often not pregnancy-specific, and used non-standardized botanical formulations. OUTLOOK: These medicinal plants offer biologically plausible pathways to reduce preeclampsia risk. However, pregnancy-focused randomized trials, dose optimization, pharmacokinetic profiling, and safety evaluations are essential before clinical integration into maternal care.
26 May 2026
Read appraisal →Journal of medical Internet research
Virtual Reality Interventions for Stress Reduction in the General Population: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Increasing mental demands across multiple life domains underscore the importance of effective individual stress management to mitigate the adverse health consequences of chronic stress. Growing evidence suggests that virtual reality (VR) interventions constitute an effective approach to stress reduction. OBJECTIVE: This systematic review and meta-analysis aimed to examine and compare application areas of VR interventions for stress reduction in the general population and to identify potential predictors of effectiveness based on sample characteristics and intervention design. METHODS: Five databases (MEDLINE, CINAHL, CENTRAL, PsycInfo, and Web of Science) were systematically searched for randomized controlled trials investigating the effectiveness of VR interventions for stress reduction in the general population. Studies were included if they primarily focused on stress reduction, included a neutral control condition, and reported a validated measurement of perceived stress. Trials targeting mental disorders or those conducted in the context of medical procedures were excluded. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias using the Cochrane Collaboration's tool. Effects were synthesized using pooled standardized mean differences, and relevant predictors were evaluated through subgroup analyses and meta-regressions. RESULTS: A total of 55 relevant studies met the inclusion criteria, with 37 investigating single-session and 18 multisession interventions (ranging from 1 to 42 sessions over 2 days to 6 months). The meta-analysis included 39 studies with 4024 participants (sample sizes 24-409; mean ages 19.2-70.6 years). Intervention types included VR-based nature exposure (21), biophilic architectural elements (6), guided meditation (9), interactive tasks (4), and other approaches (1). On average, VR interventions significantly reduced perceived stress level (-0.55, 95% CI -0.70 to -0.40; P<.001; I2=76%; 95% prediction interval [PI] -1.32 to 0.23), anxiety (-0.88, 95% CI -1.23 to -0.54; P<.001; I2=87%; 95% PI -2.07 to 0.31), and depression (-0.34, 95% CI -0.47 to -0.21; P<.001; I2=0%; 95% PI -0.47 to -0.21), and enhanced positive emotion (-0.64, 95% CI -0.84 to -0.46; P<.001; I2=62%; 95% PI -1.28 to -0.02). In addition, the decrease in systolic blood pressure was significant (-0.20, 95% CI -0.37 to -0.04; P=.02; 95% PI -0.37 to -0.04). Univariate linear regression analyses identified multiple sessions (R²=15%, P=.02) as a significant predictor of VR-based stress reduction. Further analyses of technical and content-related characteristics revealed that a higher image refresh rate (t15=-2.36, 2-tailed; P=.04) was associated with stronger intervention effects. CONCLUSIONS: This review highlights the considerable potential of VR interventions for reducing psychological stress in the general population and extends recent evidence by quantifying the effects of a broad range of VR-based stress reduction approaches and, for the first time, comparing diverse intervention characteristics. VR represents a promising alternative, particularly in contexts where access to real-life nature or conventional relaxation methods is limited or resource-intensive.
26 May 2026
Read appraisal →Journal of medical Internet research
Association Between Wearable Device Adoption and Health-Related Lifestyle Behaviors: Retrospective Cohort Study
BACKGROUND: Wearable devices are increasingly adopted for personal health monitoring, but evidence on their long-term associations with health-related lifestyle behaviors in real-world population settings remains limited. OBJECTIVE: This study examined the longitudinal association between wearable device adoption and engagement in health-related lifestyle behaviors using a nationally representative panel dataset from South Korea. METHODS: We analyzed data from the 2016 and 2022 waves of the Korea Media Panel survey. Health-related lifestyle behaviors in the physical, social, and cultural domains were operationalized as estimated annual activity counts based on self-reported frequency measures. We used a difference-in-differences framework with generalized estimating equations to compare changes in these behaviors between new wearable adopters and nonadopters adjusting for demographic and socioeconomic characteristics. Relative changes were estimated using Poisson models with a log link, and subgroup analyses were conducted to explore variation across sociodemographic groups. As a sensitivity analysis, inverse probability of treatment weighting was additionally applied to assess the robustness of the findings to observed baseline imbalance. RESULTS: Wearable device adoption was associated with greater increases in total, physical, and cultural health-related lifestyle activities over time. In the difference-in-differences model, adopters showed greater relative increases in total activity (rate ratio [RR] 1.24, 95% CI 1.08-1.35), physical activity (RR 1.36, 95% CI 1.12-1.64), and cultural activity (RR 1.78, 95% CI 1.31-2.42) than nonadopters. Subgroup analyses showed limited evidence of consistent heterogeneity and should be interpreted cautiously. Sensitivity analyses using inverse probability of treatment weighting showed overall patterns broadly similar to those of the primary analyses. CONCLUSIONS: In this nationally representative panel study, wearable device adoption was associated with greater increases in total, physical, and cultural health-related lifestyle activities over time, whereas no clear association was observed for social activity. These findings should be interpreted as associative rather than causal given the observational design and the inability to directly assess parallel trends.
26 May 2026
Read appraisal →Current obesity reports
How Have Researchers Estimated the Impact of Excess Weight on Mortality? A Systematic Review.
OBJECTIVE: The aim of this review is to describe and reflect on the methodology of the studies that estimate the attributable mortality (AM) to excess weight. The purpose was to provide a methodological framework and serve as a starting point to improve the estimation of AM to excess weight. This will lead to more comparable estimates over time and across countries. METHODS: A systematic review was conducted considering MEDLINE (Ovid), Embase and Web of Science databases. Studies that had estimated AM to excess weight published until December 2024 were included. Data extraction was performed in an ad-hoc table based on the STREAMS-P tool, and a descriptive analysis of the methodology employed was performed. RESULTS: The search strategy yielded 2,163 results. Twenty-three studies estimating AM in four continents were included. The majority estimated AM to all-cause mortality (n = 9) and for cardiovascular diseases (n = 9), followed by cancer without specifying which type of cancer (n = 7). Variation was observed in the body mass index (BMI) cut-off points used to define overweight, obesity and excess weight. Thirteen studies applied the population attributable fraction (PAF) formula denoted by PAF = [P (RR-1)] / [1 + P (RR-1)]. Most of the relative risks (RR) derived from meta-analysis (n = 14), and only a minority studies (n = 6) used RR from national studies based on the population for which AM was estimated. CONCLUSION: This review emphasizes methodological constraints on AM estimation to excess weight. Causes of death causally associated with excess weight should be prioritized rather than relying solely on all-cause mortality. The ideal scenario would be to consider lag times; RR derived from the population under study and to use WHO BMI cut-offs and measured data rather than self-reported weight and height. The absence of this data should not prevent AM estimation if limitations are acknowledged. Expanding available estimates worldwide is essential to improve understanding of the mortality burden associated with excess weight.
26 May 2026
Read appraisal →Circulation journal : official journal of the Japanese Circulation Society
Association Between Ventricular-Arterial Coupling and C-Reactive Protein in Patients at Risk for Heart Failure
BACKGROUND: Although ventricular-arterial coupling (VAC) and C-reactive protein (CRP) are associated with the pathogenesis of heart failure (HF), their association in patients at risk of HF remains unclear. METHODS AND RESULTS: VAC was assessed by arterial elastance (Ea) to left ventricular endsystolic elastance (Ees) ratio (Ea/Ees) using the noninvasive single-beat method in 112 hypertensive patients. Log-transformed high-sensitivity CRP (hs-CRP) and categorical hs-CRP were associated with Ea/Ees on univariate analysis. Categorical hs-CRP was independently associated with Ea/Ees after adjusting for atherosclerotic risk factors, cardiac function and vascular function (β=0.27, P=0.003). CONCLUSIONS: hs-CRP was independently associated with VAC assessed by Ea/Ees in patients at risk of HF.
25 May 2026
Read appraisal →Medicine
Effect of a sports-medicine-guided football program on physical fitness in adolescents: A controlled school-based trial
Integrating artificial intelligence (AI) with sports medicine principles into school-based physical education may enhance physical fitness and reduce injury risk among adolescents. This study aimed to evaluate whether a 10-week sports-medicine-guided football curriculum augmented with explainable machine-learning feedback produces greater improvements in physical fitness and injury-prevention outcomes than a standard physical-education curriculum. A total of 195 healthy junior high school students (mean age 14.1 ± 0.7 years) participated in a controlled school-based intervention and were allocated to either an AI-supported sports-medicine curriculum (intervention) or a standard curriculum (control). Physical fitness outcomes, including lung capacity, estimated maximal oxygen uptake (VO2max), sprint performance, standing long jump, and middle-distance running performance (800 m girls/1000 m boys), were assessed before and after the 10-week program. Outcomes were analyzed using 2-way mixed analysis of variance to test Group × Time interactions. Injury risk prediction was supported by an explainable machine-learning ensemble model (CatBoost + Gradient Boosting) interpreted using SHAPley Additive explanations. Significant Group × Time interactions were observed for lung capacity (P < .01) and VO2max (P < .01), indicating greater improvements in the intervention group compared with controls. The intervention group also demonstrated superior improvements in sprint performance, middle-distance running, and lower-limb power (P < .05), while no significant between-group differences were observed for upper-body strength (P > .05). A school-based football curriculum integrating sports-medicine principles with explainable AI-driven feedback leads to significantly greater improvements in key physical-fitness outcomes than a standard physical-education curriculum. This approach directly supports individualized training adaptation and injury-risk awareness, offering a scalable model for enhancing adolescent health and safety in school physical education.
25 May 2026
Read appraisal →BMC psychology
Single-exposure messages for child sexual abuse prevention: a randomized controlled online trial testing educational and humanizing approaches
BACKGROUND: Child sexual abuse and exploitation, including online material depicting such acts, constitute a major public health concern. Prevention efforts likely benefit from public understanding and destigmatization, yet communication strategies on this topic face persistent misconceptions and strong emotional reactions. This randomized controlled online study examined whether brief educational and humanizing communication interventions influenced knowledge, attitudes, and behavioral intentions related to prevention, while avoiding adverse emotional or ethical effects. METHODS: A total of 2,019 adults from Spain and Portugal were recruited via an online panel and randomly assigned to one of four conditions: an educational message, a humanizing message with empathy focus, a humanizing message with prevention focus, or a neutral control message. Each intervention consisted of a short video and accompanying text presented as part of a health information campaign. Measures included affective state, stigma-related attitudes, literacy about therapeutic options, openness toward the topic, and behavioral intention to seek information. RESULTS: Exposure to any intervention message led to short-term changes in affective responses, with lower positive valence and slightly higher arousal compared to the control group (medium effect for valence, small for arousal). No significant differences emerged for stigma dimensions, knowledge, openness, or behavioral intentions. All three intervention formats performed similarly. The study did not identify measurable short-term adverse or rebound effects on stigma. Dropout rates did not differ across conditions, but languages. CONCLUSIONS: Brief communication interventions elicited short-term affective responses without evidence of increased stigma or avoidance on the measured outcomes. While the present single exposures appear insufficient to change attitudes or behavior, such formats may serve as a foundation for repeated, multi-channel prevention messaging integrated into broader educational efforts. TRIAL REGISTRATION: German Clinical Trials Register (DRKS), DRKS00038927, retrospectively registered on 09/01/2026.
25 May 2026
Read appraisal →Journal of medical systems
Machine Learning-Predicted Risk Trajectories for Incident Chronic Kidney Disease and Associations with Post-CKD Outcomes
Chronic kidney disease (CKD) is a progressive condition where risk accumulates before clinical onset. However, strategies for evaluating CKD risk remain limited, and the trajectories of risk over time are poorly understood. We enrolled 51,156 individuals without CKD in the University of California health system between 2012 and 2024. A debiased XGBoost model was developed to predict CKD risk, and the predicted probabilities from this model at each visit were used to derive risk trajectories through latent class growth mixture modeling. The model achieved a time-dependent area under the curve of 0.95 (95% confidence interval (CI), 0.94-0.96), a C-index of 0.93 (0.92-0.94), and a Brier score of 0.027 (0.026-0.028) for predicting 5-year CKD occurrence. Similar predictive performance was observed for 1 to 4 years. Longitudinal patterns of predicted CKD risk revealed three distinct trajectories: gradual (75.9%), progressive (10.1%), and rapid increase (14.0%). Compared to the gradual trajectory, the progressive trajectory was associated with a higher risk of estimated glomerular filtration rate (eGFR) decline of ≥ 25% (adjusted hazard ratio (HR) 1.31; 95% CI, 1.06-1.61) and ≥ 50% (HR 1.59; 95% CI, 1.09-2.31) following CKD onset. The progressive and rapid trajectory was linked to an increased risk of cardiovascular events (HR 1.84; 95% CI 1.18-2.91) and all-cause mortality (HR 2.36; 95% CI 1.49-3.73). Our machine learning model demonstrated strong predictive performance for incident CKD risk and identified three distinct risk trajectories that were associated with different post-CKD outcomes, highlighting the importance of early risk monitoring.
25 May 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Training a Smoking Status Probabilistic Model Using Cotinine Levels in a Large Claims Database
INTRODUCTION: Smoking status is an important confounder for many epidemiologic studies, yet it is not well documented in common sources of real-world data, including administrative claims. Probabilistic models can be used to create a proxy for smoking status, yet most published models have been trained using self-reported data. The objective of this study was to train a smoking status probabilistic model using cotinine values available in a large claims database. METHODS: Beneficiaries were included if they had at least one cotinine measurement and were categorized as a "current smoker" if their serum or plasma cotinine value was ≥5 ng/mL or urine cotinine value was ≥30 ng/mL. Predictors were collected across one year prior to the cotinine assessment date. The model was fit using logistic regression with stepwise forward selection. Model performance was assessed using discrimination and calibration. RESULTS: The final model yielded an area under the receiver operating characteristic curve of 0.77 (95%CI:0.75-0.78) and was well calibrated across most prediction deciles. The strongest predictors included diagnosis codes for smoking and drug abuse, and number of medications. The model was found to be highly specific, yet not sensitive at probability cutoffs ≥0.2. CONCLUSIONS: A smoking status model was developed and internally validated for application in claims data, using available cotinine values to define smoking status and found to have acceptable discrimination and calibration. The model is based on 26 predictors, fewer than other similar published smoking status models. External validation of the model should be a next step toward utilizing the model for epidemiological research. IMPLICATIONS: This study tests the utility of cotinine values to validate a smoking status probabilistic model, which has not been done in the literature to date. The results were robust to various cotinine levels used to define smoking status, per current guidance. The final model uses only 26 factors to predict smoking status, simplifying the application of the model in other claims databases.
25 May 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Patterns of e-Cigarette Use and Smoking Cessation Outcomes: Secondary Analysis of a Large Randomised Controlled Trial to Inform Clinical Advice
INTRODUCTION: Limited data exist on how to use e-cigarettes (EC) to optimize smoking cessation. METHODS: We examined associations between patterns of EC use and outcomes at 1 year in a large trial (N = 886) comparing nicotine replacement therapy (NRT) and EC. RESULTS: Use of tobacco flavor was limited and associated with a lower smoking cessation rate compared to other flavors (relative risk; RR = 0.56, 95% CI = 0.35 to 0.89). EC users reduced nicotine strength over time. Abstainers using EC reported lower urges to smoke than abstainers using NRT at both weeks 1 (b = -0.25, 95% CI = -0.45 to -0.04) and 4 (b = -0.37, 95% CI = -0.58 to -0.16). Participants using both cigarettes and EC (dual users) at week 1 were more likely to stop smoking at week 4 than exclusive smokers (RR = 4.45, 95% CI = 1.96 to 10.10). Dual users at 4 weeks and 6 months were also more likely to achieve validated 50% reduction in smoke intake at 1 year (RR = 2.37, 95% CI = 1.36 to 4.11 and RR = 4.56, 95% CI = 2.71 to 7.66, respectively). CONCLUSIONS: Non-tobacco flavors were preferred and may be also more effective. Urges to smoke were lower in EC users than in users of NRT. Dual use was associated with a reduction in smoking and quitting smoking later on. IMPLICATIONS: Clinicians advising smokers wanting to use EC as a quitting aid can consider recommending non-tobacco flavors; explaining that EC reduce urges to smoke more than combination NRT; and reassuring those worried about dual use that such use is associated with reduced smoking and a higher chance of stopping smoking altogether later on.
25 May 2026
Read appraisal →PloS one
A hybrid BiLSTM and rule-based system for integrated diabetes prediction and personalized guidance
Diabetes is a common chronic disease that needs early diagnosis and proper management to avoid severe complications. While current Artificial Intelligence (AI) tools generate predictive information, they often lack an integrated element for post-diagnosis support in order to fill in this critical gap in patient self-management. This research proposes and validates a hybrid System which aims to bridge this gap. The methodology is based on a novel, fused dataset (PIMA and Type 2 Diabetes) that was carefully preprocessed following a leakage-safe protocol in order to increase generalizability. The system architecture is a combination of two different critical components: Strong Bidirectional Long Short term Memory (BiLSTM) model for prediction and rule based engine for creating personalized lifestyle recommendations. In order to validate the efficacy of the BiLSTM model, seven traditional machine learning (ML) models and standard deep learning (DL) models have been comparatively tested, in which BiLSTM model has demonstrated a better generalization and prediction performance. Rigorous 10-fold cross validation was used to validate the system, which came up with an accuracy of 84.02%, precision of 87.89%, and recall of 80.50%. This research concludes that by successfully combining the high-performance predictive engine and real-time guidance module, it is possible to develop a holistic clinically relevant tool to close the loop between diagnosis and proactive self-management.
25 May 2026
Read appraisal →Science advances
Generalizability of an AI-based mammogram risk score (MRS) for breast cancer among diverse populations of women
Conventional prediction models incorporating genetic and clinical factors including breast density underperform in non-European populations. We investigated an artificial intelligence-derived mammogram risk score (MRS), a summary of texture features that captures intrinsic breast-tissue characteristics-the substrate for cancer development. This study leveraged data from two North American screening cohorts totaling >226,000 women, including non-Hispanic white, non-Hispanic Black, East Asian, South Asian, and Indigenous women. MRS distributions showed nonsignificant shifts (and similar SDs) between cohorts and across race and ethnic subgroups. MRS increased with age and was significantly associated with breast cancer risk, with hazard ratios per SD ranging from 2.24 [95% confidence interval (CI), 2.03 to 2.46] to 2.32 (95% CI, 2.25 to 2.39) after age adjustment. Associations remained significant within all subgroups. Calibration was excellent across the racial and ethnic groups and across full-field digital mammograms and tomosynthesis. These findings establish MRS as a strong predictor that is independent of race or ethnicity, demonstrating its potential for broader clinical utility.
24 May 2026
Read appraisal →Journal of ethnopharmacology
Therapeutic potential of pharmacological components of Salvia miltiorrhiza against atherosclerosis: A preclinical systematic review and meta-analysis
ETHNOPHARMACOLOGICAL RELEVANCE: Atherosclerosis (AS) severely threatens global health, while current therapies exhibit limitations. Recognized as a 'superior-grade' herb in the Shennong Ben Cao Jing, Salvia miltiorrhiza Bunge (Danshen) has been shown in modern studies to protect against cardiovascular diseases. AIM OF THE STUDY: The aim of this study was to investigate the therapeutic potential and protective mechanism of pharmacological components of Salvia miltiorrhiza against AS. MATERIALS AND METHODS: Relevant animal studies were collected from 8 databases, namely, PubMed, Web of Science, Embase, Cochrane Library, CNKI, Wanfang Data, VIP, and SinoMed. Risk of bias of the included studies was evaluated using the SYRCLE's tool. Statistical analysis was performed using R 4.2.0 and Python 3.14.2 software. Machine learning model was trained to predict optimal intervention parameters and was subsequently validated. RESULTS: A total of 64 studies were included. The pharmacological components of Salvia miltiorrhiza ameliorated atherosclerotic plaque formation and stability, and modulated various biomarkers, including lipid profiles, inflammatory cytokines, oxidative stress indicators, endothelial function markers, as well as matrix metalloproteinases. Machine learning identified an optimal Tanshinone IIA regimen against AS, which was defined as a single dose of 33.18 mg/kg dose over 84 days and demonstrated predictive robustness in validation. CONCLUSIONS: The pharmacological components of Salvia miltiorrhiza attenuate AS by regulating lipid metabolism, anti-inflammatory and antioxidant actions, improving endothelial function, modulating of vascular smooth muscle cells, remodeling extracellular matrix, and regulating programmed cell death. These findings provide translational insights that pave the way for subsequent preclinical and early-clinical studies, pending systematic validation through more rigorous research.
24 May 2026
Read appraisal →Studies in health technology and informatics
Hybrid AI Decision Support in Healthcare: Insights into Usability and Trust
Decision Support Systems (DSS) increasingly integrate Artificial Intelligence (AI) to enhance clinical reasoning, yet adoption depends on professionals' trust and usability. This study explored how hybrid AI decision support, combining rule-based guideline logic with generative reasoning, can be designed and evaluated in practice. Using a human-centred design approach within preventive child healthcare, a prototype integrating Large Language Models (LLMs) into an existing guideline-based DSS was developed and tested. Usability and trust were evaluated with nine healthcare professionals through task observations, usability scales, and interviews. The system showed good usability and moderate trust; highest scores were for traceability, workflow integration, and autonomy, while confidence indicators and contextual reasoning needed improvement. The study demonstrates the feasibility and acceptability of hybrid AI DSS in child healthcare and provides initial design requirements for usable, trustworthy, and human-centred AI integration across clinical domains.
24 May 2026
Read appraisal →JMIR research protocols
Effect of a Personalized Mobile Health Intervention Using Artificial Intelligence (the WARIFA App) Versus a Nonpersonalized Intervention on User-Defined Objectives, Healthy Lifestyles, and Management of Type 1 Diabetes (T1D): Protocol for a Randomized Controlled Trial
BACKGROUND: Noncommunicable diseases are the leading cause of death worldwide. Cardiovascular and respiratory diseases, cancer, and type 2 diabetes share common risk factors that can be addressed: physical activity, a healthy diet, and avoiding smoking and alcohol. The Watching the Risk Factors (WARIFA) mobile health app was created for general health awareness and to support users in adopting healthier behaviors, as well as to support type 1 diabetes (T1D) self-management. OBJECTIVE: This study aims to evaluate the effectiveness of the WARIFA app with personalized artificial intelligence (AI)-driven messages, compared to a nonpersonalized version, in promoting health-related behavior change among the general population and individuals with T1D. METHODS: A total of 108 European participants, including individuals with T1D, were to be randomized (computer-generated sequence, double-blind, 1:1 ratio) to an intervention or control group. In the intervention group, participants used the WARIFA app with personalized messages and the use of AI. This applied to certain functionalities, such as providing recommendations on healthy dietary habits based on food logging and offering advice and encouragement through daily step tracking. It also provided risk predictions for cardiovascular and respiratory diseases, cancer, and type 2 diabetes. Participants with T1D were offered glucose predictions based on previous measurements. In the control group, participants used a WARIFA app without personalized messages or AI. Both WARIFA app versions offered access to air quality and UV index information for the geographical area, as well as displaying physical activity in the form of daily steps and sleep hours, as well as glucose results for participants with T1D. Both groups were provided with an activity monitor and used the WARIFA app for 8-12 weeks. The primary outcome is a self-defined goal, chosen from a set of proposed objectives at baseline and assessed at the end of the study using a Likert scale (1 to 10 points, 0 being no achievement at all and 10 being full achievement of the objective). Secondary outcomes include: engagement with the app, changes in lifestyle behavior, body composition, lipid profile, glycated hemoglobin (T1D only), hypoglycemic events (T1D only), and health-related quality of life, as well as acquired knowledge, self-efficacy, and usability. RESULTS: The clinical trial took place between January and June 2025. A total of 88 participants were finally recruited. The data are being analyzed, and the results are expected to be published in 2026. CONCLUSIONS: There is evidence that improving lifestyle behavior can prevent noncommunicable diseases. In this study, we aim to evaluate the effectiveness of the WARIFA app to improve lifestyle behaviors and T1D management. TRIAL REGISTRATION: ClinicalTrials.gov NCT06918444; https://clinicaltrials.gov/study/NCT06918444. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/84510.
23 May 2026
Read appraisal →Journal of medical Internet research
Effectiveness of a Blended Intervention to Promote Physical Activity Among Office Employees: Randomized Controlled Trial
BACKGROUND: Regular moderate-to-vigorous physical activity (MVPA) reduces the risk of noncommunicable diseases, yet one-third of adults globally fail to meet MVPA recommendations. Office employees, among the least active groups, face heightened risks due to prolonged sedentary behavior and barriers like lack of time, fatigue, and low motivation. Although scalable, web-based interventions frequently face challenges, such as low engagement, high attrition, and limited personalization. Blended interventions, combining digital tools with interactive components, show promise but lack robust evidence among office employees. OBJECTIVE: This study aimed to evaluate the effectiveness of a theory- and evidence-based blended intervention in increasing MVPA among office employees. METHODS: This 24-week, 3-arm randomized controlled trial included 141 office employees, randomized equally into blended (web-based+electronic workshops [e-workshops]), web-based only, and control groups. The intervention was developed using the intervention mapping framework, ensuring a systematic, evidence- and theory-based design tailored to the needs of the target population. All participants accessed a study website; the control group used the "Library" module with general MVPA information, while intervention groups also accessed a tailored, theory-driven "Intervention" module. The blended group additionally attended 3 live e-workshops (weeks 2, 4, and 8). MVPA was objectively measured using hip-worn accelerometers at baseline (T1), postintervention (12 wk, T2), and follow-up (24 wk, T3). Retention, engagement, and acceptability were assessed. Analyses followed the intention-to-treat principle, and generalized linear mixed models were used to evaluate between-group differences at T2 and T3, adjusted for baseline MVPA, sex, and ActiGraph LEAP (Ametris) wear time. RESULTS: A total of 141 participants (mean age 32.1, SD 9.2 y; 75/141, 53.2% female) were randomized evenly across groups with comparable baseline characteristics, except for sex (P=.002). The blended group showed significantly greater MVPA increases than the control at T2 (β coefficient=0.247, 95% CI 0.013-0.480; P=.04) and T3 (β coefficient=0.373, 95% CI 0.139-0.607; P=.002), and greater increases than the web-based at T2 (β coefficient=0.287, 95% CI 0.062-0.512; P=.01) and T3 (β coefficient=0.368, 95% CI 0.138-0.597; P=.002). Sensitivity analyses confirmed the robustness of these findings. Retention and engagement rates were 83% and 56%, respectively. Participants reported high acceptability across all domains. CONCLUSIONS: A blended intervention integrating tailored web content with interactive e-workshops significantly increased MVPA at T2 and T3 compared with both web-based and control groups. This success stems from the rigorous intervention mapping framework, which systematically identified effective behavior change and habit formation theories, while the blended approach addressed key limitations of traditional and web-based methods. These findings underscore the potential of evidence- and theory-based blended approaches to address common barriers to MVPA and promote sustainable MVPA in workplace settings, offering promising opportunities for broader scalability and personalization.
23 May 2026
Read appraisal →Journal of medical Internet research
Measuring eHealth Literacy in the European Economic Area, Switzerland, and the United Kingdom: Scoping Review
BACKGROUND: Digital tools continue to evolve and have the potential to improve health care delivery. However, they are associated with challenges, including accessibility issues and health misinformation. Individuals need eHealth literacy (eHL) to reliably use these tools, and providers require appropriate eHL measurement approaches to offer targeted solutions. For around 2 decades, researchers have been operationalizing and measuring eHL. OBJECTIVE: This paper aims to provide an up-to-date overview of how eHL has been assessed in recent years in the European Economic Area, Switzerland, and the United Kingdom and which methodological limitations need to be considered. METHODS: A scoping review was conducted. Records were searched via CINAHL, PubMed, and Google Scholar on January 31, 2025, and January 28, 2026. Peer-reviewed empirical papers published in German or English since 2020 that measured eHL in the European Economic Area, Switzerland, or the United Kingdom were included. The synthesis covered the publication trend, eHL measurement approaches and associated limitations reported in the included papers, eHL measurement frequency, countries and languages, and samples. RESULTS: In the final analysis, 132 papers published between 2020 and 2025 were included. The number of publications per year showed an overall upward trend (2020: 11/132, 8.33%; 2025: 35/132, 26.52%). Nine self-report eHL measurement instruments were used, the eHealth Literacy Scale being the most frequent (94/132, 71.21%). All included papers (132/132, 100%) reported data collection via surveys, and digital surveys were common (71/132, 53.79%). Reported limitations included potential self-report biases (37/132, 28.03%), selection biases due to the data collection modes (26/132, 19.70%), and limitations specific to 5 eHL measurement instruments (18/132, 13.64%). Most included papers (121/132, 91.67%) reported eHL results from a single measurement per participant. Data were collected in 22 countries within the target regions. The distribution of eHL measurement instruments varied considerably between countries. Data were collected in 22 languages within the target regions. Patients or individuals with health problems were a frequent target group (64/132, 48.48%). Most papers (104/132, 78.79%) described studies covering broad adult age ranges. Sample sizes ranged from ≤50 (7/132, 5.30%) to >2000 participants (8/132, 6.06%). CONCLUSIONS: To our knowledge, this is the first scoping review synthesizing eHL measurement limitations reported in empirical papers from the European Economic Area, Switzerland, and the United Kingdom. The identified limitations reported in the included papers potentially lead to biased results. Therefore, health care providers and researchers should take various factors into account when selecting eHL measurement approaches, such as eHL measurement purpose, target population, and data collection setting. Future research should address these constraints by adapting and developing new or revised eHL measurement instruments, including translated and culturally adapted versions. Policymakers should encourage health care providers to conduct methodologically well-founded eHL measurements as a basis for targeted solutions.
23 May 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Mediators of the Effect of Opt-In Versus Opt-Out Treatment on Smoking Cessation in a Randomized Clinical Trial
INTRODUCTION: Smoking cessation interventions are effective. Most smokers do not receive them, in part because smokers are required to "opt-in". Changing the default (CTD) randomized clinical trial, conducted at a large academic medical center, demonstrated that CTD to providing treatment unless refused (opt-out) improved quit rates. The objective of this study was to evaluate if treatment participation and default-theory based variables explained the effect of opt-out on cessation. METHODS: We conducted a secondary analysis of CTD data (N = 739). Descriptive statistics and correlations of the baseline variables were provided. Structural equation models were employed to investigate whether the treatment assignment was causally mediated by a priori proposed mediators including perceived endorsement, counseling participation, and medication utilization. RESULTS: The mean age of the participants was 51.39 (SD = 14.71) years, among whom 349 (47.23%) were females, and the majority was White (428, 57.92%). The indirect effects from the opt-out treatment arm to counseling participation to smoking cessation (b = 0.201, 95% CI = 0.105 to 0.303), and from opt-out to perceived endorsement to counseling participation to smoking cessation (b = 0.010, 95% CI = 0.002 to 0.022) were significant. The indirect effect from opt-out to perceived endorsement to counseling participation to medication utilization to smoking cessation was marginally significant (b = .001, 95% CI = 0.000 to 0.003). CONCLUSIONS: The opt-out approach enhanced patients' sense that their counselor endorsed and/or recommended treatment, as well as counseling participation. The results partially support our hypothesis that the advantage of Opt-out care would increase treatment participation and would do so by psychological variables thought to underpin the efficacy of defaults. IMPLICATIONS: Our results strengthen evidence for the value of delivering smoking cessation as a default (opt-out rather than opt-in) by illuminating likely mechanisms of action. These include increasing participation in counseling and two default-based theoretical mechanisms, perceived endorsement and implied endorsement.
23 May 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Identifying Key Predictors of Smoking Cessation Success: Text-Based Feature Selection Using a Large Language Model
INTRODUCTION: The most effective way to reduce mortality and morbidity among current smokers is to quit smoking. Although about half of smokers attempted to quit, only one-tenth succeeded in 2022. Understanding key predictors of smoking cessation success would inform smoking cessation interventions and increase quitting rates. METHODS: We analyzed data from waves 5 and 6 of the Population Assessment of Tobacco and Health (PATH) study (December 2018 to November 2021). Using OpenAI's GPT-4.1, we identified the top 45 variables from wave 5 that are highly predictive of 12-month smoking abstinence in wave 6, based on descriptions of survey variables. We then validated the predictive power of the GPT-4.1-selected variables by comparing the performance of eXtreme Gradient Boosting (XGBoost) trained on different sets of variables. Finally, we derived insights into the top 10 variables, ranked according to their SHapley Additive exPlanations values. RESULTS: The performance of XGBoost trained with all possible wave 5 variables and the 45 selected variables was almost identical (AUC:0.749 vs AUC:0.752). The top 10 variables included past 30-day smoking frequency, minutes from waking up to smoking first cigarette, important people's views on tobacco use, prevalence of tobacco use among close associates, daily electronic nicotine product use, emotional dependence, and health harm concerns. CONCLUSIONS: The high predictive performance of XGBoost, when trained on the selected variables, underscores the efficiency and efficacy of GPT-4.1-based feature selection. The top 10 variables include various risk factors that have been previously reported in the literature for their influence on smoking behavior. IMPLICATIONS: Our findings do not establish causal relationships between the selected predictors and 12-month smoking abstinence. However, identifying these key predictors provides valuable insights into the factors highly associated with smoking cessation success. This study demonstrates the ability of OpenAI's GPT-4.1 to perform feature selection using only the textual descriptions of variables. The efficient and successful application of GPT-4.1 for variable selection highlights the potential of integrating artificial intelligence tools into tobacco research to guide resource-efficient and targeted intervention strategies.
23 May 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
The Public Health Impacts of Flavored Cigar Bans: A Systematic Review and Meta-Analysis
INTRODUCTION: This review summarizes research on the public health impacts of flavored cigar bans, which are policies that restrict the sale of cigars containing characterizing flavors. METHODS: A systematic literature search was conducted using PubMed, Scopus, PsycINFO, Cumulative Index to Nursing and Allied Health Literature, and Applied Social Sciences Index and Abstracts. Studies were included if they evaluated the impact of a real-world or hypothesized flavored cigar ban. The primary outcome was tobacco use behavior. Secondary outcomes included retailer compliance, sales, and the tobacco industry response to flavored cigar bans. Two reviewers independently screened articles for inclusion, extracted data, and assessed study quality. We also conducted a meta-analysis using random effects models to pool estimates on tobacco use behavior after a flavored cigar ban. This review was registered with PROSPERO (CRD42023481603). RESULTS: Of 2,399 articles identified in the preliminary search, 55 were included in the review. Many real-world studies found declines in cigar use post-ban, specifically among youth. However, other studies found no significant changes in cigar use. Among studies examining sales, there were declines in flavored cigar sales but increases in sales of non-flavored cigars and other tobacco products. Among studies assessing hypothesized tobacco use behavior, pooled results indicate that 8% (95% CI: 2%, 27%) of flavored cigar users intend to quit tobacco altogether, 45% (95% CI: 39%, 50%) would switch to non-flavored cigars or cigarettes, and 41% (95% CI: 32%, 51%) would switch to other tobacco products. CONCLUSIONS: Flavored cigar bans may reduce cigar use and improve public health when policies are comprehensive and well-enforced. IMPLICATIONS: Overall, findings from this review suggest that flavored cigar bans may reduce tobacco use and have the potential to improve public health, but not all research is consistent. Several studies examining the impacts of flavored cigar bans on tobacco use found a decrease in cigar use among youth, but some found that there was no significant change in cigar use. Research on policy impacts among adults is limited. To maximize intended policy impact, monitoring of retailer compliance and the tobacco industry's response to flavored cigar bans is needed.
23 May 2026
Read appraisal →BMJ open
Australian key stakeholder views regarding implementation of atrial fibrillation screening: a qualitative evaluation
OBJECTIVE: To understand the issues impacting atrial fibrillation (AF) screening and what needs to be considered for a successful national screening programme in Australia. DESIGN: Qualitative design using semistructured interviews and thematic analysis. SETTING: Australian Health. PARTICIPANTS: Six broad stakeholder groups were identified: charities/patient support, healthcare providers, professional bodies, government, research (including Indigenous health) and industry. METHODS: Semistructured interviews were conducted with 25 representative participants. Iterative thematic analysis was used. Coding was driven by the research questions (the current context; is a national screening programme warranted and approaches to a national screening programme) and an inductive approach where novel groupings of information were identified. FINDINGS: The key findings are grouped into four areas. (1) Current opportunistic general practitioner-led screening is ad hoc and fragmented. ISSUES: poor remuneration; lack of health sector collaboration and prioritisation; consumers lack awareness. (2) Systematic screening of all in-scope patients not considered feasible and concerns over lack of evidence. (3) Alternative approaches to increase screening include innovative approaches inside and outside general practice and use of smart technology. (4) Recommendations: (a) Support general practices and address remuneration and workflows; (b) Ensure a clear pathway to treatment; (c) Address data security, management and integration and sensitivity issues with wearable devices; (d) Promote collaboration between key organisations; (e) Address research gaps and (f) Generate culturally appropriate consumer education to promote consumer demand. CONCLUSIONS: Most stakeholders were broadly supportive of AF screening but agreed that current approaches were fragmented and not sufficient. If the forthcoming research evidence supports screening effectiveness on major outcomes, stakeholders envisaged a semi-systematic approach tailored to specific health settings, rather than a formalised systematic national screening programme.
23 May 2026
Read appraisal →Journal of medical Internet research
Toward Participatory Precision Health With Co-Designed Recommendations: Systematic Review of Just-in-Time Adaptive Interventions in Adolescents and Young Adults
BACKGROUND: The transition from adolescence to young adulthood (age 10-25 years) constitutes a sensitive developmental period marked by rapid biological, psychological, and social change, during which preventive health interventions can shape long-term outcomes. Mobile health tools offer accessible opportunities for tailored support for this population, but often adapt poorly to dynamic contexts, resulting in inconsistent engagement and effects. Just-in-time adaptive interventions (JITAIs), which tailor support in real time using ongoing data, are increasingly explored as precision health strategies. However, how these mechanisms are designed, implemented, and evaluated for adolescents and young adults (AYAs) has not been systematically reviewed. OBJECTIVE: This review aimed to synthesize the evidence on JITAIs developed for AYAs, examine how their adaptive mechanisms have been designed to support specific health goals and changing AYA contexts, and assess methodological reporting quality to inform future precision health intervention development. METHODS: We conducted a systematic review in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and SWiM (Synthesis Without Meta-Analysis) reporting guidelines. Twelve databases were searched for peer-reviewed studies published from 2013 to 2025. Eligible studies focused on participants aged 10 to 25 years and reported real-time adaptive mobile health interventions consistent with JITAI design principles. Two reviewers independently conducted screening, data extraction, and methodological quality appraisal using the Joanna Briggs Institute checklists. AYA coauthors contributed to all phases. Due to substantial heterogeneity in study populations, intervention content, adaptive mechanisms, comparators, and outcome measurements, findings were synthesized narratively, and no meta-analysis was conducted. RESULTS: A total of 61 unique interventions were included. JITAIs for AYAs addressed substance use (n=24, 39.3%), mental health (n=23, 37.7%), and physical health or chronic conditions (n=14, 23%). JITAI tailoring mechanisms relied predominantly on self-reported behavioral data. Decision rules were typically symptom threshold-based, and decision points were commonly daily or event-triggered. Methodological concerns with reporting on intervention administration, participant selection, and outcome measurement reliability were pervasive across all studies, limiting the interpretability of observed effects and cross-study comparisons. Ethical considerations, including researcher positioning and reflexivity, alongside the depth of reporting around participatory AYA engagement in design and implementation, were also inconsistent. CONCLUSIONS: This review contributes a novel perspective to AYA digital health by moving beyond intervention outcomes to examine how core adaptive mechanisms are operationalized for AYAs across multiple health domains, while also integrating AYA perspectives into the interpretation of findings and recommendations. Unlike prior reviews focused primarily on adults or specific conditions, it identifies broader contextual, methodological, and ethical considerations relevant to AYA precision health. These findings highlight the need for more transparent, contextually responsive, and youth-centered adaptive interventions, alongside more rigorous designs for evaluating adaptive intervention components in daily life contexts.
23 May 2026
Read appraisal →JMIR formative research
MyLungHealth, a Patient-Facing Education Tool for Lung Cancer Screening: Qualitative User-Centered Design Study
BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide, with low-dose computed tomography screening demonstrating an approximately 20% reduction in mortality among high-risk individuals. Despite this benefit, screening prevalence remains suboptimal, with often less than 20% of eligible individuals reported to be up to date on screening. Shared decision-making is essential for effective lung cancer screening (LCS) implementation, with decision aids shown to enhance patient knowledge and engagement. OBJECTIVE: The aim of this study is to identify patient preferences, concerns, and design considerations through qualitative evaluation of MyLungHealth, a personalized patient-facing educational tool for LCS integrated with electronic health records, and to describe how these findings informed iterative design modifications. METHODS: We employed qualitative research methods through focus groups (n=34) and individual interviews (n=18) with individuals who met screening eligibility criteria. Participants were recruited from the University of Utah Health and New York University Langone Health between May and December 2023. Feedback was analyzed using Braun and Clarke's thematic analysis principles. RESULTS: Six themes were organized into three overarching domains. Domain A included interpretation and impact of personalized risk information: theme 1, difficulties interpreting risk information, and theme 2, varied impacts of risk information on motivation. Domain B included autonomy, privacy, and user interface preferences: theme 3, desire for autonomy and control over personal health data, and theme 4, preference for straightforward language and multiple information formats. Domain C included integration with clinical workflows and patient portal systems: theme 5, expectations for integration with health care provider workflows, and theme 6, mixed experiences with personal health record systems. These insights led to key design modifications, including simplified risk presentation, multimodal content delivery options (video and text), and implementation of electronic health record alerts for clinicians. CONCLUSIONS: The user-centered design process for MyLungHealth revealed important considerations for developing effective patient education tools for LCS. The findings highlighted the need for simplified risk presentation, personalized information delivery, and integration with clinical workflows. These findings underscore the importance of balancing comprehensive risk communication with user accessibility.
23 May 2026
Read appraisal →JMIR research protocols
A Large, Diverse, Urban Cardiovascular Health e-Cohort in Childhood and Adolescence: Protocol for the Young Hearts Study
BACKGROUND: Cardiovascular health (CVH), defined as a composite of 8 health factors and behaviors ranging from body composition to sleep duration, is strongly associated with the risk for future cardiovascular events in adults. However, there is little data on CVH among contemporary, diverse cohorts of children and adolescents. OBJECTIVE: This protocol describes the completed recruitment of a diverse pediatric e-cohort based in Chicago. We include details about ongoing longitudinal data collection for CVH behaviors, as well as neighborhood and environment-based socioeconomic factors, and linkage to electronic health records (EHRs) for direct capture of CVH factors. METHODS: Young Hearts (YH) uses a longitudinal cohort design, linking participant responses to annual electronic surveys to EHRs. Parents/guardians of children aged 0-17 years and adolescents aged 18 years are asked to complete surveys at 3 e-visits, completed annually in consecutive years. Adolescents aged 12-17 years are invited to complete surveys at each e-visit to provide self-reports of their data in addition to their parents/guardians. As adolescents reach the age of majority, they re-consent and complete their remaining e-visit data. Participants' clinical data are abstracted from EHRs at 6 health systems in Chicago and linked to their survey data. The main study outcome is the CVH score, adapted from the Life's Essential Eight framework for use in pediatric populations. We will also study component CVH behaviors and factors, each assessed using developmentally-appropriate measures, including body mass index, blood pressure, lipids, glycemic control, diet, physical activity, sleep, and smoking exposure. Our innovative study design will allow us to model trajectories of pediatric CVH from birth through 20 years of age. Funding for this study was provided by the National Heart, Lung, and Blood Institute (R01HL155864). RESULTS: The YH study, funded in March 2021, recruited, enrolled, and consented 7114 children and adolescents aged 0-18 years between April 2022 and February 2025. Baseline surveys have been completed by 6651 participants, of whom 3259 (49%) were female, and the mean age was 7.4 (SD 5.4) years. Data collection is still in progress, with completion anticipated in February 2027. We aim to publish results beginning in 2026. CONCLUSIONS: This study will define the epidemiology of cardiovascular risk factors in a diverse, contemporary cohort of US children and adolescents and identify behavioral and structural individual- and area-level economic, psychosocial, and lifestyle factors associated with identified disparities. We anticipate that the YH cohort will become an ongoing resource for pediatric CVH research.
23 May 2026
Read appraisal →Occupational and environmental medicine
Lymphoid neoplasms and benzene exposure using a revised classification scheme: systematic review and meta-analysis
Lymphoid neoplasms are a diverse group of cancers derived from lymphocytes, with classification recently updated by the 5th edition of the WHO Classification of Haematolymphoid Tumours (WHO-HAEM5). Benzene, a well-known carcinogen, is widely used in industries and environmental exposures vary; however, its association with specific lymphoid neoplasm subtypes remains unclear due to historical classification challenges. This study aimed to clarify the risk of lymphoid neoplasms related to benzene exposure using the updated WHO-HAEM5 framework.A systematic review and meta-analysis were conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eligible studies were comparative human studies evaluating benzene exposure and lymphoid neoplasm risk, with sufficient data for risk estimates. Inclusion criteria specified study designs, participants, exposures and outcomes based on clinical diagnoses. Data were independently extracted by multiple reviewers. The main outcome was risk of lymphoid neoplasms and subtypes analysed by random-effects meta-analysis with heterogeneity and bias assessments.From 1488 records, 95 studies met criteria (65 occupational and 30 environmental exposure). Benzene exposure significantly increased overall lymphoid neoplasm risk (RR 1.26; 95% CI 1.18 to 1.35). B-cell neoplasms showed elevated risk (RR 1.26; 95% CI 1.16 to 1.37), including mature B-cell neoplasms, Hodgkin lymphoma and plasma cell neoplasms. T-cell and natural killer cell neoplasm risk was not significantly increased overall. Occupational exposures conferred higher risks with lower heterogeneity than environmental exposures.Benzene exposure is strongly associated with increased risk of lymphoid neoplasms, particularly B-cell subtypes, supported by the refined WHO-HAEM5 classification reducing heterogeneity. This study underscores the importance of biologically informed disease classification and the need for targeted occupational safety and environmental health policies.The review protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (Registration ID: CRD420251063844) .
23 May 2026
Read appraisal →ACS sensors
Artificial Intelligence-Driven Wearable Sensors for Cardiovascular Health Monitoring
Wearable sensing technologies hold great promise for continuous and non-invasive monitoring of cardiovascular health, offering new avenues for early detection and effective management of cardiovascular diseases. Despite these advances, several critical challenges remain, including inefficiencies in sensor design, the degradation of calibration model accuracy over time, and difficulties in processing complex physiological signals under real-world conditions. The integration of artificial intelligence (AI) into wearable cardiovascular monitoring systems presents transformative solutions to these limitations by improving system performance, adaptability, and clinical applicability. In this review, we provide an overview of current wearable technologies for cardiovascular monitoring, including their mechanisms, characteristics, and limitations. An in-depth discussion is subsequently presented on AI-driven wearable cardiovascular monitoring, encompassing five critical facets: sensor design, sensor calibration, signal processing, pattern recognition, and disease management. Furthermore, we analyze the challenges associated with the widespread use of AI-based wearable systems and provide insights into potential strategies for addressing these challenges. This review is expected to serve as a roadmap for future research and development in the rapidly evolving field of intelligent cardiovascular health monitoring.
23 May 2026
Read appraisal →JMIR formative research
Patients' mHealth Apps Usage and Data Privacy, Security, and Confidentiality Concerns: Exploratory Study
BACKGROUND: The Technology Adoption Model (TAM) offers a potential framework for elucidating the relationships between data privacy or security concerns and behavioral intention, perceived usefulness (PU), and perceived ease of use (PEOU) of mobile health (mHealth) apps, particularly for patients' self-care management. In Saudi Arabia, limited information is available on these pertinent research areas despite the government's relentless efforts to bolster the use of mHealth apps. OBJECTIVE: This study applies the TAM and the psychosociocultural framework to explore the influence of patients' data privacy and security concerns on the PU, PEOU, and behavioral intention to use mHealth apps for self-care management in Saudi Arabia. METHODS: A cross-sectional study was conducted by recruiting patients using mHealth apps for self-care from various provinces in Saudi Arabia. Research instruments were developed based on the components of 2 theories: the psychosociocultural framework and TAM, which were then piloted, validated, and distributed to participants via Google Forms. Linear regression models were performed to test the hypothesized relationships. RESULTS: Overall, 567 patients using mHealth apps participated in the study. Slightly more than one-third (217/567, 38.2%; range 35.6%-41.4%) of the participants expressed a high level of concern regarding data privacy, confidentiality, and security, with significant predictors being female gender, higher educational qualifications, and younger age groups (<46 years). About 18% to 25% of the variance in PU, PEOU, and behavioral intention to use mHealth apps was explained by the tested factors. Patients were more likely to have higher PU following a unit decrease in data confidentiality (β=.31; P=.01) and security concerns (β=.47; P=.01). The PEOU of mHealth apps increased as users demonstrated less concern regarding data privacy (β=.18; P=.001), confidentiality (β=.24; P<.001), and security (β=.43; P=.02). Likewise, behavioral intention to use mHealth apps also increased significantly following a reduction in respondents' concerns toward data privacy (β=.18; P=.02), confidentiality (β=.24; P=.03), and security issues (β=.36; P=.01). CONCLUSIONS: Specific demographic factors and concerns regarding data security and privacy influence patients' PU, PEOU, and behavioral intention to use mHealth apps for self-care management. Targeting the age-, education-, and gender-based differences regarding the usage of mHealth apps. Health care providers and policymakers may consider age-, education-, and gender-based differences when developing strategies to improve the adoption of mHealth apps among the Saudi patient population.
22 May 2026
Read appraisal →JMIR research protocols
Mobile Health Technology for Personalized Tobacco Cessation Support in Laos (Project Support Laos): Protocol for a Randomized Controlled Trial
BACKGROUND: Tobacco use remains the leading cause of preventable morbidity and mortality in Lao People's Democratic Republic (Lao PDR). Despite the prevalence of cigarette smoking in Lao PDR (51% in men and 7% in women), no national tobacco treatment programs are available. Therefore, the development and evaluation of sustainable tobacco cessation interventions suitable for widespread adoption in Lao PDR are pressing public health needs. OBJECTIVE: This project aims to adapt our theoretically and empirically based mobile health (mHealth) technology to help people quit smoking cigarettes in Lao PDR. METHODS: Our mHealth approach includes a fully automated, interactive, personalized, smartphone-delivered intervention for behavioral treatment. This project includes 2 main phases. In the first phase, we use formative research methods to adapt our intervention content to the sociocultural context, language, and communication styles of Laotians. In the second phase, we conduct a randomized controlled trial to evaluate the efficacy of our mHealth intervention. In the trial, adult smokers are recruited through 2 national hospitals: Setthathirath Hospital in Vientiane and Champasak Hospital in Champasak Province. Participants (n=500) are randomized to either the standard care (SC; n=250) or automated treatment (AT; n=250) group. SC consists of brief advice to quit smoking delivered by research staff, self-help written materials, and a 2-week supply of nicotine replacement therapy (transdermal patches). AT consists of all SC components plus a fully automated smartphone-based treatment program that involves interactive and personalized proactive messages, images, or videos. The primary health outcome of the trial is biochemically confirmed self-reported 7-day point prevalence abstinence 12 months post study enrollment. Secondary outcomes include abstinence at 3 and 6 months post enrollment. RESULTS: This study was approved by the ethical review boards of the respective domestic and international institutions. Data collection for the formative phase occurred from January 2022 to May 2023, and data analyses are ongoing. Data collection for the trial phase is ongoing and is expected to be completed by the end of 2026. CONCLUSIONS: If the proposed project is successful, it has the potential to transform health care services for tobacco treatment throughout Lao PDR and, ultimately, to significantly reduce tobacco-induced morbidity and mortality in the country. The AT's potential for widespread adoption and sustainability is enhanced by the direct involvement of Lao governmental stakeholders at multiple national institutes. Furthermore, the US-Lao collaborative work and capacity-building activities in this project will contribute to creating a knowledge base for mHealth research applications and advancing mHealth research capacity in Lao PDR.
22 May 2026
Read appraisal →The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
Integrated metabolic and inflammatory biomarkers for early association and prediction of adverse pregnancy outcomes: a multicenter validation study
BACKGROUND: Adverse pregnancy outcomes (APOs) affect long-term maternal and offspring health. Conventional metabolic markers, including the triglyceride-glucose (TyG) index, inadequately capture pregnancy-related immuno-inflammatory dysregulation. We propose a composite index, TyNIR, integrating metabolic and inflammatory parameters, and assess its associations with APOs and predictive performance using machine learning. METHODS: Data were collected from two tertiary centers in Fujian, China (2023-2025), including 1,741 pregnancies and an external validation cohort of 300. Early-pregnancy parameters were used to derive TyNIR. Primary outcomes were gestational diabetes mellitus (GDM), gestational hypertension (GH), preterm birth (PB), low birth weight (LBW), and premature rupture of membranes (PROM). Multivariable regression, subgroup, and interaction analyses assessed associations between TyNIR and APOs. A feature selection approach developed the PREMIER model, which was compared with TyG and other models and externally validated. RESULTS: TyNIR was positively associated with GDM, GH, and PB. After adjustment, the highest quartile showed increased risks, with odds ratios (ORs) of 3.86 (95% CI 2.33-6.39) for GDM, 3.05 (1.74-5.36) for GH, and 3.45 (1.24-9.66) for PB. No associations were observed for LBW or PROM. The PREMIER model achieved area under the curve (AUC) values of 0.710 ± 0.031, 0.736 ± 0.040, and 0.742 ± 0.073, outperforming TyG and other models. External validation confirmed consistent associations and superior predictive performance. CONCLUSIONS: TyNIR consistently predicts risks of GDM, GH, and PB. The TyNIR-based PREMIER model shows robust predictive performance with external validation. These findings support early identification of high-risk pregnancies and may guide personalized management of APOs.
22 May 2026
Read appraisal →JMIR infodemiology
Japanese-Language AI Agent System for Human Papillomavirus Vaccine Infoveillance and Public Communication: Development and Feasibility Evaluation
BACKGROUND: Human papillomavirus (HPV) vaccine hesitancy remains a significant public health challenge in Japan, where proactive vaccination recommendations were suspended between 2013 and 2021. The resulting information gap between medical institutions and vaccine-hesitant populations is exacerbated by misinformation on social media platforms. Traditional public health communication strategies cannot address individual queries while simultaneously monitoring population-level discourse. OBJECTIVE: This study aimed to develop and conduct a feasibility evaluation of a dual-purpose artificial intelligence agent system that delivers verified HPV vaccine information to the public through a conversational interface while generating infoveillance reports for medical institutions based on user interactions and social media discourse. METHODS: We implemented a system with 3 components: a vector database integrating 139,803 documents, including academic papers, Japanese government sources, news media, and social media posts; a retrieval-augmented generation chatbot using a ReAct agent architecture with iterative multitool orchestration across 5 specialized knowledge sources; and an automated report generation system with modules for news analysis, research synthesis, social media sentiment analysis, including stance classification and topic modeling, and user interaction pattern identification. System performance was assessed using both automated and manual evaluation protocols on a scale from 0 to 5. RESULTS: The entire system functioned as expected. For single-turn evaluation, the chatbot achieved mean scores of 4.83 (SD 0.67; 95% CI 4.71-4.93) for relevance, 4.89 (SD 0.53; 95% CI 4.79-4.97) for routing, 4.50 (SD 1.29; 95% CI 4.27-4.70) for reference quality, 4.90 (SD 0.62; 95% CI 4.78-4.99) for correctness, and 4.88 (SD 0.54; 95% CI 4.78-4.96) for professional identity, with an overall mean of 4.80. Multiturn evaluation yielded higher mean scores: 4.94 for context memory (SD 0.25; 95% CI 4.84-5.00) and an overall mean of 4.98, with topic centering and identity achieving 5.00. The report generation system achieved high scores across all sections: 4.83 for completeness (SD 0.37; 95% CI 4.73-4.94), 4.88 for correctness (SD 0.33; 95% CI 4.77-4.96), and 4.12 for helpfulness (SD 0.48; 95% CI 3.98-4.27). Reference validity achieved perfect scores (5.00) across all periods, with citation correctness averaging 4.21 (SD 0.58; 95% CI 3.96-4.46). CONCLUSIONS: This feasibility study demonstrated that an integrated artificial intelligence agent system can support both public HPV vaccine communication and social media infoveillance in a Japanese-language context. Prospective deployment with real users is needed to assess actual public health impact.
22 May 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Respiratory Syncytial Virus Vaccination in Solid Organ Transplant Recipients: Interim Findings From a Phase 3 Trial of mRNA-1345
BACKGROUND: Solid organ transplant (SOT) recipients are at increased risk for severe respiratory syncytial virus (RSV) disease due to chronic immunosuppression. METHODS: In this ongoing, open-label, phase 3 trial, adults ≥18 years with a history of liver, kidney, or lung transplant received 2 doses of mRNA-1345 RSV vaccine (50 µg) 56 days apart. Primary endpoints were tolerability, safety, and RSV-A/RSV-B neutralizing antibody (nAb) responses on Day 85; secondary endpoints included immunogenicity on Days 29 and 181. Cell-mediated immunity was an exploratory endpoint assessed in a subset of participants. RESULTS: 146/150 participants (median: age, 57 years; time since transplant, 4.7 years) received both doses. Reactogenicity was mild to moderate and transient. No vaccine-related discontinuations, deaths, adverse events of special interest (AESIs), or events of transplant rejection were reported within 28 days after any dose. One dose was immunogenic across all SOT types, with 4.9- and 3.4-fold increases in RSV-A/RSV-B nAb GMTs by Day 29, respectively. A second dose resulted in modest additional increases over baseline (RSV-A, 7.1-fold; RSV-B, 5.2-fold). Added benefit of the second dose was more apparent in participants with kidney and lung transplant, <2 years post-transplant, and on mycophenolate. Responses remained above baseline through Day 181. Polyfunctional CD4⁺ T-cell responses were robust and sustained; CD8⁺ responses were also observed. CONCLUSIONS: mRNA-1345 was well tolerated and immunogenic in SOT recipients. A single dose induced nAb responses across subgroups, with potential additional benefit from a second dose in specific groups. Durable antibody and cellular responses support mRNA-1345 as a preventive strategy for RSV in this vulnerable population. CLINICAL TRIALS REGISTRATION: NCT06067230.
22 May 2026
Read appraisal →Open heart
Prognostic value of CT-derived fractional flow reserve for major adverse cardiovascular events in patients with suspected or known coronary artery disease: a systematic review and meta-analysis
BACKGROUND: Fractional flow reserve derived from CT (FFR-CT) enables non-invasive functional assessment of coronary stenoses in patients with suspected or known coronary artery disease, but evidence regarding its prognostic value remains fragmented. We conducted a systematic review and meta-analysis to quantify the association between abnormal FFR-CT and major adverse cardiovascular events (MACE), updating the 2022 meta-analysis by Nørgaard et al METHODS: We searched PubMed, Embase and Scopus through December 2025 for studies comparing outcomes in patients with suspected or known coronary artery disease with FFR-CT ≤0.80 versus >0.80 (PROSPERO: CRD420261276897). Random-effects meta-analysis with Hartung-Knapp-Sidik-Jonkman adjustment was performed. Subgroup analyses examined FFR-CT technology, geography and follow-up duration. Risk of bias was assessed using the Quality in Prognosis Studies (QUIPS) tool and certainty of evidence using Grading of Recommendations, Assessment, Development and Evaluations (GRADE) method. RESULTS: Twenty-two studies with 20 067 patients were included, representing a fourfold increase from the prior meta-analysis. Follow-up ranged from 12 to 120 months. Patients with FFR-CT ≤0.80 had significantly higher MACE risk (HR 3.94; 95% CI 2.92 to 5.31; p<0.0001) with substantial heterogeneity (I²=79.9%). However, restricting to hard endpoints (death/myocardial infarction; k=6) yielded HR 3.28 (95% CI 2.25 to 4.79) with zero heterogeneity (I²=0%). No significant differences emerged across FFR-CT technologies (p=0.812), including HeartFlow, machine learning and deep learning algorithms. Trim-and-fill analysis suggested possible publication bias, with adjusted HR 2.35. GRADE certainty was moderate. CONCLUSIONS: Abnormal FFR-CT is associated with nearly fourfold increased risk of MACE in patients with suspected or known coronary artery disease, with consistent prognostic value across technologies. The absence of heterogeneity for hard endpoints supports FFR-CT as a reliable prognostic marker. These findings address the evidence gap identified by current guidelines regarding FFR-CT prognostic utility. PROSPERO REGISTRATION NUMBER: CRD420261276897. CLINICAL TRIAL NUMBER: Not applicable (as this is a Systematic Review and Meta-Analysis and not a Clinical Trial).
22 May 2026
Read appraisal →The British journal of ophthalmology
Impact of artificial intelligence on the availability, accessibility, acceptability and quality of ophthalmic disease screening services: a scoping review
This scoping review examines the existing literature on the application of artificial intelligence (AI) in screening for eye diseases, with a focus on evaluating whether AI-assisted diagnostic technologies enhance the availability, accessibility, acceptability and quality of screening services. 42 original studies were selected for in-depth analysis, including those employing health economic evaluations. Methodological quality was assessed using the Mixed Methods Appraisal Tool, with the majority of studies demonstrating high quality-24 scored 5/5, 15 scored 4/5 and the rest scored 3/5. Among the included studies, 34 compared manual screening with either AI-assisted or fully AI-driven approaches. Availability was the most frequently studied aspect (28 studies), followed by acceptability (12 studies), whereas accessibility and service quality were less commonly addressed. Overall, AI shows significant potential to improve the cost-effectiveness of eye care services and enhance patient access, particularly in remote or underserved regions. It was also well-accepted by patients, with high satisfaction and improved referral compliance. The findings suggest that AI holds promise for advancing eye disease screening, although large-scale, long-term trials are needed to effectively integrate AI into the reconstruction of screening processes and the reshaping of eye health service systems.
22 May 2026
Read appraisal →Journal of medical Internet research
A Digital Toolkit for Weight Loss Maintenance in European Adults (NoHoW): 2×2 Factorial Randomized Controlled Trial
BACKGROUND: Digital approaches to weight management have the potential to produce cost-effective and scalable weight management solutions. Effective behavior change interventions typically promote self-regulation of energy balance behaviors, which may be enhanced by incorporating emotion regulation strategies. OBJECTIVE: This study aimed to evaluate the effectiveness of a digital behavior change toolkit for weight loss maintenance in European adults who had achieved ≥5% weight loss in the previous 12 months. We hypothesized that a combined intervention targeting self-regulation or motivation and emotion regulation would be more effective than either component alone, and that each would outperform an active control. METHODS: The Navigating to a Healthier Weight (NoHoW) trial was a 2×2 factorial randomized, single-blind, controlled trial involving 1627 adults who had achieved ≥5% weight loss in the previous 12 months (initial BMI ≥25 kg/m2) across 3 European centers (the United Kingdom, Denmark, and Portugal). The trial evaluated a digital toolkit for weight management subsequent to an initial ≥5% weight loss in the prior 12 months. Participants were assigned using adaptive stratified sampling to one of four groups: (1) self-regulation or motivation (n=403), (2) emotion regulation (n=416), (3) combined motivation and emotion (n=408), or (4) active control (generic content, regular self-weighing, and Fitbit use, n=400). The primary outcome was weight change from baseline to 12 months. Prespecified secondary outcomes included cardiometabolic markers. Linear models adjusted for recruitment center, sex, age group, BMI group, and pretrial weight loss. Subgroup analyses were conducted by sex. RESULTS: At 12 months, 76% (364/1627) of participants remained in the study. In the primary ITT analysis in all participants, none of the intervention arms (motivation, emotion, or combined) differed significantly from the active control for weight change at 12 months. Completer and per-protocol analyses produced similar patterns and did not change the overall interpretation. In the per-protocol sample, men regained 0.14 kg, and women regained 0.54 kg of their pretrial weight loss. Subgroup analyses indicated a small effect of the motivation intervention in men, but this was not clinically meaningful and did not alter the primary null findings. Nearly half of ITT participants regained weight, and no significant intervention effects were observed for cardiometabolic secondary outcomes. CONCLUSIONS: The NoHoW trial was the first large-scale, multicountry 2×2 factorial randomized controlled trial to evaluate a digital-only toolkit based on self-regulation or motivation and emotion regulation techniques for weight loss maintenance. NoHoW found no evidence in the primary ITT analysis that digital interventions targeting self-regulation or emotion regulation improved weight loss maintenance compared with the active control. A small subgroup effect in men should be interpreted cautiously and does not change this conclusion. The trial provides evidence on both the limitations and potential of digital behavior change interventions for long-term weight outcomes. Future digital interventions may benefit from enhanced engagement and tailored content to improve long-term weight outcomes.
21 May 2026
Read appraisal →Oral health & preventive dentistry
Screening for Periodontitis Using Blood Biomarkers and Demographic Data: A Machine Learning Study
PURPOSE: Periodontitis is a common chronic disease associated with systemic conditions such as diabetes and cardiovascular disease. Diagnosis typically relies on dental examinations and radiographs, which may be underutilised by individuals who avoid, delay, or lack dental care. This study evaluated the potential of routine blood biomarkers and demographic data for screening moderate-to-severe periodontitis. METHODS AND MATERIALS: Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2011-2012 adults aged ≥ 30 years (N = 3,338). Periodontal status was classified using CDC/AAP definitions. An XGBoost classifier was trained on 77 features spanning demographics, complete blood count, glycemic and hemo-globin markers, heavy metals, and additional biochemical assays. Model performance was assessed with Accuracy, Precision, Recall, and F1 score. SHapley Additive exPlanations (SHAP) were used for interpretability. Subgroup analyses were conducted by age and gender. RESULTS: The model achieved 61.1% Accuracy, 57.4% Precision, 94.6% Recall, and an F1 score of 71.5%. Performance was higher in males (F1 = 78.2) than in females (F1 = 63.9%). SHAP identified age, gender, blood cadmium, blood lead, and glycohaemoglobin (HbA1c) as top predictors. CONCLUSION: Routine blood biomarkers and demographics can be leveraged for non-dental screening of periodontitis, offering a feasible preventive strategy in primary care. The model achieved high recall, minimising false negatives, and identified biologically plausible predictors. This approach may help flag at-risk individuals, particularly in underserved populations, and support integration of oral health into general medical care.
