Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 137 appraisals
The Knee
Artificial intelligence in the management of sports knee injuries: a narrative review.
INTRODUCTION: Sports-related knee injuries are common and debilitating, often leading to chronic pain, early osteoarthritis, and reduced performance. Artificial Intelligence (AI) has emerged as a promising tool to improve their prevention, diagnosis, prognosis, and rehabilitation. This review summarises current evidence on the clinical applications, limitations, and future directions of AI and machine learning in sports-related knee injuries. METHODS: A narrative review of PubMed, Embase, Medline and Web of Science was conducted, examining recent literature on AI-based models in musculoskeletal and sports medicine. The review was categorised into key domains: injury prediction and prevention, diagnostic imaging performance, AI-enabled clinical workflows, alongside postoperative and rehabilitation outcome modelling. RESULTS: AI algorithms demonstrate strong potential across the sports knee injury continuum. Predictive models analysing biomechanical and physiological data have achieved high area under the curve (AUC) values, in some cases above 0.90, in experimental and pilot setting when identifying athletes at risk of ACL rupture or overuse injuries, while machine learning approaches have been used to predict graft failure, revision surgery, and return-to-sport. However, most remain investigational rather than clinically deployable, with limited explainability, insufficient external validation, and training datasets that are often narrow or unrepresentative of broader athletic populations. CONCLUSION: AI has the potential to transform the management of sports-related knee injuries through more predictive, personalised, and precise care. However, wider clinical adoption will require multicentre validation, improved interpretability, and robust ethical and regulatory oversight. With further development, AI may enhance injury prevention, recovery, and improve long-term joint health outcomes in athletes.
3 Aug 2026
Read appraisal →AIDS care
"It's a lifeline": perspectives and experiences of health technology use among HIV care providers in rural Florida
The use of health technology, such as telehealth and mobile health used to support or deliver health care, is a promising strategy to improve HIV outcomes among people with HIV (PWH) in the rural South. In-depth interviews were conducted with 21 HIV care providers in rural Florida to understand the perspectives and experiences of using health technology to deliver care for PWH. Thematic analysis was applied. Emerging themes were organized under four domains: current and prior health technology practices, drawbacks to health technology, benefits of health technology, and potential facilitators of health technology. Providers revealed that health technology use has expanded rapidly since onset of the COVID-19 pandemic, especially use of video calls, but they preferred multiple communication methods. Drawbacks to health technology included lack of human connection, increased burden to providers and patients, confidentiality concerns, technology malfunctioning, discomfort with technology, and financing constraints. Benefits included improved communication and access to care, social connection and community support, and reduced HIV-related stigma. Potential facilitators included health technology that is user-friendly, and economic incentives to increase technology use. Health technology holds promise for addressing barriers to HIV care, however, its drawbacks must be addressed to ensure effective implementation in the rural South.
3 Aug 2026
Read appraisal →Sleep medicine
Concordance of wearable device sleep metrics with patient-reported sleep quality: A systematic review
BACKGROUND: Commercial wearable devices increasingly monitor sleep, but their concordance with patient-reported sleep quality remains poorly characterized. This systematic review evaluates concordance between wearable sleep metrics and validated subjective measures. METHODS: Following PRISMA guidelines, we searched PubMed/MEDLINE, Embase, and Cochrane Library through October 2025 for studies comparing consumer wrist-worn actigraphy-based devices with validated subjective sleep quality measures in adults (≥18 years). Two reviewers independently performed screening, extraction, and QUADAS-2 quality assessment. GRADE criteria evaluated evidence certainty. RESULTS: Five observational studies (2006 participants) were included. Wearable devices showed poor to moderate agreement with subjective assessments, explaining only 2.5-16.2% variance. Total sleep time moderately correlated with same-day diaries (r = 0.367), but devices failed to capture Pittsburgh Sleep Quality Index scores. Agreement varied substantially by population: good sleepers showed 82.4% concordance versus 39.4% in insomnia patients (p = 0.006). Clinical populations and older adults demonstrated poor agreement. Polysomnography concordance was also poor: sleep efficiency showed fair intraclass correlation coefficient values (0.478-0.570) with systematic overestimation (+1.75% to +7.9%), sleep onset latency correlated poorly (r = 0.033), and wake after sleep onset was underestimated (-7 to -30 min). Evidence certainty ranged from low to moderate. CONCLUSIONS: Commercial wearable sleep trackers demonstrate poor to moderate agreement with validated subjective sleep quality measures, with significant population-specific variation. Device data should complement, not replace, validated subjective assessments, as current technology inadequately captures patient-reported sleep quality and shows systematic bias in objective parameters.
3 Aug 2026
Read appraisal →Sleep medicine reviews
Artificial intelligence in sleep medicine I: Diagnosis, treatment, care, and research
Artificial intelligence (AI) is transforming sleep medicine (SM) through improved diagnostics, therapeutics, and research. AI enhances diagnostic accuracy, treatment personalization, workflow efficiency. However, implementation requires careful validation and oversight. This review explores AI applications across the sleep disorders spectrum. AI in diagnostic SM covers applications including polysomnography and home sleep testing. We examine how AI detects early neurodegenerative changes in REM sleep behavior disorder, identify novel biomarkers, and optimize chronotherapeutic timing. Wearables complement these advances by continuously monitoring sleep patterns, movement and physiological signals in natural settings, generating rich datasets ideal for AI analysis. In therapeutics, machine learning models enhance sleep apnea phenotyping, enabling precise treatment selection, and improve CPAP adherence prediction. AI helps identify biomarkers to optimize the timing of chronotherapeutic interventions. In medical care, natural language processing facilitates unstructured clinical chart data extraction. Implementation challenges include data standardization, algorithmic bias, and the "generalization gap." We provide a structured framework for clinical implementation, emphasizing validation requirements and ethical considerations. Large datasets using routine clinical care, cohort studies, and wearable derived data create vast amounts of data. In return, AI tools and methodologies enhance and expand development of phenotypes and endotypes with the final goal of personalized and precision SM.
2 Aug 2026
Read appraisal →Current opinion in pediatrics
Clinical implementation of artificial intelligence in adolescent mental healthcare
PURPOSE OF REVIEW: This review aims to summarize recent literature on artificial intelligence (AI) tools for adolescent mental health, including the types of tools available, their clinical applications, effectiveness, and safety, as well as relevant ethical considerations. RECENT FINDINGS: For clinicians, AI can facilitate clinical documentation, enhance therapy, and support the diagnosis process. Adolescents show interest in using AI for their mental healthcare and can benefit from AI-guided therapy apps and chatbots. Most studies that show effectiveness focus on depression treatment. Many tools are only in the prototyping stage, not tested on clinical samples, or lack safety measures, highlighting the need for further safety evaluation before specific app recommendations can be made. SUMMARY: AI is increasingly being implemented in pediatric health systems and adolescents' daily lives. Adolescent medicine practitioners should recognize the growing potential for certain AI applications to enhance access and support adolescents, review and utilize those applications that have empirical support of efficacy, and that provide guardrails for safe and ethical use.
1 Aug 2026
Read appraisal →JMIR mHealth and uHealth
Wearable Devices for Monitoring and Management of Comorbid Obstructive Sleep Apnea and Hypertension: Scoping Review
BACKGROUND: As an established driver of hypertension, obstructive sleep apnea (OSA) generates a significant cardiovascular burden when both disorders are present. This intersection not only compromises nocturnal hemodynamics but also hampers long-term clinical management. Yet, current health care delivery rarely integrates the simultaneous and continuous tracking of these dual burdens. While wearable technology provides a noninvasive, pragmatic toolset for synchronized physiological monitoring, research remains largely siloed within single-disease frameworks. Consequently, clinical evidence supporting wearable applications specifically for comorbid populations remains sparse. OBJECTIVE: In this scoping review, we summarized the current applications of wearable technology for comorbid OSA and hypertension. The analysis primarily outlines device categories, monitored physiological indicators, prevalent clinical scenarios, and existing challenges. METHODS: The search for relevant literature spanned PubMed, Web of Science, Embase, and IEEE Xplore, covering the period from January 2015 to February 2026. Eligible studies included adults and used wearable or portable technologies to objectively track sleep- or respiratory-related indicators and cardiovascular/blood pressure metrics. Study selection, data charting, and evidence synthesis were conducted using a 2-reviewer process and descriptive and narrative approaches. RESULTS: Our initial search yielded 739 records. Following title and abstract screening, we reviewed 54 full texts, ultimately finalizing a cohort of 13 eligible studies. Published between 2015 and 2026 across 9 countries, these articles capture data from 5596 participants. Most were observational studies and device validation studies. Evaluated device types included wrist-worn devices, fingertip contact devices, patch and single-lead devices, and multiparameter portable monitoring systems. The clinical applications mainly focused on screening and risk stratification for comorbid OSA and hypertension, monitoring of abnormal nocturnal blood pressure and hemodynamic changes, and cardiovascular risk assessment and remote longitudinal management. However, research remains sparse, and different devices vary greatly in reference standards, diagnostic thresholds, and validation pathways; therefore, their clinical translational value still requires further validation. CONCLUSIONS: Wearable devices may complement traditional assessment by providing continuous nocturnal and longitudinal data. However, at present, they are more suitable as auxiliary monitoring and risk warning tools in comorbidity management and cannot yet replace standard sleep studies or standard blood pressure monitoring. Future research should further shift from feasibility validation to large-sample, prospective, multicenter clinical studies in comorbid populations.
1 Aug 2026
Read appraisal →PloS one
Integrating smoking cessation into HIV care settings: A systematic review and meta-analysis of effectiveness and the evidence gap in cost-effectiveness.
The prevalence of smoking among people living with HIV (PLWH) is higher than in the general population, and PLWH who smoke are at increased risk of both smoking- and HIV-related comorbidities. As most PLWH reside in low- and middle-income countries (LMICs), there is a need for effective and cost-effective smoking cessation interventions in resource-constrained settings. We systematically reviewed and meta-analyzed the effectiveness of smoking cessation interventions for PLWH and looked for economic evaluations. Four databases (PubMed, Cochrane, Scopus, Web of Science) were searched up to March 23rd, 2026. Interventional and quasi-experimental studies evaluating smoking cessation interventions for PLWH were included. Risk of bias was assessed using Cochrane's risk-of-bias tool for randomized studies and the Effective Public Healthcare Panacea Project tool for non-randomized studies. Thirty-two articles met the inclusion criteria. Most evidence originated from high-income countries, with three randomized controlled trials conducted in LMICs (Kenya, South Africa and Vietnam). No economic evaluations were identified. Smoking cessation interventions varied in type, duration, intensity, and mode of delivery. Overall, studies had low to moderate risk of bias. GRADE assessments indicated moderate-certainty evidence that pharmacological interventions (RR 1.86, 95% CI 1.42-2.45) and tailored, intensive behavioral support (RR 1.34, 95% CI 1.05-1.71) increase smoking abstinence compared with standard care. This review indicates that pharmacological and tailored, intensive behavioral support interventions can support smoking cessation among PLWH, including emerging evidence from LMICs, but the absence of economic evaluations limits guidance for resource-constrained settings. Future research should prioritise implementation strategies and economic evaluations to support scalable integration into routine HIV care. (PROSPERO Registration no: CRD42022313630).
1 Aug 2026
Read appraisal →BMJ open ophthalmology
Screening for diabetic retinopathy with artificial intelligence in a primary care setting: a comparative cost analysis
OBJECTIVE: Cost analysis of autonomous artificial intelligence (AI)-based screening of diabetic retinopathy (DR) for adults with diabetes at a primary care clinic. METHODS AND ANALYSIS: This study provides a comparative cost analysis of actual results using AI-based DR screening with counterfactual results based on all patients going through the physician-based referral system. A cost analysis is conducted using cost data from published sources, provincial billing codes, statistical sources, and patient characteristics from a clinical study to compare autonomous AI-based screening for DR versus physician-based screening. Costs considered include direct costs of operating the AI system, physician fees, and indirect costs to patient time. Along with total cost comparisons, a cost per DR case detected is estimated and a sensitivity analysis based on variations in AI costs is provided. RESULTS: Over the study period, 202 participants were screened for DR using autonomous AI. The majority (93.6%, n=189) of AI-based DR screening exams were completed successfully. The AI-based scenario results in total direct costs of $C7919.04 and indirect costs of $C5728.80, resulting in total costs of $C13 647.84 per 100 patients. The traditional physician-based approach results in total direct costs of $C8240 and indirect costs of $C19 998.09, resulting in total costs of $C28 238.09 for 100 patients. When costs are converted to costs per unit outcome, the total cost per diagnosed DR case is $C620.36 for the AI-based approach and $C1283.55 for the physician-based approach; the AI-based cost per diagnosed case was 52% lower. CONCLUSION: Given the lower cost per diagnosed case of the AI-based approach, there are advantages to the implementation of AI-based screening for DR.
1 Aug 2026
Read appraisal →BMC ophthalmology
The effect of lithium on the structure and function of the human retina: a systematic review
BACKGROUND: Bipolar disorder and depression are associated with structural and functional changes in the retina, including a thinner retinal nerve fibre layer (RNFL). Lithium is widely considered the most effective treatment for bipolar disorder, but its mechanism of action is not fully understood. We assessed research looking at the effect of lithium on structural or functional retinal outcomes in humans. METHODS: Searches using the terms 'Lithium' AND 'retina' were carried out to identify peer reviewed studies assessing the impact of lithium on retinal structure or function. These included those with or without a control group comparison, pre- and post- lithium comparisons and observational studies. There were no exclusions based on the quantity or preparation of lithium administered, or the length of administration. Risk of bias was assessed using the Joanna Briggs Institute (JBI) critical appraisal tool for Analytical Cross Sectional Studies, and a narrative synthesis and tabulated summary of the included studies was completed. RESULTS: Seven studies assessing structural outcomes and 10 reporting functional ones were identified, all highly heterogenous and with multiple limitations. Structural outcomes were derived exclusively from optical coherence tomography (OCT) with retinal nerve fibre layer (RNFL) being the most common measurement. There was no evidence of differences in the RNFL between participants with bipolar disorder taking lithium and healthy controls in two larger studies. In six studies looking at differences in those with bipolar disorder taking lithium and those taking valproate, two showed no signs of difference and four showed evidence of thicker RNFL in the lithium group. Studies reporting functional outcomes reported a statistically significant effect of lithium on at least one functional measure, derived from electrooculography, electroretinography, and dark adaptation thresholds. CONCLUSIONS: Current evidence suggests that lithium is likely to have an effect on the retina but limitations in all studies mean better designed and adequately powered prospective studies are required. REGISTRATION: PROSPERO database (Number-CRD42024516635).
31 July 2026
Read appraisal →The Spanish journal of psychology
A Systematic Review of the Risk and Protective Factors for Suicide in Autism Spectrum Disorder
The shorter lifespan and increased prevalence of physical and psychiatric comorbidities in autistic people is a challenge for mental health. There is a high risk of suicide-related behaviors in autism, which are related to mental health issues, social isolation, and other factors. Here we aimed to systematically review the evidence on risk and protective factors of suicidal behaviors in autistic people. Electronic datasets (PubMed, Scopus, Web of Science, and PsycInfo) were searched for empirical articles on the risk and protective factors for suicide in autism. The PRISMA guidelines were followed, and a series of inclusion and exclusion criteria were applied to the retrieved data. This review included 19 studies reporting multiple risk factors associated with suicide-related behaviors. Noteworthy contributors among these factors include psychiatric comorbidities, bullying, cyberbullying, teacher harassment, and cognitive functions. The evidence for protective factors is limited; however, social relationships are highlighted. Identifying risk and protective factors is paramount for detecting early warning signs and enabling the prevention and intervention of suicidal behavior. Our review identified key sociodemographic, clinical, and cognitive risk factors to take into account as alarm-signs when identifying suicidal behaviors in autism.
31 July 2026
Read appraisal →Archives of orthopaedic and trauma surgery
Shoulder injuries in rugby union: a systematic review and meta-analysis
Shoulder injuries are common in Rugby Union, resulting in prolonged absence and considerable burden. Reported incidence, severity, and mechanisms vary widely, reflecting the multiple high-impact actions in the sport. Various management approaches exist to treat a wide range of pathologies. This systematic review and meta-analysis aimed to synthesise evidence on the epidemiology, mechanisms, and treatment of shoulder injuries in rugby. PUBMED, SCOPUS, and WEB OF SCIENCE were searched from database inception until 1st June 2025. From 1,099 abstracts screened, 37 studies were included. The pooled match incidence was 11.02 injuries/1,000 player-hours (95% CI 7.23-16.82), though heterogeneity was very high (I² = 97%) and the 95% prediction interval was wide (2.11-57.61 injuries/1,000 player-hours), indicating the pooled rate should be read as an average across diverse populations rather than a single representative value. Higher rates were seen in male elite (13.74/1,000 h) and high school/university players (12.28/1,000 h). Training incidence was considerably lower (0.20/1,000 h). Substantial between-study heterogeneity was observed across all incidence analyses, reflecting genuine variation across playing levels, sex, and surveillance methods. Tackling was the predominant injury mechanism, most often affecting the tackler. Bankart, Latarjet, and Bristow procedures all produced favourable reported outcomes for surgical management of anterior shoulder instability. These were indirect comparisons drawn from observational cohorts, however, and comparative effectiveness between techniques cannot be determined from the available evidence. Shoulder injuries in rugby occur far more often in matches than in training, result in prolonged time loss, and are most sustained in tackling. Surgical stabilisation is associated with favourable reported outcomes, though the comparative findings should be regarded as hypothesis-generating, and high-quality comparative studies are warranted. Greater attention to prevention, improved reporting of mechanisms, and focused research on female players should be prioritised.
31 July 2026
Read appraisal →JMIR cancer
Piloting a Clinical Decision Support System for Unintended Weight Loss in Primary Care: Mixed Methods Study on Early Cancer Detection
BACKGROUND: Delayed cancer diagnosis leads to poorer outcomes. Unintended weight loss (UWL) is a nonspecific symptom associated with cancer and other serious conditions, which can make it complex to identify the underlying cause. Clinical decision support systems (CDSSs) can provide evidence-based recommendations to facilitate timely investigation and diagnosis. OBJECTIVE: This study aimed to pilot the implementation of a CDSS for improving early cancer detection in primary care patients with UWL. METHODS: Five practices reviewed patients identified by the UWL CDSS. Staff were interviewed on the acceptability and feasibility of the CDSS. Interviews were analyzed thematically using 2 relevant frameworks: the acceptability of health care interventions and the sociotechnical model for evaluation of digital interventions framework. Clinical audits assessed the correct identification of UWL, follow-up rates, and patient characteristics. RESULTS: Of 60 patients identified by the CDSS as potentially having UWL, 36 (60%) had true UWL. Among the misclassified cases, most patients (16/55, 30%) were intentionally trying to lose weight; this intention was documented as free text in the clinical notes for 98% (58/60) of patients and in a structured field for only 20% of patients. Of the 36 patients, 5 (14%) were actively recalled by their practices for further follow-up; the others (31/36, 86%) were not recalled, as most were deemed already under appropriate follow-up. By 6 months, 94% (34/36) of the cohort had received follow-up care regardless of whether they had been formally recalled. One-third (12/36, 33%) of patients had no additional symptoms, while 36% (13/36) had recorded additional abdominal symptoms. Mental health conditions accounted for 19% of diagnoses linked to UWL consultation. Practice staff were generally receptive to the UWL CDSS concept. It was particularly appreciated by practices with strong quality improvement processes and prior CDSS experience. A high proportion of misclassified patients, poor workflow integration, and conflicting patients' agendas were cited as implementation barriers. CONCLUSIONS: Our study revealed challenges and potential benefits of implementing a CDSS for identifying patients with UWL at risk of cancer in primary care. While integration issues and misclassification of patients were noted, high rates of follow-up care were observed regardless of the CDSS. These high follow-up rates prompt consideration of whether they reflect Australian primary care and whether a CDSS with these characteristics is the most appropriate approach to support early cancer detection. Future research should focus on improving CDSS integration with existing workflows, enhancing correct identification through access to clinical notes and the use of more sophisticated digital methods (eg, AI). These findings highlight the potential of CDSS in improving patient care and the complexities involved in their successful implementation.
30 July 2026
Read appraisal →JMIR mHealth and uHealth
Perceptions of Adults Aged 50 Years and Older Regarding the Use of Wearable mHealth Technologies to Promote Physical Activity: Systematic Review and Meta-Ethnography
BACKGROUND: Despite advances in wearable mobile health (mHealth) technologies and their associated apps designed to promote physical activity, and the importance of adapting them to users, little is known about older adults' perceptions of these technologies. OBJECTIVE: This review aimed to synthesize and analyze qualitative evidence exploring the perceptions of adults aged 50 years and older regarding areas to improve, barriers to, and facilitators of wearable mHealth technologies (activity trackers and companion apps) to promote physical activity. METHODS: A qualitative systematic review and meta-ethnography was conducted. Comprehensive searches were performed across 8 databases (MEDLINE, Scopus, Web of Science, CINAHL, The Cochrane Library Plus, PsycINFO, ProQuest, and ÍnDICEs-CSIC) for articles published in English or Spanish between January 2013 and January 2024. The synthesis followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and ENTREQ (Enhancing Transparency in Reporting the Synthesis of Qualitative Research) guidelines. RESULTS: Ten articles met the inclusion criteria and were synthesized using meta-ethnography. Three main themes emerged: (1) barriers to promoting physical activity caused by wearable mHealth technologies: personal barriers (physical aspects, perceptions about technology, and personal preferences), technological barriers (functionality, content, design, alarms, availability, and accessibility), and environmental barriers (season of the year); (2) personal facilitators (consideration that these apps improve health, perceptions about technology, and personal preferences), technological facilitators (functionality, content, and design), relational facilitators (technological and social support), environmental facilitators (seasonal variations), and health care professionals (support and monitoring by health care services); and (3) personal areas (perceptions about technology and personal preferences), technological areas (functionality, content, and design), and relational areas (technological support). CONCLUSIONS: Although older adults acknowledge the potential of wearable mHealth technologies to promote physical activity, their effective engagement is hindered by distinct personal, technological, and environmental barriers. Bridging the digital divide requires designers to prioritize user-centered, age-friendly interfaces that are integrated with continuous support from health care professionals. To promote genuine health equity, future research must rigorously report intersectional demographics to ensure that mHealth interventions mitigate, rather than inadvertently exacerbate, existing disparities.