21 May 2026
Read appraisal →JMIR medical informatics
Machine Learning Prediction Model for Dyslipidemia and Its Association With Atherothrombotic Events in 3 Independent Cohorts From South Korea, Japan, and the United Kingdom: Algorithm Development and Validation Study
BACKGROUND: Dyslipidemia is a multifactorial and complex condition that warrants investigation through advanced analytical approaches such as machine learning (ML). Few previous ML studies predicting dyslipidemia have been validated across multiple international populations. OBJECTIVE: This study aimed to develop an ML model to predict the 5-year incidence of dyslipidemia using routinely collected health examination data. To ensure generalizability, the model was externally validated in populations from South Korea, Japan, and the United Kingdom. Furthermore, the clinical relevance of the model-derived risk was evaluated by examining its association with atherosclerotic outcomes, including acute myocardial infarction and cerebral infarction. METHODS: This study was conducted using 3 independent, large-scale, population-based cohorts. The discovery cohort from South Korea (n=471,650) was used for model training and internal validation, while 2 validation cohorts from Japan (validation A; n=7,255,685) and the United Kingdom (validation B; n=408,725) were used for external validation. We evaluated various ML-based models using 23 features extracted from regular health screening data to predict the new onset of dyslipidemia within 5 years. Shapley Additive Explanations values were calculated to assess feature importance. To ensure the robustness of the proposed ML model, we evaluated the risk of atherothrombotic events (acute myocardial infarction or cerebral infarction) based on the model probability (tertiles; T1, T2, and T3) using a Cox proportional hazards model. RESULTS: In the discovery cohort, soft-voting ensemble learning with Light Gradient Boosting Machine and categorical boosting exhibited performance metrics of area under the receiver operating characteristic curve (AUROC) of 0.783, precision of 37.9%, and area under the precision-recall curve of 0.469. The model showed moderate discriminatory performance in the external validation cohorts (cohort A: AUROC 0.744; precision 27.2%; and cohort B: AUROC 0.687; precision 5.07%). Shapley Additive Explanations value analysis identified smoking, alcohol intake, and physical activity as the most important features for predicting dyslipidemia. Finally, a higher model probability (T3 vs reference) was pronounced with an increased risk of acute myocardial infarction (adjusted hazard ratio 2.34, 95% CI 1.84-2.97) and cerebral infarction (adjusted hazard ratio 2.43, 95% CI 2.19-2.71). CONCLUSIONS: This multinational study developed and validated an ML-based model using routine health checkup data to predict the 5-year risk of new-onset dyslipidemia, which was also associated with atherosclerotic events.
21 May 2026
Read appraisal →BMC neurology
The impact of caffeine consumption on migraine: a systematic review.
BACKGROUND: Migraine is a common, disabling neurological disorder affecting over one billion people worldwide, characterized by recurrent unilateral, pulsating headaches lasting 4-72 h. Caffeine, a widely consumed psychoactive alkaloid found in coffee, tea, and other beverages, is frequently implicated as both a trigger and a treatment for attacks. OBJECTIVES: We aim to comprehensively evaluate the association between caffeine exposure and migraine by integrating observational evidence with Mendelian Randomization (MR) studies. METHODS: A comprehensive search was conducted through PubMed, Scopus, Web of Science, and the Cochrane Library till August 2025. We included primary studies assessing the impact of caffeine exposure on migraine risk. RESULTS: 19 studies (nine cross-sectional, seven MR, and three cohort) were included in our review. MR studies included over one million participants, while cross-sectional studies involved over 43,000 participants, and the cohort studies included 421 migraine cases. MR reflected lifelong genetic liability (population-level, chronic exposure) and associated with a reduced risk with overall odds ratio (OR) ranging from 0.53 to 0.71, with stronger associations reported in migraine with aura (ORs as low as 0.37-0.39). Observational studies captured short-term, acute effects and showed that abrupt withdrawal or acute excessive intake (≥ 3 drinks/day) can trigger attacks in some people (P = 0.024). While habitual moderate use was generally not linked to higher average migraine burden. CONCLUSIONS: Lifelong genetic liability to higher coffee/caffeine intake is associated with a reduced risk of migraine especially for migraines with aura. However, acute excessive intake or sudden changes in caffeine habits can trigger attacks in some people. More future large, well prospective cohorts and carefully designed MR studies are needed to confirm our results and clarify the precise impact of caffeine on migraine risk.
20 May 2026
Read appraisal →Cancer causes & control : CCC
Prediction models for early detection and diagnosis of lung cancer in people who have never smoked: a systematic review and critical appraisal
BACKGROUND: Never-smokers can develop lung cancer, but this possibility is often overlooked by patients and physicians. We conducted a systematic review to identify existing prediction models that could be implemented in primary care or at a population level to facilitate early detection and diagnosis of lung cancer in never-smokers. METHODS: This study was registered on PROSPERO (ref: CRD42023374471). We systematically searched literature on the Medline, Embase, PsycINFO, and CINAHL databases published before 22 January 2025, with additional hand searching. Primary care or population-level data from subjects including never-smokers had to be used for model derivation. Studies involving specialized tests (radiological or genetic) were excluded. We used CHARMS to guide data extraction and critical appraisal, the TRIPOD statement for model evaluation, and PROBAST for risk of bias assessment. RESULTS: Among 2,431 studies retrieved, 31 models were included. Eight models were developed exclusively for never-smokers, but none were at low risk of bias. Among 23 models derived from never- and ever-smokers, five were at low risk of bias. Two were diagnostic models with 1-2 years prediction horizons, and three were prognostic models with 2-10 years prediction horizons. Methodological issues from the included studies were identified, analyzed, and discussed. CONCLUSION: This systematic review critically appraises and summarizes key information from currently available prediction models for lung cancer in never-smokers. The findings can inform future research to improve care and services for this underserved population.
20 May 2026
Read appraisal →Journal of the American Heart Association
Effect of Different Types of Whole Dietary Pulses on Established Therapeutic Lipid Targets for Cardiovascular Risk Reduction: An Updated Systematic Review and Dose-Response Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Although dietary pulses are recognized by major clinical practice guidelines to reduce cholesterol and coronary heart disease risk, intake is low. There are no health claims for any pulse for cholesterol reduction, which could support uptake. We therefore conducted a systematic review and dose-response meta-analysis of randomized trials of the effect of different types of whole dietary pulses on lipid targets. METHODS: MEDLINE, Embase, and the Cochrane Library were searched through March 2025 for trials ≥3 weeks. The primary outcome was low-density lipoprotein-cholesterol. Secondary outcomes were other lipid targets. Independent reviewers extracted data and assessed risk of bias. Certainty of evidence was assessed using Grading of Recommendations Assessment, Development, and Evaluation. RESULTS: Thirty-eight trials (52 trial comparisons, n=2095) with a median of 6 weeks and dose of 130 g/d (0.5-0.67 cup/d) showed that whole dietary pulses decreased low-density lipoprotein-cholesterol (mean difference, -0.14 mmol/L [95% CI, -0.19 to -0.08]), non-high-density lipoprotein-cholesterol (-0.22 mmol/L [95% CI, -0.30 to -0.14]), apoB (apolipoprotein B) (-0.08 g/L [95% CI, -0.13 to -0.03]) and high-density lipoprotein-cholesterol (-0.03 mmol/L [95% CI, -0.05 to -0.01]) with no effects on other lipids. Analyses by pulse type showed similar results. A linear inverse relationship was shown for beans up to 1 cup/d for low-density lipoprotein-cholesterol (coefficient, -0.25 mmol/L/0.5 cup [95% CI, -0.48 to -0.02]) and non-high-density lipoprotein-cholesterol (-0.45 mmol/L/0.5 cup [95% CI, -0.71 to -0.18]). Grading of Recommendations Assessment, Development, and Evaluation was moderate-to-high for all outcomes, except apoB (very low). CONCLUSIONS: Whole dietary pulses likely result in small important-to-moderate reductions in lipid targets and trivial reductions in high-density lipoprotein-cholesterol. Similar effects were observed across pulse types with an inverse dose-response gradient for beans up to 1 cup/d. Future studies on chickpeas, dried peas, and lentils are warranted. REGISTRATION: URL: https://www.crd.york.ac.uk/PROSPERO/view/CRD42023432826; Unique identifier: CRD42023432826.
20 May 2026
Read appraisal →Journal of the American Heart Association
Sex Differences Among Young Patients With ST-Segment-Elevation Myocardial Infarction: A Systematic Review and Meta-Analysis
BACKGROUND: Female sex is associated with lower incidence of myocardial infarction, especially at younger ages. However, sex differences in mortality and coronary artery disease patterns among young patients with ST-segment-elevation myocardial infarction are not well established. This study evaluated sex differences in in-hospital and 1-year all-cause death and in the extent of coronary artery disease in patients aged ≤45 years with ST-segment-elevation myocardial infarction. METHODS: A systematic review (December 9, 2025) was conducted using PubMed, Cochrane, Embase, major cardiology conference proceedings, and ClinicalTrials.gov. Studies comparing sexes for outcomes were included. Non-English publications, abstracts without full text, and studies published before 2010 were excluded. A random-effects meta-analysis was performed, with results presented as relative risks (RR) and 95% CIs. Heterogeneity was assessed using τ and I2, risk of bias using the Risk of Bias in Nonrandomized Studies of Interventions tool. The study was registered on the International Prospective Register of Systematic Reviews. RESULTS: Eleven studies including 738 029 patients with ST-segment-elevation myocardial infarction (22% women) were analyzed. Female sex was associated with higher unadjusted in-hospital (RR, 1.56 [95% CI, 1.40-1.74]; τ=0.04; I2=0%) and 1-year mortality rate (RR, 1.78 [95% CI, 1.50-2.12]; τ=0.00; I2=0%). Women more frequently had single-vessel disease (73% versus 60%; RR, 1.13 [95% CI, 1.03-1.23]; τ=0.07; I2=67%), while men had 2-vessel (23% versus 18%; RR, 0.76 [95% CI, 0.68-0.85]; τ=0.00; I2=0%;) and 3-vessel disease (16% versus 9%; RR, 0.49 [95% CI, 0.27-0.86]; τ=0.41; I2=33%). CONCLUSIONS: In young patients with ST-segment-elevation myocardial infarction, female sex is associated with higher unadjusted in-hospital and 1-year death despite less extensive CAD. Further studies adjusting for comorbidities and standardizing CAD definitions are needed.
20 May 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
Predicting incident type 2 diabetes using wearable activity and polygenic risk: A survival-modeling study in All of Us
BACKGROUND: Evidence is limited on whether daily step counts are uniform across individuals or whether combining them with genetic risk improves prediction of type 2 diabetes (T2D), both essential for precision prevention. We aimed to assess whether objectively measured daily step count and Polygenic Risk Score (PRS) predict the incidence of T2D, and to evaluate the predictive performance of machine-learning models. METHODS: We conducted a prospective cohort study of 4589 adults in the National Institutes of Health's All of Us Research Program with valid Fitbit step data and whole-genome-derived PRS, excluding individuals with T2D before or within a 180-day lead-in period. Incident T2D was defined by HbA1c ≥ 6.5%, plasma glucose ≥126 mg/dL, or an All of Us T2D condition record. We evaluated Cox and machine-learning survival models. FINDINGS: Over a median of 2.92 years (15 340 person-years), 265 participants developed T2D (5.77% cumulative incidence; 17.27 per 1000 person-years). Risk-reducing thresholds were ∼7000 steps/day (P < .001) but differed by PRS group (∼7800 high vs ∼5800 low; P < .001). Each additional 1000 steps/day was associated with lower T2D risk (adjusted hazards ratio, 0.83; 95% CI, 0.79-0.88; P < .005), whereas each 1-SD higher PRS was associated with higher T2D risk (adjusted hazards ratio, 2.62; 2.32-2.96; P < .005). Adding steps to a clinical model increased the C-index from 0.748 to 0.774; adding PRS increased it to 0.867. Penalized Cox achieved the highest discrimination (C-index 0.859), followed by survival support vector machine (0.85) and classical Cox (0.846). Calibration was best with Random Survival Forests. INTERPRETATION: Step-count thresholds for reducing T2D risk varied across genetic risk groups, indicating that step counts do not confer uniform protection. Step counts and PRS provided independent, complementary predictive information, and their combination improved prediction of incident T2D.
20 May 2026
Read appraisal →Archives of disease in childhood
Effects of metformin on anthropometric indices and hormones and adipocytokine concentrations in children with overweight or obesity: a systematic review and meta-analysis of randomised controlled trials
OBJECTIVE: The aim of this systematic review was to assess the effect of metformin on anthropometric indices, hormones and adipocytokine concentrations in children with overweight or obesity. METHODS: Eligible studies were searched via PubMed, Scopus, Cochrane Central Register of Controlled Trials, alongside clinical trial registries and the reference lists of included studies, from inception to October 2024. Screening was carried out independently by two reviewers, involving title-abstract screening for relevance, followed by full-text review for eligibility confirmation. Data extraction was performed by two independent investigators using an identical Excel spreadsheet. A meta-analysis was conducted using the random-effects model. RESULTS: A total of 19 original studies were deemed eligible for this systematic review. Metformin was more effective than placebo in improving Body Mass Index (BMI), BMI-Z score, body weight, fasting insulin and leptin concentrations (mean difference (MD) -1.07 kg/m2, 95% CI -1.62 to -0.52; MD -0.09, 95% CI -0.13 to -0.05; MD -3.20 kg, 95% CI -4.21 to -2.19; MD -3.38 μU/mL, 95% CI -6.00 to -0.75; MD -3.73 ng/mL, 95% CI -6.28 to -1.18, respectively). There was no difference between metformin and placebo on adiponectin and resistin concentrations, as well as adiponectin to leptin ratio. CONCLUSION: Metformin may have a role as an adjuvant therapy in managing overweight and obesity among the paediatric population. Regarding the concentrations of adipokines and hormones, the results are inconclusive. More randomised controlled trials with rigorous methodology and homogeneity in population and intervention characteristics are needed to shed light on this topic. TRIAL REGISTRATION NUMBER: PROSPERO CRD42023401732.
20 May 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
Associations of Metabolic Phenotypes with Cardiac Structure and Clinical Outcomes: Insights from the UK Biobank
CONTEXT: Metabolic syndrome and obesity represent major cardiovascular risk factors, yet their combined effects on cardiac structure and clinical outcomes remain incompletely understood. OBJECTIVE: To examine associations between metabolic phenotypes, cardiac magnetic resonance imaging (CMR), and clinical outcomes. DESIGN: Prospective study with a median 5.1-year follow-up. SETTING: Population-based cohort from the UK Biobank. PARTICIPANTS: A total of 22 789 participants (mean age 64.1 ± 7.5 years, 52.6% female) were categorized as metabolically healthy nonobese (MHN) vs obese (MHO) or unhealthy nonobese (MUN) vs obese (MUO), based on obesity (body mass index ≥30 kg/m²) and metabolic status (prevalent diabetes or both hypertension and hyperlipidemia). MAIN OUTCOME MEASURE(S): Primary and secondary endpoints were major adverse cardiovascular events (MACE) and all-cause mortality, respectively. CMR included left ventricular ejection fraction (LVEF, %), end-diastolic volume (LVEDV, mL), myocardial mass (g), wall thickness (mm), cardiac output (L/min), and left atrial volume (mL). Linear regression and Cox proportional hazards models, adjusted for age, sex, and smoking, assessed associations between metabolic phenotypes, CMR, and clinical outcomes. RESULTS: In adjusted models, all phenotypes demonstrated increased WT (MHO: β=.53 [.50, .55]; MUN: β=.24 [.21, .27]; MUO: β=.66 [.61, .70]), compared to MHN. LVEDV was increased in obesity phenotypes (MHO: β=4.67 [4.07, 5.27]; MUO: β=5.14 [4.05, 6.22]) and decreased in MUN (β=-1.25 [-1.95, -.55]), while MUO showed reduced LVEF (β=-.48 [-.91, -.04]). MACE risk was increased in unhealthy phenotypes (MUN: adjusted hazard ratio (aHR) = 1.55 [1.16-2.07]; MUO: aHR = 1.95 [1.28-2.97]). All phenotypes showed increased all-cause mortality risk (MHO: aHR = 1.65 [1.23-2.21]; MUN: aHR = 1.41 [1.05-1.90]; MUO: 2.10 [1.41-3.15]). CONCLUSION: Metabolic phenotypes show distinct cardiac structural changes and increased mortality risk, supporting their potential in cardiovascular risk stratification.
19 May 2026
Read appraisal →JMIR infodemiology
Performance of Large Language Models in the Cognitive Analysis of Misinformation: Evaluation Study
BACKGROUND: Public discourse is significantly impacted by the rapid spread of misinformation on social media platforms. Human moderators, while capable of performing well, face many challenges due to scalability. While large language models (LLMs) show great potential across various language tasks, their capacity for cognitive and contextual analysis, in detecting and interpreting misinformation, remains less evaluated. OBJECTIVE: This study evaluates the effectiveness of LLMs in detecting and interpreting misinformation compared to human annotators, focusing on tasks requiring cognitive analysis and complex judgment. Additionally, we analyze the influence of different prompt engineering strategies on model performance and discuss ethical considerations for using LLMs in content moderation systems. METHODS: We evaluated 4 OpenAI models against a panel of human annotators using a subset of posts from the MuMiN dataset. Each model and human annotator responded to structured questions on misinformation, following an established cognitive framework. Both human annotators and LLMs also provided scores indicating how confident they were in their responses. Various prompting strategies were used in this research, including: 0-shot, few-shot, and chain-of-thought, with performance evaluated through precision, recall, F1-score, and accuracy. We used statistical tests, including the McNemar test, to quantitatively assess differences between LLM and human ratings of misinformation. RESULTS: GPT-4 Turbo with chain-of-thought prompting achieved the highest performance of all LLMs for detecting misinformation, with an accuracy of 67.2% and an F1-score of 78.3%, but was outperformed by human annotators, who achieved 70.1% accuracy and an F1-score of 81%. LLMs performed well in tasks involving logical reasoning and straightforward misinformation detection, but struggled with complex judgments, including detecting sarcasm, understanding misinformation, and analyzing user intent. LLM confidence scores positively correlated with accuracy in simpler tasks (r=0.72, P<.01) but were less reliable in subjective and complex contextual evaluations. CONCLUSIONS: LLMs show significant potential for automating misinformation detection. Their limitations in understanding and interpreting these posts highlight the current necessity of human oversight. A hybrid framework combining LLMs for preliminary screening with human moderators for more complex evaluation presents a promising future direction. Future research could prioritize the fine-tuning of LLMs using datasets that emphasize cognitive and emotional linguistic features, alongside the development of advanced prompting techniques.
19 May 2026
Read appraisal →JMIR mHealth and uHealth
Multimodal Personalized Mobile Health Just-in-Time Adaptive Intervention for Occupational Stress Management: Pilot Study
BACKGROUND: As digital health solutions gain traction, there is an urgent need for effective, person-centered stress management tools for employees. Advances in wearable stress monitoring and machine learning now enable the collection of high-resolution, real-time data and the delivery of personalized interventions with respect to both timing and content. Despite this technological progress, there remains a notable paucity of mobile health (mHealth) interventions that capitalize on these capabilities to implement just-in-time adaptive interventions (JITAIs). OBJECTIVE: This pilot study aimed to examine the preliminary effects of the RELAX mHealth app, which uses a JITAI framework to reduce occupational stress, with a particular focus on initial indications of stress reduction, the influence of the app's degree of personalization on these effects, and overall user experience. METHODS: A 2-arm randomized trial was conducted with 46 employees across two phases of 3 weeks each. Participants used the RELAX mHealth app, which adapts intervention timing based on real-time stress data and tailors content according to users' preferences. Stress was assessed via multiple heart rate variability (HRV) parameters measured with wearable sensors, alongside subjective questionnaires and ecological momentary assessment (EMA) questionnaires. Usability and user satisfaction were also evaluated. In the second phase, a group randomization was implemented, with two groups of different degrees of in-app personalization. RESULTS: The findings were mixed. While selected subjective stress indicators decreased from baseline to the end of the study, one physiological measure increased. Retrospective chronic stress (P=.048) and arousal related to the most recent stress event prior to EMA entry (P=.02) were reduced; however, the low frequency (LF)-to-high frequency (HF) ratio of HRV increased (P=.03). Other stress variables, such as work-related stress, valence of daily experiences, and momentary arousal, showed no change over the course of the study. No between-group differences were observed with respect to the assigned degree of personalization. Additionally, usability increased (P<.001), whereas satisfaction decreased at a trend level (P=.05). CONCLUSIONS: Although the results provide selective preliminary indications of stress reduction with app use, the variability of outcomes raises concerns about its current practical utility. The effects are also discussed with regard to the specific design of the prototype and the study setup. Technical issues and user discomfort were noted despite increasing usability over the study period, indicating areas for improvement in the developed prototype. Given the absence of a control group and the use of interventional arms only, no definitive conclusions about effects can be drawn. Nevertheless, this study highlights both the potential and the challenges of a personalized mHealth approach to occupational stress management. Further refinement of the app and more comprehensive evaluations are needed to fully understand the impact and functional usability of this JITAI-based approach in real-world settings.
19 May 2026
Read appraisal →Circulation
Effect of APOC3 Inhibition With Olezarsen on Coronary Atherosclerosis: Essence-TIMI 73b Imaging Study
BACKGROUND: Whether lowering triglyceride-rich lipoproteins and remnant cholesterol favorably modifies coronary atherosclerosis is unclear. Olezarsen, an antisense oligonucleotide that targets apolipoprotein C-III, reduces triglycerides by ~60% and remnant cholesterol by ~70%, has a neutral effect on LDL (low-density lipoprotein) cholesterol (LDL-C), and reduces apoB (apolipoprotein B) by ~15% in moderate hypertriglyceridemia. We investigated the effect of olezarsen on coronary plaque in adults with largely moderate hypertriglyceridemia. METHODS: We conducted a coronary computed tomography angiography study within Essence-TIMI 73b, a randomized, placebo-controlled trial of olezarsen versus placebo that enrolled patients between November 2022 and February 2024. Inclusion criteria were triglycerides ≥150 mg/dL (2.26 mmol/L), presence of or high risk for cardiovascular disease, and, for this imaging study, noncalcified plaque on baseline coronary computed tomography angiography. The primary end point was percent change from baseline to 12 months in noncalcified plaque volume. RESULTS: Of 468 participants (349 olezarsen, 119 placebo), the median age was 63 years (interquartile range, 56-70); 31% were women, and 97% received lipid-lowering therapy. Median baseline triglycerides were 249 mg/dL (interquartile range, 197-331), and remnant cholesterol was 53 mg/dL (interquartile range, 38-76). Median baseline noncalcified plaque volume was 125.3 mm³ (interquartile range, 63.2-213.3). At 6 months, olezarsen reduced triglycerides by 63.9%, remnant cholesterol by 71.9%, and apoB by 16.0% over placebo, with no difference in LDL-C. The percent change in noncalcified plaque volume from baseline to month 12 did not differ between olezarsen and placebo (placebo-adjusted least-squares mean difference, 2.98% [95% CI, -3.4 to 9.3]; p=0.36). No significant differences between olezarsen and placebo were observed for changes in low-attenuation, calcified, or total plaque volumes at 12 months. CONCLUSIONS: Despite substantial triglyceride and remnant cholesterol lowering, treatment with olezarsen for 12 months on top of standard-of-care lipid-lowering therapy in patients with largely moderate hypertriglyceridemia did not affect noncalcified coronary plaque volume. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT05610280.
19 May 2026
Read appraisal →Human vaccines & immunotherapeutics
Coverage and determinants of childhood immunization in Somalia: A systematic review and meta-analysis
Childhood immunization is a cost-effective public health intervention, yet coverage remains suboptimal in Somalia. This systematic review and meta-analysis aimed to pool childhood immunization coverage and its determinants in Somalia. The electronic database search strategy yielded 11 eligible cross-sectional studies conducted between 2010 and 2025. Using Comprehensive Meta-Analysis Version 5 (Biostat, Englewood, NJ, USA), the pooled coverage for receiving at least one vaccine was 57.9%, while completion of the four basic vaccines was 16.1%, Full immunization coverage was 42.2%. Children born in health facilities were more likely to be immunized than home births (AOR = 2.10; 95% CI: 1.87-2.35). Unexpectedly, mothers with ≥4 ANC visits had lower odds of child immunization (AOR = 0.27; 95% CI: 0.24-0.30). Maternal education increased immunization likelihood (AOR = 1.14; 95% CI: 1.09-1.20). Children in rural areas were likely to be immunized than urban counterparts (AOR = 1.55; 95% CI: 1.49-1.61). These findings highlight the urgent need to strengthen integrated maternal and child health services.
19 May 2026
Read appraisal →Scientific reports
RDE-DR: robust deep ensemble CNNs for automated diabetic retinopathy detection from fundus images
Diabetic retinopathy (DR) is a leading cause of preventable blindness, motivating the development of reliable automated screening systems. This work proposes a Robust Deep Ensemble for Diabetic Retinopathy detection (RDE-DR) by analyzing ensemble fusion strategies. Four pre-trained convolutional neural networks (ResNet50, VGG16, VGG19, and DenseNet121) are trained using CLAHE-enhanced APTOS 2019 fundus images and integrated through seven heterogeneous fusion mechanisms, including voting-based, rank-based, and fuzzy-integral-inspired strategies. A consistent evaluation protocol is adopted, incorporating threshold optimization and probabilistic calibration analysis to validate robustness, decision margins, and accuracy-precision trade-offs. Experimental results show that multiple fusion techniques achieve comparable high performance and stable behavior on the APTOS 2019 benchmark, with the best configuration reaching 98.64% accuracy, 98.40% precision, 98.92% recall, 98.66% F1-score, and 99.78% Area-Under-Curve (AUC). Beyond peak accuracy, the study provides insights into ensemble reliability, calibration characteristics, and practical design choices for medical image classification systems. These results show that integrating transfer learning with CLAHE preprocessing and ensemble fusion yields stable experimental performance on the APTOS 2019 benchmark, suggesting potential for future medical decision support.
18 May 2026
Read appraisal →European journal of preventive cardiology
Targeting better hypertension control with the usage of the ESH smartphone application
AIMS: Uncontrolled blood pressure is a main issue in the management of hypertensive patients, while mobile health (mHealth) offers a new perspective. This study aimed to investigate the efficacy of an mHealth app-based intervention, the European Society of Hypertension (ESH) CARE, in the management of uncontrolled hypertensive patients. METHODS AND RESULTS: This was a two-arm randomized, controlled trial conducted among 107 hypertensive patients, with uncontrolled office (≥140/90 mmHg) and 24-h ambulatory blood pressure (≥130/80 mmHg). After antihypertensive drug treatment initiation or intensification, patients were 1:1 randomized to application assisted strategy (AAS) group or usual care group, with scheduled visits at 1 and 3 months. In ASS group there was a virtual visit, while in usual care group there was an office visit. At 6 months, there was a final visit assessment with office and ambulatory blood pressure (BP) measurement. The mean age of the total population (n = 107) was 52.9 ± 9.6 years, 50.5% were women, without any significant differences in the baseline characteristics. At 6 months, 50% in the usual care group and 78.2% in the AAS group (P = 0.002) achieved both systolic and diastolic ambulatory BP targets (<130/80 mmHg). Regarding office BP, there was a reduction of 19.9 ± 9.1/12.4 ± 7.7 mmHg in the usual care group and 25.1 ± 6.4/16.3 ± 6.3 mmHg in the AAS group, while regarding ambulatory BP, there was a reduction of 6.6 ± 9.2/3.3 ± 6.2 mmHg and 10.9 ± 9.1/6.4 ± 5.5 mmHg, respectively. CONCLUSION: Follow-up of hypertensive patients with the ESH Care App is feasible and contributes to a significantly better office and out-of-office BP control after 6 months. Uncontrolled blood pressure is a main issue in the management of hypertensive patients, while mobile health (mHealth) offers a new perspective. This study aimed to compare the efficacy of an mHealth app-based—ESH Care App—intervention to usual clinical care, in the management of uncontrolled hypertensive patients. Telemonitoring with the ESH Care App is associated with: Significantly improvement in office and ambulatory blood pressure control. Significantly greater reduction in office and ambulatory BP measurements.
18 May 2026
Read appraisal →The American journal of cardiology
CagriSema Versus Semaglutide Monotherapy or Placebo for Obesity: A Systematic Review and Meta-Analysis of Randomized Controlled Trials with GRADE Assessment
The obesity epidemic is a major health burden that enhances susceptibility to a broad spectrum of metabolic-associated comorbidities, ranging from fatty liver disease and endocrine dysfunction to traditional risks like type 2 diabetes mellitus and cardiovascular disease. Glucagon-like peptide-1 receptor agonists, including semaglutide, facilitate weight loss alongside glucose metabolism. The dual therapy CagriSema, which combines semaglutide with cagrilintide was developed. We systematically searched MEDLINE (via PubMed), Web of Science, Scopus, and Cochrane Library, from inception to July 2025, for randomized controlled trials (RCTs) comparing CagriSema with semaglutide monotherapy or placebo in patients with obesity. Four RCTs (n = 4,419) were included (CagriSema: 3,055; control: 1,364). Pooled analysis showed that CagriSema significantly reduced percent weight loss (Cohen's d: -1.38; 95% CI: -1.84 to -0.91; I² = 94.8%). CagriSema also resulted in greater absolute weight loss (MD: -11 kg), waist circumference (MD: -9.41 cm), and systolic blood pressure (MD: -7.06 mmHg). Gastrointestinal adverse events were more frequent (RR: 1.32). CagriSema therapy was associated with superior weight reduction compared with semaglutide or placebo. In conclusion, CagriSema achieves greater weight loss than semaglutide or placebo but increases gastrointestinal adverse events, warranting careful tolerability monitoring and longer-term data.
18 May 2026
Read appraisal →Environmental pollution (Barking, Essex : 1987)
A systematic review of low-level ambient fine particulate matter (PM2.5) exposures and adverse cardiovascular health outcomes
Associations between exposure to fine particulate matter and impacts in the cardiorespiratory system are well-established, but associations are expected to vary based on exposure duration and concentration. Domestic and international agencies set standards and guidelines for PM2.5 in part to protect public health. This systematic review explores if associations exist between adverse cardiovascular endpoints and PM2.5 exposure when the concentrations are below regulatory standards. In total, 1349 documents were screened, with 95 articles meeting criteria (i.e. peer-reviewed, PM2.5 concentrations reported, cardiovascular endpoints measured). Of the included articles, 67% (64 articles) observed significant associations with adverse cardiovascular endpoints with variable findings based on specific cardiovascular endpoints (e.g. direct measurements - 69% of articles, cardiovascular endpoints - 44% of articles, events and mortality - 76% of articles, and biomarkers - 67% of articles). While not universally consistent, significant associations were observed between PM2.5 exposure and adverse cardiovascular effects, even at levels deemed compliant with current regulations. This review highlights the necessity for further research across diverse geographical settings and populations to clarify the implications of low-level PM2.5 exposure on cardiovascular health. The findings advocate for a review of regulatory standards for PM2.5 exposure in the context of cardiovascular health.
17 May 2026
Read appraisal →Clinica chimica acta; international journal of clinical chemistry
A laboratory-driven clinical decision support system algorithm for personalized and automated cardiovascular risk stratification in primary care patients.
OBJECTIVE: To develop and evaluate an automated clinical decision support system (CDSS) capable of computing both categorical and exact-percentage cardiovascular risk (CVR) in routine clinical practice. METHODS: We developed and implemented an automated CDSS (AlinIQ®) for CVR assessment within the Clinical Laboratory Department of Hospital Universitari i Politècnic La Fe (València, Spain), applied to patients referred from Primary Care. The analytical profile-requested either via automatic trigger or proactive clinician order-included total cholesterol, HDL-C, LDL-C, non-HDL-C, triglycerides, lipoprotein(a) [Lp(a)], serum creatinine, and estimated glomerular filtration rate. Additional required inputs were smoking status, systolic blood pressure, country of origin, and age at diabetes onset. The CDSS automatically computed SCORE2, SCORE2-OP, SCORE2-Diabetes, and SCORE2 Asia-Pacific, generating both categorical strata and exact-percentage CVR. Lp(a)-adjusted CVR was derived using coefficients from the Spanish Atherosclerosis Society. Following the ESC 2025 Focused Update, patients meeting predefined clinical criteria for moderate, high, or very high CVR were directly assigned to the corresponding category without SCORE calculation. The system also incorporated modifying risk factors, generating standardized interpretive comments and personalized lipid targets. RESULTS: Over one month, 2289 screenings were requested; 189 (8.3%) were excluded. A total of 171 patients (7.5%) were classified as high risk based on score-based calculation (164 [95.9%] triggered automatically), and 29 (1.6%) as very high risk (23 [79.3%] triggered automatically). 119 individuals (6.8%) were reclassified to a higher risk category after Lp(a)-based adjustment. CONCLUSION: This CDSS provides a scalable and reproducible framework for laboratory-driven cardiovascular prevention.
17 May 2026
Read appraisal →Journal of affective disorders
Biological age acceleration associated with mental and behavioural disorders: Evidence from the UK biobank cohort.
BACKGROUND: Mental and behavioural disorders may accelerate biological ageing, but comprehensive evidence integrating observational and causal approaches is lacking. METHODS: We performed observational and Mendelian randomisation (MR) analyses. First, 502,411 UK Biobank participants (78,674 with ICD-10 Mental and Behavioural Disorders) were propensity score matched (1:1). Biological Age Acceleration (BAA = biological age - chronological age) was regressed on mental and behavioural disorder status, and robustness checks were conducted using heteroscedasticity-robust and cluster-robust standard errors. Second, two-sample MR assessed causal effects of genetic liability to six mental and behavioural disorders on four ageing biomarkers, using strong genetic instruments (F > 10) and validated through sensitivity analyses including MR-Egger, Cochran's Q, and leave-one-out tests. RESULTS: Ever having a diagnosis of a mental and behavioural disorder was associated with higher BAA (β = 0.261, 95% CI: 0.229-0.293, P < 0.001). Strongest associations were observed for disorders of psychological development (β = 0.717), organic mental and behavioural disorders (β = 0.395), and mental and behavioural disorders due to psychoactive substance use (β = 0.338). BAA increased with comorbidity count (P-trend <0.001). MR indicated potential causal effects of depression (OR = 5.12 for frailty index), insomnia (OR = 2.04 for frailty index), and anxiety (OR = 1.06 for frailty index) on accelerated ageing, with no evidence of horizontal pleiotropy (MR-Egger intercept P > 0.05). Bipolar disorder was associated with slower epigenetic ageing (OR = 0.71 for GrimAge). Alcohol use disorder increased facial ageing (OR = 1.02). Schizophrenia showed no significant associations. CONCLUSIONS: Mental and behavioural disorders are observationally and robustly linked to accelerated biological ageing, with a dose-response by comorbidity. Genetic evidence supports potential causal roles for depression, insomnia, and anxiety in promoting ageing, while bipolar disorder may relate to slower epigenetic ageing. Biological ageing should be considered in the long-term management of mental health.
17 May 2026
Read appraisal →International journal of food microbiology
Machine learning-based modeling for monitoring and predicting the detection rate and severity of pathogenic microorganisms in seafood
Pathogenic microbial contamination in seafood presents persistent risks to food safety and public health. Conventional monitoring methods frequently lack adequate sensitivity and specificity, particularly under dynamic environmental conditions. To address these limitations, this study developed a machine learning (ML) framework utilizing microbial testing data from seafood samples collected in Yantai, China (2014-2024), with 2025 data reserved for external validation. The framework simultaneously predicts pathogen prevalence and severity levels. And, exploratory data analysis assessed variable distributions and statistical associations with detection rates. Time series analysis identified significant long-term trends and seasonal patterns, while spatial analysis revealed heterogeneity in contamination risk across administrative regions, particularly in coastal economic zones and major ports. Applying a dynamic threshold optimization strategy, detection rates were predicted using six ML algorithms: ROSE-LASSO, LightGBM, XGBoost, k-NN, CART, and SVM. LightGBM demonstrated optimal performance with 97.6% specificity and 99.9% positive predictive value (PPV) at an optimized threshold of 0.157. External validation confirmed its robustness, yielding 91.2% sensitivity, a 92.4% F1 score, and 93.6% PPV. For three-class severity classification, XGBoost achieved 93.7% overall accuracy and high sensitivity (80.0%) for high-risk samples, with an AUC of 0.989. By integrating spatiotemporal environmental features with adaptive thresholding, this framework provides a scalable approach for pathogen risk identification within coastal seafood supply chains, supporting early warning systems and evidence-based resource allocation.
17 May 2026
Read appraisal →JMIR medical informatics
Multimodal Fusion of Echocardiogram Images and Electronic Medical Records for Heart Disease Screening: Retrospective Algorithm Development and Validation Study
BACKGROUND: Echocardiography is a fundamental imaging modality for the diagnosis of heart disease (HD), but its interpretation remains operator-dependent and lacks standardized, data-driven decision support. Although artificial intelligence has improved image-based diagnosis, the added value and interpretability of integrating routinely collected electronic medical records (EMRs) with echocardiogram (ECHO) for large-scale screening remain underexplored. OBJECTIVE: This study aims to develop an explainable artificial intelligence framework that integrates multimodal data, including ECHO images and EMRs, with derived temporal and clinical features, to enhance the screening and interpretability of heart disease diagnosis. METHODS: In this retrospective single-center study, we analyzed 26,936 patients with linked ECHO and EMR records (1470 with clinically confirmed HD and 25,466 without). We constructed cross-sectional EMR features (demographics, comorbidities, and registration history) and extracted view-specific image features with a simplified Inception-v3 backbone. Five modality-specific feature vectors (4 imaging views + EMR) were concatenated and input to an extreme gradient boosting classifier within a patient-level stratified 5-fold cross-validation framework. To mitigate class imbalance during training, non-HD cases in the training folds were randomly downsampled to a 1:3 HD:non-HD ratio; held-out test folds preserved the original prevalence. Post hoc explainability was provided via Grad-CAM heatmaps for images and Shapley additive explanations analysis for EMR features. RESULTS: For the primary binary task (presence vs absence of HD), the multimodal model achieved an AUC of 0.8147 (SD 0.009) on held-out test folds, compared with 0.7785 (SD 0.014) for the image-only baseline, and 0.7343 (SD 0.009) for the EMR-only baseline. At the clinically selected decision threshold, sensitivity, specificity, positive predictive value, and negative predictive value were 84.6% (SD 1.6%), 72.4% (SD 2%), 15% (SD 2.3%), 98.8% (SD 0.3%) for the multimodal model, versus 82.3% (SD 1.9%), 73.5% (SD 2%), 15.2% (SD 2.1%), 98.6% (SD 0.3%) (image-only), and 77.5% (SD 2.1%), 67% (SD 2.3%), 11.9% (SD 1.7%), 98.1% (SD 0.4%) (EMR-only). Grad-CAM visualizations qualitatively highlighted anatomically and physiologically meaningful regions (eg, valvular structures and abnormal flow jets), while Shapley additive explanations analysis identified age, registration years, sex, and hypertension among the top EMR contributors-findings that align with established cardiovascular risk factors. CONCLUSIONS: Integrating echocardiographic images and EMR data in an explainable multimodal framework yields improved and clinically plausible screening performance for heart disease. Future work should focus on external multicenter validation, quantitative assessment of visual explanations against expert annotations, and prospective studies assessing clinical use and workflow integration.
17 May 2026
Read appraisal →JMIR formative research
Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines
BACKGROUND: Complication risks in children and adolescents with type 1 diabetes (T1D) can lead to serious health outcomes if not detected early. Despite the availability of clinical data, there remains a gap in interpretable tools that support risk stratification in this age group, particularly in alignment with local clinical guidelines. OBJECTIVE: The purpose of this study is to develop a clinically interpretable model that classifies the risk levels of T1D complications-acute, chronic, and low-using real-world data and expert clinical rules derived from the Saudi Diabetes Clinical Practice Guidelines. METHODS: A pediatric T1D dataset comprising of 306 patients was preprocessed through structured cleaning and feature engineering. Risk labels were constructed using Saudi Diabetes Clinical Practice Guidelines-derived rules. Feature selection was performed using a hybrid approach that combined the SHAP (Shapley Additive Explanations) analysis with exhaustive feature selection. A decision tree model was trained and optimized via cross-validation, using the F1-score as the primary performance metric. RESULTS: The final model achieved a high mean F1-score of 0.9876 with a low variance of 0.0189, using only 5 clinical features: BMI, hypoglycemia, disease duration, hemoglobin A1c, and impaired glucose metabolism. These features were consistently ranked as the most influential. The resulting decision tree offered a transparent logic path, enhancing its clinical interpretability and usability. CONCLUSIONS: This study demonstrates that a simple and interpretable model, guided by national clinical guidelines, can effectively predict the risk levels of T1D complications in children and adolescents. Its strong performance, clarity, and reliance on a small number of clinically meaningful features make it a promising candidate for integration into clinical decision support systems. This supports a shift toward predictive and personalized diabetes care.
17 May 2026
Read appraisal →PloS one
Multi class photoplethysmography-based deep model for cardiovascular disease classification
BACKGROUND: cardiovascular disease is the leading global cause of mortality. Photoplethysmography (PPG), widely embedded in consumer wearables, offers a scalable diagnostic modality. However, prior approaches are often constrained by handcrafted features, binary classification, and poor generalizability, limiting their clinical impact. METHODS: A deep hierarchical convolutional neural network (CNN) was designed to extract both morphological and rhythmic characteristics directly from raw photoplethysmography (PPG) signals. The architecture employs progressively structured convolutional filter hierarchies to capture multi-scale signal features. To enhance signal stability and training efficiency, a dual-stage normalization strategy was implemented, consisting of Z-score standardization followed by Min-Max scaling. In addition, batch normalization and dropout regularization were incorporated to improve model generalization and reduce the risk of overfitting. The proposed framework was trained and evaluated on a multi-source dataset comprising 612 patients and 2,448 annotated PPG segments distributed across six diagnostic classes: atrial fibrillation (AF), heart failure (HF), acute coronary syndrome (ACS), cerebral vascular accident (CVA), deep vein thrombosis (DVT), and normal sinus rhythm (NSR). RESULTS: The model achieved an overall accuracy of 93.48%, a macro-average F1-score of 0.9386, and a Cohen's Kappa of 0.8968, indicating "almost perfect" agreement. AF and HF were detected with flawless precision and recall (1.000), while ACS achieved high sensitivity (recall 0.964). Errors were primarily confined to physiologically related conditions (e.g., ACS vs. CVA). Inference efficiency was demonstrated with <5 ms per segment on consumer-grade hardware, confirming feasibility for real-time applications. CONCLUSION: The proposed framework advances beyond lightweight but underpowered or overly complex models by combining representational depth with computational efficiency. Limitations include the need for external, multi-center validation and explainability integration. This study establishes a robust foundation for PPG-based, multi-class cardiovascular diagnostics, supporting clinical decision support and next-generation wearable health technologies.
17 May 2026
Read appraisal →European journal of pediatrics
Leveraging artificial intelligence to predict non-adherence to pediatric immunization schedules: a systematic review
UNLABELLED: Immunization is one of the most effective interventions to prevent infectious diseases. Identifying individuals at risk of non-adherence to immunization schedules could enable early interventions to improve coverage. With the increasing availability of large datasets with sociodemographic, medical, and geographic data, artificial intelligence (AI) offers new possibilities for identifying predictive patterns. We performed a systematic literature review of original studies that used AI to predict immunization non-adherence in children. Searches were conducted in PubMed, Embase, and Web of Science and no restrictions were applied regarding publication year, language, or study design. Study characteristics, dataset size, AI methods, performance metrics, and validation approaches were extracted. Risk of bias was assessed using PROBAST + AI. A total of 843 records were identified; after screening, 12 studies were included. Nine of 12 studies were conducted in low- and middle-income countries, used cross-sectional survey data, and analyzed vaccines from routine immunization schedules. Model performance was generally high; 9/12 studies reported an accuracy or an area under the receiver operating characteristic curve exceeding 80%, but no study conducted external validation. Predictors clustered into seven categories: maternal, paternal, family, child, geographic, wealth, and others. Among maternal and child-related factors, prenatal care and previous immunization history had a high predictive weight. CONCLUSION: AI demonstrates a promising potential for identifying individuals at risk of vaccine non-adherence. However, the lack of external validation raises concerns about model overfitting and limits real-world applicability. Demographic health surveys were routinely repeated across countries and time periods and could therefore represent an ideal resource for external validation. WHAT IS KNOWN: • Geographic, sociodemographic, and medical factors contribute to non-adherence to the immunization schedule. • Identifying these individuals can guide early interventions to improve immunization coverage. WHAT IS NEW: • Artificial intelligence can reveal unknown influential factors; prenatal care and delivery setting emerged as key factors in this systematic review.
17 May 2026
Read appraisal →European journal of preventive cardiology
Elevated blood pressure levels among 533 167 adults living in Sub-Saharan Africa: a systematic review and meta-analysis
AIMS: Synthesizing contemporary data from sub-Saharan African countries, we did a systematic review and meta-analysis of blood pressure (BP) levels and hypertension among adults living in the region. METHODS AND RESULTS: We searched PubMed and other databases to identify studies published from 1 January 2010 to 31 December 2021. We used a random-effects model to estimate the pooled-prevalence of hypertension and mean systolic/diastolic BP overall and on a sex- and age-specific basis. Heterogeneity (I²) was assessed via the χ² test on Cochran's Q statistic. We identified 170 high-quality studies (195 samples) comprising 533 167 adults living in 26 countries. The pooled prevalence of hypertension was 30.5% (95% CI 28.4-32.6%). Overall mean systolic/diastolic BP was 128 (95% CI 127-129)/80 (95% CI 79-80) mmHg, with males recording higher mean BP levels (3.10 [95% CI 2.30-3.90]/0.69 [95% CI 0.10-1.29] mmHg) compared with females. Reflecting increasingly higher BP levels with age, the pooled estimates of hypertension prevalence initially rose three-fold (from 10.6% [95% CI 8.2-13.0%] to 30.9% [95% CI 27.8-34.0%]) in those aged 21-30 to 41-50 years, and then two-fold to 66.4% (95% CI 64.2-68.7%) among those aged 71-80 years, respectively. Hypertension prevalence was lower in healthy weight [28.4% (95% CI 26.1-30.6%)] compared with overweight [35.8% (95% CI 31.4-40.1%)] adults. Regionally, prevalent hypertension was lowest in those living in Eastern Africa [27.2% (95% CI 24.8-29.7%)]. CONCLUSION: Our findings suggest a steep age-related pattern of increasing BP levels in the region that will adversely affect millions of people within the next 10-20 years without urgent intervention. A detailed review and analysis of 170 studies involving more than half a million adult men and women living in 26 sub-Saharan Africa countries, revealed that one in three have elevated blood pressure/hypertension.Elevated blood pressure levels, and the markedly increased risk of suffering a debilitating or fatal cardiovascular event at an early age, rose steeply from around one in ten affected at age 20–30 years to around one in three by the age of 50 years.Beyond the influence of age, the risk of having elevated blood pressure was different, given it increased markedly for overweight men and women, whilst those living in (mainly poorer) Eastern African countries had the lowest blood pressure levels.
16 May 2026
Read appraisal →European journal of preventive cardiology
Cuffless Blood Pressure Monitoring Devices: Technical Foundations and Clinical Implications
Cuffless blood pressure (BP) monitoring devices represent a promising innovation in hypertension management. This scientific statement provides a comprehensive update on these emerging technologies, their specific validation requirements, their potential clinical applications, and their present and future challenges. These devices generate considerable interest by enabling non-invasive BP measurement without arterial occlusion, thereby eliminating the discomfort associated with traditional cuff-based monitoring, particularly during sleep. The technologies on which these devices are based comprise a heterogeneous group, primarily utilizing pulse wave propagation time or waveform analysis through contact or non-contact sensors. They can be categorized as continuous or intermittent, automated or manual, calibration-free or requiring cuff/demographic calibration, and wearable or stationary. This technological diversity necessitates validation protocols distinct from those used for conventional cuff-based monitors, with specific requirements for each device category. Potential clinical applications include widespread out-of-office BP monitoring, unbiased assessment of circadian BP patterns and BP variability, improved detection of nocturnal hypertension, enhanced treatment adherence and long-term BP control, and continuous monitoring in hospital settings. Additionally, their lower cost compared with conventional technologies could enhance the early detection of hypertension in resource-limited settings. However, due to insufficient accuracy validation, this scientific statement does not recommend their use in clinical decisions in spite of their potential interest, in line with international guidelines not recommending their use in hypertension management. Key challenges ahead include developing standardized validation protocols, establishing normative BP data, managing the resulting burden on clinicians in handling huge volumes of data, exploring additional haemodynamic parameters, and advancing sensor technology, mathematical models, and algorithms.
16 May 2026
Read appraisal →PloS one
Deep learning-based bimodal speech and facial expression recognition of miners' unsafe emotions
Under the influence of unsafe emotions, miners' ability to perceive risks is hindered, which can easily lead to decision-making errors and safety accidents. To recognize unsafe emotions exhibited by miners during operations, this study proposes a deep learning-based bimodal framework that integrates speech and facial expression features. A convolutional neural network (CNN) combined with a bidirectional long short-term memory (Bi-LSTM) network is employed to model local spectral patterns and temporal dependencies in speech signals, and ShuffleNet-V2 is used to capture deep facial features. In addition, three feature enhancement strategies are proposed to improve the generalization ability of the model. By constructing a dataset containing five categories of miners' unsafe emotions for network training, the model achieves a mean recognition accuracy of 85.56%. Furthermore, we conducted a preliminary field test of the bimodal model in a real mining environment. The results provide preliminary evidence of its potential applicability in real-world mining conditions.
16 May 2026
Read appraisal →Journal of global health
Global prevalence of underweight and overweight/obesity among migrant workers: a systematic review and meta-analysis
BACKGROUND: Migrant workers are increasingly recognised as a nutritionally vulnerable population due to unstable working conditions, disrupted food environments, and limited access to health services. However, global estimates of malnutrition in this group remain fragmented. We aimed to identify the global prevalence of underweight and overweight/obesity among migrant workers and identify key moderating factors. METHODS: We conducted a systematic review and meta-analysis following PRISMA guidelines. We searched seven databases without restrictions on language, region, or year of publication. Observational studies reporting the prevalence of underweight (body mass index (BMI) < 18.5 kg/m2) or overweight/obesity (BMI ≥ 25 kg/m2) among migrant workers were included. We calculated pooled prevalence estimates using generalised linear mixed models with random effects. We assessed heterogeneity with I2 and Cochran's Q statistics. Moderator analyses were conducted using meta-regression and subgroup comparisons. Further, we appraised study quality using the Joanna Briggs Institute tools, and assessed the certainty of the evidence using GRADE. RESULTS: We included 30 studies involving 135 404 migrant workers with a mean age of 32.3 years (standard deviation = 11.1). The pooled prevalence was 4.7% (95% confidence interval (CI) = 3.1, 6.9) for underweight and 43.7% (95% CI = 39.8, 47.6) for overweight/obesity, suggesting a substantial double burden of malnutrition. Underweight was more common among local migrants and in lower-middle-income settings, particularly in South Asia, whereas overweight/obesity was more common among international migrants, especially in high-income countries and in Europe and Central Asia. Moderator analyses identified female proportion, destination country or region, income level, and marital status as significant moderators for both outcomes. CONCLUSIONS: Migrant workers experience a substantial double burden of malnutrition, although the pattern varies across migration contexts. Undernutrition remains a concern in lower-income settings, whereas overweight and obesity are more common in high-income settings. Integrating nutrition-sensitive strategies into labour and migration health policies, including healthier food environments, routine nutritional screening, and culturally appropriate interventions, may help reduce health inequalities in this population. REGISTRATION: INPLASY: 202540030.
16 May 2026
Read appraisal →European journal of preventive cardiology
Effect of incretin-based therapies on blood pressure: a systematic review and meta-analysis
AIMS: Hypertension and obesity frequently coexist and synergistically increase cardiovascular (CV) risk. Incretin-based therapies with glucagon-like peptide-1 receptor agonists (GLP-1-RAs), gastric inhibitory polypeptide (GIP)/GLP1-RAs, and glucagon/GIP/GLP-1RAs lead to substantial weight loss. However, their antihypertensive efficacy and safety profile have not been comprehensively quantified. Our study aimed to evaluate the effects of incretin-based therapies on office systolic blood pressure (BP) (SBP), diastolic BP (DBP), all-cause mortality, and key safety outcomes, i.e. hypoglycaemia and pancreatitis episodes, in adults with overweight or obesity. METHODS AND RESULTS: We searched PubMed, EMBASE, and ClinicalTrial.gov from inception to 30 April 2024 for randomized controlled trials (RCTs) comparing incretin-based therapy with placebo and ≥1 month of follow-up. The primary outcome was change in SBP; secondary outcomes were change in DBP, all-cause mortality, hypoglycaemia, and pancreatitis. Random effects meta-analyses generated mean differences (MDs) or risk ratios (RRs) along with 95% confidence intervals (CIs). Heterogeneity was explored with I2 statistics, subgroup analyses, and meta-regression. Eighty-five RCTs encompassing 90 977 participants (median follow-up time 8 months) met the eligibility criteria. GLP1-RAs reduced SBP by 3.4 mmHg (95% CI 2.8-4.0) and DBP by 0.9 mmHg (95% CI 0.5-1.2). A higher weight loss was significantly associated with a greater reduction in BP. The most significant BP reduction was associated with dual (SBP 5.1 mmHg; DBP 1.8 mmHg) and triple (SBP 6.6 mmHg; DBP 2.1 mmHg) receptor agonists. All-cause mortality was reduced by 18% (RR 0.82, 95% CI 0.76-0.90). Incretin-based therapy did not increase the risk of hypoglycaemia (RR 1.05, 95% CI 0.83-1.33) or pancreatitis (RR 0.84, 95% CI 0.61-1.15). CONCLUSION: Incretin-based therapy led to a modest but clinically meaningful BP reduction and lower all-cause mortality in adults with overweight or obesity, without excess in hypoglycaemia or pancreatitis episodes. There was a significant association between weight loss and the reduction in SBP and DBP. Dual and triple agonists exhibited the most pronounced antihypertensive effect. These findings support the use of incretin-based therapies as part of an integrated and multidisciplinary approach to managing obesity and hypertension, with multiple agonists showing particular promise. Our findings also underscore the need for long-term RCTs to clarify weight-independent mechanisms and the durability effect of BP reduction. Incretin-based therapy, now widely used for weight loss, also modestly reduces blood pressure and mortality, providing an additional health benefit to people with obesity or diabetes.Across 85 clinical trials (more than 90 000 adults), incretin-based therapy lowered systolic blood pressure by about 3 mmHg; the most significant falls occurred with the dual and triple agonists, and every kilogram lost made the reduction larger.Incretin-based therapy use was associated with an 18% lower mortality, with no increase in hypoglycaemia or pancreatitis episodes, supporting their safe dual role in weight control and cardiovascular protection.
16 May 2026
Read appraisal →JMIR mHealth and uHealth
Engagement of Users in Digital Health Applications: Scoping Review
BACKGROUND: Mobile health (mHealth) uses mobile technology as a tool for prevention and health promotion. Research indicates that user engagement is crucial for effective mHealth interventions and improved health outcomes. However, many studies report low adoption rates, rapid decline after initial use, and a lack of acknowledgment of user implications in achieving outcomes. Thus, conceptualizing participation in mHealth is essential to identify key determinants for engaging users. OBJECTIVE: This scoping review aims to identify and characterize the attributes and definitions of user engagement in mHealth, examine engagement methods, and analyze barriers and facilitators influencing participation. METHODS: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, Scopus, Web of Science, and PubMed databases were searched for publications between 2000 and 2025 with a 2-stage selection process. RESULTS: Out of 2489 articles identified, 1416 were screened, and 52 met the inclusion criteria. Half were recently published in the last 5 years (2020-2025). Existing literature focused on digital interventions for specific populations and health topics. Analysis revealed four main perspectives on engagement in mHealth: (1) usage metrics, (2) subjective user experiences, (3) a hybrid approach that combines both, and (4) a goal-oriented perspective (behavior change or health outcomes). CONCLUSIONS: To understand the complexity and multifactorial nature of participation, it is relevant to conceptualize it as a dynamic mechanism enabling users to achieve their objectives. Both quantitative use and subjective user experience should be integrated to reach the optimal intervention dose. Recognizing users' evolving needs, uniqueness, and their socioenvironmental context interdependence, it is essential to involve users in all stages (design, implementation, and iterative evaluation of mHealth). Findings will inform an e-Delphi study to establish consensus on engagement criteria.
16 May 2026
Read appraisal →European heart journal
Foundation models for electrocardiogram interpretation: clinical implications.
BACKGROUND AND AIMS: The 12-lead electrocardiogram (ECG) remains a cornerstone of cardiac diagnostics, yet existing artificial intelligence (AI) solutions for automated interpretation often lack generalizability, remain closed source, and are primarily trained using supervised learning (SL), which requires extensive labelled datasets and may limit adaptability across diverse clinical settings. Self-supervised learning (SSL) can potentially overcome these limitations by learning robust representations from unlabelled data. To address these challenges, this study developed and compared two open-source foundational ECG models: DeepECG-SL, a supervised multilabel ECG model, and DeepECG-SSL, a self-supervised model. METHODS: Both models were trained on over 1 million ECGs using a standardized preprocessing pipeline and automated free-text extraction from ECG reports to predict 77 cardiac conditions. DeepECG-SSL leveraged unlabelled data through self-supervised contrastive learning and masked lead modelling before fine-tuning for downstream tasks, while DeepECG-SL was trained directly on labelled diagnostic data in an end-to-end fashion. Performance was evaluated across seven private, multilingual healthcare systems and four public ECG repositories, with assessment of fairness by age and sex, and investigation of privacy vulnerabilities as well as memory and compute requirements. RESULTS: DeepECG-SSL achieved micro-averaged area under the receiver operating characteristic curves (AUROCs) across all 77 cardiac conditions for ECG interpretation of 0.990 [95% confidence interval (CI): 0.990, 0.990] on the internal dataset (MHI-ds), 0.981 (95% CI: 0.981, 0.981) on external public datasets (UKB, CLSA, MIMIC-IV and PTB), and 0.983 (95% CI: 0.983, 0.983) on external private datasets (UW, UCSF, JGH, NYP, MGH, CSH and CHUM), while DeepECG-SL demonstrated AUROCs of 0.992 (95% CI: 0.992, 0.992), 0.980 (95% CI: 0.980, 0.980), and 0.983 (95% CI: 0.983, 0.984), respectively. Fairness analyses revealed minimal disparities (true-positive rate and false-positive rate difference <0.1) across age and sex groups for both models. DeepECG-SSL demonstrated superior performance on limited-data digital biomarker tasks, with the largest improvements in long QT syndrome (LQTS) genotype classification (AUROC 0.931 vs 0.850, P = .026, n = 127 ECGs) and 5 year atrial fibrillation risk prediction (AUROC 0.742 vs 0.734, P < 0.001, n = 132 050 ECGs), while achieving superior performance in left ventricular ejection fraction ≤40% classification (AUROC 0.926 vs 0.917, P < 0.001, n = 25 252 ECGs) and comparable performance in LQTS detection (AUROC 0.767 vs 0.735, P = 0.117, n = 934 ECGs). CONCLUSIONS: This study establishes SSL as a promising paradigm for ECG analysis, particularly in settings with limited annotated data, enhancing accessibility, generalizability, and fairness in AI-driven cardiac diagnostics. By releasing model weights, preprocessing tools, and validation code, this work aims to support robust, data-efficient AI diagnostics across diverse clinical environments and questions.
16 May 2026
Read appraisal →European journal of preventive cardiology
Visit-to-visit blood pressure variability and cardiovascular outcomes: a systematic review and dose-response meta-analysis
AIMS: Visit-to-visit blood pressure variability (VVV BPV) is a recognized risk factor for cardiovascular disease (CVD) that is underutilized in clinical practice. The reliability of electronic health record (EHR) data in estimating BPV and predicting CVD remains uncertain. This study compared BPV estimation methodologies using EHR vs. non-EHR data and examined dose-response associations with CVD. METHODS AND RESULTS: A systematic review and meta-analysis was conducted across five databases (MEDLINE, Scopus, EMBASE, CINAHL, and Web of Science) for studies published from January 2012 to August 2024. Studies assessing VVV BPV in adults and its association with CVD outcomes (e.g. myocardial infarction, stroke, heart failure, and cardiovascular mortality) were included. A dose-response meta-analysis (DRMA) evaluated BPV thresholds linked to increased CVD risk using standard deviation (SD) and coefficient of variation (CV). A total of 4926 studies were screened, 49 of which met the inclusion criteria. No consensus has emerged on BPV estimation methodologies, although non-EHR studies have followed stricter protocols. The meta-analysis showed that VVV BPV predicted any CVD outcome. Effect sizes were comparable between EHR [the hazard ratio (HR): 1.17, 95% confidence interval (CI): 1.09-1.24] and non-EHR (HR: 1.14, 95% CI: 1.10-1.17) studies (P-value = 0.468). A BPV threshold of SD 6.72 mmHg or CV 9.05% was linked to a 10% higher CVD risk. CONCLUSION: The EHR data reliably estimate BPV, yielding effect sizes similar to those of non-EHR sources. A non-linear dose-response relationship suggests that a higher BPV increases CVD risk. Visit-to-visit blood pressure variability needs to be incorporated into clinical practice, and further research is required to identify strategies to implement and scale up into routine workflow. Visit-to-visit blood pressure variability (VVV BPV) is capable of predicting CVD development in 6 years. However, its clinical use remains limited, and the reliability of electronic health records (EHR) in measuring BPV is uncertain. Having a standard deviation of VVV BPV ≥6.72 mmHg or a coefficient of variation of VVV BPV ≥9.05% mmHg is linked to a 10% higher CVD risk. Blood pressure variability estimates from EHR data were comparable to those from non-EHR studies, suggesting that EHR-based BPV assessment is reliable for CVD risk evaluation.
16 May 2026
Read appraisal →European heart journal
Artificial intelligence-based quantification of breast arterial calcifications to predict cardiovascular morbidity and mortality
BACKGROUND AND AIMS: Women are underdiagnosed and undertreated for cardiovascular disease (CVD). Automatic quantification of breast arterial calcification (BAC) on screening mammography can identify women at risk for CVD. This study aimed to determine whether artificial intelligence-based automatic quantification of BAC from screening mammograms predicts CVD and mortality beyond PREVENT scores in a large, racially diverse, multi-institutional population. METHODS: This retrospective cohort study included 123 762 women from two healthcare systems who had screening mammograms. Breast arterial calcification was quantified using a transformer-based neural network for segmentation. Breast arterial calcification severity was categorized as zero (0 mm2), mild (>0-10 mm2), moderate (>10-25 mm2), and severe (>25 mm2). Kaplan-Meier analysis, Cox proportional hazards, and Fine-Gray competing event models were used to examine the association between BAC and major adverse cardiovascular events (MACE). RESULTS: Breast arterial calcification was detected in 16.1% (internal cohort) and 20.6% (external cohort) of women and provided significant prognostic value incremental to the PREVENT score. In PREVENT adjusted models, a clear dose-response was observed. Compared with zero BAC, mild [internal: hazard ratio (HR) 1.32, 95% confidence interval (CI) 1.10-1.59; external: HR 1.28, 95% CI 1.17-1.39], moderate (internal: HR 1.75, 95% CI 1.23-2.50; external: HR 1.79, 95% CI 1.55-2.06), and severe BAC (internal: HR 3.29, 95% CI 2.15-5.05; external: HR 2.80, 95% CI 2.36-3.32) were all prognostic for any MACE. Each 1 mm2 increase in BAC conferred an additional 2%-3% risk for MACE (P < .001). CONCLUSIONS: Automatically quantified BAC is an independent predictor of MACE and mortality, adding prognostic value to the PREVENT score. This approach may provide an opportunistic cardiovascular risk assessment during routine mammography screening without additional radiation exposure to guide earlier and more effective preventive care for women.
16 May 2026
Read appraisal →European heart journal
Wearable devices and cardiovascular health: revolutionizing remote monitoring and disease prevention
Wearable devices are transforming cardiovascular medicine by enabling continuous monitoring of physiologic and behavioural measures outside of traditional clinical settings. Smartwatches and activity trackers, the most widely used wearables, employ motion and biometric sensors to measure physical activity, sleep quality, heart rate, and rhythm. By converting health goals into objective, quantifiable measures, wearable devices empower patients to assume a more active role in their health while providing clinicians with novel opportunities for longitudinal, real-world assessment. Clinical applications span the cardiovascular continuum from lifestyle interventions targeting physical activity and sleep to the remote management of chronic conditions such as heart failure. Widespread clinical adoption of wearables remains limited by challenges, such as variability in device methodology, data outputs, validation, and intended use; incompatibility with existing electronic health records; and the lack of standardized, evidence-based workflows for clinicians to efficiently interpret and act upon wearable data. This review summarizes the current landscape of wearable technologies in cardiovascular medicine by highlighting key clinical applications, evidence gaps in the existing literature, the role of artificial intelligence, and barriers to implementation. We discuss strategies to enhance clinical integration and strengthen the current evidence base while also providing practical guidance to help clinicians navigate commonly encountered clinical scenarios.
16 May 2026
Read appraisal →Journal of medical Internet research
A Digital Diabetes Self-Management Education and Support Program Integrated With Continuous Glucose Monitoring for Type 2 Diabetes: Randomized Controlled Trial
BACKGROUND: Previous research has demonstrated that the use of continuous glucose monitoring (CGM) can improve glycemic control in people with type 2 diabetes when used regularly alongside a digital diabetes self-management education and support (DSMES) program. However, to date, there is limited evidence showing the benefits of a digitally delivered DSMES program combined with real-time CGM for adults with type 2 diabetes. OBJECTIVE: The objective of this study is to evaluate the impact of a DSMES program coupled with CGM on hemoglobin A1c (HbA1c) and CGM-derived glycemic measures compared to usual care for adults with type 2 diabetes over 6 months. METHODS: Participants with type 2 diabetes and HbA1c of 8% or higher (64 mmol/mol) who were not using mealtime bolus insulin (aged 26-83 y; mean HbA1c 9.6%, SD 1.4% [mean 81.2 mmol/mol, SD 15.8 mmol/mol]) were randomly assigned to a digital DSMES+CGM integrated solution (n=51) or usual care (n=49) for 6 months. The primary outcome was HbA1c. The secondary outcomes were CGM-derived glycemic measures, including glucose management indicator, percent time in range 70 to 180 mg/dL, percent time above range (>180 mg/dL), percent time below range (<70 mg/dL), and mean glucose. Linear mixed effects models were used for intention-to-treat analyses. RESULTS: HbA1c was lower among the intervention group versus the usual care group at 3 months (difference=-0.7%, 95% CI -1.4% to -0.1% or difference=-8.1 mmol/mol, 95% CI -15.5 to -0.7 mmol/mol; P=.03) and at 6 months (difference=-0.6%, 95% CI -1.4% to 0.2% or difference=-6.9 mmol/mol, 95% CI -15.7 to 1.9 mmol/mol; P=.12) but only reached statistical significance at 3 months. CGM-derived glycemic measures, including glucose management indicator (difference=-0.9%, 95% CI -1.7% to -0.1%; P=.03), time in range (difference=14.6%, 95% CI 1.0% to 28.2%; P=.04), time above range (difference=-14.9%, 95% CI -29.0% to -0.9%; P=.04), and mean glucose (difference=-36.4 mg/dL, 95% CI -70.0 to -2.9 mg/dL; P=.03), also significantly improved for the intervention group versus the usual care group at 6 months. CONCLUSIONS: The combination of digital DSMES+CGM is effective for supporting adults with type 2 diabetes in managing their condition and has the potential to reduce the risk of long-term health complications.
16 May 2026
Read appraisal →BMJ open gastroenterology
Development and validation of multivariable prognostic machine learning models to identify patients at risk for inadequate bowel preparation when undergoing colonoscopy: a prospective, multicentre study
OBJECTIVE: Inadequate bowel preparation impairs the accuracy of colonoscopy and increases the burden on patients and healthcare systems. Consequently, the quality of bowel preparation is an important quality indicator. We aim to develop and validate multivariable prognostic models for the identification of patients at risk of inadequate bowel preparation using machine learning (ML). METHODS: Demographic and clinical data from consecutive patients ≥18 years of age who underwent colonoscopy at six centres in Germany were prospectively collected. Adequate bowel preparation was defined as Boston Bowel Preparation Scale ≥6 with a value ≥2 in each colonic segment. We used statistical and ML methods to build prognostic models and to compare them to published models. RESULTS: Overall, we analysed 2652 patients, including 699 (26.4%) inpatient procedures. The mean patient age was 57.6 years (SD 16 years), and 48.9% were women. In 1401 (52.8%) patients, the indication was screening or surveillance, and 1035 (39%) patients had a first-time colonoscopy. The rate of inadequate bowel preparation was 16%. Sensitivities, specificities and areas under the curve of predictive models obtained by generalised boosting models, Ranger, support vector machine (radial), CatBoost and Net Regularised Generalised Linear Models were 0.51-0.86, 0.52-0.83 and 0.71-0.74, respectively. They were only marginally superior to a logistic regression model. All models had high negative predictive values >0.9 for inadequate bowel preparation. To detect one patient with inadequate bowel preparation, 8-10 patients need to be evaluated. CONCLUSIONS: Predictive models to identify patients at risk for inadequate bowel preparation obtained by ML showed comparable results compared with a logistic regression model. TRIAL REGISTRATION NUMBER: DRKS00018878.
16 May 2026
Read appraisal →The British journal of general practice : the journal of the Royal College of General Practitioners
Does recommending healthy diets in general practice benefit secondary prevention of cardiovascular disease? A systematic review and meta-analysis.
BACKGROUND: Suboptimal diet is the main preventable risk factor globally for cardiovascular disease (CVD), posing major burden for primary care. However, healthy dietary recommendations' evidence in patients with established CVD is unclear. AIM: Systematically review and meta-analyse dietary patterns' effect on CVD patients' all-cause and CVD mortality, and CVD recurrence. METHOD: Publication databases were searched for randomised controlled trials (RCTs) (≥6 months follow-up) and prospective cohort studies. Random-effects meta-analysis calculated relative-risks (RR) and 95% confidence-intervals (95% CI). Cochrane bias risk tools assessed study quality. RESULTS: Screening 28 291 abstracts identified 41 articles meeting inclusion criteria. Nineteen articles from 14 unique RCTs conducted 1956-2020, enrolling 14 065 CVD patients, compared fat-replacement, Mediterranean, low-fat, low-sodium, and high-fibre diets with minimal/no dietary advice. Pooled results showed no diet intervention significantly impacted mortality/CVD outcomes. However, stratified meta-analysis revealed a Mediterranean-diet significantly reduced CVD mortality (RR: 0.40; 95% CI: 0.19, 0.83). Twenty-two cohort studies, conducted 1976-2017 for 104 386 CVD patients, predominantly assessed a Mediterranean diet (60%), showing greatest healthy dietary pattern adherence significantly reduced all-cause mortality by 29% (0.71; 0.65, 0.78), CVD mortality by 25% (0.75; 0.66, 0.84), and CVD recurrence by 17% (0.83; 0.75, 0.92). Overall, there was considerable between-study heterogeneity (RCTs: I2 = 34-60%, cohorts: I2 = 49-76%) partly explained by disparate geographical locations, and studies were mainly classified as of some concerns (65% RCTs) or high bias risk (52% cohorts). CONCLUSION: GPs providing healthy dietary advice, particularly the Mediterranean diet, likely enhances CVD secondary prevention. However, cogent studies with clearly defined diets are required to bolster the evidence.
16 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
Integrating liquid biopsies and artificial intelligence for early cancer detection: A systematic review and meta-analysis
INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine learning (ML) approaches have been proposed to synthesize these complex biological data for the development of diagnostic classifiers. This study aims to evaluate the integration of ML with circulating cell-free DNA (cfDNA) analysis for early cancer detection. METHODS: Medline, Embase, Cochrane, and Web of Science were searched in July 2025. Eligible studies combined ML and cfDNA features to distinguish cancer patients (stages I-III) from non-cancer controls. Summary diagnostic performance metrics and their 95% confidence intervals (CI) were calculated. RESULTS: The study included 109 articles permitting analyses for lung (n = 34), liver (n = 29), colorectal (n = 28), pancreatic (n = 16), breast (n = 17), esophageal (n = 12), ovarian (n = 13), gastric (n = 9), head and neck (n = 4), and mixed (n = 27) cancer types. Specificity was consistently high across all tumor types and stages (94%-99%). Sensitivity ranged from 72% to 92% for stage I-III, 44-91% for stage I, 71-98% for stage II and 83-99% for stage III. In the pooled study population, neural networks (90%, 95% CI: 81%-95%), random forest (86%, 95% CI: 77%-92%) and heterogeneous ensemble learning (85%, 95% CI: 79%-89%) demonstrated the highest sensitivity. The stratified analysis by classifier feature revealed 86% (95% CI: 80%-90%) sensitivity for fragmentation and 81% (95% CI: 76%-85%) for methylation, with 92%-96% specificity. CONCLUSION: ML and cfDNA profiling show potential for early cancer detection, with ensemble methods, neural networks and random forests achieving the best overall performance. Fragmentomic features provide the highest sensitivity.
16 May 2026
Read appraisal →The British journal of general practice : the journal of the Royal College of General Practitioners
Pollution to prevention: digital decision support for air pollution and exercise counselling in general practice.
BACKGROUND: Outdoor exercise supports physical, mental, and social wellbeing. However, exposure to air pollution during activity can increase cardiorespiratory risk. The Royal College of Physicians (A Breath of Fresh Air, 2025) urges clinicians to address environmental determinants of health, including air quality, during routine care, including annual checks. Patients increasingly use digital tools providing air-quality data and actionable behavioural recommendations, yet the quality, evidence base, and integration of these tools into clinical workflows remain unclear. AIM: To evaluate digital tools that help patients modify exercise behaviour based on air-pollution exposure to inform the development of a custom chatbot to support exercise counselling in primary care. METHOD: A scoping review of app libraries, grey literature, and peer-reviewed studies identified digital tools providing air-quality data with actionable exercise recommendations. Data extraction covered functionality, pollutants, validation, and quality using the Mobile App Rating Scale (MARS) and Behaviour Change Technique Taxonomy (BCTTv1). RESULTS: Seven tools were identified: IQAir, Plume Labs, BreezoMeter, EPA AirNow, PurpleAir, airTEXT, and AirTrack. MARS scores ranged from 2.9 to 4.3 (mean = 3.6/5.0), highest for functionality and aesthetics but lower for information accuracy and engagement. Behaviour change content was limited to general health information, and no tools demonstrated formal validation or clinical integration. CONCLUSION: Existing tools provide accessible air-quality data but lack individualised recommendations for exercise. Insights informed a custom chatbot embedding air-quality and exercise advice into GP cardiorespiratory annual reviews. Next steps include further development of the custom chatbot and pilot implementation in general practice to assess feasibility, clinician uptake, and patient outcomes.
16 May 2026
Read appraisal →Journal of affective disorders
Machine learning and SHAP-based interpretation of dietary antioxidants' impact on comorbid MDD and suicidal ideation.
OBJECTIVE: To develop and validate a machine learning model for predicting major depressive disorder (MDD) with suicidal ideation (SI) by incorporating dietary antioxidants, and to elucidate the specific contribution of these antioxidants in the prediction. METHODS: Data were obtained from NHANES 2007-2010. Dietary antioxidants, including vitamins, minerals, and polyphenols, were the primary predictors. Demographic, lifestyle, and health-related variables were also included. Collinear variables were removed, class imbalance was corrected, and data were normalized prior to modeling. Twelve machine learning algorithms were compared using a systematic benchmarking protocol within the sklearn framework: Random Forest (RF), LightGBM, AdaBoost, XGBoost, Extra Trees, CatBoost, Gradient Boosting Decision Trees, Support Vector Machine, Decision Tree, Gaussian Naïve Bayes, Stochastic Gradient Descent, and K-Nearest Neighbors. SHapley Additive exPlanation (SHAP) values were calculated to interpret feature contributions within the best-performing model. RESULTS: A total of 9306 participants were included, of whom 247 with MDD comorbid with SI. After preprocessing, 31 dietary antioxidants and 9 demographic and health-related variables were retained for modeling. The RF classifier achieved the highest performance with an accuracy of 83%, precision of 0.825, recall of 0.838, area under the ROC curve of 0.927, and F1 score of 0.831. SHAP analysis identified vitamin C, kaempferol, myricetin, peonidin, luteolin, eriodictyol, hesperetin, pelargonidin, and zinc as the most influential predictors. CONCLUSION: The RF model exhibited superior predictive capability for comorbid MDD and SI. SHAP analysis highlighted the critical roles of dietary antioxidants, particularly vitamin C and kaempferol, in predicting these outcomes.
15 May 2026
Read appraisal →Journal of medical Internet research
Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence
BACKGROUND: Machine learning (ML) and deep learning (DL) show promise for fall risk prediction, but prior reviews focused mainly on real-time fall detection, in-hospital falls, or conventional statistical models. The performance of ML-DL-based models for predicting future falls in community-dwelling older adults remains unclear. OBJECTIVE: This study aimed to review ML-DL studies for predicting future falls among community-dwelling older adults and meta-analyze discrimination where feasible. METHODS: Six databases were searched from inception to September 23, 2024, with updates on August 31, 2025, and February 28, 2026. We included longitudinal studies developing or validating ML-DL models to predict future falls in community-dwelling adults aged ≥60 years and excluded real-time detection, simulated or no fall, and inpatient studies. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Areas under the curve (AUCs) were meta-analyzed using Hartung-Knapp-Sidik-Jonkman random-effects models with 95% CIs. Heterogeneity, 95% prediction intervals (PIs), sensitivity analyses, and subgroup analyses were conducted. RESULTS: After screening 10,253 records, 28 (0.3%) studies were included; 18 (64.3%) focused on general older adults. Prediction horizons ranged from 3 months to 7 years, and fall incidence ranged from 1.6% to 46.6%. Twenty-three (82.1%) studies applied ML, and 5 (17.9%) studies used DL. Input modalities included text (n=18, 64.3%), sensor (n=5, 17.9%), image (n=1, 3.6%), and multimodal data (n=4, 14.3%). Common predictors included age, sex, fall history, depression, and basic daily activities. Only one model underwent external validation. Calibration reporting was sparse. All models were rated at high risk of bias. Ten models were meta-analyzed, yielding a pooled AUC of 0.79 (95% CI 0.69-0.87) with extreme heterogeneity (τ2=0.64; τ=0.80; I2=99.8%; Q=4128.99). The confidence-distribution bootstrap PI was 0.20 to 0.99, indicating substantial uncertainty in expected performance across new populations. Subgroup analyses indicated moderation by sample size and population type, with higher discrimination in specific populations than in general samples; however, the specific population subgroup included only 2 studies. Although all participants were community dwelling, some cohorts were recruited through clinically enriched pathways rather than general community sampling. CONCLUSIONS: ML-DL models show potential for identifying community-dwelling older adults at elevated future fall risk; however, wide PIs, limited external validation, and high risk of bias suggest real-world performance may be optimistic. The pooled AUC should be interpreted as a summary of reported discrimination under study-specific conditions, predominantly from internally validated, high-risk-of-bias models, rather than as a robust estimate of transportable real-world performance. This review extends prior reviews by focusing on community-dwelling settings and by integrating PROBAST, Hartung-Knapp-Sidik-Jonkman meta-analysis, PIs, and modality-specific synthesis to evaluate both discrimination and uncertainty. Findings support the use of ML-DL models for proactive fall prevention while emphasizing the need for validation and context-specific implementation.
15 May 2026
Read appraisal →BMJ open
Prediction of inpatient falls and key predictors using machine learning applied to electronic health records: a retrospective cohort study in a tertiary hospital in Türkiye
OBJECTIVE: Traditional fall risk tools are often inaccurate and burdensome. This study aims to develop a predictive model for inpatient falls and identify the most influential variables using machine learning applied to electronic health record data. DESIGN: A retrospective cohort study. SETTING: A large tertiary university hospital in Türkiye. PARTICIPANTS: Adult patients (≥18 years) hospitalised in a university hospital between January 2017 and June 2023. PRIMARY OUTCOME MEASURES: Occurrence of inpatient falls recorded in incident reporting systems. RESULTS: A total of 518 fallers were identified and compared with 3121 non-fallers. Fallers were significantly older (median 68.5 vs 64 years, p<0.001), had longer hospital stays (16 vs 12 days, p<0.001) and higher comorbidity burden (p<0.001). They were also more likely to receive high fall-risk medications (86% vs 76%, p<0.001) and had a higher prevalence of mental disorders (26.3% vs 17.2%, p<0.001). The random forest quantile classifier demonstrated the highest discrimination (area under the curve (AUC) 0.821, 95% CI 0.781 to 0.861), with balanced sensitivity (0.799, 95% CI 0.734 to 0.867) and specificity (0.722, 95% CI 0.691 to 0.755). Pairwise comparisons using DeLong's test showed significantly higher AUC compared with several models (adjusted p=0.001), while differences with eXtreme Gradient Boosting were not significant (p=0.240). Key predictors included comorbidity burden, age, number of fall-risk-increasing medications and laboratory variables such as eosinophil and basophil counts, blood urea nitrogen, sodium and red blood cell count. CONCLUSION: Machine learning models using electronic health data can predict inpatient falls and reveal key risk factors. The random forest quantile classifier offers a promising approach for improving fall risk prediction in imbalanced clinical datasets.
15 May 2026
Read appraisal →Journal of the American College of Cardiology
Global Hypertension 2000 to 2020: Trends, Disparities, and Progress in Awareness, Treatment, and Control
BACKGROUND: Hypertension is the leading preventable risk factor for premature death worldwide. Accurate estimates of prevalence and control are critical for developing public health priorities. OBJECTIVE: This study sought to examine global variations in hypertension prevalence, awareness, treatment, and control by world economic region and compare secular changes from 2000 to 2020. METHODS: A systematic search of MEDLINE from January 1995 to November 2024 was supplemented with manual searches of article references. Population-based cross-sectional surveys reporting age- and sex-specific hypertension prevalence and using standardized blood pressure measurement methods were included. Sex- and age-specific prevalence by country was applied to population counts to calculate the number with hypertension regionally and worldwide. Country-specific percentages of awareness, treatment, and control were applied to numbers of adults with hypertension to obtain regional and global estimates. Hypertension was defined as mean systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or use of antihypertensive medication. RESULTS: We included 287 studies conducted in 6,060,567 adults from 119 countries. In 2020, 33.0% (95% CI: 32.5%-33.5%), or 1.71 (95% CI: 1.65-1.77) billion, of the world's adults had hypertension: 30.1% (95% CI: 29.4%-30.9%), or 395 (95% CI: 394-396) million, in high-income countries and 33.4% (95% CI: 32.8%-33.9%), or 1.32 (95% CI: 1.26-1.38) billion, in low- and middle-income countries. From 2000 to 2020, age-standardized prevalence decreased by 2.7% and absolute burden increased by 76 million in high-income countries compared with increases of 5.8% in prevalence and 651 million in absolute burden in low- and middle-income countries. From 2000 to 2020 in high-income countries, awareness increased from 57.7% to 69.2%, treatment from 42.9% to 66.3%, and control from 16.4% to 40.2%. More modest increases were observed in low- and middle-income countries: awareness from 29.1% to 46.1%, treatment from 20.7% to 30.8%, and control from 6.4% to 13.6%. CONCLUSIONS: The burden of hypertension is increasing, as are disparities between high-income countries and low- and middle-income countries. Urgent efforts are needed to address hypertension worldwide, particularly in low- and middle-income countries.
15 May 2026
Read appraisal →PloS one
Health related quality of life among hypertensive adults living in rural Vietnam: Results from a cluster-randomised controlled trial
BACKGROUND: There is a growing burden of hypertension (HTN) among adults living in rural Vietnam, which is associated with reduced health related quality of life (HRQoL). Few community-based interventions have, however, attempted to improve the quality of life in patients with uncontrolled HTN. This study aimed to examine the impact of a multi-component intervention on HRQoL in adults with uncontrolled HTN. METHODS: This cluster-randomized controlled trial was conducted in sixteen communities (8 intervention and 8 comparison) living in a rural setting in Vietnam (2017-2022). Consenting adults with uncontrolled HTN were enrolled. The comparison arm received training sessions about HTN prevention and management and patient education materials. The intervention arm received information similar to the comparison group and three enhancement components, namely a storytelling intervention, home blood pressure (BP) self-monitoring, and expanded community health worker services. The primary outcome was the differential change in HRQoL over 12-month follow-up period, measured using the 12-Item Short Form Health Survey (SF-12), which generates a Physical Component Summary (PCS-12) and a Mental Component Summary (MCS-12). The total HRQoL score was calculated as the sum of PCS-12 and MCS-12, with higher scores indicating better HRQoL. RESULTS: A total of 671 patients were studied; their mean age was 66 years and 55% were women. At the 12-month follow-up, the intervention group showed a significant increase in their PCS-12 with a multivariable-adjusted difference of 4.2 points (95% CI: 2.0-6.4) compared with the control group. While the MCS-12 scores increased for both groups, their differential change over 12 months was not statistically significant (multivariable-adjusted difference: 1.4 points; 95% CI: -0.6; 3.5). CONCLUSION: Our results demonstrate that a multicomponent intervention effectively improved overall HRQoL with a significant impact on physical health-related HRQoL in individuals with uncontrolled HTN. TRIAL REGISTRATION: ClinicalTrials.gov, Registration number: https://clinicaltrials.gov/study/NCT03590691, (registration date July 17, 2018).
15 May 2026
Read appraisal →Journal of medical Internet research
Leveraging Text Messaging and Behavior Theory to Improve Colorectal Cancer Screening in Federally Qualified Health Centers: Cohort Study
BACKGROUND: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the United States. Despite recommendations for screening to begin at the age of 45 years, significant disparities persist, particularly among medically underserved populations. OBJECTIVE: This study examines the effectiveness of SMS text messaging reminders in improving CRC screening rates across 2 large federally qualified health centers (FQHCs) serving vulnerable populations. METHODS: The study included 4822 adults aged ≥45 years, receiving care at 2 large FQHC networks in Texas and California. Participants were assigned to one of four groups: (1) control (no SMS text messages), (2) single-outreach SMS text overdue message, (3) three-week SMS overdue and reminder text messages, and (4) six-week SMS text messages that were informed by behavior theory. Data were collected from May 2023 to July 2024. The outcome measure was a binary indicator of whether the participant underwent 1 of 3 CRC tests, fecal immunochemical test, colonoscopy, and Cologuard, within 90 days of completing the SMS text messaging reminders. Independent variables included demographic, geographic, clinical, and primary care access variables. Multivariate logistic regression models were used to examine associations between CRC screening completion and the SMS text messaging reminder groups, adjusting for covariates. Adjusted odds ratios (aORs) and 95% CIs were reported. RESULTS: In the combined 3-test model, patients in the single-outreach SMS text message (aOR 1.22, 95% CI 1.00-1.47) and the 3-week SMS text message (aOR 1.27, 95% CI 1.05-1.53) groups had higher odds of completing the screening test compared to those in the control group. Within the fecal immunochemical test-only model, patients in the 3-week SMS text message group (aOR 1.25, 95% CI 1.00-1.56) were more likely to complete the screening test. Within the Cologuard-only model, patients in the 3-week SMS text message group (aOR 7.01, 95% CI 1.96-25.07) and the 6-week SMS text message group (aOR 5.75, 95% CI 1.53-21.61) had significantly higher odds of CRC screening completion. CONCLUSIONS: The findings highlight that moderate-frequency SMS text messaging reminders can effectively increase CRC screening rates in FQHCs; however, critical factors include the timing and frequency of these reminders. The 3-week intervention was associated with improved screening uptake, whereas the 6-week theory-informed intervention did not demonstrate a significant advantage over the control group, potentially reflecting a ceiling effect or message fatigue associated with more frequent messaging. Additionally, the study highlights unique screening patterns that contradict previous literature, underscoring the importance of a tailored approach for vulnerable communities.
15 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
When bad turns good: a systematic review on cholesterol and LDL in longitudinal patient cohorts with Parkinson's disease
BACKGROUND: While the aetiology of Parkinson's disease (PD) involves genetic and environmental factors, emerging evidence has suggested a surprising link between lipid metabolism - particularly serum total cholesterol (TC) and low density lipoprotein cholesterol (LDL-C) - and the pathogenesis of PD. Cholesterol plays a vital role in neuronal membrane integrity, myelination, and synaptic function. However, its peripheral concentration and regulatory mechanisms in the central nervous system (CNS), remain incompletely understood in the context of PD. OBJECTIVE: This review aims to systematically examine the current literature on the relationship between TC and LDL-C with the risk and progression of PD in longitudinal patient cohorts. METHODS: A systematic literature search was conducted using PubMed with the keywords "Parkinson's disease" AND "cholesterol" and "Parkinson's disease" AND "LDL." Inclusion criteria encompassed English-language longitudinal human studies published before October 2024, with data on cholesterol levels in relation to PD incidence or progression. Fourteen studies met the eligibility criteria. RESULTS: Twelve of the 14 included studies reported an inverse association between lower serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels and increased risk for PD onset as well as faster disease progression. Notably, this pattern was not uniform across all subgroups: age, sex, BMI, and statin use modulated the strength and direction of associations. CONCLUSION: Despite some heterogeneity across studies, there is growing evidence that lower TC and LDL-C may be associated with a higher risk and more rapid progression of PD. Future research should focus on mechanistic studies and stratified analyses to clarify whether and how cholesterol modulation could contribute to neuroprotective strategies in PD.
15 May 2026
Read appraisal →Journal of medical Internet research
Stimulating Future-Oriented Thinking and Goal-Achievement Through the Future Self Using Virtual Reality and a Smartphone App: Randomized Controlled Trial
BACKGROUND: Research suggests that identification with one's future self-encompassing vividness, connectedness, and valence-plays a key role in motivating future-oriented choices and goal pursuit. Interventions aiming to strengthen future self-identification have been shown to reduce maladaptive behaviors and promote well-being, but traditional approaches often rely heavily on imagination. Emerging technologies offer novel opportunities to make the future self more vivid and tangible, potentially reducing cognitive burden, and enhancing intervention effectiveness. OBJECTIVE: This randomized controlled trial evaluated the effectiveness of a digital intervention on the following primary outcomes: future self-identification, future orientation, consideration of future consequences, self-defeating behavior, goal commitment, and goal achievement. METHODS: In this parallel, 3-arm randomized controlled trial, 321 first-year students from a large public university in the Netherlands were randomized (1:1:1) with blocks of 9 to a smartphone app-based future-self intervention, an immersive virtual reality (VR) version of the same intervention, or an active goal-setting control condition. Participants assigned to the smartphone or VR condition engaged with the intervention over a 3-week period, during which they interacted with their 10-year-old self, their future self. Participants in the control condition received no further support. Each of the 3 conditions consisted of 107 participants, who were all included in the analyses following the intention-to-treat principle. Due to the nature of the intervention, blinding was not possible. RESULTS: Compared to the goal-setting control condition, both intervention conditions yielded significant short-term improvements on all 3 aspects of future self-identification (smartphone: dvividness=0.49; dvalence=0.44; dconnectedness=0.43; VR: dvividness=0.35; dvalence=0.44; dconnectedness=0.43), and a small decline in vividness (smartphone: d=-0.36; VR: d=-0.23) and connectedness (smartphone: d=-0.36; VR: d=-0.32) at 6-month follow-up. Additionally, the intervention buffered declines in future orientation during the study period (smartphone: d=.16; VR: d=0.18), and VR delivery led to significantly higher weekly goal achievement (d=0.88). However, effects on future orientation were not sustained over time (smartphone: d=-0.18; VR: d=-0.25), resulting in similar levels across conditions at 6-month follow-up. Furthermore, no significant effects emerged for other primary or secondary outcomes, such as self-defeating behavior (smartphone: doverall=-0.10; VR: doverall=-0.03), impulsivity (smartphone: doverall=-0.07; VR: doverall=-0.09), or academic performance (η2partial=0.00). No adverse events were reported in any of the conditions. CONCLUSIONS: This randomized controlled trial innovatively compares smartphone and immersive VR future-self interventions. Findings suggest that digital interventions leveraging visual and interactive representations of the future self can strengthen future self-identification and future orientation, and support (short-term) goal pursuit. Together, they highlight the potential of scalable digital future self approaches for educational and preventive health contexts.
15 May 2026
Read appraisal →Journal of the American College of Cardiology
Blood Pressure Levels and Outcomes in Type 2 Diabetes: Dose-Response Meta-Analysis of 5.87 Million Cohort Participants
BACKGROUND: The dose-response relationships between systolic and diastolic blood pressure (SBP and DBP) levels and risks of all-cause mortality, cardiovascular and renal outcomes in type 2 diabetes remain poorly characterized, particularly at lower blood pressure (BP) levels. OBJECTIVE: This study aimed to examine risk patterns of major clinical outcomes across a wide range of BP levels in participants with type 2 diabetes. METHODS: We systematically searched PubMed, Embase, and Web of Science from inception to November 30, 2024, for cohort studies assessing associations of BP levels with all-cause mortality, cardiovascular, and renal outcomes. One-stage mixed-effects dose-response meta-analysis was conducted to assess the curvilinear associations. RESULTS: A total of 89 cohorts from 113 articles of 5,875,364 participants with type 2 diabetes were identified. A J-shaped association was observed for SBP with all-cause mortality. J-shaped associations were also observed for SBP with cardiovascular events and for DBP with all-cause mortality, with flattened risks at lower BP levels. After excluding studies with participants who had baseline cardiovascular diseases or cancer, a significantly lower risk of cardiovascular events was observed at lower SBP levels, as well as a flattened risk of all-cause mortality. Positive linear or monotonic trends were observed for renal events, estimated glomerular filtration rate decline, and development or progression of albuminuria. CONCLUSIONS: In type 2 diabetes, BP is linearly or monotonically associated with most cardiovascular and renal outcomes. Low SBP does not appear to be associated with an elevated risk of all-cause mortality compared with higher BP targets, suggesting that previously reported associations are likely due to reverse causation and unmeasured confounding.
15 May 2026
Read appraisal →Journal of the American College of Cardiology
Single-Pill Low-Dose Triple Combination Therapy vs Standard-Dose Monotherapy in Patients With Mild-to-Moderate Hypertension
BACKGROUND: Single-pill low-dose combination (LDC) antihypertensive therapy is an emerging strategy for improving blood pressure (BP) control. Although previous phase II and pragmatic studies suggested promise, these are the first phase III, double-blind, active-controlled trials comparing a single-pill ultra-low-dose triple combination with standard-dose monotherapy. OBJECTIVES: In these studies, we sought to compare a single-pill combination of 1.67 mg amlodipine, 16.67 mg losartan potassium, and 4.17 mg chlorthalidone (LDC-ALC) with standard-dose monotherapy-5 mg amlodipine or 50 mg losartan potassium-in patients with mild-to-moderate hypertension. METHODS: HM-APOLLO-301 (Study 301, May 2022-June 2023) and HM-APOLLO-302 (Study 302, March-December 2024) were multicenter, randomized, double-blind, active-controlled, phase III studies in South Korea. Study 301 compared LDC-ALC and amlodipine; Study 302 compared LDC-ALC and losartan. Adults (≥19 years of age) with systolic blood pressure (SBP) 140 to <180 mm Hg and diastolic blood pressure (DBP) <110 mm Hg after 4-week placebo run-in were randomized to 8 weeks of treatment. The primary endpoint was SBP reduction at week 8, assessed first for noninferiority, then for superiority. RESULTS: In Study 301, LDC-ALC exhibited noninferiority to amlodipine in reducing SBP at week 8 (upper bound of 1-sided 97.5% CI: 2.8 mm Hg [<3 mm Hg noninferiority margin]), and similar efficacy (least-squares mean change: -19.1 vs -19.9 mm Hg; 95% CI: -1.5 to 3.1; P = 0.495), with similar DBP reduction and blood pressure control rate. In Study 302, LDC-ALC was both noninferior (upper bound of one-sided 97.5% CI: -0.6 mm Hg) and superior to losartan (least-squares mean change: -19.9 vs -16.4 mm Hg; 95% CI: -6.6 to -0.2; P = 0.037) in SBP reduction at week 8, with greater DBP reduction and a higher blood pressure control rate than losartan. Adverse events were similar between groups (LDC-ALC vs amlodipine: 11.7% vs 13.9%; LDC-ALC vs losartan: 6.4% vs 3.3%), with ≤1% treatment withdrawals and no serious drug-related events. CONCLUSIONS: Single-pill LDC-ALC achieved BP reductions similar to those with amlodipine and greater than those with losartan monotherapy over 8 weeks, with similar short-term tolerability. These findings support LDC-ALC as an effective and well tolerated alternative initial therapy for mild-to-moderate hypertension, expanding the set of validated strategies alongside established monotherapies. (A Study to Evaluate Efficacy and Safety of HCP1803 in Patients With Essential Hypertension [HM-APOLLO-301; NCT05362110]; A Study to Evaluate Efficacy and Safety of HCP1803 Compared to RLD2001-1 in Patients with Essential Hypertension [HM-APOLLO-302, NCT06438172]).
14 May 2026
Read appraisal →Current heart failure reports
Emerging Artificial Intelligence Tools for the Screening of Structural and Valvular Heart Disease
PURPOSE OF REVIEW: Structural heart disease (SHD) encompasses diseases involving the heart valves, chambers, walls, and muscles. Current diagnostic methods have limited accessibility and predictive value. This review aims to present recent advances in artificial intelligence (AI)-guided tools in the screening of SHD and valvular heart disease (VHD), and to present challenges and opportunities for their use in clinical practice. RECENT FINDINGS: AI-guided models trained on ECGs, chest X-rays, and coronary artery calcium scans have a high accuracy in the diagnosis of SHD, heart failure, low left ventricular ejection fraction, and VHD. Some of these models can highlight the signals that influence their predictions, improving explainability. The use of AI in screening for SHD and VHD could lead to earlier diagnosis, enhanced accuracy, and better accessibility. However, outcome data on earlier diagnosis using these tools is required before broad deployment.
14 May 2026
Read appraisal →Journal of the American College of Cardiology
Behavioral Economics and Medication Adherence for Hypertension: A Randomized Clinical Trial
BACKGROUND: Nonadherence to antihypertensive medications is common. Mobile health (mHealth)-based behavioral economic interventions may improve adherence, but remain largely untested, especially in vulnerable populations. OBJECTIVE: The study sought to test whether an mHealth incentive lottery would lower systolic blood pressure (SBP) and improve adherence. METHODS: BETTER-BP (Behavioral Economics Trial To Enhance Regulation of Blood Pressure) was a randomized trial conducted in 3 safety-net clinics in New York City. Eligible participants were adults with hypertension prescribed at least 1 antihypertensive medication, with SBP >140 mm Hg, and poor self-reported adherence. In the intervention arm, an incentive lottery was administered via SMS messaging. All participants received passive adherence monitoring. The intervention lasted 6 months, with continued monitoring until 12 months. The primary clinical endpoint was change in SBP at 6 months. The primary process endpoint was adequate antihypertensive medication adherence (≥80% days adherent) from baseline to 6 months. RESULTS: Four-hundred participants (265 intervention:135 control) were enrolled with median age 57 years, 60.5% women, 61.5% Hispanic, and 20.3% non-Hispanic Black. Over 70% had Medicaid or no insurance. At 6 months, intervention arm participants were twice as likely to achieve adequate adherence (71% vs 34%; adjusted risk ratio: 2.04; 95% CI: 1.58-2.63), but there was no significant change in mean SBP (-6.7 mm Hg intervention vs -5.8 mm Hg control; P = 0.62). From 6 to 12 months, adherence was similar (31% intervention vs 26% control; adjusted risk ratio: 1.17; 95% CI: 0.83-1.65). CONCLUSIONS: In a diverse safety-net population, the BETTER-BP intervention doubled the rate of adequate antihypertensive medication adherence but did not reduce SBP at 6 months.
14 May 2026
Read appraisal →Journal of Korean medical science
Cardiovascular Risk Stratification in Youth-Onset Type 2 Diabetes Using Machine Learning
BACKGROUND: The rising incidence of youth-onset type 2 diabetes mellitus (T2DM), along with the risk of early cardiovascular complications, is concerning. Brachial-ankle pulse wave velocity (baPWV) and carotid intima-media thickness (cIMT) are noninvasive markers of arterial stiffness and atherosclerosis. They serve as important markers for cardiovascular risk assessment. This study aimed to apply machine learning models to identify high-risk individuals who may benefit from early and targeted cardiovascular screening. METHODS: This retrospective study included 129 patients with youth-onset T2DM who underwent baPWV and cIMT measurements between January 2018 and July 2024. High-risk groups were defined as having values of ≥ mean + 1 standard deviation for baPWV (arterial stiffness) and cIMT (atherosclerosis). Clinical predictors were evaluated using linear, logistic regression, and machine learning analyses. Multiple machine learning models were trained using oversampling and cross-validation techniques to enhance prediction performance. RESULTS: Among the models tested, the gradient boosting model with adaptive synthetic sampling oversampling achieved the best performance in predicting both arterial stiffness (accuracy 0.81) and atherosclerosis prediction (accuracy 0.92). Age and hypertension were consistently identified as the most important factors for arterial stiffness. For atherosclerosis risk, traditional analysis identified dyslipidemia, male sex, and duration of illness as relevant factors; machine learning more clearly emphasized low-density lipoprotein cholesterol and triglyceride levels as key predictors of increased cIMT. CONCLUSION: Hypertension and age were consistent predictors of arterial stiffness, while atherosclerosis risk factors were further clarified with lipid parameters by machine learning analysis. These findings suggest that conventional and machine learning analyses offer complementary strengths. Their combined use may enable earlier to detect nuanced cardiovascular risk patterns and support early and targeted vascular screening in youth-onset T2DM.
14 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Metabolic, demographic, and behavioral risk factors predict brain structural variability in the general population: an analysis of the human connectome project young adults
BACKGROUND: Understanding whether commonly available metabolic, demographic, and behavioral factors can explain variability in brain structure may support the development of accessible predictive approaches. This study aims to evaluate the ability of machine learning regression models to predict brain structural measures in young, neurologically healthy adults using clinically accessible risk factors. METHODS: Data were drawn from the Human Connectome Project Young Adults dataset, including 1082 participants aged 22-37. Predictors comprised metabolic measures (systolic and diastolic blood pressure, hematocrit, body mass index, hemoglobin A1C, thyroid stimulating hormone) and demographic/behavioral variables (age, gender, race, education level, smoking history, alcohol use). Brain structural parameters from MRI included gray matter (GM) and white matter (WM) volumes, surface areas, and cortical thickness. Distance correlation (dcor) guided feature selection, followed by machine learning modeling. Model performance was evaluated using normalized mean absolute error (NMAE), normalized root-mean-squared error (NRMSE), and coefficient of determination (R²). SHAP values were used for feature interpretation. RESULTS: XGBoost consistently outperformed other algorithms, showing strong predictive accuracy for total brain segmentation, supratentorial, and total GM volumes (NMAE < 0.35, R² > 0.80). In contrast, cortical surface predictions (left superior frontal) showed moderate performance (NMAE > 0.50, R² < 0.60). SHAP analysis highlighted age, gender, education level, and diastolic and systolic blood pressure as the most influential predictors of brain structure. CONCLUSIONS: The findings highlight the methodological value of regression-based approaches for estimating brain structural variability and support their potential role as complementary tools for brain health research.
14 May 2026
Read appraisal →Journal of molecular neuroscience : MN
Development and Validation of a SHAP-Interpretable Machine Learning Model for Stroke Risk Prediction Using Circulating MicroRNA Biomarkers
BACKGROUND/OBJECTIVE: Stroke remains a leading cause of morbidity and mortality worldwide. Circulating microRNAs (miRNAs) have emerged as promising non-invasive biomarkers for cardiovascular disease diagnosis and risk stratification. However, their integration into predictive models remains limited by challenges in feature selection, model robustness, and interpretability. This study aims to develop an interpretable machine learning framework for predicting stroke incidence using serum miRNA signatures. METHODS: We analyzed serum miRNA expression profiles from 1,785 human samples (173 stroke patients and 1,612 non-stroke controls) obtained from the GEO dataset GSE117064. Differential expression analysis was performed to identify significantly dysregulated miRNAs using linear modeling and empirical Bayes moderation. LASSO logistic regression was then applied to select predictive miRNA features. Five supervised machine learning classifiers-logistic regression, support vector machine (SVM), random forest, XGBoost, and k-nearest neighbors-were evaluated using 10-fold cross-validation. The best-performing model (SVM) was further interpreted using Shapley Additive exPlanations (SHAP) to assess individual miRNA contributions to prediction. To validate the robustness and clinical generalizability of the identified signatures, quantitative real-time PCR (qPCR) and Next-Generation Sequencing (NGS) were performed on independent sets of serum samples. The pre-trained SVM model was applied to the NGS data to verify its classification performance in an external cohort. RESULTS: A total of 604 miRNAs were differentially expressed between stroke and non-stroke groups, including 206 downregulated and 398 upregulated candidates. LASSO regression identified 66 non-zero-coefficient miRNAs with potential predictive value. Among the classifiers tested, the SVM model achieved the highest average accuracy of 0.9983 and perfect AUC (1.0000), demonstrating superior performance and stability. SHAP analysis revealed that a subset of miRNAs, including hsa-miR-3648, hsa-miR-1290, and hsa-miR-6765-3p, had the greatest impact on classification outcomes, providing mechanistic insights and enhancing model interpretability. qPCR results preliminarily supported the differential expression patterns of key miRNAs. Furthermore, NGS analysis of an independent validation cohort (n = 10) demonstrated distinct hierarchical clustering of the selected biomarkers. The pre-trained SVM model preliminarily supported its cross-platform validity by classifying these external samples with an overall accuracy of 80% (sensitivity 80%, specificity 80%), suggesting the potential generalizability of the identified miRNA panel across different quantification platforms. CONCLUSIONS: This study demonstrates the utility of integrating differential expression analysis, regularized feature selection, and machine learning with SHAP-based interpretation to identify and validate serum miRNA signatures predictive of stroke. The successful external validation using NGS highlights the robustness of the identified biomarkers and their potential for cross-platform application. The results provide a transparent, data-driven framework for biomarker discovery in clinical risk prediction and support the development of non-invasive diagnostic tools for stroke detection.
14 May 2026
Read appraisal →Journal of medical systems
VR-Based Physical Activity and Self-Management Interventions for Glycemic Control and Behavioral Outcomes in Type 2 Diabetes: A Systematic Review and Meta-Analysis
A growing body of original research has recently investigated virtual reality (VR)-based interventions for physical activity and self-management, yet yielded inconsistent results. This systematic review and meta-analysis aims to evaluate the effectiveness of VR-based physical activity and self-management interventions in improving glycemic control and modifying health-related behaviors among patients with type 2 diabetes mellitus (T2DM). A total of 10 studies involving 1,005 adults with T2DM were included, comprising 5 randomized controlled trials (RCTs), 1 non-randomized controlled trial (nRCT), and 4 self-controlled before-after studies. Of these, participants in the intervention groups received VR-based interventions, including immersive or interactive VR programs for diabetes self-management education or physical activity promotion, with most intervention durations ranging from 8 to 24 weeks. Control groups received traditional approaches, including routine care, in-person diabetes self-management programs, and content-equivalent 2D websites. The pre-post intervention analysis revealed that glycated hemoglobin (HbA1c) levels decreased significantly post-intervention (mean difference (MD) = -0.50, 95% CI: -0.92 to -0.08, p = 0.02). While VR-based physical activity and self-management interventions did not demonstrate superior efficacy to traditional intervention methods in glycemic control or behavioral outcomes, their unique advantages - including enhanced adherence, greater accessibility, and reduced spatiotemporal constraints - position them as a viable alternative for diabetes management. Nevertheless, the field requires more rigorously designed RCTs to establish efficacy and a systematic exploration of VR implementation protocols to maximize therapeutic benefits.
14 May 2026
Read appraisal →PloS one
Comparing machine and deep learning models for pediatric anxiety classification using structured EHRs and area-based measures of health data
OBJECTIVE: This retrospective, case-control study with internal validation evaluates the performance of machine learning (ML) and deep learning (DL) models in classifying pediatric patients at risk for anxiety disorders using structured electronic health records (EHRs) and area-based measures of health (ABMH). The aim is to enable proactive care by monitoring potential anxiety onset across developmental stages. METHODS: We trained a series of ML models (Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, XGBoost) and DL models (LSTM, GRU, RETAIN, Dipole) using structured EHR data from 30-day windows prior to diagnosis. Two datasets were used per age group: one with structured EHR data only, and another including both EHR and ABMH data. ML models were trained using short-term cross-sectional features, while DL models leveraged full longitudinal patient histories. Performance was assessed using AUROC, AUPRC, PPV, NPV, F1 score, and accuracy. Due to differences in input scope, model performance reflects both algorithmic and temporal design differences and is not intended as a direct comparison between ML and DL. RESULTS: ML models offered strong baseline performance, with XGBoost achieving AUROC scores of 0.817 (EHR) and 0.816 (EHR+ABMH) for 8-year-olds. Adding ABMH features did not significantly improve performance. DL models, particularly RETAIN and Dipole, achieved the highest AUROC values (e.g., Dipole: 0.853 with EHR, 0.857 with EHR+ABMH for 8-year-olds), outperforming other DL and ML models within their respective design constraints. CONCLUSION: Both ML and DL models successfully identified likely anxiety onset using structured EHR data. DL models using longitudinal data achieved the highest performance, while XGBoost provided a robust ML baseline. The minimal impact of ABMH features highlights integration challenges, and performance variation across ages emphasizes the need for age-stratified modeling approaches.
14 May 2026
Read appraisal →Current nutrition reports
Digital Solutions for the Control of Lifestyle Parameters in Individuals With High Cardiometabolic Risk: a Scoping Review
BACKGROUND: Digital technologies are increasingly used to promote healthier behaviors and prevent noncommunicable diseases (NCDs). Diet and physical activity are key determinants of cardiometabolic health, where digital tools can support self-monitoring and behavior change. OBJECTIVE: This scoping review aimed to evaluate the effectiveness of digital health technologies in improving diet quality and adherence, assessing body composition, and promoting physical activity among adults at cardiometabolic risk. METHOD: A systematic search was performed in Web of Science and PubMed (2000-2025) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines and the PICo framework. Inclusion criteria focused on digital solutions for diet and exercise in adults with overweight, obesity, or metabolic complications. RESULTS: 15 studies were included (8 randomized controlled trials, 2 pilot studies, and 5 validation studies). Most intervention studies reported significant improvements in weight reduction, body mass index, or waist circumference, especially when mobile applications or wearable devices were combined with personalized feedback. Validation studies confirmed a high reliability of AI-based image analysis tools for estimating body composition compared with DXA. Improvements in dietary adherence and physical activity levels were also observed in several studies. However, heterogeneity in study design, short follow-up duration, and variability in outcome measures limited comparability across studies. Major gaps included limited long-term evidence, scarce use of AI-driven personalized interventions, insufficient reporting of measurement validity, and underrepresentation of vulnerable populations. CONCLUSION: Digital health technologies, including mobile apps, wearables, and AI-based systems, show promising potential as a complementary tool to clinician-guided care. They can enhance dietary adherence, promote self-monitoring, and support healthier lifestyles, although their long-term effectiveness remains to be confirmed. Future research should prioritize long-term randomized trials, standardized outcomes measures, validation of AI-based tools in diverse populations, and structured integration of digital interventions into clinical pathways.
14 May 2026
Read appraisal →European journal of heart failure
Effect of influenza vaccination in patients with decompensated heart failure: a systematic review and meta-analysis
AIMS: In patients with heart failure (HF) influenza vaccination has shown beneficial effects in preventing cardiac decompensations. However, no conclusive results have been achieved in the few studies that have evaluated the impact of vaccination during episodes of acute HF (AHF) decompensation. We conducted a systematic review and meta-analysis to determine the possible effects of influenza vaccination on all-cause mortality in patients diagnosed with AHF. METHODS: PubMed, Medline, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews databases were searched for longitudinal studies comparing patients with AHF vaccinated against influenza with unvaccinated patients. The primary outcome selected for meta-analysis was 1-year all-cause mortality, and secondary outcomes consisted of other outcomes reported in at least in two different studies. Statistical heterogeneity was determined by calculating the I² statistic. Individual adjusted results were pooled using a random effects model. Sensitivity analysis was run for the primary outcome by removing each individual study and then re-doing the meta-analysis. RESULTS: Up to 30 June 2025, five observational cohort studies examining the effect of influenza vaccination on 1-year all-cause mortality in AHF patients had been published. Statistical heterogeneity was low (I2 = 33.7%), meaning that between-study results were consistent. Pooled analysis of confounder-adjusted hazard ratio (HR) for all-cause mortality in vaccinated patients was 0.89 (95% CI 0.83-0.96) compared with unvaccinated patients. All sensitivity analyses rendered very similar results. In-hospital and 90-day mortality were reported in three and two studies and showed similar reductions in risk, with an adjusted odds ratio of 0.85, 95% CI 0.70-1.01, and adjusted HR of 0.86, 95% CI 0.76-0.96; respectively. Isolated data from single studies suggest no effect on hospitalization following discharge after the AHF episode. CONCLUSIONS: Influenza vaccination is associated with a lower short- and long-term all-cause mortality in patients with decompensated HF; however, as all the studies included in this meta-analysis were observational, these results could be subject to residual confounding and causality cannot be directly inferred from them.
14 May 2026
Read appraisal →PloS one
Efficacy and safety of the ayurvedic formulation 'Trikatu' as an add-on to standard care in dyslipidemia: Study protocol for a randomized, double-blind, placebo-controlled trial evaluating lipid parameters, and gut microbiota.
BACKGROUND: Dyslipidemia is a leading modifiable risk factor for cardiovascular disease (CVD), with early and sustained LDL-C reduction offering significant preventive benefits. However, in low- and middle-income countries, long-term adherence to statin therapy remains alarmingly low, highlighting the need for culturally acceptable and safer adjunctive options. Trikatu, a classical Ayurvedic formulation traditionally known to enhance metabolism, may offer supportive benefits in lipid regulation. This study protocol was developed to assess the efficacy and safety of Trikatu as an add-on to standard statin therapy on lipid parameters and assess changes in gut microbiota in response to the intervention and identify microbial correlates associated with favorable lipid outcomes. METHODS: This randomized, double-blind, placebo-controlled clinical trial is being conducted at AIIMS Bhubaneswar, a tertiary care center. Total 170 participants (aged 25-60 years) with moderate to high Atherosclerotic cardiovascular disease (ASCVD) risk. Dyslipidemia, as defined by the 2019 ACC/AHA Guidelines and indicated for statin therapy, will be randomized in a 1:1 ratio to receive either Trikatu (1000 mg) or matching placebo, administered orally twice daily after food for 12 weeks along with standard statin therapy (dose/intensity as per guidelines). Primary outcome includes change in fasting serum LDL-C from baseline to 12 weeks. Secondary outcomes include changes in total cholesterol, HDL-C, triglycerides, glycemic markers (fasting glucose, HbA1c, insulin, HOMA-IR), inflammatory and metabolic markers (hs-CRP, TNF-α, IL-6, adiponectin, ghrelin, ApoA1, ApoB), resting blood pressure, and gut microbiota. The proportion of participants achieving normal cholesterol levels (<200 mg/dL) will also be assessed. Drug compliance and any adverse events will also be recorded. DISCUSSION: In individuals with moderate to high ASCVD risk, achieving and sustaining optimal lipid control remains a cornerstone of cardiovascular prevention. However, poor long-term adherence to statin therapy, especially in low-resource settings, limits real-world impact. By assessing Trikatu, effects on lipid profiles, glycemic and inflammatory markers, and gut microbiota, the study aims to explore its potential to augment cardiovascular risk reduction in a holistic and culturally acceptable manner. If proven effective and safe, Trikatu could serve as a valuable complementary strategy in dyslipidemia management. CLINICAL TRIAL REGISTRATION: Clinical Trial Registry of India (CTRI/2023/04/051942) Registered on 25/04/2023.
13 May 2026
Read appraisal →PloS one
Healthy living environments create healthy physical activity-related habits (ELEVATE): A protocol for a smartphone-based mixed-method study
Physical inactivity remains a key risk factor for adverse public health outcomes, disproportionately burdening lower socioeconomic groups. While individual-level interventions may temporarily increase walking-related physical activity (WPA) for some, they rarely achieve population-wide benefits. In contrast, built and natural environments are better positioned to support sustained WPA at the population level. Despite the importance of the environment to WPA, state-of-the-art research has limitations: it often relies on cross-sectional self-reports, uses coarse, simplistic environmental indicators, and focuses narrowly on residential neighborhoods, overlooking the diverse streetscapes people encounter in daily mobility. Using a transdisciplinary approach, ELEVATE addresses these gaps by 1) assessing how adults perceive and experience the built and natural streetscape environment during their WPA, 2) longitudinally assessing the relationships between streetscape environments and WPA, and 3) developing an artificial intelligence-powered system to suggest place-based environmental interventions that promote WPA. ELEVATE will employ a mixed-methods study design grounded in participatory co-creation, on-site and walk-along interviews to capture the everyday practices and experiences of WPA. We will use deep learning to extract objective environmental features and perceived streetscape qualities from street view images. A sample of approximately 200 adults residing in Amsterdam, The Netherlands, will be recruited. To examine relationships between micro-scale streetscape features and WPA, we will collect seven days of intensive longitudinal data using smartphones, including global positioning system (GPS)-based mobility and step counts. We will use spatiotemporal clustering to identify where people are most and least physically active, and mixed models to assess associations between these environmental measures and step counts. Qualitative and quantitative results will be triangulated in co-created focus groups. Finally, to support evidence-based public health and urban planning interventions, we will develop an AI-powered recommender system that generates context-sensitive (re)design guidelines framing the built and natural environment as a modifiable determinant of WPA. This study will generate evidence on how micro-scale built and natural street environments encountered during daily mobility are associated with WPA in adults. The findings are expected to inform place-based public health strategies by identifying modifiable environmental characteristics associated with physical activity and socioeconomic inequalities in urban settings.
13 May 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Deep-learning endomicroscope with large field-of-view and depth-of-field for real-time in vivo imaging of epithelial cancer hallmarks
In vivo microscopy (IVM) has shown great promise to improve early detection of epithelial precancer, but it suffers from fundamental trade-offs that limit the resolution, field-of-view (FOV) and depth-of-field (DOF). Here, we present PrecisionView, a compact, deep learning-enabled endomicroscope that breaks these constraints and achieves 20 mm2 FOV and 500 µm DOF with 4 µm resolution, representing approximately 5× increase in FOV and 8× larger DOF compared to conventional IVM with similar resolution. PrecisionView integrates a deep learning-optimized phase mask and real-time reconstruction, enabling rapid in vivo assessment of two key hallmarks of cancer: epithelial cell nuclear morphology and subsurface microvasculature through fluorescence and reflectance imaging. By imaging the oral cavity of healthy volunteers and cervical specimens with precancerous lesions, PrecisionView generates large-scale (1 to 3 cm2) coregistered maps of cellular and vascular structures, revealing distinct microscopic patterns associated with anatomic structures and precancerous lesions. Our results suggest the potential of this computational endomicroscope to address the unmet need for early cancer detection at the point of care.
13 May 2026
Read appraisal →PloS one
Burden and predisposing factors of physical inactivity among adults in Africa: Systematic review and Meta-analysis
INTRODUCTION: Physical inactivity is a primary driver of global mortality, data on its specific impact in Africa remains fragmented. We conducted this study to provide the first comprehensive pooled prevalence and identify predisposing factors contributing to physical inactivity among African adults. METHOD: We searched PubMed, Scopus, Embase, and Google Scholar for studies published between March 1, 2010, and March 31, 2025. A quality assessment of the studies was performed through the Newcastle-Ottawa Scale (NOS). The random-effect (DerSimonian) model was used to calculate the aggregated rates of physical inactivity among adults and their predisposing factors. Heterogeneity was assessed using the I2 statistic, along with subgroup and sensitivity analyses. Publication bias was evaluated using Egger's test, and all analyses were performed in STATA, version 17. RESULTS: This systematic review and meta-analysis included 34 studies with a total of 41,521 participants. The pooled prevalence of physical inactivity was 45% (95% CI: 35%-55%), and there was considerable heterogeneity (I2 = 98.4%). The highest prevalence of 57% (95% CI: 0.33, 0.81) I2 = 97.7 seen in 2013 and the lowest prevalence 28% (95% CI: 0.03, 0.53) I2 = 94.3%) observed in 2011. This study identified that, being female (OR = 1.87; 95% CI: 1.55, 2.25), Adults aged ≥ 60, (OR=2.18; 95% CI:1.98,4.77), being obese (OR=3.52; 95% CI: 2.26,5.49), urban dweller (OR=1.91, 95% CI:1.24,2.95), being overweight (OR=2.03; 95% CI: 1.61,2.68), depression symptom (OR= 1.34;95% CI: 1.07,1.68) and more drinking of alcohol (OR=1.73; 95% CI: 1.18,2.64), were significant predisposing factors associated with prevalence of physical inactivity. CONCLUSION: A high level of physical inactivity was observed amongst the African adults. Our findings indicate that female gender, older age, being overweight and obese, high levels of alcohol consumption and depression are significant predictors of physical inactivity in the African population. Promoting awareness about the positive impacts of regular exercise on health among women and an elderly age group could effectively lower the chances of hypokinetic diseases.
13 May 2026
Read appraisal →Journal of medical Internet research
Effectiveness of Digital Health Interventions in Older Adults With Frailty and Sarcopenia: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Frailty and sarcopenia represent substantial global health challenges, frequently diminishing patients' quality of life through impaired muscle function and physical performance. Digital health interventions (DHIs) have shown promise in mitigating these conditions among older adults. However, outcomes of such interventions in this demographic are inconsistent, and a thorough synthesis of existing evidence is lacking. OBJECTIVE: This study aimed to evaluate the effectiveness of DHIs in older adults with frailty and sarcopenia. METHODS: A comprehensive search of PubMed, Web of Science, MEDLINE, Embase, and Cochrane Library was conducted from their inception until January 2026 to identify randomized controlled trials. Meta-analyses were performed using R software (R Foundation for Statistical Computing). Study quality was evaluated using the revised Cochrane Risk of Bias Tool 2.0 (Cochrane Collaboration), and evidence certainty was assessed using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). RESULTS: From 3506 records, 16 studies were included. DHIs significantly improved total skeletal muscle mass (weighted mean difference [WMD] 1.01, 95% CI 0.08-1.94, 95% prediction interval [PI] -0.95 to 2.96), gait speed (WMD 0.09, 95% CI 0.03-0.15, 95% PI -0.1 to 0.26), Timed Up and Go Test (TUGT: WMD -0.52, 95% CI -1.02 to -0.03, 95% PI -1.93 to 0.85), 30-second Chair Stand Test (30CST: WMD 2.19, 95% CI 0.89-3.48, 95% PI -1.59 to 5.66), balance (standardized mean difference [SMD] 0.61, 95% CI 0-1.21, 95% PI -0.94 to 2.13), and quality of life (SMD 0.16, 95% CI 0.05-0.27, 95% PI 0.04-0.28). No significant improvements were observed in Appendicular Skeletal Muscle Mass Index (ASMI), grip strength, 6-minute walk test (6MWT), 2-minute walk test (2MWT), Short Physical Performance Battery (SPPB), or BMI. Although the pooled effect was favorable, the wide 95% PI suggests substantial between-study heterogeneity. Subgroup analysis stratified by intervention duration revealed significant intersubgroup differences in ASMI (χ²₁=9.93; P=.0016), indicating interventions lasting ≥12 weeks were more effective for improving ASMI (WMD 0.28, 95% CI 0.06-0.50, 95% PI -0.30 to 0.83). Subgroup analysis stratified by intervention type showed significant intersubgroup differences in balance (χ²₃=9.89; P=.0195), with exergame-based interventions showing significant effects (SMD 0.83, 95% CI 0.26-1.40). CONCLUSIONS: This systematic review is the first to quantify the disease-specific efficacy of DHIs in improving muscle function, physical performance, and quality of life among older adults with frailty and sarcopenia, demonstrating their unique value as a scalable complementary approach. By overcoming geographical and resource constraints, DHIs support underserved populations. However, low evidence quality and heterogeneity warrant cautious interpretation. The 95% PIs indicate that actual effects may vary with population characteristics and implementation contexts. Nonetheless, DHIs represent a promising and cost-effective strategy for service expansion. Future high-quality studies are needed to better understand their effectiveness and implementation across settings.
13 May 2026
Read appraisal →Oral health & preventive dentistry
Prevalence and Risk of Dental Erosion Linked to Carbonated Drinks in Adolescents: A Systematic Review and Meta-Analysis
PURPOSE: To determine the prevalence of dental erosion due to carbonated beverage consumption among adolescents and the association between both variables through a systematic review and meta-analysis. METHODS AND MATERIALS: This study was conducted in accordance with the preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines for systematic reviews. A comprehensive literature search was performed in PubMed, Scopus, Web of Science, LILACS, and SciELO. Observational studies were included, and the PEO framework was used to identify dental erosion and non-erosion (outcome) and intake of carbonated beverages (exposure) in adolescents (population). The prevalence of dental erosion and odds ratio (OR) with 95% confidence interval (95%CI) to determine the risk of carbonated beverages were estimated with MedCalc V.23.0.2 and Review Manager (RevMan V.5.4.1) software, respectively. A P value 0.05 was considered to indicate statistical significance. RESULTS: A total of 24 studies met the eligibility criteria. The pooled sample included 21,541 adolescents, and the overall erosion prevalence was 37.6% (95%CI 26.3, 49.7%). The meta-analysis revealed a significant association between carbonated beverage consumption and dental erosion (OR = 1.98, 95%CI: 1.42-2.77; 7785 participants; I2 = 80%; P 0.0001). CONCLUSION: Dental erosion is a prevalent condition among adolescents, with a pooled prevalence of 37.6%. Carbonated beverage consumption among adolescents is associated with a 98% increased risk of dental erosion.
13 May 2026
Read appraisal →Lancet (London, England)
Long-term effects of colonoscopy screening on colorectal cancer incidence and mortality: a multicountry, population-based randomised controlled trial
BACKGROUND: We previously reported the 10-year effects of colonoscopy screening on colorectal cancer incidence and mortality. Here, we report the effects after 13 years of follow-up. METHODS: In this multicountry, population-based randomised controlled trial, 84 583 men and women aged 55-64 years at enrolment from Norway, Poland, and Sweden were randomly allocated (1:2) to colonoscopy screening or no screening and analysed. The primary outcomes were colorectal cancer incidence and mortality after 10-15 years of follow-up in intention-to-screen analyses, with first analysis after 10 years, and repeated every other year or at longer intervals. This trial is registered with ClinicalTrials.gov, NCT00883792, and is ongoing. FINDINGS: At 13 years of follow-up, colorectal cancer incidence was 375 colorectal cancers (1·46%) of 28 217 individuals in the screening group and 912 colorectal cancers (1·80%) of 56 366 individuals in the no-screening group. The risk ratio (RR) was 0·81 (95% CI 0·71-0·90) in intention-to-screen analyses and 0·55 (0·33-0·81) in per-protocol analyses. The risk for proximal colorectal cancer was 129 (0·51%) in the screening group versus 283 (0·56%) in the no-screening group (RR 0·91 [0·71-1·09]), and the risk for distal colorectal cancer was 224 (0·87%) in the screening group versus 563 (1·11%) in the no-screening group (RR 0·79 [0·65-0·89]; interaction p<0·0001). In men, the colorectal cancer risk was 214 (1·69%) of 14 154 in the screening group and 541 (2·19%) of 28 247 in the no-screening group (RR 0·77 [0·64 to -0·88]); in women, the risk was 161 (1·24%) of 14 063 in the screening group versus 371 (1·43%) of 28 119 in the no-screening group (RR 0·87 [0·70 to 1·02]; interaction p<0·0001). Colorectal cancer mortality was 106 (0·41%) of 28 217 in the screening group and 236 (0·47%) of 56 366 in the no-screening group (intention-to-screen RR 0·88 [0·68-1·08], per-protocol RR 0·70 [0·26-1·25]). The observed colorectal cancer mortality in the non-screening group (0·47%) was substantially lower than expected at the time of designing the trial (0·82%). INTERPRETATION: One colonoscopy significantly reduced colorectal cancer incidence but not mortality over 13 years. Colorectal cancer mortality was lower in both study groups than when the trial was designed. FUNDING: The Norwegian Research Council, the Nordic Cancer Union, the Norwegian Cancer Society, and the Health Fund of South-East Norway.
13 May 2026
Read appraisal →Expert review of vaccines
Population-level impact of HPV vaccination: a global systematic review of ecological, cross-sectional, and cohort studies
BACKGROUND: Human papillomavirus (HPV) causes approximately 4.5% of cancers globally, with the highest burden in low- and middle-income countries (LMICs). Since their introduction in 2006, HPV vaccination programs have led to substantial declines in HPV-related outcomes, although impact varies across settings. RESEARCH DESIGN AND METHODS: We conducted a systematic review to evaluate the population-level impact of HPV vaccination on HPV infection, cervical intraepithelial neoplasia grade 2 or higher (CIN2+), genital warts, invasive cervical cancer (ICC), and oropharyngeal cancer (OPC), and examined the influence of coverage, age at initiation, and vaccine type. The review followed PRISMA 2020. RESULTS: Of 13,549 records screened, 63 were included: 9 assessed HPV infection, 24 on CIN2+, 25 on genital warts, and 7 on ICC. Greatest reductions were observed in settings with at least 70% coverage and early vaccination prior to sexual debut, typically achieved through school-based programs. Reported declines ranged from 58-100% for HPV infection, 30-88% for CIN2+, 60-90% for genital warts, and 70-88% for ICC. CONCLUSIONS: HPV vaccination offers strong protection, especially when delivered early and at high coverage within schools. Expanding access and prioritizing underserved populations are essential to achieving global cancer prevention goals. Limitations include heterogeneity across designs, outcome definitions, and follow-up.
13 May 2026
Read appraisal →Vaccine
The immune response of the mRNA vaccine CS-2034 as a heterologous booster before and after breakthrough infection: A follow-up study.
BACKGROUND: The emergence of highly mutated SARS-CoV-2 variants challenges vaccine protection. Evaluating long-term immune protection from boosters is crucial. The experiment compares neutralizing antibody dynamics and ACE2-RBD binding inhibition induced by mRNA and inactivated vaccine boosters, and assesses the impact of breakthrough infections and age on immune responses. METHODS: This study is based on a previous randomized, double-blind, parallel controlled trial. Healthy participants aged ≥18 who had received ≥3 inactivated COVID-19 vaccine doses. Volunteers (32 aged 18-59 and 32 ≥ 60) were stratified by age and vaccine type (24 mRNA, 8 inactivated in each age group). Pregnant women, those with severe chronic diseases or a history of allergies are excluded. Neutralizing antibodies were measured via microneutralization assay, and ACE2-RBD binding inhibition by high-sensitivity electrochemiluminescence. The study assessed changes in antibody levels and ACE2-RBD binding inhibition pre- and post-infection across age groups to evaluate vaccine efficacy and durability. (Registed number: ChiCTR2200064575). RESULTS: The neutralizing antibody titers from mRNA vaccine showing stronger and more consistent responses against the original strain and variants like XBB and BA.5. After breakthrough infections, mRNA-vaccinated individuals show a significant and lasting boost in antibodies, while the increase in inactivated vaccine recipients is modest. By age, mRNA-vaccinated people over 60 experience a faster antibody decline post-vaccination, whereas the inactivated vaccine group sees an initial rise at three months before declining. For ACE2-RBD binding inhibition, mRNA vaccines maintain higher early-phase rates, while inactivated vaccines show a delayed rise at three months followed by decline. CONCLUSIONS: mRNA vaccines induce stronger neutralizing antibodies and broader immunity than inactivated vaccines. Breakthrough infections boost antibody levels more in mRNA recipients than in inactivated vaccine recipients. Antibody decline is faster with mRNA vaccines in people over 60, while inactivated vaccines show a modest but weaker antibody increase against variants in the elderly.
13 May 2026
Read appraisal →Vaccine
Factors influencing rabies vaccines immunogenicity during post-exposure prophylaxis: a systematic literature search and meta-analysis
BACKGROUND: Rabies post-exposure prophylaxis (PEP) requires timely appropriate wound management and vaccination (including concomitant rabies immunoglobulin [RIG], depending on vaccination history and wound severity) to ensure rapid increases in rabies virus neutralizing antibody (RVNA) titers, a surrogate marker of protection. In this systematic review and meta-analysis, we assessed factors influencing the immunogenicity of rabies vaccines administered as PEP. METHODS: PubMed, Embase, and The Cochrane Library were systematically searched for randomized controlled trials published in English, French, Spanish, or Portuguese from January 1, 1983 to March 28, 2022 reporting the immunogenicity of rabies vaccines, specifically rabies human diploid cell vaccine (Imovax Rabies), purified Vero cell rabies vaccine (Verorab), and purified chick embryo cell rabies vaccine (RabAvert/Rabipur), as part of PEP in healthy individuals. Pooled RVNA geometric mean titers (GMTs) and seroconversion rates at Day 14 (D14) post-vaccination, determined by rapid fluorescent focus inhibition test, were estimated using random effects meta-analyses. RESULTS: We identified 1040 studies, of which 67 (9689 participants across 140 intervention groups) were included. The pooled RVNA GMT across all vaccines at D14 post-vaccination was 8.19 (95% CI: 6.73-9.98) and did not differ significantly by vaccine, administration route, or RIG use; however, there were significant interlaboratory differences. Meta-regression analysis, including titration year, demonstrated a negative association with log-transformed RVNA GMTs, equating to a 4.18% reduction (95% CI: -5.89 to -2.44) in log-transformed GMTs per year during the period assessed. Across all vaccines assessed, the pooled seroconversion rate by D14 post-vaccination was 98% (95% CI: 97-98; n = 7023; I2 = 64%) and was not impacted by vaccine, administration route, RIG use, test laboratory, or titration year. CONCLUSIONS: Although RVNA GMTs appear to have decreased by titration year, there was no clinically meaningful impact on immunogenicity, and the three licensed rabies vaccines assessed are expected to provide similar protection.