30 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Suicide in neurodegenerative diseases: a systematic review.
BACKGROUND AND OBJECTIVE: Suicide is a public health issue, which differs from suicidality, the continuum from suicidal ideation to the suicidal act, including suicide attempts and completed suicide. The main goal of the present study is to determine the relationship between Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS) with suicidality. METHODS: This is a systematic review aiming to determine the relationship between AD, PD, ALS, and MS with suicidality following PRISMA 2020 guidelines by collecting cross-sectional, case-control, and cohort studies; case series; case reports; and retrospective and prospective studies from Google Scholar, PubMed, and Cochrane Library. The protocol of this systematic review was registered on PROSPERO; the registration number is CRD420261422354. RESULTS: From 2247 records identified from electronic databases, only 24 articles were included: three studies focusing on AD, nine on PD, and six studies focusing on ALS and MS, respectively. These studies exhibited moderate to low risk of bias. Despite the broad differences regarding the neurochemistry, pathophysiology, diagnosis, symptoms, and treatments of the selected diseases, patients are at a higher risk of suicidality. Depression and low social connectivity are the most prevalent risk factors. Suicidality is mainly detected during the first years post-diagnosis in PD, ALS, and MS patients, while the results in AD are confusing. CONCLUSIONS: Data about this topic is scarce and largely varying. Further research is required to elucidate this paradigmatic realm, fostering awareness, enhancing therapies, and providing explanations and interpretations of the mechanisms involved.
30 July 2026
Read appraisal →Current psychiatry reports
Postpartum Suicidality beyond Depression: a Systematic Review of Risk Profiles and Prevention Gaps
PURPOSE OF REVIEW: Suicidal ideation and behavior during the postpartum period are increasingly recognized as important contributors to maternal morbidity and mortality, particularly in high-income countries. Although postpartum depression is a well-established risk factor, emerging evidence suggests that suicidality may also occur independently and therefore remain underrecognized in clinical practice. This systematic review aimed to synthesize current evidence regarding the prevalence of suicidal ideation and behavior in the postpartum period and to identify associated risk factors. RECENT FINDINGS: A systematic search of PubMed, Web of Science, EMBASE, and PsycINFO was conducted up to March 2025 following PRISMA guidelines. Twenty studies met inclusion criteria, encompassing 352,726 postpartum women across diverse international settings. Reported prevalence of suicidal ideation ranged from 2.2% to over 50%, depending on the population studied and assessment methods used. The most frequently identified risk factors included postpartum depression, anxiety, trauma history, intimate partner violence, unplanned pregnancy, low socioeconomic status, low social support, and adverse childhood experiences. Notably, several studies reported suicidal ideation even in the absence of clinical depression. Only a limited number of studies addressed preventive interventions. Postpartum suicidality appears to be a multifactorial phenomenon that cannot be explained solely by depressive symptomatology. The findings highlight the need for broader screening strategies that incorporate psychosocial and trauma-related factors in addition to depression. Integrated and trauma-informed approaches to screening and prevention may improve early identification of at-risk individuals. Future research should prioritize longitudinal designs and culturally sensitive investigations to better inform prevention and intervention strategies.
28 July 2026
Read appraisal →Signal transduction and targeted therapy
Mucosal immunity and vaccine development
The mucosal system, which includes the respiratory, gastrointestinal, and urogenital tracts, serves as a primary entry point for pathogens, with a unique immune microenvironment and specialized defense mechanisms. In recent years, especially following the onset of the COVID-19 pandemic, there has been increasing recognition of the importance of mucosal immunity, motivated by an enhanced comprehension of its fundamental mechanisms. Currently, strategies based on mucosal delivery systems to administer antigens and induce strong mucosal protective immunity have become a key focus in the development of mucosal vaccines. Compared with conventional intramuscular delivery, mucosal vaccination can simultaneously elicit a robust local mucosal response, effectively block pathogen entry into the local mucosa, and generate systemic immune responses to prevent symptomatic infections and severe disease. In addition, mucosal delivery offers advantages such as ease of administration and low invasiveness, making it a more widely acceptable approach to vaccination. In the present study, we conducted a systematic review of the mechanisms of mucosal immunity, the technological platforms for mucosal vaccines, and proposed a perspective on the challenges and future directions for the development of next-generation mucosal vaccines, with the goal of enhancing public knowledge and awareness regarding mucosal immunity and its possible effects on global health.
27 July 2026
Read appraisal →Medicine
Performance of DeepSeek V3 and ChatGPT-4o in answering esophageal cancer-related questions
Esophageal cancer remains a significant global health issue. ChatGPT-4o and DeepSeek V3 can provide the public with health-related knowledge about esophageal cancer. This study aimed to evaluate the accuracy of DeepSeek V3 and ChatGPT-4o in responding to health knowledge questions related to esophageal cancer. Fifty-two questions related to esophageal cancer were classified into themes of basic knowledge, diagnosis and molecular biology, management of local and locoregional diseases, management of advanced and metastatic diseases, clinical case analysis and patient frequently asked questions (FAQs). These questions were entered into DeepSeek V3 and ChatGPT-4o to obtain responses, and 2 experienced gastroenterologists independently evaluated the accuracy and temporal stability of each response. Overall, the scores of DeepSeek V3 and ChatGPT-4o on all questions were 4 (3-4), and there was no statistically significant difference between the 2 groups. The final scores of DeepSeek V3 in basic knowledge, diagnosis and molecular biology, management of local and locoregional diseases, management of advanced and metastatic diseases, clinical case analysis, and FAQs were 4 (3-4), 4 (3-4), 4 (3-4), 3 (3-4), 4 (4-4), and 4 (4-4), respectively, while the scores of ChatGPT-4o were 4 (3-4), 3 (2-4), 4 (3-4), 3 (3-4), 4 (4-4), and 4 (4-4), respectively. For temporal stability across 2 independent test runs, DeepSeek V3 presented inconsistent responses on 2 questions, and ChatGPT-4o on 1 question; no statistically significant differences were found in overall and subgroup scores between the 2 runs for both models (all P > .05). ChatGPT-4o and DeepSeek V3 showed favorable accuracy and comprehensive responses to most of the 52 esophageal cancer-related questions in this study, but our findings do not confirm their general reliability for esophageal cancer health information in routine clinical or public use.
27 July 2026
Read appraisal →Medicine
Survival benefit of concurrent beta-blocker use in cancer patients treated with radiotherapy: A systematic review and meta-analysis
BACKGROUND: Both preclinical and retrospective studies have implicated β-adrenergic signaling in cancer progression, leading to interest in β-blockers (BBs) as adjunctive anticancer agents. There is evidence that they may enhance the sensitivity of cancer cells to radiotherapy and other conventional therapies. We conducted a systematic review and meta-analysis to explore the radiosensitizing effects of BBs. METHODS: PubMed, the Cochrane Library, Embase, Web of Science, the China National Knowledge Infrastructure, the China biology medicine database, Wan fang Data, and the VIP database were searched for articles in both English and Chinese from the establishment of the databases to September 8, 2025, to identify studies comparing outcomes according to beta-blocker use (yes vs no) in patients with solid tumors treated with radiotherapy. The primary endpoint was overall Survival (OS), Secondary objectives included 1, 2, 5-Year Survival, disease-free survival, distant metastasis-free survival, locoregional progression-free survival and progression-free survival (PFS). RESULTS: 8 studies (3165 patients), including 7 retrospective cohort studies and 1 randomized controlled trial, were analyzed. The most common cancer was non-small cell lung cancer (n = 5). BB use was associated with significantly improved OS (hazard ratio [HR] 0.73, 95% confidence interval: 0.64-0.83). Benefits were also observed for disease-free survival (HR 0.69) and Distant Metastasis-Free Survival (HR 0.62), indicating reduced recurrence and metastasis. CONCLUSION: In this meta-analysis, the use of BBs during or around radiotherapy may be associated with longer OS in cancer patients, suggesting that it may enhance the efficacy of radiotherapy and provide a survival benefit. Alternatively, the effect of β-blockers on survival may shift from a nonspecific effect to an intriguing cancer-specific effect over time. Beta-blockers are an intriguing option to explore in prospective studies of patients with solid tumors undergoing radiotherapy.
26 July 2026
Read appraisal →Journal of pediatric endocrinology & metabolism : JPEM
Beyond A1c: a narrative review of mental health integration in pediatric type 1 diabetes care
Background Youth with type 1 diabetes (T1D) experience substantially higher rates of depression, anxiety, diabetes distress, attention-deficit/hyperactivity disorder (ADHD), and disordered eating behaviors (DEB) than peers. These conditions may impair self-management and contribute to worsened glycemic outcomes.ContentWe synthesize prevalence and consequences of common comorbidities; summarize International Society for Pediatric and Adolescent Diabetes (ISPAD) and American Diabetes Association (ADA) guidance on psychosocial assessment; and appraise integrated models: co-located behavioral health, stepped care, and telehealth integration. We map barriers (workforce, workflow, stigma, fragmented communication, digital inequities) to concrete actions.SummaryPsychological morbidity and glycemic outcomes may be connected through behavioral and neuroendocrine pathways and family dynamics. Programs embedding behavioral health in pediatric diabetes care facilitate routine psychosocial screening and are associated with improved engagement and quality of life, and in some models, improved glycemic outcomes.OutlookTo close the implementation gap, teams should standardize routine psychosocial screening, embed trained mental-health professionals, use stepped-care models matching interventions to symptom severity, track referral follow-through, and leverage hybrid telehealth. Policy levers include reimbursement for integrated visits and investment in the behavioral-health workforce. The consistent associations between psychological and glycemic outcomes in youth with T1D support integrating psychosocial care as a core component of comprehensive diabetes management.
25 July 2026
Read appraisal →Nanotechnology
DFT and machine learning investigation of Au/Pt-decorated SnS2 monolayers for asthma and COPD diagnosis
Asthma and chronic obstructive pulmonary disease (COPD) are among the most prevalent chronic respiratory diseases worldwide, affecting hundreds of millions of people and contributing significantly to global morbidity and mortality. This work introduces a novel Au/Pt-decorated SnS2heterostructure for exhaled NO2detection, representing the new study to explore its role in lung disease diagnostics. It demonstrates ppb level NO2detection, a key biomarker for asthma and COPD, enabling early and differentiation of lung conditions by providing quantitative analysis of trace-level gases, which are often elevated in inflamed airways. While two-dimensional (2D) SnS2offers strong potential as a sensing platform, prior studies relied mainly on density functional theory (DFT) based gas sensing. Here, we present unprecedented integration of DFT and machine learning (ML) to investigate the gas sensing performance of pristine and Au/Pt-decorated SnS2monolayers. DFT analysis revealed enhanced adsorption and charge transfer upon noble-metal decoration, with Pt-SnS2showing optimal characteristics for asthma and COPD detection. Five ML models were trained on DFT and experimental-derived descriptors to rapidly predict the sensing behaviour of multiple gases, including NO2, among which XGBoost achievingR2= 0.9961. Both ML and DFT methods consistently identified NO2as the most sensitive analyte. This novel DFT-ML synergy not only validates fundamental adsorption mechanisms but also provides a scalable pathway for accelerated screening and design of high-performance gas sensors. Our findings establish a new prototype for integrating ML with first-principles simulations in the design of next-generation 2D material-based sensing devices.
24 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Differential effects of hormone therapy formulations on Parkinson's disease risk: a systematic review and meta-analysis
BACKGROUND: The relationship between menopausal hormone therapy (HT) and Parkinson's disease (PD) risk remains controversial, with inconsistent findings potentially driven by differences in hormonal formulations. METHODS: We conducted a systematic review and meta-analysis following PRISMA 2020 guidelines. MEDLINE/PubMed and EMBASE were searched between January 8 and March 14, 2026. Observational studies evaluating the association between menopausal HT and PD risk were included. Effect estimates were pooled using random-effects models. Subgroup analyses were performed according to HT formulation (estrogen-only vs. combined estrogen-progestin therapy). A multilevel meta-analysis was conducted to account for within-study dependence. RESULTS: Fourteen studies including 753,749 participants (4,433 PD cases) were analyzed. Overall, HT was not significantly associated with PD risk (RR 1.08; 95% CI 0.94-1.23; I² = 49.4%). In subgroup analyses, combined therapy was associated with an increased PD risk (RR 1.40; 95% CI 1.07-1.82), whereas estrogen-only therapy showed no significant association (RR 1.01; 95% CI 0.81-1.27). Multilevel analysis yielded consistent results (combined: RR 1.33; 95% CI 1.00-1.77; estrogen-only: RR 1.03; 95% CI 0.83-1.27), with no statistically significant interaction between formulations (p = 0.12). CONCLUSIONS: Combined menopausal HT was associated with an increased risk of PD, while estrogen-only therapy showed no significant association. Although differences between formulations were not statistically significant, these findings suggest that hormone composition may influence neurological outcomes and warrant further investigation into individualized HT strategies.
23 July 2026
Read appraisal →Depression and anxiety
Reducing Public Stigma Toward Suicide-Loss Survivors Through Brief Video Interventions: A Randomized Controlled Trial
BACKGROUND: Suicide-loss survivors (SLSs) experience substantial and often enduring psychological burden. These difficulties are compounded by public stigma, underscoring the need for scalable approaches to shift attitudes at the population level. In this study, we examined whether brief survivor-narrative videos can reduce public stigma of SLSs. METHODS: In a randomized controlled trial, 1351 adults (18-50) completed baseline measures and were allocated to view either a brief SLS narrative or a psychoeducational control. Public stigma toward SLSs and trait impressions were assessed at baseline and postexposure. RESULTS: Relative to control, the SLS video arm showed clear improvements immediately and at 30 days: stigma scores were lower and trait impressions more favorable, with attenuation over time. Item-level analyses indicated sizable immediate reductions for "Disconnected" (-24%) and "Cowardly" (-16%), and smaller but significant decreases for "Immoral" and "Irresponsible" (-12% each). CONCLUSIONS: Brief survivor-narrative videos can shift public attitudes toward SLSs and maintain part of that change over 1 month. As a low-cost, scalable complement to postvention, brief video contact offers a practical lever to improve the social climate surrounding SLSs. Deployed widely and reinforced over time, it can move communities from blame to empathy, strengthen everyday support, and advance survivors' recovery.
23 July 2026
Read appraisal →Current pain and headache reports
Chronic Pain and the Risk of Dementia: A Systematic Review and Meta-Analysis
BACKGROUND: The relationship between chronic pain and dementia risk has been extensively studied, but findings remain inconclusive. We conducted an updated meta-analysis to synthesize evidence from recent large-scale cohort studies. METHODS: Two authors independently and systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Chinese National Knowledge Infrastructure for cohort studies published through April 2025, with a minimum follow-up of one year. Hazard ratios (HRs) were considered equivalent to risk ratios (RRs) assuming low event rates. Random-effect models pooled risk ratios (RR) with 95% confidence intervals (CI). Subgroup analyses and meta-regression explored heterogeneity. RESULTS: Of 3,823 publications, 30 studies met the inclusion criteria. After excluding five studies with overlapping populations, 25 studies involving 2,091,835 participants were pooled in the primary analysis. Individuals with chronic pain had a 28% higher dementia risk (RR = 1.28, 95% CI = 1.18-1.38). Heterogeneity was high (I²=91%). Subgroup analyses showed migraine, headaches, arthritis-related pain, and widespread pain were associated with increased risk. The association was significant in Asia but not in America or Europe, and when using ICD (but not DSM) criteria. Chronic pain increased the risk of Alzheimer's disease and non-vascular dementia, but not vascular dementia. Study quality, region, measurement methods, and pain types influenced the relationship. Pain type was a source of heterogeneity. CONCLUSIONS: In this meta-analysis of cohort studies, chronic pain was associated with an increased risk of dementia. Due to the observational nature of the included studies and high heterogeneity, this association should not be interpreted as causal. Further research is needed to determine whether pain management can mitigate dementia risk.
23 July 2026
Read appraisal →JMIR mental health
Optimizing Treatment Strategies in the Bipolar Disorder Spectrum With Classical AI Approaches: Systematic Review of Performance, Bias, and Clinical Applicability
BACKGROUND: Bipolar disorder (BD) is a complex and heterogeneous psychiatric condition, characterized by fluctuating clinical courses that affect approximately 1%-2% of the global population in their lifetime. Despite pharmacological advances, treatment response varies significantly among patients, making the identification of individualized treatment strategies a major challenge. Artificial Intelligence (AI), through its classical approaches, has emerged as a powerful tool in precision psychiatry to identify subtle patterns in complex data and inform personalized clinical decisions. OBJECTIVE: The present systematic review aimed to examine the current evidence on classical AI-supported treatment optimization in the BD spectrum. METHODS: The review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Four databases (PubMed, Web of Science, Scopus, and Embase) were searched for original studies published after 2015 on the application of classical AI in the treatment of BD in adult patients. Publication bias was evaluated by visual inspection of a funnel plot. The methodological quality, risk of bias, and clinical applicability of the predictive models were assessed using the Prediction Model Risk Of Bias Assessment Tool for prediction models using regression or AI methods (PROBAST+AI; PROBAST+AI Working Group) tool. RESULTS: A total of 35 studies were included and classified into 5 outcome-based categories, including acute symptomatic response, long-term maintenance response, relapse and readmission risk, safety and dose optimization, and brain aging and phenotyping. Acute symptomatic response models performed modestly (pooled area under the curve [AUC] 0.68), while imaging improved accuracy (74%-77%). Long-term maintenance response models showed moderate-to-high performance (pooled AUC 0.80), with biomarker- and cellular-based models reaching 96%-99% accuracy. Relapse and readmission prediction achieved a pooled AUC of 0.71, with digital phenotyping and rule-based methods performing best (AUC 0.85-0.88). Safety and dose optimization models achieved 85%-97% accuracy. Brain aging and phenotyping studies highlighted accelerated brain aging in BD, partially mitigated by lithium, and revealed novel data-driven subgroups. However, 3 studies were considered at high risk of bias due to small sample sizes associated with disproportionately high-performance estimates. An additional study was identified as potentially biased because it lay markedly distant from the funnel plot's confidence line. Finally, the PROBAST+AI assessment revealed a high risk of bias in most studies, primarily due to data analysis limitations, small sample sizes, and lack of external validation. CONCLUSIONS: The adoption of classical AI tools in BD serves as a driver for therapeutic optimization, although current AI tools in BD should still be considered exploratory rather than ready for clinical use. Effective implementation in real-world clinical scenarios requires more robust, transparent, and externally validated models to ensure reliability and generalizability.
22 July 2026
Read appraisal →Scientific reports
The role of AI in combating misinformation: leveraging text mining and social networking analysis
Misinformation on social media can be a severe threat to social trust, safety, and health of the population, especially in times of an epidemic like the Monkeypox outbreak. This study specifically focuses on a hybrid RoBERTa-GRU architecture designed to capture both contextual semantics and temporal dependencies in social media discourse. This research presents how the combination of text mining and social network analysis enables Artificial Intelligence (AI) to support misinformation detection. The proposal of a hybrid architecture that integrates RoBERTa and GRU-based embeddings in a contextual fashion and GRU-based modelling of sequential patterns helps identify and substantiate misinformation in social media posts. Based on a curated X (formerly Twitter) dataset consisting of Monkeypox posts (5787 posts), the model provided state-of-the-art results, with ROC-AUC 0.9979 and Cohen's kappa 0.9887; standalone baselines were also surpassed. Results reveal that the proposed transformer-RNN hybrid effectively captures both semantic depth and temporal relationships in misinformation detection tasks. In addition to performance, the paper addresses the limitations of dataset bias, multilingual constraint issues, and scalability as related to cross-linguistic applicability, multimodal study, and performance in real-time and resource-limited systems. The study adds value in this emerging body of knowledge on AI-driven social media analytics by offering practical guidance for mitigating health-related misinformation online.
22 July 2026
Read appraisal →JMIR formative research
Development of a User-Informed Decision Aid for Contraceptive Decision-Making Among Adolescents and Young Adults: Qualitative Study
BACKGROUND: Adolescents and young adults seeking contraceptive care face many considerations related to differences in contraceptive indications and knowledge, which are often overlooked and can lead to contraceptive nonadherence or nonuse as well as adverse health outcomes. OBJECTIVE: This study aimed to develop a digital decision aid that meets the contraceptive decisional needs of adolescents and young adults from diverse backgrounds. METHODS: We developed a web-based decision aid using a user-centered design framework and the International Patient Decision Aid Standards. The design and development process was informed by a literature review and consultations with scientific experts, health informatics specialists, clinicians experienced in adolescent reproductive health, and a diverse group of adolescent and young adult advisors. We gathered feedback on the decision aid's content and functionality from clinicians, adolescents, and young adult stakeholders during focus group interviews conducted across 2 user testing cycles. Each focus group was recorded and transcribed, and the data were analyzed using a focus group guide to identify key attributes, patterns, and perspectives among users. Two qualitative researchers used rapid qualitative analysis to explore and summarize findings across 4 key domains, which contributed to the refinement of the decision aid content and the improvement of its functionality. RESULTS: Twenty-four clinicians, adolescents, and young adult participants from diverse backgrounds shared their perspectives on the decision aid's content, relevance, design, and usability across 2 user testing cycles conducted from February 2023 to June 2024. The decision aid includes a survey, a decision algorithm that generates a summary of contraceptive method recommendations, infographics, and a health care provider summary view. The decision algorithm applies a weighted scoring system ranging from +1 or -1 to +10 or -10 for each method, reflecting the user's primary indication for seeking contraception, contraceptive use preferences, and relevant health history. CONCLUSIONS: This approach allowed us to capture rich perspectives from a diverse group of stakeholders that accounted for the unique contraceptive decision-making needs of adolescents and young adults, resulting in a functional, youth-informed decision aid prototype, MyPlanMyChoice, ready for pilot and feasibility evaluation in a clinical setting.