13 May 2026
Read appraisal →Med (New York, N.Y.)
Mazdutide 9 mg in Chinese adults with a body mass index ≥30 kg/m2 but without diabetes: A phase 2 randomized controlled trial
BACKGROUND: Once-weekly dual glucagon and glucagon-like peptide-1 receptor agonist mazdutide, administered at 4 and 6 mg, provides substantial weight loss in Chinese adults with overweight or obesity. This trial aimed to evaluate the efficacy and safety of mazdutide 9 mg in Chinese adults with obesity. METHODS: In this randomized, double-blind, placebo-controlled phase 2 trial (NCT01904913), participants with a body mass index (BMI) ≥30 kg/m2 and without diabetes were randomized 3:1 to receive mazdutide 9 mg or placebo for 24 weeks. The primary endpoint was the percentage change in body weight from baseline to week 24. FINDINGS: Eighty participants were randomized and received mazdutide 9 mg (n = 60) or placebo (n = 20). At week 24, the mean percentage change in weight from baseline was -12.78% with mazdutide 9 mg and 1.80% with placebo (treatment difference: -14.58% [95% confidence interval (CI) -18.00, -11.16], p < 0.0001). Weight reduction ≥5% was achieved by 81.7% of participants with mazdutide after 24-week treatment, while no participants with placebo achieved this threshold. Mazdutide treatment was associated with greater improvements in cardiometabolic risk factors vs. placebo. The most common adverse events included nausea (50.0% with mazdutide vs. 0% with placebo), diarrhea (38.3% vs. 10.0%), and vomiting (36.7% vs. 10.0%), predominantly mild to moderate in severity. CONCLUSIONS: Mazdutide 9 mg was safe and led to substantial weight reductions in Chinese adults with a BMI ≥ 30kg/m2 but without diabetes after 24-week treatment. These results support further clinical development of mazdutide 9 mg for the treatment of obesity. FUNDING: This study was sponsored by Innovent Biologics.
12 May 2026
Read appraisal →European journal of nutrition
Association of daily diet and incidence of head and neck cancer: a prospective cohort study of UK biobank participants
PURPOSE: The association between daily diet and head and neck cancer (HNC) remains unclear. Therefore, this study aimed to systematically investigate the relationship between different types of diet and incident HNC. METHODS: This cohort study analyzed UK Biobank (UKB) data (2006-2021, n = 480,577) using Cox proportional hazards models to evaluate the associations between daily diet and incident HNC and its subtypes: laryngeal cancer (LC), oral cancer (OC), and oropharyngeal cancer (OPC). Subgroup analyses were further performed for sex, age, body mass index (BMI), and smoking status. RESULTS: This large prospective study identified significant dietary associations with head and neck cancer (HNC) risk during 11.8 years of follow-up. Protective associations emerged for moderate dried fruit consumption (Hazard Ratio (HR) = 0.77-0.81), daily milk intake (> 299 mL/day, HR = 0.71), and regular coffee drinking (HR = 0.77-0.84). Conversely, elevated risks were observed with frequent red meat (≥ 4 times/week, HR = 1.20), non-oily fish (≥ twice/week, HR = 1.45), refined grains (≥ 3 bowls or slices/week, HR = 1.14), and habitual salt addition (HR = 1.21). Subtype-specific patterns include the protective effects of whole grains and vegetables against laryngeal cancer and reduced oral cancer risk with limited poultry consumption. Notable variations were observed in sex (sex-specific milk/oily fish effects), age (differential fruit/vitamin impacts), and BMI (heightened red meat risk in normal-weight individuals). CONCLUSION: Our findings demonstrated significant associations between dietary patterns and HNC risk, identifying both protective and risk-enhancing food components. These results highlight the potential of dietary interventions in HNC prevention, with varying effects across cancer subtypes and demographic groups.
12 May 2026
Read appraisal →Vaccine
Enhancing uptake of respiratory vaccinations in asthma and chronic obstructive pulmonary disease (COPD) patients: a systematic review.
INTRODUCTION: Despite influenza, pneumococcal and COVID vaccines being widely recommended for patients with chronic respiratory disease, vaccination rates in this cohort remain low. The aim of this systematic review is to identify interventions which are effective in increasing respiratory vaccination rates in adults with chronic obstructive pulmonary disease (COPD) or asthma. METHODS: The inclusion and exclusion criteria for the study can be found in the PROSPERO protocol (PROSPERO registration no: CRD42025588565). A search was run across four databases (MEDLINE, Embase, CENTRAL and ClinicalTrials.gov) in February 2025, which returned 2537 studies. Eleven studies were deemed to meet the study inclusion/exclusion criteria and these were narratively synthesised: four randomised clinical trials (RCTs), six longitudinal studies and one observational cohort study. Risk of bias was assessed using the Cochrane's Risk of Bias (RoB-V2) and ROBIN-I-V2 tools. Studies were categorised according to the COM-B model of behaviour change. RESULTS: All 11 studies consisted of COPD populations, with four studies also including asthma patients. Interventions focused on patient education, with/without involvement of a healthcare professional (HCP). Nine of the eleven studies showed a statistically significant improvement in influenza and/or pneumococcal vaccination rate with an intervention. No studies assessing COVID vaccine uptake in this population were suitable for inclusion. Most studies targeted patients' capability to get vaccinated through improving patients' and HCPs' knowledge. Fewer studies focused on social opportunity (e.g. support from other patients/HCPs) or automatic motivation (e.g. reminders). DISCUSSION: The published literature in this area is currently limited. Most studies are non-randomised and are at high-risk of bias, making meta-analysis not possible. Further research should assess the practicalities (physical opportunity) of vaccination, especially in low-income economies (where most respiratory patients reside) and standardising research methods to allow for future meta-analysis. OTHER: There is no funding for this review.
12 May 2026
Read appraisal →Physiological measurement
A multimodal fusion network for heart sound abnormality detection and classification
Objective.Accurate physiological assessment of cardiac function from heart sounds remains challenging due to background noise, variable heart rates, and the need for reliable cardiac-cycle segmentation. This study aimed to develop a fully E2E deep learning framework that extracts diagnostic information directly from raw heart sound recordings for cardiac abnormality detection and classification.Approach.We propose HS-MMNet, an E2E multi-modal deep learning framework designed for physiological heart sound analysis. Recordings are preprocessed (normalization and 25-400 Hz bandpass filtering) and divided into fixed-length 2.5 s segments. A Convolution Head with multi-atrous spatial pyramid and channel-spatial attention extracts fine-grained local temporal patterns from the filtered 1-D waveform. A Transformer Head captures long-range spectro-temporal dependencies from Log-Mel spectrograms. These hypotheses are iteratively fused by a novel multi-hypothesis cross-attention module with cyclic query-key-value assignment and a hypothesis-mixing MLP, enabling rich cross-site interaction and effective suppression of noise and non-informative regions. Recording-level classification is obtained via a fully connected layer.Main results.On the PhysioNet/CinC Challenge 2016 dataset, HS-MMNet achieved 94.80% accuracy, 92.10% sensitivity, 96.85% specificity, 87.50% precision, and 89.74%F1-score, outperforming all previously reported methods. On the balanced five-class Yaseen dataset (normal, aortic stenosis, mitral regurgitation, mitral stenosis, mitral valve prolapse), it attained 99.60% macro-averaged precision, recall, andF1-score with only four misclassifications in 1000 recordings, establishing new state-of-the-art (SOTA) benchmarks.Significance.HS-MMNet represents an advance in automated physiological measurement from heart sounds. By eliminating cardiac cycle detection and multi-channel requirements while achieving SOTA diagnostic performance, it provides a practical, scalable solution for accurate cardiovascular screening with primary-care and low-resource settings.
12 May 2026
Read appraisal →The aging male : the official journal of the International Society for the Study of the Aging Male
SPISE index and ensemble machine learning refine cardiovascular risk stratification in stage 0-3 CKM syndrome
BACKGROUND: While the single-point insulin sensitivity estimator (SPISE) shows promise as an insulin resistance biomarker, its association with cardiovascular disease (CVD) in early CKM stages (0-3) remains underexplored. METHODS: We analyzed 6480 participants with CKM stage 0-3 from the China Health and Retirement Longitudinal Study. CVD outcomes were assessed relative to SPISE index levels. An ensemble machine learning model was employed to predict CVD risk. RESULTS: 6480 subjects were enrolled, of whom 967 developed CVD. After stratifying participants into SPISE quartiles (Q1-Q4) and adjusting for covariates, higher quartiles were linked to a lower CVD risk. This study developed an LR+GMM (Logistic Regression + Gaussian Mixture Model) ensemble model to predict CVD risk using five strong predictors: SPISE, high-density lipoprotein cholesterol (HDL-c), diastolic blood pressure (DBP), body mass index (BMI), and glycated hemoglobin (HbA1c). The model performed well, achieving an accuracy (ACC) of 0.986 and an area under the receiver operating characteristic curve (AUC) of 0.932. CONCLUSIONS: The SPISE index is a significant inverse predictor of CVD risk in individuals with stage 0-3 CKM syndrome. The LR+GMM ensemble model, incorporating the SPISE index and four clinical metrics, demonstrated outstanding predictive performance.
12 May 2026
Read appraisal →Journal of medical Internet research
The Digital Exposome: A Life Course Framework for Health in the Digital Age
Digital technologies are reshaping human behavior, health care delivery, and population health; however, their cumulative effects across the lifespan remain underexplored. This viewpoint argues that exposures arising from interactions with digital technologies should be formally integrated into exposome science as a distinct, measurable component of the human environment. Our aims are to (1) redefine the digital component of the exposome (the digital exposome) within the broader exposome framework, (2) examine its life course implications for health and equity, and (3) outline a research and policy agenda to enable its systematic measurement and integration into clinical and public health practice. Digital technology-related exposures can confer benefits such as enhanced health monitoring, personalized interventions, improved access to care, and the promotion of healthy behaviors. However, they may also introduce potential risks, including mental health challenges, cognitive and circadian disruptions, sedentary lifestyles, exposure to misinformation, and widening inequities among vulnerable populations. Despite their ubiquity, digital technology-related exposures remain poorly integrated into clinical medicine, epidemiology, or public and global health policies. Drawing on interdisciplinary evidence from exposure science, epidemiology, and digital phenotyping research, we propose a refined conceptual definition of the digital exposome grounded in the classical exposome domains. We propose redefining the digital exposome as the full spectrum of exposures resulting from interactions or proximities with digital technologies and their combined influence on health across the lifespan. This framework conceptualizes digital technology-related exposures as a dynamic set of environmental influences operating through sociotechnical, behavioral, and biological pathways over the life course. To operationalize this framework, we discuss practical approaches using validated behavioral instruments, objective device use logs, ecological momentary assessments, smartphone-based digital phenotyping, and wearable sensing technologies. Systematic measurement, large-scale longitudinal studies, and harmonized exposure metrics are needed to characterize the cumulative health impacts of digital environments more accurately. Emerging tools such as digital markers or biomarkers and digital phenotypes offer promising opportunities to link real-world technology use with physiological and biological outcomes, thereby supporting precision medicine and population health strategies. Ethical governance, privacy safeguards, and equity considerations must be embedded from the start, drawing on emerging exposomethics frameworks. Recognizing the digital exposome as a modifiable determinant of health offers a foundation for evidence-based guidance, prevention strategies, and policy interventions suited to increasingly digital societies. By integrating digital technology-related exposures into exposome science, clinical practice, and public health research, this viewpoint seeks to foster interdisciplinary dialogue, guide future empirical work, and support the development of safer and more equitable digital environments across the lifespan.
12 May 2026
Read appraisal →Scientific reports
Hypnotic safety suggestions reduce cortisol awakening response and morning heart rate in daily life.
With stress being causally linked to the most frequently occurring mental and physical health problems, effective coping with acute stress is essential for promoting overall well-being and building long-term stress resilience. However, most interventions aimed at improving stress regulation require extended periods of training and practice. In a previous study, we demonstrated that the Jena Safety Anchor-a brief, hypnosis-based intervention-effectively reduced subjective stress responses in a controlled laboratory setting using the Trier Social Stress Test (TSST). The present study examined whether this effect extends to everyday life. A total of 80 participants (n = 40 female) were randomly assigned to either a hypnosis group or a control group (n = 40 per group). Over the course of two weeks, all participants collected saliva samples at home to assess their cortisol awakening response (CAR), an indicator of anticipatory stress, reflecting the body's preparation for expected demands upon waking. Additionally, all participants continuously recorded their heart rate via smartwatches during the two data collection weeks. The hypnosis group received a single guided session of the Jena Safety Anchor at the end of the first week, while the control group came to the laboratory but received no intervention. CAR was measured via three saliva samples taken within one hour after waking on specified days. Morning heart rate was measured between 6 and 9 a.m. Results revealed a significant reduction in CAR and morning heart rate in the hypnosis group during the second week, following the intervention. In contrast, no change in CAR and morning heart rate was observed in the control group. These findings show that a single session of the Jena Safety Anchor can effectively reduce physiological stress anticipation in the morning. This points to its potential as a rapid, low-effort intervention for enhancing stress coping and fostering resilience. It offers a promising approach to mental health promotion that is both time-efficient and easily integrated into everyday routines.
12 May 2026
Read appraisal →Tobacco control
Impact of Instagram and TikTok influencer marketing on perceptions of e-cigarettes and perceptions of influencers in young adults: a randomised survey-based experiment
BACKGROUND: Pairing e-cigarettes with healthy lifestyle contexts in influencers' promotional social media posts may increase e-cigarette use risk among young adults. This study examined the effects of e-cigarette and healthy lifestyle content on young adults' perceptions of influencer credibility, harm perceptions of, and susceptibility to use, e-cigarettes. METHODS: In this survey-based online, repeated-measures experiment (2023), California young adults (N=1494, Mage=23 (SD=1.8); 63% female, 51% Hispanic) were randomly shown 10 (total) Instagram and TikTok videos, featuring influencers promoting e-cigarettes alongside healthy lifestyle activities (experimental group), or a healthy lifestyle activity alone (control). After watching each video, participants rated perceived influencer credibility (eg, honesty). After watching all videos, harm perceptions of e-cigarettes were assessed among all participants and susceptibility to e-cigarette use was assessed among e-cigarette never-users. Harm perceptions and susceptibility outcomes were compared between groups (experimental vs control) and between participants who perceived influencers as credible versus non-credible, using binomial generalised linear mixed effects models. RESULTS: Participants in the experimental group were more likely to report lower harm perceptions (adjusted OR (AOR) 1.12; 95% CI 1.03; 1.21) and higher susceptibility to e-cigarette use (AOR 1.22, 95% CI 1.11; 1.34), than participants in the control condition. Similar results were found when influencers were perceived as credible, but not when they were perceived as non-credible. CONCLUSIONS: Social media influencers' posts promoting e-cigarette use along with healthy activities may contribute to young adult e-cigarette use. Decreasing perceived credibility of influencers could decrease the negative effects of their promotional posts. TRIAL REGISTRATION NUMBER: NCT06433466.
12 May 2026
Read appraisal →Journal of global health
Folate and global health review series, part 3: syntheses on cardiovascular, cerebrovascular, and metabolic diseases.
BACKGROUND: Cardiovascular and metabolic diseases account for an increasing share of morbidity and mortality globally. Folic acid supplementation has been linked to a lowered risk of stroke and some metabolic indicators due to its involvement in homocysteine and one-carbon metabolism and its role in the production of nitric oxide; however, the evidence on these associations is inconclusive. METHODS: We searched MEDLINE, Embase, CINAHL, the Cochrane Library, and the Database of Abstracts of Reviews of Effects from inception to February 2024 for systematic reviews and meta-analyses investigating the associations of folate (dietary intake, supplementation, or blood concentrations) with any cardiometabolic outcome. We performed screening, data abstraction, and risk of bias assessment in duplicate, and assessed the credibility of the evidence using predefined criteria. RESULTS: We identified 113 unique associations from 49 reviews. The included syntheses mostly had low risk of bias of and provided pooled risk estimates from intervention trials or prospective cohorts. A larger volume of evidence was available for composite cardiovascular outcomes, coronary heart disease, and stroke compared to other outcomes. No association reached a convincing or highly suggestive level of credibility. Six directional associations and five null associations met the criteria for a suggestive level of credibility. Three dose-response relationships, all at suggestive levels of credibility, supported an association between higher dietary folate intake and a reduced risk of coronary heart disease and stroke. CONCLUSION: The available evidence on the association between folate status and cardiometabolic outcomes primarily focuses on secondary prevention of cardiometabolic diseases and substantially underrepresents low- and middle-income countries. More large-scale studies are warranted to validate a relationship between folate status and cardiometabolic events or indicators. Overall, the evidence landscape around folate and cardiometabolic diseases appears to be limited both in volume and scope. REGISTRATION: PROSPERO: CRD42021265041.
12 May 2026
Read appraisal →Journal of medical Internet research
Effectiveness of mHealth Interventions Aimed at Promoting Physical Activity and Reducing Sedentary Behavior on Work-Related Outcomes Among Workers: Systematic Review
BACKGROUND: Mobile health (mHealth) technologies have gained popularity and may play a key role in promoting health-related behaviors. However, the impact of mHealth interventions on work-related outcomes remains unclear. OBJECTIVE: We aimed to provide an overview of the effectiveness of mHealth interventions designed to encourage physical activity and decrease sedentary behavior on work-related outcomes. METHODS: A search was conducted in MEDLINE, Web of Science Core Collection, the Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews, and Ichushi-Web for all publications up to September 12, 2025, without language or date restrictions. We included studies which (1) investigated the impact of mHealth interventions promoting physical activity and reducing sedentary behavior on work-related outcomes such as absenteeism, presenteeism, work productivity, work performance, and workability; (2) were designed as a randomized controlled trial or nonrandomized study of interventions; (3) were conducted among workers; and (4) were published as full-text original articles in Japanese or English. Two researchers independently conducted the screening of titles and abstracts, followed by a full-text review to confirm study eligibility and extract the data. We assessed the risk of bias using the Cochrane Collaboration Risk of Bias tool for randomized trials (RoB 2) and the Risk Of Bias In Nonrandomized Studies of Interventions (ROBINS-I). Findings were narratively synthesized. RESULTS: In total, 4022 records were identified from databases and other sources. After removing duplicates, 3631 studies were screened, and 17 studies (12 randomized controlled trials and 5 nonrandomized studies of interventions) met the inclusion criteria. Of the 17 eligible studies with 18,805 participants, 12 showed a favorable effect direction on ≥1 work-related outcome, but the majority exhibited a high or critical risk of bias, raising concerns about the quality of the research. There was considerable heterogeneity across studies. CONCLUSIONS: Scientific evidence suggests that health promotion efforts that incorporate mHealth components to encourage physical activity and reduce sedentary behavior may have a favorable impact on work-related outcomes. However, the low methodological quality of the studies and their high heterogeneity due to the participant characteristics, intervention content, use of diverse work-related assessment tools, and different follow-up periods make it difficult to draw definitive conclusions about the effectiveness of the interventions. This review is the first to comprehensively synthesize evidence on the impact of mHealth interventions on work-related outcomes, extending prior reviews that primarily focused on traditional workplace-based interventions without mHealth components. This review also contributes to the field by offering guidance for future studies, including the potential value of megastudy designs that evaluate multiple interventions within the same population over the same period on the same outcomes. As a practical implication, these findings suggest that mHealth technologies may serve as a complementary strategy for supporting occupational health and productivity.
12 May 2026
Read appraisal →European journal of clinical pharmacology
Effects of hypoxia-inducible factor prolyl hydroxylase inhibitors on lipid profiles in patients with chronic kidney disease: a systematic review and meta-analysis
BACKGROUND: Hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs) are novel oral agents for treating anemia in chronic kidney disease (CKD), with potential effects on lipid modulation. We aimed to systematically evaluate the effects of HIF-PHIs on lipid profiles and cardiovascular outcomes in CKD patients. MATERIALS AND METHODS: PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), and Embase (Ovid) were searched for randomized controlled trials comparing HIF-PHIs with erythropoiesis-stimulating agents (ESAs) or placebo in dialysis-dependent (DD) or nondialysis-dependent (NDD) CKD patients. Primary outcomes included changes in low-density lipoprotein cholesterol (LDL-C), total cholesterol, triglycerides, and high-density lipoprotein cholesterol (HDL-C). Secondary outcomes included cardiovascular outcomes, including cardiovascular death, myocardial infarction, stroke, and all-cause mortality. RESULTS: A total of 20 trials involving 12,155 patients were analyzed in this review. Roxadustat significantly reduced LDL-C (mean difference [MD], -16.07 mg/dL; 95% CI, -17.92 to -14.21; 14 randomized controlled trials [RCTs], 10,510 patients), total cholesterol (MD, -25.25 mg/dL; 95% CI, -29.70 to -20.81; 10 RCTs, 5,538 patients), triglycerides (MD, -19.70 mg/dL; 95% CI, -30.78 to -8.61; 9 RCTs, 4,616 patients), but also decreased HDL-C (MD, -4.91 mg/dL; 95% CI, -6.80 to -3.02; 9 RCTs, 5,132 patients). Desidustat significantly reduced LDL-C and total cholesterol, but showed no significant effects on triglycerides or HDL-C, whereas molidustat showed no significant lipid-lowering effects. Overall, treatment with HIF-PHIs was not associated with significant differences in cardiovascular death (RR, 1.00; 95% CI, 0.84 to 1.18; 10 RCTs, 9,371 patients), myocardial infarction (RR, 1.12; 95% CI, 0.90 to 1.38; 15 RCTs, 11,265 patients), stroke (RR, 1.18; 95% CI, 0.86 to 1.61; 14 RCTs, 11,136 patients), or all-cause mortality (RR, 1.06; 95% CI, 0.96 to 1.17; 19 RCTs, 11,903 patients), compared with ESAs or placebo. CONCLUSION: Roxadustat showed the most substantial lipid-lowering effects, while desidustat showed significant reductions in LDL-C and total cholesterol but no significant effects on triglycerides or HDL-C, and molidustat showed no significant effects. Despite these changes in lipid profiles, no significant differences in cardiovascular outcomes were observed for these three HIF-PHIs, compared with ESAs or placebo.
11 May 2026
Read appraisal →JMIR formative research
Feasibility of Integrating Wearable Devices and Ecological Momentary Assessment for Real-Time Environmental Exposure Estimation: Proof-of-Concept Study
BACKGROUND: Environmental exposures such as heat and air pollution are critical determinants of health, yet traditional assessment methods rely on stationary monitors or residential address proxies that fail to capture the exposures that individuals experience throughout the day. OBJECTIVE: This pilot study aimed to assess the feasibility of integrating ecological momentary assessment (EMA), wearable devices, and continuous GPS tracking to capture real-time environmental exposures and to explore associations with concurrent health outcomes. METHODS: In total, 7 young adults (aged approximately 16 to 24 years; 5/7, 71% female) wore Fitbit Charge 6 watches from July 2025 to August 2025 (mean 28.1, SD 1.1 days), recording sleep quality and duration, resting heart rate, breathing rate, heart rate variability, and physical activity. GPS location measured at up to 5-minute intervals (mean 19.7, SD 25.8 measurements per day) was linked to ambient temperature, humidity, and air pollution data (particulate matter <2.5 um or <10 um in diameter, nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide) derived from monitoring stations, satellites, and climate models using data-integration algorithms accessed via an application programming interface. EMA surveys administered 3 times per day captured participants' emotional states and location (inside or outside). Feasibility targets were ≥3 GPS measurements per day, ≥1 survey completed per day, and complete sleep data on ≥50% of days. We examined exploratory bivariate correlations between environmental exposures, physiological measures, and self-reported mood, adjusting for multiple comparisons using false discovery rate correction. RESULTS: Of the 7 participants, 5 (71%) met predefined feasibility targets. Mean compliance included 565 (SD 457) GPS coordinates per participant, 1.4 (SD 0.2) EMA surveys per day, and complete Fitbit sleep data on 64% (SD 27%) of days. Surveys identified barriers to compliance, including perceived complexity of the system and forgetting to put the Fitbit watch back on after removing it. Exploratory correlations (6/7, 86% of participants with complete Fitbit data) revealed associations between nitrogen dioxide and heat exposure and reduced heart rate variability (a marker of parasympathetic tone), and between air pollutants (sulfur dioxide) and increased negative emotions. Heat exposure showed a paradoxical pattern of lower self-reported sadness but reduced heart rate variability with higher levels of heat exposure. Given the small sample size, these correlations should be considered preliminary and hypothesis generating rather than definitive findings. CONCLUSIONS: This study demonstrates that the multimodal integration of wearable devices, GPS tracking, and EMA is feasible for capturing real-time environmental exposures and concurrent health outcomes in young adults. This approach addresses critical exposure misclassification issues in environmental health research that relies on residential addresses as proxies. Preliminary patterns suggest complex relationships between environmental exposures and both physiological and emotional outcomes, warranting further investigation in larger, more diverse samples. This approach could inform future personalized environmental health interventions.
11 May 2026
Read appraisal →PloS one
Research on interpretable machine learning modeling and spatial prediction of cross-landform landslide disaster mechanism: A case study of Shaanxi Province
The differential understanding of the landslidetriggering mechanisms across various geomorphic units is vital for enhancing regional disaster prevention. This study investigates the Loess Plateau area in northern Shaanxi and the Qinba Mountain Area in southern Shaanxi as the study areas, and constructs a 15dimensional evaluation factor system covering topography, geology, vegetation, and human activities. After eliminating collinearity factors via double tests of the Pearson correlation coefficient and variance inflation factor, Bayesian optimization is used to optimize hyperparameters for CatBoost, Random Forest, LightGBM, and XGBoost, and the SHAP framework is combined to perform global attribution, single-factor dependency analysis, and interaction-effect quantification. The results demonstrate that CatBoost performs best across both geomorphic areas, achieving AUCs of 0.8307 and 0.8252 in the test sets. In northern Shaanxi, a discrete patch pattern driven by "human-water" coupling is observed, with population density and the topographic moisture index contributing 37.2%. In contrast, Southern Shaanxi exhibits a continuous, band-shaped distribution controlled by "structure-topography" is observed, and elevation and lithology dominate the model's decision-making. The SHAP-dependent map identified cross-geomorphic differences in the population density threshold of 99.83 people/km2, the slope threshold of 2.21° in northern Shaanxi, and the elevation threshold of 1194.73m in southern Shaanxi. Furthermore, the interaction network revealed an antagonistic effect of DEM and POP in northern Shaanxi and of DEM and LULC in southern Shaanxi. The framework proposed a quantitative, explainable basis for differentiated disaster prevention strategies in this research.
11 May 2026
Read appraisal →Tobacco control
Vaping cessation interventions: a systematic review and meta-analysis
OBJECTIVE: Electronic nicotine delivery systems (ENDS), such as e-cigarettes, have surged in popularity. While long-term risks remain unclear, known dangers include nicotine addiction, E-cigarette or Vaping Use-Associated Lung Injury and potential extrapulmonary effects on the heart, immune system and neurodevelopment. Addressing ENDS addiction may require strategies akin to smoking cessation. However, evidence is lacking; there are no currently approved vaping-cessation aids. The objective of this review is to summarise the available literature addressing interventions for vaping cessation. DATA SOURCES AND STUDY SELECTION: A librarian-assisted search was conducted in MEDLINE, Embase and Scopus, as well as preprints in Europe PMC, Open Science Framework, and the grey literature searches until January 2024. Six included studies used self-report methods to determine the abstinence rate, while one study used cotinine-level-verified self-reported abstinence. We included randomised controlled trials (RCTs) that examine 'rates of vaping cessation' outcomes. An intention-to-treat approach was used for data extraction, and random-effects meta-analyses models were applied. DATA SYNTHESIS: Seven RCTs were included, which studied pharmacological therapy, digital interventions and/or educational content. The primary meta-analyses demonstrated a statistically significant increase in the odds of achieving 7-day point prevalence abstinence (PPA) (OR 1.52, 95% CI 1.15 to 2.01, number of participants=3244, moderate-certainty) and continuous abstinence (OR 2.71, 95% CI 1.31 to 5.61, number of participants=164, low-certainty) following intervention. A non-significant increase in odds was noted at 30-day PPA (OR 1.32, 95% CI 0.72 to 2.42, number of participants=1994, very low certainty). CONCLUSIONS: Vaping cessation interventions increase 7-day PPA rates at 1-12 months follow-up and this was consistent across prespecified subgroup analyses of the intervention type, outcome and duration of follow-up. More high-quality studies with reproducible findings are needed to enhance the certainty of the evidence and guide clinical interventions. PROSPERO REGISTRATION NUMBER: CRD42022383670.
11 May 2026
Read appraisal →Journal of medical Internet research
Effects of Stand-Alone Digital Lifestyle Interventions on Weight-Related Outcomes in Adults With Overweight or Obesity: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Obesity is a major global health concern, and scalable digital solutions are urgently needed. While digital lifestyle interventions (DLSIs) have shown promise, prior meta-analyses often included hybrid formats with human support, limiting insights into the effectiveness of fully digital interventions. OBJECTIVE: This study aimed to evaluate the independent effects of standalone DLSIs-defined as interventions delivered exclusively via digital platforms without in-person or adjunctive support-on anthropometric and dietary outcomes in adults with overweight or obesity. METHODS: We searched MEDLINE, Embase, PsycINFO, Web of Science, and the Cochrane Library from inception through March 4, 2026. Eligible studies were randomized controlled trials (RCTs) evaluating stand-alone DLSIs in adults with overweight or obesity. Interventions were included if they targeted diet or physical activity exclusively through digital platforms. We included fully automated, asynchronous, or one-to-many synchronous systems without individualized support. Studies involving hybrid interventions, including one-to-one synchronous human interaction, nonadult populations, or non-RCT designs, were excluded. Two independent reviewers performed study selection and data extraction. Risk of bias was assessed using the Cochrane Risk of Bias 2.0 tool (Cochrane Bias Methods Group). Meta-analysis used a random-effects model with the Hartung-Knapp-Sidik-Jonkman method, and heterogeneity was assessed using I2 statistics. The certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation approach. RESULTS: A total of 19 RCTs involving 3556 participants were included. Stand-alone DLSIs significantly improved anthropometric outcomes compared to controls (standardized mean difference 0.26, 95% CI 0.14-0.38; 95% prediction interval [PI] -0.16 to 0.68; P<.001; 19 studies; n=3556; I2=56.1%), corresponding to an additional weight loss ranging from 2.62 kg to 6.55 kg, depending on the baseline body weight. Significant improvements were also found in dietary outcomes (standardized mean difference 0.26, 95% CI 0.04-0.48; 95% PI -0.29 to 0.81; P=.008; 8 studies; n=1365; I2=57.5%). Subgroup analyses for anthropometric outcomes revealed significant differences only by control group type (P<.001), with waitlist controls showing the largest effect. For dietary outcomes, no significant subgroup differences were found (P>.05). While most studies showed a low risk of bias, substantial statistical heterogeneity was observed in some outcomes. Consequently, the certainty of evidence for both outcomes was rated as moderate. CONCLUSIONS: This review is innovative as it is the first to isolate the pure efficacy of stand-alone DLSIs by excluding synchronous human support. Our findings provide moderate-certainty evidence that these tools are effective for weight management and dietary improvement without human intervention. While stand-alone DLSIs offer a highly scalable, cost-effective first-step intervention, the PIs included zero, and substantial heterogeneity was observed, suggesting that benefits may vary across settings. Future research should identify user characteristics that maximize engagement with unguided digital tools.
11 May 2026
Read appraisal →CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne
Screening for high-risk drinking and alcohol use disorder: update of the 2023 national clinical practice guideline
BACKGROUND: Current screening tools for harmful alcohol consumption have fallen out of step with recent guidance on the health risks of alcohol. To address this gap, the Canadian Research Initiative in Substance Matters updated the screening recommendations for high-risk drinking and alcohol use disorder (AUD) in the 2023 national clinical practice guideline. METHODS: Following a systematic review of literature published between Jan. 1, 2013, and Feb. 24, 2023, that examined screening tools for high-risk drinking and AUD, the updated recommendations were developed by a multidisciplinary national committee, which included people with lived and living experience. We scored the recommendations and certainty of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation tool. We used the Appraisal of Guidelines for Research and Evaluation II instrument and the Guidelines International Network's principles for disclosure of interests and management of conflicts to ensure the update met international standards for transparency, high quality, and methodological rigour. RECOMMENDATIONS: Acknowledging that time constraints are the most commonly reported barrier to universal alcohol screening, we developed 5 recommendations involving a simple screening method, to identify and address both unhealthy alcohol consumption and more serious problems related to alcohol. The recommendations include asking all patients about alcohol consumption and providing educational support to all those who drink above Canada's Guidance on Alcohol and Health's low-risk threshold. We propose a simple screening algorithm to optimize and tailor further intervention, including when to assess for possible AUD. INTERPRETATION: The revised screening recommendations represent a timesaving and pragmatic approach intended to be a resource for universal screening for alcohol risks and problems. The recommendations streamline the process of identifying and addressing the health needs of those who consume alcohol in a hazardous way or may have more serious problems related to alcohol.
11 May 2026
Read appraisal →European heart journal
Cardiovascular benefit of continuous positive airway pressure according to high-risk obstructive sleep apnoea: a multi-trial analysis
BACKGROUND AND AIMS: Randomized trials of continuous positive airway pressure (CPAP) treatment for obstructive sleep apnoea (OSA) in patients with cardiovascular disease have not detected reduced risk of major adverse cardiovascular and cerebrovascular events (MACCEs). This study tested whether the cardiovascular benefit of CPAP occurs preferentially in high-risk OSA, characterized by greater OSA-related heart rate acceleration or hypoxaemia. METHODS: In a post hoc analysis of pooled Randomized Intervention with Continuous Positive Airway Pressure in Coronary Artery Disease and Obstructive Sleep Apnoea, Impact of Continuous Positive Airway Pressure on Patients with Acute Coronary Syndrome and Nonsleepy Obstructive Sleep Apnoea, and Sleep Apnoea Cardiovascular Endpoints Study randomized trials; outcomes were stratified by high-risk OSA status, defined by heart rate response following OSA respiratory events >9.4 b.p.m. (third tertile) or oxygen desaturation area under baseline (hypoxic burden) > 87.1% min/h (third tertile). Cox mixed models quantified the CPAP treatment effect on MACCE (including cardiovascular mortality, myocardial infarction, and stroke) within high-risk OSA and the difference vs low-risk status (primary test). Secondary analyses examined participants without excessive sleepiness (Epworth <11 points) or without increased blood pressure (systolic/diastolic <140/90 mmHg). RESULTS: In 3549 participants, 16.6% and 16.3% reached the MACCE endpoint with CPAP (n = 1778) and usual care (n = 1771), respectively. The CPAP treatment effect was greater in participants with vs without high-risk OSA [interaction hazard ratio (iHR) .69, 95% confidence interval (CI) .50-.95, Pinteraction = .024; Nhigh-risk = 1832]. The differential effect was stronger in those without excessive sleepiness (iHR .59, 95% CI .41-.84; Nhigh-risk = 1509), or without increased blood pressure (iHR .54, 95% CI .36-.81; Nhigh-risk = 1244). Continuous positive airway pressure benefits in high-risk OSA were observed alongside harm in low-risk OSA. CONCLUSIONS: Continuous positive airway pressure preferentially improves cardiovascular outcomes in high-risk OSA, while harm in low-risk OSA may counteract this effect. These findings provide a pathway to identify patients likely to benefit.
11 May 2026
Read appraisal →Journal for immunotherapy of cancer
Safety and immunogenicity of a tri-antigen vaccine targeting IGFBP-2, HER2, and IGF-IR in participants with non-metastatic breast cancer
BACKGROUND: Ductal carcinoma in situ (DCIS) is a preinvasive form of breast cancer. Current treatment consists of surgery, radiation, and often systemic therapy exposing patients to unnecessary health risks. Vaccines targeting DCIS may be a way to intercept preinvasive lesions and prevent the development of invasive breast cancer. METHODS: We developed a Th1 selective multiantigen, polyepitope plasmid-DNA vaccine encoding segments of IGFBP-2, HER2, and IGF-IR, all antigens expressed in hormone receptor positive and negative DCIS. We then performed a Phase I study in participants with non-metastatic breast cancer with no evidence of disease. The primary objective was to assess the safety of 3 monthly intradermal doses (150, 300, or 600 µg) of the tri-antigen vaccine with granulocyte macrophage colony-stimulating factor as an adjuvant. 32 participants were enrolled, 10 per dose level. Toxicity evaluations occurred monthly with vaccination and at 1 and 6 months after the last vaccine. Blood was collected at baseline and at 1 and 6 months after the last immunization to assess cellular immune responses. Participants were followed annually for 5 years for long-term toxicity. RESULTS: There was no significant difference in adverse events (AEs) across dose levels and all related AEs were grades 1 or 2. All doses were immunogenic; responders included 70% of participants at the 150 µg dose level, 67% at the 300 µg dose, and 40% at the 600 µg dose level. All participants at the 300 µg dose retained significant Th1-antigen-specific immune response at 6 months after end of immunizations. T-cells derived from vaccine immunologic responders exhibited gene expression profiles that indicated an increased metabolic fitness as compared with immunologic non-responders. CONCLUSIONS: The tri-antigen vaccine appears safe and immunogenic. The intermediate dose (300 µg) was chosen as the Phase II dose due to the long-term persistence of immunity after vaccination. The vaccine will be studied in Phase II trials for the treatment of DCIS. TRIAL REGISTRATION NUMBER: NCT02780401.
11 May 2026
Read appraisal →BMJ open
Does previous pregnancy experience improve folic acid supplementation uptake? A systematic review and meta-analysis.
OBJECTIVE: To examine whether multiparous women have more or less folic acid uptake than primiparous women. OUTCOMES: Preconceptional, periconceptional and postconceptional folic acid use among all pregnant women and high risk pregnant women. DESIGN: Systematic review and meta-analysis. DATA SOURCES: MEDLINE-Ovid, CINAHL Ultimate, Scopus and ProQuest Medical. ELIGIBILITY CRITERIA: Observational epidemiological studies comparing folic acid use between primiparous and multiparous women, published in English from January 1994 to June 2024. DATA EXTRACTION AND SYNTHESIS: Two reviewers screened all papers meeting the eligibility criteria. One reviewer completed data extraction and assessed study quality using an adapted version of the Newcastle-Ottawa Scale. Three other reviewers independently assessed 10% of the studies as a quality check. Random-effects (DerSimonian and Laird) meta-analyses combined results for each outcome. Heterogeneity, risk of publication bias and certainty of evidence were assessed. RESULTS: Of the 10 982 records identified, 81 studies involving 826 855 women were included in the review. 27 studies were conducted in Europe, 22 in Asia, 11 in North America, 7 in Africa, 7 in Australia, 5 in the Middle East and 2 in South America. Multiparous women were consistently less likely to take folic acid before and during pregnancy than primiparous women. For preconceptional use, the odds were 29% lower among multiparous women (adjusted OR (aOR): 0.71; 95% CI 0.64 to 0.78; n=25 studies; I2=88.67%), and 58% lower in multiparous high-risk women (aOR: 0.42, 95% CI 0.27 to 0.64; n=3 studies; I2=27.28%). For periconceptional use, the odds were 32% lower among multiparous women (aOR: 0.68; 95% CI 0.63 to 0.74; n=28 studies; I2=85.46%). Postconception, the odds were 21% lower among multiparous women (aOR: 0.79; 95% CI 0.74 to 0.85; n=33 studies; I2=85.91%). By the second trimester, there was no significant difference between the two parity groups (aOR: 0.96; 95% CI 0.87 to 1.05; n=4 studies; I2=0.00%). The certainty of evidence was low for preconceptional, periconceptional and postconceptional uptake due to heterogeneity, and moderate for preconceptional uptake among high-risk women. CONCLUSIONS: Multiparous women were less likely to take folic acid preconceptional, periconceptional and postconceptionally, despite their previous pregnancy experience. Barriers to folic acid supplement uptake among multiparous women need to be identified, and strategies to address them in preconception, antenatal and interconception care should be investigated. PROSPERO REGISTRATION NUMBER: CRD42024553241.
11 May 2026
Read appraisal →Journal of medical Internet research
Characterizing Physical Activity Trajectories Preceding Incident Major Depressive Disorder Diagnosis With Consumer Wearable Devices in the All of Us Research Program: Retrospective Nested Case-Control Study
BACKGROUND: Low physical activity (PA) is a well-established risk factor for major depressive disorder (MDD). However, the temporal dynamics of PA preceding an incident clinical diagnosis of MDD remain poorly characterized, particularly using long-term, objective measures collected in real-world settings. OBJECTIVE: This study aimed to characterize trajectories of wearable-measured PA during the year preceding incident MDD diagnosis and identify the timing of within-person changes. METHODS: We conducted a retrospective nested case-control study using linked electronic health record and wearable (Fitbit) data from the All of Us Research Program. Adults with at least 6 months of valid Fitbit PA data in the 12 months preceding diagnosis were included. Incident MDD cases were identified based on a first electronic health record-recorded diagnosis and matched to MDD-free controls on age, sex, BMI, and calendar time of diagnosis, with up to 4 controls per case. Daily steps and moderate to vigorous PA (MVPA) were aggregated into monthly averages. Linear mixed-effects models were used to compare prediagnostic PA trajectories between cases and controls over a retrospective time scale from -12 to 0 months. Among cases, within-person contrasts were used to identify when PA levels first showed statistically significant deviations relative to levels observed 12 months before diagnosis. Exploratory analyses assessed heterogeneity by demographic factors. RESULTS: The analytic cohort included 4104 participants (n=829, 20.2% incident MDD cases and n=3275, 79.8% matched controls; n=3355, 81.7% women; median age 48.4, IQR 36.3-61.3 years). Compared with controls, individuals who developed MDD exhibited consistently lower overall PA and significant downward trajectories in both daily steps and MVPA during the year preceding diagnosis (global trajectory tests; P<.001 for both outcomes). Differences widened progressively over time, indicating accelerating declines as diagnosis approached. Among cases, statistically significant changes in daily step counts emerged approximately 4 months before diagnosis (-145, 95% CI -253 to -37 steps vs month -12; P=.02) and reached -428 (95% CI -531 to -326) steps at diagnosis (P<.001). Declines in MVPA emerged approximately 5 months before diagnosis (-2.48, 95% CI -4.32 to -0.64 minutes; P=.02) and reached -5.61 (95% CI -7.35 to -3.86) minutes at diagnosis (P<.001). Furthermore, exploratory analyses suggested heterogeneity in prediagnostic trajectories across demographic subgroups, including steeper declines among men, more pronounced reductions in activity intensity at older ages, and persistently lower activity levels with flatter trajectories among individuals with obesity. CONCLUSIONS: Unlike prior studies lacking objective PA assessment before MDD diagnosis, this study linked wearable and clinical data to characterize long-term prediagnostic trajectories in real-world settings. We observed sustained within-person declines emerging 4 to 5 months before diagnosis, providing insights into temporal dynamics preceding clinical recognition. These findings suggest that wearable-based monitoring may offer scalable early signals for risk stratification, prevention, and intervention for MDD.
11 May 2026
Read appraisal →JMIR mHealth and uHealth
mHealth-Based Gamification Interventions to Promote Health Among Older Adults: Scoping Review
BACKGROUND: Healthy aging has emerged as a global priority. However, older adults' participation in health promotion programs remains low, and traditional health promotion models have achieved limited success in fostering sustained engagement among this population. Mobile health (mHealth)-based gamification interventions offer a promising way to address these challenges. However, no published reviews support or oppose the use of mHealth-based gamification interventions as health promotion strategies in older adults. OBJECTIVE: The study aimed to identify mHealth interventions using gamification to promote health among older adults. METHODS: Our scoping review was conducted following the Joanna Briggs Institute recommendations for scoping reviews and Arksey and O'Malley's framework. The process followed PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines and PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Literature Search Extension) checklist. A comprehensive literature search was conducted across 8 databases: PubMed, Scopus, Web of Science, Embase, Cochrane Library, CINAHL, PsycARTICLES, and IEEE Xplore Digital Library, from their inception to December 10, 2025. Two reviewers independently screened titles, abstracts, and full texts via Rayyan, with disagreements resolved by a third reviewer. RESULTS: This scoping review identified 11 studies. Only 1 article was published before 2022. The interventions were found to improve enjoyment and motivation (n=5), cognitive function (n=3), physical activity (n=2), and digital literacy (n=2). Individual studies also reported improvements in mental health (n=1) and adherence (n=1), a reduction in suicidal ideation (n=1), improvements in physical function (n=1), the promotion of social engagement (n=1), and the identification of mild cognitive impairment (n=1). Game elements used were ranked by frequency as progress, challenges, goals, levels, reward, sensation, storytelling or narration, leaderboard, surprise, and avatar. No research was found to use the game element of "social sharing." mHealth types included augmented and virtual reality-based training systems, wearable devices, mobile phones, tablets, and Windows platforms and devices. Notably, only 4 studies applied theoretical frameworks, and 3 omitted the concrete approach to gamification. CONCLUSIONS: As the first scoping review to identify and map mHealth-based gamification interventions for older adults, this study highlights their potential as an innovative approach to health promotion. By systematically synthesizing evidence regarding intervention designs, gamification strategies, and preliminary health outcomes, it establishes a foundation for future inquiry. However, this review is limited by the small number of included studies, precluding broad generalizations. Future research should assess long-term impacts, integrate theoretical frameworks, establish reporting guidelines, design personalized social-interactive interventions, and expand to broader health domains. Ultimately, these insights provide targeted guidance for developing age-appropriate digital health solutions, contributing to the realization of active aging.
10 May 2026
Read appraisal →Human vaccines & immunotherapeutics
Incidence of Bacille Calmette-Guérin associated lymphadenitis in healthy children: A systematic review and meta-analysis
Few systematic reviews or meta-analyses have addressed on the incidence of Bacille Calmette-Guérin (BCG) lymphadenitis in healthy children. A systematic literature search was independently conducted by two researchers using four English and Korean databases (PubMed, Scopus, KMbase, RISS). The risk of bias was assessed. A total of 52 peer-reviewed journal articles (about 1.8 million children) were included in meta-analyses. The incidence proportion of BCG lymphadenitis ranged from 0.0% to 25.6%. The pooled incidence proportion was 2.5% (95% CI, 1.6-3.6; I2 = 99%). Studies employing active (vs. passive) surveillance reported a significantly higher incidence proportion of lymphadenitis, as did randomized controlled trials (vs. prospective and retrospective cohort studies). Also, the incidence significantly differed according to study quality, publication period, and BCG strain. This study highlights substantial variability in the incidence of BCG lymphadenitis among diverse studies and underscores the influence of surveillance methods, study design, and vaccine strain on reported outcomes.
10 May 2026
Read appraisal →Journal of physiology and biochemistry
Effects of acute, subacute, and chronic exercise on plasma s-Klotho levels: a systematic review and meta-analysis
Exercise exerts beneficial effects on multiple physiological systems, influencing biomarkers associated with aging processes and chronic diseases. This systematic review and meta-analysis aimed to evaluate the acute, subacute, and chronic effects of exercise on s-Klotho levels in healthy individuals and patients with chronic diseases. A comprehensive search was conducted in electronic databases. Included studies were randomized and non-randomized studies assessing the effects of aerobic, resistance, or combined exercise on s-Klotho levels. Interventions were classified as acute (single session), subacute (< 12 weeks), or chronic (≥ 12 weeks). Risk of bias was assessed using RoB 2 and ROBINS-I tools, and quality of evidence was evaluated with the GRADE framework. Forty-one studies were included in the qualitative synthesis, and 30 in the meta-analysis, comprising 2,765 participants (18-85 years). Twenty-five involved healthy individuals, while 21 included patients with chronic diseases. Acute and subacute aerobic exercise increased s-Klotho levels in healthy individuals (SMD 0.69; 95%CI 0.41-0.97) and diseased populations (SMD 0.62; 95%CI 0.11-1.12). Chronic exercise (12-36 weeks) showed significant increases in healthy (SMD 0.57; 95%CI 0.33-0.82) and diseased populations (SMD 1.51; 95%CI 0.87-2.16). Resistance exercise thrice weekly demonstrated the highest effect (SMD 1.60; 95%CI 0.81-2.38). Most studies had high risk of bias, with quality of evidence ranging from very low to moderate. Exercise significantly increases s-Klotho levels, with acute and subacute aerobic sessions benefiting healthy and diseased populations, while chronic resistance exercise elicited greater responses in diseased populations. Despite promising findings, low methodological quality suggests cautious interpretation.
10 May 2026
Read appraisal →Clinical oral investigations
Does endodontic treatment modify serum inflammatory markers of cardiovascular risk in individuals with asymptomatic apical periodontitis? a systematic review and meta-analysis.
OBJECTIVE: Plasma inflammatory biomarkers linked to cardiovascular risk have been associated with asymptomatic apical periodontitis. However, it remains unclear whether endodontic treatment can reverse these alterations. This review evaluated the effect of endodontic treatment on inflammatory markers in individuals with asymptomatic apical periodontitis. MATERIALS AND METHODS: A comprehensive search was conducted in PubMed/Medline, Embase, Web of Science, Scopus, VHL, gray literature, and reference lists between October and November 2022, with an update in September 2025. Risk of bias was assessed using the Newcastle-Ottawa Scale, and the certainty of evidence using the GRADE approach. Random-effects meta-analysis estimated pooled mean differences (MD) and 95% confidence intervals (95%CI) for serum inflammatory markers concentrations between treated individuals and controls (α = 5%). RESULTS: The search identified 6,295 records; sixteen studies were assessed and eight included in the quantitative synthesis. All studies showed moderate risk of bias, and evidence certainty was very low. Meta-analysis suggested possible reductions in C-reactive protein (CRP) [MD = 0.76 (95% CI: - 0.15, 1.67)] , interleukin-6 (IL-6) [MD = 0.81 (95% CI:-0.27, 1.90)], and tumor necrosis factor-alpha (TNF-α) [MD = 1.04 (95% CI:-0.38, 2.46)] after endodontic treatment, with levels similar to control groups. CONCLUSION: Evidence, although limited, suggests endodontic treatment may lower serum CRP, IL-6, and TNF-α levels in asymptomatic apical periodontitis patients. CLINICAL RELEVANCE: Endodontic treatment of asymptomatic apical periodontitis may help reduce systemic inflammatory biomarkers associated with cardiovascular risk, reinforcing its potential role beyond local infection control.
10 May 2026
Read appraisal →Journal of biopharmaceutical statistics
Revolutionizing cardiovascular disease classification through machine learning and statistical methods
BACKGROUND: Cardiovascular diseases (CVDs) include abnormal conditions of the heart, diseased blood vessels, structural problems of the heart, and blood clots. Traditionally, CVD has been diagnosed by clinical experts, physicians, and medical specialists, which is expensive, time-consuming, and requires expert intervention. On the other hand, cost-effective digital diagnosis of CVD is now possible because of the emergence of machine learning (ML) and statistical techniques. METHOD: In this research, extensive studies were carried out to classify CVD via 19 promising ML models. To evaluate the performance and rank the ML models for CVD classification, two benchmark CVD datasets are considered from well-known sources, such as Kaggle and the UCI repository. The results are analysed considering individual datasets and their combination to assess the efficiency and reliability of ML models on the basis of various performance measures, such as precision, kappa, accuracy, recall, and the F1 score. Since some of the ML models are stochastic, we repeated the simulation 50 times for each dataset using each model and applied nonparametric statistical tests to draw decisive conclusions. RESULTS: The nonparametric Friedman - Nemenyi hypothesis test suggests that the Extra Tree Classifier provides statistically superior accuracy and precision compared with all other models. However, the Extreme Gradient Boost (XGBoost) classifier provides statistically superior recall, kappa, and F1 scores compared with those of all the other models. Additionally, the XGBRF classifier achieves a statistically second-best rank in terms of the recall measures.
10 May 2026
Read appraisal →International journal of medical informatics
Development of an interpretable machine learning model for predicting 4-year chronic kidney disease risk in elderly hypertensive patients
INTRODUCTION: Age and hypertension are key drivers of renal impairment, predisposing older hypertensive adults to faster kidney function decline and higher mortality. We aim to develop an interpretable machinelearning model to predict 4-year chronic kidney disease (CKD) risk in this population. METHODS: Our study incorporated 4,142 hypertensive patients from the Health and Retirement Study (HRS) 2010 and 2012 cohorts for model development and internal validation, with additional temporal validation performed within the HRS 2006 and 2008 cohorts. External validation was conducted using three distinct subcohorts derived from the China Health and Retirement Longitudinal Study (CHARLS) database. Feature selection was implemented through an integrated LASSO-Boruta algorithm, followed by model construction using eight machine learning approaches. Discriminative performance was rigorously evaluated through multiple metrics, including receiver operating characteristic (ROC) curve analysis, accuracy, sensitivity, specificity, and Brier score. The optimal model underwent interpretability analysis via SHapley Additive exPlanations (SHAP) to elucidate decision-making mechanisms and was subsequently deployed as a web-based clinical prediction tool. RESULTS: Using a combined LASSO-Boruta strategy, we identified nine routinely available predictors for model development. In the training set, SVM achieved the highest AUC (0.735), closely followed by XGBoost (0.734); notably, in the temporal validation cohort, XGBoost was the only model with an AUC > 0.700 (0.702). Overall performance metrics derived from confusion matrices, together with Brier scores, suggested that XGBoost provided a favorable balance between sensitivity and specificity while maintaining acceptable probabilistic calibration. Calibration curves further suggested that XGBoost showed relatively stable agreement between predicted and observed risks across datasets, supporting its selection for subsequent SHAP-based interpretation and web deployment; SHAP identified age as the leading contributor to CKD risk. CONCLUSIONS: We developed an interpretable model using routine clinical indicators to predict 4-year CKD risk in elderly hypertensive adults, with applicability across Asian and Caucasian populations.
9 May 2026
Read appraisal →The Journal of asthma : official journal of the Association for the Care of Asthma
Prediction model for cardiovascular mortality risk in asthma patients based on machine learning
BACKGROUND: Asthma is a common chronic respiratory disease, cardiovascular disease (CVD) mortality constitutes a major public health concern. At present, no personalized tools are available to accurately estimate CVD mortality risk in this population. Accordingly, a machine learning model was developed to predict incident CVD mortality of asthma. METHODS: Data were extracted from NHANES (2007-2012). Predictors were selected by LASSO and multivariable Cox regression. Discrimination and calibration were quantified with ROC curves and calibration curves, nomogram and web calculator were subsequently deployed. Incremental predictive value relative to ASCVD risk and Framingham risk scores were assessed with the integrated discrimination improvement and net reclassification improvement. Six machine learning algorithms were trained, and feature importance of the optimal model was interpreted with SHAP values. RESULTS: Among 2,033 adults participants with asthma, with mean age 44 years, and they comprised 834 males (41.02%) and 1199 females (58.98%). Twelve variables (age, sex, smoking, hypertension, diabetes, CKD, SBP, LYM, UA, K, FENO, and PEF) were retained as predictors of CVD mortality. Calibration was stable across training sets (C-index = 0.863) and validation sets (C-index = 0.832). The pulmonary function test augmented NHANES model outperformed both ASCVD risk and Framingham risk scores. RF and XGB model achieved the highest discrimination. CONCLUSION: A machine learning model integrating routine clinical and pulmonary function test indices was developed. The model enables accurate estimation of long term CVD mortality risk with asthma, facilitating early identification of high risk individuals and informing personalized preventive strategies.
9 May 2026
Read appraisal →Journal of diabetes science and technology
Explainable Machine-Learning Models to Predict Weekly Risk of Hyperglycemia, Hypoglycemia, and Glycemic Variability in Patients With Type 1 Diabetes Based on Continuous Glucose Monitoring
BACKGROUND AND OBJECTIVE: The aim of this study was to develop and validate explainable prediction models based on continuous glucose monitoring (CGM) and baseline data to identify a week-to-week risk of CGM key metrics (hyperglycemia, hypoglycemia, glycemic variability). By having a weekly prediction of CGM key metrics, it is possible for the patient or health care personnel to take immediate preemptive action. METHODS: We analyzed, trained, and internally tested three prediction models (Logistic regression, XGBoost, and TabNet) using CGM data from 187 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach combined with feature engineering deployed on the CGM signals was used to predict hyperglycemia, hypoglycemia, and glycemic variability based on consensus targets (time above range ≥5%, time below range ≥4%, coefficient of variation ≥36%). The models were validated in two independent cohorts with a total of 223 additional patients of varying ages. RESULTS: A total of 46 593 weeks of CGM data were included in the analysis. For the best model (XGBoost), the area under the receiver operating characteristic curve (ROC-AUC) was 0.9 [95% confidence interval (CI) = 0.89-0.91], 0.89 [95% CI = 0.88-0.9], and 0.8 [95% CI = 0.79-0.81] for predicting hyperglycemia, hypoglycemia, and glycemic variability in the interval validation, respectively. The validation test showed good generalizability of the models with ROC-AUC of 0.88 to 0.95, 0.84 to 0.89, and 0.80 to 0.82 for predicting the glycemic outcomes. CONCLUSION: Prediction models based on real-world CGM data can be used to predict the risk of unstable glycemic control in the forthcoming week. The models showed good performance in both internal and external validation cohorts.
9 May 2026
Read appraisal →Annals of epidemiology
Population-based clustering of co-occurring social determinants: An application of unsupervised machine learning
PURPOSE: This study aimed to develop a cluster-based measure of multiple co-occurring social determinants of health by applying unsupervised machine learning to a population-based cohort, offering a data-driven approach to organize complex social exposures. METHODS: Unsupervised clustering was applied to a population-based cohort of Ontario respondents to six-cycles of the Canadian Community Health Survey (2001-2012) linked to the Canadian census and vital statistics data. Clusters were evaluated using internal metrics, visualization techniques, descriptive analysis and theoretical considerations to determine the optimal number of clusters. Sensitivity analyses were integrated across the iterative clustering process. Premature mortality rates were generated assess validity. RESULTS: Optimal clustering solutions included 4-clusters and 6-clusters. Both cluster solutions revealed distinct social typologies. The 6-cluster solution offered greater granularity and theoretical interpretability. The 4-cluster solution showed greater heterogeneity within certain marginalized groups. Premature mortality rates differed meaningfully across clusters, supporting the clustering approach in capturing risk associated with social exposure. CONCLUSIONS: Unsupervised machine learning methods identified meaningful population subgroups reflecting complex patterns of social exposures. This approach offers a flexible, data-driven method for characterizing social exposures that can be considered alongside theoretical frameworks and used for equity monitoring, intervention planning and policy development.
6 May 2026
Read appraisal →Journal of diabetes science and technology
Plantar Thermogram Analysis Using Deep Learning for Diabetic Foot Risk Classification
AIMS: Thermography is a noninvasive method to identify patients at risk of diabetic foot ulcers. In this study, we employed thermography and deep learning to stratify patients with diabetes at risk of developing foot ulcers. METHODS: We prospectively recorded clinical data and plantar thermograms for adult patients with diabetes who underwent diabetic foot screening. A total of 153 thermal images were analyzed using a deep learning algorithm to determine the risk of diabetic foot ulcers. The neural network was trained using a balanced dataset consisting of 98 thermal images (49 normal and 49 abnormal), with 80% allocated for training and 20% for validation. The trained model was then validated on a separate testing dataset consisting of 55 thermal images (42 normal and 13 abnormal). The neural network was trained to prioritize higher sensitivity in identifying at-risk feet for screening purposes. RESULTS: Participants had a mean age of 63.1 ± 12.6 years (52.3% female), and 62.1% had been diagnosed with diabetes for more than 10 years. The average body mass index was 27.5 ± 5.6 kg/m2. Of the thermal images, 91 were classified as category 0 and 62 as categories 1 to 3, according to the diabetic foot risk classification system of the International Working Group on the Diabetic Foot. Using five-fold cross-validation, the neural network model achieved an overall accuracy of 71.8 ± 4.9%, a sensitivity of 81.2 ± 10.0%, and a specificity of 64.0 ± 7.4%. Additionally, the Matthews correlation coefficient was 0.46 ± 0.08%Conclusions:These results suggest that thermography combined with deep learning could be developed for screening purposes to stratify patients at risk of developing diabetic foot ulcers.
4 May 2026
Read appraisal →Journal of proteome research
Stability-Based Machine Learning Identifies a Minimal Two-Protein Serum Signature for Early Silicosis
BACKGROUND: The early diagnosis of silicosis, an irreversible fibrotic lung disease, is challenged by the low sensitivity of current radiological methods in early-stage disease and their susceptibility to interobserver variability. Consequently, a pressing need exists for noninvasive, objective biomarkers to facilitate timely detection and intervention. METHODS: We employed a multistage study design comprising a discovery cohort (57 Stage I silicosis patients, 57 matched controls) and an independent, unmatched validation cohort (40 patients, 40 controls). Serum protein profiles were generated using Olink targeted proteomics. We utilized a rigorous, stability-based machine learning framework, which integrated Lasso, Random Forest, and SVM-RFE algorithms over 100 iterations, to perform feature selection and identify a robust biomarker signature from the discovery cohort. Based on the selected features, a logistic regression model was subsequently constructed, and its performance was evaluated using both internal and external validation. RESULTS: Our discovery strategy identified a two-protein signature comprising IL8 and CCL3. This signature demonstrated excellent diagnostic performance in the discovery cohort, achieving a cross-validation AUC of 0.986 (95% CI: 0.975-1.000). Importantly, the model's robustness was confirmed in the heterogeneous validation cohort, where it achieved an outstanding AUC of 0.973 (95% CI: 0.936-1.000), with 95.0% specificity and 77.5% sensitivity. Bioinformatic analysis revealed that decreased serum levels of IL8 and CCL3 were associated with silicosis, providing novel diagnostic biomarkers and highlighting a complex, paradoxical shift in circulating chemokines during early-stage disease.
4 May 2026
Read appraisal →Pakistan journal of pharmaceutical sciences
Network pharmacology and machine learning reveal multi-target mechanisms of poly herbal formulation against atherosclerosis
BACKGROUND: Herbs, like Allium sativum, Ginkgo biloba and Nerium oleander are traditional medicinal plants that have been used to treat atherosclerosis and cardiovascular disease. This study provides valuable insights into how network pharmacology (NP) and emerging machine learning are utilized to identify potential drug candidates from these plants for treatment of atherosclerosis. METHODS: NP analysis was employed to screen compounds and their potential gene targets from databases and tools e.g. IMPPAT, PubChem, KNAPSACK, Swiss ADME, Swiss Target Prediction, Disgenet and GeneCard. Cytoscape 3.10.2 was employed to visually understand these networks. DAVID database was used for functional and enrichment analysis of the genes validated through molecular docking using PyRx and Discovery Studio. RESULTS: Computational tools and bioinformatics approaches showed a few core compounds, such as Quercetin, Naringenin, Luteolin, Kaempferol, Apigenin, Daidzein, Luteolin-7-olate, Pinocembrin, Pregnenolone and Fisetin found to be effective against atherosclerosis. Pathway analysis revealed that mechanism of atherosclerosis development is directly associated with cholesterol metabolism, cellular senescence, Ras, NF-κB and PI3K-Akt signaling pathways. CONCLUSION: NP and molecular docking analysis suggested that screened compounds may inhibit progression of atherosclerosis by modulating key associated pathways. Hence, this machine learning aided NP study provides basis for understanding and recognizing the activity of these plants in treating atherosclerosis.
4 May 2026
Read appraisal →Molecular psychiatry
A five-year risk prediction model of cardiovascular disease in individuals with bipolar disorder: a nationwide register study from Sweden
Cardiovascular disease (CVD) risk prediction models for the general population may not provide accurate predictions in individuals with bipolar disorder (BD) who have elevated risks of cardiometabolic conditions and premature mortality. Therefore, we aimed to: 1) develop a five-year CVD risk prediction model in this population by using nationwide register data from Sweden, 2) investigate whether the performance improved when we considered additional risk factors, including psychiatric comorbidity, psychotropic medication, and socio-demographic variables, compared to using established CVD risk factors only, and 3) whether machine learning approach provided improvements compared to standard logistic regression models. We followed 33,933 persons with BD aged 30-82 years old, without previous CVD, from the date of BD diagnosis registered between 2007-2014, for up to five years. The logistic regression model containing only established risk factors yielded an area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval 0.74-0.78) in the test dataset, while the logistic regression model and the best performing machine learning model including additional predictors yielded similar results (AUC was 0.77 (0.75, 0.79) in both models). The performance of logistic regression models slightly improved with additional predictors when continuous risk scores were used. In conclusion, standard logistic regression and established CVD risk factors may be sufficient to predict CVD in individuals with BD when using population register-based data from Sweden. External validation across diverse healthcare settings and rigorous assessment of clinical impact will be crucial next steps before implementing these models in clinical practice.
4 May 2026
Read appraisal →IEEE transactions on bio-medical engineering
Extending Multiscale Characterization of Heart Rate Variability via Deep Learning for Mortality Risk Prediction
OBJECTIVE: To improve mortality risk prediction from heart rate variability (HRV) signals by capturing nonlinear scaling patterns often overlooked by traditional linear analyses. METHODS: This study combines detrended moving average (DMA) analysis with convolutional neural networks (CNNs). DMA curves were computed from 2-hour overlapping windows of 24-hour Holter ECG recordings in 916 survivors and 70 nonsurvivors. A CNN was trained to extract features from these curves and benchmarked against models using traditional HRV and clinical features. RESULTS: The CNN achieved an ROC-AUC of 0.72 and an adjusted hazard ratio of 2.129 for daytime recordings, outperforming standard models. Two patient groups emerged based on DMA scaling patterns. Group 1, with dominant short-term scaling, exhibited reduced slopes in nonsurvivors, suggesting impaired autonomic adaptability. Group 2 showed earlier transitions between short- and long-term behavior, where reduced long-term slopes more strongly predicted mortality. Integrated gradients analysis identified key timescales in the DMA curve driving model predictions. CONCLUSION: DMA combined with CNNs enhances HRV-based mortality risk stratification and reveals distinct physiological scaling patterns associated with survival outcomes. SIGNIFICANCE: This study highlights the potential of DMA and CNNs in improving mortality risk stratification and providing mechanistic insights into HRV dynamics, with implications for personalized health monitoring.
4 May 2026
Read appraisal →Sports health
Evaluation of Exertional Sweat Loss Estimates in Wearable Technology
BACKGROUND: Wearable technology shows promise for monitoring exertional sweat loss, yet its accuracy requires evaluation. This study evaluated the accuracy of 2 commercially available smartwatches for estimating sweat loss compared with gold standard methods. HYPOTHESIS: Sweat loss values measured via wearable technology will be determined accurate compared with gold standard methods. STUDY DESIGN: Cross-sectional. LEVEL OF EVIDENCE: Level 2. METHODS: A total of 111 participants (age, 40 ± 14 years; height, 171.0 ± 8.9 cm; body mass, 70.8 ± 14.7 kg; VO2max, 46.8 ± 8.7 ml/kg/min) completed up to 4 running trials under varying conditions (indoor/outdoor, distances from 2.5 km to 20 km, and interval runs). Sweat loss estimates from each smartwatch were compared with criterion measurements based on change in nude body mass pre- and postexercise. Validity statistics included tests for error (mean absolute error [MAE], mean absolute percentage error [MAPE]), linearity (Lin's concordance correlation coefficient [CCC], Pearson's [r], and Deming regression), equivalence (CI for difference in means), and Bland-Altman plots for bias assessment. Stratified analyses by sex, weight, and fitness category were also completed, along with a chi-square test of independence. RESULTS: Both devices demonstrated acceptable overall correlation (CCC range, 0.71 to 0.90) but substantial errors in estimating exertional sweat loss (MAPE range, 25.38% to 33.21%). Stratified analyses yielded similar results, with no additional analyses meeting the combined validity thresholds. CONCLUSION: Wearable devices offer a promising and convenient tool for general sweat loss tracking but lack the precision to replace laboratory methods for hydration management. Traditional methods remain essential for the most accurate measurements. CLINICAL RELEVANCE: Wearable devices provide an accessible option for sweat loss monitoring. This study highlights their potential for general tracking while emphasizing the need for laboratory methods when accuracy is critical, ensuring better guidance for hydration strategies in sports performance and health applications.
4 May 2026
Read appraisal →PloS one
Optimizing LSTM networks and feature selection algorithms using GEE data
Feature selection uncertainty and suboptimal model configuration in flood susceptibility mapping (FSM) are critical challenges for disaster risk reduction. This study introduces a novel integrated framework that couples feature selection strategy with metaheuristic-optimized deep learning for high-precision FSM in the flood-prone Khuzestan Province, Iran. An initial set of 19 factors was sourced from Google Earth Engine (GEE). The most influential variables were identified using an ensemble of nine feature selection methods, including Boruta, Boruta-SHAP, Elastic-Net, Mutual Information, Permutation Importance, Recursive Feature Elimination (RFE), Sequential Forward Selection (SFS), Stability Selection, and Deep Feature Importance. For model development, 1,000 sample points were used, consisting of 500 randomly selected non-flood points (value 0) and 500 flood points (value 1), with the trained model subsequently generalized to the entire study area. In this process, a frequency-based consensus rule was applied, whereby variables were retained only if selected by a majority of methods. This process established the Normalized Difference Vegetation Index (NDVI) and Daily Minimum Temperature (TMMN) as the most critical predictors. A Long Short-Term Memory (LSTM) was developed using this optimal feature set and subsequently enhanced through hyperparameter optimization with five advanced metaheuristic algorithms, including WOA, GWO, OOA, CSA, and HOA. The model validation demonstrated that optimization significantly boosted performance, with the LSTM-WOA model emerging as superior, achieving the highest F1-Score (0.88) and Cohen's Kappa (0.75). The final FSM identified the northwestern and central regions as the highest susceptibility. The study innovation lies in its formalized consensus feature selection and comparative metaheuristic optimization, providing a reliable tool for FSM in arid and semi-arid regions.
3 May 2026
Read appraisal →PloS one
Auditing the impact of social media's policy shift on anti-vaccine discourse: A large language model-driven empirical study
The sudden termination of X's (formerly Twitter) misinformation policy on November 23, 2022, provides an opportunity to assess the effects of lifting content moderation restrictions on vaccine-related discourse. This study examines changes in the prevalence, thematic composition, and engagement of anti-vaccine discourse following X's policy shift, analyzing tweets from a seven-day period before and after the policy termination (November 16-30, 2022), excluding the announcement date itself from regression analyses. Using GPT-4o for stance classification, thematic categorization, and stance consistency assessment, with validation through external benchmarks and cross-annotator agreement, we find that anti-vaccine tweets increased significantly post-policy (OR = 1.60, 95% CI: 1.50-1.72), particularly via retweets, suggesting content amplification. Sensitivity analyses excluding highly retweeted content revealed that the policy change was also associated with increased creation of new anti-vaccine content. Thematically, health concerns over vaccination became more prominent, while conspiracy-related and anti-mandate narratives declined in relative prevalence. Stance consistency in quote tweets increased, indicating reinforced ideological alignment in anti-vaccine discourse. These results suggest that content moderation policies may constrain both the volume and amplification of anti-vaccine content, with policy removal associated with rapid shifts in discourse patterns.
3 May 2026
Read appraisal →JMIR aging
The Effectiveness of Digital Interventions to Increase Preventive Care Uptake in Older Adults: Systematic Review
BACKGROUND: Older adults face increasing health risks associated with aging and chronic disease; yet, uptake of recommended clinical preventive services remains low. Digital health interventions have the potential to enhance access and engagement, but their effectiveness in older adult populations remains unclear. OBJECTIVE: This systematic review aimed to examine the range and types of digital clinical preventive service interventions and assess their impact on preventive care uptake among older adults. METHODS: We conducted a systematic review of peer-reviewed research literature published since 2014. Eligible studies included experimental and quasi-experimental designs evaluating digital interventions targeting community-dwelling adults aged 60 years and older. Interventions focused on high-priority preventive services, including cancer screening and adult immunizations. Data were extracted using a standardized form and synthesized narratively due to heterogeneity in study designs and outcomes. RESULTS: In total, 24 studies involving over 1.3 million participants from 11 countries were included. Interventions used a range of digital tools, including telephone calls, SMS text messages, patient portals, and video-based education. While some digital and automated interventions demonstrated modest improvements in preventive services uptake, results were mixed. Interventions incorporating personalized elements (eg, tailored telephone counseling or in-person education) were generally more effective than generic, automated communications. Few studies reported on digital literacy support or intervention reach, and engagement with digital platforms was often low. CONCLUSIONS: Digital interventions can support modest improvements in preventive services uptake among older adults, particularly when personalized or combined with human interaction. However, assumptions of digital fluency and limited reporting on engagement constrain generalizability. Future research should prioritize inclusive design, detailed reporting, and strategies that address digital equity to better support older adult populations.
3 May 2026
Read appraisal →Journal of medical Internet research
Application of Digital Health Technologies in Cardiac Rehabilitation for Patients With Coronary Heart Disease: Scoping Review
BACKGROUND: The high mortality and recurrence rates associated with coronary heart disease (CHD) impose substantial health care costs and economic burdens globally. Identifying effective interventions to improve patient outcomes is paramount. Digital health technologies (DHTs) offer novel solutions to overcome the challenge of low participation rates in traditional cardiac rehabilitation (CR). OBJECTIVE: This review aims to systematically map the scope of application, intervention objectives, and evaluation metrics of DHTs in CR for patients with CHD, thereby providing a structured evidence base for future research and practice. METHODS: This scoping review adheres to the Joanna Briggs Institute's methodology and is reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A systematic search was conducted across 5 major databases, PubMed, Web of Science, Embase, Cochrane Library, and EBSCO, covering the period from inception to February 2026. Inclusion criteria were developed based on the participants, concept, and context framework. Studies focused on the application of various DHTs within CR settings for patients with CHD. Eligible literature comprised randomized controlled trials, quasi-randomized controlled trials, and longitudinal before-and-after studies published in peer-reviewed journals. Two researchers (XZ and ZL) independently conducted literature screening and data extraction. Findings were presented through a comprehensive narrative synthesis and evidence gap maps. RESULTS: A total of 43 studies were included, predominantly randomized controlled trials (n=40). Findings revealed (1) diverse technological formats, categorized into 3 main types: digital health tools, real-time remote support, and asynchronous communication. Multitechnology combined interventions have become the mainstream model (36/43, 83.7%). (2) Intervention objectives were multifaceted, consolidating into 4 dimensions: motivation and guidance, knowledge and skills, monitoring and security, and social and group dynamics. (3) Evaluation metrics were multidimensional, encompassing clinical physiological indicators, health behaviors, patient-reported outcomes, service use rates, and technological feasibility. DHTs demonstrated positive effects in improving short-term physiological function and health behaviors; however, evidence remains insufficient regarding their impact on long-term clinical outcomes such as reducing adverse events. CONCLUSIONS: The innovation of this scoping review lies in integrating highly heterogeneous evidence to reveal the field's evolution from isolated tools toward systematic, integrated solutions. Research confirms that DHTs effectively overcome temporal and spatial constraints, enhancing rehabilitation accessibility and engagement. They serve as crucial strategic tools for bridging geographical disparities in health care resources and advancing equity in cardiovascular health services. However, the evidence base remains limited, including insufficient long-term efficacy data and inadequate exploration of vulnerable populations such as older people and those with low digital literacy. Future research urgently requires large-scale, long-term follow-up clinical trials, alongside enhanced studies on adaptability for specific populations and considerations of health equity. This will propel digital CR toward greater scientific rigor, universal applicability, and precision.
3 May 2026
Read appraisal →Circulation
Prognostic Factors for Long-Term Risk of Stroke After Transient Ischemic Attack or Minor Stroke: A Systematic Review and Meta-Analysis
BACKGROUND: Patients with a transient ischemic attack (TIA) or minor stroke have an increased risk of subsequent stroke that persists for at least 10 years. We aimed to identify prognostic factors associated with long-term risk of stroke in this patient group and estimate their population attributable fraction (PAF). METHODS: A systematic review was performed of MEDLINE, Embase, and Web of Science for cohort studies including patients with TIA or minor stroke that evaluated factors for subsequent stroke over a follow-up period of ≥1 year. We pooled hazard ratios adjusted for relevant confounders using random-effect meta-analysis and determined the PAF of factors based on their pooled prevalence and adjusted hazard ratio. We assessed certainty of evidence using the grading of recommendations, assessment, development, and evaluation approach. The study is registered in PROSPERO (CRD42023476551). RESULTS: From 14 732 identified citations, we included 28 cohort studies comprising 86 810 patients with TIA or minor stroke (median age, 69 years [interquartile range, 65-71]; 52%-60% male patients). Factors that had high certainty evidence of association with increased long-term risk of stroke included older age (adjusted hazard ratio, 1.04 per year increase [95% CI, 1.02-1.05]); male sex (1.25, 1.15-1.36; PAF 13.0% [95% CI, 7.8-18.7]); atrial fibrillation (1.34, 1.18-1.52; 3.8% [95% CI, 0.3-9.9]); diabetes (1.52, 1.32-1.75; 7.7%, 3.1-14.1); hypertension (1.60, 1.31-1.94; 19.3%, 8.4-31.6); ischemic heart disease (1.67, 1.28-2.18; 10.7%, 2.8-22.9); history of stroke or TIA before the index event (1.70, 1.43-2.02; 12.0%, 5.2-21.4); smoking (1.29, 1.05-1.60; 11.2%, 1.0-30.7); ABCD2 (age, blood pressure, clinical features, duration of TIA, and presence of diabetes) score of ≥4 (1.59, 1.31-1.94; 18.0%, 2.9-39.9); presence of acute infarct on neuroimaging (1.97, 1.41-2.74; 19.0%, 5.2-38.9), including diffusion-weighted imaging-positive lesions (1.86, 1.02-3.37; 14.0%, 7.0-25.0); minor stroke as index event (1.75, 1.35-2.27 versus TIA; 28.0%, 10.2-47.6); presentation with aphasia or dysarthria (1.45, 1.24-1.69; 19.2%, 0.2-53.1); presentation with paresis (1.45, 1.15-1.84; 22.0%, 3.8-43.4); and etiologic stroke subtypes, including cardioembolism (2.16, 1.53-3.05; 14.6%, 3.1-33.5), large-artery atherosclerosis (2.19, 1.68-2.86; 13.2%, 5.1-25.5), and small-vessel disease (1.69, 1.14-2.49; 16.8%, 5.0-34.3). CONCLUSIONS: These findings can help identify patients with a particularly enduring risk of stroke who are most likely to benefit from ongoing monitoring and treatment and facilitate the development and implementation of targeted stroke prevention strategies.
3 May 2026
Read appraisal →JMIR mHealth and uHealth
Alcohol Use Trajectories During the First 72 Weeks of WHOOP Wearable Platform Membership: Observational Cohort Study
Among 30,000 new wearable device users (11.6 million person-days), self-reported alcohol consumption significantly declined over 72 weeks, with the daily probability of drinking decreasing by 5.8 percentage points (P<.001) and reductions across age and biological sex.
2 May 2026
Read appraisal →JMIR aging
Machine Learning-Derived Cardiovascular Aging Phenotypes From Cardiac Function and Stroke Risk in the UK Biobank: Cohort Study
BACKGROUND: Cardiovascular magnetic resonance (CMR) is widely used across various cardiac conditions and systematically assesses cardiac anatomical structures and functional dynamics. Machine learning (ML) can accurately predict outcomes and understand the inherent features of clinical data. OBJECTIVE: This study aimed to derive CMR phenotypes related to cardiovascular aging, investigate the relationship between these phenotypes and stroke risk, and relearn these phenotypes using supervised ML. METHODS: We enrolled 36,467 participants without stroke and extracted CMR parameters from the UK Biobank, with follow-up data extending until September 30, 2023. Using the generative topographic mapping technique, we identified latent grid nodes among participants and then derived phenotypes through agglomerative hierarchical clustering. We used supervised ML models to predict cardiac function phenotypes and used Cox proportional hazards models to assess the association between these phenotypes and long-term stroke risk. RESULTS: We enrolled 36,467 participants in the study. The mean age was 54.9 (SD 7.5) years, with 17,442 (47.8%) male participants. During a mean follow-up time of 14.7 (SD 1.1) years, 500 (1.4%) participants developed stroke and 664 (1.8%) participants died, respectively. After generative topographic mapping modeling, we identified 2 distinct phenotypes: phenotype 1, characterized by adverse cardiac function and an accumulation of cardiovascular risk factors, reflecting cardiovascular aging; and phenotype 2, associated with a lower risk of stroke (hazard ratio 0.695, 95% CI 0.559-0.864; P=.001), which remained significant after accounting for competing mortality (hazard ratio 0.578, 95% CI 0.484-0.691; P<.001). We selected the random forest model as the optimal model for the phenotypes, demonstrating high accuracy (area under the curve 0.914, 95% CI 0.911-0.918 for training and 0.867, 95% CI 0.858-0.876 for validation) and calibration ability (Brier score 0.111, 95% CI 0.109-0.113 for training and 0.132, 95% CI 0.127-0.137 for validation). CONCLUSIONS: By integrating unsupervised and supervised ML methods, we identified cardiovascular aging-related phenotypes that demonstrate robust predictive ability for incident stroke, which may have the potential to improve preventive and therapeutic strategies for high-risk populations.
2 May 2026
Read appraisal →Stroke
Preterm Delivery and Long-Term Risk of Maternal Stroke: Systematic Review and Meta-Analysis
BACKGROUND: Adverse pregnancy outcomes are linked to increased risk of maternal cardiovascular disease. However, the association between preterm delivery (PTD) and long-term risk of stroke in the mother remains uncertain, particularly in the case of spontaneous PTD. METHODS: A systematic review and meta-analysis were performed to provide an up-to-date synthesis of the evidence on the association between PTD and long-term maternal stroke. PubMed, CINAHL, and Web of Science were searched for relevant articles published between January 1, 2000, and May 6, 2025. Eligible studies included cohort and case-control studies examining populations of parous women. Primary exposure was defined as any PTD; secondary exposures were spontaneous PTD and medically indicated PTD. The primary outcome was defined as any stroke; secondary outcomes were ischemic and hemorrhagic stroke. Two reviewers screened studies, extracted data, and performed quality assessments. Pooled unadjusted and adjusted effect estimates (as defined by the original study) were calculated separately using random effects inverse variance models. RESULTS: Eleven thousand twenty-five studies were screened for eligibility. Across 21 studies including a total of 8.7 million participants, PTD was consistently associated with increased stroke risk. The follow-up period ranged from 8 to 57 years. The primary meta-analysis demonstrated a positive association between any PTD and any stroke (adjusted risk ratio [aRR], 1.66 [95% CI, 1.34-2.05]). A high level of heterogeneity was observed (I2=97%, τ2=0.15), possibly due to variations in exposure definition, outcome ascertainment, and follow-up durations. Spontaneous PTD (aRR, 1.37 [95% CI, 1.15-1.64]) and medically indicated PTD (aRR, 2.08 [95% CI, 1.70-2.54]) were associated with any stroke. PTD was positively associated with ischemic (aRR, 1.59 [95% CI, 1.45-1.75]) and hemorrhagic stroke (aRR, 1.44 [95% CI, 1.06-1.94]). CONCLUSIONS: Women who have experienced a PTD, including spontaneous PTD, may be at increased risk of stroke in later life. These women may benefit from targeted cardio-metabolic preventive interventions postpartum to reduce their risk.
2 May 2026
Read appraisal →The Cochrane database of systematic reviews
Vitamin D for preventing acute respiratory infections in children up to five years of age
RATIONALE: Acute respiratory infections (ARIs) are a leading cause of morbidity and mortality in children under five years of age and contribute to healthcare visits and hospitalisations globally. Vitamin D deficiency is common amongst pregnant women and young children. Given that vitamin D supplementation is safe, affordable, and easy to administer, its potential to reduce ARI-related healthcare visits holds substantial public health relevance. This review evaluates the benefit and harm of vitamin D supplementation in preventing ARI-related healthcare visits in young children, aiming to inform supplementation policies during pregnancy and early childhood. OBJECTIVES: To determine the benefit and harm of vitamin D supplementation for preventing ARIs in children up to five years of age. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, CINAHL, Web of Science, WHO Global Index Medicus, and four clinical trial registries on 18 March 2025. ELIGIBILITY CRITERIA: We included randomised controlled trials (RCTs) comparing vitamin D to placebo, or a higher dose (≥ 1000 IU) versus a lower dose (≤ 1000 IU) administered from early pregnancy through to five years of age. We excluded studies involving children with chronic respiratory or systemic conditions. OUTCOMES: Critical outcomes: 1) healthcare visits made by children for an ARI: (a) the proportion of children with healthcare visits made for ARIs; (b) the mean number of healthcare visits made for ARIs per child; and 2) hypercalcaemia: the number of pregnant women and children with hypercalcaemia. RISK OF BIAS: We assessed risk of bias using the Cochrane risk of bias (RoB) 1 tool for outcomes reported in the summary of findings tables. SYNTHESIS METHODS: We synthesised outcome data using meta-analyses, calculating risk ratios (RR) for dichotomous, and mean differences (MD) for continuous outcomes, each with 95% confidence intervals (CI). Where meta-analysis was not feasible, we summarised the results narratively. We used GRADE to assess the certainty of the evidence for pre-specified outcomes. INCLUDED STUDIES: We included 107 RCTs involving 31,521 participants. Vitamin D supplementation was administered during pregnancy, early childhood, or both, and in some cases to both infants and lactating mothers. Interventions used vitamin D2, D3, both, or unspecified forms, with dosages ranging from daily administration to large single or quarterly boluses. Most were delivered orally, typically on a daily schedule, but also weekly or quarterly, over durations ranging from a single dose to 18 months. Settings included hospitals, day-care centres, communities, and homes. SYNTHESIS OF RESULTS: Vitamin D supplementation may result in a slight reduction in the proportion of children who make ARI-related healthcare visits compared to placebo (risk ratio (RR) 0.95, 95% confidence interval (CI) 0.91 to 1.00; P = 0.03; 10 studies, 2447 participants; low-certainty evidence), but probably does not reduce the mean number of ARI-related healthcare visits per child (mean difference (MD) 0.07, 95% CI -0.06 to 0.20; P = 0.32; 3 studies, 561 participants; moderate-certainty evidence). Comparisons between higher and lower doses of vitamin D probably do not reduce the proportion of children making ARI-related healthcare visits (RR 0.94, 95% CI 0.81 to 1.10; P = 0.46; 2 studies, 1382 participants; moderate-certainty evidence), and do not reduce the mean number of ARI-related healthcare visits per child (MD -0.10, 95% CI -0.59 to 0.39; P = 0.69; 1 study, 579 participants; low-certainty evidence). No cases of hypercalcaemia were reported among pregnant women in trials comparing vitamin D supplementation with placebo. In higher versus lower dose comparisons, supplementation may result in little to no difference in the risk of hypercalcaemia among pregnant women (RR 1.61, 95% CI 0.60 to 4.31; P = 0.34; 6 studies, 2379 participants; low-certainty evidence). Similarly, the evidence that vitamin D supplementation has little to no effect on the risk of hypercalcaemia in children, when compared with placebo, is very uncertain (RR 0.81, 95% CI 0.53 to 1.24; 7 studies, 1542 participants; very low-certainty evidence) and may result in little to no difference in the risk of hypercalcaemia in children when higher doses are compared to lower doses (RR 1.23, 95% CI 0.82 to 1.85; 7 studies, 1287 participants; low-certainty evidence). AUTHORS' CONCLUSIONS: We found low-certainty evidence that vitamin D supplementation during pregnancy or early childhood may result in a slight reduction in the proportion of children under five years of age who make ARI-related healthcare visits, but probably does not reduce the mean number of healthcare visits for ARIs per child. This finding highlights the need for large, well-designed, placebo-controlled trials to confirm the potential benefit. We found moderate-certainty evidence that higher-dose vitamin D supplementation, compared with lower doses, probably does not reduce the proportion of children making healthcare visits for ARIs and does not reduce the mean number of ARI-related healthcare visits per child. Hypercalcaemia occurs infrequently in both pregnant women and children receiving vitamin D supplementation. Where hypercalcaemia could be measured, specifically in children receiving vitamin D versus placebo, vitamin D supplementation may have little to no effect on the risk of hypercalcaemia (very low-certainty evidence). In both children and pregnant women, a higher dose of vitamin D compared to a lower dose may result in little to no difference in the risk of hypercalcaemia (low-certainty evidence). FUNDING: This Cochrane review had no dedicated funding. REGISTRATION: Protocol (2022) DOI: 10.1002/14651858.CD015111.
2 May 2026
Read appraisal →Scandinavian journal of primary health care
Overdiagnosis in atrial fibrillation screening with wearables
PURPOSE: Wearable technologies are increasingly used to screen for atrial fibrillation (AF), often outside formal clinical pathways. While this holds promise for early detection, the impact on patient outcomes remains unclear. We aimed to examine the hypothesis whether intermittent and continuous AF screening using wearable-like technologies leads to overdiagnosis in asymptomatic, high-risk individuals. METHODS: This hypothesis-generating analysis draws on available RCTs screening asymptomatic, high-risk individuals for AF using wearable-like ECG technologies. It is not a systematic review, as the field remains too early-stage for formal evidence synthesis. Eligible studies included either intermittent (≥10 recordings) or continuous monitoring for ≥1 week, reflecting typical smartwatch use. Primary outcomes included AF diagnoses, anticoagulant use, and adverse events. Overdiagnosis was inferred when excess AF detection did not translate into improved clinical outcomes. Analyses used Mantel-Haenszel random-effects models. RESULTS: Continuous screening nearly tripled AF diagnoses (OR 2.84; 95% CI, 1.61-5.02) and doubled anticoagulant use compared to controls. Intermittent screening showed a non-significant 1.8-fold increase. Neither method significantly reduced adverse outcomes. A pooled analysis revealed a marginal reduction in negative events (OR 0.94, 95% CI 0.90-0.99). Overdiagnosis estimates varied by method, ranging from 8-29% based on persistent differences in cumulative incidence after the screening period has ended and 47-65% based on increased diagnoses and treatment during the screening phase that are not followed by improved patient-relevant outcomes. CONCLUSION: Wearable-enabled screening for AF in high-risk patients increases diagnosis and treatment without clear benefit in clinical outcomes. These findings support the hypothesis of overdiagnosis and findings highlight the need for cautious integration of consumer technologies into primary care. Further trials with extended follow-up in low-risk populations are needed to clarify when AF detection improves outcomes versus when it leads to unnecessary medicalisation and harm.
30 Apr 2026
Read appraisal →Biomedizinische Technik. Biomedical engineering
Blood pressure estimation using single photoplethysmography signal based on ResNet-BiGRU
OBJECTIVES: Conventional cuff-based blood pressure (BP) measurement provides only intermittent readings, whereas photoplethysmography (PPG)-based methods enable continuous and noninvasive monitoring. This study aims to develop a deep learning framework for accurate, cuffless BP estimation using a single PPG signal. METHODS: A hybrid deep neural network, termed ResNet-BiGRU, was developed by integrating residual convolutional blocks with bidirectional gated recurrent units to jointly capture morphological and temporal features. The UCI Cuff-Less Blood Pressure Estimation Dataset (a subset of MIMIC-II), which contains synchronized PPG and arterial blood pressure (ABP) signals from 942 subjects, was used for model training and validation. After applying a 0.5-8 Hz bandpass filter and segmenting into 5 s windows, the data were split 80/20 for training and validation. External evaluation was conducted using the VitalDB dataset, which provides synchronized PPG and ABP recordings from surgical patients under diverse physiological conditions. RESULTS: The model achieved mean absolute errors (MAE) of 4.78 mmHg for systolic BP (SBP) and 2.98 mmHg for diastolic BP (DBP) on UCI, and 8.15 mmHg for SBP and 4.59 mmHg for DBP on VitalDB. CONCLUSIONS: The ResNet-BiGRU model demonstrates accurate, robust, and generalizable cuffless BP estimation, showing strong potential for wearable health monitoring applications.
28 Apr 2026
Read appraisal →BMJ paediatrics open
Metabolically obese normal weight phenotype in children and adolescents aged 3 to 18 years: a systematic review
BACKGROUND: Children with normal Body Mass Index (BMI) may still exhibit adverse metabolic profile-termed as the metabolically obese normal weight (MONW) phenotype, placing them at increased risk of long-term cardiometabolic complications. OBJECTIVE: To systematically synthesise the evidence of the MONW phenotype in children and adolescents aged 3-18 years, and to identify early-life determinants. METHODS: This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and searched PubMed/ MEDical Literature Analysis and Retrieval System On-Line (MEDLINE), Scopus, Web of Science (WoS), Google Scholar, Embase and Cochrane Library for studies from 2000 to January 2026. Observational studies of children and adolescents aged 3-18 years with normal BMI and at least one metabolic abnormality were included. Two reviewers independently screened the studies, extracted data and assessed risk of bias. Searches were conducted between March 2025 and January 2026, and 11 studies met the predefined inclusion and exclusion criteria. RESULTS: The reported prevalence of MONW ranged from 10.6% to 56.2%, varying by diagnostic criteria and population. Children with MONW consistently showed higher visceral adiposity, insulin resistance (IR), dyslipidaemia, hypertension, impaired glucose metabolism and elevated markers of low-grade inflammation despite having normal BMI. Early-life determinants included extremes of birth weight, rapid infant weight gain, adverse maternal metabolic status, unhealthy dietary patterns and sedentary behaviour. Overall risk of bias across studies was judged to be low to moderate, with the main concerns relating to residual confounding and use of heterogeneous MONW definitions. CONCLUSIONS: MONW represents a clinically relevant but under-recognised paediatric phenotype. Reliance on BMI alone may delay identification of vulnerable children. Early risk stratification integrating metabolic proofing and central adiposity measures may enable timely prevention of long-term metabolic disease. Harmonised operational definitions of MONW and robust longitudinal life-course studies are required to inform evidence-based screening thresholds and preventive interventions.
27 Apr 2026
Read appraisal →PloS one
Effects of peppermint (Mentha x piperita L.) oil on cardiometabolic outcomes in patients with pre- and stage 1 hypertension: A placebo randomized controlled trial.
Hypertension represents the predominant risk factor for cardiovascular disease morbidity and mortality; with significant healthcare utilization and expenditure. Pharmaceutical management is habitually adopted; although its long-term effectiveness remains ambiguous, and accompanying adverse effects are disquieting. Peppermint, which is rich in menthol and flavonoids, may exert potential benefits relevant to hypertension. This trial aimed to explore the effects of twice-daily peppermint oil supplementation in individuals with pre- and stage 1 hypertension. A 20 day, parallel randomized, placebo-controlled trial was adopted (NCT05561543). 40 individuals with pre- and stage 1 hypertension were randomly assigned to receive 100 μL per day of either peppermint oil or peppermint-flavoured placebo. The primary trial outcome was the between-group difference in systolic blood pressure from baseline to 20 days. Secondary outcome measurements were the between-group differences in anthropometric, haematological, diastolic blood pressure/resting heart rate, psychological wellbeing, and sleep efficacy indices. Statistical analysis was conducted on an intention-to-treat basis using baseline-adjusted linear regression models comparing post intervention values between trial arms with the corresponding baseline value entered as a covariate; adjusted mean differences (b), 95% confidence intervals, and effect sizes (d) were calculated. In relation to the primary outcome, adjusted systolic blood pressure at 20 days was significantly lower (b = -8.48 mmHg, 95% CI = -14.24 to -2.73, d = -0.94) in the peppermint trial arm (baseline = 130.05 mmHg, 20 days = 121.97 mmHg) than in placebo (baseline = 130.93 mmHg, 20 days = 131.05 mmHg). Loss to follow-up (N = 1) and adverse events (N = 1) were low, both occurring in the peppermint arm, and compliance was very high in the peppermint (93.3%) trial arm. Given the substantial health and economic burden associated with hypertension worldwide, these findings suggest that twice-daily peppermint supplementation may represent a simple, low-cost, and well-tolerated strategy to support blood pressure reduction in this population. Trial registration ClinicalTrials.gov NCT05561543.
27 Apr 2026
Read appraisal →PloS one
Resilient road safety modeling through spatially disaggregated explainable AI
Understanding spatial disparities in traffic accident severity is essential not only for improving transport safety but also for advancing sustainable and inclusive transportation systems. Road crashes represent a persistent public health and equity challenge, directly linked to the United Nations Sustainable Development Goals (SDG 3: Good Health and Well-Being and SDG 11: Sustainable Cities and Communities). This study proposes an explainable, spatially segmented machine learning framework to examine urban-rural heterogeneity in crash outcomes, using disaggregated accident data from Kent, UK (2022-2024). By treating urban and rural systems as separate analytical units, the study captures risk heterogeneity that conventional pooled approaches often obscure. Among five tested models, Random Forest achieved the best performance and was further interpreted using SHapley Additive exPlanations (SHAP) to uncover how key factors differ in influence across spatial contexts. The results reveal a behavioral risk profile dominating in urban areas and infrastructure-driven risks in rural environments. These findings highlight the need for context-sensitive, evidence-based interventions that ensure transport equity across regions. Additionally, the study contributes to sustainable governance frameworks, enabling spatially adaptive risk mitigation, inclusive policy design and the long-term resilience of transport infrastructures.
27 Apr 2026
Read appraisal →Journal of medical Internet research
Effectiveness of Digital Serious Games on Knowledge and Attitudes in Public Health Education: Systematic Review and Bayesian Network Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Inadequate health literacy and low engagement challenge public health education. Digital serious games show potential to enhance health knowledge and attitudes. However, the comparative effectiveness of different game formats is unclear. OBJECTIVE: This study aimed to evaluate and compare the effectiveness of different digital serious game formats in improving public health knowledge and attitudes. METHODS: This systematic review and Bayesian network meta-analysis followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Seven databases (PubMed, CINAHL, Embase, PsycINFO, Cochrane Library, Scopus, and Web of Science) were searched from January 2000 to October 2025. An updated search in February 2026 identified no additional studies. Eligible studies were randomized controlled trials (RCTs) involving nonprofessional participants comparing digital serious games with traditional or noninteractive education. Standardized mean differences and 95% credible intervals were pooled using Bayesian network models with random effects. Subgroup analyses examined population characteristics, intervention duration, health topic, and delivery format. Risk of bias was assessed using the Cochrane risk-of-bias tool, and evidence certainty was rated using the Grading of Recommendations Assessment, Development and Evaluation. RESULTS: Forty randomized controlled trials from 19 countries (N=8764 participants) were included. Digital serious games significantly improved knowledge (standardized mean difference 0.66, 95% CI 0.32-0.99; I²=89.1%) and attitudes (standardized mean difference 0.50, 95% CI 0.27-0.76; I²=80.7%) compared with traditional education. Multisession interventions showed larger effects than single-session interventions for knowledge (0.76 vs 0.43) and attitudes (0.53 vs 0.30), with greater improvements among adolescents, nonpatient populations, and Asian studies. Network meta-analysis showed low heterogeneity (I²=8% for knowledge; 3% for attitudes). Mobile app-based, computer-offline, and web-based games ranked highest for knowledge; computer-offline, web-based, and virtual reality games ranked highest for attitudes. Evidence certainty was moderate for knowledge and low-to-moderate for attitudes. CONCLUSIONS: Digital serious games improve public health knowledge and attitudes across diverse contexts. Using a Bayesian network meta-analysis of randomized controlled trials, this review compares the relative effectiveness of different game formats. Mobile app-based, computer-offline, and web-based games most improved knowledge; computer-offline, web-based, and virtual reality formats most improved attitudes. Multisession interventions were more effective than single-session ones, particularly for adolescents and nonpatient populations. These findings guide scalable digital health education strategies. Future research requires adequately powered trials, longer follow-up, and standardized frameworks.
27 Apr 2026
Read appraisal →PloS one
Machine learning identifies pupil size and corneal thickness as key predictors of axial elongation rate
PURPOSE: This study aimed to develop a machine learning-based prediction model for myopia progression using ocular biometric parameters to provide an objective assessment tool for clinical practice. METHODS: A retrospective analysis was conducted on patients treated at Shanghai Parkway Health Ophthalmology Department as the training set, and myopic individuals from the Optometry Center of Peking University People's Hospital as the validation set. Demographic and biometric data were collected, including central corneal thickness (CCT), axial length (AL), corneal curvature (K-value), anterior chamber depth (ACD), corneal diameter (WTW), and pupil size (PS). Seven machine learning models (e.g., XGBoost, random forest, support vector machine) were employed for modeling, with performance optimized via 5-fold cross-validation. Model accuracy was evaluated using mean squared error (MSE) and the coefficient of determination (R²), and variable importance was analyzed. RESULTS: No statistically significant differences were observed in baseline characteristics between the training and validation sets (all P > 0.05). The XGBoost model demonstrated the best performance, achieving R² = 0.913 (MSE = 0.005) on the training set and R² = 0.766 (MSE = 0.016) on the test set. Variable importance analysis revealed pupil size (score 100) and corneal thickness (40.88) as the key predictors of axial elongation rate, followed by age of onset (17.96). CONCLUSION: The machine learning-based prediction model effectively utilizes ocular biometric data to assess myopia progression risk, with pupil size and corneal thickness identified as core predictive factors. This model provides a quantitative tool for early clinical intervention. Future studies should expand the sample size and incorporate additional biomarkers to optimize performance.
27 Apr 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
ACT on Vaping: Pilot Randomized Controlled Trial of a Novel Digital Health App With Text Messaging for Young Adult Vaping Cessation
BACKGROUND: There is no published evidence to support the efficacy of any digital vaping cessation program for young adults (YAs) at differing levels of readiness to quit. In this pilot randomized controlled trial, we evaluated the preliminary acceptability and efficacy of a program for vaping cessation based on acceptance and commitment therapy (ACT on Vaping), delivered via a smartphone app and text messaging. METHODS: YAs aged 18-30 (n = 61) were randomized 1:1 to ACT on Vaping (n = 31) or incentivized text message control (n = 30). Outcome data were collected at 3 months post-randomization. Results were compared against a priori benchmarks for acceptability (satisfaction of ≥3.5 on 5-point scale) and efficacy relative to control (meeting at least one of three): ≥1-point difference in Contemplation Ladder change scores; ≥5 percentage difference in 24-hour quit attempts, ≥5 percentage difference in cotinine-confirmed 30-day point prevalence abstinence (PPA) from all non-therapeutic nicotine/tobacco. RESULTS: Satisfaction with ACT on Vaping averaged 3.8, exceeding the acceptability benchmark. A higher proportion of participants in the ACT on Vaping arm reported a 24-hour quit attempt (87.5% vs. 75.9%), exceeding the efficacy benchmark. Both changes in quit readiness (+0.96 in ACT on Vaping vs. +0.72 in control) and cotinine-confirmed 30-day PPA (4.2% in ACT on Vaping vs. 0% in control) were descriptively higher for ACT on Vaping but did not reach the benchmark level for efficacy. CONCLUSIONS: ACT on Vaping had promising acceptability and preliminary efficacy. A fully powered trial of ACT on Vaping is warranted to evaluate its efficacy. IMPLICATIONS: Digital interventions are a promising yet under-researched approach for reaching and supporting YAs to quit vaping. This proof-of-concept pilot randomized controlled trial evaluated a novel mobile health application and associated text messaging program (ACT on Vaping) for young adult vaping cessation and found preliminary evidence for acceptability and efficacy relative to an incentivized text message control arm, warranting evaluation in a fully powered trial as a next step. TRIAL REGISTRATION: NCT05897242.
26 Apr 2026
Read appraisal →European journal of preventive cardiology
Economic evaluation of alternative cardiac rehabilitation programmes compared to centre-based cardiac rehabilitation programmes: a systematic review.
AIMS: Traditional centre-based cardiac rehabilitation (CBCR) has several drawbacks, including high costs, limited accessibility, and low utilisation rates. As a result, alternative cardiac rehabilitation programmes (ACRPs), such as home-based rehabilitation and telerehabilitation, have been developed. This systematic review aims to synthesise existing evidence on the economic evaluation of ACRPs compared to CBCR. METHODS AND RESULTS: We searched databases of MEDLINE, Embase, CINAHL, Cochrane CENTRAL, Web of Science, and EconLit, as well as trials registers of Clinicaltrials.gov, International Clinical Trials Registry Platform, and EU Clinical Trials Register. The last search date was 20 March 2025. We included randomised controlled trials that directly compared ACRP with CBCR in terms of cost and effectiveness. Study quality was assessed using the Cochrane Risk of Bias tool and the Drummond 10-item checklist. Both qualitative and quantitative syntheses were conducted, with random-effects models applied in the meta-analysis. Thirteen studies met the inclusion criteria, comprising 2040 participants. Meta-analysis showed a non-significant trend favouring CBCR over ACRP for EQ-5D (SMD = -0.32; 95% CI: -0.71 to 0.06; I2 = 79%). For QALY, there was no difference between ACRP and CBCR (SMD = -0.02; 95% CI: -0.19 to 0.15; I2 = 0). Ten of the thirteen studies reported lower costs for ACRP compared with traditional CBCR. Three of five economic evaluations found ACRP to be cost-effective. CONCLUSION: ACRPs demonstrate cost-effectiveness comparable to traditional CBCR. This review highlights the potential of ACRPs, such as home-based rehabilitation and telerehabilitation, as solutions for broader implementation in CR practice. This study reviewed existing research to compare the cost and effectiveness of alternative cardiac rehabilitation programmes with traditional centre-based programmes.Alternative cardiac rehabilitation programmes provide similar health benefits to centre-based programmes.They are often less expensive, with most studies showing lower costs.
26 Apr 2026
Read appraisal →BMJ open
Effects of nurse-led shared decision-making on low-dose CT uptake and screening outcomes in high-risk populations: a systematic review and meta-analysis
OBJECTIVES: To evaluate the effects of nurse-led shared decision-making (SDM) on lung cancer screening outcomes, including low-dose CT (LDCT) uptake, benign findings, early cancer detection and willingness to participate among high-risk populations. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed, Medline via OvidSP, Cochrane Central Register of Controlled Trials, EMBASE via OvidSP, Web of Science, Scopus, grey literature databases and clinical trial registries were searched from inception to March 2025. ELIGIBILITY CRITERIA: Studies evaluating nurse-led SDM interventions in high-risk lung cancer populations, reporting outcomes including LDCT uptake rates, screening results (Lung-RADS (Lung Imaging Reporting and Data System) classifications), early-stage cancer detection or willingness to participate. Randomised controlled trials, quasi-experimental studies and observational studies were included. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently extracted data and assessed risk of bias using the Risk of Bias in Non-randomised Studies of Interventions (for non-randomised studies) and Cochrane Risk of Bias 2.0 (for randomised controlled trials). Meta-analyses were conducted using random-effects models. Meta-regression explored sources of heterogeneity. RESULTS: 13 studies (n=13 608 participants) were included, comprising 10 single-arm studies and three comparative studies. In single-arm studies without control groups, nurse-led SDM programmes achieved a pooled LDCT uptake rate of 98% (95% CI 28% to 100%; I²=99%), and willingness to participate was 68% (95% CI 24% to 93%; I²=98%). In comparative studies, nurse-led SDM showed no significant difference in LDCT uptake compared with usual care (RR 1.00, 95% CI 0.99 to 1.02; I²=0%), suggesting non-inferiority rather than superiority. Among individuals who completed screening, 81% (95% CI 77% to 85%) had benign or low-risk findings (Lung-RADS [Lung Imaging Reporting and Data System] I/II), and 2% (95% CI 1% to 3%) were diagnosed with early-stage lung cancer, rates consistent with benchmark screening trials. Meta-regression identified female sex as positively associated with uptake (β=0.54, p<0.001), while current tobacco use was negatively associated (β=-0.37, p=0.033). The risk of bias was moderate to serious across studies. CONCLUSIONS: Comparative evidence suggests that nurse-led SDM achieves equivalent LDCT uptake to standard care approaches, indicating feasibility as an alternative service delivery model. However, the predominance of single-arm studies, high heterogeneity and moderate-to-serious risk of bias limit causal inference. High uptake rates in single-arm studies likely reflect selection bias rather than intervention effectiveness. Current evidence supports the feasibility but not the superiority of nurse-led SDM. Well-designed randomised controlled trials are needed to establish comparative effectiveness and cost-effectiveness before recommending widespread integration of nurse-led SDM into lung cancer screening programmes. TRIAL REGISTRATION NUMBER: PROSPERO CRD420251033595. https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=1033595.
26 Apr 2026
Read appraisal →JMIR mHealth and uHealth
A Deep Neural Network for Interpreting Wearable Electrocardiogram Data in Atrial Fibrillation: Prospective Observational Diagnostic Accuracy Study
BACKGROUND: Atrial fibrillation (AF) and atrial flutter (AFL) are common arrhythmias associated with the risk of ischemic stroke, which can be reduced with anticoagulation therapy. Thus, early diagnosis of AF and AFL is essential. However, diagnosis may be challenging due to the paroxysmal and asymptomatic nature of these arrhythmias. OBJECTIVE: Current diagnostic workflows involve time-consuming and resource-intensive manual review of noisy signals and prolonged recordings. We evaluated a mobile system that combines a wireless wearable single-lead chest strap electrocardiogram (ECG) and a novel deep neural network (DNN)-based artificial intelligence (AI) method for detecting AF/AFL episodes, AF/AFL burden, and rhythm change and estimated the delay in the detection of rhythm change from AF/AFL to sinus rhythm. We also assessed the rhythm classification performance. METHODS: A total of 116 patients with recent-onset AF or AFL undergoing cardioversion were monitored using a mobile single-lead chest strap ECG system. Simultaneously, a 3-lead Holter ECG served as the reference. The DNN-based AI analyzed the single-lead chest strap ECG data to detect AF/AFL, non-AF/AFLrhythm, and noninterpretable segments, as well as to estimate AF/AFL burden and detect rhythm change. Performance metrics included sensitivity, specificity, positive predictive value, negative predictive value, and intraclass correlation coefficient for AF and AFL burden estimation. RESULTS: The sensitivity and specificity for detecting AF/AFL were 91.9% (204.9/223.0 h) and 99.6% (242.4/243.5 h), respectively. The sensitivity for detecting AF was 96.2% (191.5/199.0 h), whereas it was 55.8% (13.4/24.0 h) for detecting AFL. The positive predictive value and negative predictive value for AF/AFL detection were 99.5% (204.9/206.0 h) and 93.1% (242.4/260.5 h), respectively. The intraclass correlation coefficient between the AF/AFL burden estimated by the DNN-based AI method and that derived from the physician-interpreted reference ECG was 0.96 (95% CI: 0.94-0.97; P<.001). Rhythm change detection occurred within 1 minute in most cases. CONCLUSIONS: The mobile single-lead chest strap ECG system powered by a DNN-based AI algorithm demonstrated strong performance in detecting AF, estimating AF burden, and recognizing rhythm change to sinus rhythm. This AI-driven approach enables automated and accurate rhythm analysis, supporting clinical decision-making. Further validation in real-world ambulatory settings is warranted.
25 Apr 2026
Read appraisal →Journal of medical Internet research
Personalized Smartphone Messaging for Secondary Prevention After Percutaneous Coronary Intervention: Randomized Controlled Trial
BACKGROUND: Cardiac rehabilitation improves outcomes after percutaneous coronary intervention (PCI), but participation remains suboptimal due to barriers such as distance, cost, and lack of referral. Mobile health interventions may enhance accessibility and engagement. OBJECTIVE: This study evaluated the effectiveness and user acceptability of a smartphone messaging app (AnSim) in improving cardiovascular risk factors among patients who recently underwent PCI. METHODS: A 2-arm randomized controlled trial was conducted at 2 Korean hospitals; 120 patients who had undergone PCI within the prior month were randomized (1:1) to receive AnSim plus usual care (intervention) or usual care alone (control). The intervention comprised personalized messages tailored by behavioral stage and risk profile, delivered 6 times/week for 6 months. Outcomes were assessed at baseline, 6 months, and 9 months. The primary outcome was change in blood pressure (BP) at 6 and 9 months. Secondary outcomes were lipid profiles, hemoglobin A1c, BMI, smoking status, physical activity, and achievement of guideline-recommended risk-factor targets. User acceptability was assessed by questionnaire. RESULTS: Overall, 115 (95.8%; intervention: n=58, control: n=57) participants completed follow-up at 6 months and 110 (91.7%; intervention: n=54, control: n=56) at 9 months. At 6 months, systolic BP was 124.9 (SD 16.7) mmHg in the intervention group and 124.8 (SD 13.1) mmHg in the control group (between-group difference 0.1 mmHg, 95% CI -5.4 to 5.6), and diastolic BP was 80.2 (SD 11.4) mmHg vs 78.4 (SD 10.6) mmHg (difference 1.8 mmHg, 95% CI -2.3 to 5.9). At 9 months, systolic BP was 126 (SD 14.2) mmHg vs 128 (SD 15.4) mmHg (difference -2 mmHg, 95% CI -7.6 to 3.6), and diastolic BP was 80.8 (SD 13.2) mmHg vs 81.3 (SD 12.0) mmHg (difference -0.5 mmHg, 95% CI -5.3 to 4.3). No significant between-group differences were observed in secondary outcomes. In post hoc analyses, participants with above-median message reading were more likely to achieve ≥4 of 5 guideline-recommended risk-factor targets at 9 months (69% vs 19.2%; relative risk 3.59, 95% CI 1.57-8.18; P<.001) and logged more diary entries (mean difference 203.4 entries, 95% CI 79.3-327.5; P=.001). BP responders at 9 months read messages on more days than nonresponders (110.3 vs 83.3 d; mean difference 27 d, 95% CI 4.6-49.3; P=.02). Among survey respondents (54/60, 90%), 87% (47/54) found the messages helpful and 81.4% (44/54) wished to continue. Two events occurred in the control group (P=.50). CONCLUSIONS: This trial found no between-group improvement in BP with low-intensity personalized messaging compared to an attention-matched diary app, but exploratory analyses suggest engagement may be a key determinant of potential benefit. Unlike comprehensive app-based cardiac rehabilitation programs, AnSim provides scalable behavioral reinforcement that can be integrated into post-PCI follow-up with minimal burden. These findings support future pragmatic trials optimizing engagement strategies and evaluating longer-term clinical and implementation outcomes.
25 Apr 2026
Read appraisal →The Nurse practitioner
Artificial intelligence for early detection of aortic stenosis: The emerging role of NPs.
Aortic stenosis is associated with significant morbidity and mortality if left untreated. Nurse practitioners in primary care are uniquely positioned to identify early signs of this condition and are often the frontline provider for patients. Unfortunately, barriers to screening, diagnosis, referral, and treatment remain significant. Artificial intelligence technologies are emerging to address these barriers. This article highlights the potential impact of artificial intelligence in transforming aortic stenosis management and explores its integration into nurse practitioner practices to improve outcomes.
25 Apr 2026
Read appraisal →JMIR human factors
Personalizing Mobile Apps for Health Behavioral Change According to Personality: Cross-Sectional Validation of a Preference Matrix
BACKGROUND: Mobile health (mHealth) apps are increasingly used to support healthy lifestyle behaviors through features such as health tracking and personalized reminders. Personalized messaging, tailored to users' profiles, has been shown to improve engagement and retention in health-related contexts. Prior research has linked personality traits, based on the Big Five model, to preferences for specific app mechanisms, leading to the development of a preference matrix for personalizing mHealth apps. This matrix comprises 15 mechanisms derived from behavior change techniques and gamification elements, intended to guide developers in optimizing engagement according to user profiles. OBJECTIVE: This study aimed to validate this preference matrix by examining whether the associations between mechanisms and Big Five personality traits reported in the literature align with user preferences observed in an experimental setting. METHODS: A cross-sectional study was conducted using an online survey that collected demographic data, mHealth app usage, and personality traits. Participants were presented with mockups illustrating 15 mechanisms and were asked to select their preferred options. Logistic regression and ordinal logistic regression analyses were performed to examine associations between personality traits, mechanism selection, and motivation scores. All analyses were adjusted using the Bonferroni correction to account for multiple comparisons. RESULTS: A total of 214 participants completed the survey (mean age 29.42, SD 10.41 y; n=118, 55.1% women; n=89, 41.6% men; n=5, 2% identifying as other; and n=2, 1% nonrespondents). Higher conscientiousness significantly increased the likelihood of selecting the collection mechanism (eg, collecting badges or points; odds ratio [OR] 1.87, 95% CI 1.27-2.75). For competition (eg, competing with other users), conscientiousness (OR 3.22, 95% CI 1.73-6.00) and agreeableness (OR 1.93, 95% CI 1.08-3.45) were significant predictors. Preferences for rewards (eg, virtual incentives such as points or virtual currency) were associated with conscientiousness (OR 2.36, 95% CI 1.53-3.63) and neuroticism (OR 1.97, 95% CI 1.36-2.86). Additionally, 4 mechanisms-self-monitoring, progression, challenge, and quest-were selected by more than half of the participants, independent of personality traits. CONCLUSIONS: The findings partially validate the proposed preference matrix. Conscientiousness consistently emerged as a key predictor of preference across multiple mechanisms, highlighting its central role in engagement with gamified mHealth features. While some mechanisms appear to have universal appeal, others show personality-specific preferences, underscoring the value of combining baseline mechanisms with targeted personalization strategies in mHealth app design.
25 Apr 2026
Read appraisal →The British journal of ophthalmology
Two-year outcomes of repeated red light therapy in premyopic children: sustained efficacy and rebound effects
BACKGROUND: To evaluate the long-term prevention effectiveness and rebound effect of repeated red light therapy (RRLT) in children with premyopia over 2 years. METHODS: A total of 108 premyopic children (cycloplegia spherical equivalent refraction (SER): -0.50 to +0.75 D) were enrolled and followed for 24 months. Participants were randomly assigned to the RRLT or control groups. The RRLT was administered two times per day for 3 min per session, with at least 4-hour interval. At the beginning of the second year, participants receiving RRLT were further randomised into continued treatment and washout subgroups. Axial length (AL), SER and subfoveal choroidal thickness (SChT) were measured. RESULTS: Over 2 years, the RRLT group showed significantly smaller AL elongation (0.26 mm; 95% CI 0.18 to 0.35 mm) and SER progression (-0.21 D; 95% CI -0.35 to -0.08 D) compared with the controls (AL: 0.43 mm; 95% CI 0.36 to 0.49 mm; SER: -0.66 D; 95% CI -0.79 to -0.52 D). The RRLT group also demonstrated significantly less SChT thinning (-2.44 µm; 95% CI -16.11 to 11.23 µm) than the controls (-44.12 µm; 95% CI -53.05 to -35.19 µm). After RRLT cessation in the second year, the washout subgroup exhibited significantly faster AL elongation and more SChT thinning than the controls, with no significant difference in SER progression. CONCLUSIONS: The 2-year RRLT intervention effectively retarded AL elongation and SER progression in premyopic children by 0.17 mm and -0.45 D, respectively. Notably, a significant rebound effect was observed in AL growth following 1-year RRLT cessation.
25 Apr 2026
Read appraisal →La Medicina del lavoro
Artificial Intelligence in Occupational Health Surveillance: Evaluating AI-Assisted ILO Classification of Radiographs of Pneumoconioses
BACKGROUND: Pneumoconioses remain an important occupational health issue, particularly in low- and middle-income countries. The International Labour Organization (ILO) Classification standardizes chest radiograph interpretation but requires trained readers and is affected by inter-reader variability. This study evaluated whether generative multimodal artificial intelligence (AI) models can approximate ILO-based diagnostic reasoning. METHODS: Eighty-two chest radiographs from the official NIOSH B Reader syllabus were analysed using four AI systems (GPT-4o, GPT-5, MedGemma-4B, MedGemma-27B). Each image was evaluated with a standardized prompt based on the 2022 revised ILO guidelines using deterministic settings. Model outputs were mapped to ILO codes and compared with the official answer keys of the ILO Standard Radiograph Set used for B Reader training and examination. Performance metrics included balanced accuracy, sensitivity, specificity, precision, and Matthews correlation coefficient (MCC). Bootstrap 95% confidence intervals, McNemar's test, and Cohen's κ assessed performance variability and agreement. RESULTS: All four AI models showed moderate diagnostic performance, with balanced accuracy ranging from 60.8% to 70.3%. Sensitivity remained limited (35.5%-54.9%), while specificity was consistently high (84.6%-86.2%). MedGemma-27B performed best for small opacities, GPT-5 for pleural abnormalities and for technical quality. Large opacities and rare findings were systematically under-detected. Statistical comparisons showed significant differences between models, although agreement patterns were broadly similar. CONCLUSION: All AI models partially followed structured ILO radiographic criteria but did not achieve expert-level performance, confirming that they cannot replace certified B Readers. Larger, real-world datasets are needed to assess their potential clinical utility as supportive tools in occupational health surveillance programs.
25 Apr 2026
Read appraisal →Annals of medicine
Artificial intelligence and wearable sensors in sports injury risk prediction: current status and future perspectives.
BACKGROUND: Artificial intelligence (AI) and wearable sensors are increasingly reshaping sports injury risk prediction by enabling continuous, individualized, and data-driven assessment. DISCUSSION: This review summarizes recent advances in wearable technologies - including inertial measurement units (IMUs), electromyography (EMG), physiological monitors, and flexible electronics - and their use in capturing biomechanical, physiological, and psycho-physiological indicators relevant to injury risk. Deep learning (DL) models, particularly those capable of temporal and multimodal fusion, have shown promise in predicting injuries such as anterior cruciate ligament (ACL) tears, muscle fatigue, and stress fractures. Through representative applications and mechanism-informed analysis, we highlight the growing role of interpretable AI, real-time feedback, and behavior-integrated predictive systems. CONCLUSIONS: Key challenges remain, including data heterogeneity, limited generalizability, compliance issues, and privacy concerns. Future directions point toward personalized modeling, explainable systems, federated learning, and digital twin frameworks. Together, these advancements mark a shift toward proactive, intelligent injury prevention across athletic and clinical settings.
24 Apr 2026
Read appraisal →Journal of medical economics
Cost-effectiveness of tirzepatide versus semaglutide for patients with obesity or overweight in the US: evidence from the SURMOUNT-5 head-to-head phase-3 trial
PURPOSE: This study evaluated the cost-effectiveness (from the United States [US] societal perspective) of tirzepatide at its maximum-tolerated-dose (MTD) compared to semaglutide (MTD), both administered adjunct to a reduced-calorie diet and increased physical activity. The analysis focused on individuals with obesity (body mass index [BMI] ≥ 30 kg/m2), or overweight (BMI ≥27 to <30 kg/m2 + ≥1 obesity-related complication), using data from the head-to-head Phase-3 SURMOUNT-5 trial (patients without type 2 diabetes [T2D]). PATIENTS AND METHODS: This patient-level simulation modeling study assessed the cost and long-term clinical outcomes of tirzepatide (MTD) versus semaglutide (MTD), using data from the SURMOUNT-5 trial population. The modeled population were at risk of developing obesity-related complications including cardiovascular disease (CVD) and obstructive sleep apnea (OSA), amongst others. These outcomes were modeled using cardiometabolic parameters including weight, systolic blood pressure, high-density lipoprotein, glycated hemoglobin (HbA1c) and total cholesterol, by assessing their impact on healthcare and wider societal costs, quality of life, and mortality. Incremental cost-effectiveness ratios (ICERs; cost/quality-adjusted life year [QALY]) and incremental net health benefit (iNHBs) were calculated, and uncertainty was assessed through sensitivity and scenario analyses. RESULTS: Tirzepatide (MTD) was estimated to be less costly and more efficacious compared to semaglutide (MTD) with per patient cost savings of $41,688, 0.506 QALYs gained and positive iNHB of 0.784, indicating a net health benefit for tirzepatide. The model predicted that per 1,000 patients, 70 fewer patients will develop T2D, 10 fewer will develop CVD with tirzepatide (MTD) and patients spend 3.07 more years living with moderate/severe OSA when treated with semaglutide (MTD). CONCLUSION: Based on this simulation model, using head-to-head SURMOUNT-5 trial data, tirzepatide (MTD) had lower total costs and higher QALYs compared to semaglutide (MTD). This supports that tirzepatide (MTD) is a cost-effective treatment option for individuals with obesity or overweight compared to semaglutide (MTD). This study focused on evaluating the cost-effectiveness of two weight management drugs, tirzepatide and semaglutide, for adults in the US who are overweight or have obesity. Using data from the SURMOUNT-5 trial, the analysis showed that tirzepatide was more effective and less costly, providing better weight loss and health benefits compared to semaglutide.The findings revealed that for every 1,000 individuals treated with tirzepatide, there were 70 fewer cases of type 2 diabetes and 10 fewer cases of heart disease compared to those treated with semaglutide. Additionally, patients on semaglutide experienced a longer duration living with moderate or severe sleep apnea. The study highlighted that tirzepatide led to greater improvements in reducing weight and returning blood sugar levels to normal. Over a lifetime, tirzepatide was found to save $41,688 per patient and provide an additional 0.5 quality-adjusted life years (QALYs; a measure that helps compare how much different treatments improve both length and quality of life), emphasizing its advantages both financially and health-wise.Tirzepatide also demonstrated a reduction in absenteeism (when people are not at work because of illness or health problems) and presenteeism (when people are at work but not fully productive because they are unwell), meaning fewer days lost from work compared to semaglutide, thus highlighting productivity benefits. Overall, the research supports that tirzepatide offered a more cost-effective and beneficial treatment option for weight management in the US healthcare context compared to semaglutide.
24 Apr 2026
Read appraisal →Renal failure
Explainable machine learning integrating bioelectrical impedance for 6-month cardiovascular risk in peritoneal dialysis
Peritoneal dialysis (PD) is a common treatment for end-stage renal disease, yet cardiovascular disease (CVD) remains a major cause of morbidity. Inadequate fluid management can elevate CVD risk, and bioelectrical impedance spectroscopy (BIS) is increasingly used to assess fluid status. This study aimed to develop an artificial intelligence model integrating BIS measurements and medical history to predict major adverse cardiovascular events (MACEs) within six months in clinically stable PD patients. Data were stratified by MACE occurrence and subject, with 80% assigned to training and 20% to testing. Class imbalance was addressed using the synthetic minority over-sampling technique. Four algorithms - logistic regression, random forest, XGBoost, and deep neural networks - were trained using fivefold cross-validation and grid search for optimal hyperparameters. Model performance was evaluated with area under the ROC curve (AUC), calibration plots, and decision curve analysis (DCA). Feature ablation experiments compared models using all 15 features, only 11 BIS-recorded features, and only 4 medical history features. The random forest model achieved the highest performance (AUC = 0.88), with CVD history as the most influential predictor. Isotonic regression calibration improved probability alignment (brier score = 0.0451). DCA suggested potential clinical benefit. While the model relied on four medical history features, its initial sensitivity was modest (0.68). Integrating BIS features significantly enhanced diagnostic sensitivity (0.84). The random forest model, based on 15 clinically accessible features, accurately predicts the risk of MACE within six months in clinically stable PD patients, demonstrating strong discriminatory ability with a performance reaching 88%.
24 Apr 2026
Read appraisal →JMIR formative research
Multimodal Sentiment and Emotion Analysis Framework for Personalized Health Coaching Messages: Proof-of-Concept Study
BACKGROUND: Text generation approaches in health care communication have evolved along 2 major paths. The first path involves generative adversarial networks, progressing from basic architectures to specialized variants like Text-to-Text Generative Adversarial Network (TT-GAN) and Time and Frequency Domain-Based Generative Adversarial Network (TF-GAN), which address challenges in discrete text generation through techniques such as Gumbel-Softmax and reinforcement learning. The second path emerges from transformer-based architectures, particularly Generative Pretrained Transformer-2 (GPT-2), which uses extensive pretraining and self-attention mechanisms to generate contextually appropriate text. GPT-2's transformer architecture enhances persuasive health communication by generating personalized messages using various strategies like task support, dialogue support, and social support for effective health interventions. OBJECTIVE: This study aimed to use GPT-2 as a generative method to construct persuasive text in a dataset and compare the performance of sentiment analysis and emotion detection analysis. METHODS: We combined sentiment analysis tools (VADER [Valence Aware Dictionary and Sentiment Reasoner] and TextBlob) with emotion detection methods (Text2Emotion and NRCLex [National Research Council Lexicon]) to analyze health coaching messages across different persuasive types: reminder, reward, suggestion, and praise. RESULTS: TextBlob and VADER achieved accuracies of 57% and 69%, respectively, while RoBERTa (robustly optimized BERT approach)-sentiment outperformed them with an accuracy of 88%. Emotion detection showed a high prevalence of "joy" and "happy" labels (93.69% positive skew). While transformers excel in accuracy, lexicon-based models like VADER offer a better performance-efficiency balance for real-time health communication systems. For emotion detection, all categories showed perfect accuracy (1.0), while trust showed mixed results, with precision, recall, and F1-score values ranging from 0.81 to 0.96. The emotion detection analysis revealed varying success rates across different emotions, with some categories, such as anger and neutral, showing reasonable performance and others, such as trust, showing mixed performance. CONCLUSIONS: This research contributes to understanding the emotional dynamics of persuasive health communication and highlights both the capabilities and limitations of current natural language processing tools in analyzing health-related persuasive messaging. This proof-of-concept study using synthetically generated data establishes a methodological framework for multimodal sentiment and emotion analysis. The findings require validation with real-world health coaching messages before clinical deployment.
24 Apr 2026
Read appraisal →European journal of preventive cardiology
i-CARE: a smartphone-based intervention to enhance cardiac rehabilitation in coronary artery disease-a randomized controlled trial
AIMS: The global shortage of healthcare professionals creates challenges in managing chronic diseases. This study evaluated a remotely delivered, theory-based self-care support intervention, 'Internet-based CArdiac Rehabilitation Enhancement (i-CARE)', delivered through a smartphone application to enhance self-management in patients with coronary artery disease (CAD). METHODS AND RESULTS: In this assessor-blinded, two-arm randomized controlled trial conducted at two regional hospitals in Hong Kong, community-dwelling adults with angiographically confirmed CAD were randomly assigned (1:1) to standard care or the i-CARE intervention. i-CARE included personalized risk assessment, skill-building, and app-based support to improve self-care behaviours. The primary outcome was change in CAD self-care behaviours; secondary outcomes were self-reported health status, health-related quality of life (HRQoL), physiological, and biomarkers. Of 294 participants randomized (n = 147 per group), the i-CARE group showed significant greater improvements in self-care maintenance [β = 4.055, 95% confidence interval (CI) 1.420-6.691, P = 0.003] and self-care confidence (β = 5.609, 95% CI 1.089-10.129, P = 0.015) at 3 months, sustained at 6 months. For secondary outcomes, the intervention also resulted in significant reductions in systolic (β = -3.725, 95% CI -7.322 to -0.128, P = 0.042) and diastolic (β = -2.153, 95% CI -4.228 to -0.079, P = 0.042) blood pressure and improvements in HRQoL at 3 months (β = 0.208, 95% CI 0.073-0.343, P = 0.003), with persistent benefits for diastolic blood pressure and HRQoL sustained at 6 months. No significant differences were observed for cardiac-specific health status, blood glucose, lipid profile, or waist-to-height ratio. CONCLUSION: Remotely delivered i-CARE intervention effectively promote self-care among CAD patients and may help address critical healthcare workforce shortages, while also conferring secondary benefits in blood pressure and HRQoL. Cardiac rehabilitation is a comprehensive management approach with proven clinical benefits to delay disease progression. However, the participation rate in cardiac rehabilitation remains low due to common barriers such as time constraints and need for transportation to centres. Mobile health applications offer innovative solutions to overcome these barriers providing multidimensional support to meet the complex needs of coronary artery disease (CAD) patients and enhance their self-management capabilities. This study aimed to evaluate the effects of an internet-based Cardiac Rehabilitation Enhancement (i-CARE) intervention among community-dwelling adults with angiographically diagnosed CAD to enhance self-care behaviours. The findings showed promising potential of digital health interventions to improve self-care behaviours, blood pressure control, and health-related quality of life in CAD patients.