22 July 2026
Read appraisal →JMIR cancer
Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review
BACKGROUND: Large language model (LLM)-based conversational agents are increasingly used in health care, yet their capacity to support genuine multiturn dialogue remains underexplored. In oncology, where patients and caregivers experience complex informational and emotional needs throughout the disease trajectory, conversational agents may support information provision, symptom consultation, and emotional assistance. However, research specifically examining multiturn conversational agents designed for patients with cancer and informal caregivers remains limited. OBJECTIVE: This scoping review aimed to map the research landscape of LLM-based multiturn conversational chatbots developed for patients with cancer and informal caregivers, focusing on system design, intervention purposes, evaluation approaches, safety considerations, and transparency of LLM-related components. METHODS: This scoping review followed the Joanna Briggs Institute methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Six databases-PubMed, Embase, Scopus, Web of Science, CINAHL, and PsycINFO-were searched for studies published between January 2022 and January 2026, with supplementary searches conducted in IEEE Xplore Digital Library and ACM Digital Library in May 2026. Studies were included if they described LLM-based chatbots designed for patients with cancer or informal caregivers that supported multiturn conversational interaction. Two reviewers independently conducted the study selection and data extraction. RESULTS: Eight studies met the inclusion criteria. Most studies focused on prototype development, with limited research evaluating clinical outcomes. ChatGPT-based models were the most commonly used LLMs, and retrieval-augmented generation techniques were applied in several studies. Chatbots were primarily designed for emotional support or information provision. Evaluation approaches varied widely, including response quality, psychological outcomes, and user experience. However, no studies evaluated interaction-level characteristics such as conversational continuity or context retention, and only 2 studies reported any conversational memory mechanism. Reporting on safety risks, mitigation strategies, prompt design, model parameters, and adherence to LLM reporting guidelines was often limited or absent. CONCLUSIONS: This scoping review identified only 8 studies on LLM-based multiturn conversational chatbots for patients with cancer and informal caregivers. The field remains at an early stage, characterized by prototype-oriented development, heterogeneous design and evaluation approaches, and inconsistent safety and transparency reporting. Future development should prioritize genuine conversational capability, safety management, and transparent reporting.
22 July 2026
Read appraisal →Food & function
Efficacy of different dietary fibers for chronic idiopathic constipation: a systematic review and network meta-analysis
This study aims to compare the efficacy and safety of different dietary fiber subtypes in treating patients with chronic idiopathic constipation (CIC) through network meta-analysis (NMA). PubMed, Embase, Cochrane Library, and Web of Science were searched from inception to October 23, 2025. The risk of bias was evaluated using the Cochrane RoB 2.0 tool, and treatment rankings were calculated using the surface under the cumulative ranking curve (SUCRA). 17 randomized controlled trials (RCTs) involving 1423 adults with CIC were included. NMA suggested that viscous soluble fibers were associated with greater efficacy compared with placebo in relieving defecation difficulty (high certainty, class 1), and were also associated with improvements in stool consistency and stool frequency relative to placebo (moderate certainty, class 1). According to SUCRA rankings, viscous soluble fibers tended to have a higher probability of favorable performance compared with other dietary fiber subtypes in improving straining during defecation (moderate certainty), stool frequency (low certainty), and stool consistency (low certainty), and was associated with a relatively favorable tolerability profile (moderate certainty). Insoluble dietary fibers showed the highest ranking probability for improvement in overall constipation symptom scores; however, this finding was supported by very low certainty evidence. Overall, the findings suggest that viscous soluble fibers have a higher probability of improving stool consistency, stool frequency, and straining during defecation. In contrast, insoluble fibers showed a relatively higher ranking for improvement in overall constipation symptom scores. These results support a symptom-oriented and individualized approach to dietary fiber selection in the management of CIC.
21 July 2026
Read appraisal →European journal of clinical pharmacology
Clinical decision support systems for polypharmacy optimization in older patients: a narrative review
PURPOSE: Multimorbidity and polypharmacy are increasingly prevalent in the older population and are associated with a higher risk of potentially inappropriate medications (PIM), drug-drug interactions (DDI), and adverse drug reactions (ADR). Although medication review (MR) and deprescribing are effective strategies, their manual implementation can be complex, time-consuming, and prone to clinical variability. Clinical Decision Support Systems (CDSS) offer advanced digital solutions to optimize polypharmacy by analyzing multidimensional clinical data and generating personalized recommendations. METHODS: A narrative review was conducted to identify and compare the main CDSS developed for the polypharmacy management, MR and deprescribing in older adults and patients with multimorbidity. Systems were classified as manual, hybrid, or automated according to their data acquisition modalities. Operational characteristics, integration into clinical workflows, decision-support functions, generated outputs, and available validation evidence across different healthcare settings were assessed. Owing to the narrative nature of the review and the heterogeneity of the included evidence, no formal risk-of-bias assessment or certainty-of-evidence evaluation was performed. RESULTS: Manual CDSS require direct data entry by clinicians and are associated with a high operational burden. Hybrid systems combine automatic data acquisition with manual integration, balancing efficiency and clinical oversight. Automated systems, integrated into electronic health records (EHR), provide real-time decision support with minimal human intervention. Considerable heterogeneity was observed across identified platforms in terms of automation, implementation characteristics, and stage of validation, with evidence ranging from development and feasibility studies to observational analyses and randomized controlled trials (RCT). CONCLUSION: CDSS represent promising tools for safer and more effective management of polypharmacy in complex patients. Advanced integration into clinical workflows and systematic use of multidimensional data may enhance their impact. However, the heterogeneity of available systems and the variability in their level of clinical validation highlight the need for comparative studies, pragmatic trials, and real-world implementation evaluations. Such studies are necessary to clarify the impact of different CDSS models on prescribing appropriateness, medication-related risks, patient outcomes, and long-term sustainability within routine healthcare settings.
20 July 2026
Read appraisal →Biomedical physics & engineering express
Benchtop validation of a low-cost wearable triaxial accelerometer
Wearable inertial sensors have become a cornerstone technology for detecting fall-related events, such as tripping while walking, in older adults. However, low-cost devices must undergo rigorous validation to ensure fidelity and stability in measurement. This study aimed to evaluate the concurrent validity of a newly developed, low-cost triaxial accelerometer by comparing its linear acceleration measurements with those obtained from a widely used commercial reference sensor (RS). A methodological validation study was conducted to assess the accuracy and precision of the proposed accelerometer. Triaxial linear acceleration data were simultaneously collected from the new device (sensor under test, SUT) and a commercial RS (Physilog®5, GaitUp, Lausanne, Switzerland). Measurements were obtained across three independent axes from 49 SUT units, with three repeated trials performed for each axis. The RS exhibited a more negative mean bias on the mediolateral (x) axis and a slightly larger mean bias on the vertical (y) axis. The SUT showed a greater mean bias (offset) on the anteroposterior (z) axis. Root mean squared error values were highly similar between thexandyaxes (<3.2% difference), while thez-axis presented moderately larger errors (∼9%). Mean absolute error was similar in thexandyaxes, whereas thez-axis was slightly higher. Visual waveform comparisons demonstrated strong overlap in mean time-series profiles, and near-unity correlations indicated high correspondence in signal variation. Bland-Altman analysis confirmed minimal bias and narrow limits of agreement for thexandyaxes, with greater variability observed along thezaxis, but no evidence of substantial systematic deviation. Overall, the low-cost accelerometer (∼60 USD) showed strong agreement with the commercial reference device (>1 000 USD), supporting its validity for three-dimensional linear acceleration measurement under controlled laboratory conditions. This low-cost device represents a promising solution for scalable motion monitoring and fall-related event detection.
20 July 2026
Read appraisal →Science advances
A wearable biomechanical system for medical evaluation of soft tissue disorders
Evaluating soft tissue elasticity provides critical biomechanical insights essential for the precise characterization of various physiological and pathological conditions. Here, we present the clinical validation of a wireless, compact wearable system designed for direct, location-specific monitoring of the elastic modulus of the skin and underlying tissues via vibro-rotational biomechanical dynamics. Validated by computational models and experiments, the device uses a tunable skin interface to enable depth-controlled measurements across diverse anatomical sites. Two human subject studies, one involving patients with cancer-related lymphedema and the other involving patients with systemic scleroderma, yield data that correlate with standard clinical metrics. This technology offers the potential for longitudinal assessments in these and other contexts, in both clinical and home settings.
20 July 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
A machine learning approach to profiling anxiety risk among cancer patients on treatment
PURPOSE: Previous literature has identified multiple risk factors for anxiety among individuals with cancer. However, the relative importance across many interrelated variables is not clear. Further, it is unknown how combined psychosocial factors and demographic/clinical characteristics increase an individual's vulnerability to anxiety. The aims of this study are to systematically test the following: (1) the best predictors of anxiety, (2) the best-performing classifiers for distinguishing high versus low anxiety, and (3) the most informative combinations of psychosocial features and demographic/clinical characteristics that heighten an individual's vulnerability to anxiety. METHODS: Machine learning (ML) models were used. For Aim 1, we tested the predictors of anxiety using the models of regularized regressions, GAMS, and best subset selection. For Aim 2, models tested included support vector machines (SVM), random forest, nearest neighbors, and XGBoost as the classifiers to predict high versus low anxiety. For Aim 3, gradient-boosted decision tree models were trained to examine the most important combinations of candidate predictors. We used SHapley Additive exPlanations (SHAP) to improve the interpretability of our ML models. RESULTS: Across five imputed datasets, using linear regression as the primary benchmark (MSE = 69.945 ± 8.131; MAE = 6.636 ± 0.361), elastic net showed the lowest average prediction error (MSE = 65.486 ± 8.323; MAE = 6.504 ± 0.357), although differences among predictor-based models were modest. For classifiers, random forest achieved the highest mean accuracy (0.751 ± 0.015), kNN achieved the highest PR-AUC (0.797 ± 0.016), and logistic regression achieved the highest ROC-AUC (0.835 ± 0.005). Participation self-efficacy was the most consistent key predictor of anxiety across models. The most important risk combinations of demographic and psychosocial/symptom variables included age-symptom communication barriers, age-cancer coping self-efficacy, ECOG performance status-depressive symptoms, the type of helper-psychological well-being, and education-psychological well-being. CONCLUSION: Our findings may inform risk stratification in clinical settings and facilitate personalized intervention designs aiming at relieving anxiety among cancer patients.
19 July 2026
Read appraisal →Biomedical physics & engineering express
Simulation-driven deep learning for the diagnosis of middle ear pathologies using wideband acoustic immittance
Wideband acoustic immittance (WAI) provides comprehensive frequency-dependent information for diagnosing middle ear pathologies. However, the scarcity of clinical data and complex response patterns significantly hinder automated diagnosis, particularly in data-limited scenarios. To address this issue, this study proposes a simulation-driven computer-aided diagnosis framework for WAI based on finite element (FE) modeling. Latin hypercube sampling was employed to systematically perturb key physiological parameters of the human ear FE models, generating a standardized virtual WAI dataset comprising 12 000 samples across the 0.2-6 kHz frequency range. The dataset includes four middle ear conditions: normal ear, ossicular chain discontinuity, ossicular chain fixation, and otitis media with effusion. Based on this dataset, a lightweight convolutional neural network tailored for multi-channel WAI inputs, termed WAIHybrid, was developed. It was benchmarked against traditional feature-based machine learning models and five representative deep learning architectures on simulated data and subsequently evaluated on an external clinical dataset comprising 206 ear-level WAI records. WAIHybrid achieved a macro-F1 of 96.30% and a balanced accuracy of 96.29% on an independent simulated test set. On the external clinical dataset, the corresponding values were 87.76% and 88.07%, respectively. Response-level comparisons, learned-representation analyses, and Integrated Gradients maps identified partial class-related correspondence between the simulated and clinical data, residual simulation-to-clinical discrepancy, and class-dependent channel-frequency attribution patterns. These findings support a simulation-driven proof of concept for automated WAI analysis in data-limited middle ear assessment. Further evaluation in larger, more balanced, and clinically heterogeneous cohorts is needed.
19 July 2026
Read appraisal →JMIR human factors
Engaging Older Adults With Neurocognitive Disorders in Digital Health Technologies: Scoping Review
BACKGROUND: Population aging is associated with a growing prevalence of neurocognitive disorders among adults aged 65 years and older. Digital health technologies offer promising opportunities to support cognitive health and well-being in this population. However, their effectiveness largely depends on users' level of engagement. Despite the recognized importance of engagement in digital health, limited evidence exists on how engagement is conceptualized, measured, and related to intervention outcomes among older adults living with neurocognitive disorders. OBJECTIVE: This scoping review aimed to describe how engagement with digital health technologies among older adults with neurocognitive disorders is conceptualized and measured, examine the relationship between engagement and the effectiveness of digital health interventions, and identify factors that facilitate or hinder engagement. METHODS: A scoping review was conducted following the Joanna Briggs Institute methodological guidance and reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. A comprehensive search strategy, developed in collaboration with an information specialist, was applied to MEDLINE, Embase, CINAHL, Web of Science, and Google Scholar, without date restrictions. Empirical studies involving adults aged 65 years and older living with neurocognitive disorders and using digital health technologies were included. Study selection and data extraction were performed independently by at least 2 reviewers, and the results were synthesized narratively. RESULTS: Of the 1665 records identified after duplicate removal, 2 studies met the inclusion criteria. One study examined computerized cognitive stimulation and cognitive engagement programs among community-dwelling older adults with mild neurocognitive disorders, whereas the other explored the use of a personalized digital reminiscence application in long-term care settings among individuals with major neurocognitive disorders. No study used a validated instrument to directly measure engagement. However, observable indicators and markers related to the behavioral, cognitive, and affective components of engagement were reported. Both studies also documented concurrent cognitive or psychosocial outcomes. Factors facilitating engagement included professional support, content personalization, and involvement of informal caregivers, whereas limiting factors included cognitive fluctuations, fatigue, technical complexity, and reliance on external support. CONCLUSIONS: This scoping review highlights a significant gap in the literature regarding the explicit conceptualization and standardized measurement of engagement with digital health technologies among older adults living with neurocognitive disorders. The findings underscore the need to develop and apply multidimensional, context-sensitive engagement measurement tools tailored to this population to better understand and optimize digital health interventions.
18 July 2026
Read appraisal →International ophthalmology
Machine learning-based model for identifying liver injury in patients with thyroid-associated ophthalmopathy
PURPOSE: To explore the relationship between thyroid-associated ophthalmopathy (TAO) and liver injury and establish a model for identifying liver injury by using machine learning so as to provide an effective diagnostic tool for liver injury in patients with TAO. METHOD: A single-center retrospective study was conducted to collect the clinical data of 318 patients with TAO admitted to a hospital from 2016 to 2022. The patients were divided into a TAO liver injury group (104 cases) and TAO normal liver function group (214 cases) according to whether the patients had normal liver function. The multivariate binomial logistic regression model was used to analyse the risk factors for liver injury in patients with TAO. Feature selection was performed using the random forest algorithm. The research data were divided into a training set and a test set at a ratio of 6:4. Taking whether accompanied by liver injury (0 = no, 1 = yes) as the outcome variable, models were established based on logistic regression, random forest, support vector machine, and decision tree. The performance of the models was evaluated using metrics, including sensitivity, specificity, positive predictive value, negative predictive value, Youden index, and accuracy. RESULT: The random forest method was used for feature screening, and variables ranked among the top 10 according to either mean decrease accuracy or mean decrease Gini were selected for model construction. In five-fold cross-validation, the RF model showed the highest accuracy of 0.937 and AUC of 0.977. In the test set, the RF model also showed good discrimination, with an AUC of 0.973, and the SVM model showed the highest AUC of 0.986, while SVM and LR achieved the highest accuracy of 0.914. CONCLUSION: This study shows that a classification model based on machine learning can effectively identify the risk of liver damage in patients with TAO.
18 July 2026
Read appraisal →Journal of behavioral addictions
Problematic internet use and borderline personality features: A systematic review informed by the alternative model of personality disorders.
BACKGROUND AND AIMS: Associations between problematic internet use (PIU) and borderline personality disorder (BPD) have been reported, but the literature is highly heterogeneous, with some studies assessing overall BPD severity and others examining individual traits (e.g., emotional lability, impulsivity, dissociation). This review synthesizes these approaches using the Alternative Model of Personality Disorders (AMPD), examining how BPD-relevant pathological personality traits (Criterion B) and impairments in self and interpersonal functioning (Criterion A) relate to PIU. METHODS: Following PRISMA 2020 guidelines, we searched PsycINFO, Web of Science, PubMed, Embase, and Scopus for studies examining associations between PIU (e.g., general use, social media, gaming, smartphone use) and personality traits or functioning in adults. Study quality was assessed using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. RESULTS: Thirty studies (N = 21,358) met inclusion criteria. Emotional lability and impulsivity showed the most consistent associations with PIU across outcome types. Few studies examined impairments in personality functioning, but available evidence suggests that identity disturbance may predict PIU over time and that interpersonal dysfunction is associated with greater use of online environments for reassurance and emotion regulation. Associations involving dissociation and detachment were relatively weak and less consistent. DISCUSSION AND CONCLUSIONS: Studies varied widely in design, measures, and theoretical approach, limiting direct comparison across studies. Despite this heterogeneity, findings indicate that emotional lability, impulsivity, and identity disturbance appear to represent transdiagnostic vulnerabilities that increase risk for PIU and shape patterns of online engagement.
18 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Association between circadian rhythm disturbances and cognitive decline in the elderly: a systematic review
BACKGROUND: Circadian rhythm dysregulation may contribute to sleep-wake disorders and cognitive impairment. In the elderly, circadian abnormalities have been observed in Mild Cognitive Impairment (MCI), suggesting a possible association between neurocognitive deficits and circadian dysfunctions. OBJECTIVE: To analyze the association between circadian rhythm disturbances and cognitive decline in the population aged ≥ 60 years. METHODS: This systematic review included studies evaluating the association between circadian rhythm disturbances and cognitive decline in elderly individuals aged 60 years or older, with self-reported or objectively measured sleep data and cognitive assessment via questionnaires. Studies tracking progression from MCI to Alzheimer's disease (AD) were retained, as they capture circadian changes across the MCI-to-dementia continuum. Studies that evaluated other sleep disorders and/or other conditions of cognitive impairment exclusively or that did not specify the mean age of the participants were excluded. RESULTS: Ten studies were included, totaling 5,731 participants (5,707 eligible for analysis). Circadian exposures were organized into three domains: (1) actigraphic rest-activity rhythm metrics (amplitude, robustness, interdaily stability, intradaily variability, and acrophase); (2) sleep quality and architecture parameters (efficiency, fragmentation, latency, WASO, and melatonin timing); and (3) circadian-disrupting lifestyle exposures (rotating night shift work). Across these domains, circadian dysregulation was consistently associated with a higher risk of development or progression of MCI and dementia. CONCLUSION: Circadian and sleep disturbances negatively impact cognitive health in the elderly, reinforcing the need for further research on this association and its public health implications.
17 July 2026
Read appraisal →European journal of cardiovascular nursing
The psychological effects of interventions targeting informal caregivers of patients with cardiovascular disease-a systematic review.
AIMS: To explore the psychological effects of interventions aimed at supporting informal caregivers involved in the care and treatment of patients with cardiovascular disease. METHODS AND RESULTS: Databases (PubMed, CINAHL, Embase, Cochrane Library, and PsycInfo) were searched for studies in accordance with the Cochrane Handbook guidelines. Inclusion criteria were: caregivers of patients with one or more cardiovascular diseases, patient and caregiver >18 years, caregivers included in the intervention, and, reporting of psychological outcomes specific to caregivers. Study designs were randomized controlled trials with a follow-up periods of >2 months. The RoB 2.0 bias assessment tool was used to assess risk of bias. Fifteen studies from nine countries were identified. Most interventions consisted of multiple components including educational face-to-face sessions, telephone support, and/or written resources. The analysis showed inconsistent results on caregiver outcomes, but significant improvements were reported in eight studies in at least one caregiver outcome. Analysis indicated that the studies providing more frequent contact with caregivers and patients were more likely to report significant improvements in caregiver outcomes. Risk of bias was judged as low in three studies, some concerns in nine studies, and as high in three studies. CONCLUSION: Due to inconsistency in results, this review yield uncertainty about whether interventions targeting caregivers of patients with cardiovascular disease improve caregivers' psychological outcomes. Therefore, further research is needed to develop effective interventions for caregivers, as they play a vital role in the daily care and treatment of patients with cardiovascular disease. REGISTRATION: PROSPERO: CRD420250654618.
17 July 2026
Read appraisal →Pulmonology
Systematic review of prognostic scores and individual predictor variables for short-term mortality after acute pulmonary embolism.
BACKGROUND: For patients with acute pulmonary embolism (PE), assessment of prognosis helps with risk stratification, triage for level of care, management strategy, and communication among healthcare workers and patients. We sought to identify prognostic models and individual factors associated with short-term outcomes after acute symptomatic PE. METHODS: We performed a systematic review of prognostic factors for PE, searching MEDLINE, Embase, and Web of Science for records up to 1 June 2024. Studies of any design evaluating potential prognostic models or individual variables (not contained in the models) associated with short-term mortality after acute PE were included. RESULTS: We identified 314 studies that included 2,495,115 patients. Of these, 225 studies included 2,267,952 patients and evaluated 24 prognostic models for patients with acute PE. The most frequently used validated models were the simplified Pulmonary Embolism Severity Index (sPESI) (127 studies), the original PESI (79 studies), and the European Society of Cardiology risk schema (34 studies). Each model-development study had a c-index ≥ 0.7. Individual factors associated with prognosis included older age, presence of coexisting conditions, abnormal clinical signs and symptoms, clot burden, markers of right‑ventricle dilatation/dysfunction and myocardial injury, altered laboratory results indicating impaired haemodynamics, and tests that assess for systemic inflammation. Pooled odds ratios for variables not contained in any eligible prognostic model ranged from 1.43 (for D-dimer) to 2.65 (for right heart thrombi). CONCLUSIONS: This systematic review identified 24 prognostic models and 18 individual variables distinct from the prognostic models associated with short-term mortality after acute PE.
17 July 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
The effects of glucose-dependent insulinotropic polypeptide on net splanchnic blood flow in lean humans.
CONTEXT: Glucose-dependent insulinotropic polypeptide (GIP) is an incretin hormone with potent vasoactive and metabolic effects in adipose tissue, but its effects on splanchnic blood flow (SBF) in humans remain unclear. Investigating potential regional differences is important for understanding the vascular actions of GIP in humans. OBJECTIVE: The aim of this study was to examine the effects of GIP on SBF, both independently and in combination with hyperglycemia and hyperinsulinemia. METHODS: In a randomized, controlled crossover study, 8 healthy, lean male participants underwent 4 separate experimental conditions. The interventions included intravenous infusions of either GIP at a rate of 1.5 pmol-1 kg-1 minute-1 or saline, administered alone or in combination with a hyperglycemic and hyperinsulinemic clamp, respectively. Splanchnic blood flow was measured by Fick's Principle after catheterization of a hepatic vein, using indocyanine green as indicator. RESULTS: Splanchnic blood flow remained comparable across all experimental conditions, including GIP and saline infusions, both with and without the hyperglycemic and hyperinsulinemic clamp (P = .42). CONCLUSION: Under the applied conditions, GIP does not appear to play a substantial role in the acute regulation of net SBF, either alone or in combination with induced hyperglycemia and hyperinsulinemia.
17 July 2026
Read appraisal →The Journal of the American Academy of Orthopaedic Surgeons
Clinical Outcomes and Safety Profile of Open and Arthroscopic Subtalar Joint Arthrodesis: A Systematic Review
BACKGROUND: Open and arthroscopic subtalar arthrodesis are safe and effective surgeries for patients with notable arthritis; however, not an established superiority among them has been found. This systematic review aims to present all of the available literature on studies directly comparing both surgeries. METHODS: Two independent authors completed a systematic literature search using the following databases: PubMed, Embase, and the Cochrane library. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocol and the Cochrane Handbook guidelines were followed. In addition, the MINORS score was used to evaluate the quality and bias of the nonrandomized controlled trials. Our search criteria included only studies that included both open and arthroscopic subtalar arthrodesis. RESULTS: A total of four studies including 125 open and 130 arthroscopic surgeries met the inclusion criteria for the current systematic review. The mean age and body mass index of patients in the open and arthroscopic groups were 50.0 ± 2.3 and 47.9 ± 1.4 years and 29.0 ± 3.7 and 29.5 ± 2.6 kg/m 2 , respectively. Patients in the open cohort and arthroscopic cohort were followed for a mean of 16.9 ± 6.5 and 15.8 ± 4.7 months, respectively. Union rate was 95.4% ± 4.6% vs. 93.6 ± 4.0%, and the time to fusion was 14 ± 1.6 vs. 10.5 ± 1.6 weeks. The overall complication rate among patients who underwent open arthrodesis was 44.0%, whereas the complication rate was 39% among patients who underwent arthroscopic arthrodesis. Patients stayed in the hospital for 2.2 ± 0.9 days following an open procedure versus 1.1 ± 0.5 days following minimally invasive procedures. CONCLUSION: The current systematic review found a high rate of union among both group of patients. Although trends suggested higher union rates and fewer complications in the arthroscopic cohort, no notable differences were found. Although limited, we hope that this systematic review may help to guide evidence-based decisions when planning the surgical management of patients with severe subtalar arthritis.
17 July 2026
Read appraisal →International journal of medical informatics
A scoping review on conversational AI in mental health: A human-centered perspective.
BACKGROUND: Conversational AI offers scalable mental health support, with large language models (LLMs) enabling personalized interactions. Human-centered design is critical in this domain, yet a comprehensive synthesis from this perspective is lacking. This review maps conversational AI research in mental health across the patient journey and develops a human-centered taxonomy to guide future design. METHODS: Following PRISMA guidelines, we conducted a comprehensive search across fifteen multidisciplinary databases. We systematically analyzed the literature across six dimensions: research foci, mental disorder types, target populations, AI technologies, data sources, and evaluation metrics. A consensual taxonomy research method was employed to develop a human-centered design framework. RESULTS: Of 10,293 identified records, 677 studies met the inclusion criteria. Analysis reveals a marked increase in publications since 2020, predominantly from computer science (449 studies), followed by medicine (148) and social sciences (80). Research is skewed toward detection (23%) and intervention (66%) stages, with prevention (8%) and maintenance (3%) receiving less attention. Mood, anxiety, and stress-related disorders are the most investigated conditions. LLMs have emerged as the predominant AI technology, particularly within intervention and maintenance stages. Data sources continue to rely heavily on text-based inputs, with multimodal approaches still limited in adoption. Evaluation metrics vary significantly by discipline, reflecting limited cross-disciplinary integration. Through thematic synthesis, we developed a human-centered taxonomy comprising four primary dimensions: Emotional Sensitivity to Users, User-Centric Interaction Design, Human-AI Collaboration and Capability Enhancement, and Ethics and Accountability, with a total of thirteen sub-dimensions. CONCLUSIONS: This review provides a comprehensive, human-centered mapping of conversational AI research in mental health across the patient journey. Critical gaps remain in stage coverage, disorder diversity, population inclusivity, multimodal data integration, and interdisciplinary evaluation. The proposed taxonomy offers a structured framework to align AI development with human-centered principles, fostering empathetic, ethical, effective, and equitable mental health support.
16 July 2026
Read appraisal →Journal of affective disorders
Ecological momentary interventions for depression: A systematic review and meta-analysis
INTRODUCTION: Depression remains a prevalent condition with many individuals experiencing residual symptoms despite standard treatments. Ecological momentary interventions (EMIs) offer real-time, ecologically-valid microinterventions, yet their clinical utility and implementation challenges in depression remain underexplored. METHOD: A systematic search of six databases (inception to 25 July 2025) identified randomised controlled trials (RCTs) evaluating EMIs for depression. Pooled effect sizes assessed differences relative to comparator conditions. RESULTS: Sixteen RCTs involving 1258 participants were included. EMIs significantly reduced depressive symptoms at posttest (Hedges' g = 0.57; 95% CI = 0.33-0.80; p < 0.001) and at follow-up ranging from 10 to 32 weeks (Hedges' g = 0.43). High heterogeneity reflected variability in EMI formats. EMIs also showed a statistically significant improvement in quality of life (Hedges' g = 0.44; 95% CI = 0.17-0.71; p < 0.01), supporting their broader impact beyond symptom reduction. Engagement averaged 64.91% (SD = 17.18), ranging 37.5%- 88.0%, with variation across reporting methods. Acceptability and usability ratings were consistently high. Intervention dosage and risk of bias were significant moderators of treatment efficacy. Subgroup analyses revealed greater benefits from EMIs incorporating psychoeducation and active practice, and fully-automated formats. CONCLUSION: The review highlights EMI efficacy in reducing depressive symptoms and improving quality of life, with intervention dosage and methodological rigor emerging as key moderating factors. Reporting engagement remains a challenge, and substantial heterogeneity underscores the need for refined trial designs to isolate the active EMI components to best optimise treatment outcomes in depression. Future studies should prioritise strategies strengthen methodological rigor and optimising user engagement. PROSPERO No. CRD420251105663.
16 July 2026
Read appraisal →Journal of affective disorders
The association between obsessive-compulsive symptoms and self-compassion: A meta-analysis and systematic review.
BACKGROUND: Self-compassion, defined as the ability to respond to personal suffering with mindful awareness, self-directed kindness, and recognition of shared humanity, has been linked to reduced risk and severity of psychopathology. No systematic reviews have examined the relationship between self-compassion and obsessive-compulsive (OC) symptoms. METHOD: We conducted a systematic review and meta-analysis of peer-reviewed quantitative studies reporting associations between validated measures of self-compassion and OC symptoms. Eleven studies (N = 3336) met inclusion criteria. Random-effects models estimated pooled correlations for total symptoms and four core OC symptom dimensions (symmetry/ordering, unacceptable thoughts, checking/responsibility, and contamination/washing). Moderator analyses examined clinical status and cultural orientation. RESULTS: Across studies, self-compassion was moderately and negatively associated with OC symptom severity (r = -0.34, 95% CI [-0.39, -0.30]), with moderate heterogeneity (τ2 = 0.002, I2 = 40%). All four symptom dimensions were inversely associated with self-compassion, with the strongest association for unacceptable thoughts (r = -0.41). Neither clinical status nor cultural orientation significantly moderated effects. Funnel plot symmetry and fail-safe N analyses indicated minimal publication bias. DISCUSSION: Findings suggest that lower self-compassion is reliably linked to greater OC symptom severity across studies, samples (clinical vs. community), and cultures (individualist vs collectivist), particularly for unacceptable thoughts. These results highlight self-compassion as a potential therapeutic target in OCD, warranting further investigation through longitudinal and interventional studies.
15 July 2026
Read appraisal →Psychological medicine
Mechanisms of mindfulness-based cognitive therapy in difficult-to-treat depression: moderation and mediation analyses from the RESPOND trial.
BACKGROUND: Mindfulness-based cognitive therapy (MBCT) was developed for relapse prevention in people with remitted depression but is increasingly used for those with difficult-to-treat depression (DTD). A key question regarding this broader application is whether ongoing depressive symptoms constrain therapeutic responsiveness or disrupt MBCT's proposed mechanism, decentering. We explored whether baseline depressive severity moderates clinical outcomes, whether changes in decentering mediate treatment effects, and whether this mediation varies by baseline severity. METHODS: Secondary moderation, mediation, and moderated mediation analyses were conducted using data from the RESPOND randomized trial (N = 234), comparing MBCT plus treatment as usual (TAU) with TAU alone in adults not remitted after high-intensity psychological therapy. Depressive symptoms (PHQ-9) and decentering (Experiences Questionnaire) were assessed at baseline, post-treatment (10 weeks), and follow-up (34 weeks). Analyses were conducted using structural equation modelling. RESULTS: Higher baseline severity predicted greater symptom improvement across both groups. Treatment-related increases in decentering partially mediated the effect of MBCT on depressive symptoms at follow-up. Although baseline severity did not moderate the treatment effect, it moderated the indirect effect, with decentering more strongly associated with symptom reduction among those with higher baseline depression. Severity did not moderate the acquisition of decentering skills. CONCLUSIONS: Concerns that more severe depressive symptoms limit the effectiveness of MBCT were not supported. MBCT's core mechanism remained operative under substantial symptom burden, with clinical impact amplified at higher severity. These findings reduce key uncertainties regarding the application of MBCT in DTD and support its use across a broad range of symptom severity.
15 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 →European journal of clinical pharmacology
Clomiphene citrate versus testosterone replacement therapy in male hypogonadism: a systematic review of literature and meta-analysis
INTRODUCTION: Male hypogonadism, including age-related and functional forms, is characterized by insufficient testosterone production often associated with obesity and metabolic comorbidities. Testosterone replacement therapy (TRT) increases serum testosterone but suppresses spermatogenesis and may cause adverse effects. Selective estrogen receptor modulators (SERMs), such as clomiphene citrate, have emerged as an alternative approach to restore endogenous testosterone while preserving fertility. This study aimed to compare the efficacy of clomiphene citrate versus testosterone replacement therapy (TRT) in increasing serum testosterone levels in men with hypogonadism. METHODS: We conducted a systematic review of the literature in PubMed, Embase, Scopus, The Cochrane Library, and Google Scholar to identify studies comparing clomiphene citrate and testosterone replacement therapy (TRT) in men with male hypogonadism'. The primary outcome was the change in serum testosterone levels before and after treatment. Secondary outcomes included variations in other hormonal parameters, and clinical symptom improvement assessed through standardized instruments. RESULTS: A total of 11 studies with 1512 patients were included (764 on clomiphene citrate; 748 on testosterone replacement therapy, TRT). For serum testosterone, the overall pooled effect showed no significant difference between clomiphene and TRT (MD = 6.64 ng/dL; 95% CI -35.35 to 48.63; I² = 80.6%). Subgroup analyses indicated injectable testosterone achieved higher levels than clomiphene (1 study, 62 patients; MD = -290.50; 95% CI -518.78 to -62.22), whereas gel-based TRT showed no difference (8 studies, 1012 patients; MD = 24.29; 95% CI -18.91 to 67.48; I² = 76.1%). For sexual function, three studies (199 patients; 85 on clomiphene; 114 on TRT) assessing post-treatment libido (ADAM 1-5) showed lower libido scores with clomiphene than TRT (MD = - 0.54; 95% CI - 0.87 to - 0.21; P = 0.009; I² = 18.8%). CONCLUSIONS: No statistically significant difference in serum testosterone levels was observed between clomiphene citrate and testosterone gel, although the evidence is limited by high heterogeneity and some concerns to high risk of bias. TRT was associated with greater improvements in libido. These findings should be interpreted as preliminary, and large-scale randomized trials incorporating clinical and fertility endpoints are needed.
13 July 2026
Read appraisal →Depression and anxiety
The Impact of Psoriasis Treatment on Depression and Suicide Risk: A Systematic Review
BACKGROUND: Psoriasis is a long-lasting inflammatory condition of the skin associated with various comorbidities, including depression and suicidal ideation. Management strategies for psoriasis include symptom alleviation, quality-of-life enhancement, and prevention of disease progression. Psoriasis treatments include topical therapies, phototherapy, oral systemic medications, and biologics. OBJECTIVES: In this review, we evaluate the impact of psoriasis treatments on depression and suicidal ideation in affected patients. METHODS: We systematically searched multiple databases, including PubMed, Scopus, and Web of Science, until September 30, 2024, to identify relevant articles. Studies that examined the effects of psoriasis therapies on depression and suicidal thoughts were included. Data on treatment modalities, psychological health outcomes, and psoriasis severity was obtained. Quality evaluation instruments, such as JBI, consolidated standards of reporting trials (CONSORTs), Center for Evidence-Based Management (CEMBa), and appraisal tool for cross-sectional studies (AXIS), were used to appraise study quality. RESULTS: Ten studies met inclusion criteria, predominantly focusing on biologic therapies. Biologics like guselkumab, brodalumab, and bimekizumab have shown notable reductions in depression symptoms, probably through the alleviation of psoriasis severity and the enhancement of quality-of-life. Suicidal ideation occurred in some cases, especially with brodalumab; however, a definitive causal relationship was not established. CONCLUSIONS: Treatments for psoriasis, especially biologics, have shown some advantages in reducing depression symptoms as well as relieving skin symptoms. However, cautious monitoring is required due to the possible hazards of suicidal thoughts with certain therapeutic options such as biologics. Addressing the complex issues of psoriasis requires comprehensive therapy that includes dermatologists and mental health specialists.
13 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 →JMIR medical informatics
Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review
BACKGROUND: Psychiatric clinical notes in electronic health records (EHRs) provide rich longitudinal information that can support clinical decision-making. Using historical medical data can enable earlier identification of mental illness, better characterization of disease trajectories, and more personalized treatment planning. Natural language processing (NLP) transforms these unstructured notes into analyzable representations for research and care. OBJECTIVE: This study aims to systematically summarize NLP methodologies for psychiatric clinical notes, compare major modeling paradigms and application areas, and highlight emerging large language model (LLM) trends, key challenges, and future research directions. METHODS: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, a literature search was conducted for articles on NLP methods based on psychiatric clinical notes published from January 2021 to December 2025 in Ovid MEDLINE, Ovid EMBASE, PubMed, Scopus, Web of Science, the ACM Digital Library, and ScienceDirect. This scoping review analyzed NLP methods applied to psychiatric clinical notes, focusing on major trends, identifying suitable features for traditional machine learning (ML)-based models, applications of pretrained language models (PLMs), and key challenges. Approaches were categorized as rule-based, traditional ML, hybrid, deep learning (DL), and LLM-based methods across information extraction and text classification tasks. RESULTS: In total, 101 studies were eligible for inclusion. Rule-based methods (n=36) and hybrid approaches (n=34) remained the most widely used techniques, largely favored for their interpretability in handling nuanced, subjective clinical notes. These were followed by DL (n=15), traditional ML (n=10), and LLM-based approaches (n=6). Traditional ML studies relied heavily on engineered features, which could be grouped into 5 broad categories: domain knowledge features, lexical and statistical features, vector-based semantic features, emotion-related features, and temporal features. PLMs improved performance mainly through domain adaptation and task-specific fine-tuning, enhancing the handling of psychiatric language, medical terminology, and clinical note structure. LLM-based studies, although still limited in number, indicated a growing shift toward generative and reasoning-based applications. CONCLUSIONS: Hybrid NLP approaches remain dominant, combining domain rules with ML for extraction and classification. DL approaches continue to advance, with domain adaptation supporting medical terminology and clinical semantics. LLMs may further automate complex workflows via zero-shot capabilities and reasoning, alongside growing interest in temporal modeling and multimodal integration. Key future needs include improved generalizability across institutions, privacy protection, and careful attention to ethical implications in clinical deployment.
13 July 2026
Read appraisal →JMIR mental health
Digital Mental Health Research Priorities, Revisited for the AI and Large Language Model Era
Digital mental health has become an established part of mental health care, but the rapid arrival of large language models and other artificial intelligence (AI) tools has refocused attention on the evidence needed to guide the field. This editorial updates the research priorities articulated by JMIR Mental Health in 2023, while reaffirming their emphasis on equity, replicability, privacy, efficacy, and engagement. While the importance of these priorities has not changed in recent years, the urgency with which they must now be applied has. As digital tools become more clinically consequential, research must move beyond demonstrating that a technology is feasible, usable, or novel. The field now needs studies that clarify how these tools work, for whom they are beneficial, under what conditions they may cause harm, and how they can be ethically integrated into care. We call for research that is transparent about the technologies being studied, grounded in meaningful clinical questions, attentive to safety, and designed to produce knowledge that remains useful as specific products and models change.
13 July 2026
Read appraisal →Vaccine
Instruments for measuring parents' vaccine hesitancy towards their children based on the COSMIN guidelines: A systematic review
BACKGROUND: Parental vaccine hesitancy undermines immunization gains, leading to resurgent outbreaks and increased child mortality. Reliable assessment instruments are urgently needed to guide effective interventions and improve coverage. Despite multiple vaccine hesitancy assessment instruments, no systematic reviews have evaluated the measurement properties of these instruments against established methodological standards, limiting evidence-based selection. OBJECTIVE: To systematically evaluate the methodological quality and measurement properties of vaccine hesitancy assessment instruments for parents of 0-18 children, providing an evidence-based basis for selecting appropriate instruments. DESIGN: Measurement properties are systematically reviewed according to the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines. METHODS: Six electronic databases were systematically searched from inception until July 15, 2025. Methodological quality was assessed using the COSMIN Risk of Bias Checklist and measurement properties were synthesized according to the COSMIN criteria. A modified Grading, Recommendations, Assessment, Development, and Evaluation system was used to assess the certainty of evidence. RESULTS: Forty studies evaluating 31 vaccine hesitancy assessment instruments were included. Based on COSMIN evidence grading, the Malay version of the modified vaccine hesitancy scale (MVHS-M), the Vietnamese version of the parent attitudes about childhood vaccines survey (PACV-Viet), and the parental attitude scale towards vaccination (PASV) received Category A recommendation. The general vaccine hesitancy scale (GVHS), vaccination attitudes examination (VAX), vaccine acceptance instrument (VAI), and vaccine barriers assessment tool (VBAT) received Category C recommendation, while others were Category B recommendation. CONCLUSIONS: This systematic review identified MVHS-M, PACV-Viet, and PASV as effective tools for assessing vaccine hesitancy in parents, suitable for both research and clinical use. Future research should comprehensively assess their measurement properties, especially exploring measurement error and responsiveness. REGISTRATION: A protocol was registered on the PROSPERO (CRD420251070194).
12 July 2026
Read appraisal →Journal of medical Internet research
Evolution of Regional Information Infrastructures Integrating Health and Social Care in Scotland: Qualitative Study
BACKGROUND: Expectations of integrating health and social care providers have driven the development of digital solutions aimed at overcoming interoperability challenges and ensuring access to information needed for integrated care across fragmented services. However, challenges persist in aligning diverse coding practices, heterogeneous data-sharing mechanisms, and stakeholder needs. OBJECTIVE: We examine how expectations of interoperability and integrated care have shaped the growth of regional information infrastructures in Scotland, using the Key Information Summary (KIS), a summary record that shares key patient information from general practitioner records with out-of-hour services, ambulance services, hospitals, social workers, and caregivers across multiple care settings, as a case study. METHODS: This qualitative study examined the development, implementation, and adoption of KIS in Lothian, Scotland, across health and care settings, where it has been in use for 13 years. Multisited ethnography was used to understand how technology design, implementation, and adoption were shaped by social, organizational, cultural, and political factors. Data were collected through interviews with users, vendors, and implementers; observations of technology use and multidisciplinary team meetings; and documentary analysis of policies, user guides, and internal reports. A hybrid analytical approach was applied: the Technology, People, Organization, and Macroenvironment framework guided initial coding, while the sociology of expectations and information infrastructure theory were used inductively to trace evolving visions of integration, and the long-term development of regional information infrastructure. RESULTS: Data included 54 qualitative interviews, 20 hours of observation, and 59 documents collected between April 2024 and March 2025. Findings illustrate how information infrastructures for integrating health and care providers evolved through successive concerted efforts, conceptualized as waves. Three waves were identified, each characterized by attempts to interlink disparate information systems used by various health and care providers. The first wave focused on linking health care providers by developing networks and architectures required for sharing clinical information, which later supported the development and sharing of KIS. Subsequent waves sought to interlink information systems used by health care providers with those used by local authorities and social care providers. In the absence of shared data standards across these sectors, interoperability was achieved by extending the existing health care-centric infrastructure to different social care settings through workarounds such as providing proxy access to hospital systems and secure emailing networks. CONCLUSIONS: This work illustrates how regional information infrastructures for integrated care evolve through orchestrated waves of change. Some expectations for change required coordinated, system-level action, such as setting up standards, networks, and architecture, while others were realized through local adaptations. Integrating health and care providers through digitalization is a long-term process requiring sustained coordination, with progress often occurring through incremental, local extensions. Policies must support adaptive, long-term coordination, balancing system-level initiatives with local adaptations to achieve meaningful integration.