24 Apr 2026
Read appraisal →JMIR medical informatics
Modeling Diabetes Risk and Progression With Public Health Data: Ontology-Guided, Simulation-Capable Digital Twin Study
BACKGROUND: Digital twins (DTs) offer a paradigm for health care by enabling data-driven, simulation-capable representations of individual health trajectories. However, DT development remains limited by the scarcity of standardized, temporally structured, and multidomain data suitable for modeling chronic disease progression. Most existing DT studies rely on narrowly scoped or proprietary datasets, restricting generalizability. Public health datasets, such as the Midlife in the United States study, provide rich biopsychosocial information but are underused due to structural complexity and lack of semantic integration frameworks. OBJECTIVE: This study aimed to develop and evaluate an ontology-guided, agent-orchestrated framework for constructing offline, simulation-capable, and progression-aware DTs from public health datasets. Using diabetes as a case study, the framework integrates agent-based orchestration, medical ontologies, and large language model (LLM)-assisted semantic reasoning with machine learning to support explainable feature structuring, risk prediction, and predictive "what-if" progression analysis. METHODS: A 6-stage DT framework was developed and applied to Midlife in the United States wave 2 (baseline) and wave 3 (follow-up) data. Ontology- and LLM-assisted feature selection identified predictors across biological, behavioral, psychosocial, and socioeconomic domains. Cleaned and harmonized data were used to train predictive models (random forest, eXtreme gradient boosting, and logistic regression) to estimate diabetes onset at follow-up. A state-transition simulator was implemented to model between-wave progression dynamics, quantify transitions across low-, medium-, and high-risk states, and evaluate predictive "what-if" scenarios such as weight reduction and lifestyle improvement. Model performance was assessed using accuracy, F1 score, area under the receiver operating characteristic curve (AUC), and calibration metrics. RESULTS: From 9976 candidate variables, ontology- and LLM-guided selection retained the top 200 relevant predictors spanning biological, behavioral, psychosocial, and socioeconomic domains. Predictive modeling achieved strong discrimination, with random forest (AUC=0.82, accuracy=0.76) and eXtreme gradient boosting (AUC=0.81, accuracy=0.75) outperforming logistic regression (AUC=0.78). The state-transition simulator reproduced realistic progression patterns: 33.9% (1414/4174) of participants changed risk states between waves, and the high-risk group increased from 10.8% (451/4174) to 32.2% (1344/4174). Next-state prediction accuracy reached 92.5%. Predictive "what-if" analyses showed that with a simulated 10% weight reduction, model-estimated diabetes cases decreased by 98 (from 576 to 478). A placebo test (0% weight change) produced less than 0.3% difference in risk distribution, confirming model stability. CONCLUSIONS: This study presents a foundational, ontology-guided, and agent-orchestrated framework for constructing offline, simulation-capable, and progression-aware DTs from public datasets. By combining semantic reasoning, multidomain predictors, and predictive "what-if" progression simulation, the framework transforms static population data into longitudinal, interpretable representations of individual health trajectories. The proof-of-concept application to diabetes demonstrates that public health data can support robust and explainable DT models for exploratory risk analysis and hypothesis generation, without implying causal intervention effects or direct clinical decision support.
24 Apr 2026
Read appraisal →European journal of preventive cardiology
Aerobic exercise and cardiovascular outcomes in patients with diabetes undergoing percutaneous coronary intervention: a nationwide population-based study
AIMS: We investigated the impact of aerobic exercise on clinical outcomes in patients with diabetes undergoing percutaneous coronary intervention (PCI). METHODS AND RESULTS: We analyzed a nationwide prospective population database from the Korean National Health Insurance System. We included 8225 patients with diabetes who had undergone PCI and documented their aerobic exercise habits before and after the procedure (mean interval: 2.0 years). The patients were categorized into four groups: persistent non-exercisers, new exercisers, exercise discontinuers, and exercise maintainers. The primary outcome was major adverse cardiovascular events (MACE), a composite of all-cause death, myocardial infarction, revascularization, and heart failure. We assessed the risk of clinical events using inverse probability-weighted Cox proportional hazards models. During a mean follow-up of 4.9 years, exercise maintainers, discontinuers, and new exercisers were associated with a significantly lower risk of MACE compared to non-exercisers. The lowest risk was observed in exercise maintainers (maintainers: aHR, 0.78; 95% CI: 0.71-0.86; discontinuers: aHR, 0.88; 95% CI: 0.79-0.98; new exercisers: aHR, 0.89; 95% CI: 0.80-1.00). A J-curve relationship between the amount of aerobic exercise and adverse clinical outcomes was observed, with the lowest risk identified at 1000-1499 MET-min/week. Benefits diminished with exercise intensity ≥1500 MET-min/week. CONCLUSION: Aerobic exercise maintenance after PCI was associated with a reduced risk of cardiovascular events in patients with diabetes, with a moderate amount of exercise providing maximal benefit. Therefore, aerobic exercise is advisable for patients with diabetes undergoing PCI, and the optimal exercise dosage warrants further research. LAY SUMMARY: This large-scale prospective population study, based on the Korean National Health Insurance System, demonstrated that maintaining aerobic exercise in patients with diabetes undergoing percutaneous coronary intervention is significantly associated with a lower risk of cardiovascular events.A J-curve relationship was observed between the amount of aerobic exercise and clinical outcomes, with the lowest risk observed at 1000-1499 MET-min/week for both new exercisers and the entire study population.However, exercising beyond 1500 MET-min/week offers diminishing returns, highlighting the need for balanced exercise intensity.
24 Apr 2026
Read appraisal →European journal of preventive cardiology
A comprehensive analysis of the impact of high-intensity interval vs. moderate-intensity continuous training on global and regional myocardial function in patients early after acute myocardial infarction: the STRAICT randomized controlled trial
AIMS: This study aims to compare the effect of moderate-intensity continuous training (MICT) vs. isovolumic high-intensity interval training (HIIT) on left ventricular (LV) myocardial function early post-acute myocardial infarction (AMI). Training-induced changes were analysed at both global and segmental levels using advanced deformation echocardiographic imaging techniques. METHODS AND RESULTS: One hundred AMI patients were randomly assigned to participate in an 8-week training programme comprising either MICT (n = 50) or isovolumic HIIT (n = 50), starting within 6 weeks post-AMI. Before and after cardiac rehabilitation (CR), we carried out a comprehensive and detailed evaluation of myocardial function, including measurements of LV longitudinal strain (LS) and circumferential strain and torsional mechanics, LV myocardial work (MW) and mechanical dispersion [post-systolic shortenings (PSS)] indexes, and atrial strains. Mixed models were used to evaluate the effect of MICT and HIIT on speckle-tracking echocardiography parameter. Both MICT and HIIT resulted in a similar improvement in the amplitude and timing of LV LS (time effect P < 0.001 in each case), as depicted by the comparable increase in global LS [HIIT: -0.82%, 95% confidence interval (CI): -0.19/-61; MICT: -1.11%, 95% CI: -1.17/-0.51; time ∗ group P = 0.71], MW index and efficiency, constructive MW, and reduction in the number of PSS and wasted MW (time ∗ group P > 0.12 in each case) in the two groups. These improvements were observed in all myocardial territories, regardless of previous ischaemic risk status (culprit vessel-by-time-by-group interactions, P > 0.60 in each case). Circumferential strains remained unchanged in both groups while there was a trend (time ∗ group P = 0.04) for LV torsion to decrease for MICT (P = 0.06) and increase for HIIT (P = 0.36) after CR. Left atrial strains during the reservoir and conduit phases improved (time effect P < 0.05) similarly in MICT vs. HIIT (time ∗ group P > 0.54). V̇O2peak (P < 0.001) and quality of life (P < 0.001) improved equally in MICT and HIIT after CR. CONCLUSION: Early supervised exercise-based CR, whether using MICT or isovolumic HIIT, improves longitudinal myocardial function in both culprit and non-culprit territories after AMI. Exercise-based cardiac rehabilitation (CR) is an effective strategy for reducing cardiovascular morbidity and mortality in patients after a myocardial infarction (MI). While moderate-intensity continuous training (MICT) has been the traditional form of aerobic exercise in CR, high-intensity interval training (HIIT) is gaining popularity due to its demonstrated feasibility, safety, and potential superiority in improving physical fitness. The impact of CR on heart function after MI is not well understood. It is still unclear whether HIIT is more effective than MICT for improving heart function. This uncertainty is due to limitations in previous studies, such as differences in total workload realized, which is an important factor in CR. It is also unclear whether different types of exercise training have different effects on parts of the heart that were damaged due to reduced blood flow during the heart attack compared with the areas that were not affected. This study aims to fill these gaps by comparing HIIT and equal-volume MICT using advanced echocardiographic imaging techniques to evaluate overall and regional heart function, focusing on both affected and unaffected areas of the heart. We found thatExercise-based CR initiated early after the heart attack effectively improves cardiac function, with HIIT as effective as isovolumic MICT.These improvements were observed equally with both MICT and HIIT across all areas of the heart, confirming its ability to functionally recover even in regions previously impacted by reduced blood flow.
24 Apr 2026
Read appraisal →European journal of preventive cardiology
Comparison of patient characteristics and health outcomes between self-selected centre-based cardiac rehabilitation and hybrid cardiac telerehabilitation: a prospective cohort study
AIMS: Data on self-selected modes of delivery of cardiac rehabilitation (CR) are limited. This study compared centre-based CR (cbCR) with hybrid cardiac telerehabilitation (hCTR) in terms of patient characteristics, change in physical and mental functioning, and achievement of guideline-directed treatment targets. METHODS AND RESULTS: From May 2022 to December 2023, consecutive patients with cardiovascular diseases were enrolled at a tertiary centre into a 3 month cbCR or hCTR programme based on shared decision-making. Changes in exercise capacity, anxiety, depression, and health-related quality of life (hrQoL) scores from admission to the discharge visit were compared between CR modalities. The achievement of two-step blood pressure and LDL-cholesterol (LDL-C) goals, as well as the HbA1c goal in patients with diabetes at the discharge visit, was compared between CR modalities. Out of 1292 patients screened, 406 (21% females, age 60.4 ± 12.7 years) were eligible and completed the study. Of those, 72% chose cbCR and 28% chose hCTR. Patients in hCTR were 3 years younger and exhibited higher baseline peak VO2 (91 vs. 80% of predicted), better hrQoL, and lower depression and anxiety scores. No significant differences were found in improvements in physical or mental functioning or in meeting blood pressure and HbA1c targets between the two groups. A smaller proportion of hCTR participants achieved the LDL-C Step I target (56 vs. 69% in cbCR). CONCLUSION: Overall, hCTR attracted slightly younger patients with better baseline health, but both modalities showed similar effects on most health outcomes. Centre-based cardiac rehabilitation was associated with tighter lipid control, which could be related to more intense counselling or patient preference. Outpatient cardiovascular rehabilitation was equally effective when performed with thrice weekly supervised exercise training sessions or partly performed at home while monitored with fitness trackers and a biosensor vest. Patients who self-select digitally monitored home training were slightly younger, fitter, and had better quality of life with lower levels of anxiety and depression. Blood lipid control, weight loss, and smoking cessation therapies need to be enhanced by tighter control and more intensive medical therapy particularly in patients choosing to perform part of the exercise sessions at home.
23 Apr 2026
Read appraisal →JMIR formative research
Effects of a Modified Exposure Claim for an e-Cigarette on Claim Comprehension, Behavioral Intentions, and Risk Perceptions Among US Adult Tobacco Users and Nonusers: Randomized Experimental Study
BACKGROUND: Effective communication about the relative risks of cigarettes and e-cigarettes can help increase switching away from cigarettes while minimizing unintended use. OBJECTIVE: This study examined comprehension of a proposed modified exposure claim (MEC) about an e-cigarette (IQOS VEEV, the study product [SP]) and the effects of claim exposure on SP use intentions and risk perceptions among adult tobacco users and nonusers. METHODS: Adult smokers with no intention to quit smoking (S-NIQ, n=606), adult smokers with an intention to quit smoking (S-IQ, n=600), adult e-cigarette users (ECU, n=630), adult former smokers (FS, n=619), adult tobacco and nicotine products (TNP) never-users aged 18-24 years (n=648), and adult TNP never-users aged 25 years and older (n=749; total N=3852) participated in a randomized between-groups online experimental study. Participants viewed a marketing brochure for the SP with (test condition) or without (control condition) an embedded MEC. Outcome measures included claim comprehension, intention to use the SP regularly, and perceived health risk to self from using the SP or smoking cigarettes. RESULTS: Most participants were female (n=2110, 54.8%), had a mean age of 40.2 (SD 14.93) years, and were equally split across the 4 US regions. S-IQ and S-NIQ were long-term, frequent cigarette smokers, while 91.4% (566/619) of FS were long-term quitters. ECU on average used e-cigarettes ≥15.2 times per day, and the majority of them (552/630, 87.6%) had started using e-cigarettes more than 12 months before. Most participants correctly understood the key elements of the claim: the SP produces lower levels of harmful chemicals compared to cigarettes (1818/1926, 94.4%), and switching completely from cigarettes to the SP reduces exposure to harmful chemicals (1832/1926, 95.1%). In both conditions, positive intention to use the SP was high among ECU (control: 238/314, 75.8% vs test: 249/315, 79%; P=.33), moderate among S-IQ (control: 127/299, 42.5%; test: 166/299, 55.5%; P<.001) and S-NIQ (control: 140/299, 46.8%; test: 166/307, 54.1%; P=.07), low among FS (control: 28/306, 9.2%; test: 33/312, 10.6%; P=.55), and very low among adult TNP never-users aged 18-24 years (control: 3/330, 0.9%; test: 8/318, 2.5%; P=.11), and adult TNP never-users aged 25 years and older (control: 5/373, 1.3% vs test: 12/375, 3.2%; P=.09). All groups understood that the SP posed a lower health risk compared to cigarettes. In all groups, claim exposure was associated with significantly lower risk perception of the SP relative to cigarettes (all comparisons, P<.001). CONCLUSIONS: The tested MEC has the potential to benefit public health by simultaneously increasing already high levels of SP use intention and reducing SP risk perceptions relative to cigarettes among adult tobacco users while generating low levels of use intention among tobacco nonusers.
23 Apr 2026
Read appraisal →JMIR research protocols
WhatsApp-Led mHealth Intervention for Hypertension Management Among Adults Aged 40 to 59 Years in Kerala, India: Protocol for a Mixed Methods Study
BACKGROUND: Medication adherence and lifestyle modification remain key in the management of hypertension. Leveraging mobile technologies to improve patient adherence has been found to be effective in chronic disease management. Given the high mobile penetration and the opportunities it provides for encouraging lifestyle modification, an app such as WhatsApp, which is a free-to-use and real-time messaging platform, could facilitate patient-clinician interactions. We present a protocol for a WhatsApp-led mobile health (mHealth) intervention to improve adherence to medication and healthy lifestyles among adults aged 40 to 59 years in Kerala, India. OBJECTIVE: The study aims to assess the impact of the WhatsApp-led mHealth strategy on blood pressure measures and adherence to medication and lifestyle modification. The study also focuses on exploring participants' views on feasibility, facilitators, and challenges, as well as their intention to use this WhatsApp-led mHealth strategy. METHODS: The study will follow a multiphase mixed methods (sequential explanatory) design with an initial quantitative phase (quasi-experimental study design) followed by a qualitative phase (phenomenological approach). Multistage cluster sampling will be adopted. Using a survey checklist and blood pressure measurements, community-dwelling adults aged 40 to 59 years will be screened for hypertension. Approximately 120 participants with hypertension will be assigned each to the intervention and control arms. All the participants will be advised to follow routine hypertension care from health care providers. Additionally, participants in the intervention arm will receive regular health messages and reminders over WhatsApp. A baseline survey will be conducted at enrollment, and a follow-up survey will be conducted after 3 months at the end of the intervention. The qualitative phase will involve follow-up interviews. Participants will be selected through quota sampling from those with the lowest and highest adherence to medication and lifestyle modification after receiving the intervention. Statistical analysis for survey data and thematic analysis for qualitative data will be conducted. RESULTS: The study received ethics approval (IHEC/CUK/2024/04) from the Institutional Human Ethics Committee of the Central University of Kerala. All 240 participants were recruited by July 2025. The study completed data collection in August 2025 and is expected to finish data analysis by April 2026. The study results are expected to be submitted for publication in July 2026. CONCLUSIONS: The study will be a potential strategy to address nonadherence to treatment and lifestyle regimens, which is a critical bottleneck to controlling hypertension and preventing complications. The study could be proof for a WhatsApp-led mHealth strategy that can be integrated into hypertension management at the community level, especially for improving hypertension awareness and reminders for behavior change.
22 Apr 2026
Read appraisal →Journal of neurology
The association of night shift work with the risk of acute ischemic cerebrovascular syndrome: a longitudinal study of 204,261 UK Biobank participants
BACKGROUND: Shift work is highly prevalent globally, yet evidence linking it to ischemic cerebrovascular disease remains inconsistent. Existing studies are limited by unsystematic quantification of night shift exposure and outcome definitions failing to capture the clinical continuum of ischemic events. We performed a large prospective cohort study using refined shift pattern classification and acute ischemic cerebrovascular syndrome (AICS) as the primary end point to enable a robust evaluation of the association between shift patterns and ischemic cerebrovascular disease. METHODS: 204,261 participants with employment information from the UK Biobank were included. Cox proportional hazards regression was used to assess associations between current shift work and AICS risk, with cross-classification by shift status and job duration. We further quantified average lifetime night shift frequency and cumulative duration among participants with complete occupational histories. Transient ischemic attack (TIA) and ischemic stroke (IS) were analyzed separately to identify the main driver of AICS and potential heterogeneity. RESULTS: During a median follow-up exceeding 14 years, irregular shift work was significantly associated with elevated AICS risk (HR 1.118, 95% CI 1.019-1.227, P = 0.019), while always night shift work was not (HR 0.904, 95% CI 0.707-1.157, P = 0.423). AICS risk was driven by irregular shift patterns rather than duration. Lifetime night shift frequency and duration showed significant dose-response relationships with AICS. CONCLUSIONS: Irregular shift work increases AICS risk relative to non-shift work, and lifetime night shift exposure exhibits a clear dose-response effect. IS constitutes the primary driver of the composite AICS end point.
20 Apr 2026
Read appraisal →International journal of biometeorology
Random forest-based medical geography: geographical distribution of reference values for LP-PLA2 in healthy adults in China
Lipoprotein-associated phospholipase A2 (LP-PLA2), a vascular-specific inflammatory marker, is associated with coronary heart disease (CHD), a highly prevalent condition among middle-aged and older adults. It explores the relationship between environmental factors in environmentscience, geography, statistics and medicine and the reference value of LP-PLA2 in China. A geographic distribution prediction model for Lp-PLA2 medical reference values was developed using optimized machine learning approaches. The support vector machine (SVM) model employed a polynomial kernel function, with hyperparameters tuned via grid search and cross-validation to determine the optimal classifier configuration. Concurrently, the random forest algorithm was applied to mitigate overfitting and perform regression analysis on the dataset. Further considering the influence of geographical factors on the 100 LP-PLA2 medical reference values in city and county units from 2000 to 2024, using 24 geographical factors as independent variables, to fit 2317 LP-PLA2 medical reference values, and then explore the spatial variation characteristics of LP-PLA2 medical reference values. Further spatial interpolation was visualized on the national map. The medical reference value of LP-PLA2 was significantly correlated with longitude, altitude, annual average wind speed, topsoil clay percentage, topsoil reference capacity, topsoil organic matter content, and total convertible amount of topsoil, respectively. The geospatial distribution map shows that the whole value was high in the east and low in the west, with significant differences in longitude. The geographical characteristics of any given region, when analyzed through either the SVM prediction model or the derived geographical distribution map, enable precise determination of region-specific Lp-PLA2 reference values. This establishes an evidence-based foundation for clinical decision-making and research design in healthcare institutions and medical research organizations.
20 Apr 2026
Read appraisal →Blood pressure
Acupuncture for primary hypertension: a systematic review and meta-analysis of randomised controlled trials
OBJECTIVE: This systematic review and meta-analysis evaluated the efficacy and safety of acupuncture for primary hypertension, based on randomised controlled trials (RCTs) published between 2015 and 2024. METHODS: We searched seven major databases for RCTs comparing acupuncture to control interventions. The primary outcomes were changes in systolic blood pressure (SBP) and diastolic blood pressure (DBP). Meta-analyses utilised random-effects models, and the Cochrane Risk of Bias tool assessed study quality. RESULTS: Seven RCTs (n = 812) were included. Acupuncture was associated with lower SBP [mean differences (MD) = -6.73 mmHg, 95% confidence interval (CI): -8.84 to -4.62] and DBP (MD = -6.59 mmHg, 95% CI: -9.42 to -3.76). Heterogeneity was substantial for SBP (I2 = 73.3%) and considerable for DBP (I2 = 90.9%). Notably, the SBP analysis included seven studies, whereas the DBP analysis included six studies, and the similar absolute pooled reductions should be interpreted cautiously, given the small and not fully overlapping study sets. Exploratory subgroup analyses suggested blood pressure reductions across several acupuncture modalities, though each subgroup contained very few studies. Adverse-event reporting was limited and inconsistent across studies. Formal tests for publication bias were underpowered given the small number of included studies. CONCLUSIONS: Acupuncture may have blood pressure-lowering effects in patients with primary hypertension, but the evidence is limited by substantial heterogeneity, a small number of studies and considerable variation in intervention type and exposure. Larger, well-designed, multi-centre RCTs with standardised protocols and longer follow-up are needed to confirm these findings and establish optimal treatment parameters. High blood pressure (hypertension) is a major health concern worldwide. Acupuncture, a component of traditional Chinese medicine, has been used to manage blood pressure, but evidence of its effectiveness remains limited. This review examined seven recent randomised controlled trials involving 812 participants to assess whether acupuncture reduces blood pressure in people with primary hypertension. The findings suggest that acupuncture may reduce both systolic and diastolic blood pressure; however, the included studies were few and varied considerably in the type of acupuncture used, the number of treatment sessions (ranging from a single session to 60 sessions over 12 weeks) and the populations studied. Reported side effects were minor, such as temporary pain at the needle site, though safety reporting was incomplete. Therefore, while the results are encouraging, they are not definitive. Further large-scale, rigorously designed trials are needed before firm conclusions can be drawn about the role of acupuncture in hypertension management.
20 Apr 2026
Read appraisal →BMJ open
Cost-effectiveness of osteoporotic fracture risk assessment in people with intellectual disabilities: a UK NHS modelling study
OBJECTIVES: We compared the cost-effectiveness of alternative fracture risk assessment strategies for people with intellectual disabilities (ID) aged ≥40 years from a UK National Health Services perspective over a lifetime horizon. DESIGN: Cost-effectiveness analysis using a lifetime decision-analytical model. SETTING: UK primary care, with data from literature and national databases. PARTICIPANTS: People with ID. INTERVENTIONS: Three strategies were assessed: (S1) Risk assessment using the UK QFracture score; (S2) use of IDFracture (a fracture risk prediction tool specifically developed for adults with ID); and (S3) conducting a one-time dual-energy X-ray absorptiometry (DXA) scan in all. S1 and S2 were followed by DXA scan for those at risk. At-risk individuals received treatment according to UK practice (bisphosphonates plus vitamin D and calcium for osteoporosis, and vitamin D and calcium alone for osteopenia). PRIMARY OUTCOME MEASURES: Direct healthcare costs and quality-adjusted life years (QALYs), and incremental cost-effectiveness ratio (ICER). RESULTS: In the base case, S2 (ICER: -£2568/QALY) was dominant (ie, less costly and more effective) and S3 (ICER: £1678/QALY) was cost-effective relative to S1 for major osteoporotic fracture (MOF). For hip fracture, S2 (ICER: £32 116/QALY) and S3 (ICER: £49 536/QALY) were not cost-effective relative to S1 under the National Institute for Health and Care Excellence-recommended cost-effectiveness thresholds. Findings from the sensitivity analyses were predominantly consistent with the base-case results. Subgroup analyses showed that age-specific and gender-specific strategies could be used. CONCLUSION: For people with ID aged ≥40 years, a proactive approach to risk assessment for MOF is not only clinically beneficial, but also cost-effective.
20 Apr 2026
Read appraisal →PloS one
Risk prediction for cardiovascular related diseases using PRS and EHR in the Framingham Heart Study
Cardiovascular disease is a leading cause of mortality and rising healthcare costs worldwide. Fortunately, the disease is preventable, and addressing risk factors can significantly reduce its effects. Over the past decade, risk prediction models have advanced significantly, with polygenic risk scoring analysis, which is often used in combination with clinical health information for prediction. However, most previous cardiovascular disease prediction studies based on polygenic risk scores have focused on a single specific disease or event, such as cardiac events. Given the complex nature of the cardiovascular disease, which involves a combination of genetic and environmental factors, a comprehensive analysis of the disease prediction results is essential. In this study, we investigate the genetic and environmental factors contributing to cardiovascular disease by utilizing data from the Framingham Heart Study, a leading cardiovascular cohort. We compared the prediction performance of different methods across various scenarios and assessed performance using various evaluation metrics to identify the best-fitting model for six cardiovascular related diseases. We also analyzed the feature importance of genetic and clinical variables, noting that different variables had varying effects on each disease. Our findings demonstrated the performance of prediction algorithms in forecasting cardiovascular disease by utilizing genetic and clinical factors, as well as highlighting the importance of each feature in the disease prediction. While models relying solely on polygenic risk score showed relatively low prediction performance for some diseases, integrating genetic information with clinical data improved prediction performance in most cases. For certain diseases, particularly those known to be heritable, polygenic risk scores demonstrated predictive ability, suggesting that they may serve as standalone predictive tools. We believe our study reveals the value of combining polygenic risk scores with clinical variables and expect that our thorough analysis can inform study designs tailored to specific diseases and research objectives.
20 Apr 2026
Read appraisal →Global health action
Lifestyle interventions to prevent gestational and type 2 diabetes among migrant women from low- and middle-income countries: a systematic review.
Migrant women from low- and middle-income countries (LMICs) living in high-income settings experience disproportionately high risk of gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM). This review aimed to identify and synthesise culturally adapted lifestyle interventions for preventing or managing GDM and T2DM among migrant women from LMICs, focusing on intervention components, cultural adaptation strategies, and behavioural and metabolic outcomes. Five databases (PubMed, Embase, Scopus, CINAHL, Cochrane Central) were searched using Preferred Reporting Items for Systematic reviews and Meta-Analysis 2020 guidelines. Eligible studies included experimental designs involving lifestyle interventions delivered to migrant women from LMICs in high-income countries, reporting outcomes related to GDM or T2DM targeting behaviour change. Data were synthesised narratively; study quality was appraised using RoB2 for RCTs and a structured narrative approach for non-randomised designs. Certainty of evidence was evaluated using GRADE. Eight studies met the inclusion criteria. Sample sizes ranged from 28 to 641 participants. Intervention duration varied from 6 weeks to 12 months. Most interventions incorporated atleast one culturally tailored component, such as bilingual delivery, culturally adapted dietary education, or community-based engagement. Improvements were reported across dietary behaviours, physical activity, glycaemic measures, or diabetes-related knowledge; however, effect sizes were modest and inconsistent. Interventions combining dietary modification, physical activity, and culturally adapted delivery demonstrated greater improvements than exercise-only or digital-only programmes. Overall certainty of evidence ranged from low to moderate. Culturally adapted, multi-component lifestyle interventions show promise for improving behavioural and metabolic outcomes among migrant women from LMICs; however, the evidence base remains limited. Main findings: Culturally adapted, multi-component lifestyle interventions show modest but inconsistent improvements in behavioural and metabolic outcomes among migrant women from low- and middle-income countries with overall low-to-moderate certainty of evidence.Added knowledge: This review highlights cultural adaptation as a key feature of intervention effectiveness and identifies important evidence gaps, including limited high-quality studies and challenges in comparing heterogeneous interventions.Global health impact for policy and action: Policies should prioritise scalable, culturally responsive interventions – such as bilingual and community-based approaches – to reduce diabetes disparities and improve maternal health outcomes in migrant populations.
19 Apr 2026
Read appraisal →Scientific reports
A hybrid dual-stream CNN framework with dynamic data augmentation and improved Manta Ray Foraging Optimization for robust glaucoma detection.
A progressive neurological condition, glaucoma is one of the main causes of irreversible blindness in the globe. Early detection is crucial to preventing irreversible vision loss however conventional diagnostic methods are often time-consuming, and heavily reliant on clinical expertise. This study presents an innovative deep learning framework designed to automate glaucoma detection while addressing key challenges such as data imbalance, image quality inconsistencies, and the need for accurate feature extraction. The proposed framework begins with a dedicated preprocessing pipeline that enhances image clarity, and isolates clinically relevant regions of interest. To handle class imbalance, a novel hybrid data augmentation strategy is introduced, combining geometric transformations with adaptive Gaussian noise injection that adjusts intensity according to image characteristics, thereby simulating realistic clinical variability. At its core, the framework leverages a dual-stream CNN architecture that integrates DenseNet121 for structural feature extraction and ResNet50 for texture representation. A lightweight channel-wise attention mechanism is then introduced to selectively emphasize clinically significant channels while suppressing redundant features, thereby balancing efficiency and interpretability. To optimize model performance while reducing computational overhead, an Improved Manta Ray Foraging Optimization (IMRFO) algorithm is employed. IMRFO enhances the standard MRFO with Partial Centroid Opposition-Based Learning (PCOBL) to dynamically fine-tune hyperparameters, including augmentation settings and transfer learning configurations. Experimental validation was conducted on four public benchmark datasets, ACRIMA, Drishti-Gs, ORIGA, and RIM-ONE-DL, demonstrating the framework's superior performance across all evaluation metrics. The model achieved 100.00% accuracy, precision, recall, and AUC on both ACRIMA and Drishti-Gs, with losses of 0.003 and 0.001, respectively. On ORIGA, it reached 99.70% accuracy, 99.80% precision, 99.30% recall, and 99.50% AUC (loss = 0.01). On RIM-ONE-DL, the model scored 99.90% accuracy, 99.70% precision, 99.50% recall, and 99.70% AUC (loss = 0.006). These findings confirm the framework's robustness and clinical applicability for effective glaucoma screening.
19 Apr 2026
Read appraisal →Scientific reports
Food preferences and mortality risk in the prospective cohort of UK Biobank participants.
Food preferences may serve as less biased proxies for self-reported food intake in predicting health outcomes but have not yet been linked to mortality risk. We included 177,148 UK Biobank participants who completed a food preference questionnaire (FPQ), with a mean follow-up of 3.4 years (607,779 person-years; 3355 deaths). High versus low preference for 140 food items was tested using Cox regression with Holm adjustment. Sensitivity analyses excluded participants with key health conditions, additional supplement use, or short follow-up, added BMI and physical activity as covariates, and redefined preference groups. Higher preference for asparagus (hazard ratio (95% confidence interval)): 0.69 (0.62, 0.77)), aubergine (0.79 (0.72, 0.87)), black pepper (0.72 (0.64, 0.81)), broccoli (0.69 (0.61, 0.79)), butternut squash (0.75 (0.68, 0.83)), and extra virgin olive oil (0.67 (0.58, 0.78)) was linked to lower all-cause mortality, whereas higher preference for regular fizzy drinks (1.38 (1.24, 1.54)) was linked to higher mortality across all analyses. Specific food preferences are associated with all-cause mortality risk. FPQs may, therefore, complement conventional dietary assessments. Further studies should examine their associations with metabolic and cardiovascular outcomes.
19 Apr 2026
Read appraisal →JMIR aging
Leveraging Digital Health Technologies to Assess Older Adults' Frailty and Nutritional Status: Two Cross-Sectional Studies
BACKGROUND: With the rising prevalence of aging worldwide, there is a necessity for reliable and frequent assessments of older individuals' health status to manage and potentially prevent age-related complications. Digital health technologies (DHTs), such as wearable devices, provide an opportunity to gather objective, continuous, and unobtrusive measurements, enabling effective health management in everyday life. OBJECTIVE: We aimed to evaluate the relationship between digital endpoints and frailty, nutritional status, and patient-reported outcomes (PROs) in older individuals, as well as quantified their compliance and comfort with DHTs. METHODS: In two cross-sectional studies, namely the Geriatric Anorexia Studies 1 and 2, including three in-clinic visits and 2-3 weeks of at-home monitoring, 94 participants (mean age 72.98 years, SD 6.28 years) were stratified based on their frailty status (n=39, 41%, nonfrail; n=45, 48%, prefrail; and n=10, 11%, frail) and nutritional status (n=70, 74%, with normal nutrition and n=24, 26%, at risk for malnutrition), as assessed in a clinical setting using the Fried Frailty Score and the Simplified Nutritional Appetite Questionnaire, respectively. We remotely monitored older adults using different DHTs. At home, participants were monitored with a wrist accelerometer for physical activity, continuous glucose monitoring (CGM) for glucose concentration, a digital body scale for weight and body composition, and a digital nutritional scale for meal tracking. Compliance with devices was assessed via wear time and correct at-home usage, while comfort was evaluated using questionnaires. The association between digital endpoints and frailty/nutritional status was investigated via linear regression, followed by ANOVA, and the relationship between digital endpoints collected at home and PROs was evaluated via Spearman's ρ. Weight and body composition were also assessed during in-clinic visits with a research-grade scale, used to validate the at-home digital body scale measurements, via the intraclass correlation coefficient, Pearson's R, and Bland-Altman plots with mean bias. RESULTS: Physical activity digital endpoints collected at home were significantly different across frailty and nutritional groups and significantly correlated with self-reported appetite, fatigue, and physical function (eg, for mean daily activity in the maximum 60 minutes of activity, ρ=0.28, -0.23, and 0.47, respectively). More than 80% of participants reported that all devices were mostly to very acceptable to wear/use: 95% (88/93) for the body scale, 86% (43/50) for CGM, 80% (40/50) for the nutrition scale, and 91% (84/93) for the wrist accelerometer. Compliance ranged from 60% of monitoring days for the digital nutrition scale to 90% for the wrist accelerometer. CONCLUSIONS: Physical activity digital endpoints show clear prognostic significance for frailty and malnutrition in older individuals and reflect self-reported measures. DHTs can be reliably deployed at home for older individuals to measure their physical activity, weight and body composition, glucose concentration, and meal intakes, thus enabling patient-centric, data-centric clinical trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT04858932; https://clinicaltrials.gov/study/NCT04858932; ClinicalTrials.gov NCT05211973; https://clinicaltrials.gov/study/NCT05211973.
18 Apr 2026
Read appraisal →JMIR human factors
Interactive, Personalized Patient Decision Aid for COVID-19 Vaccination in Canada: User-Centered Design Approach
BACKGROUND: The COVID-19 pandemic highlighted the need for practical digital health tools to support informed decision-making amid rapidly evolving evidence and widespread misinformation. OBJECTIVE: We iteratively developed and refined VaxDA-C19, a bilingual (English and French) web-based patient decision aid designed to support informed decision-making in Canada about COVID-19 vaccination. VaxDA-C19 integrates interactive and personalized features aimed to enhance vaccine confidence, reduce cognitive overload, and respond to diverse informational needs. METHODS: VaxDA-C19 was developed using an iterative, user-centered design approach. Throughout the development process, we involved a citizen panel, health care professionals, user experience designers, and scientific experts to guide refinements. We also conducted usability testing sessions with adults in Canada, using semistructured interviews, comparative testing, and think-aloud protocols with thematic analysis. We ultimately conducted 4 design cycles in total with adults in Canada (users) and expert reviewers (experts). Cycle 1 involved 9 people (9 users), cycle 2 involved 22 people (22 users), cycle 3 involved 9 people (3 users and 6 experts), and cycle 4 involved 9 people (9 experts). RESULTS: In cycle 1, user feedback guided design decisions about how to present quantitative information and technical vaccine descriptions more simply. In cycle 2, while most users (9/11, 82%) favored in-depth explanations of vaccine development, a few raised concerns about content that could be perceived as politically charged. Cycle 3 identified usability improvements, including more explicit navigation controls, simplified medical terminology, and optimized interactive components (avatars and sliders). Expert reviews in cycle 4 refined linguistic consistency, mobile responsiveness, content transparency, and scientific accuracy, emphasizing explicit instructional guidance and bilingual accessibility. CONCLUSIONS: Our iterative process produced a personalized, bilingual digital decision aid to support evidence-informed, values-congruent decisions about COVID-19 vaccination. A randomized controlled trial will further evaluate VaxDA-C19's impact on vaccination intentions, knowledge retention, emotional responses, decisional conflict, and decisional regret. If it proves effective, the patient decision aid may also be used as a platform to support other vaccine decisions, namely, influenza, measles, shingles, pertussis, and potentially other emerging infectious diseases.
18 Apr 2026
Read appraisal →BMJ open
Comparison of the effects of eHealth-delivered sedentary breaks versus physical activity promotion interventions on sedentary time: protocol for a systematic review and meta-analysis
INTRODUCTION: Prolonged sedentary behaviour (SB) is an independent risk factor for adverse health outcomes, with current WHO guidelines emphasising both increased physical activity (PA) and reduced sitting time. While electronic health (eHealth) interventions offer scalable solutions, the comparative effectiveness of two dominant strategies: sedentary break interventions (frequent interruptions of sitting) versus PA promotion (structured activity sessions) remains unclear. This systematic review and meta-analysis protocol aims to compare the effectiveness of interventions targeting breaks in SB versus increased PA in reducing sedentary time through meta-analysis of randomised controlled trials (RCTs) comparing either intervention to a control condition, while exploring key moderating factors including participant characteristics, intervention type, duration, eHealth delivery mode, theoretical basis, use of behaviour change techniques (BCTs), PA intensity and SB frequency. METHODS AND ANALYSIS: This protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. A comprehensive search will be conducted in PubMed, Embase, Web of Science and the Cochrane Library from inception to 31 July 2025, with language restrictions limited to English and Chinese publications. RCTs comparing eHealth-delivered interventions promoting sedentary breaks with those increasing PA in adults will be included. The primary outcome is sedentary time (objectively measured or self-reported), and secondary outcomes include health outcomes such as cardiometabolic markers, fatigue and well-being. Two reviewers (SC and XN) will independently screen studies, extract data and assess risk of bias using validated tools. Meta-analyses will be performed if sufficient homogeneous data are available, comparing changes in sedentary time. Subgroup analyses will explore effects by participant characteristics including gender (male vs female), age groups (<60 vs ≥60 years), intervention type (sedentary breaks vs PA), duration (short-term ≤3 months vs medium-term >3 to <6 months vs long-term ≥6 months), eHealth platform/delivery mode (eg, smartphone apps vs websites vs wearable device prompts vs text messages), theoretical basis (eg, theory-driven interventions vs non-theoretical interventions), use of BCTs (eg, interventions incorporating BCTs vs interventions without BCTs), SB break frequency (eg, every 30 min vs every 60 min) and PA promotion intensity (eg, light vs moderate vs vigorous). A narrative synthesis will be conducted for studies unsuitable for quantitative analysis. ETHICS AND DISSEMINATION: Ethical approval is not required as this study involves secondary analysis of published data. Findings will be disseminated through peer-reviewed publication and conference presentations. PROSPERO REGISTRATION NUMBER: CRD420251042994.
18 Apr 2026
Read appraisal →Journal of global health
National, regional and provincial prevalence of peripheral artery disease in Chinese adults in 2023: an updated systematic review and modelling study
BACKGROUND: Peripheral artery disease (PAD), a common atherosclerotic condition characterised by the narrowing or obstruction of peripheral arteries, has become a substantial public health concern in China. However, its prevalence across the country at the provincial level has never been quantified. Through an updated systematic review and modelling study, we aimed to estimate the national, regional, and provincial prevalence of PAD among Chinese individuals aged 30-89 years in 2023. METHODS: We conducted an updated literature search in PubMed, Embase, MEDLINE, the China National Knowledge Infrastructure, Wanfang, and the Chinese Science and Technology Journal Database to identify studies published between 4 March 2017 and 12 August 2024 reporting on the prevalence of PAD in the general Chinese population. We also identified and included studies from previous systematic reviews. We then used a multilevel mixed-effects meta-regression model to generate the age- and sex-specific prevalence estimates of PAD among individuals aged 30-89 years at the national level. We calculated pooled odds ratios (ORs) for factors associated with PAD using a random-effects meta-analysis and incorporated them into an 'associated factor-based model' to estimate regional and provincial prevalence. RESULTS: We included 54 articles from all searches. Model-based estimates indicated that the overall prevalence of PAD among Chinese adults aged 30-89 years in 2023 was 5.47% (95% confidence interval (CI) = 4.64-6.44), equivalent to 49.90 million (95% CI = 42.32-58.73) affected individuals. Prevalence increased progressively with age, ranging from 4.23% (95% CI = 3.58-5.00) among individuals aged 30-34 years to 18.37% (95% CI = 15.90-21.13) among those aged 85-89 years. The overall prevalence was higher in females (6.19%; 95% CI = 5.25-7.27) than males (4.76%; 95% CI = 4.03-5.61). Regionally, Northeast China had the highest prevalence of PAD at 5.87% (95% CI = 4.98-6.91), while Central China had the lowest at 5.26% (95% CI = 4.46-6.19). At the provincial level, the prevalence of PAD was the highest in Beijing (6.40%; 95% CI = 5.43-7.53) and lowest in Tibet (4.35%; 95% CI = 3.68-5.12). Female sex, current smoking, hypertension, diabetes, coronary heart disease, and stroke were identified as factors significantly associated with PAD. CONCLUSIONS: We found that PAD is prevalent in China, with pronounced age-related trends, sex differences, and regional disparities. These findings could inform targeted public health initiatives and appropriate resource allocation. REGISTRATION: PROSPERO: CRD420251118923.
18 Apr 2026
Read appraisal →Sleep
Multimodal cardiovascular risk profiling using self-supervised learning of polysomnography
STUDY OBJECTIVES: Polysomnography (PSG) provides a comprehensive assessment of brain, cardiac, and respiratory activity during sleep. While it is widely used for diagnosing sleep disorders, its potential to assess future health risks has not been fully explored. This study aimed to develop and evaluate an interpretable framework to identify physiological patterns in PSG data linked to cardiovascular disease (CVD) outcomes, without relying on manual annotations (e.g. sleep stages). METHODS: We developed a self-supervised deep learning model that extracts meaningful patterns from multi-modal signals (electroencephalography [EEG], electrocardiography [ECG], and respiratory signals [Resp]). The model was trained on data from 4398 participants. Projection scores were derived by contrasting embeddings from individuals with and without CVD outcomes. External validation was conducted in an independent cohort with 1093 participants. The source code is available on https://github.com/miraclehetech/sleep-ssl. RESULTS: The projection scores revealed distinct and clinically meaningful patterns across modalities. ECG-derived features were predictive of both prevalent and incident cardiac conditions, particularly CVD mortality. EEG-derived features were predictive of incident hypertension and CVD mortality. Resp added complementary predictive value. Combining projection scores with the Framingham Risk Score consistently improved prediction, with area under the curve values ranging from 0.607 to 0.965 in the Sleep Heart Health Study test cohort and 0.710 to 0.807 across four of five outcomes in the external validation cohort, demonstrating robust cross-cohort generalizability. CONCLUSIONS: Our findings demonstrate that the proposed framework can generate individualized CVD risk scores directly from PSG data. The resulting projection scores have the potential to be integrated into clinical practice, enhancing risk assessment and supporting personalized care. Statement of Significance This study is among the first to apply a self-supervised framework for cardiovascular risk profiling using polysomnography (PSG) data. By transforming electroencephalography, electrocardiography, and Resp into interpretable projection scores, we identified physiological markers predictive of multiple cardiovascular outcomes. These risk profiles, combined with traditional risk scores, significantly improved prediction across both internal and external cohorts. Our findings highlight the untapped potential of PSG signals beyond sleep staging, providing an interpretable, scalable, and clinically actionable approach for personalized cardiovascular risk stratification.
17 Apr 2026
Read appraisal →JMIR medical informatics
Research on Risk Transfer Pathways for Lung Cancer Among Middle-Aged and Older Individuals Using Deep Reinforcement Learning: Retrospective Cohort Study
BACKGROUND: Nowadays, lung cancer has a significantly high incidence rate worldwide. The mortality rate of lung cancer continues to rise; it is more common in middle-aged and older individuals and poses a great threat to human health. OBJECTIVE: This study aimed to assess the lung cancer risk among middle-aged and older individuals in a timely manner and to establish an efficient pathway for the risk transfer. METHODS: We proposed a deep reinforcement learning model based on deep Q-network (DQN) to explore the risk transfer pathway for lung cancer among middle-aged and older individuals. Risk stratification of lung cancer occurrence was deduced through deep neural network. The DQN model was developed using the Health and Retirement Study cohort for model training and internal validation. We also used the China Health and Retirement Longitudinal Study cohort for model external validation. Transfer simulation of multiple pathways in different cycles was calculated in a stratified risk groups-leveraged DQN model. RESULTS: We developed and evaluated the DQN method for optimizing the risk transfer pathway among middle-aged and older individuals, with accuracy ranging from 0.917 (95% CI 0.896-0.928) to 0.949 (95% CI 0.909-0.961) and area under curve ranging from 0.906 (95% CI 0.887-0.933) to 0.927 (95% CI 0.893-0.938). External validation was conducted to assess the model's effectiveness and availability. A total of 8780 and 3763 samples from the Health and Retirement Study were used for model training and internal testing, respectively, and 16,442 samples from the China Health and Retirement Longitudinal Study were used for external validation. The results showed that DQN models illuminated the optimal risk transfer pathways for stratified risk groups. Lung cancer incidence in the high risk group had declined by 68.2% through risk transfer of model-based simulation, which had declined by 56.9% in the medium risk group. Through simulative intervention and risk transition deduced from the DQN model, lung cancer incidences of the high risk, medium risk, and low risk groups were obviously decreased. CONCLUSIONS: A DQN-based deep reinforcement learning model was proposed and validated to develop and simulate a risk transfer pathway of lung cancer among middle-aged and older individuals. Risk stratification supplied an effective foundation for lung cancer risk transition.
17 Apr 2026
Read appraisal →BMJ open
mHealth-based Healthy Lifestyle Promotion (MYLIFE) for metabolic syndrome risk reduction in Chinese employed adults: protocol for a cluster-randomised controlled trial
INTRODUCTION: Metabolic syndrome (MetS) poses a significant public health challenge among employed adults. Lifestyle modifications have been shown to be effective in preventing the onset and progression of MetS in employed adults, and the widespread adoption of mobile and wearable technologies introduces an appealing approach to mHealth lifestyle interventions. When widely implementing smartphone-based interventions for employed adults, enhancing equitable access and promoting wearable device use for those with MetS is a more cost-effective way to reduce health disparities, particularly in resource-limited settings. However, relevant evidence is currently lacking. OBJECTIVE: This study aims to design an mHealth-based Healthy Lifestyle Promotion (MYLIFE) trial, a three-arm randomised controlled trial (RCT), to evaluate the effectiveness of a smartphone-based lifestyle intervention and a combined smartphone-based lifestyle intervention with intensive intervention for MetS individuals using wearable devices, in reducing MetS risk among employed adults. METHODS AND ANALYSIS: This is a three-arm, parallel, single-blind, cluster RCT with a 12-week intervention and a total follow-up of 1 year. The trial will recruit 120 workplaces from the Chinese Cohort of Working Adults in a 1:1:1 ratio to either a control group or one of two intervention groups. Within these workplaces, a total of at least 348 eligible participants will be enrolled. The regular mHealth group will receive smartphone-based intervention. The intensive mHealth group will receive smartphone-based intervention for all participants, and an additional wearable device-intensive intervention for those with MetS. The primary outcome is the Chinese MetS Z score at 12 weeks. Secondary outcomes include MetS Z score, prevalence of MetS, specific MetS components, questionnaire-based indicators, and clinical outcomes at 12 weeks and 1 years. The main analysis will follow the intention-to-treat principle, using mixed-effects models to assess between-group differences in outcomes. ETHICS AND DISSEMINATION: The protocol has been approved by the Ethics Committee of the West China School of Public Health and the West China Fourth Hospital, Sichuan University (Gwll2025024). The findings will be published in peer-reviewed journals and presented at scientific conferences. TRIAL REGISTRATION NUMBER: ChiCTR2500101904.
17 Apr 2026
Read appraisal →Infection and immunity
Maternal influences on oral microbiome development and implications for early childhood health: a systematic review
A deeper understanding of how maternal-infant interactions shape the establishment and diversification of the oral microbiome could have significant clinical applications; however, relatively few studies emphasize early maternal-infant microbial connections. This systematic review provides a longitudinal analysis of oral microbiome development from birth to five years, focusing on the relationship between maternal and infant microbiomes. We conducted a systematic search (June 2025) using keywords "mother," "children," "oral microbiome," and "longitudinal" across PubMed, Cochrane Library, and Embase. Twelve studies fulfilled the inclusion criteria: longitudinal design, healthy mother-child dyads, saliva sample collection, and relevant age range. We excluded review articles, non-English publications, and studies with overlapping data. Results were synthesized by developmental stage and topic. Overall, current literature agrees that the mother is an important source of exposure for initial colonization of the newborn's oral microbiome. Several studies indicated that the oral microbiome at birth is diverse and unspecialized, composed mostly of maternally derived strains. Rapid selection occurs over the first few weeks, as the relative abundances of typical oral bacterial species increase. Throughout the first year, increases in diversity strengthen the resemblance between infant and maternal microbiomes. The microbiome appears to stabilize around 3-5 years. In conclusion, maternal-infant connections play a significant role in influencing oral microbiome development during the first 5 years of life. This review highlights the need for future studies to incorporate larger, longitudinal designs with metadata and advanced tools to clarify the roles of delivery mode, tooth eruption, and parental lifestyle habits in shaping early oral microbiome development.
17 Apr 2026
Read appraisal →Journal of the American College of Cardiology
Risk-Guided Atrial Fibrillation Screening With Artificial Intelligence-Enabled Electrocardiogram Models: A VITAL-AF Trial Analysis
BACKGROUND: Screening for atrial fibrillation (AF) may lead to earlier detection and initiation of preventive measures. Current AF screening approaches using a guideline age-based threshold of ≥65 years have shown limited yield. OBJECTIVES: In an AF screening trial, we assessed whether the screening effect was larger among individuals at elevated AF risk using validated clinical and electrocardiogram (ECG)-based artificial intelligence (AI) risk models. METHODS: VITAL-AF was a cluster-randomized trial of patients aged ≥65 years treated at 1 of 16 primary care practices affiliated with Massachusetts General Hospital. Patients randomized to a screening practice were screened using a single-lead ECG. Among VITAL-AF participants without prevalent AF with at least one 12-lead ECG within 3 years before enrollment, we estimated AF risk using 3 validated models derived outside of VITAL-AF: the Cohorts of Heart and Aging Research in Genomic Epidemiology-AF (CHARGE-AF) clinical score, an AI-based model using a 12-lead ECG alone (ECG-AI), and a model combining ECG-AI and CHARGE-AF (CH-AI). Two-year incident AF discrimination was assessed by the time-dependent area under the receiver-operating characteristic curve (AUROC) and average precision. AF screening effect was defined as the difference in 2-year incident AF diagnosis rate (per 100 person-years) in screening vs control across AF risk deciles. RESULTS: Of 30,630 VITAL-AF participants without prevalent AF, 16,937 had pretrial ECG and clinical data. Each score discriminated 2-year AF risk according to AUROC (CHARGE-AF: 0.711 [95% CI: 0.671-0.749]; ECG-AI: 0.784 [95% CI: 0.743-0.819]; CH-AI: 0.788 [95% CI: 0.754-0.824]) and average precision (0.0952 [95% CI: 0.0836-0.112]; 0.132 [95% CI: 0.113-0.157]; 0.133 [95% CI: 0.117-0.159]). An AF screening effect was observed in the top decile of CH-AI (AF diagnosis rate in screening 10.07/100 person-years [95% 8.28-11.87] vs 7.76 [95% 6.30-9.21] in control, P < 0.05), corresponding to a difference in AF diagnosis rate of 2.32/100 person-years (95% CI: 0.01-4.63) and number-needed-to-screen of 43 per year. CONCLUSIONS: Use of ECG-based AI and clinical factors identified individuals at particularly high risk for AF who may benefit from screening. Findings suggest a trade-off between increasing AF screening efficiency and decreasing population coverage (ie, restriction of the screening pool). Future studies are needed to determine whether a risk-based approach is optimal or whether consideration of additional clinical- and systems-level factors (eg, access, health care system engagement) can further refine AF screening strategies. (Screening for Atrial Fibrillation Among Older Patients in Primary Care Clinics [VITAL-AF]; NCT03515057).
16 Apr 2026
Read appraisal →Biological psychiatry
From Snapshots to Stable Outcomes: Resting-State Functional Magnetic Resonance Imaging-Based Prognosis of Functioning in Patients With Psychosis Risk or Recent-Onset Depression
BACKGROUND: Early recovery of functioning is critical for favorable outcomes in psychotic and affective disorders. Transdiagnostic brain activity patterns may capture pathways for poor outcomes before clinical manifestation, thereby supporting timely prevention and intervention. METHODS: Using machine learning, we evaluated the transdiagnostic prognostic value of resting-state functional magnetic resonance imaging fractional amplitude of low-frequency fluctuations (fALFF) (slow-5 and slow-4 sub-bands) for functional outcomes in patients at clinical high risk for psychosis (n = 217) or with recent-onset depression (n = 198) from the multisite PRONIA (Prognostic Tools for Early Psychosis Management) study. Leave-site-out cross-validation assessed the geographic generalizability of models across disability and symptom domains, with outcomes defined as snapshots at 9- or 18-month follow-up or across both time points. We examined diagnosis-specific performance, generalization to recent-onset psychosis (n = 140), and negative symptoms and the added value of fALFF over clinical prognostication. RESULTS: Transdiagnostic models predicting stable good functioning across follow-ups showed up to 10% higher balanced accuracy (BAC) than snapshot models. Decreased slow-5 fALFFs in the default mode network, executive control network (ECN), and dorsal attentional network (DAN) and increased fALFF in the salience network, ECN, and DAN predicted impairment with BAC = 67% (sensitivity = 65%, specificity = 70%, p < .001). This model generalized to recent-onset psychosis (BAC = 62%, sensitivity = 64%, specificity = 59%, p < .001) and predicted (BAC = 65%, sensitivity = 66%, specificity = 65%, p < .001) and was mediated by negative symptoms. Slow-5-based models improved prognostic accuracy over expert ratings in disability (BACraters = 66%, BACraters+slow-5 = 75%, W = 1680, p < .001) and symptom (BACraters = 61%, BACraters+slow-5 = 71%, W = 1444, p < .001) domains. CONCLUSIONS: We highlighted the prognostic value of fALFF for functional impairment in psychosis risk and early depression. Leveraging trajectorial information, we identified candidate imaging biomarkers to improve prognostication, thereby supporting personalized prevention and recovery strategies.
16 Apr 2026
Read appraisal →Open heart
Chronotype-aligned exercise timing in middle-aged adults at cardiometabolic risk: a randomised controlled trial
OBJECTIVE: To investigate whether aligning exercise timing with chronotype enhances cardiometabolic and sleep-related benefits in sedentary adults with cardiovascular risk factors. METHODS: In this 12-week randomised controlled trial conducted in Lahore, Pakistan (January-June 2025), 150 sedentary adults (aged 40-60) with at least one cardiovascular risk factor were recruited and categorised as morning-type or evening-type using the Morningness-Eveningness Questionnaire and validated by 48 hour core body temperature monitoring. Participants were randomised into a chronotype-aligned exercise (CAE) group exercising at their preferred time, or a chronotype-misaligned exercise (CME) group exercising at their non-preferred time. Moderate-intensity aerobic training (5 sessions/week, 40 min/session) was supervised. Primary outcomes included systolic and diastolic blood pressure (BP) and heart rate variability (RMSSD); secondary outcomes included peak oxygen consumption (VO₂ peak), low density lipoprotein (LDL), fasting glucose and sleep quality (PSQI), assessed pre- and post-intervention. RESULTS: Of 150 randomised participants, 134 completed the study (CAE: n=64; CME: n=70). CAE led to significantly greater improvements in systolic BP (-10.8 vs -5.5 mm Hg, p=0.002), diastolic BP, RMSSD, VO₂ peak, LDL, fasting glucose and PSQI scores compared with CME. Repeated measures analysis of variance (ANOVA) revealed significant group×time interactions across all outcomes (eg, systolic BP: η²=0.095, p=0.005). The reduction in systolic BP in the CAE group was substantial and significantly greater than in the CME group. CONCLUSION: Aligning exercise timing with individual chronotype significantly enhances cardiometabolic and sleep-related outcomes in at-risk adults. Chronotype-based exercise prescriptions may offer a cost-effective, personalised approach to improving cardiovascular health.
16 Apr 2026
Read appraisal →Food & function
The effect of anthocyanins through diet and supplementation on cognitive function in older adults: a multi-centre randomised controlled clinical trial
Anthocyanins, a flavonoid subclass present in certain blue, purple and red fruits and vegetables, have potential neuroprotective properties due to their anti-inflammatory, antioxidant, and signalling effects. Clinical and epidemiological studies of anthocyanins provide promising evidence for attenuation of memory loss and improved cognitive function. We investigated the effects of anthocyanins, provided through diet or supplementation, on cognitive function and related parameters. A multi-centre, 24-week randomised, parallel 3-arm clinical trial was conducted in participants aged 60-85 years with self-reported memory complaints and scoring ≤13 on the Memory Index Score within the Telephone Montreal Cognitive Assessment (T-MoCA). Participants were randomised to high anthocyanin diet, freeze-dried product derived from blackcurrants (250 mg anthocyanins per day), or control (placebo). The primary outcome was auditory episodic memory functioning. Secondary outcomes were additional cognitive functions, subjective memory complaints and self-reported depression symptoms, blood pressure, inflammatory biomarkers, lipid profile, vascular and microvascular endothelial function tests. Mixed linear modelling evaluated changes over time using both intention-to-treat and completer analyses. Of 110 participants enrolled (mean age 69.2 years SD 6.7; 36 males, 74 females), 94 completed the study. There was no treatment × time effect on any primary or secondary outcomes. We postulate that the potential anti-inflammatory, antioxidant, and signalling effects of anthocyanins were insufficient to significantly modulate any outcomes due to participants' overall good health and lack of relevant cognitive decline. We recommend future interventional studies focus on subjects with diagnosed cognitive impairment, such as MCI and dementia, and those with more severe comorbidities, particularly chronic inflammatory conditions. Trial Sponsor: National Health and Medical Research Centre (NHMRC) Dementia Collaborative Research Centre.
15 Apr 2026
Read appraisal →Food & function
Metabolic and inflammatory effects of oleuropein and olive leaf extract: a systematic review and meta-analysis
Background: Olive leaves, a by-product of olive oil production, are rich in oleuropein, a phenolic compound with antioxidant and anti-inflammatory properties. This systematic review and meta-analysis evaluated the effects of olive leaf extract (OLE) and oleuropein on glucose metabolism, lipid profile, and inflammatory markers in randomized clinical trials (RCTs). Methods: The review was registered in PROSPERO (CRD42022375615) and followed PRISMA guidelines. PubMed, Scopus, and Web of Science were searched up to January 2026 without restrictions. Eligible studies were RCTs in adults comparing OLE or oleuropein with placebo or control. Study selection, data extraction, and risk of bias (RoB 2) assessment were performed independently by two reviewers; certainty of evidence was assessed with GRADE. Meta-analyses were conducted when at least two trials reported suitable data. Results: Eleven RCTs were included (4 short-term and 7 continuous-intake trials). Short-term crossover studies showed heterogeneous effects on postprandial glycemia and insulin responses, precluding meta-analysis. In continuous-intake, parallel-design RCTs, pooled analyses demonstrated no statistically or clinically significant effects on glycemic or lipid outcomes. Evidence regarding inflammatory markers was scarce and of low certainty. Conclusion: The current evidence does not support clinical recommendations for OLE supplementation aimed at health benefits. Data from methodologically consistent and robust trials indicate no statistically significant metabolic or inflammatory effects under usual human consumption patterns, whereas evidence from other study designs remains inconclusive. Further high-quality RCTs are required to clarify potential metabolic benefits.
15 Apr 2026
Read appraisal →Analytical chemistry
Dual-Signal Amplification Strategy for Synchronous Monitoring of Stress Hormones and Metabolic Indicators
The synchronous monitoring of stress hormones and metabolic indicators is crucial for long-term health management, yet it remains a significant challenge for wearable sensors, primarily due to the ultralow abundance of biomarkers in sweat and the vulnerability of weak electrochemical signals to interference. Herein, we report a wearable electrochemical sensor based on a dual-signal amplification strategy for the synchronous and highly sensitive detection of cortisol, glucose (Glu), and uric acid (UA). The CuFe-PBA/CC substrate provides a platform featuring high surface area, electrocatalytic activity, and dual signal amplification capabilities, which are the primary sources of high sensitivity. The MIP layer enhances the effective concentration of target substances near active sites through a local enrichment effect. Combined with the substrate's high catalytic activity, this amplifies the electrochemical signal specific to the target. It synergizes with an exogenous redox probe ([Fe(CN)6]3-/4-) to establish a dual-amplification pathway, which enhances the current output to the milliampere level and substantially improves the signal-to-noise ratio. To ensure the optimal performance of the sensor, the preparation parameters were optimized via the integration of machine learning. This integrated sensing system exhibits high selectivity, superior sensitivity (with detection limits of 44.78 fM for cortisol, 0.064 μM for Glu, and 0.089 μM for UA), and excellent stability (retaining 88% of the initial signal after 100 cycles of mechanical deformation). Furthermore, we fabricated the sensor into a watch-like form factor, integrated it with high-precision wearable electronic modules, and achieved real-time wireless detection of sweat cortisol, Glu, and UA in ex vivo sweat monitoring experiments. This work provides a novel solution for objective stress assessment and metabolic status monitoring, holding promise for advancing long-term health management applications.
15 Apr 2026
Read appraisal →Tobacco control
Generative AI in a new era of computer model-informed tobacco research: a short report
BACKGROUND: Social media influencers who promote e-cigarettes on Instagram or TikTok for tobacco brands use marketing tactics to increase the appeal of their promotional content, for example, depicting e-cigarettes alongside healthy lifestyle or entertainment imagery that could decrease youths' risk perceptions of e-cigarettes. Monitoring the prevalence of such content on social media using computer vision and generative AI (artificial intelligence) can provide valuable data for tobacco regulatory science (TRS). METHODS: We selected 102 Instagram and TikTok videos posted by micro-influencers in 2021-2024 who promoted e-cigarettes alongside posts featuring four themes: cannabis, entertainment, fashion or healthy lifestyle. We used OpenAI's GPT-4o multimodal large-scale visual linguistic model to detect the presence of nicotine vaping, cannabis vaping, fashion, entertainment and healthy lifestyle. The model did not require any additional training and improved its performance as we modified the text prompt. RESULTS: The model's accuracy was 87% for nicotine vaping, 96% for cannabis vaping, 99% for fashion, 96% for entertainment and 98% for healthy lifestyle. CONCLUSIONS: Generative AI can achieve accurate object detection with zero-shot learning (no additional training of the pretrained model). This model can be applied to big data-scale sample sizes of images and videos to detect e-cigarette-related and other substance-related promotional content and contexts (eg, healthy lifestyle) used for the promotion of these products on social media, providing valuable data for TRS.
15 Apr 2026
Read appraisal →JMIR aging
Web-Based Personalized Machine Learning Recommendations to Enhance Shared Decision-Making in Prostate-Specific Antigen Screening: Randomized Controlled Trial
BACKGROUND: Prostate‑specific antigen (PSA) screening involves complex trade‑offs between early detection and the risks of overdiagnosis. For older adults (aged ≥50 years), shared decision‑making (SDM) is often hindered by limited health literacy, sensory or cognitive impairments, and multimorbidity, which complicate risk comprehension. Traditional decision aids provide foundational knowledge but are often nonpersonalized. Machine learning (ML) may offer individualized recommendations, yet the psychological and behavioral effects of ML‑assisted SDM in geriatric populations remain poorly characterized. OBJECTIVE: This study aimed to develop and evaluate a web‑based, ML‑driven decision aid integrated into an SDM workflow to provide personalized PSA screening recommendations and to assess its effects on decisional conflict (primary outcome), state anxiety, and decision satisfaction among middle‑aged and older men. METHODS: The study followed a 2‑stage design. First, a model establishment group (n=507) was used to train and evaluate 6 ML algorithms based on clinical and values‑clarification data. A random forest model was selected for its superior performance (mean area under the curve 0.933, SD 0.350; 95% CI 0.902-0.963). Second, a randomized controlled trial was conducted with 367 participants (mean age 64.34, SD 10.30 years) randomly assigned 1:1 to the ML suggestion group (MLSG; n=185) or the control group (CG; n=182). Both groups received video‑based education, counseling, and values clarification; only the MLSG received an ML‑generated "second opinion" recommendation. Primary and secondary outcomes were assessed using the Decisional Conflict Scale (DCS), Spielberger State‑Trait Anxiety Inventory (STAI), and Satisfaction with Decision scale. RESULTS: In the randomized controlled trial (n=367), the MLSG reported significantly lower decisional conflict than the CG (total DCS score: mean difference [MD] -3.77, 95% CI -5.55 to -1.99; Cohen d=-0.44; P<.001). The MLSG reported greater perceived support (DCS7: adjusted P=.03), more adequate advice (DCS9: adjusted P<.001), and higher decision confidence (DCS10: adjusted P=.03; DCS11: adjusted P<.001). Regarding psychological well‑being, although total anxiety scores did not differ, the MLSG reported reduced worry (STAI item 6: MD -0.98, 95% CI -1.20 to -0.76; d=-0.89; adjusted P<.001) and increased calmness (STAI item 1: MD 0.30, 95% CI 0.06-0.54; d=0.25; adjusted P=.01). Decision satisfaction was higher in the MLSG across all items (total Satisfaction with Decision score: MD -7.38, 95% CI -8.54 to -6.18; P<.001). Behavioral choices were strongly influenced by the ML recommendation: participants in the MLSG who received an "accept" recommendation were more likely to select "accept" (34/67, 50.7%) than those in the CG (44/182, 24.2%; P<.001). When the system suggested "not now," only 17.8% (21/118) chose "accept," which was lower than in the CG. CONCLUSIONS: Integrating personalized ML recommendations into SDM workflows provides emotional scaffolding for older men, reducing decisional distress and enhancing confidence without undermining autonomy. By addressing geriatric‑specific vulnerabilities through a facilitated digital interface, this ML‑driven approach complements traditional clinical consultations. These findings support the scalable integration of artificial intelligence-assisted decision support to foster patient‑centered care in aging populations.
15 Apr 2026
Read appraisal →Nature communications
A group 1 hemagglutinin stem vaccine elicits broad humoral responses against influenza in phase 1/2a study
Vaccines targeting the conserved stem region of influenza hemagglutinin (HA) may induce broad immunity and reduce reliance on strain-matched seasonal formulations. This Phase 1/2a study evaluates safety and immunogenicity of a universal influenza vaccine component comprising a soluble trimeric stabilized group 1 HA-stem protein (INFLUENZA G1 mHA). Healthy adults aged 18-to-45 received one or two doses at 45 µg or 135 µg, with or without Al(OH)₃. Primary endpoints were safety and reactogenicity. All regimens were well-tolerated with no vaccine-related serious adverse events. Secondary and exploratory endpoints included binding, neutralizing, and ADCC responses compared to a matched quadrivalent seasonal influenza vaccine cohort. A single dose induced robust binding antibody responses to H5 HA and homologous stem HA, with 6.5-16.4-fold increases at Day 29 versus baseline, exceeding responses elicited by the seasonal influenza vaccine. Additionally, we demonstrate broader binding to a panel of group 1 HAs, with responses maintained for at least one year. Neutralizing antibody and ADCC responses at Day 29 showed 7.9-15-fold and 4.9-8.6-fold increases, respectively. Importantly, serum from vaccinated participants protect mice against lethal heterologous H5N1 challenge. This study provides proof of concept that the HA stem vaccine elicits broad and durable influenza A group 1-directed immunity. This trial is registered with ClinicalTrials.gov (NCT05901636).