12 July 2026
Read appraisal →Vaccine
Interventions to improve pre-school vaccination timeliness: a systematic review.
BACKGROUND: Improving the timeliness of pre-school vaccinations (children aged 0 to 5 years) is an important public health goal to prevent outbreaks and maximise protection during early childhood. Multiple studies have highlighted the need for effective public health interventions to improve timely vaccination, and consequently the overall effectiveness of vaccination programmes. We aim to synthesise evidence on vaccination timeliness interventions and subsequently provide recommendations to improve pre-school vaccination timeliness in England. METHODS: We conducted a systematic review of randomised controlled trials (RCTs) and non-randomised studies. Studies were eligible if they were conducted in high-income countries and evaluated an intervention to improve timeliness of any pre-school vaccination on the United Kingdom (UK) national childhood vaccination schedule. Five databases and grey literature were searched to February 2025. Risk of bias was assessed using Cochrane risk of bias tools. Data were analysed using random-effects meta-analyses and synthesis without meta-analysis (using effect direction plots). RESULTS: Of the 10,385 records from database searches and 1490 records from citation searches, 33 studies were eligible (15 RCTs, 18 non-randomised studies). Most studies were conducted in the USA (n = 24) and reported on the timeliness of multiple pre-school vaccines (n = 23). Twelve studies (36%) were judged as serious or critical risk of bias, eleven at moderate and ten at low risk. Various intervention groups were identified: call-recall (n = 10), quality improvement (n = 9), education (n = 5), multicomponent (n = 5), combination vaccines (n = 2), communication (n = 1) and vaccination schedule change (n = 1). We found limited evidence from a small number of studies of a potential beneficial effect of combination vaccines, communication, quality improvement, education and schedule change interventions. CONCLUSION: We identified several possible interventions to improve pre-school vaccination timeliness. However, we found limited quantity and quality of evidence on this topic. We recommend further high-quality studies evaluating interventions to improve vaccination timeliness in England, and application of consistent vaccination timeliness definitions.
12 July 2026
Read appraisal →JMIR formative research
Exploring Breast Cancer Survivors' Preferences for Text Messaging-Based Mobile Health Interventions Targeting Sleep and Physical Activity: Qualitative Study
BACKGROUND: Sleep disturbances and low physical activity are common among breast cancer (BC) survivors and are associated with increased morbidity and mortality. Given the increased access to technological devices and the growing popularity of SMS text messaging-based mobile health interventions, these tools have the potential to both address sleep disturbances and promote physical activity in a scalable and cost-effective way. To understand and make effective use of these tools, it is important to consider the preferences of BC survivors with sleep disturbances, including how SMS text messaging-based mobile health interventions could deliver interventions involving physical activity and sleep hygiene. OBJECTIVE: The objective of this study was to explore the perspectives and preferences of BC survivors regarding text messaging-based mobile interventions targeting sleep and physical activity. METHODS: Three focus groups (n=13 participants) and 3 individual interviews (n=3) were conducted from May 2020 to March 2021 with 16 BC survivors (mean age=59.3, SD 8.9 y) currently experiencing sleep disturbances. The interview questions focused on their experiences with poor sleep and preferences for text messaging-based mobile health interventions. Thematic analysis was applied to the deidentified transcriptions of audio recordings. RESULTS: Three themes were identified: (1) attitudes toward health interventions delivered through text messaging, (2) specific user needs, and (3) technology usage habits and preferences. Most participants reported a positive attitude toward the possibility of using technology to help improve their sleep and increase their physical activity. Most expressed a high level of acceptance toward some technologies, such as text messaging and mobile apps, but not others, such as voice interactions. In terms of desired features, reminders and accountability features, such as meeting physical activity goals, were mentioned most frequently. In addition, incorporating bedtime and relaxation exercise reminders was thought to be helpful. Regarding time and frequency, a daily reminder scheduled for 1 hour before bedtime was found to be acceptable. CONCLUSIONS: The insights have been used to guide the development of a messaging-based mobile health intervention for improving sleep and physical activity in BC survivors. Future research will focus on delivering an intervention addressing these health behaviors and assessing its acceptability and effectiveness.
11 July 2026
Read appraisal →BMJ open
Shared decision making in pain care: a systematic review study protocol of decision aids
INTRODUCTION: Shared decision making (SDM) in healthcare is an ethical imperative and essential to patient-centred care. SDM is particularly useful when several preference-sensitive treatment options exist and in the setting of chronic conditions or longitudinal management. Chronic pain management embodies these characteristics, yet SDM often remains insufficient in this population. Decision aids are designed to facilitate SDM by helping patients and clinicians understand treatment options, clarify patient values, and guide collaborative decision processes. Despite their importance, the use of decision aids in pain management is inconsistent and their reported effectiveness has been variable. However, the current landscape of decision aids for pain management has not been described. This lack of understanding of what tools exist, how they are structured, what decisions they address and how they were developed makes it difficult to advance implementation efforts or identify meaningful gaps in available resources. As such, the purpose of this systematic review is to identify and characterise decision aids for pain management across the lifespan. Specifically, this review will describe the clinical decisions addressed, decision aid structures and delivery formats, development processes and outcomes used to evaluate the impact of existing decision aids. METHODS AND ANALYSIS: Electronic searches were performed in PubMed, CINAHL and Ovid Embase from inception through January 2026. Studies will be assessed for quality using the Mixed Methods Appraisal Tool. Data will be extracted and presented with the aim of describing the: (a) content and structures of pain-related decision aids, (b) development processes used to create existing pain-related decision aids and (c) outcome measures used to evaluate the impact of decision aids in clinical care. ETHICS AND DISSEMINATION: This review does not require ethics approval. Findings will be disseminated to clinicians, researchers and patients through journal publications, conference presentations and in collaboration with patient partners. PROSPERO REGISTRATION NUMBER: CRD420251085288.
11 July 2026
Read appraisal →BMJ open
Development and acceptability of gist-based decision aids for prostate and breast cancer screening: a two-phase qualitative interview study in an online setting.
BACKGROUND: Traditional approaches to designing decision aids have focused on providing completeness of information and quantitative detail. An alternative approach based on psychological research emphasises understanding the essence or 'gist' of the decision. Few gist-based decision aids exist. The objective of this study was to develop novel gist-based decision aids for prostate and breast cancer screening and evaluate their acceptability. METHODS: Men aged 40-60 years and women aged 40-49 years eligible for prostate or breast cancer screening were recruited from ResearchMatch (National Institutes of Health) and a public hospital located in New York City. Three rounds of semistructured interviews were performed to determine self-reported comprehensibility and acceptability, with iterative modifications after each phase, including: Phase 1: initial feedback; Phase 2, Round 1: evaluating our modifications; and Phase 2, Round 2: confirming final content. RESULTS: A total of 80 participants were involved in the two-phase qualitative interview process; Phase 1 and Phase 2 included 32 and 48 participants, respectively. Racial distribution was 50% white, 30% black and 10% Asian; 9% of participants were Hispanic or Latino. Most participants were highly educated. Interview themes established content validity of the final decision aids. In a quantitative analysis of the final decision aids using a questionnaire with fixed response options, acceptability was high, with two-thirds finding the length and amount of information of the decision aid to be optimal. The majority found the decision aid visually appealing, easy to read and easy to get through, and indicated that it held their interest and did not require much mental effort to read. One-quarter said the tool made them feel somewhat nervous, and three-quarters not at all. Most participants could understand and relate to the images, graphs and patient stories in the decision aid. After Phase 2, 45 of the 48 participants (94%) said they would find the decision aid helpful when making a decision. CONCLUSION: This study describes the iterative development and pilot testing of a novel gist-based decision aid for cancer screening, demonstrating high acceptability. Our findings provide a foundation for randomised trials comparing gist-based and traditional tools.
10 July 2026
Read appraisal →Brain imaging and behavior
Associations between olfactory dysfunction and structural MRI findings in Parkinson's disease: a systematic review
Olfactory dysfunction is a common and early non-motor symptom of Parkinson's disease (PD). Structural Magnetic Resonance Imaging (MRI) studies have reported changes in olfactory-related brain regions, but findings remain inconsistent. This review synthesizes MRI evidence to clarify neuroanatomical correlates of olfactory dysfunction in PD. We performed a systematic review to find the associations between olfactory dysfunction and structural MRI findings in people with PD. A systematic search in PubMed, Scopus, and Web of Science yielded 20 eligible studies. The eligible studies involved 1,092 patients with PD. Although there were inconsistent regions of interest and types of analysis, we identified structural changes in the olfactory sulcus and bulb, cortical gray matter, hippocampus, parahippocampus, and basal ganglia that are associated with olfactory dysfunction. Olfactory dysfunction in PD is linked to structural changes in primary olfactory, limbic, and cortical regions. These MRI findings may serve as early biomarkers, but standardized, longitudinal studies are needed to confirm their diagnostic or prognostic value.
10 July 2026
Read appraisal →International ophthalmology
The relationship between diabetic retinopathy and intestinal microbiota: a systematic review and meta-analysis
PURPOSE: The results of human observational studies on the correlation between gut microbiota and diabetic retinopathy (DR) are discrepant. This meta-analysis aimed to evaluate the specificity of the gut microbiota in DR patients compared to patients with type 2 diabetes mellitus (T2DM). METHODS: All published literature up to October 2024 was searched by two researchers on PubMed, Embase and Web of Science databases. Diversity and gut microbiota composition were the main outcomes. The meta-analysis was conducted in Review Manager (RevMan) Version 5.3. RESULTS: Eight studies, investigating gut microbiota by collecting stool samples, conducted in China and India were included in this meta-analysis, involving a total of 486 individuals in the T2DM (n = 258) and DR (n = 228) groups. No significant difference in alpha-diversity was observed between T2DM and DR patients. The pooled estimate showed that, at the phylum level, the abundances of Patescibacteria, and Synergistetes were significantly lower, and Verrucomicrobia were considerably higher in DR patients than in T2DM patients. At the genus level, DR patients had an increase in Bacteroides compared to T2DM. CONCLUSIONS: In this meta-analysis with a small number of studies and relatively high heterogeneity, changes in gut microbiota were associated with DR, commonly reflected by a reduction in beneficial species and an increase in pathogenic species influencing metabolic pathways.
10 July 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Risk prediction for lung cancer screening: a systematic review and meta-regression.
BACKGROUND: Lung cancer (LC) remains the deadliest cancer, often diagnosed at advanced stages. Screening reduces mortality in high-risk individuals. Eligibility criteria in European and US screening guidelines have recently expanded. Therefore, we conducted an updated systematic review of risk-based models for identifying candidates for low-dose computed tomography screening and post-screening nodule classification. METHODS: We systematically searched Embase and Medline (January 2020-January 2026), identifying studies proposing new risk models in the context of LC screening. We separated models by pre- and post-screening risk stratification. Data extraction included study design, population, model type, risk horizon and model performance metrics. We performed an exploratory meta-regression of areas under the curve (AUCs) to assess whether sample size, model type, validation type and inclusion of biomarkers were associated with performance. RESULTS: Of 2462 records, 91 were included. 56 models were for screening selection (30 included biomarkers) and 35 for post-screening nodule classification. Regression-based models predominated, though machine-learning approaches were increasingly common. Discrimination ranged from moderate (AUC∼0.70) to excellent (>0.90), with biomarker and imaging-enhanced models often outperforming models without. Calibration was inconsistently reported and fewer than half underwent external validation. CONCLUSION: We identified 91 risk prediction models for LC, developed after 2020. Although many demonstrated promising discrimination across both screening selection and post-screening management, most remain insufficiently mature for clinical adoption, as their performance and practical value outside the original study setting are uncertain. Future work should prioritise external validation, updating and comparative evaluation of existing models, and prospective implementation studies rather than continued development of additional models.
10 July 2026
Read appraisal →Journal of the American Heart Association
Social Determinants of Health and Cardiovascular Disease-Related Outcome Disparities Among Breast Cancer Survivors: A Systematic Review
BACKGROUND: Breast cancer is the most commonly diagnosed malignancy worldwide. Breast cancer survivors face an increased risk of cardiovascular disease (CVD), a leading cause of death in this group. Social determinants of health (SDoHs), operationalized as economic, environmental, and psychosocial factors, play an important role in CVD disparities. However, few studies have examined how SDoHs are associated with CVD disparities in this population, and no systematic review has addressed their multilevel influences. METHODS: This systematic review summarizes the current evidence on the relationships between SDoHs at different levels and CVD disparities among breast cancer survivors. Using the 2024 American College of Cardiology/American Heart Association framework, SDoHs were categorized at the individual, interpersonal, or community levels. Studies published in peer-reviewed journals between January 1, 2010, and November 30, 2025, were identified through searching electronic databases (PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Web of Science, PsycINFO) and citations. RESULTS: Of 6550 unique records, 37 articles that addressed the impact of SDoHs on CVD outcomes were selected. Most (n=31) were conducted in the United States and used retrospective designs (n=29). Most (n=30) focused on individual-level SDoHs such as race, income, or education; 2 on interpersonal-level SDoHs (ie, psychosocial stress); and 13 on community-level SDoHs, including neighborhood socioeconomic status and residential area. Black race, lower neighborhood socioeconomic status, and rural residence were associated with a higher incidence of CVD and increased cardiovascular death. CONCLUSIONS: This review highlights the urgent need to address SDoHs and emphasizes the importance of multilevel interventions to reduce CVD disparities among breast cancer survivors.
9 July 2026
Read appraisal →Journal of medical Internet research
Magnetic Resonance Imaging-Based Artificial Intelligence in Predicting Prostate Cancer Biochemical Recurrence: Systematic Review and Meta-Analysis
BACKGROUND: Artificial intelligence (AI) has emerged as a promising tool for prostate cancer (PCa) risk stratification and outcome prediction. However, current studies often lack multicenter external validation, have limited sample sizes, present significant intermodel variability, and face overfitting concerns. OBJECTIVE: This study aimed to comprehensively evaluate the diagnostic performance of magnetic resonance imaging (MRI)-based AI models in predicting biochemical recurrence (BCR) of PCa. METHODS: Systematic searches were conducted in the PubMed, Embase, Web of Science, and Cochrane Library databases up to January 13, 2026. Studies were included that involved participants diagnosed with PCa, used MRI-based AI for predicting BCR, and had clearly defined reference standards. The quality of the included studies was assessed using the PROBAST+AI tool. A bivariate random effects model was used to pool sensitivity, specificity, and area under the curve (AUC) statistics. RESULTS: A total of 28 studies were included, with 2623 patients in internal validation and 1134 patients in external validation. Diagnostic contingency tables were reconstructed from published performance metrics for most studies, while others were extracted from receiver operating characteristic curves due to the lack of direct reporting. In the internal validation set, pooled sensitivity was 0.80 (95% CI 0.73-0.86; prediction interval [PI] 0.48-0.99), specificity was 0.83 (95% CI 0.77-0.89; PI 0.49-1.00), and AUC was 0.86 (95% CI 0.83-0.89; PI 0.74-0.99). In the external validation set, pooled sensitivity was 0.82 (95% CI 0.72-0.91; PI 0.54-0.99), specificity was 0.83 (95% CI 0.71-0.92; PI 0.49-1.00), and AUC was 0.84 (95% CI 0.79-0.90; PI 0.70-0.98). No statistically significant differences were observed between internal and external validation in sensitivity (P=.73), specificity (P>.99), AUC (P=.53), or diagnostic odds ratio (P=.98). Medical Net and Extreme Gradient Boosting achieved the highest sensitivity and AUC, whereas multiple kernel learning and support vector machine had the highest specificity. Subgroup and meta-regression analyses suggested that AI method, model type, timing of MRI acquisition, and treatment modality may contribute to heterogeneity. CONCLUSIONS: This meta-analysis innovatively realizes the quantitative direct comparison of MRI-based AI model performance between internal and external validation cohorts for PCa BCR prediction. It comprehensively evaluates AI performance across diverse PCa treatment modalities and integrates machine learning and deep learning approaches. For the field, it identifies key sources of performance heterogeneity (eg, MRI acquisition timing and treatment modality) and quantifies the sensitivity-specificity trade-off in integrated radiomic-clinical models, advancing the systematic understanding of MRI-based AI for BCR prediction. In real-world practice, it provides actionable guidance to prioritize pretreatment MRI for AI model development and clinical BCR assessment and underscores the urgent need for standardized imaging protocols and prospective multicenter studies, laying a foundation for the safe clinical translation of these AI tools as adjunctive decision support instruments.
8 July 2026
Read appraisal →British journal of sports medicine
Comparative effectiveness of physical therapy interventions in adults with rotator cuff tendinopathy: a systematic review and network meta-analysis
OBJECTIVE: To compare the effects of different physical therapy interventions and identify the most effective intervention on pain, function, quality of life (QoL) and adverse events (AEs) in adults with rotator cuff (RC) tendinopathy. DESIGN: Systematic review and network meta-analysis (NMA) of randomised clinical trials (RCTs). DATA SOURCES: Seven databases and two trial registries were searched up to March 2024. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: RCTs comparing physical therapy interventions to any other physical therapy intervention, sham, placebo, waiting list or no treatment on pain, function, QoL and AEs in adults with RC tendinopathy. DATA SYNTHESIS: A frequentist NMA using a random-effects model was performed. Risk of bias and certainty of the evidence were assessed using the revised Cochrane risk-of-bias tool and the Grading of Recommendations, Assessment, Development and Evaluation approach, respectively. RESULTS: 89 RCTs (5532 participants) were included. Exercises targeting the shoulder muscles may reduce pain (standardised mean difference (SMD) -0.79, 95% CI -1.33 to -0.26) and improve function (SMD 0.74, 95% CI 0.34 to 1.14) compared with no treatment but the evidence is very uncertain. Exercises targeting shoulder and scapular muscles in addition to percutaneous electrolysis (SMD -1.58, 95% CI -2.68 to -0.48) and to trigger point dry needling (SMD 3.10, 95% CI 1.99 to 4.22) seem to be the most effective interventions on pain and function at the end of treatment, respectively. CONCLUSION: Most of the interventions identified may not be superior to isolated shoulder exercises alone, which might be considered a reasonable first-line approach, but the evidence is very uncertain and most interventions are informed by limited data. High-quality research is needed to improve evidence of physical therapy interventions in adults with RC tendinopathy. PROSPERO REGISTRATION NUMBER: CRD42024527176.
8 July 2026
Read appraisal →Journal of medical systems
Automatic Sleep Staging Using Cardiorespiratory Signals: A Systematic Review of Methodologies and Performance
Cardiorespiratory-based methods offer promising alternatives to traditional PSG for longitudinal sleep monitoring, holding significant systemic medical value for scalable sleep health management. This systematic review synthesizes methodological frameworks and performance outcomes of automatic sleep staging using cardiorespiratory signals. Four databases were searched and a total of 35 studies published since 2010 were identified. The analysis revealed that cardiorespiratory signal-based sleep staging achieved a practically meaningful accuracy of 70%, with no significant performance differences observed among signal modalities (cardiac signals, cardiorespiratory signals, or cardiac/cardiorespiratory signals combined with other non-EEG modalities) or between modeling algorithms (traditional machine learning vs. deep learning). However, we identified significant methodological heterogeneity and several critical model failure modes that hinder clinical translation, including the widespread lack of external validation, consistently poor classification of the N1 sleep stage, and limited generalization across diverse patient populations. To realize the technology's potential, future research must establish consensus-driven methodological guidelines and rigorously validate algorithms on large, demographically and clinically diverse datasets. These advances are essential for integrating cardiorespiratory-based sleep staging into healthcare systems as a scalable tool for population-level screening, longitudinal monitoring, and tiered clinical decision support.
8 July 2026
Read appraisal →European journal of psychotraumatology
Assessment of functional impairment related to post-traumatic stress in children and adolescents: a systematic review
Background: Post-traumatic stress disorder (PTSD) often impairs children's and adolescents' functioning across different life domains. Significant functional impairment is a diagnostic criterion for PTSD in both the DSM-5 and the ICD-11, but the methods in terms of domains covered and scoring rules to assess functional impairments are quite inconsistent.Objective: This systematic review evaluates instruments specifically designed to assess post-traumatic stress symptoms (PTSS), PTSS modules within diagnostic interviews, and generic functional impairment measures used to capture PTSS-related functional impairment in children and adolescents.Method: A systematic search was conducted across six databases for studies published between January 2010 and November 2025. Eligible papers included the development or psychometric evaluation of PTSS-specific measures, PTSS modules in diagnostic interview, and generic functional impairment measures validated in samples exposed to trauma or adverse childhood experiences. Data were extracted and categorized based on assessed content domains, scoring methods, and psychometric properties, with results stratified by age group (6 years and younger versus 7 years and older).Results: From 3519 records, 30 papers met the inclusion criteria. Thirteen PTSS-specific instruments, one diagnostic interview module, and one generic functional impairment measure were identified. Together, these instruments assessed 11 different life domains, although considerable variation existed in the domains covered across tools. Scoring methods also varied, with approximately half using dichotomous response formats and the remainder using multi-categorical scales. Psychometric properties of the functional impairment scales were reported in only 20.7% of publications.Conclusion: Current tools for assessing PTSS-related functional impairment in youth lack standardization. There is a critical need for well-validated instruments that comprehensively capture the presence and severity of PTSS-related functional impairment. This systematic review identified and evaluated 15 instruments, comprising PTSS-specific measures, diagnostic interview modules, and generic functional impairment tools, used to assess PTSS-related functional impairment in children and adolescents.The instruments collectively assessed 11 different life domains; however, substantial variability existed in the domains covered, and scoring methods ranged from dichotomous formats to Likert-type scales.Only 20.7% of papers reported psychometric properties for functional impairment items, highlighting the critical need for standardized, developmentally appropriate tools with robust validation in children and adolescents with potentially traumatic experiences.
8 July 2026
Read appraisal →Journal of medical economics
Evaluation of nirmatrelvir/ritonavir treatment for COVID-19 on health-related outcomes: a global economic value systematic literature review.
AIMS: Nirmatrelvir/ritonavir (NMV/r)i is an antiviral drug indicated for the treatment of patients with mild to moderate coronavirus disease 2019 (COVID-19) who are at high risk of progressing to severe disease. We performed an updated economic systematic literature review (eSLR) of NMV/r to build upon evidence reported in our previous eSLR and additionally examine the health-related outcomes associated with NMV/r and any potential effect of long COVID. METHODS: A systematic search of Embase, PubMed, Cochrane, and EconLit and of conference and health technology assessment agency websites was performed to identify economic analyses published between January 2022 and October 2025. RESULTS: Of the 33 included economic evaluations, most were cost-utility analyses (n = 16) and used an analysis of a short-term decision tree with a long-term Markov model (n = 11). Several types of health-related endpoints were reported by studies, with most including hospital-related endpoints (n = 20), quality-adjusted life-years (QALYs) (n = 18), and death-related endpoints (n = 18). Positive health-related outcomes were associated with NMV/r treatment, including reductions in hospitalizations and deaths and an increase in QALYs. NMV/r treatment was also associated with a reduced number of patients with long COVID complications. LIMITATIONS: Most studies were conducted in high-income countries, were based on different COVID-19 variants, and were limited in data on the long COVID population. CONCLUSION: In addition to monetary benefits, NMV/r provides short-term and long-term health-related benefits to patients with mild to moderate COVID-19. This study provides further support for NMV/r as a cost-effective treatment option.
7 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 →Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
AI-enhanced non-invasive diagnosis of chronic kidney disease using LIBS of fingernail biomarkers
Chronic kidney disease (CKD) is a systemic condition that leads to progressive renal failure and metabolic imbalances that may be detected in the keratinized bio-tissues of the body such as fingernails. Nevertheless, still its early detection is difficult due to the invasive nature of current clinical screening approaches. This research paper provides a precise, non-invasive, and AI-enhanced diagnostic model of CKD screening through the combination of Laser-Induced Breakdown Spectroscopy (LIBS) and the cutting-edge ensemble machine learning to determine the elemental patterns of fingernail biomarkers. In this study, 55 participants with chronic kidney disease and 45 healthy controls were taken as the subjects and their emission spectra were measured with a nanosecond LIBS spectrometer using a laser of a fundamental wavelength @ 1064 nm. To handle the high-dimensional spectral data, Principal Component Analysis (PCA) was used as a feature extraction approach, forming the basis for novel machine-learning models. A sophisticated ensemble learning algorithm specifically Extreme Gradient Boosting (XGBoost) was applied to examine the biochemical change between CKD and control samples and the predictive quality was tested by a relative comparison with a Support Vector Machine (SVM) algorithm. Using 10-fold cross-validation, the XGBoost model outperformed SVM, attaining 97% accuracy, 98% sensitivity, 96% precision and specificity, and a 97% F1-Score. Moreover, external validation on a separate dataset showed that the model is robust and generalizable with 95% accuracy, 97% sensitivity, and an F1-score of 95%. The results conclude that fingernail-based LIBS integrated with ensemble machine learning is a potential and non-invasive instrument for CKD classification.
6 July 2026
Read appraisal →Cancer causes & control : CCC
Post-diagnosis physical activity in relation to mortality among gynecological cancer survivors
PURPOSE: Physical activity may play a supportive role in cancer survivorship. However, evidence on the association between post-diagnosis physical activity and mortality among women with gynecological cancer remains limited and inconsistent. METHODS: We conducted a systematic review of the literature published between 1949 and January 2026. Eligible observational studies were identified, and random-effects meta-analyses were performed to estimate pooled hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between post-diagnosis physical activity and all-cause mortality among women diagnosed with gynecological cancer. RESULTS: A total of ten eligible studies on endometrial, ovarian, and cervical cancer were included, collectively reporting 3,867 deaths. High levels of post-diagnosis physical activity, compared with low levels, were associated with lower mortality (HR: 0.65; 95% CI 0.54-0.78). This inverse relationship was evident in both endometrial and ovarian cancer survivors (endometrial cancer: HR: 0.60; 95% CI 0.43-0.83; ovarian cancer: HR: 0.71; 95% CI 0.58-0.86). Medium levels of physical activity tended to be inversely associated with mortality (HR: 0.88; 95% CI 0.76-1.02). CONCLUSION: Higher levels of physical activity after a gynecological cancer diagnosis were associated with improved survival. The results suggest that physical activity may represent a modifiable lifestyle factor with the potential to improve long-term outcomes among gynecological cancer survivors. IMPLICATIONS FOR CANCER SURVIVORS: This supports the potential value of integrating physical activity into survivorship care, although further high-quality prospective studies are needed to strengthen causal inference.
6 July 2026
Read appraisal →JMIR formative research
Exploring Informal Caregivers' Perception of the Olera Digital Caregiving Assistance Platform for Dementia Care: Mixed Methods Evaluation Study
BACKGROUND: Informal caregivers of people living with dementia often experience high rates of caregiver burnout while providing care. Although there are many websites and mobile apps available to help caregivers, many do not use digital tools. The Olera platform was developed to be an easily adoptable web-based support tool, connecting caregivers with long-term services and supports, financial assistance, and educational resources. The platform was developed based on the Build-Measure-Learn framework with input from caregiver needs assessments and usability studies. OBJECTIVE: This study aims to evaluate the quantitative and qualitative feedback of informal caregivers of people living with dementia on the second iteration of the Olera platform. The primary objective was to assess caregivers' acceptance of this caregiving platform. The secondary objective was to use qualitative methods to explore (1) the study cohort's challenges in daily caregiving to determine and compare them with prior literature, (2) their experience when using the Olera platform, and (3) their attitudes toward integrating artificial intelligence in caregiver services for future studies and platform development. METHODS: Caregivers were recruited through various sources and screened for eligibility through an initial survey. Participants used the platform for 4 weeks and completed a survey with an adapted Technology Acceptance Survey (TAS) and qualitative open-ended questions at the end of the testing period. TAS responses were summarized with descriptive statistics, while ANOVAs, t tests, and linear regressions were used to compare the differences in the overall TAS scores by caregiver characteristics. Qualitative feedback data on the platform's usefulness were analyzed via a thematic analysis framework approach. RESULTS: A total of 65 caregivers in the United States completed the study, with a mean age of 59.9 (SD 9.8) years. The majority were female (61/65, 95.3%), non-Hispanic or Latino White (45/65, 69.2%), and the adult child of their care recipient (42/65, 64.6%). Evaluation of the Olera platform showed a high acceptance rate, with each TAS item scoring above 5.0 and an overall TAS score of 5.83 (SD 0.85) out of 7. Higher platform use frequency was associated with higher TAS ratings in technology acceptance (F3,61=7.88, P<.001). Thematic analyses elicited the caregiving challenges, evaluation of the Olera platform, and feedback on artificial intelligence-assisted support. CONCLUSIONS: The Olera platform is an example of a beneficial web-based tool, though key features were requested to be included in the next iteration. Additionally, data supported prior findings regarding informal caregiver challenges and the insufficiency of conventional support mechanisms, indicating a need for more innovative digital solutions. Future research and development efforts using the Build-Measure-Learn approach are necessary to further iterate the platform's key features, enhance the tool, involve more informal caregivers in its improvements, and serve as a model for customizable, person-centered online care support.
6 July 2026
Read appraisal →International health
Antimicrobial stewardship interventions currently implemented at primary healthcare settings across low- and lower-middle-income countries (LLMICs)
BACKGROUND: Antimicrobial resistance (AMR) is a top global public health and development threat. Antimicrobial stewardship programs (AMSPs) are one of the most cost-effective interventions to optimize the use of antimicrobials. This study reviews AMSPs that have been implemented in low- and lower middle-income countries. METHODS: A systematic search was conducted on electronic databases including MEDLINE, PubMed, Embase, OVID, Web of Science and Cochrane Library on 18 July 2024 for published papers from 2014 to 2024 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Relevant published literature was then selected based on the established inclusion/exclusion criteria. Each article was screened by two independent reviewers. Data were extracted and synthesized in the review. RESULTS: Of the 425 articles screened, only 13 were eligible for review and included in this study. Two studies were multinationals. Five studies were randomized controlled trials. Among the three key focuses of AMSPs, most of the interventions focused on optimizing antibiotic use (n=8), followed by improving diagnostics and monitoring (n=3) and education and training (n=2). The most commonly reported barriers to implementing AMSPs was a lack of resources (n=9). Facilitators reported included knowledge of AMS (n=8), availability of educational and training resources (n=8), adequate funding (n=6), accountable and transparent procedures (n=5) and positive communication within healthcare facilities (n=4). CONCLUSIONS: All included studies show improvement in AMS through innovative programs. However, only a few have been adopted nationwide and influence policy formulation in the country. We recommend adoption of effective AMSPs into the national strategic planning and implementation across primary health settings.
5 July 2026
Read appraisal →Sleep & breathing = Schlaf & Atmung
Which nocturnal hypoxemia parameters are associated with ambulatory blood pressure monitoring in adults with obstructive sleep apnea? A systematic review.
OBJECTIVE: Systemic arterial hypertension affects up to 50% of patients with obstructive sleep apnea (OSA) and represents one of the main modifiable cardiovascular risk factors. Hypoxemia is a recognized marker of OSA severity; however, it remains unclear which specific desaturation parameters during sleep, such as oxygen desaturation index (ODI), mean and minimum oxygen saturation (SpO2), percentage of sleep time with peripheral oxygen saturation < 90% (T90%) or hypoxic burden (HB) are most consistently associated with elevated blood pressure (BP). This systematic review aims to evaluate, in patients with OSA, the impact between nocturnal desaturation parameters and elevated BP as measured by ambulatory blood pressure monitoring (ABPM). METHODS: A literature search was performed in PubMed, Web of Science, Scopus and Embase databases, covering all records available up to June 2025. RESULTS: A total of 5,587 records were identified through database searches, and 24 studies met the inclusion criteria. The studies revealed heterogeneous results, with some showing significant associations between hypoxemia indices and hypertension, while others did not. Evidence suggests that ODI3% is commonly related to increased BP, whereas minimum SpO2, mean SpO2 and T90% yielded variable findings. HB was evaluated in only one study, suggesting a potential role in BP reduction after CPAP; however, the available evidence remains preliminary. CONCLUSION: ODI3% showed the most consistent association with increased BP across studies, whereas other hypoxemia indices yielded more variable results. Evidence regarding HB remains preliminary and warrants further investigation. These findings suggest that although ODI currently appears to be the most reliable marker, evidence from a single study indicates that composite indices such as HB may represent more informative predictors of BP outcomes and deserve further investigation in future studies.
5 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Telemedicine for headache disorders in real-world practice: from patient experience to health system impact.
BACKGROUND: Telemedicine has expanded rapidly in neurological care and is increasingly applied to headache management to improve access and continuity of follow-up. However, patient experience and healthcare system impact in real-world practice remain insufficiently explored. OBJECTIVE: The objective of this narrative review is to evaluate clinical effectiveness, patient experience, and healthcare system impact in the management of headache disorders. METHODS: A narrative review of the literature was conducted, including randomized controlled trials, observational studies, real-world evidence contributions, and reviews addressing telemedicine in headache care. Evidence was synthesized thematically, focusing on clinical outcomes, patient acceptance, and implementation aspects. RESULTS: Available evidence supports the clinical effectiveness and safety of telemedicine in selected headache patients, particularly in non-acute settings and structured follow-up pathways. Outcomes assessed using validated instruments (HIT-6, MIDAS, VAS) are generally comparable to in-person visits. High patient satisfaction and willingness to continue telemedicine are consistently reported, mainly due to convenience and time and cost savings. Real-world studies suggest telemedicine may improve access to specialist care and optimize healthcare resource utilization, although challenges related to digital literacy, organizational requirements, and equity remain. CONCLUSIONS: Telemedicine is a valuable component of contemporary headache management when integrated into structured clinical pathways. Beyond clinical effectiveness, patient experience and organizational context are critical for successful implementation. Future research should focus on standardized care models, larger populations, and long-term real-world outcomes.
5 July 2026
Read appraisal →Infectious diseases of poverty
Broadly neutralizing antibodies for HIV therapy in clinical trials: a systematic review
BACKGROUND: Lifelong daily antiretroviral therapy (ART) effectively suppresses human immunodeficiency virus type 1 (HIV-1) replication but does not eradicate the virus, underscoring the urgent need for long-acting antivirals and functional cure strategies. Broadly neutralizing antibodies (bNAbs) have emerged as a promising approach for achieving durable HIV-1 remission. In this systematic review, we summarize recent advances in the development of bNAbs for HIV-1 treatment. METHODS: We searched PubMed, Embase, and Web of Science for clinical trials published up to March 22, 2026. We included data evaluating intravenously administered bNAbs, with or without concomitant conventional ART, and comparing them with placebo, ART or no intervention. These data were used to evaluate the pharmacokinetics, antiviral efficacy, resistance profiles, immunologic effects, and safety of intravenously administered bNAbs. RESULTS: LS-modified bNAbs extended half-life by 2- to 5-fold relative to their parental counterparts (e.g., VRC01LS: 71 vs 15 days), although viremia reduced half-life by 20-40%. In viremic participants harboring bNAb-sensitive virus, monotherapy achieved viral load (VL) reductions of 0.93-1.8 log₁₀ copies/ml, with rebound occurring within approximately 4-8 weeks, whereas combinations regimens achieved declines of up to 2.04 log₁₀ copies/ml and delayed rebound to 15-33 weeks. No consistent reduction in total reservoir size was observed, although early intervention and baseline viral sensitivity appeared to limit reservoir expansion. Resistance emerged through epitope-proximal mutations, with cross-resistance observed mainly among antibodies targeting shared epitope classes. Overall, bNAbs were well tolerated, with rare discontinuations (0.4%) and low immunogenicity. CONCLUSIONS: LS-engineered bNAbs exhibit a longer half-life than their parental antibodies. Combination regimens achieve greater VL reductions and a longer delayed rebound compared with monotherapy, indicating that LS-modified multi-epitope bNAb cocktails are promising for long-acting HIV-1 remission. To become clinically competitive, larger resistance-guided trials are needed to extend dosing intervals, improve resistance mitigation, and define optimal integration with other long-acting therapies.
4 July 2026
Read appraisal →Journal of medical Internet research
Sensor-Based Monitoring of Knee Osteoarthritis Symptoms in Free-Living Settings: Scoping Review
BACKGROUND: Knee osteoarthritis is a heterogeneous condition characterized by chronic pain, stiffness, and fatigue that fluctuate rapidly over time. Traditional clinical assessments provide only static diagnoses of disease severity, failing to capture the dynamic, day-to-day symptom variability that impacts patient quality of life. While wearable technologies offer the potential for continuous, high-frequency monitoring, previous reviews have examined general technological interventions for knee osteoarthritis management, yet they lack a specific synthesis of technologies for symptom monitoring. OBJECTIVE: This study aims to synthesize current research on sensor technologies used for the continuous monitoring of knee osteoarthritis symptoms in free-living or simulated daily environments. Specifically, the review seeks to (1) map sensor modalities to specific symptom domains (biomechanical, physiological, and behavioral); (2) evaluate the alignment between objective sensor metrics and patient-reported outcome measures; and (3) identify gaps in current monitoring paradigms. METHODS: A systematic literature search was conducted across PubMed, Embase, Web of Science, and IEEE Xplore. The review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Eligibility criteria included studies involving participants with knee osteoarthritis using wearable or portable sensors capable of continuous monitoring (eg, inertial measurement units and electrocardiography) and assessing clinical symptoms (eg, pain, fatigue, and stiffness). Studies relying solely on stationary laboratory equipment (eg, force plates) without a portable component were excluded to ensure relevance to real-world applicability. Data regarding sensor types, sampling frequencies, monitored symptoms, and the statistical association between objective features and subjective symptom severity (key findings) were extracted. RESULTS: A total of 16 studies met the inclusion criteria. The summary constructed from the results revealed a distinct technological saturation: the majority of studies (n=6) used inertial measurement units to quantify biomechanical deficits (eg, gait asymmetry and range of motion), which showed robust correlations with functional limitations. In contrast, there was a notable scarcity of research using physiological sensors (eg, electrocardiography and bioimpedance) to monitor systemic symptoms. Crucially, findings highlighted a significant discrepancy between subjective and objective data, particularly in sleep monitoring, where poor self-reported sleep quality predicted pain exacerbations despite stable objective actigraphy metrics. Furthermore, most systems operated as passive data loggers, with a lack of integration into active feedback loops. CONCLUSIONS: Unlike previous reviews focused solely on biomechanics, this study innovatively maps the use of sensors across a multidimensional symptom spectrum, revealing a critical gap in the monitoring of fatigue and physiological stress. The findings suggest that current sensor applications are limited by a lack of integration with subjective patient experiences. Real-world implementation requires a hybrid monitoring paradigm that combines the ecological validity of wearable sensors with the clinical relevance of patient-reported outcomes. This approach paves the way for digital phenotyping and active feedback systems, offering a personalized strategy for managing the complex symptom burden of knee osteoarthritis.
3 July 2026
Read appraisal →The Journal of international medical research
Examining the state of telehealth for mental health and substance use care after the coronavirus disease 2019 pandemic: An integrative review.
ObjectiveThe objective of this integrative review was to synthesize literature and provide implications for clinical practice on telehealth use among patients with serious mental illness and substance use disorders following the coronavirus disease 2019 pandemic.MethodsAn integrative review guided by Socio-Technical Systems Theory was applied. The PubMed, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, Medline, Academic Search Complete, and Gale Health and Wellness databases were searched for publications from 1 January 2019 to 1 December 2024. Articles selected according to the established inclusion and exclusion criteria were evaluated using a rapid critical appraisal checklist developed by Fineout-Overholt and Melnyk.ResultsAmong the 172 articles reviewed, 16 peer-reviewed and 2 government publications were included. Four themes were identified: (a) treatment adherence; (b) satisfaction reported by patients and providers; (c) telehealth policy developments; and (d) access to services. Telehealth supported continuity of care, improved satisfaction, and improved access. Technological and financial barriers restricted equitable access. Policy changes enabled broader adoption; however, regulatory approaches varied across jurisdictions.ConclusionTelehealth remains an integral method for delivering mental health and substance use care following the coronavirus disease 2019 pandemic. Greater focus is needed on long-term effectiveness and limitations of telehealth.
3 July 2026
Read appraisal →Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Implementation and Evaluation of Virtual Care in Canadian Health Care Systems: A Scoping Review
OBJECTIVE: This scoping review examined available evidence in implementation and evaluation of virtual care in Canada. Virtual care saw recent uptake due to the COVID-19 pandemic; however, to ensure quality of care, rigorous implementation and evaluation frameworks are needed. METHODS: Peer-reviewed and gray literature were searched to determine extent, range, and nature of evidence surrounding implementation and evaluation of virtual care based on the guidelines of the Joanna Briggs Institute. Although virtual care can encompass synchronous and asynchronous modalities, this review focused on synchronous virtual care, defined as real-time interactions between patients and providers via videoconferencing or telephone. Search included MEDLINE, EMBASE, Psych Info, and CINAHL databases and national and provincial health system, professional organization, and regulatory websites. Inclusion criteria included videoconferencing or telephone and English and French Canadian sources. Citations were screened by two researchers at title, abstract, and full-text levels. RESULTS: Two hundred and eight (208) manuscripts were included for analysis. High numbers of studies on patient satisfaction, process outcomes, and barriers were identified, with underrepresentation of health and systems outcomes and impact evaluations. There were very few studies examining hybrid care, planetary health, and use of virtual care with equity-deserving groups. DISCUSSION: This scoping review identified areas of importance for future research, including the use of virtual care in rural and remote regions, inpatient, long-term, and emergency settings, hybrid care, economic and planetary health impacts, and artificial intelligence. As well, enhancing standardization of implementation and evaluation guidelines will optimize quality of care and best practice.
3 July 2026
Read appraisal →Child and adolescent psychiatric clinics of North America
Technology-Enabled Crisis Care for Youth: Bridging the Gap
The article discusses technological advancements in mental health care for youth in crisis, addressing workforce shortages and enhancing care delivery. Technologies like remote patient monitoring, telehealth, mobile health applications, virtual reality therapy, and artificial intelligence improve symptom tracking, communication, and treatment accessibility. Clinical decision support systems and therapy chatbots enhance crisis assessment and management. Various settings employ telepsychiatry and extend reach, while digital therapeutics and monitoring ensure ongoing support. Challenges include regulatory compliance, data privacy, and technological limitations. Future directions emphasize interoperability, safeguards for new and adapting technologies, patient-centered care, and integrated digital health systems.