15 Apr 2026
Read appraisal →Tobacco control
Cigarette filter ventilation, product appeal and regulatory options: a review of the influence of ventilation on consumers' sensory and risk perceptions
OBJECTIVE: Cigarette filter ventilation may enhance product appeal, support initiation and sustained smoking, and increase smoking prevalence. We reviewed recent evidence on how filter ventilation affects consumers' perceptions of product appeal and considered product use preferences to inform regulatory options. DATA SOURCES: PubMed, Embase and PsycINFO databases were searched up to December 2023. Systematic search strategies used terms based on cigarette filter ventilation, and multiple constructs comprising product appeal and user perceptions. STUDY SELECTION: Two reviewers screened all 917 retrieved titles and abstracts independently and applied a consensus strategy to identify 16 articles that met eligibility criteria. DATA EXTRACTION: Extracted data focused on study design, dependent variables or outcomes, and major conclusions. DATA SYNTHESIS: While remaining popular, awareness and risk perception of ventilated cigarettes is generally low. Cigarettes with filter ventilation are perceived as smoother and less irritating than non-ventilated cigarettes. These modified sensory experiences shape perceptions of reduced health risks. Pack text descriptors and colours convey further information to consumers about smoothness, supporting lowered risk perceptions. CONCLUSIONS: Filter ventilation and its associated marketing continue to influence consumers' perceptions of smoothness creating misperceptions about health risks. Standardised ('plain') packaging regulations are recommended to restrict communication of ventilation themes as well as evidence-based health communications to correct consumer misperceptions about smoothness and health risks. Since filter ventilation has no benefits for public health, policy makers could consider banning filter ventilation or even disallowing filters.
15 Apr 2026
Read appraisal →Biomedical physics & engineering express
A multi-task cross-attention strategy to segment and classify polyps
Polyps are the main biomarkers for diagnosing colorectal cancer. Their early detection and accurate characterization during colonoscopy procedures rely on expert observations. Nevertheless, such a task is prone to errors, particularly in morphological characterization. This work proposes a multi-task representation capable of segmenting polyps and stratifying their malignancy from individual colonoscopy frames. The approach employs a deep representation based on multi-head cross-attention, refined with morphological characterization learned from independent maps according to the degree of polyp malignancy. The proposed method was validated on the BKAI-IGH dataset, comprising 1200 samples (1000 white-light imaging and 200 NICE samples) with fine-grained segmentation masks. The results show an average IoU of 83.5% and a recall of 94%. Additionally, external dataset validation demonstrated the model's generalization capability. Inspired by conventional expert characterization, the proposed method integrates textural and morphological observations, allowing both tasks, polyp segmentation and the corresponding malignancy stratification. The proposed strategy achieves the state-of-the-art performance in public datasets, showing promising results and demonstrating its ability to generate a polyp representation suitable for multiple tasks.
14 Apr 2026
Read appraisal →Journal of medical Internet research
Large Language Model-Based Analysis of Statin Therapy Discussions and Sentiment on Social Media: Cross-Sectional Observational Study
BACKGROUND: Statin therapy, despite proven cardiovascular benefits, remains underused. Social media platforms may capture patient perspectives that are less visible in clinical encounters. OBJECTIVE: This study aimed to characterize themes, sentiment, and decision-making factors related to statin therapy through large language model (LLM)-based analysis of Reddit discussions. METHODS: This cross-sectional observational study analyzed English-language Reddit posts and comments mentioning statins from January 2022 to May 2025, identified via keyword-based Reddit application programming interface searches (≤1000 posts per keyword). A total of 5328 retrieved discussions (n=1661, 31.2% posts and n=3667, 68.8% keyword-containing comments) from public subreddits were included. Themes, sentiments (positive, neutral, or negative), guideline-informed clinical relevance, information-seeking behavior, adverse effect mentions, decision factors, and adherence-related content were extracted using an LLM-based pipeline. RESULTS: Among 5328 discussions, prominent topics included adverse effects (n=1697, 31.9%), decision-making references related to laboratory results and physician advice (n=2767, 51.9% and n=2034, 38.2%, respectively), and alternative approaches (n=2485, 46.6%). Overall sentiment was neutral in 34% (n=1812) of discussions, negative in 30.9% (n=1646), and positive in 16.9% (n=900); the remainder were mixed or unclear. Statin-directed sentiment was neutral in 44.1% (n=2350) of discussions, negative in 25.2% (n=1343), and positive in 12.5% (n=666); the remainder did not express statin-directed sentiment. High clinical relevance was identified in 12.6% (n=672) of discussions. Adherence-related issues were mentioned in 29.8% (n=1587) of discussions. Among adverse effect mentions, muscle pain (n=129, 7.6%) and fatigue (n=110, 6.5%) were common. CONCLUSIONS: LLM-enabled analysis of Reddit discourse highlights substantial negative sentiment, adherence-related concerns, and adverse effect narratives surrounding statin therapy. These findings suggest opportunities for patient-centered communication and shared decision-making strategies that address symptom attribution, uncertainty, and information needs in digital information environments.
12 Apr 2026
Read appraisal →Australian journal of primary health
National and international guidelines for preconception care and comparison with Australian guidelines: a systematic review
BACKGROUND: Guidelines for preconception care are resources that are systematically arranged based on evidence and local contexts to enhance the delivery of high-quality and consistent healthcare. The Royal Australian College of General Practitioners Guidelines (RACGP) for preventive activities in general practice (the Red Book) contains specific components of preconception care items, yet this guideline primarily focuses on general preventive healthcare and may not include a comprehensive list of preconception items based on more recent evidence. This review aimed to compare the recent RACGP guidelines (the 2024 Red Book) with national and international guidelines for preconception care. METHODS: Electronic databases, including PubMed, Embase, Web of Science, CINAHL, and Scopus, were systematically searched following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. We also searched clinical guideline registries on professional organisations' websites. Searches were conducted from May to June 2024 and in July 2025. RESULTS: We identified seven other national and international guidelines that recommended what should be done as part of preconception health in clinical practice. Preconception care elements that were not included in the current RACGP guidelines that have evidence of effectiveness were the inclusion of men in preconception care, screening for intimate partners and domestic violence, screening for chlamydia and gonorrhoea infections, and screening for female genital mutilation as part of preconception care. CONCLUSIONS: This review identified four preconception care elements that may potentially improve the implementation of preconception care in Australian practice. This work informs the next steps towards developing a comprehensive preconception medical record tool for use in general practice.
12 Apr 2026
Read appraisal →Molecular biomedicine
Machine learning integrated clinical-proteomics data identifies a 6-protein panel signature for atherosclerotic severity and enhanced patient stratification
Atherosclerosis, a major cause of adverse cardiovascular events and mortality rates worldwide, stems from sustained lipid accumulation and subsequent chronic inflammation within the arterial walls. An early identification of patients at risk is crucial to prevent life-threatening thrombotic events and provide effective and personalized treatments. Leveraging the power of machine learning (ML) to enhance diagnostics and biomarker discovery, we applied a high-throughput approach using five ML classification algorithms (MLCA), integrating clinical and serum proteomic data from patients with carotid atherosclerotic stenosis (AT, n:60), dyslipidemic patients (DLP, n:55), and healthy controls (HC, n:66). As a result, a robust 6-protein panel (B2M, GPV, MMP9, PLF4, TSP1, and FB isoforms) was identified with a ROC-AUC value > 0.9 for all algorithms applied, highly discriminating AT patients compared to DLP or CTRL. The levels of these proteins were further validated in an independent external cohort, including patients presenting with acute atherothrombotic stroke, corroborating the potential of this panel as biomarker for atherosclerosis severity. In addition, the combined clinical-proteomic ML approach provided a more accurate patient stratification than the clinical or proteomic analysis alone. Mechanistically, the identified biomarkers highlight the importance of platelet activation, uncontrolled angiogenesis and intraplaque haemorrhage in the atherosclerotic process, underscoring the need for multipathway therapies to prevent unwanted thrombotic events.
12 Apr 2026
Read appraisal →Nature communications
Exposome-wide patterns predict brain health in aging
Promoting brain health is vital for well-being and reducing healthcare burdens. Brain health as measured with the Brain Age Gap (BAG) - the difference between chronological and predicted brain age- relates to many factors. However, a holistic view, integrating the range of factors an individual brain is exposed to, is missing for understanding how the exposome shapes brain health. After computing BAG as an indicator of grey matter (GM) health, we predicted it using machine learning based on 261 exposome variables (spanning biomedical, environmental, lifestyle, socio-affective, and early life domains) in UK Biobank participants. Exposome data can predict GM health with factors pertaining to cardiovascular and bone health, along with alcohol and smoking, nutrition and diabetes showing greater contribution to the prediction. In such domains, life period and duration of exposure appeared crucial. These findings call for early prevention in cardiovascular and metabolic health to promote life-long brain health.
12 Apr 2026
Read appraisal →Heart (British Cardiac Society)
Subclinical atherosclerosis in women after pre-eclampsia: a systematic review and meta-analysis
BACKGROUND: Women with pre-eclampsia are at increased risk of later-life cardiovascular disease (CVD). Despite suggestions that women affected by pre-eclampsia should undergo routine CVD screening, uniform recommendations are lacking. Subclinical atherosclerosis provides a window of opportunity to identify and treat those at risk of CVD. Coronary artery calcium scoring (CAC), carotid intima-media thickness (CIMT) and ankle brachial index (ABI) are effective methods of detecting subclinical atherosclerosis. This systematic review sought to explore associations between timing of measurement and presence of subclinical atherosclerosis in women with a history of pre-eclampsia. METHODS: We searched the MEDLINE, EMBASE and CINAHL databases for all studies which reported subclinical atherosclerosis in women with a history of pre-eclampsia. Common and random effects models were used to examine the associations between pre-eclampsia and subclinical atherosclerosis. RESULTS: 35 case-control studies comprising 20 235 women were included in the final analysis. 3376 had a history of pre-eclampsia. Among the 26 studies assessing CIMT, pre-eclampsia was associated with increased CIMT (standardised mean difference 0.63 (95% CI 0.32, 0.93)). This difference was present during pregnancy (0.65 (95% CI 0.33, 0.98)) and persisted for 12 months post partum (0.84 (95% CI 0.20, 1.47)) and beyond (0.50 (95% CI 0.01, 0.99)). Pooled analysis of the eight CAC studies also demonstrated that the odds of having subclinical atherosclerosis were 1.57 (95% CI 1.39, 1.77) times higher in women with a history of pre-eclampsia. CONCLUSIONS: Among women with a history of pre-eclampsia, subclinical atherosclerosis can be seen during pregnancy and persists long term. Our review supports the importance of early follow-up in patients who have had pre-eclampsia.
12 Apr 2026
Read appraisal →Vaccine
Increasing HPV vaccine uptake using a "start at 9" strategy: A pilot program
BACKGROUND: Human papillomavirus (HPV) vaccine completion rates by 13 years remain suboptimal. Earlier HPV vaccination may result in improved timely completion rates. We aim to describe the impact of recommending HPV vaccine at 9 years of age on vaccine completion rates. METHODS: We implemented a multi-level program (including policy development, provider and office personnel education, electronic health record changes, and use of vaccine promotional items) to facilitate routine HPV vaccine recommendation at 9 years at 2 pediatric practices. Monthly chart reviews were performed 6 months before through 18 months after program implementation. Outcome measures included rates of HPV vaccination and documentation of HPV vaccine during the visit. Quarterly data (age, HPV vaccination status, gender, race, ethnicity, insurance, and primary care provider type) were obtained from the electronic health records of 9-12-year-olds served by the practice from baseline through 18 months after program implementation. RESULTS: Following program implementation, the median percentage of HPV vaccine uptake among 9-10-year-olds increased from 8% to 41% at the urban practice and from 3% to 23% at the suburban practice. Similarly, HPV vaccine completion rates among 9-12 year olds increased from 9% to 14%, p < 0.001 and from 6% to 16%, p < 0.001, respectively. Documentation of provider vaccine recommendation strongly correlated with HPV vaccination. CONCLUSION: Implementation of a multi-level program to routinely recommend the HPV vaccine at 9 years of age is feasible and effective in increasing in HPV vaccine completion by 13 years of age.
12 Apr 2026
Read appraisal →PloS one
Inconsistent condom use and its associated factors among female sex workers in African countries: Systematic review and meta-analysis
BACKGROUND: Inconsistent condom use represents the most proximal behavioral risk factor for acquisition and transmission of sexually transmitted infections, including human immunodeficiency virus. However, certain situations hinder female sex workers from practicing consistent condom use. This study aimed to assess the pooled estimate of inconsistent condom use among female sex workers and identify factors associated with it. METHODS: This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, 2020 reporting checklist. Electronic databases (PubMed, Cochrane Library, Epistemonikos, Hinari, and Science Direct), Google Scholar, and other university repositories were searched until March 20, 2024, based on the eligibility criteria. Three independent reviewers screened the titles, abstracts, and full texts. Two independent reviewers extracted the data. The Joanna Briggs Institute quality appraisal checklist was used. The Higgin's I² test was used to quantify heterogeneity. Pooled analysis was conducted using a random-effects model. Sensitivity and subgroup analyses were done. Publication bias was assessed using Egger's regression test and funnel plot. The pooled prevalence and statistical association were declared at a p-value < 0.05 with the 95% CI. RESULTS: A total of 24 studies involving 23,496 female sex workers with a median age of 27.3 years were included. The overall pooled prevalence of inconsistent condom use among FSW in Africa was estimated at 46.73% (95% CI: 37.60, 55.86), I² = 99.59%, and p = 0.00. Condom availability (AOR = 0.68; 95% CI: 0.50, 0.92), depression (AOR = 1.51; 95% CI: 1.00, 2.30), no education (AOR = 1.87; 95% CI: 1.19, 2.93), two or more nonpaying clients (AOR = 2.90; 95% CI: 1.51, 5.54), having >9 current client number (AOR = 0.46; 95% CI: 0.29, 0.74), violence (AOR = 1.74; 95% CI: 1.33, 2.27), and police harassment (AOR = 2.28; 95% CI: 1.03, 5.05) were significant factors. CONCLUSION AND RECOMMENDATION: Inconsistent condom use was high in Africa. Factors including availability of condoms, depression, and education, having two or more nonpaying clients, client numbers, violence, and police harassment were significant factors. Strategies like improving peer education, providing mental health support, empowering women, and improving female sex workers educational status, ensuring condom availability, and strengthening supply for easily accessible condoms can decrease inconsistent condom use and protect FSWs from STI including HIV.
11 Apr 2026
Read appraisal →PloS one
Association between hypertension and impaired lung function among adults: A systematic review and meta-analysis
BACKGROUND: Impaired lung function and hypertension are both significant global public health concerns, and their potential association has recently drawn considerable interest. This systematic review and meta-analysis aims to investigate the association between impaired lung function and hypertension, and vice versa. METHODS: We searched CINAHL (EBSCOhost), MEDLINE (PubMed), Scopus and Web of Science up to 22nd July 2025 for observational studies involving adults (≥ 18 years) reporting hypertension and impaired lung function. Grey literature was also searched, and data extraction was verified independently. The review was registered with PROSPERO (CRD42023427631). Random effects models estimated pooled ORs with 95% CIs. Subgroup analysis and meta-regression were performed to identify potential sources of heterogeneity. Quality assessment was conducted using Newcastle-Ottawa Scale and JBI checklist and publication bias were appraised. RESULTS: This systematic review included 28 articles, of which 26 articles were selected for the meta-analysis. Of these, 17 studies (n = 134823) examined impaired lung function as the exposure, while 9 studies (n = 12522) investigated hypertension as the exposure. Our findings revealed that the pooled unadjusted effect of lung function impairment on hypertension was 1.70 (95% CI: [1.53-1.88]), while the pooled adjusted effect was lower at 1.40 (95% CI: [1.31-1.49]). In contrast, the pooled unadjusted effect of hypertension on lung impairment was 3.00 (95% CI: [1.86-4.82]), but the pooled adjusted effect decreased to 1.94 (95% CI: [1.51-2.50]). The findings indicate that obstructive, restrictive and mixed lung function impairments are significantly associated with hypertension. Restrictive lung impairment consistently shows a stronger association with hypertension compared to obstructive impairment when impaired lung function is the exposure. CONCLUSION: This study concludes that there is a significant positive association between impaired lung function and hypertension in both directions, with stronger temporal evidence suggesting that impaired lung function may precede hypertension, while the reverse causal direction lacks such evidence.
11 Apr 2026
Read appraisal →Open heart
Effect of a mobile health intervention optimised with artificial intelligence on blood pressure and other cardiovascular risk factors in adults with high blood pressure: rationale and design of My Intelligent Cardiac Assistant (MICArdiac) randomised controlled trial
INTRODUCTION: Control of blood pressure (BP) continues to be a challenge globally. Clinical trials have shown home BP monitoring and text-message interventions to lower BP. Integrating these to personalise supportive messaging in response to changing BP and activity through leveraging artificial intelligence (AI) could improve BP control. The aim of this trial is to examine the impact on BP, compared with a text-message only programme, of the My Intelligent Cardiac Assistant (MICArdiac) programme. MICArdiac is a 6-month programme comprising tracking of BP, physical activity and heart rate informing a content stream of AI-driven personalised messages to encourage BP self-management, as-necessary medical review to up-titrate medicines and cardiovascular preventative behavioural change. METHODS AND ANALYSIS: MICArdiac is a prospective randomised open-blinded endpoint randomised controlled trial with 1:1 (intervention:control) allocation and active control. Individuals aged 35 years old or above with high BP are randomised to the MICArdiac programme or to a text-message cardiovascular education programme. Recruitment started in primary care and hospital clinics in Western Sydney and moved to decentralised direct-to-community recruitment in the Australian Capital Territory, New South Wales and Victoria. Randomisation and allocation concealment occur via a secure web-based system. The primary outcome is mean daytime systolic BP measured by 24-hour Ambulatory Blood Pressure Monitoring at 6 months. Primary analysis will follow intention-to-treat principles; data analysts will be blinded. Process evaluation will be conducted. ETHICS AND DISSEMINATION: Ethics approval was obtained from Western Sydney Local Health District Human Ethics Research Committee (2021/ETH11379). TRIAL REGISTRATION NUMBER: ACTRN12622000091707.
11 Apr 2026
Read appraisal →PloS one
Risk factors for spontaneous abortion following hepatitis E vaccination during and shortly before pregnancy: Further analysis from a cluster-randomized trial
BACKGROUND: A recent randomized trial of Hepatitis E Vaccine (HEV239) found an elevated risk of spontaneous abortion (SAB) in women given HEV239 during or within ninety days before pregnancy. Here we present additional analyses to better understand this risk. METHODS: We investigated whether maternal risk profiles of SAB or gestational age at SAB differed between women receiving HEV239 versus control Hepatitis B vaccine (HBV). We also assessed whether HEV239-vaccinated women who experienced SABs had distinct short-term adverse reactions compared to HEV239-vaccinated women who had live births, exploring potential inflammatory mechanisms. Finally, we assessed maternal exposure to HEV239 in narrowly defined pre-last menstrual period (LMP) windows to more precisely demarcate pre-LMP at which HEV-239 vaccination was associated with an elevated risk of SAB. Chi-square tests and Mann-Whitney U tests were performed for bivariate comparisons. Poisson models were used for multivariable analyses. FINDINGS: Baseline maternal risk factors for SAB (age, body mass index, parity, and histories of SAB, hypertension, or diabetes) were similar between the SABs in HEV239 and HBV groups. Median gestational age at SAB was comparable (10.5 weeks [IQR 10-13] for HEV239 vs. 11 weeks [IQR 9-12.2] for HBV, p = 0.783). No increased short-term adverse events (pain: 9% vs. 9%; fever: 2% vs. 1%) were observed among HEV239 recipients who experienced SABs compared to those who did not. An elevated risk of SAB was observed in HEV239 recipients vaccinated 31-60 days pre-LMP (Relative Risk 4.3 [95% CI 1.3-19.3], p = 0.026). INTERPRETATION: Our analyses did not uncover associations pointing to a biological mechanism underlying the original observation of an increased SAB rate connected to HEV239 vaccination shortly before or during pregnancy. Targeted studies such as placental examinations and genetic testing are needed to investigate potential mechanisms.
11 Apr 2026
Read appraisal →Biomedical physics & engineering express
Differentiating long QT syndrome genotypes using electrocardiographic geometric parameterization and machine learning approaches
Long QT Syndrome (LQTS) is an inherited cardiac disorder characterized by dysfunctional cardiac ion channels, which result in prolonged QT intervals on electrocardiograms (ECGs). LQTS can lead to severe clinical manifestations, including syncope, ventricular arrhythmias, and sudden cardiac death. Effective genotype-specific management strategies are essential to mitigate the risk of life-threatening arrhythmias. This study aims to achieve automatic discrimination among the LQT1, LQT2, and LQT3 genotypes to enable targeted treatment and prevention strategies. Utilizing ECG data from the Telemetric and Holter ECG Warehouse's LQTS database, our methodology involves an automated extraction process of short ECG signals, geometric parameterization techniques, and classification using a two-stage cascade of binary support vector machine classifiers. The input features for the classifiers are derived from Lead I ECG signals sampled at 200 Hz, highlighting the potential application in developing single-lead ECG monitoring devices and applications, such as widely used smartwatches, for which short recording duration, low sampling frequency, and arm-to-arm lead measurements are fundamental prerequisites for practical use. The proposed classifier achieved 71% weighted accuracy on out-of-sample data (LQT1: 65% recall, 58% precision; LQT2: 79% recall, 82% precision; and LQT3: 71% recall, 77% precision). Our findings demonstrate the feasibility of noninvasive genotype differentiation for LQTS based on the morphological analysis of ECG signals, providing an advancement in the field of personalized cardiology and the development of portable diagnostic tools.
11 Apr 2026
Read appraisal →Psychological medicine
Interventions to prevent antipsychotic-induced weight gain and metabolic complications in individuals with a first-episode psychosis and minimal antipsychotic exposure: a systematic review and meta-analysis
BACKGROUND: Rapid weight gain commonly occurs following the onset of first-episode psychosis (FEP), leading to cardiometabolic disease. Most weight gain in FEP occurs in the first 3 months of treatment, offering a critical window for prevention. Despite this, most studies aiming to prevent antipsychotic-induced weight gain include people with chronic illness or people who have had lengthy exposure to antipsychotic medication. We aimed to synthesize and analyze the literature on interventions aimed at preventing antipsychotic-induced weight gain. METHODS: We conducted a systematic review in PsycInfo, MEDLINE, CINAHL, and EMBASE of studies that examined the effectiveness of interventions in preventing antipsychotic-induced weight gain in FEP. We examined their effect on weight gain and a range of cardiometabolic markers. RESULTS: We screened 2,092 articles, 13 of which were eligible. Behavioral interventions, all three of which consisted of a multidisciplinary team approach, resulted in a mean of 3.05 kg less weight gain than treatment-as-usual (95% CI 1.36 kg to 4.73 kg). Pharmacological interventions displayed marked clinical and statistical heterogeneity, with each of the seven trials in adults using a different pharmacological intervention. Few studies collected comprehensive data on metabolic health. Only two pharmacological studies, and five studies in total, have been published since 2010. CONCLUSIONS: Despite the importance of preventing weight gain in FEP, there have been few recent studies investigating this topic. Our results indicate that multidisciplinary team interventions are effective in preventing weight gain in FEP and should be offered to all patients.
11 Apr 2026
Read appraisal →Open heart
Sex differences in risk factor control and medication adherence post-ACS: insights from the TEXTMEDS randomised clinical trial
INTRODUCTION: Poor adherence to risk factor control and life-saving medications is a key factor affecting long-term patient prognosis. Evidence indicates that sex plays a significant role in the uptake of both pharmacological and non-pharmacological interventions, ultimately influencing long-term outcomes. This study aimed to quantify sex differences in risk factor management and medication adherence following acute coronary syndrome (ACS). METHODS: This is a secondary analysis of the TEXTMEDS randomised clinical trial - a single-blind, multicentre randomised controlled trial of patients post-ACS. We compared sex differences in achieving clinical and lifestyle targets for secondary prevention, namely blood pressure control (<140/90 mm Hg), low-density lipoprotein cholesterol (LDL-C) (<1.8 mmol/L), healthy body mass index (BMI) (<25 kg/m²), regular physical activity (Global Physical Activity Questionnaire score ≥600), smoking status and adherence to cardioprotective medications (aspirin, beta blockers, ACE/angiotensin receptor blockers, statins, antiplatelets), using adjusted logistic regression models. Medication adherence was defined as taking ≥80% of prescribed doses in the month prior to follow-up, across all five drug classes, unless contraindicated. RESULTS: Of 1379 patients (mean age 58.5±10.7 years; 1095 (79.4%) male), females were less likely than men to achieve LDL-C targets (adjusted OR (aOR): 0.61, 95% CI 0.45 to 0.82) and engage in regular physical activity (aOR: 0.61, CI 0.47 to 0.80), but more likely to achieve a healthy BMI (aOR: 1.47, CI 1.04 to 2.06). Female patients are less likely to adhere to their medication compared with male counterparts (aOR: 0.68, CI 0.50 to 0.92). However, this association weakened and lost statistical significance after further adjustment for socio-economic factors (aOR: 0.71, CI 0.50 to 1.03). There were no significant interactions between sociodemographic or clinical factors and sex in relation to overall medication adherence (P-interactions >0.05). CONCLUSION: This study reveals that female patients are less likely to achieve LDL-C targets and engage in physical activity but more likely to maintain a healthy BMI. Although females showed lower medication adherence, this association weakened after adjusting for socio-economic factors. These findings highlight the importance of sex-sensitive strategies focusing on risk factor control and medication adherence for improving cardiovascular health outcomes. TRIAL REGISTRATION NUMBER: ACTRN12613000793718.
11 Apr 2026
Read appraisal →International journal for equity in health
Community participation in responding to health misinformation
Responses to health misinformation increasingly emphasize the importance of community engagement, yet the role of participation and power sharing remains under-theorized and inconsistently described in the peer-reviewed literature. Drawing on findings from the systematic review Overcoming Health Misinformation in Marginalized Groups, which examined 32 studies evaluating approaches to tackling health misinformation, we reflect on how the depth and quality of community participation shape trust, engagement, and intervention success. We argue that sustained partnership, shared decision-making, and local ownership enhance message credibility and reach by embedding health information within trusted social contexts. We propose strengthening equity-focused responses through structured reporting tools that center power, trust, and co-production as practical design principles rather than implementation add-ons. Making participation visible is critical if community engagement is to move beyond rhetoric and contribute meaningfully to addressing health misinformation in contexts of low institutional trust.
11 Apr 2026
Read appraisal →Psychological medicine
Inflammatory cytokine alterations in genetic and clinical high risk groups of psychosis: a systematic review and network meta-analysis
BACKGROUND: Inflammation has been implicated in psychosis, but its role in individuals at clinical (CHR) and genetic (GHR) high-risk remains unclear. We therefore conducted a network meta-analysis (NMA) to compare circulating cytokine levels across CHR, GHR, and healthy control (HC) groups. METHODS: We systematically searched multiple databases up to February 2025, extracting cytokine levels (plasma/serum) from CHR, GHR, and HC groups. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were estimated using random-effects models. Given that no direct head-to-head comparisons between CHR and GHR were available, indirect comparisons were performed through the common comparator (HC). The transitivity assumption was assessed by comparing key study and participant characteristics across comparisons. RESULTS: Thirty studies were included (CHR: 1601, GHR: 675, HC: 1980). NMA estimates indicated higher IL-6 levels in CHR compared with GHR, while IL-6 and IL-1β levels were lower in GHR compared with HC. In pairwise subgroup analyses, CHR converters showed higher IL-13 levels than non-converters. The evidence network was sparse and star-shaped, with all CHR-GHR estimates relying exclusively on indirect comparisons. CONCLUSIONS: This study represents the first NMA to synthesize cytokine alterations in individuals at high risk for psychosis using indirect evidence. Elevated IL-6 in CHR individuals suggests immune activation, whereas reduced IL-6 in GHR may reflect a distinct immune profile. Increased IL-13 levels in converters highlight potential involvement of Th2-related pathways during transition to psychosis. However, the sparse nature of the evidence network necessitates cautious interpretation of the findings, and larger, standardized multi-center studies are required for confirmation.
11 Apr 2026
Read appraisal →Acta dermato-venereologica
Predicting Melanoma Impact on the Swedish Healthcare System from the Adult Population Using Machine Learning on Registry Data
Melanoma incidence has increased in Western countries over the past 50 years, leading to significant healthcare costs. In Sweden, comprehensive healthcare registries enable large-scale prediction studies using machine learning. Several machine learning models were evaluated to predict melanoma diagnoses using Swedish registry data, assessing the added value of diagnostic and medication data beyond demographics. The study included all adults in Sweden with continuous residency for 9.5 years (n = 6,036,186). The outcome was a melanoma diagnosis, including melanoma in situ, recorded within 5 years after the index date (31 December 2014). Predictors included age, sex, income, education, marital status, region of birth, diagnoses, and dispensed drugs. Models tested were logistic regression, gradient boosting, random forests, and a neural network. A total of 38,582 individuals (0.64%) developed melanoma. The gradient boosting model using all predictors performed best, with an area under the receiving operating characteristics curve (AUC) of 0.735 (95% confidence interval [CI], 0.725-0.746). When diagnosis and medication data were excluded, AUC dropped to 0.681 (95% CI: 0.670-0.691). The findings highlight that including healthcare codes improves predictive performance, and demonstrate the utility of Swedish registries for computational phenotyping. This approach may support early detection of melanoma and targeted follow-up.
10 Apr 2026
Read appraisal →PloS one
Prevalence and determinants of inadequately controlled diabetes in Qatar's public primary care settings: A cross-sectional study
BACKGROUND: The prevalence of type 2 diabetes mellitus (T2DM) is rapidly increasing worldwide, causing serious morbidity and mortality. Proper management and control of T2DM are essential in slowing and reducing complications. Measuring the adequacy of diabetic control, therefore is one of the ways of evaluating the success of health care system and the individuals in addressing disease morbidity and mortality. This study was proposed to assess the extent of uncontrolled T2DM in ambulatory adults managed in the public primary care settings of Qatar over five years. In addition, it aimed to predict the risk of uncontrolled disease based on age, sex, nationality and comorbidity. METHODS: A cross sectional review of Electronic Health Records of 84,512 adult individuals with T2DM having at least one HbA1c blood test result per year during the 5 years study period (1st January 2018, to 31st December 2022) was analyzed. The term "inadequately controlled" T2DM was used to define an individual with HbA1c measurements of 7% or higher. RESULTS: The yearly prevalence of inadequately controlled T2DM remained stable at a high rate (53%) during the last three years of observations. Aging increases the probability of having inadequately controlled T2DM, but this was attributed to comorbidities. Adjusting for those comorbidities will show that younger ages are the ones at higher risk of being uncontrolled. Additionally, the study reported that hypertension and dyslipidemia are the most frequently prevalent comorbidities, nevertheless retinopathy, although less frequent, was the most important predictor for inadequately controlled T2DM. CONCLUSIONS: The study highlights the significant healthcare challenge posed by T2DM in Qatar due to its high prevalence and associated complications. Males, South Asian ethnic background and Qatari locals have the highest probability of being inadequately controlled. Overall, the study emphasizes the need for targeted interventions to address the challenges of controlling diabetes, particularly among high-risk groups identified in the study.
10 Apr 2026
Read appraisal →PloS one
MaxGRNet: A multi-axis vision transformer with improved generalization for eye disease classification using explainable AI with insertion-deletion operations on fundus images
Eye diseases, including diabetic retinopathy (DR), glaucoma, and cataracts, represent a major global health concern and can lead to severe visual impairment or blindness if not identified in a timely manner. This study proposes a novel eye disease classification framework based on a multi-axis vision transformer (MaxViT) applied to color fundus images with Explainable Artificial Intelligence (XAI) techniques to enhance model transparency. The proposed architecture integrates transformer-based attention mechanisms with Global Response Normalization (GRN)-based multi-layer perceptron (MLP) layers to capture complex spatial and contextual relationships within fundus images effectively. The model was evaluated on a publicly available eye disease classification dataset using a five-fold cross-validation strategy to assess its robustness and generalization. The experimental results show that the proposed approach consistently outperforms conventional Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) variants, including ResNet50, Swin-T, MaxViT-T, and ViT-B16. The model achieved a macro-averaged test accuracy, precision, and recall values of 96.75%, 96.70%, and 96.80%, respectively, with paired statistical t-tests confirming that these improvements were statistically significant. Rigorous preprocessing techniques were employed to improve data consistency, and XAI-based visual explanations provided insights into the model's decision-making process, supporting interpretability in ophthalmic image analysis. Overall, the proposed MaxViT-based framework is robust and computationally feasible for research-oriented evaluation approaches for automated fundus image classification, highlighting the potential of advanced transformer architectures for future decision-support and research-oriented ophthalmic applications.
10 Apr 2026
Read appraisal →Sexual health
Co-creation and evaluation of people-centered mobile health messages to promote PrEP adherence among GBMSM in China: a feasibility study
BACKGROUND: Co-creation has the potential to generate people-centered mobile health (mHealth) interventions to improve PrEP adherence. This study generated people-centered mHealth messages with Chinese gay, bisexual and other men who have sex with men (GBMSM) via a structured co-creation approach, and evaluated the messages quality, and assessed the feasibility of the co-creation approach. METHODS: A three-step co-creation framework was implemented with 19 GBMSM PrEP users and six HIV researchers between 2021 and 2023. Messages were assessed via 10-point scales, and the degree of usefulness by co-creators and 783 participants from a PrEP demonstration trial. Thematic analysis identified PrEP adherence barriers and participants' reflections from meeting notes and materials, with descriptive statistics summarizing evaluation results. RESULTS: Four levels of PrEP adherence barriers were identified (insufficient education, efficacy concerns, inadequate behavioral skills, PrEP stigma). Based on these barriers, 25 mHealth messages (19 posters, three videos, three text messages) were co-created, covering nine themes (e.g. missed dose coping, side-effect management). Seventeen high-quality messages were selected, with mean scores of 8.25 (relevance), 7.86 (appropriateness) and 7.25 (elaboration). Among GBMSM PrEP users who received the messages, 90.4% (708/783) valued the co-created messages as helpful. Reflections from the co-creators highlighted the value of the participatory process, confirming that co-creation effectively engages GBMSM PrEP users. CONCLUSIONS: Co-creation groups are feasible for Chinese GBMSM to identify context-specific PrEP adherence barriers and develop people-centered mHealth messages, supporting the value of co-creation for PrEP adherence intervention development in this population.
9 Apr 2026
Read appraisal →The New England journal of medicine
Immunogenicity and Safety of vYF, a Yellow Fever Vaccine - A Phase 2 Trial
BACKGROUND: A next-generation, live-attenuated yellow fever vaccine, vYF, was developed in Vero cells to improve vaccine supply and availability. The safety of and immune response to vYF as compared with those of the licensed yellow fever vaccine, YF-VAX, are unclear. METHODS: In this year 1 interim analysis of a phase 2, observer-blinded, randomized, active-controlled trial, we randomly assigned healthy adults 18 to 60 years of age in a 2:1 ratio to receive vYF or YF-VAX as a single vaccine injection on day 1. Neutralizing antibody titers were measured on day 29, month 6, and year 1. The primary analysis focused on the per-protocol population, which included participants with no history of yellow fever infection or vaccination and with no protocol deviations. Noninferiority would be shown if the lower limit of the two-sided 95% confidence interval of the between-group difference in the percentage of participants with seroconversion was greater than -5 percentage points on day 29. RESULTS: A total of 568 participants were enrolled: 382 in the vYF group and 186 in the YF-VAX group; 329 and 156 participants, respectively, were included in the per-protocol population for the noninferiority analysis. Seroconversion by day 29 occurred in 99.7% of participants receiving vYF and 99.4% of those receiving YF-VAX (difference, 0.3 percentage points; 95% confidence interval, -1.2 to 3.2, which met the criterion for noninferiority). Neutralizing antibody geometric mean titers were similar in the two vaccine groups, peaking on day 29 (in the vYF group, 1:2654 among participants with no history of yellow fever infection or vaccination and 1:1312 among participants with a history of yellow fever infection or vaccination; in the YF-VAX group, 1:3147 and 1:1079, respectively), then decreasing through 1 year after vaccination (in the vYF group, 1:401 among participants with no history of yellow fever infection or vaccination and 1:490 among participants with a history of yellow fever infection or vaccination; in the YF-VAX group, 1:548 and 1:646, respectively). No major safety concerns were identified. The safety profiles were similar in the two vaccine groups: solicited adverse events were reported by 208 of 367 participants (56.7%) in the vYF group and 113 of 185 participants (61.1%) in the YF-VAX group, and unsolicited adverse events were reported by 99 of 379 participants (26.1%) and 39 of 186 participants (21.0%), respectively. CONCLUSIONS: In this trial, vYF had immunogenicity and safety profiles similar to those of YF-VAX. (Funded by Sanofi; ClinicalTrials.gov number, NCT04942210.).
9 Apr 2026
Read appraisal →PloS one
The Relationships between physical activity, sedentary behaviour, sleep, and dementia: A systematic review and meta-analysis of cohort studies
OBJECTIVE: This study aimed to summarize the observational evidence from prospective cohort studies examining the associations of regular physical activity, sedentary behaviour, and sleep duration with incident dementia among community-dwelling adults aged 35 years and older. METHODS: Systematic literature searches (1946 to August 2025) of CINAHL, EMBASE, MEDLINE, PSYCINFO, and SPORTDISCUS were performed. Eligible studies included community-dwelling adults aged 35 + years with at least one year of follow-up and valid measures of movement behaviours and dementia outcomes. Studies were excluded if they included participants with baseline dementia, lacked risk estimates for all-cause dementia. Grey literature was excluded. Random effects meta-analysis generated pooled risk ratio (RR) and 95% confidence intervals (CI). Primary exposures were defined using national thresholds for physical activity, sedentary time, and sleep duration. Subgroup analyses were performed by age and follow-up duration. RESULTS: Forty-nine studies with physical activity (n = 2,855,529), 17 studies on sleep duration (n = 1,344,170), and three studies on sedentary duration (n = 295,809) were included. Regular physical activity significantly reduced the risk of incident dementia (pooled RR = 0.75, 95% CI = 0.68 to 0.82), though heterogeneity was substantial and partially explained by subgroup analyses. Prolonged sedentary behaviour (8 + hours/day sitting) increased dementia risk (RR = 1.27, 95% CI = 1.17 to 1.39) with low heterogeneity. Moreover, both short (<7 hours; RR = 1.18, 95% CI = 1.09 to 1.28) and long (>8 hours; RR = 1.28, 95% CI = 1.15 to 1.43) sleep were linked to higher dementia risk compared with 7-8 hours. Heterogeneity was moderate to substantial. CONCLUSION: Regular physical activity, less sedentary time and appropriate nightly sleep (7-8 h) may be associated with reduced risk of dementia and are potentially modifiable factors in the prevention or delay of dementia. Future studies with middle-aged adults and longer-term follow-up including changes in movement behaviours over time are needed to better understand the relationship between physical activity, sedentary behaviour, and sleep for dementia risk. PROSPERO REGISTRATION: CRD42021272054.
9 Apr 2026
Read appraisal →The New England journal of medicine
Intensive LDL Cholesterol Targeting in Atherosclerotic Cardiovascular Disease
BACKGROUND: Despite guideline recommendations, evidence from randomized trials evaluating the appropriate low-density lipoprotein (LDL) cholesterol target for secondary prevention in patients with atherosclerotic cardiovascular disease remains limited. METHODS: In this open-label superiority trial conducted in South Korea, we randomly assigned patients with atherosclerotic cardiovascular disease in a 1:1 ratio to a target LDL cholesterol level of less than 55 mg per deciliter (1.4 mmol per liter) (intensive-targeting group) or less than 70 mg per deciliter (1.8 mmol per liter) (conventional-targeting group). The primary end point was a composite of death from cardiovascular causes, nonfatal myocardial infarction, nonfatal stroke, any revascularization, or hospitalization for unstable angina at 3 years. Safety was also assessed. RESULTS: Of 3048 patients who underwent randomization, 1526 were assigned to the intensive-targeting group and 1522 to the conventional-targeting group. The median follow-up was 3.0 years. The median LDL cholesterol level during the trial was 56 mg per deciliter (1.4 mmol per liter) in the intensive-targeting group and 66 mg per deciliter (1.7 mmol per liter) in the conventional-targeting group. A primary end-point event occurred in 100 patients (Kaplan-Meier estimate of cumulative incidence, 6.6%) in the intensive-targeting group and in 147 patients (Kaplan-Meier estimate of cumulative incidence, 9.7%) in the conventional-targeting group (hazard ratio, 0.67; 95% confidence interval, 0.52 to 0.86; P = 0.002). The incidence of prespecified safety end points was similar in the two trial groups, except for a lower incidence of creatinine elevation in the intensive-targeting group. CONCLUSIONS: Among patients with atherosclerotic cardiovascular disease, targeting an LDL cholesterol level of less than 55 mg per deciliter resulted in a lower risk of cardiovascular events at 3 years than targeting a level of less than 70 mg per deciliter. (Funded by the Cardiovascular Research Center and Yuhan; Ez-PAVE ClinicalTrials.gov number, NCT04626973.).
9 Apr 2026
Read appraisal →Brain : a journal of neurology
Leveraging machine learning to predict Parkinson's disease using pre-symptomatic proteomics data
In Parkinson's disease, non-motor symptoms precede the characteristic motor manifestations by up to 20 years. However, predicting the disease risk remains challenging. We applied machine learning to 2937 plasma proteins assayed in UK Biobank participants with Parkinson's disease to predict disease outcome up to 14 years before diagnosis. Of the 446 proteins found to be dysregulated in incident cases compared with controls, a subset of 23 predicted disease with an area under the curve (AUC) value of 0.78 in incident and 0.795 in prevalent cases. The utility of the identified proteomic signatures was validated in an independent cohort, based on 16 proteins shared across datasets, leading to a validation AUC of up to 0.76. Parkinson's disease-related pathways, including neuron death and amyloid-β clearance, were enriched up to 9 years before diagnosis. Furthermore, a co-expression network analysis revealed protein modules associated with disease risk and time-to-diagnosis. Our findings underscore the potential of applying machine learning to large-scale proteomics data from pre-symptomatic patients for early disease prediction.
9 Apr 2026
Read appraisal →Physiological measurement
Towards fair and trustworthy heart rate estimation from wrist-worn photoplethysmography: a multi-wavelength dataset and uncertainty-aware deep learning approach evaluated across skin tones, sexes, and motion conditions
Objective.To improve fairness (reduced disparities across skin tones and sexes) and trust (well-calibrated uncertainty metrics that indicate unreliable predictions) in wrist-worn photoplethysmography (PPG) heart rate estimation. While PPG offers a convenient, low-cost method for continuous heart rate monitoring, accuracy is often degraded by motion artefacts and demographic variation.Approach.We collected a multi-wavelength (blue, green, red, IR) wrist-worn PPG dataset covering diverse activities, skin tones, and sexes, and developed a multi-branch PPG-accelerometer fusion convolutional neural network quantifying both aleatoric and epistemic uncertainty (the latter via Monte Carlo dropout, concrete dropout, and deep ensembles). We compared wavelength sets, quantified demographic effects, and applied uncertainty-aware post-processing to filter out high-uncertainty predictions. Evaluation used leave-one-subject-out cross validation across both the collected multi-wavelength dataset and five commonly used single-wavelength datasets.Main results.Multi-wavelength fusion improved accuracy under motion, with blue-green-red-IR achieving the lowest mean absolute error (MAE). Baseline error was higher for participants with darker skin tones and for females; uncertainty-guided rejection reduced MAE and effectively eliminated these gaps (reducing parity gaps from 1.2 BPM to 0.0 BPM for skin tone). Concrete dropout provided the best calibration-accuracy-efficiency trade-off, achieving the lowest average miscalibration area (MA=0.12) while maintaining competitive MAE.Significance.Combining calibrated uncertainty with group-aware evaluation yields a more fair and trustworthy wrist-worn PPG heart rate estimation pipeline, supporting responsible deployment in consumer and sports contexts and informing bias-aware clinical development.
9 Apr 2026
Read appraisal →European journal of pediatrics
Predicting coronary artery abnormalities in Kawasaki disease: Model development and validation
Routine echocardiography at one month after diagnosis is the current standard for screening coronary artery abnormalities (CAA), a major complication of Kawasaki disease. The study aimed to develop and validate models to predict CAA and assess whether routine echocardiography could be safely reduced in low-risk patients. Two prospective multicenter Japanese registries were utilized: PEACOCK (development/internal validation) and Post-RAISE (external validation). Variables obtained within one week of diagnosis were used to predict CAA at one month after diagnosis, defined as a maximum coronary artery Z score (Zmax) ≥ 2. The models included simple models using the previous maximum Z score only, logistic regression models, and machine learning models (LightGBM and XGBoost). Discrimination, calibration, and clinical utility were assessed. Among 4,973 PEACOCK and 2,438 Post-RAISE patients, the CAA incidence was 5.5% and 6.8%, respectively. Twenty-two models were developed using 29 variables. For external validation, a simple model using the maximum Z score at week 1 produced an area under the curve (AUC) of 0.79; adding other variables or using more complex models did not increase the AUC by more than 0.02. The models failed to efficiently reduce the number of echocardiographic examinations while minimizing missed cased of CAA.Conclusion: Until superior predictors are identified, routine echocardiography at one month after diagnosis should remain the standard practice.
9 Apr 2026
Read appraisal →Blood pressure
Effects of whole-body vibration training on resting blood pressure in adults: a systematic review and meta-analysis of randomised controlled trials
OBJECTIVE: To investigate the effect of whole‑body vibration training (WBVT) on resting blood pressure (BP) in adults and its moderating factors through a meta‑analysis. METHODS: PubMed, Cochrane Library, Embase, and Web of Science were systematically searched up to 2 December 2025, with systolic BP (SBP) and diastolic BP (DBP) as the primary outcomes. Using Stata 17.0 software, effect sizes were pooled with a random‑effects model, and results were reported as weighted mean differences (WMDs) with 95% confidence intervals. RESULTS: This meta-analysis included 14 randomised controlled trials (RCTs) involving 490 adult participants. WBVT significantly reduced SBP (WMD = -6.57 mmHg, 95% CI [-8.84, -4.29]; p < 0.01) and DBP (WMD = -3.01 mmHg, 95% CI [-4.32, -1.71]; p < 0.01) in adults. Subgroup analyses showed greater BP reduction in individuals with higher baseline BP (SBP: -11.34 mmHg [95% CI: -13.45, -9.23]; p < 0.01; DBP: -6.15 mmHg [95% CI: -7.46, -4.83]; p < 0.01) and in those who were overweight or obese (SBP: -8.99 mmHg [95% CI: -11.18, -6.80]; p < 0.01; DBP: -3.98 mmHg [95% CI: -5.17, -2.78]; p < 0.01). Interventions using combined movement patterns also yielded more pronounced effects (SBP: -7.12 mmHg [95% CI: -9.73, -4.51]; p < 0.01; DBP: -3.30 mmHg [95% CI: -5.61, -0.79]; p < 0.01). CONCLUSION: WBVT significantly reduced resting BP in adults, particularly effective in those with higher baseline BP, overweight/obese, or using combined exercise protocols, but further high-quality RCTs are needed. This meta-analysis demonstrates that WBVT significantly reduces resting BP in adults, with an average reduction of 6.57 mmHg in SBP and 3.01 mmHg in DBP. Subgroup antihypertensialyses further revealed that the anve effect was more pronounced in individuals with elevated baseline BP (SBP ≥130 mmHg or DBP ≥80 mmHg) and in those who were overweight or obese (BMI ≥25 kg/m2). Additionally, training protocols incorporating compound movements, such as squats combined with heel raises, were superior to those using simple standing postures. These findings hold clear clinical significance. The magnitude of BP reduction achieved by WBVT, particularly the approximate 6.57 mmHg decrease in SBP, is expected to translate into a significant reduction in cardiovascular risk. Prior evidence indicates that for every 5 mmHg reduction in SBP, the risk of major cardiovascular events decreases by approximately 10%. Therefore, WBVT provides an effective, safe, and promising non-pharmacological adjunctive management strategy for adults, especially for higher-risk groups (e.g. individuals with hypertension or those who are overweight/obese) and for those who have difficulty engaging in traditional exercise.
9 Apr 2026
Read appraisal →Journal of medical Internet research
A Gamified Mobile Health Intervention to Promote Physical Activity, Executive Function, and Mental Health in College Students: Randomized Controlled Trial
BACKGROUND: College students commonly experience suboptimal health conditions, including insufficient physical activity (PA), excessive body weight, and declining physical fitness. Traditional interventions face low adherence, while gamified mobile health (mHealth) programs may improve engagement and outcomes. OBJECTIVE: This study aimed to evaluate the feasibility and effectiveness of a novel gamified, incentive-based mHealth intervention on primary outcomes (PA and adherence) and secondary outcomes (physical fitness, body composition, executive function [EF], and mental health). METHODS: A 2-arm parallel-group randomized controlled trial (RCT) was conducted in 2025 at Yantai University with 160 college students (18-25 years; BMI 18.5-30.0) who were randomized 1:1 (computer-generated, sex-stratified blocks of 4; concealed allocation) to the intervention group (IG) or control group (CG; n=80 each); major exclusions were contraindications to exercise, severe physical/mental illness, recent PA interventions, or psychotropic medication use. Both used the same fitness watch-app system and identical PA targets (≥150 min moderate-to-vigorous physical activity [MVPA] per week or ≥900 metabolic equivalent-minutes [MET-min] per week); IG additionally received team-based gamification (competition, points/leaderboards, feedback, and rewards), while CG received monitoring only. PA and adherence were monitored throughout the 8-week intervention; other outcomes were assessed at baseline and 8 weeks (fitness, body composition, EF, and mental health). Open-label with blinded outcome assessors/analysts; intention-to-treat (ITT) with multiple imputation. RESULTS: At 8 weeks, data were available for 154 participants (IG 78; CG 76); all 160 were analyzed per ITT. Compared to the CG, the IG demonstrated significantly higher mean levels in all primary PA outcomes over 8 weeks (daily steps: mean 10,356, SD 1245 versus 8242, SD 1087; Δ=2114; d=1.81, 95% CI 1.44-2.18; P<.001; daily MVPA: mean 71, SD 15 versus 43, SD 12 min; Δ=28 min; d=2.06, 95% CI 1.68-2.45; P<.001; and weekly MET-min: mean 1650, SD 310 versus 1340, SD 285; Δ=310; d=1.04, 95% CI 0.71-1.37; P<.001). Adherence was also higher in the IG (both P<.001). Key secondary outcomes, including skeletal muscle mass (Δ +0.54 kg), body fat percentage (Δ -0.46 percentage points), 800/1000 m run time (Δ -6.5 s), and depressive symptoms (Δ -2.0 points), significantly favored the IG (all P<.001). No serious adverse events occurred. CONCLUSIONS: This RCT demonstrated that integrating gamification into an mHealth platform significantly enhanced PA, intervention adherence, and selected health outcomes among college students. The key innovation lies in the trial design, which held the technology platform and activity goals constant across groups, thereby allowing a clearer estimate of the incremental contribution of gamification. Unlike many existing studies, this approach reduces confounding from co-occurring intervention components in between-group comparisons. It therefore provides more comparable and methodologically robust evidence on the specific efficacy of gamification in mHealth interventions. For real-world implementation, this model leverages the existing smartphone and wearable ecosystem, offering a low-cost, scalable strategy for university health-promotion programs.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
Development and Validation of a Machine Learning Model for Incident Heart Failure Prediction in Chronic Kidney Disease: A Multicenter Cohort Study
BACKGROUND: Chronic kidney disease (CKD) and heart failure (HF) share pathophysiological mechanisms, rendering HF one of the most burdensome cardiovascular complication in CKD. Current HF prediction models, derived from the general population, exhibit limited accuracy in CKD, thus necessitating a CKD-specific risk model and clinical implementation. METHODS: The development set comprised 52 251 patients with CKD from the China Renal Data System (70% training; 30% internal validation). External validation used 21 798 patients from independent Chinese hospitals and 3323 UK Biobank participants. Outcome was 5-year new-onset HF. Five machine learning models were developed, with performance assessed via area under the curve and compared using DeLong test. The top-performing extreme gradient boosting model was simplified via forward stepwise selection; feature importance quantified using Shapley additive explanations. RESULTS: In the Chinese external validation cohort, the extreme gradient boosting model outperformed others (area under the curve, 0.879 [95% CI, 0.871-0.887]; DeLong P<0.05), with an area under the curve of 0.851 (95% CI, 0.831-0.870) in the UK Biobank cohort. The model was simplified to 9 predictors; Shapley additive explanations analysis ranked estimated glomerular filtration rate and albuminuria as among the most important features. In Chinese and UK Biobank external validation sets, the simplified extreme gradient boosting model showed a ΔC-statistic of 0.050 (0.045-0.056) and 0.006 (-0.013-0.024) versus the Atherosclerosis Risk in Communities risk score, and 0.194 (0.183-0.204) and 0.145 (0.107-0.182) versus the Predicting Risk of Cardiovascular Disease Events Equation, respectively. A web-based calculator was deployed (https://clinician.shinyapps.io/HF_Risk_Predictor_for_CKD/). CONCLUSIONS: The 9-variable extreme gradient boosting model tailored for patients with CKD may help predict HF risk in this high-risk population. Validation across diverse populations is warranted to confirm its generalizability.
8 Apr 2026
Read appraisal →Annals of medicine
Intervention components, training dose, and adherence in exercise-based prevention of hamstring strain injury in football: a systematic review and meta-analysis
OBJECTIVE: To quantify associations between exercise-based prevention programmes and hamstring strain injury (HSI) risk in football participants, and whether training dose and adherence modify effects. METHODS: Six databases were searched to 1 October 2025. Randomised and cluster-randomised trials comparing HSI prevention programmes with usual practice or warm-up in football participants were included. Random-effects meta-analysis pooled risk ratios (RRs); subgroup analyses and meta-regression assessed effect modification. RESULTS: Fifteen trials (n = 7,465) were analysed. Programmes reduced HSI risk (RR = 0.51, 95% CI 0.36-0.71), with I²=57% and a prediction interval crossing the null (0.18-1.40). Based on a control event rate of 7.8%, absolute risk reduction was 3.8% (38 fewer HSIs per 1000 participants; 95% CI 23-50 fewer). Effects were stronger for shorter interventions (1-6 months; RR = 0.43) than longer interventions (7-10 months; RR = 0.77; P for interaction=0.04), and for elite/semi-professional players (RR = 0.38) than amateur players (RR = 0.77; P for interaction = 0.02). Training frequency and weekly volume did not modify effects, whereas adherence did. High adherence (≥75%) was associated with lower HSI risk (RR = 0.36, 95% CI 0.28-0.48), whereas low adherence (<75%) showed no clear benefit (RR = 0.92, 95% CI 0.68-1.23; P for interaction <0.00001). Each 10% increase in adherence corresponded to an RR multiplier of 0.83 (approximately 17% lower RR). Certainty of evidence was low. CONCLUSION: Exercise-based programmes reduce HSI risk in football when implementation supports sustained adherence. Effects may be stronger in shorter interventions and elite populations, but evidence remains insufficient to differentiate programme types or components.
8 Apr 2026
Read appraisal →Journal of medical Internet research
Social Media Intervention Based on the Information-Motivation-Behavioral Skills Model Promotes HIV Testing and Reduces High-Risk Behaviors Among Men Who Have Sex With Men in Resource-Limited Settings in China: Randomized Controlled Trial
BACKGROUND: Social media intervention may enhance HIV prevention among men who have sex with men, but the effect of this intervention in resource-limited settings remains unclear. OBJECTIVE: This randomized controlled trial evaluated whether a social media intervention grounded in the information-motivation-behavioral skills (IMB) model could be beneficial for HIV prevention among men who have sex with men in resource-limited settings. METHODS: Participants were recruited in Nanning, China, between April 2023 and April 2024. Eligible participants were randomly assigned to either the social media intervention group or the routine HIV prevention services control group. Participants in the intervention group received a 3-month social media intervention, which included completing video-based tasks. Baseline surveys were conducted, followed by follow-up surveys every 3 months, for a total of 2 follow-ups. Outcomes included HIV testing uptake, high-risk behavior, AIDS-related knowledge, safe sex self-efficacy, and attitude. RESULTS: A total of 180 eligible men who have sex with men were enrolled (90 per group). Follow-up rates were 97.8% (88/90) and 95.5% (86/90) for the intervention and control groups, respectively. At the follow-ups, the intervention group demonstrated significantly higher uptake of HIV testing, a lower proportion of participants reporting high-risk sexual behaviors, and higher condom use self-efficacy compared to the control group (all P<.05). After controlling for sociodemographic variables, generalized estimating equations analysis revealed that the intervention group had significantly higher odds of HIV testing (risk ratio [RR] 1.739, 95% CI 1.110-2.730), HIV self-testing (RR 2.306, 95% CI 1.593-3.340), and consistent condom use (RR 2.457, 95% CI 1.636-3.690) than the control group. Cochran-Armitage trend tests within the intervention group revealed that with increasing intervention duration, both HIV testing and HIV self-testing significantly increased, while high-risk sexual behaviors significantly decreased (all P<.05). CONCLUSIONS: The social media intervention guided by the IMB model demonstrated a positive effect on expanding HIV testing coverage, reducing high-risk behavior, enhancing AIDS-related knowledge, and improving safer sex self-efficacy among men who have sex with men in resource-limited settings. These findings provide valuable guidance for future HIV prevention and control efforts targeting this population.
8 Apr 2026
Read appraisal →Journal of medical Internet research
Electrocardiogram-Based Mental Stress Detection Amid Everyday Activities Using Machine Learning: Model Development and Validation Study
BACKGROUND: Frequent, sustained stress is linked to poor health and requires monitoring for early intervention. Electrocardiograms (ECG) are promising biomarkers because they can be recorded noninvasively and continuously using wearable devices. However, tracking stress with ECG is challenging because daily activities elicit responses similar to mental stress (MS), and various mental stimuli that individuals encounter complicate the use of machine learning (ML) models trained on a limited set of stressors. OBJECTIVE: We (1) evaluated the ability of ML models to distinguish MS episodes from a composite "no-stress" background, including rest and low- to moderate-intensity activities; (2) assessed their generalizability to new stressors and participants; and (3) tested robustness to lower sampling rates and fewer features, to explore their suitability for lightweight wearables. METHODS: We used a comprehensive ECG dataset sampled at 1000 hertz from 127 participants who underwent various mental stressors and engaged in diverse physical activities. A 30-second window was used to extract 55 features from time, frequency, nonlinear, and morphological domains. We trained a logistic regression (LR) model and an extreme gradient boosting (XGBoost) model, splitting the data into 60/20/20 for training, validation, and testing. Shapley additive explanation values were computed to explain model predictions. Additional analyses included leave-one-stressor-out; downsampling to 500, 250, and 125 hertz; a time-window sensitivity analysis; and reducing the number of features to as few as 5. RESULTS: XGBoost achieved an area under the receiver operating characteristic curve (AUROC) of 0.741 (95% CI 0.701-0.783) and an area under the precision-recall curve (AUPRC) of 0.706 (95% CI 0.658-0.753), compared with 0.724 (95% CI 0.678-0.772) and 0.691 (95% CI 0.639-0.742) for LR. The mean performance difference between XGBoost and LR was 0.017 for AUROC (95% CI 0.001-0.032) and 0.015 for AUPRC (95% CI -0.001 to 0.037; clustered bootstrap analysis using 2000 participant-level resamples), suggesting that LR performs comparably to the nonlinear XGBoost model. Both models were robust to downsampling and feature reduction (10 features retained >93% of performance). Extending the analysis window to 60 seconds improved model performance across all sampling rates, highlighting a trade-off between rapid detection and overall performance. When evaluating discrimination from physical activity, models achieved acceptable specificity for light physical activity (XGBoost: 0.787; LR: 0.794) but poor specificity for moderate physical activity (XGBoost: 0.418; LR: 0.444). Both models generalized to most unseen stressors, although performance varied across stressors, with limited transfer to the social-evaluative stressor. Feature importance analysis revealed fuzzy entropy and frequency-based features as key predictors. CONCLUSIONS: ML models can detect MS with high sensitivity and remain robust to lower sampling rates and fewer features. Generalization to novel stressors was stressor-dependent. Importantly, our results highlight challenges in distinguishing stress-related cardiac responses from those caused by physical exertion, revealing critical limitations of single-sensor ECG approaches for MS detection.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
Exposure to Volatile Organic Compounds and Cardiovascular Diseases: A Systematic Review and Meta-Analysis
BACKGROUND: Environmental pollution is an often-neglected contributor to cardiovascular disease (CVD). Volatile organic compounds (VOCs) are a subset of ubiquitous pollutants. Recent evidence has suggested exposure to VOCs as a possible contributor to CVD. We aimed to review the evidence for such a link between VOC exposure and cardiovascular outcomes. METHODS: PubMed, Web of Science, and Scopus were systematically searched for ecological and observational studies evaluating associations between exposure to VOCs and cardiovascular outcomes. The available evidence was synthesized qualitatively, and meta-analyses were conducted wherever multiple estimates were identified for a shared association. Sensitivity analyses were performed to evaluate the influence of individual studies on the pooled estimates. RESULTS: Thirty-six studies were included in the review. Pooled random-effects estimates for associations between VOC mixtures (odds ratio, 1.57 [95% CI, 0.79-3.11])/toluene, ethylbenzene, and xylene and CVD trended toward a positive effect while being imprecise. The pooled estimate for the association between exposure to benzene and CVD death also trended toward a positive effect (meta relative risk, 1.09 [95% CI, 0.99-1.21]; I2=91.4 [95% prediction interval, 0.82-1.46]). However, serious inconsistencies were noted on the significance and the magnitude of association. Fixed-effect analyses indicated significant positive effects in all evaluated associations. CONCLUSIONS: Evidence from studies evaluating ambient and individual VOC exposures suggests a possible association with increased CVD and CVD death. However, the heterogeneity noted between specific VOCs' effects, possible confounding, limited long-term exposure assessment, and sparse evaluation of mixture/copollutant effects warrant cautious interpretation of these findings and highlight the need for future high-quality prospective studies.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
Blood Pressure Parameters and Risk of Cognitive Decline and Dementia in Type 2 Diabetes: Results From the ADVANCE Trial
BACKGROUND: Blood pressure (BP) variability and cumulative BP load are significantly associated with cardiovascular disease risk beyond mean systolic BP, but less is known regarding their associations with cognitive decline/dementia and whether these associations differ by cognitive function at baseline or by sex. The aims of this study were to determine associations of different BP parameters with cognitive decline/dementia in patients with type 2 diabetes and explore differences by mild cognitive impairment at baseline and sex. METHODS: Using data from the ADVANCE (Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation) study, BP parameters were calculated from an 18-month exposure window comprising measurements at 3, 4, 6, 12, and 18 months, after randomization. Logistic regression was used to estimate the odds ratio (OR) per SD higher and 95% CI for the associations of BP parameters with the composite outcome of cognitive decline (≥3 points from baseline on the Mini-Mental State Examination) or clinical diagnosis of dementia. RESULTS: Of the 11 140 ADVANCE participants, 9586 patients had 5 complete BP measurements within the 18-month exposure window. After a mean follow-up of 3.5 years, 1674 (17.5%) participants were diagnosed with cognitive decline and/or dementia. Overall, variability and baseline pulse pressure (PP), but not BP load, were associated with higher odds of cognitive decline/dementia (OR: variability in systolic BP, 1.11 [95% CI, 1.05-1.17]; diastolic BP, 1.11 [95% CI, 1.05-1.17]; PP, 1.05 [95% CI, 1.00-1.11]; mean arterial pressure, 1.13 [95% CI, 1.07-1.19]; and baseline PP, 1.19 [95% CI, 1.13-1.25]). There were no differences by mild cognitive impairment at baseline or sex. CONCLUSIONS: Higher BP variability and baseline PP, but not mean BP or BP load, were associated with higher odds of cognitive decline/dementia in patients with type 2 diabetes. BP variability and PP may be important therapeutic markers for the preservation of brain health. REGISTRATION: URL: https://clinicaltrials.gov; Unique Identifier: NCT00145925.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
Beyond Composite Indices: Comprehensive Social Determinants Improve Heart Failure Readmission Prediction
BACKGROUND: Heart failure (HF) hospitalization readmissions are associated with a high mortality rate and strain the health care system. Both clinical factors and social determinants of health (SDOH) predict HF readmissions, but the optimal approach to incorporating area-level SDOH data remains unclear. METHODS: We merged census tract- and county-level SDOH measures with electronic health record data in a retrospective cohort of 33 579 Black and White patients with HF (Emory Healthcare, 2010-2018). Six combinations of electronic health record data with 752 area-level SDOH were evaluated using multiple machine learning models (logistic regression, random forest, XGBoost) to predict 30-day HF readmission. Models were assessed for predictive performance using area under the receiver operating characteristic curve and algorithmic fairness across race using the equalized odds ratio. RESULTS: Expanded SDOH predictor sets improved predictive performance and algorithmic fairness compared with traditional SDOH indices. The XGBoost model using expanded SDOH alongside clinical predictors provided better predictive performance (area under the receiver operating characteristic curve, 0.671) and improved algorithmic fairness across patient race (equalized odds ratio, 0.437) than models with traditional indices (area under the receiver operating characteristic curve, 0.632; equalized odds ratio, 0.329). Feature-importance analysis revealed that specific environmental predictors (housing cost burden, air quality) ranked among top predictors alongside clinical biomarkers. County-level SDOH outperformed census tract-level SDOH and matched prediction performance of clinical predictors alone. CONCLUSIONS: Including hundreds of individual SDOH indicators rather than traditional composite indices improves machine learning models' ability to predict 30-day HF readmissions. While performance gains are modest, inclusion of specific environmental factors may provide greater clinical utility and improved equity across racial groups than composite SDOH measures for guiding further research and preventive interventions.
8 Apr 2026
Read appraisal →Environmental science & technology
Molecular Representation and Closed-Loop Validation for Toxicity Assessment of Organic Compounds in Ambient Air PM2.5
Although the health impacts of fine particulate matter (PM2.5) are primarily attributed to its chemically diverse composition rather than to mass concentration, assessing the toxicity of PM2.5 constituent compounds remains highly challenging due to chemical complexity and limited experimental scalability. This study introduces an interpretable machine learning (ML) framework using a curated A549 cytotoxicity data set (19,841 compounds) that integrates customized Molecular Access System (MACCS) fingerprints, six base models, and a stacking ensemble meta-model, all optimized with a biobjective strategy to assess the toxicity of organic compounds in PM2.5. The stacking ensemble model demonstrated satisfactory performance (test AUC > 0.8), exhibiting good generalization, adaptability, and robustness. Nontargeted analysis generated and experimentally validated a prediction set from the Hong Kong PM2.5 samples (51 of 387 compounds confirmed from 13 classes), demonstrating broad applicability on an independent Nanjing PM2.5 set (572 compounds). Key substructures driving toxicity, identified through a SHapley Additive exPlanations (SHAP) analysis and cell experimental validation, revealed that PM2.5 compounds with aromatic rings and nitrogen-based functional groups (e.g., aromatic α,β-unsaturated ketones, aromatic amines, aromatic nitro compounds, and tertiary amines) likely contribute to high toxicity. The derived "structure-toxicity" rules narrow the search space from thousands of compounds to those containing critical substructures, enabling efficient prioritization of toxic components and providing a foundation for improving model specificity and predictive accuracy in future studies.