3 July 2026
Read appraisal →Nano letters
Wearable Self-Powered Biomedical Smart Sensors Deriving from E-Waste
Power requirements represent a critical challenge for wearable sensors. Self-powered sensing systems enabled by miniaturized energy-storage devices (MESDs) offer a promising solution. However, the proliferation of MESDs inevitably generates electronic waste (e-waste), which causes environmental concerns. Transient electronics that degrade into eco-friendly residuals provide opportunities for the development of green power sources. Herein, flexible e-waste-friendly power sources based on degradable MXene films were developed for the integration of wearable, self-powered biomedical sensors. The proposed transient MXene film-based supercapacitors (TMFSCs) possess good energy storage capability and mechanical flexibility and can be completely degraded into eco-friendly residuals within minutes. Furthermore, a wearable self-powered biomedical smart sensor was designed for real-time monitoring of pulse signals in real-life scenarios, and the obtained pulse rate is conducive to early evaluation of human health. Collectively, TMFSCs are considerably competitive for future eco-friendly flexible MESDs toward next-generation sustainable wearable and portable sensing electronics.
3 July 2026
Read appraisal →Experimental eye research
Artificial intelligence applications in OCT and OCTA for diabetic retinopathy: A systematic review
PURPOSE: Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide. Optical coherence tomography (OCT) and OCT angiography (OCTA) provide detailed retinal imaging, enabling early detection of microvascular changes. This study aims to systematically review artificial intelligence (AI), particularly deep learning (DL), applications for DR detection and analysis using OCT and OCTA images. METHODS: A comprehensive literature search was conducted across PubMed, Web of Science, Scopus, IEEE Xplore, and Embase for studies published up to March 2026. A total of 1007 articles were identified, of which 175 studies met the inclusion criteria following the PRISMA study selection process. RESULTS: DL-based approaches consistently demonstrated superior performance compared to traditional machine learning (ML) methods, with reported AUC values typically ranging from 0.90 to 0.99 across classification and segmentation tasks. Convolutional neural networks (CNNs), Vision Transformers (ViTs), and encoder-decoder architectures such as U-Net showed strong performance in detecting key DR biomarkers, including microaneurysms, macular edema, and neovascularization. However, performance variability was observed depending on dataset size, imaging modality, and annotation quality. CONCLUSIONS: AI-driven analysis of OCT and OCTA images offers significant potential for automated DR detection. Despite promising results, challenges such as limited public datasets, lack of cross-institutional validation, and model interpretability remain. Future research should focus on multimodal integration, explainable AI, and large-scale validation to enhance clinical applicability.
2 July 2026
Read appraisal →Journal of diabetes science and technology
The Impact of Virtual Consultations on Quality of Care for Patients With Type 2 Diabetes: A Systematic Review and Meta-Analysis
BackgroundVirtual consultations (VC) have transformed healthcare delivery, offering a convenient and effective way to manage chronic conditions such as Type 2 Diabetes (T2D). This systematic review and meta-analysis evaluated the impact of VC on the quality of care provided to patients with T2D, mapping it across the six domains of the US National Academy of Medicine (NAM) quality-of-care framework (ie, effectiveness, efficiency, patient-centeredness, timeliness, safety, and equity).MethodsA systematic search was conducted in PubMed/MEDLINE, Cochrane, Embase, CINAHL, and Web of Science for the period between January 2010 and December 2024. Eligible studies involved adult T2D patients, evaluated synchronous VCs, and reported outcomes relevant to NAM quality domains. Two independent reviewers performed screening, and studies were assessed using the Mixed Methods Appraisal Tool (MMAT). A narrative synthesis was conducted for each quality domain, and a meta-analysis of HbA1c levels was performed using random-effects models.ResultsIn total, 15 studies involving 821 014 participants were included. VCs were comparable with face-to-face care in effectiveness, efficiency, patient-centeredness, and timeliness, with improvements in accessibility and patient satisfaction. Mixed results were found for safety due to limitations in physical assessments, and for equity, with older adults and those with lower digital literacy facing more challenges. The meta-analysis showed no significant difference in HbA1c reduction between VCs and face-to-face (standardized mean difference [SMD] = -0.31, 95% confidence interval [CI]: -0.71 to 0.09, P = 0.12).ConclusionVCs offer a promising alternative to in-person care, but addressing digital disparities and improving access for older adults are essential for maximizing VC potential.
2 July 2026
Read appraisal →Journal of Alzheimer's disease : JAD
Early diagnosis of Alzheimer's disease through handwriting analysis and deep learning: A review.
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders worldwide, requiring early identification for timely intervention and to slow disease progression. However, existing diagnostic approaches, while effective at later stages, remain limited in detecting early-stage AD. Handwriting analysis has recently emerged as a non-invasive, cost-effective, and ecologically valid digital behavioral biomarker that reflects neurocognitive impairment. This review examines the role of handwriting as a neurocognitive marker for AD, focusing on integrating deep learning methodologies to enhance early diagnostic accuracy. It also elucidates the neurocognitive mechanisms linking handwriting behavior and AD, addressing current methodological and translational challenges. We performed a PRISMA-informed structured literature search and narrative synthesis of handwriting- and drawing-based studies for detecting AD/mild cognitive impairment (MCI), including offline handwriting images and online pen-stroke kinematics captured by digital devices. Task paradigms, data dimensions, preprocessing pipelines, modeling strategies (traditional machine learning and deep learning), evaluation practices, and translational considerations were summarized, and studies were organized by detection purpose and analytic approach. Our findings show that handwriting-based models generally discriminate AD/MCI from healthy controls with accuracy exceeding 80%, while deep learning models (e.g., convolutional neural network and multimodal Transformer fusion) approach 90% in structured tasks like clock drawing and figure copying. Online kinematic markers (e.g., reduced velocity, prolonged in-air time, increased pausing, and pressure instability) recur across studies, and multimodal integration with speech, gait, or facial signals can further improve sensitivity and ecological validity, although most studies are small and single-center.
2 July 2026
Read appraisal →The Orthopedic clinics of North America
Wearable Devices and Artificial Intelligence to Enhance Perioperative Care in Total Knee Arthroplasty
Rapidly evolving wearable devices have the potential to transform health care delivery and enhance outcomes. Applications of wearable smart devices range from activity and motion tracking to real-time monitoring of vital signs and biochemical markers. Recent evidence provides support for the use of wearable devices in chronic disease management, rehabilitation, and perioperative care of patients with total knee arthroplasty. Data derived from these devices may enhance treatment personalization and early identification of complications. Challenges remain regarding accuracy, standardization, privacy, and patient adherence. Future advances must overcome these limitations.
2 July 2026
Read appraisal →Journal of biomedical informatics
Clinical document metadata extraction: A scoping review.
OBJECTIVES: Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast documentation heterogeneity and drift over time challenge harmonization of document metadata. Automated extraction methods have emerged to coalesce metadata from disparate practices into target schema. This scoping review aims to catalog research on clinical document metadata extraction, identify methodological trends and applications, and highlight gaps warranting further investigation. METHODS: We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to identify articles from Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science and external sources that perform clinical document metadata extraction, either primarily as a methodology study, secondarily as a feature for a downstream application, or for analysis. We initially identified and screened 342 articles published between 2011 and 2025, then comprehensively reviewed 77 we deemed relevant to our study. RESULTS: Among the 77 articles included in our full text review, 49 were methodological, 22 used document metadata as features in a downstream application, and 6 analyzed document metadata composition. We observe myriad purposes for methodological study and application types. Available labelled public data remains sparse except for structural section datasets. Methods for extracting document metadata have progressed from largely rule-based and traditional machine learning with ample feature engineering to transformer-based architectures with minimal feature engineering. DISCUSSION AND CONCLUSION: Clinical document metadata extraction research has accelerated over recent years. The emergence of large language models has enabled broader exploration of generalizability across tasks and datasets, allowing the possibility of advanced clinical text processing systems. We anticipate that research will continue to expand into richer document metadata representations and integrate further into clinical applications and workflows.
2 July 2026
Read appraisal →Medical image analysis
A review of deep learning-based Unsupervised Anomaly Detection in brain MRI
The manual assessment of brain Magnetic Resonance Imaging (MRI) scans can be labor-intensive and time-consuming for radiologists. Deep Learning methods have demonstrated the potential to aid this process. However, their effectiveness relies on the availability of large, annotated data sets. Unsupervised Anomaly Detection (UAD) presents a promising alternative, offering the potential to identify and localize anomalies without per-pixel annotations. Instead, a normative distribution is learned using healthy data, enabling the identification of abnormalities as deviations. This allows UAD methods to detect abnormalities that were unseen during training. This appealing feature has led to numerous studies proposing innovations and novel approaches. In this work, we provide a review of the literature and systematically collect and compare the proposed approaches. We observe that UAD has made significant advancements in brain MRI analysis. However, individual approaches are often evaluated in different contexts, i.e., changes in acquisition parameters, pre- and post-processing, and anomaly scoring. This variability makes it challenging to assess which models perform best, underscoring the need for comprehensive comparative studies concerning the specific context of MRI scans. Our collection, featuring public data sets, research studies, and open implementations, is available at our GitHub repository https://github.com/FinnBehrendt/Unsupervised-Anomaly-Detection-in-Brain-MRI.
1 July 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Large language models accurately identify decision reasons in verbal reports.
Understanding the reasons behind human choices under risk is a central goal of decision scientists, but traditional methods relying on behavioral data are limited by strict invariance assumptions. We introduce a scalable analytical framework using large language models (LLMs) to analyze verbal reports and identify articulated reasons for choice between monetary lotteries. A validated LLM accurately identified predefined decision reasons in participants' free-text reports, aligning with their actual choices in 95% of trials. Our analysis reveals that the reasons behind people's decisions vary systematically and are driven more by the structure of the choice problem than by individual differences. Crucially, reasons identified from verbal reports yield more parsimonious and informative representations of decision processes compared to those inferred from choices alone; furthermore, problem-specific reason profiles achieve out-of-sample prediction accuracy that is competitive with established computational models. This work demonstrates that verbal reports are a rich data source and our analytical framework can unlock their potential, delivering results that challenge the field's foundational invariance assumptions and pave the way for more context-sensitive and interpretable models of human decision making.
1 July 2026
Read appraisal →Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
Clinical applications of artificial intelligence in autosomal dominant polycystic kidney disease
Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic kidney disorder leading to kidney failure. Recent advancements in artificial intelligence (AI) are transforming the diagnosis, risk stratification, management and prognostication in ADPKD by enabling more accurate assessments and individualized care. AI-powered imaging tools enhance the measurement of total kidney volume (TKV), improving the precision and efficiency of monitoring disease progression and more reliable assessment of therapeutic response. Machine learning algorithms can integrate genetic, imaging and clinical data to predict kidney function decline, facilitating personalized treatment strategies. In addition, AI is being used to identify genetic variants and to refine genotype-phenotype relationships, offering deeper insights into disease variability. AI-enabled monitoring technologies can support longitudinal tracking of TKV measurements obtained through magnetic resonance imaging or computed tomography, thereby improving clinical decision-making and management. Furthermore, AI can optimize clinical trial design by improving patient selection, prediction of treatment responses and safety monitoring. Despite these promising developments, integrating AI into the clinical practice remains controversial and may pose several challenges. This review highlights the emerging clinical applications of AI in ADPKD, emphasizing its potential to advance precision medicine and improve patient outcomes.
1 July 2026
Read appraisal →American journal of medical quality : the official journal of the American College of Medical Quality
Increasing Advance Care Planning in a Community Internal Medicine Clinic
INTRODUCTION: Advance care planning (ACP) supports goal-concordant care but remains underused nationally. In resident clinics, limited continuity, short visits, and lack of standardized workflows create barriers to ACP integration. In our Community Internal Medicine resident clinic, ACP discussion was documented in 2.5% of annual physicals, and 19.6% of paneled patients had an advance directive (AD) on file. We aimed to increase ACP documentation from 2.5% to 12.5% within 1 year without increasing perceived resident workflow burden. METHODS: We conducted a resident-led quality improvement project using Lean Six Sigma methodology. Three interventions were implemented sequentially: nursing distribution of ACP brochures, an electronic medical record DotPhrase to prompt and document ACP discussions, and targeted patient portal outreach to patients without an AD before annual visits. The primary outcome was documented ACP discussion during annual physicals. Secondary outcomes included AD uploads and ACP consult referrals. The balancing measure was resident-perceived workflow burden. Charts were reviewed over 5-week periods at baseline (n = 118) and after each implementation phase (n = 132, 158, 129). RESULTS: ACP documentation increased from 2.5% at baseline to 12.2%, 17.1%, and 24.0% across sequential phases (χ2(3) = 24.74, P < 0.0001). Pairwise comparisons versus baseline were significant (all P ≤ 0.005). AD upload rate increased from 1.7 to 6.0 per month, and ACP consult referrals increased from 0.0% to 10.9%. Mean perceived workflow burden decreased from 4.9 to 3.0. CONCLUSION: Workflow-embedded interventions improved ACP documentation, AD uploads, and ACP referrals without increasing perceived resident burden.
30 June 2026
Read appraisal →Diving and hyperbaric medicine
Bipolar spectrum disorders in divers: risks, recognition, and recommendations.
Bipolar disorder is a recurrent psychiatric condition characterised by episodic mood disturbances, residual functional impairment, and high rates of psychiatric and medical comorbidity. While many individuals achieve symptomatic remission, persistent deficits in cognition, emotional regulation, and insight may remain, raising concerns for participation in safety-critical activities such as scuba diving. This systematic review synthesised evidence from psychiatric, occupational, aviation, and diving medicine literature to examine the clinical course of bipolar disorder, treatment considerations, functional outcomes, and safety-relevant factors pertinent to fitness-to-dive assessments. Bipolar disorder exhibits marked heterogeneity in syndromal and functional outcomes. Even during euthymia, subtle impairments in attention, executive functioning, and decision-making may persist. Pharmacological stability is essential for diving, but treatment regimens, particularly lithium use, polypharmacy, and antidepressant therapy, introduce additional considerations. Comorbidity, circadian disruption, sleep deprivation, and reduced insight during early relapse further complicate risk assessment. Empirical data on diving outcomes in individuals with bipolar disorder are scarce, necessitating reliance on expert opinion and extrapolation from related safety-critical domains. Fitness-to-dive assessments in bipolar disorder should prioritise sustained functional stability, reliable treatment adherence, and illness insight over symptom absence alone. A cautious, individualised approach is warranted, incorporating medication effects, comorbidity, operational context, and relapse-prevention planning, supported by collaboration between mental health professionals and diving medical examiners.
30 June 2026
Read appraisal →Diving and hyperbaric medicine
Evidence-informed decision aid for fitness-to-dive assessment after otologic surgery
INTRODUCTION: Fitness-to-dive after otologic surgery is often approached conservatively, with some procedures historically labelled as absolute contraindications despite limited empirical evidence. The available literature is heterogeneous and includes clinical reports, experimental pressure studies, guidance documents, and manufacturer specifications, leading to uncertainty in clinical counseling. We aimed to characterise the available evidence regarding fitness-to-dive after otologic surgery and to develop an evidence-informed clinical decision aid. METHODS: A scoping review was conducted in accordance with PRISMA-ScR guidance. PubMed/MEDLINE, Embase, Scopus, and relevant non-indexed sources were searched. Eligible sources included clinical reports and series, experimental or hyperbaric chamber studies, guidance or consensus documents, and manufacturer statements providing explicit pressure- or depth-related information. Data were charted descriptively by procedure type and evidence stream. RESULTS: The search identified 324 records; after removal of duplicates and screening, 40 sources were included. The evidence base was predominantly non-comparative. Across procedures, recommendations emphasised postoperative stability and reliable pressure equalisation rather than surgical history alone. Canal wall down mastoidectomy was consistently portrayed as incompatible with diving, whereas selected middle ear reconstructions and stapes surgery were commonly described as potentially compatible in appropriately selected individuals. For cochlear implantation, guidance was mainly conditional and based on hyperbaric testing, limited clinical diving reports, and manufacturer-specified pressure or depth limits. Communication emerged as an additional practical consideration in cases of significant hearing loss. CONCLUSIONS: Relevant evidence is limited and heterogeneous, and does not consistently support blanket prohibitions for all otologic procedures. A function-based, individualised approach is supported, while specific higher-risk scenarios warrant restriction. Prospective registries and standardised outcome reporting are needed to refine procedure-specific recommendations.
30 June 2026
Read appraisal →European journal of nutrition
Nutrition interventions in women with polycystic ovary syndrome: a systematic review.
PURPOSE: This systematic review aimed to synthesize current evidence on the effects of various dietary interventions on anthropometric, metabolic, hormonal, inflammatory, and oxidative stress parameters in women with polycystic ovary syndrome (PCOS). METHODS: The review followed the PRISMA guidelines and was prospectively registered in PROSPERO (CRD42025641781). Searches were conducted in PubMed, Cochrane Library, EBSCO, Science Direct, Web of Science, National Thesis Center, Google Scholar, and DergiPark Academic for studies published between February 2015 and February 2025. Experimental and observational studies were included if they evaluated the independent effect of dietary interventions in adult women with PCOS. Methodological quality was assessed using the Joanna Briggs Institute (JBI) critical appraisal tools. RESULTS: A total of 38 studies were included, covering interventions such as calorie-restricted diets, low-glycemic index/load diets, ketogenic diets, intermittent fasting, dietary approaches to stop hypertension, Mediterranean-style, and other diets. Most dietary interventions demonstrated beneficial effects on body weight, body mass index, and waist circumference, as well as improvements in insulin sensitivity, reproductive hormone regulation, and menstrual regularity. However, findings related to lipid metabolism, inflammatory markers, and oxidative stress outcomes were inconsistent. CONCLUSION: Current evidence indicates that dietary interventions are crucial in improving the management of metabolic, anthropometric, hormonal, and clinical outcomes in women with PCOS. Nevertheless, the heterogeneity of dietary approaches, study designs, and outcome measures highlights the need for long-term randomized controlled trials to establish more conclusive recommendations.
30 June 2026
Read appraisal →Current allergy and asthma reports
Harnessing Machine Learning and Electronic Health Record Data to Improve Asthma Management
PURPOSE OF REVIEW: The review examines the application of machine learning (ML) and large language models (LLMs) to asthma management. We sought to identify clinically relevant applications, and particularly those that harness electronic health record data. We review methodological challenges and future directions for translating these tools into meaningful improvements in asthma care. RECENT FINDINGS: ML applied to electronic health record data has been utilized across several domains of asthma management: predicting medication response to inhaled corticosteroids and biologics, improving inhaler adherence through digital inhaler systems, and predicting exacerbation risk with moderate-to-high accuracy. Tools for patient education include clinician-guided chatbots, as well as publicly available LLMs. Limitations include accuracy, hallucinations, and patient health literacy. ML and LLMs offer promising pathways towards personalized, data-driven asthma management. However, harnessing this potential will require rigorous external validation, transparent model design, equitable implementation, and adaptive clinician oversight. Collaboration among clinicians, data scientists, and policymakers will be essential to implement these tools for patient-centered asthma care.
30 June 2026
Read appraisal →BMJ open
Comparative diagnostic accuracy of image-enhanced endoscopy modalities and artificial intelligence-assisted endoscopy for gastric intestinal metaplasia and gastric dysplasia: protocol for a systematic review and diagnostic test network meta-analysis.
INTRODUCTION: Gastric intestinal metaplasia (GIM) and gastric dysplasia are critical precursors in the Correa cascade of gastric carcinogenesis. Although multiple image-enhanced endoscopy (IEE) modalities and artificial intelligence (AI)-assisted systems have been developed to improve their detection, the comparative diagnostic accuracy of these technologies remains unclear. Direct comparative studies are limited and conventional pairwise meta-analyses do not provide a coherent framework for comparing multiple diagnostic technologies. METHODS AND ANALYSIS: This protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) and PRISMA-Network Meta-Analysis guidelines. We will search MEDLINE (Ovid), Embase (Ovid), the Cochrane Central Register of Controlled Trials (CENTRAL; Cochrane Library/Wiley), Web of Science Core Collection (Clarivate), Scopus (Elsevier), ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform from inception, without date or language restrictions. Eligible studies will be diagnostic accuracy studies evaluating IEE modalities (including narrow-band imaging with or without magnification, blue laser imaging, linked colour imaging and iScan) or AI-assisted endoscopy against histology. Comparative studies will form the primary evidence base for the diagnostic test network meta-analysis; single-arm studies will be summarised separately and will not contribute to the primary network estimates. The coprimary diagnostic parameters will be sensitivity and specificity for GIM and dysplasia. These will be analysed jointly using a hierarchical bivariate diagnostic test network meta-analysis with binomial likelihoods and correlated random effects. Diagnostic OR and area under the receiver operating characteristic curve will be treated as secondary summary measures. Two reviewers will independently screen, extract data and apply Quality Assessment of Diagnostic Accuracy Studies 2. Heterogeneity, transitivity and inconsistency will be assessed using prespecified clinical and methodological effect modifiers, model-based diagnostics and sensitivity analyses. The certainty of evidence will be assessed using Confidence in Network Meta-Analysis, with interpretation adapted to diagnostic accuracy outcomes. ETHICS AND DISSEMINATION: This study does not require ethical approval as it relies on secondary analysis of published literature. Findings will be disseminated through peer-reviewed publication and conference presentations. PROSPERO REGISTRATION NUMBER: CRD420261363078.