8 Apr 2026
Read appraisal →PloS one
A multi-modal data fusion and real-time monitoring on stroke risk prediction using federated learning
Predicting the risk of stroke is one of the critical problems in healthcare, which necessitates efficient solutions for providing accurate and prompt risk assessments while preserving data confidentiality. This work proposes a new framework using Federated Learning (FL) to combine Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) models that are essential in analyzing multimodal data. Implemented in Python, the approach incorporates two datasets: Dataset 1, which consists of Demographic data medical history, and lifestyle data, and the second dataset, which includes the normal condition and the affected stroke condition CT scan images. Imputation of missing values, feature normalization by Min-Max scaling, and handling of imbalanced classes with SMOTE make the data pre-processing procedures exhaustive. In FL architecture three clients -Client A, Client B, and Client C - process a split multimodal dataset containing static and sequential information. Each client independently trains an MLP-GRU model. Each is applied with MLP handling static features from Dataset 1 and GRU handling sequential features from Dataset 2. To update models, Federated Averaging is used on a central server, to create a global model that is then returned to the clients for further refinement. The accuracy of the proposed method averages 99.00% and surpasses other models by 2.5% including CNN, LSTM, Random Forest, and SVM. By enhancing MLP with GRU and applying them to a privacy-preserving FL framework,The study addresses the fragmented use of multimodal medical data, where clinical records and imaging are generally evaluated separately, resulting in inadequate diagnostic support. The strategy integrates complementary modalities to create a more comprehensive perspective of patient health, enhancing healthcare predictive accuracy and decision-making. This incentive is essential for improving computational methods and linking technical advancement with medical objectives like fast diagnosis and therapy planning. The introduction emphasises the therapeutic necessity of harmonising organized and unstructured data to reduce diagnostic ambiguity. A translational approach is used to discuss how multimodal integration might improve clinical workflows, develop collaborative healthcare systems, and support sustainable medical practices. This repeated emphasis links methodological advances to real-world healthcare issues, boosting the study's academic relevance sets.
7 Apr 2026
Read appraisal →Circulation
Wearable-Derived Training Load and Coronary Atherosclerosis in Middle-Aged and Older Athletes and Physically Active Controls: A New Perspective From the Master@Heart Study
BACKGROUND: Middle-aged and older endurance athletes have increased prevalence of coronary artery disease (CAD) on coronary computed tomography angiography compared with healthy controls, despite similarly low cardiovascular risk. Previous studies relied on self-reported data to quantify training load (TL), which poorly correlates with objective wearable-derived TL and may bias outcomes. The effect of objective TL on CAD risk remains unknown. METHODS: In this observational, cross-sectional analysis of the Master@Heart study, 222 men (median age, 54 [49-59] years) were included: 77 lifelong athletes, 98 late-onset athletes, and 47 controls. TL was assessed using objective wearable-derived training duration and intensity (12 consecutive months), as well as self-reported training measures. Coronary computed tomography angiography-derived CAD prevalence was compared across TL quartiles (Q) using a global unadjusted chi-square test and logistic regression, adjusted for cardiovascular risk factors and years of endurance exercise, to estimate odds ratios between Q4 and Q1. In addition, adjusted logistic regression models were fitted with continuous TL, using smoothing splines to capture potential nonlinear associations. RESULTS: Across quartiles of objective Edwards training impulse (training duration × heart rate-weighted intensity), unadjusted global differences were observed for ≥1 plaque (P<0.001), coronary artery calcification (CAC)>0 (P=0.002), and CAC>100 (P=0.012). Q4 participants had significantly higher adjusted odds of ≥1 plaque (odds ratio, 5.85; 95% CI, 2.33-14.71), CAC>0 (odds ratio, 5.03; 95% CI, 2.04-12.35), and CAC>100 (odds ratio, 3.50; 95% CI, 1.22-10.00) versus Q1. Similar associations were found for objective training duration, whereas no clear associations were observed for relative time spent in high-intensity zones. In continuous analyses, Edwards training impulse and objective training duration showed significant positive associations with ≥1 plaque and CAC>100 (P<0.05), whereas self-reported training duration was only significantly associated with CAC>100 (P<0.05). Metabolic equivalent of task-minutes per week based on self-reported TL was not associated with CAD (P>0.05). CONCLUSIONS: High training duration (hours/week), particularly when combined with cumulative high-intensity TL (Edwards training impulse), was independently associated with increased prevalence of subclinical CAD in middle-aged and older athletes and physically active controls. Exercise intensity alone, in the absence of high duration, was not clearly linked to CAD. These findings underscore the potential of objectively measured TL for understanding associations with subclinical CAD in endurance athletes. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03711539.
7 Apr 2026
Read appraisal →International ophthalmology
Retinal image-based cardiovascular risk prediction using AI-CRS: a multi-modal deep learning framework
This study presents AI-CRS, an AI-driven deep learning framework for cardiovascular risk assessment using retinal images. By combining convolutional neural networks (CNNs), attention mechanisms, and vasculature segmentation, AI-CRS overcomes the limitations of traditional methods, such as reliance on handcrafted features and single-modality data. The model integrates multi-modal information, fusing vasculature segmentation maps with raw image data to capture subtle vascular changes indicative of cardiovascular diseases. Experimental validation demonstrates that AI-CRS outperforms conventional diagnostic techniques in cardiovascular risk stratification, achieving superior sensitivity, specificity, and accuracy. The model excels in detecting early-stage disease and subtle vascular anomalies, and it shows strong generalizability across diverse datasets, patient demographics, and varying image qualities. Beyond cardiovascular risk, AI-CRS also shows promise in assessing systemic health conditions like hypertension and diabetes, demonstrating the broader utility of retinal imaging in health monitoring. The attention-driven architecture enhances model interpretability, providing clinicians with visual explanations of disease-associated vascular patterns, which is essential for clinical adoption. AI-CRS offers a non-invasive, scalable solution that can be integrated into routine clinical practice, supporting early diagnosis, personalized care, and population-wide screening. Its automated analysis reduces the need for manual feature extraction and subjective interpretation, streamlining workflows and improving efficiency. Ultimately, AI-CRS represents a significant step toward precision medicine by enabling timely interventions, reducing healthcare costs, and improving patient outcomes through non-invasive digital biomarkers.
7 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
A community based participatory research approach to evaluate barriers and facilitators for behavioral weight loss intervention implementation in a predominantly black community.
PURPOSE: Breast cancer survivors with overweight or obesity have worse breast cancer specific survival and outcomes as compared to those with average weight by Yung and Ligibel (Clin Adv Hematol Oncol 14:790-797, 2016) and Druesne-Pecollo et al. (Breast Cancer Res Treat 135:647-654, 2012). Our pilot Group-basEd Telehealth behavioral Weight Los (GET-WEL) Program showed that fewer Black breast cancer survivors (BBCS) enrolled and lost less weight than White breast cancer survivors by Allison et al. (Obesity Science and Practice 10:e70023, 2024). This study is aimed at using a community-based participatory research approach to assess barriers and facilitators of implementing a behavioral weight loss intervention among BBCS. METHODS: Eight BBCS from a predominantly Black community were invited to participate in semi-structured interviews that were voice recorded, transcribed, coded, and analyzed via comparative thematic analysis. RESULTS: Thematic analyses revealed multiple barriers within participants. These included lack of affordable healthy food access, safety concerns with regards to outdoor activities, lack of affordable fitness center memberships, time constraints related to competing work/life obligations, and steep learning curves with technology use. Most BBCS preferred an integrated community-based coach to guide their weight loss interventions via a combination of both virtual and in-person sessions. CONCLUSION: Our results indicate that a multimodal approach including nutrition education, reducing physical activity barriers, limiting time constraints by implementing both in-person and virtual platforms, and assisting with technology courses, is necessary to improve the equitable implementation of weight loss interventions. BBCS recommended utilizing established community facilities and leveraging known community members such as nutrition counselors and physical trainers to increase successful implementation.
6 Apr 2026
Read appraisal →PloS one
IOTA Tangle-based traceability framework for wheat crop supply chain
Distributed ledger technology (DLT) has emerged as a transformative solution across industries, offering decentralization, transparency, and security. Among DLT architectures, IOTA's Tangle stands out as a scalable, feeless framework well-suited for supply chain applications, particularly in critical sectors like agriculture. This work leverages IOTA's Tangle to design a secure and traceable wheat supply chain system, addressing challenges such as opacity, fraud, and inefficiency. The proposed solution is an IOTA-based framework designed for enhancing supply chain traceability of wheat, offering trustworthy, transparent, secure and scalable services. Based on smart contracts, a transaction processing system was developed to enhance transparency and traceability and automate tasks. Furthermore, we used various technologies for increased visibility and operational efficiency throughout the supply chain, including IPFS (InterPlanetary File System) for storing extensive data such as stakeholder certifications, AI (Artificial Intelligence) for predicting crop yields, and Self-Sovereign Identity (SSI) for enhanced security. In addition, a government-provided mobile app helps silo managers and police officers verify transportation credentials and prevent contraband. The implementation of this system is expected to enhance supply chain resilience and transparency, particularly in Tunisia, thereby supporting the country's goal of food autonomy.
6 Apr 2026
Read appraisal →Cardiovascular toxicology
Association Between Environmental Chemicals Phthalates and Stroke Among the US Adults: A Cross-Sectional Study
Stroke has a multifactorial etiology, and phthalates, as widely used environmental chemicals, may play an underexplored role in cerebrovascular health. This study aimed to evaluate the association between urinary phthalate metabolites and stroke risk using data from the National Health and Nutrition Examination Survey (NHANES), complemented by network toxicology, molecular docking, and machine learning approaches. We analyzed data from 10,771 adult participants in NHANES (2003-2018) who had complete records of stroke status, urinary phthalates, and clinical covariates. Multivariable logistic regression and restricted cubic spline (RCS) models were used to assess associations. We constructed protein-protein interaction networks and performed molecular docking to identify potential toxicological targets. Five machine learning algorithms were employed to develop predictive models for phthalate-related stroke risk. Higher levels of MECPP, MCPP, MNBP, and MEOHP were significantly associated with increased stroke prevalence. Network toxicology identified SRC as a key target, and molecular docking confirmed strong binding affinity between MBZP and SRC. Among the machine learning methods, Random Forest achieved the highest predictive performance (AUC = 0.833). Notably, moderate coffee consumption (≤ 170 g/day) was significantly associated with a reduced risk of stroke (OR = 0.63, 95%CI: 0.51-0.77), suggesting potential dietary mitigation against phthalate-related stroke risk. Our findings demonstrate that certain phthalates may increase stroke risk via molecular pathways involving SRC. Furthermore, moderate coffee consumption may mitigate the adverse cerebrovascular effects of these environmental pollutants.
5 Apr 2026
Read appraisal →European journal of cardiovascular nursing
Effect of exercise intensity and volume on mortality and hospitalization in patients with coronary artery disease: a systematic review and pairwise and exploratory network meta-analysis
AIMS: To ascertain the impact of varying exercise intensities and volumes on clinical outcomes of patients with coronary artery disease (CAD) via a systematic review and meta-analysis. METHODS AND RESULTS: This study included randomized controlled trials to evaluate the effects of different intensity levels and volumes of exercise training and control interventions on clinical outcomes in patients with CAD. Data from eight databases were screened, extracted, and assessed for quality by two independent reviewers. Included studies were categorized into subgroups based on exercise intensity and volume. A random-effects model was used to conduct a meta-analysis of different outcomes. To further explore the combined effects, an exploratory network meta-analysis was performed, combining exercise intensity and exercise volume. Exercise training reduces the risk of cardiovascular mortality, overall hospitalization and cardiovascular hospitalization. In subgroup analyses, both moderate-intensity and continuous training significantly reduce the risk of overall hospitalization. Furthermore, the network meta-analysis shows that moderate-intensity continuous training (MICT) significantly lowers the risk of overall hospitalization [(risk ratio (RR) = 0.66, 95% confidence interval (CI): 0.47-0.95), surface under the cumulative ranking curve (SUCRA) = 70.6%] and cardiovascular hospitalization (RR = 0.66, 95% CI: 0.44-0.99, SUCRA = 80.1%) compared with usual care. In contrast to usual care, vigorous-intensity continuous training (VICT) lowers the risk of cardiovascular hospitalization (RR = 0.35, 95% CI: 0.14-0.92, SUCRA = 94.1%). CONCLUSION: MICT significantly reduces the risk of overall hospitalization and cardiovascular hospitalization. Although the evidence is limited, VICT appears to reduce the risk of cardiovascular hospitalization. Consequently, it may become an effective exercise-based intervention for preventing such events. REGISTRATION: PROSPERO: CRD42024519198.
5 Apr 2026
Read appraisal →European journal of cardiovascular nursing
Effects of personalized structured telemedicine-based exercise cardiac rehabilitation on health outcomes in patients with coronary heart disease: a systematic review and meta-analysis
AIMS: Despite its proven effectiveness, traditional exercise-based cardiac rehabilitation (exCR) suffers from low participation rates. However, telemedicine-based exCR can overcome access barriers to traditional rehabilitation while providing personalized, structured exercise training and remote monitoring. The aim of this review is to ascertain the efficacy of personalized, structured, telemedicine-based exCR in improving health outcomes in patients with coronary heart disease (CHD). METHODS AND RESULTS: A systematic search was conducted in databases including Cochrane Library, PubMed, Web of Science, Embase, CINAHL, Scopus, Medline, China National Knowledge Infrastructure, Wanfang, VIP, and SINOMED from inception to March 2024 to identify randomized controlled trials (RCTs). The final analysis included 19 RCTs comprising 2219 participants. Among patients with CHD, telemedicine-based exCR demonstrated comparable effects to centre-based exCR across multiple outcomes, including exercise capacity, physical activity levels, cardiovascular risk factors, and quality of life (all P > 0.05). Notably, when compared with usual care, telemedicine-based exCR showed significant improvements in exercise capacity [standardized mean difference (SMD) = 0.23, 95% CI: (0.12, 0.35), I2 = 34%, P < 0.0001], physical activity level [SMD = 0.32, 95% CI: (0.09, 0.54), I2 = 53%, P = 0.006], diastolic blood pressure [mean difference (MD) = -1.54, 95% CI: (-2.89, -0.20), I2 =8%, P = 0.02], body mass index [MD = -0.54, 95% CI: (-1.94, -0.14), I2 = 4%, P = 0.008], and depression [SMD = -0.27, 95% CI: (-0.42, -0.13), I2 = 33%, P = 0.0002]. CONCLUSION: Telemedicine-based exCR is equally effective as centre-based exCR in improving key health outcomes for patients with CHD, including exercise capacity, physical activity levels, cardiovascular risk factors, and quality of life. Furthermore, telemedicine-based exCR shows superior effectiveness to usual care, with statistically significant improvements in exercise capacity, physical activity, diastolic blood pressure, and depressive symptoms. REGISTRATION: PROSPERO: CRD42024521465.
5 Apr 2026
Read appraisal →Environmental geochemistry and health
Deciphering stone mining-induced hazardous heavy metal contamination in agricultural soils using source attribution, health-dietary risk analysis, and machine learning-driven insights
Mining activities significantly contribute to the release of hazardous heavy metals (HHMs) into agricultural soils, especially where stone extraction is intensive. This study assessed metal content, source identification, and possible health risks associated with HHMs (Cd, Pb, Cr, Cu, Ni) in stone mines affected agricultural soils of India. The results revealed that Zone 1 exhibited higher concentrations of Cr (308.95 ± 23.03 mg/kg), Ni (117.11 ± 16.21 mg/kg), and Cd (4.21 ± 1.26 mg/kg), surpasses the threshold limit set by WHO/FAO for agricultural soils. In contrast, Zone 2 had considerably lower HHMs concentrations for Cr (60.24 ± 10.68 mg/kg), Ni (31.61 ± 7.52 mg/kg) and Cd (0.66 ± 0.13 mg/kg). Geospatial distribution and self-organizing map (SOM) indicated spatial clustering of HHMs, with elevated concentrations centered in the southern part of the study area. Positive matrix factorization showed that geogenic sources and mining activities affect significantly to HHMs pollution. Monte Carlo simulation revealed that children are more prone to exposure to HHMs than adults, with ingestion identified as the primary exposure route. Hazard quotient values calculated from free ion activity model exceeded the threshold limit for Pb (1.59E+00) and Cd (6.32E-01). Severity adjustment margin of exposure highlighted severe human health risk for Cr and Ni. Random forest analysis indicated that bioavailable HHMs significantly influence the accumulation of metals in rice tissues which elevating dietary risk. The integration of robust statistical tools offers insights into the dynamics of HHMs contamination and provide policymakers and environmental scientists with useful information to mitigate the potential risks caused by mining on nearby agricultural lands.
5 Apr 2026
Read appraisal →PloS one
Perception and acceptance of micronutrient-Fortified Bouillon among Non-Index Household Members: A longitudinal sub-study nested within a randomized trial in Northern Ghana
BACKGROUND: Fortified bouillon cubes offer a scalable strategy to address micronutrient deficiencies in West Africa. However, success depends on household members' perceptions and acceptance. This study assessed changes in perception and acceptance of fortified bouillon cubes over time and explored factors influencing these outcomes among non-index household members (NIHMs) from trial households in Northern Ghana. METHODS: This longitudinal mixed-methods study was nested within a double-masked randomised controlled trial. Households received micronutrient-fortified bouillon cubes (vitamin A, vitamin B12, folic acid, iron, zinc, iodine) or control cubes (iodine only) for nine months. One NIHM (≥15 years) per household (n = 1,047) was enrolled. Structured interviews were conducted during early (months 1-2; n = 731) and late (months 7-9; n = 796) phases. Composite perception (8 items) and acceptance (10 items) scores (range: 1-5; higher scores = more positive perceptions/greater acceptance) were derived from an 18-item validated instrument. Bayesian linear mixed-effects models assessed temporal changes and associated individual and household factors. Twenty-four focus group discussions (months 4-5) explored drivers and barriers. RESULTS: Mean age of NIHMs was 40 ± 14.7 years; 59% were female. Perception scores increased over seven months, from 3.4 to 4.1 (adjusted posterior mean difference [aPMD] = 0.69; 95% credible interval [CrI]: 0.57, 0.81). Acceptance remained high (4.59 to 4.58; aPMD = -0.02; 95% CrI: -0.07, 0.03). No differences were observed between trial arms. At early timepoint, perception scores were lower among household heads compared with other household members, and lower among formal employees compared with farmers. Acceptance was higher among individuals from severely food-insecure households and lower among those from mildly food-insecure households. Focus groups revealed that perceived health benefits, sensory appeal, and cultural fit drove acceptance, while infertility-related myths and food colour changes were barriers. CONCLUSION: Fortified bouillon cubes were accepted, with perceptions improving over time. Public education will be essential to address misconceptions. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05178407); Pan-African Clinical Trial Registry (PACTR202206868437931). The authors confirm that all ongoing and related trials for this intervention are registered.
5 Apr 2026
Read appraisal →BMJ open respiratory research
Associations between respiratory diseases and lung cancer risk: a secondary analysis of the large-scale prospective UK biobank cohort
INTRODUCTION: Respiratory diseases are significant risk factors for lung cancer; however, the association between acute respiratory infections and lung cancer incidence requires further exploration. METHODS: We performed a secondary analysis of the prospective UK Biobank cohort, including participants aged 37-73 years recruited from 22 assessment centres across the UK between 2006 and 2010. Cox proportional hazards models estimated HRs for incident lung cancer according to respiratory disease status, which was defined based on linked hospital inpatient records. Mediation analysis explored potential biomarkers, and Mendelian randomisation assessed causal relationships. RESULTS: During a mean follow-up of 10.44 years (4 790 738 person-years), 2189 participants developed lung cancer. Among 107 007 individuals with respiratory diseases, 1322 cases occurred (incidence rate 27.6 per 10 000 person-years), compared with 867 cases among 351 876 participants without respiratory diseases (7.9 per 10 000 person-years). Overall, respiratory diseases were associated with increased lung cancer risk (HR 2.97, 95% CI 2.75 to 3.21). Acute respiratory infections, including acute nasopharyngitis (HR 3.41; 95% CI 1.83 to 6.34), influenza (HR 3.90; 95% CI 2.53 to 6.01), viral pneumonia (HR 9.86; 95% CI 6.55 to 14.85) and bacterial pneumonia (HR 6.28; 95% CI 4.40 to 8.96), showed strong associations with lung cancer incidence. In subtype analyses, squamous cell carcinoma exhibited the highest risk elevation (HR 3.65; 95% CI 3.06 to 4.36). Mediation analysis indicated that neutrophil counts partially mediated these associations (proportion mediated up to 8%). CONCLUSION: Acute respiratory infections were associated with higher lung cancer incidence, providing hypothesis-generating evidence that may inform future risk stratification research.
5 Apr 2026
Read appraisal →Cancer causes & control : CCC
A preference study for applying mHealth to improve the quality of cervical cancer screening management from a supply and demand perspective: a discrete choice experiment.
OBJECTIVES: This study aims to investigate the preferences of women of cervical cancer screening age regarding the use of mobile health (mHealth) technologies in Xinjiang. The purpose is to enhance adherence and acceptance of mHealth applications, thereby improving the quality of cervical cancer screening follow-up for women residing in rural areas of Xinjiang. METHODS: The attributes and levels were established based on a comprehensive literature review and qualitative interviews. The choice set questionnaire was developed using SAS 9.4 through D-efficiency analysis. The research site was identified using a multi-stage sampling method, and the survey was launched in December 2024. An Optimal Logit model was selected for preference analysis, utilizing Stata 17.0. RESULTS: A total of six attributes were identified through the literature review and subsequently combined with qualitative interviews. The mixed logit regression results indicated that, among the six attributes, five were statistically significant (P < 0.05): mHealth approach, frequency of receiving notifications, reminder content, screening notification time, missionary form, and mission strategy. The results demonstrated a preference for WeChat as the mHealth approach (β = 0.230, P < 0.001, 95%CI 1.214-1.500), not accepting receiving notifications everyday (β = - 0.144, P = 0.001, 95%CI 0.797-0.940), preferred reminders containing positive information (β = 0.159,P = 0.001,95%CI1.068-1.288), opted for screening notifications 1 month in advance (β = 0.208, P < 0.001, 95%CI 1.133-1.339), and preferred the image form of education for missionary form (β = 0.118, P < 0.001, 95%CI 1.102-1.322). CONCLUSION: A one-size-fits-all approach should be avoided in the design of mHealth solutions to effectively address the diverse needs of individuals with varying preferences, thereby promoting the adoption of mHealth and improving the feasibility of its application.
5 Apr 2026
Read appraisal →American journal of human genetics
The Electronic Medical Records and Genomics study: Design and analytic framework for assessing the impact of genome-informed risk assessments.
The Electronic Medical Records and Genomics (eMERGE) Network developed and implemented a genome-informed risk assessment (GIRA) to communicate genomic (polygenic risk scores [PRSs], integrated risk scores [IRSs], and monogenic results), clinical, and family history-based risk for 11 chronic diseases and provide recommended healthcare recommendations. GIRA reports have now been returned to 23,840 participants and their providers in a large prospective cohort study. We present here the study design and analysis framework for assessing the attributable impact of GIRA return. Pre-specified outcomes include (1) provider/participant adoption of recommended healthcare actions, (2) new diagnosis of disease, (3) treatment initiation/intensification, and (4) clinical outcomes (surrogate markers or clinical events). We assess outcomes in high risk vs. not-high-risk participants, adjusting for covariates. We evaluate the effect of PRS/IRS at pre-established high-risk thresholds using regression discontinuity (RD), a quasi-experimental method that mimics randomization near a cutoff, enabling estimation of causal effects and controlling for unobserved confounders. Monogenic and family history-based risk stratification are analyzed using logistic regression. With 23,840 participants and 12 months of follow-up, the study is powered to detect differences of 2%-11% with 80% power (α = 0.05 in the adoption outcome). Longer follow-up will be required to enable assessment of new disease diagnosis, treatment changes, and clinical outcomes. Through innovative RD analyses and defined outcomes and comparison groups, this study will provide new insights into the real-world clinical impact of genomic risk assessment, address critical evidence gaps, advance understanding of genomic medicine outcomes, and inform future research.
5 Apr 2026
Read appraisal →Annals of medicine
An explainable online frailty prediction model for community-dwelling older adults based on machine learning algorithms: a cross-sectional study based on retrospective health data
BACKGROUND: Frailty is a significant health concern associated with diminished physiological reserves and increased healthcare burdens. Currently, effective models for predicting frailty risk are lacking. This study utilizes machine learning-based models to early identify community-dwelling older adults, enhancing risk assessment accuracy and guiding targeted interventions to slow frailty progression. METHODS: A cross-sectional analysis of data from 1,156 older adults across 31 community health centers in Nanjing, conducted between January and October 2024, was performed. Independent predictors of frailty were identified using univariate analysis and the least absolute shrinkage and selection operator. The dataset was divided into 70% training and 30% testing subsets. Six machine learning (ML) models were developed and their performances compared. The SHapley Additive exPlanations (SHAP) method was applied to interpret the models, and a web-based risk calculator was created. RESULTS: Our dataset showed that 22.3% of older adults were frail. Significant predictors of frailty were identified as age, education, medicine, vegetable, cognitive status, number of diseases, hemoglobin, total cholesterol, and neutrophil-to-lymphocyte ratio. Among the six ML models, Categorical Boosting (CatBoost) exhibited the highest performance, attaining an AUROC of 0.886 in the training set and 0.831 in the testing set. CONCLUSIONS: The developed CatBoost model and web calculator can be employed by general practitioners to proactively identify high-risk community-dwelling older adults, thereby enabling timely interventions to mitigate the progression of frailty. The tool's simplicity and replicability effectively facilitate the promotion and management of frailty prevention within the community. An effective model was developed to predict frailty risk in community-dwelling older adults.The integration of biomarkers and self-reported data enhances frailty prediction and optimizes risk-based intervention strategies.The CatBoost model demonstrates superior predictive performance compared to other models.The web-based tool integrates a frailty prediction model for proactive risk management.
5 Apr 2026
Read appraisal →Open heart
Women's IschemiA TRial to Reduce Events In Non-ObstRuctive CAD (WARRIOR): a randomised controlled trial
IMPORTANCE: Women with angina due to suspected ischaemia referred for coronary angiography often have no obstructive coronary artery disease (ANOCA/INOCA). OBJECTIVE: To determine if intensive medical treatment (IMT) reduces major ischaemic events (major adverse cardiovascular event, MACE) among women with suspected ANOCA/INOCA. DESIGN: Randomised, prospective, blinded-outcomes evaluation. SETTING: 71 sites in the USA. PARTICIPANTS: 2476 women with suspected ANOCA/INOCA. INTERVENTIONS: IMT-high intensity statin, ACE inhibitor (ACEi) or angiotensin receptor blocker (ARB) and aspirin versus usual care (UC). MAIN OUTCOMES AND MEASURES: Primary: all cause death, myocardial infarction, stroke/transient ischaemic attack, hospitalisation for angina or heart failure (MACE). Secondary: components of the primary, quality of life and win ratio. RESULTS: Recruitment was lower than planned (n=2476), yielding an aged population (mean, 64 years) with well-controlled blood pressure and low-density lipoprotein cholesterol at baseline, and relatively high rates of statin and ACEI/ARB use. At 2.5 years, 421 events occurred (221 in IMT, 200 in UC) with no difference in the primary outcome (HR=1.13 (95% CI 0.94 to 1.37) for IMT vs UC, p=0.20) or secondary outcomes. Hospitalisations for angina were the dominant contributor to MACE. Sensitivity analysis of contamination provided an estimated HR for IMT versus UC of 0.74 95% CI (0.352 to 1.558), p=0.43. CONCLUSIONS AND RELEVANCE: Among women with suspected ANOCA/INOCA, outcomes were dominated by chest pain and IMT did not improve outcomes, although limited power precludes concluding that it may not be helpful. The findings support the need for more investigation in this population with high burden of angina hospitalisation, health resource consumption and poor quality of life. TRIAL REGISTRATION NUMBER: NCT03417388.
4 Apr 2026
Read appraisal →The Cochrane database of systematic reviews
Pharmacotherapy for mild hypertension
RATIONALE: This is an update of a Cochrane review published in 2012 of initiation of antihypertensive monotherapy or step-up therapy in people with untreated mild hypertension (systolic blood pressure 140 to 159 mmHg or diastolic blood pressure 90 to 99 mmHg, or both) and no pre-existing cardiovascular disease. The original review demonstrated no difference in the incidence of all-cause mortality, total cardiovascular events (stroke, myocardial infarction, and congestive heart failure), stroke incidence, coronary heart disease, or withdrawal due to adverse effects (WDAEs). Evidence for antihypertensive pharmacotherapy in people with mild hypertension for primary prevention remains uncertain in the literature with conflicting studies. We therefore performed an update of the original Cochrane review to reassess whether initiation of antihypertensive pharmacotherapy compared to placebo or no treatment in people with untreated mild hypertension and no pre-existing cardiovascular disease reduces the risk of all-cause mortality, total cardiovascular events, stroke, coronary heart disease, or WDAEs. OBJECTIVES: To reassess the efficacy and risks of initiating antihypertensive pharmacotherapy in adults with untreated mild hypertension and no pre-existing cardiovascular disease. The primary objective was to reassess the risk of all-cause mortality and total cardiovascular events (defined as fatal and non-fatal strokes, myocardial infarction, and congestive heart failure). The secondary objectives were to reassess the risk of stroke (fatal and non-fatal), coronary heart disease (fatal and non-fatal myocardial infarction and sudden cardiac death), and WDAEs. SEARCH METHODS: We searched the Cochrane Hypertension Specialised Register, CENTRAL, MEDLINE, Embase, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform from inception to June 2024. ELIGIBILITY CRITERIA: We included randomized controlled trials (RCTs) of at least one-year duration comparing initiation on antihypertensive monotherapy or step-up therapy, or both, versus placebo or no treatment in participants with mild hypertension and no pre-existing cardiovascular disease. OUTCOMES: Our critical outcomes were all-cause mortality and total cardiovascular events. Important outcomes were stroke, coronary heart disease (fatal and non-fatal myocardial infarction and sudden cardiac death), and WDAEs. RISK OF BIAS: Two review authors assessed risk of bias independently and in duplicate using Cochrane's RoB 1 tool. SYNTHESIS METHODS: Two review authors performed title and abstract screening, full-text review, and data extraction independently and in duplicate. We calculated risk ratio (RR) along with 95% confidence interval (CI) for all-cause mortality, total cardiovascular events, stroke events, coronary heart disease events, and WDAEs with the Mantel-Haenszel test and a fixed-effect model. We assessed the certainty of the evidence using the GRADE approach. INCLUDED STUDIES: We included individual patient data from five trials involving a total of 9124 participants, of whom 4593 received antihypertensives and 4531 received placebo or no treatment. All five trials reported all-cause mortality; four trials reported total cardiovascular events and strokes; three trials reported coronary heart disease; and one trial reported WDAEs. SYNTHESIS OF RESULTS: There may be little to no reduction in all-cause mortality (RR 0.85, 95% CI 0.64 to 1.14; 5 trials, 9124 participants; low-certainty evidence), total cardiovascular events (RR 0.93, 95% CI 0.69 to 1.24; 4 trials, 7292 participants; low-certainty evidence), or coronary heart disease (RR 1.12, 95% CI 0.80 to 1.57; 3 trials, 7080 participants; low-certainty evidence). There may be a decreased risk of stroke (RR 0.41, 95% CI 0.20 to 0.84; 4 trials, 7292 participants; low-certainty evidence) and an increase in WDAEs (RR 4.80, 95% CI 4.14 to 5.57; 1 trial, 17,354 participants [aggregate trial-level data; individual patient data unavailable]; low-certainty evidence) with antihypertensives. We downgraded the certainty of evidence for all outcomes due to imprecision, indirectness, and risk of bias. AUTHORS' CONCLUSIONS: In people with untreated mild hypertension and no pre-existing cardiovascular disease, initiation of antihypertensive monotherapy or step-up therapy may not reduce all-cause mortality, total cardiovascular events, or coronary heart disease compared to those who received placebo or no treatment. There may be a reduction in stroke, but possibly also an increase in WDAEs. FUNDING: CIHR Grant to the Hypertension Review Group and British Columbia Ministry of Health Grant to the Therapeutics Initiative. REGISTRATION: Protocol (2007): doi.org/10.1002/14651858.CD006742. Original review (2012): doi.org/10.1002/14651858.CD006742.pub2.
4 Apr 2026
Read appraisal →Journal of medical Internet research
Effectiveness of the Components of a Digital Multiple Health Behavior Change Intervention Among Individuals Seeking Help Online (Coach): Factorial Randomized Trial
BACKGROUND: Extant digital multiple health behavior change interventions have shown promise in various populations; however, evidence for a broader approach among the general population is lacking. Moreover, existing interventions often contain several components but are typically assessed as a whole, meaning it remains unclear to what extent individual components contribute to intervention effects and how they may interact to influence health outcomes. OBJECTIVE: This study estimates the effects of 6 components of a digital health behavior change intervention on alcohol, diet, physical activity, and smoking outcomes among individuals searching for help online. METHODS: A double-blind randomized factorial trial design with 6 two-level factors was used. Adults from the general public in Sweden who were seeking help to change their behaviors were recruited through web searches and social media. Participants were eligible if they were 18 years or older and had at least one health behavior classified as unhealthy. Effects of 6 components were estimated: screening/feedback, goal-setting/planning, motivation, skills/know-how, mindfulness, and self-authored SMS text messages. Primary outcomes were weekly alcohol consumption and frequency of heavy episodic drinking, average daily fruit and vegetable consumption, weekly moderate-to-vigorous physical activity, and 4-week point-prevalence smoking. RESULTS: A total of 5419 individuals were randomized. Overall, the screening/feedback component was the most effective for changing health behaviors, along with goal-setting/planning and motivation to change. In particular, there was evidence that screening/feedback increased average daily portions of fruit and vegetables at 2 months (mean difference 0.17, compatibility interval [CoI] 0.09-0.25, probability of effect [POE] >99.9%) and at 4 months (mean difference 0.13, CoI 0.04-0.21, POE 99.9%) and reduced the frequency of heavy episodic drinking at 4 months (incidence rate ratio 0.91, CoI 0.81-1.03, POE 94.2%). Components also interacted to further improve health outcomes, most notably the combination of screening/feedback with motivation to change, which further increased fruit and vegetable consumption (2 months: mean difference 0.20, CoI 0.09-0.30, POE >99.9%; 4 months: mean difference 0.17, CoI 0.05-0.29, POE 99.8%). CONCLUSIONS: The results from this study contribute to the development of more effective interventions by providing novel insights into the effects of individual and pairwise components of complex digital health behavior change interventions. TRIAL REGISTRATION: ISRCTN Registry ISRCTN16420548; http://www.isrctn.com/ISRCTN16420548. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2022-061024.
4 Apr 2026
Read appraisal →Journal of global health
Exposure-response association between outdoor activity time and myopia risk in Chinese children and adolescents: a systematic review and meta-analysis
BACKGROUND: The prevalence of myopia among Chinese children and adolescents has increased sharply over recent decades, raising concern about modifiable environmental risk factors. Although outdoor activity is widely recognised as protective, the quantitative exposure-response relationship within the Chinese educational context remains incompletely defined. METHODS: A systematic review and meta-analysis were conducted following PRISMA guidelines. Studies examining the association between daily outdoor activity time and myopia risk among Chinese youth were identified from major English and Chinese databases (Web of Science, PubMed, CNKI, and Wanfang) through October 2025. Random-effects meta-analysis was conducted to pool odds ratios (ORs). Prespecified subgroup and sensitivity analyses examined exposure thresholds, study design, and geographic latitude. RESULTS: We included 31 studies comprising 380 215 participants. Greater outdoor activity was consistently associated with lower myopia risk (pooled OR = 0.75; 95% confidence interval (CI) = 0.71-0.80), although substantial heterogeneity was observed (I2 = 94%). Compared with <1 hour per day, outdoor exposure of 1-2 hours (OR = 0.85; 95% CI = 0.79-0.92), 2-3 hours (OR = 0.86; 95% CI = 0.78-0.95), and > 3 hours (OR = 0.74; 95% CI = 0.63-0.87) was associated with reduced risk. When dichotomised at two hours, exposure >2 hours yielded an OR of 0.74 (95% CI = 0.69-0.80). Findings were directionally consistent across study designs and latitude strata. CONCLUSIONS: Greater outdoor activity time is associated with lower myopia risk among Chinese children and adolescents. An approximate daily exposure of around two hours appears to represent a pragmatic benchmark under current educational conditions.
4 Apr 2026
Read appraisal →Journal of medical Internet research
The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis
BACKGROUND: Digital biomarkers are increasingly being used to support depression assessment by providing objective, continuous, and real-time physiological and behavioral data. However, most existing studies have focused on individual biomarkers, such as sleep or cardiac parameters, while integrative evaluations that capture the multidimensional nature of depression remain limited. OBJECTIVE: This systematic review evaluated digital biomarkers for depression and synthesized evidence on differences between individuals with depression and controls. METHODS: Eligible studies included observational or interventional studies examining digital biomarkers for depression with validated outcome measures. We searched major international and Korean databases, including MEDLINE, PsycINFO, CINAHL, IEEE Xplore, Web of Science, Cochrane Library, KISS, RISS, KMbase, and KoreaMed, from inception to December 28, 2025. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool and the Scottish Intercollegiate Guidelines Network checklist. Meta-analyses were conducted using random-effects models with the Hartung-Knapp-Sidik-Jonkman method, and other outcomes were narratively summarized. RESULTS: The search yielded 39,617 records, of which 132 studies involving 57,852 participants met the inclusion criteria. These studies encompassed various digital biomarkers, including sleep, physical activity, cardiac measures, smartphone-derived data, speech, GPS data, and circadian rhythms. A meta-analysis of 22 studies (6947 participants) revealed that individuals with depression had significantly longer sleep onset latency (5 studies; n=292; +4.75 min, 95% CI 2.46-7.04; P=.005; 95% prediction interval [PI] 0.01-10.27) and time in bed (3 studies; n=236; +31.81 min, 95% CI 18.22-45.39; P=.01; 95% PI 2.28-55.16). Physical activity counts were also significantly lower (5 studies; n=462; standardized mean difference -0.71, 95% CI -1.33 to -0.09; P=.03; 95% PI -2.18 to 0.71). Although individuals with depression showed a lower sleep efficiency, higher mean heart rate, and lower SD of normal-to-normal intervals, these differences were not statistically significant. Other digital markers yielded inconsistent results. Overall, these findings indicate that no single digital biomarker sufficiently captures depression-related changes. Instead, the results support the superiority of personalized, multimodal approaches. However, the generalizability of these findings is limited by the lack of standardized data collection protocols and high clinical heterogeneity across studies, as reflected in wide PIs. CONCLUSIONS: Certain digital biomarkers, particularly sleep onset latency and physical activity counts, showed consistent average differences between the depression and control groups. However, wide PIs indicate substantial variability across settings, suggesting that no single marker is sufficient for reliable detection. This study advances the field by providing a comprehensive meta-analysis of multidimensional digital biomarkers, establishing a quantitative foundation for objective depression screening and monitoring. These findings support the use of personalized, multimodal digital phenotyping approaches and highlight the need for standardized, clinically interpretable frameworks for real-world depression monitoring.
4 Apr 2026
Read appraisal →BMJ global health
The effects of antenatal interventions on gestational weight gain in low and middle-income countries: a systematic review
BACKGROUND: Gestational weight gain (GWG) during pregnancy is a critical factor that affects maternal and child health outcomes. The considerable burden of inadequate GWG and the rising tide of excessive GWG are overlooked challenges in low and middle-income countries (LMICs). METHODS: This systematic review assessed the impact of antenatal interventions on GWG in LMICs. Randomised controlled trials (RCTs) on antenatal interventions on GWG in LMICs were included. These interventions included educational, behavioural, nutritional supplementation and pharmacological therapies. A systematic literature search was conducted using PubMed, Embase, Web of Science, CINAHL and the Cochrane Library from the start of each database through September 2025. RESULTS: Out of the 33 642 unique articles identified, 70 articles were included in our systematic review, with 59 individual RCTs and 11 cluster RCTs. Nutritional interventions (31 studies) included food and micronutrient supplementations. Micronutrient supplementation such as multiple micronutrient supplementations was found to reduce the risk of severely inadequate or inadequate GWG among pregnant women compared to iron only or iron and folic acid supplementation (Grading of Recommendations Assessment, Development and Evaluation [GRADE]: moderate certainty). Food supplementation showed mixed results, although most trials demonstrated higher mean GWG among undernourished pregnant women and a greater likelihood of achieving Institute of Medicine-recommended ranges (GRADE: moderate certainty). Behavioural (counselling/education) interventions (20 studies) were associated with significant reductions in excessive GWG among pregnant women with overweight or obesity and improved adequacy of GWG (GRADE: moderate certainty). Physical activity (seven studies) was found to reduce the risk of excessive GWG (GRADE: moderate certainty). Combined dietary and physical activity interventions (six studies) were found to reduce the risk of excessive GWG among pregnant women (GRADE: low certainty). CONCLUSIONS: Well-structured antenatal interventions, initiated before 20 weeks of gestation and continuing beyond this period, tailored to local cultural contexts and available resources, can effectively help pregnant women in LMICs in achieving optimal GWG. PROSPERO REGISTRATION NUMBER: CRD42022366354.
4 Apr 2026
Read appraisal →Quintessence international (Berlin, Germany : 1985)
The association between concurrent oral cancer and periodontal disease: a systematic review and meta-analysis
OBJECTIVE: Since emerging studies have reported a positive association on oral cancer and periodontal disease, the present study aimed to evaluate the odds ratio of having concurrent oral cancer in patients with periodontal disease. METHOD AND MATERIALS: A systematic review following the PRISMA guidelines was conducted with three electronic databases. The inclusion criteria were structured according to the PECOS framework: (P) Human subjects aged over 18; (E) Patients with periodontal disease; (C) Individuals without periodontal disease; (O) The odds of patients developing oral and/or head and neck cancers; (S) Case-control studies. After screening, 18 clinical studies from 13 different regions, comprising a total of 31,504 participants, were included. RESULTS: A meta-analysis was performed and revealed that the odds ratio for developing oral cancer in patients with periodontal disease, compared to those without, was 3.35 (95% CI 2.63-4.26; P .001). After adjusting for potential confounding factors, the adjusted odds ratio was found to be 2.32 (95% CI 1.71-3.15; P .001). Case definition also has an impact, with odds ratio of 2.02 if using missing teeth compared to odds ratio of 2.68 by using American Academy of Periodontology clinical periodontal parameters. CONCLUSION: Clinical considerations for this specific group of patients should draw new attention for a strategic interdisciplinary care.
4 Apr 2026
Read appraisal →The New England journal of medicine
Discontinuation of Beta-Blocker Therapy after Myocardial Infarction
BACKGROUND: The role of long-term beta-blocker therapy after a myocardial infarction in patients without left ventricular systolic dysfunction or heart failure is unclear in the era of contemporary coronary-artery reperfusion and secondary prevention interventions. METHODS: We conducted an open-label, randomized, noninferiority trial at 25 centers in South Korea. Patients whose condition remained stable after a myocardial infarction, who had a left ventricular ejection fraction of at least 40% and no heart failure, and who had received beta-blocker therapy for at least 1 year after the myocardial infarction were randomly assigned in a 1:1 ratio to discontinue or to continue beta-blocker therapy. The primary end point was a composite of death from any cause, recurrent myocardial infarction, or hospitalization for heart failure. The prespecified noninferiority margin was an upper limit of the 95% confidence interval for the hazard ratio of 1.4. RESULTS: A total of 2540 patients underwent randomization; 1246 were assigned to beta-blocker discontinuation and 1294 to beta-blocker continuation. The mean age of the patients was 63.2 years, and 12.8% were women. At a median follow-up of 3.1 years (interquartile range, 2.5 to 3.5), a primary end-point event had occurred in 58 patients (4-year Kaplan-Meier estimate, 7.2%) in the discontinuation group and in 74 patients (4-year Kaplan-Meier estimate, 9.0%) in the continuation group (hazard ratio, 0.80; 95% confidence interval, 0.57 to 1.13; P = 0.001 for noninferiority). The incidence of serious adverse events was similar in the two groups. CONCLUSIONS: Among patients who received beta-blocker therapy beyond the first year after a myocardial infarction, discontinuation of beta-blocker therapy was noninferior to continuation with respect to a composite of death from any cause, recurrent myocardial infarction, or hospitalization for heart failure. (Funded by Patient-Centered Clinical Research Coordinating Center in the Ministry of Health and Welfare, South Korea; SMART-DECISION ClinicalTrials.gov number, NCT04769362.).
4 Apr 2026
Read appraisal →Journal of advanced research
Machine learning ensemble meets clinical practice: developing a real-world risk prediction model for metabolic syndrome using super learner and scorecard approaches
INTRODUCTION: Metabolic syndrome (MetS) refers to a cluster of metabolic abnormalities that significantly increase the risk of developing cardiovascular diseases (CVDs). Traditional binary definitions of MetS fall short in capturing its severity spectrum, limiting personalized risk stratification. OBJECTIVES: We aimed to develop a super learner model and multilevel risk scorecard to improve MetS risk prediction and support early cardiovascular risk identification. METHODS: This study included a total of 460,256 health examination records from Zhejiang, China (2018-2023), with 344,925 used for model development and 115,331 used for external validation. A super learner model combining multiple machine learning algorithms was constructed to predict MetS risk. Key predictors identified through feature selection were used to develop a logistic regression-based MetS risk scorecard, stratifying individuals into five risk levels for practical and interpretable applications. RESULTS: The super learner model achieved area under the receiver operating characteristics (AUCs) of 0.816 (95 % confidence interval [CI]: 0.814-0.817) and 0.810 (95 % CI: 0.808-0.813) in the development and external validation cohorts, respectively. The risk scorecard, which incorporates ten predictors, demonstrated comparable performance, with AUCs of 0.793 (95 % CI: 0.791-0.794) and 0.788 (95 % CI: 0.785-0.791) in the respective cohorts. Based on the risk scorecard, individuals were stratified into five categories: very low, low, normal, high, and very high risk levels. CONCLUSION: The super learner model provides a highly accurate tool for MetS risk prediction, whereas the risk scorecard offers a practical and interpretable solution for clinical and personal use. These models enable precise risk assessment to guide prevention and improve outcomes.
4 Apr 2026
Read appraisal →Journal of diabetes investigation
A machine learning approach to predicting severe diabetes distress in adults with type 1 diabetes mellitus.
BACKGROUND: Diabetes distress is common in patients with type 1 diabetes mellitus (T1DM). The aim of this study was to construct and validate prediction models for diabetes distress in adults with T1DM using continuous glucose monitoring (CGM) metrics. METHODS: The CGM metrics were collected from 259 adults with T1DM. Severe diabetes distress was defined as 40 points on the Problem Areas in Diabetes scale. Prediction models were developed based on ten machine learning algorithms: random forest (RF), support vector machine (SVM), Naive Bayes (NB), Neural Network (NN), k-nearest neighbor (k-NN), XGBoost (XGB), SGDClassifier (SGDC), XGB_limitet_depth (CGB_ld), L1LogisticRegression (L1), and LightGBM. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score. RESULTS: Among the ten models, accuracy in the NN model was the highest (NN: 0.744, L1: 0.731, NB: 0.718, SVM: 0.692, SGDC: 0.654, RF: 0.628, k-NN: 0.628, LightGBM: 0.615, XGBoost: 0.564, and XGB_ld: 0.564). The NN model achieved the highest AUC of 0.728 (95% confidence interval: 0.608-0.845). CONCLUSIONS: This study developed a predictive model for severe diabetes distress using machine learning, incorporating both demographic and CGM metrics in adults with type 1 diabetes mellitus. The NN model demonstrated potential as a practical tool to assist clinicians in identifying individuals at risk of severe diabetes distress.
4 Apr 2026
Read appraisal →Computer methods and programs in biomedicine
Application of artificial intelligence in colonoscopy imaging for polyp analysis-A systematic review
The colon is a major component of the digestive system, so early detection of colorectal polyps is essential in preventing colorectal cancer, which is a leading cause of cancer-related death worldwide. While colonoscopy remains the gold standard for polyp detection, its diagnostic accuracy is highly operator-dependent. Recent advances in Deep Learning (DL), a branch of Artificial Intelligence (AI), have shown substantial potential to improve colonoscopy image analysis by enhancing the accuracy, consistency, and objectivity of polyp detection, segmentation, and classification. Artificial intelligence-based systems have significantly reduced inter-observer variability and increased diagnostic efficiency, ultimately transforming the landscape of colorectal lesion assessment. This survey provides a comprehensive and critical analysis of the current status of deep learning applications in colorectal polyp analysis. We systematically review state-of-the-art methodologies across various DL architectures-including Convolutional Neural Networks (CNNs), transformer-based models, and hybrid approaches-and examine their performance on publicly available benchmark datasets. Additionally, we highlight the strengths and limitations of existing techniques, explore the clinical relevance of AI-assisted tools, and identify prevailing challenges such as data imbalance, real-time deployment, and generalizability across diverse populations and colonoscopy devices. By consolidating key advances and outlining future research directions, this review aims to serve as a valuable resource for researchers, clinicians, and developers seeking to leverage deep learning to enhance colorectal polyp detection, diagnosis, and clinical decision-making.
4 Apr 2026
Read appraisal →JMIR research protocols
Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor-Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study
BACKGROUND: As the global population continues to age, the prevalence of geriatric conditions, including dementia and frailty, is also increasing. Early identification of individuals at an elevated risk of these conditions, such as those presenting with mild cognitive impairment (MCI) or prefrailty, can provide a critical window for prompt intervention aimed at preventing or reversing disease progression. To promote such early identification, there is a burgeoning interest in the use of digital sensor technology and predictive modeling. OBJECTIVE: This study aimed to use a continuous, home-based monitoring sensor system for older adults to distinguish those exhibiting normal aging from those with MCI, early dementia, prefrailty, or frailty, and to predict their transition from normal aging to one of these conditions. METHODS: This longitudinal cohort study will recruit 200 community-dwelling adults aged ≥65 years with normal cognition or MCI at baseline. A multi-sensor system will be installed in participants' homes, including passive infrared motion sensors, door contact sensors, bed sensors, medication box sensors, wearable activity bands, and Bluetooth proximity beacons. These devices will continuously capture spatiotemporal activity patterns, mobility indicators, sleep behaviors, and medication-taking routines. Annual assessments will include standardized cognitive tests (eg, Montreal Cognitive Assessment, Mini-Mental State Examination, Rey Auditory-Verbal Learning Test, digit span, Color Trails Test, semantic fluency, Stroop), frailty measures (modified Fried phenotype, gait speed, grip strength), mental health scales, sleep quality, and psychosocial indicators. Sensor-derived features-such as gait variability, activity regularity, sleep fragmentation, and medication adherence patterns-will be integrated with clinical data to develop supervised machine learning models. Planned approaches include logistic regression, random forests, gradient boosting, and deep learning. Model performance will be evaluated using cross-validation and independent test sets. Primary metrics will include area under the receiver operating characteristic curve, sensitivity, specificity, precision, recall, and F1-score. Models will be benchmarked against gold-standard clinical diagnoses and validated using temporal subsets of the dataset. RESULTS: Enrollment for this study started in November 2019 and will continue until March 2030. As of June 2025, we have enrolled 138 participants. Full data analysis has yet to begin. CONCLUSIONS: We aim to develop a reliable and effective sensor system for in-home use that will facilitate the early detection of cognitive and physical decline. In so doing, it will add to our current understanding of digital biomarkers. It is common for older adults to seek clinical intervention only when their cognitive impairment has already reached an advanced stage. The implementation of readily deployable sensor systems within community settings presents us with opportunities for prompt intervention, which holds the potential for delaying or reversing disease progression and allowing for a greater number of functional and meaningful years.
4 Apr 2026
Read appraisal →Current opinion in lipidology
Machine learning to predict elevated lipoprotein(a).
PURPOSE OF REVIEW: Lipoprotein(a), Lp(a), is a genetically determined, lifelong risk factor for atherosclerotic cardiovascular disease (ASCVD). Despite broad guideline support for universal one-time testing, Lp(a) measurement remains rare in clinical practice. This review summarizes recent advances in machine learning-based strategies that can enhance the efficiency, yield, and equity of Lp(a) screening. RECENT FINDINGS: To date, three studies have developed and validated machine learning models to identify individuals with elevated Lp(a) using routinely available clinical variables. The ARISE framework, derived from the UK Biobank and validated across multiple US cohorts, reduced the number needed to test by more than 50% while maintaining consistent discrimination across demographic subgroups. Additional studies have confirmed the feasibility of decision-tree and neural network models to improve case finding for elevated Lp(a) in both clinical and population-based settings. SUMMARY: Machine learning-based strategies provide a scalable means of operationalizing universal Lp(a) testing recommendations within health systems. When developed using unbiased data, externally validated, and assessed for fairness and interpretability, these models can support systematic identification of individuals with elevated Lp(a) and integration of Lp(a) measurement into routine cardiovascular risk assessment.
4 Apr 2026
Read appraisal →Physiological reviews
An umbrella review of systematic reviews of the impact of wrist-worn wearables on health outcomes
We conducted an umbrella review to synthesize the evidence on the effectiveness of interventions incorporating wrist-worn wearables' feedback on diverse health outcomes including health promotion (i.e., health behaviors and disease risk perception) morbidity, mortality, functioning, and other health-related metrics in humans. We searched in MEDLINE, Web of Science, Embase, and Cochrane Library until March 18, 2025, for eligible systematic reviews. After screening 9,487 citations, we identified 39 systematic reviews, which included 98 original studies (1 observational study, 95 randomized controlled trials, and 1 pre-post study). The reviews primarily focused on adult populations, individuals with cardiometabolic conditions, and cancer survivors. The original interventional studies mainly included Fitbit (40.2%), Polar (12.4%), and ActiGraph (10.3%) devices. Over 80% of the clinical trials involved complex behavioral interventions with wearable-based feedback, and the control groups varied. Most systematic reviews were rated as low confidence, with common flaws including inadequate consideration for risk-of-bias and heterogeneity. Interventions incorporating wrist-worn activity trackers increased physical activity in diverse populations. The effect of interventions incorporating wrist-wearables' feedback on cardiometabolic risk markers, quality of life, depression/anxiety, and pain was limited and remained inconsistent. Our findings rely on existing systematic reviews, which may vary in quality, review methodologies, and comprehensiveness. There is also potential for missing more recent evidence not yet captured in these reviews. These limitations should be considered when interpreting our results. Acknowledging these caveats, wrist-worn wearables seem to increase physical activity and may have additional benefits that require further study.
4 Apr 2026
Read appraisal →GeroScience
Integrative machine learning approach to risk prediction for dementia and Alzheimer's disease
Dementia, particularly Alzheimer's disease (AD), presents a growing global health challenge characterized by cognitive decline, behavioral changes, and loss of independence. With increasing life expectancy, early diagnosis and improved clinical strategies are urgently needed. This study developed and evaluated machine learning (ML) models to predict AD risk using UK Biobank data, integrating health, genetic, and lifestyle factors. The cohort included 2878 AD cases and 72,366 controls. Among several algorithms, CatBoost performed best (ROC-AUC = 0.773), especially in females. Inputs included ICD-10 codes from 5 years pre-diagnosis, ApoE-ε4 genotype, and large collection of modifiable risk factors. Despite fewer cases, the risk predictive models for vascular dementia (VaD) outperformed the unique AD models. ApoE-ε4 was the most predictive genetic marker, while other common variants had limited utility. Key non-genetic predictors included comorbidities (e.g., diabetes, hypertension), education, physical activity, and diet. These findings highlight the value of integrating diverse data sources for dementia risk prediction and emphasize the role of sex-specific modeling and modifiable factors in early, personalized intervention strategies.
2 Apr 2026
Read appraisal →Ophthalmology
Special AAO Report: Recommendations on Screening for Hydroxychloroquine Retinopathy (2025 Revision)
BACKGROUND: The American Academy of Ophthalmology recommendations on screening for hydroxychloroquine (HCQ) retinopathy are now a decade old. This revision provides an update based on current knowledge. Major points and changes are as follows. NATURE OF TOXICITY: The retina typically remains clinically normal for many years on HCQ, but there is an unpredictable point at which signs of toxicity may develop. PATTERN OF TOXICITY: Most eyes of patients of European heritage have a parafoveal pattern of toxicity, whereas eyes of East Asian patients tend to present with pericentral toxicity, but there are many exceptions, and screening examinations should look for both patterns. DOSAGE: The recommended daily dosage remains unchanged at ≤5.0 mg/day/kg real weight. Keep under 400 mg/day for severely obese patients. RISK FACTORS: Beyond dose and duration, concurrent renal disease and tamoxifen use increase risk, as does initiation of HCQ therapy at an older age. CLINICAL TESTS: The primary screening tool should be OCT, along with wide-pattern fundus autofluorescence (FAF). Visual field (VF) and multifocal electroretinography (mfERG) are secondary confirmatory tests. Newer VF algorithms such as 24-2C can screen parafoveal and pericentral regions simultaneously. SCREENING: A baseline screening examination, including fundus, OCT, and FAF, is advised soon after HCQ is begun, not only to rule out preexisting conditions but also for comparison with later results. Annual screening with OCT and FAF is recommended while using HCQ, but may be deferred during the first 5 years if there are no significant risk factors. PROGRESSION: Mild retinopathy is unlikely to progress after discontinuation of HCQ, but severe retinopathy may continue to worsen for many years. IMPLICATIONS OF EARLY SIGNS OF TOXICITY: With earlier recognition comes greater need to communicate with patients and their medical providers regarding options to stop HCQ or continue with cautious monitoring. NEW TECHNOLOGY: There have been experimental advances in imaging and artificial intelligence analysis for HCQ screening, but none have been validated for general use or are widely available at this time. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.
1 Apr 2026
Read appraisal →JAMA
An AI-Powered Lifestyle Intervention vs Human Coaching in the Diabetes Prevention Program: A Randomized Clinical Trial
IMPORTANCE: Prediabetes is common, yet evidence-based lifestyle interventions are underutilized. OBJECTIVE: To determine whether referral to an exclusively artificial intelligence (AI)-led lifestyle intervention based on the Diabetes Prevention Program (DPP) is noninferior to referral to a human-led DPP in achieving recommended thresholds for weight loss, hemoglobin A1c (HbA1c) reduction, and weekly physical activity among adults with prediabetes and overweight or obesity. DESIGN, SETTING, AND PARTICIPANTS: This phase 3, parallel-group, pragmatic, noninferiority randomized clinical trial was conducted from October 11, 2021, to December 16, 2024 (last follow-up) at 2 US clinical sites in Baltimore, Maryland, and Reading, Pennsylvania. Adults 18 years or older with prediabetes and overweight or obesity were enrolled. INTERVENTIONS: Participants were randomized in a 1:1 ratio to receive either a referral to an AI-powered DPP lifestyle intervention delivered via a mobile app and Bluetooth-enabled digital scale or a referral to a human coach-led DPP lifestyle intervention delivered remotely. Both interventions were delivered independently of the study team over a 12-month period. MAIN OUTCOMES AND MEASURES: The primary outcome was a composite of maintaining an HbA1c less than 6.5% throughout the study and achievement of at least 5% weight loss, at least 4% weight loss plus at least 150 minutes of weekly physical activity (assessed with actigraphy), or an absolute reduction in HbA1c of at least 0.2 percentage points at 12 months. Noninferiority of referral to the AI-led DPP compared with referral to the human-led DPP was prespecified to be determined if the 1-sided 95% CI lower boundary of the risk difference did not cross -15%. RESULTS: A total of 368 participants were included (median [IQR] age, 58 [50-65] years; 71% were female, 27% were Black, 6% were Hispanic, and 61% were White; median [IQR] BMI, 32.3 [28.5-37.1]). After referral, 171 of 183 participants (93.4%) initiated the AI-led DPP and 153 of 185 (82.7%) initiated the human-led DPP. The primary outcome was achieved by 58 of 183 participants (31.7%) in the AI-led DPP group and 59 of 185 (31.9%) in the human-led DPP group (risk difference, -0.2% [1-sided 95% CI, -8.2%]), meeting the criterion for noninferiority. Findings were consistent across individual components of the composite end point and in sensitivity analyses. CONCLUSIONS AND RELEVANCE: Among adults with prediabetes and overweight or obesity, referral to a fully automated AI-led DPP was noninferior to referral to a human-led DPP in achieving a composite outcome based on weight reduction, physical activity, and HbA1c. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05056376.
1 Dec 2025
Read appraisal →Nat Hum Behav
Machine learning-assisted optimization of dietary intervention against dementia risk
A healthy diet has been associated with a reduced risk of dementia. Here we devised a Machine learning-assisted Optimizing Dietary intERvention against demeNtia risk (MODERN) diet based on data from 185,012 UK Biobank participants, 1,987 of whom developed all-cause dementia over 10 years. We first identified 25 food groups associated with dementia in a food-wide association analysis. Second, we ranked their importance using machine learning and prioritized eight groups (for example, green leafy vegetables, berries and citrus fruits). Finally, we established and externally validated a MODERN score (0-7), which showed stronger associations with lower risk of dementia-related outcomes (hazard ratio comparing highest versus lowest tertiles: 0.64, 95% CI: 0.43-0.93) than the a priori-defined MIND diet (0.75, 0.61-0.92). Across 63 health-related outcomes, the MODERN diet showed particularly significant associations with mental/behavioural disorders. Multimodal neuroimaging, metabolomics, inflammation and proteomics analyses revealed potential pathways and further support the potential of MODERN diet for dementia prevention.
1 Nov 2025
Read appraisal →Nat Med
Feasibility and clinical utility of expanded genomic newborn screening in the Early Check program
Although genomic sequencing presents groundbreaking newborn screening (NBS) opportunities, critical feasibility and utility questions remain. Here we present initial results from the Early Check program-an observational study assessing the feasibility and clinical utility of genomic NBS in North Carolina. Recruitment was statewide through mailed letters with electronic consent. Genome sequencing with analysis of 169 high actionability genes (plus 29 optional lower actionability genes) was performed using residual NBS dried blood spots. In 8 months, 1,979 newborns were screened, with 50 (2.5%) screen positives. Negative results were returned electronically, positive results by genetic counselors. Twenty-eight results (55%) were true positives, all received anticipatory guidance, surveillance and management recommendations, and referral to specialists as appropriate. We report technical feasibility and preliminary clinical utility finding, along with interpretation and follow-up challenges that hinder public health implementation. We propose standardized terminology to facilitate cross-study comparisons and accurate characterization of genomic NBS outcomes.
1 Nov 2025
Read appraisal →Cardiovasc Diabetol
Association of estimated glucose disposition rate with aging acceleration and mortality risk in individuals with cardiovascular-kidney-metabolic syndrome: evidence from two large national population-based studies
OBJECTIVE: This study investigated the relationship between estimated glucose disposal rate (eGDR), aging acceleration (AgeAccel), and mortality in adults diagnosed with cardiovascular-kidney-metabolic (CKM) stages 1 to 4. METHODS: The study utilized data from 4,826 adults with CKM syndrome stages 1 to 4, collected from the National Health and Nutrition Examination Survey (NHANES) conducted during the 2005-2010 survey cycles. The assessment of AgeAccel was performed using two complementary measures: phenotypic AgeAccel (PhenoAgeAccel) and biological AgeAccel (BioAgeAccel). Survey-weighted logistic regression and Cox proportional hazards models were used to assess the associations of eGDR with AgeAccel and mortality risk, respectively. To assess the prognostic value of eGDR for mortality risk, we implemented a suite of nine distinct machine learning models. Additionally, a nomogram was developed to enhance the clinical applicability of our findings. Furthermore, we performed causal mediation analysis to quantify the proportion of the total effect of eGDR on mortality that was mediated through AgeAccel. To ensure the robustness of the results, we replicated our primary analyses using data from the nationally representative China Health and Retirement Longitudinal Study (CHARLS) cohort. RESULTS: Our analysis included 4,826 NHANES participants, among whom we documented 831 all-cause mortality events and 208 cardiovascular disease (CVD)-specific deaths during follow-up. In multivariable-adjusted Cox regression models, each unit increase in eGDR was significantly associated with a 10% reduction in all-cause mortality risk (Hazard ratio [HR] = 0.90, 95% Confidence interval [CI] 0.86-0.93) and a 13% decrease in CVD mortality risk (HR = 0.87, 95% CI 0.81-0.93). Additionally, eGDR showed a negative association with AgeAccel, including both BioAgeAccel (odds ratio [OR] = 0.85, 95% CI 0.82-0.87) and PhenoAgeAccel (OR = 0.78, 95% CI 0.75-0.80). For predicting all-cause mortality from eGDR, the K-Nearest Neighbors (KNN) showed superior discrimination (Area Under the Curve [AUC]: 0.926), exceeding the performance of other machine learning algorithms in a comparative evaluation. Mediation analysis revealed that the protective effect of higher eGDR was partially explained by slower PhenoAgeAccel, with mediation effects accounting for 23.53% and 15.73% of the total impact on all-cause and CVD mortality, respectively. CONCLUSIONS: In the CKM population, lower eGDR levels may be associated with both AgeAccel and an increased risk of mortality, with AgeAccel potentially mediating the relationship between eGDR and mortality. These findings suggested that eGDR could serve as a potential predictor and intervention target for delaying aging and reducing mortality risk.
1 Oct 2025
Read appraisal →Nat Commun
Proteome atlas for mechanistic discovery and risk prediction of diabetic retinopathy
Proteomics offers an unprecedented opportunity to characterize and predict diabetic retinopathy (DR) with minimal invasiveness. Here we examine this in 10,873 individuals with (pre)diabetes from two ethnically distinct cohorts. By simultaneous profiling of ~3000 proteins, we identify 668 associations with mechanistically plausible directionality that constitute a comprehensive DR proteomic landscape with linkages to retinal tomographic structure and genetic predisposition, pointing to established and novel biological pathways conferring DR risk. Integrating DR proteomic profile markedly improves predictive performance beyond clinical and genetic predictors, with plexin B2, growth differentiation factor 15, and renin emerging as top proteins validated across cohorts and linked to retinal microvascular degeneration in Guangzhou Diabetic Eye Study (GDES) based on SS-OCTA. A parsimonious panel of these three proteins alone achieves comparable performance in predicting DR development and progression, while renin is confirmed as a causal promoter through genetic analyses. Our findings highlight the potential of large-scale proteomics in elucidating DR pathogenesis and advancing biomarker discovery, with broad implications for early detection and intervention.
1 Oct 2025
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