29 June 2026
Read appraisal →Journal of medical Internet research
Physical Activity Interventions Using Digital Health Interventions for Cancer-Related Fatigue in People With a History of Cancer: Scoping Review
BACKGROUND: Although exercise has been proven effective in alleviating cancer-related fatigue (CRF), traditional face-to-face programs may not be accessible due to physical, temporal, or geographical barriers. Digital health interventions (DHIs) offer scalable alternatives for promoting physical activity; however, evidence synthesizing DHI-based physical activity interventions specifically targeting CRF and their intervention characteristics remains limited. OBJECTIVE: This scoping review aimed to map the types of digital health-based physical activity interventions for managing CRF, to summarize the key characteristics of DHI modalities and CRF outcomes, and to identify knowledge gaps for future research. METHODS: A systematic literature search was conducted across 4 databases (PubMed, EMBASE, Cochrane, and PsycINFO) up to December 2025. Inclusion criteria comprised experimental studies involving adults with a history of cancer, digital exercise interventions, a control group, and fatigue outcomes. Screening and data extraction followed the Joanna Briggs Institute Manual for Evidence Synthesis and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review) standards. The protocol was registered in PROSPERO (International Prospective Register of Systematic Reviews; CRD42022304285). Interventions were classified using the Evidence Standards Framework (ESF). RESULTS: Thirty-three studies comprising 3443 participants were included, representing 32 interventions. Most were randomized controlled trials (n=30, 91%). Interventions were delivered primarily via mobile apps (11/32, 34%) and wearable devices (8/32, 25%), followed by web-based platforms, videoconferencing, exergaming, and augmented reality. Eighteen interventions (reported in 19 studies) demonstrated statistically significant between-group CRF reductions, predominantly at immediate postintervention assessments. Evidence for sustained benefit beyond 12 months was limited, and only one study evaluated ultra-long-term outcomes, which did not demonstrate maintained improvement. Populations with breast cancer accounted for the largest proportion of participants. Fatigue measurement tools varied substantially, potentially contributing to heterogeneity in effect estimates. Most interventions were classified as ESF tier C, indicating a predominant focus on clinical outcome improvement rather than individual-level self-management and system-level implementation. Wearable device-based interventions showed the highest proportion of significant CRF improvement, whereas mobile app-based interventions reported both significant and nonsignificant results. DHIs lasting more than 12 weeks appeared to be associated with more effective CRF outcomes compared to shorter programs. Direct comparisons between in-person and digital delivery were scarce. CONCLUSIONS: Digital health-based physical activity interventions can reduce CRF in people with a history of cancer, with wearable device and longer-duration interventions showing the most favorable outcomes. However, the current evidence is concentrated in populations with breast cancer, and system-level integration remains unexplored. Future research should prioritize diverse populations with cancer, longer follow-up periods, standardized fatigue measurement, and systematic examination of specific intervention components that contribute to CRF reduction. Ultimately, advancing from individual efficacy trials toward scalable, workflow-integrated digital solutions will be key to sustainable CRF management across diverse oncology settings.
29 June 2026
Read appraisal →PloS one
Determinants of dropout from lifestyle interventions for overweight polycystic ovary syndrome: An exploratory analysis of randomized controlled trial.
OBJECTIVE: This exploratory analysis of a randomized controlled trial aimed to identify baseline predictors of dropout in overweight women with polycystic ovary syndrome (PCOS) participating in a lifestyle intervention program. METHODS: An exploratory analysis was conducted using data from a randomized controlled trial involving overweight or insulin-resistant women with PCOS aged 18-45. All participants received cyclic progestin, metformin, and a structured lifestyle intervention. Dropout was defined as proactive withdrawal, missing two consecutive visits, or loss of contact for more than six months. Univariate and adjusted multivariable logistic regression models were used to identify factors associated with dropout. RESULTS: Among the participants, 61.06% (n = 69) dropped out within one year. No significant differences were observed in baseline demographic, clinical, biochemical, psychological, or dietary characteristics between completers and dropouts. However, baseline physical activity level (PAL), objectively measured using an accelerometer, was identified as the strongest predictor of dropout. Each 0.3-unit increase in PAL was associated with a 29.6% reduction in the likelihood of dropout. CONCLUSION: Baseline PAL is strongly associated with the risk of dropout. Screening for PAL in women with PCOS is recommended, and those with lower PAL should receive personalized support in addition to lifestyle interventions to improve adherence and promote weight loss.
28 June 2026
Read appraisal →BMJ open
Effectiveness and safety of pharmacist prescribing: a systematic review
OBJECTIVE: To examine the effectiveness and safety of pharmacist prescribing across multiple healthcare settings. DESIGN: A systematic review of quantitative studies. ELIGIBILITY CRITERIA FOR THE SELECTION OF STUDIES: Quantitative studies assessing the effectiveness and safety of pharmacist prescribing compared with non-pharmacist prescribing in any healthcare setting and for any healthcare population. A clear statement of pharmacists' prescriptive authority was required for inclusion in this systematic review. DATA SOURCES: A systematic search was conducted using six electronic databases: Embase (Ovid), MEDLINE (EBSCO), SCiELO, Dimensions AI, Cochrane Library and Epistemonikos. Database searches were conducted from database inception to 29 January 2025. Additional grey literature searches were conducted using Google and DuckDuckGo. Both backward and forward citation chasing were conducted for all included studies. DATA EXTRACTION AND SYNTHESIS: Data were extracted using standardised bespoke data extraction forms. The revised Cochrane Risk of Bias 2 tool for randomised controlled trials and the Risk of Bias in Non-Randomised Studies of Interventions tool were used to assess risk of bias. A narrative synthesis approach was applied following the Synthesis Without Meta-analysis guideline. The Grading of Recommendations Assessment, Development and Evaluation approach was used to assess the level of certainty of the evidence. RESULTS: Of the 39 included studies, 32 studies reported on effectiveness and 20 studies reported on safety across 15 health conditions. Healthcare settings included outpatient (n=14), primary care (n=10), community pharmacy (n=6), inpatient (n=5), emergency department (n=1) and long-term care (n=3). These studies were based in the USA (n=26), Canada (n=5), the UK (n=4), Australia (n=2) and Singapore (n=2). In total, there were 153 outcomes related to safety and effectiveness. For 74 outcomes, no significant difference was reported between pharmacist prescribing and non-pharmacist prescribing, while 46 outcomes were significantly improved with pharmacist prescribing. Four outcomes reported in favour of non-pharmacist prescribing. Inferential statistics were not reported for 29 outcomes, meaning we cannot comment on their statistical significance. The certainty of evidence was low or very low for all outcomes. CONCLUSIONS: The consistency of effectiveness and safety findings across studies, showing either no significant difference (indicating equivalence of care and outcomes) or significant improvement in pharmacist prescribing groups, suggests it is a potential policy option. Future research on implementation, public and patient preferences and cost-effectiveness would provide valuable insights into the potential benefits of pharmacist prescribing at a health system level.
28 June 2026
Read appraisal →African journal of reproductive health
A systematic review of unintended pregnancy among adolescents living with HIV in Africa
Adolescent pregnancy remains a significant public health concern across Africa, with Eastern and Southern regions experiencing the highest burden. Among adolescents living with HIV(ALHIV), the challenge is intensified by socio-economic, cultural, and healthcare factors that increase vulnerability to unintended pregnancy. This systematic review examined the prevalence of unplanned pregnancy and associated factors among adolescents living with HIV in Africa. Following PRISMA guidelines and registered in PROSPERO (CRD42024564479), a search of Medline, EMBASE, and CINAHL identified relevant studies published between 2011 and 2023. Of the 550 retrieved articles, only three met the inclusion criteria. Reported prevalence rates varied widely, ranging from 18.8% in South Africa to 60.0% and 73.9% in Kenya, indicating a substantial and uneven burden across settings. Factors associated with unintended pregnancy included involvement with boyfriends or acquaintances rather than spouses, reflecting limited agency and structural vulnerabilities. Documented adverse outcomes included miscarriage, stillbirth, and abortion, although one study did not specify outcomes. Overall, the review highlights persistently high rates of unplanned pregnancy among adolescents living with HIV and underscores the need to integrate sexual and reproductive health services into HIV care, address stigma, and strengthen context-specific interventions. Les grossesses adolescentes demeurent un problème majeur de santé publique en Afrique, les régions de l'Est et du Sud étant les plus touchées. Chez les adolescentes vivant avec le VIH (AVVIH), ce défi est exacerbé par des facteurs socio-économiques, culturels et liés aux soins de santé qui accroissent leur vulnérabilité aux grossesses non désirées. Cette revue systématique a examiné la prévalence des grossesses non planifiées et les facteurs associés chez les adolescentes vivant avec le VIH en Afrique. Conformément aux directives PRISMA et enregistrée dans PROSPERO (CRD42024564479), une recherche dans Medline, EMBASE et CINAHL a permis d'identifier les études pertinentes publiées entre 2011 et 2023. Sur les 550 articles recensés, seuls trois répondaient aux critères d'inclusion. Les taux de prévalence rapportés variaient considérablement, allant de 18,8 % en Afrique du Sud à 60,0 % et 73,9 % au Kenya, ce qui témoigne d'une prévalence importante et inégale selon les contextes. Parmi les facteurs associés aux grossesses non désirées figurait le fait d'avoir des relations avec des petits amis ou des connaissances plutôt qu'avec des conjoints, reflétant un manque d'autonomie et des vulnérabilités structurelles. Les issues défavorables documentées incluaient les fausses couches, les mortinaissances et les avortements, bien qu'une étude n'ait pas précisé ces issues. Globalement, cette analyse met en évidence des taux toujours élevés de grossesses non désirées chez les adolescentes vivant avec le VIH et souligne la nécessité d'intégrer les services de santé sexuelle et reproductive aux soins du VIH, de lutter contre la stigmatisation et de renforcer les interventions adaptées au contexte.
28 June 2026
Read appraisal →JMIR aging
Use of Wearable Technology for Measuring and Characterizing Sedentary Behavior in People With Mild Cognitive Impairment and Dementia: Systematic Review
BACKGROUND: Sedentary behavior (SB) is a critical, modifiable risk factor for adverse health outcomes. Evidence suggests that SB is higher among individuals with cognitive impairment relative to their cognitively healthy peers. However, the nature and extent of SB across cognitive impairments remains unclear, largely due to the reliance on self-report data and the lack of synthesized evidence from more accurate methodology, such as wearable devices. Wearable device-based methodologies offer a reliable means of capturing SB in real-world settings, circumventing the recall bias inherent to self-report methods. Continuous remote monitoring of SB, via wearable devices, may provide nuanced insights important for understanding SB's contribution to cognitive impairment and health consequences. OBJECTIVE: This review aims to synthesize evidence on the volume, patterns, and variability of SB across cognitive impairment and critically appraise the wearable device-based methodology used to capture SB in this population. METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched 5 databases (Embase, MEDLINE, PsycInfo, Scopus, and Web of Science) up to January 2025 for peer-reviewed English-language studies using wearable devices to measure SB in community-dwelling or aged residential care residents aged 50 years or older with cognitive impairment (PROSPERO: CRD42024616523). Study quality was assessed using an adapted version of the National Institute of Health Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Data were extracted on SB outcomes (eg, volume, pattern, and variability) and methodological characteristics (eg, device type, placement, SB classification/processing, and its corresponding validation). RESULTS: From 2824 screened records, 17 studies (2016-2025) were included. Most studies (n=11, 65%) were of "good" quality (scoring ≥5 on bias assessment). Synthesis revealed inconsistent evidence for differences in SB volume across cognitive impairment. However, individuals with dementia consistently exhibited a unique SB pattern, engaging in significantly fewer but longer sedentary bouts than other forms of cognitive impairment and cognitively intact controls. All (n=17, 100%) studies used volume metrics to describe SB, followed by pattern metrics (n=7, 41%); only 1 study reported on SB variability. Methodological appraisal found significant heterogeneity: 13 different device models across 6 body placements were used. Most studies quantified SB using count-based thresholds (counts per minute), which were largely unvalidated in cognitively impaired or older adult populations. CONCLUSIONS: This review found that participants with dementia consistently exhibited a unique pattern of SB compared to other forms of cognitive impairment and healthy controls, while evidence for differences in SB volume was inconsistent. This may indicate that differences in SB volume are not inherent to dementia pathology but may be mediated by other factors, such as neuropsychiatric symptoms or environmental influences. Furthermore, methodological heterogeneity and unvalidated thresholds were observed throughout the review, highlighting a need for standardized protocols to enhance the validity and clinical applicability of future research. TRIAL REGISTRATION: PROSPERO CRD42024616523; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024616523.
28 June 2026
Read appraisal →BMC palliative care
Factors associated with psychological distress among end-of-life care volunteers: a systematic review of quantitative and qualitative evidence
BACKGROUND: Volunteers are integral to end-of-life care, providing emotional, spiritual, and practical support. However, they often face emotionally demanding situations with limited training and supervision compared to professionals. Given the limited and fragmented literature on psychological distress experienced by end-of-life volunteers, this systematic review aimed to synthesise existing quantitative and qualitative evidence to identify factors associated with psychological distress. METHODS: We conducted a systematic literature review including qualitative and quantitative evidence. Five databases (MEDLINE, EMBASE, PsycINFO, Cochrane Database and Web of Science) were searched for original studies, complemented by citation and reference searches. Study quality was assessed using the Qualsyst tool. Quantitative findings were synthesised using an algorithm to evaluate evidence strength, and qualitative data were integrated through thematic meta-synthesis. RESULTS: Twenty-six studies (20 quantitative and 6 qualitative studies) met inclusion criteria. Quantitative research examined 49 volunteer-related, 18 service-related, and one volunteer-patient-interaction-related factor associated with anxiety, death anxiety, depression, burnout, and/or perceived stress. Moderate-strength evidence indicated that death anxiety was negatively associated with better health and well-being but unrelated to age, volunteer experience, or training. Furthermore, depression was negatively associated with volunteer training. Qualitative evidence was scarce, but highlighted additional patient-, interaction-, and service-level mechanisms. CONCLUSION: This review identifies a small, methodologically diverse evidence base on factors associated with psychological distress in end-of-life care volunteers. Quantitative evidence suggests a potential protective association between training and depression, though substantial heterogeneity limits firm conclusions. Limited qualitative evidence revealed patient-, interaction- and service-level factors that are rarely captured quantitatively. Robust theory-guided longitudinal studies are needed to better understand distress and resilience in this under-researched group.
28 June 2026
Read appraisal →Journal of global health
Habitual physical activity and sarcopenia: a systematic review and meta-analysis of prospective cohort studies
BACKGROUND: Habitual physical activity (HPA) has been associated with a lower risk of sarcopenia by enhancing skeletal muscle protein synthesis and suppressing systemic inflammation. However, the evidence for a long-term protective association remains inconclusive. Therefore, we conducted a systematic review and meta-analysis to quantify the association between HPA and sarcopenia. METHODS: We searched PubMed, the Cochrane Library, EMBASE, Cumulative Index to Nursing and Allied Health Literature, Web of Science, and the China National Knowledge Infrastructure for prospective cohort studies on the relationship between physical activity (PA) and sarcopenia. We selected English and Chinese-language literature published before 6 October 2025, and assessed study quality using the Newcastle-Ottawa Scale. Data were statistically synthesised by calculating pooled relative risks (RRs) and 95% confidence intervals (CIs) using a random-effects model with the generic inverse-variance method. RESULTS: This meta-analysis included nine prospective cohort studies involving 21 265 participants. High levels of HPA were associated with a significantly lower risk of sarcopenia compared to the low levels (RR = 0.55; 95% CI = 0.44-0.67). This protective association remained consistent in subgroup analyses stratified by gender and by compliance with international PA guidelines. Furthermore, moderate HPA was also associated with a reduced risk compared to low HPA levels (RR = 0.73; 95% CI = 0.50-0.96). CONCLUSIONS: Our analysis indicates that moderate to high levels of HPA are independently associated with a lower risk of sarcopenia, serving as a significant protective factor. However, given the methodological heterogeneity in PA measurement, further high-quality prospective studies are needed to clarify the optimal PA dose while accounting for potential reverse causality. REGISTRATION: PROSPERO: CRD420251162529.
28 June 2026
Read appraisal →Public health research & practice
Optimising SMS content for bowel cancer screening participation in Australia: cross-sectional national survey findings.
OBJECTIVES AND IMPORTANCE OF STUDY: This study aimed to identify short message service (SMS) reminder content perceived as most likely to prompt bowel cancer screening, examine differences across sociodemographic subgroups, and explore preferences for timing and frequency. METHODS: Australian residents (N = 1016) aged 50-74 years completed an online survey rating five SMS reminders presented in random order. Outcomes included perceived usefulness, likelihood of encouraging kit return, likelihood of irritation and clarity. Preferences for timing and frequency were also assessed. Bayesian multilevel modelling (cumulative probit) compared ratings across SMS types, with effect sizes expressed as standard deviation (s.d.) differences in perceived likelihood that each SMS would encourage kit return compared with a reminder-only message. Interactions with age, gender, socioeconomic status and screening history were explored. RESULTS: Compared with a 'reminder-only' message, SMS content that encouraged storing the kit near the toilet (s.d. 0.44), conveyed general practitioner endorsement (s.d. 0.32) and gave instructions (s.d. 0.22) was more likely to prompt kit return. Responses varied slightly by age and area-level socioeconomic status. Most participants preferred two to three SMS reminders (mean 2.89, s.d. 6.73). CONCLUSIONS: SMS reminders using behavioural prompts and clear, concise content may support improved screening participation. Tailored SMS messages that reflect public preferences may increase kit return rates, support national screening goals and reduce bowel cancer mortality.
27 June 2026
Read appraisal →Science advances
AI-driven tripartite classification for optimizing wearable bioelectronics in depression management
Current disease-sensing devices primarily focus on distinguishing between healthy and diseased states, effective for diagnosis but limited in guiding optimal intervention timing for prevention. We developed a tripartite framework identifying pre-disease state in depression, a reversible phase preceding irreversible onset. Using complex systems theory, we analyzed early-warning signals emerging as biological systems approach critical transitions. Continuous monitoring of nine multimodal biomarkers-spanning electrophysiological, behavioral, and biological-enabled classification into normal, pre-disease, and disease states by quantitatively defining critical points. An artificial intelligence agent classified disease states with 95.2% accuracy using multimodal data, enabled by ultrasoft neural probes for stable, low-damage recordings. Therapeutic validation with a skin-attachable wireless vagus nerve stimulator integrating soft three-dimensional electrodes demonstrated superior efficacy during pre-disease states. Subjects treated during pre-disease showed faster recovery and greater therapeutic responses, while those treated after disease onset failed to achieve full recovery. This framework provides evidence-based rationale for early intervention.
27 June 2026
Read appraisal →Biofabrication
3D printed trichome-inspired permeable bioadhesive for wearable bioelectronics
Wearable bioelectronics that adhere directly to the skin have broad applications. However, achieving optimal breathability remains a significant challenge because of sweat accumulation at the device-skin interface. Conventional approaches, such as porous structures, often limit the functional versatility of wearable bioelectronics. To address this gap, we propose a sweat-removable skin sticker (SRSS) with a hierarchical trichome-inspired channel architecture that rapidly removes sweat from the interface while maintaining robust skin adhesion. The SRSS was fabricated through a hybrid process, in which the trichome-inspired hierarchical microchannel architecture was created by direct ink writing, enabling the controlled deposition of viscous ink with high structural fidelity. Through the multi-level ribs design, the SRSS demonstrated an effective area-normalized horizontal water removal rate (25.6 ml cm-2min-1) significantly higher than the human sweat secretion rate (0.38-2.85 × 10-3ml cm-2min-1)-approximately three orders of magnitude. This feature reduces sweat accumulation in wearable bioelectronics, thereby enhancing user comfort. Unlike conventional porous materials, the SRSS relies on channel based adhesive interface design that remains compatible with attached wearable bioelectronics, such as temperature sensors in real time. This work therefore presents a structurally engineered permeable bioadhesive interface for wearable bioelectronics.
27 June 2026
Read appraisal →JMIR mental health
Coproduction Without Youth? Closing the Participation Gap in Digital Mental Health Research
Young people are among the most intensive users of digital and generative artificial intelligence (GenAI)-enabled mental health tools, yet they remain underrepresented in the research and design processes that shape these technologies. Although participatory approaches such as co-design and patient and public involvement are widely endorsed as best practices, youth involvement in digital youth mental health (DYMH) research is often inconsistent, superficial, or limited to late-stage consultation. This participation gap risks producing interventions that are misaligned with young people's lived experiences, priorities, and vulnerabilities, particularly in the context of rapidly evolving and scalable GenAI systems. This Viewpoint aims to reexamine the underlying drivers of the participation gap in DYMH research; clarify how participation is conceptualized and implemented across disciplines; and propose concrete, actionable recommendations to support more meaningful and consistent youth involvement across the research life cycle. We draw on interdisciplinary literature from digital mental health, human-computer interaction, child-computer interaction, and health research policy. Our Viewpoint integrates conceptual frameworks (eg, Lundy's model of participation), existing reviews of co-design practices, and emerging evidence on GenAI in mental health. We adopt a life cycle-oriented perspective to examine how youth participation is distributed across stages of research and development, including problem formulation, design, implementation, and evaluation. We identify 3 interrelated drivers of the participation gap. First, conceptual and linguistic fragmentation obscures what participation entails in practice, with terms such as co-design, participatory design, user-centered design, and patient and public involvement used inconsistently across disciplines. Second, youth involvement is uneven across the research life cycle, with participation often concentrated in early ideation or usability testing but largely absent from upstream decision-making and downstream evaluation. Third, institutional barriers-including ethics review processes, consent requirements, funding constraints, and adult-centric research norms-systematically limit meaningful youth partnership. These challenges are amplified in the context of GenAI, where opaque "black box" systems, simulated therapeutic interactions, and rapid deployment cycles introduce distinct risks if youth perspectives are not integrated. We propose a set of minimum expectations to address these gaps, including explicit specification of participatory models, life cycle mapping of youth involvement, reporting of youth influence on decisions, dedicated funding for participation, proportional ethics frameworks, and mechanisms for youth-informed governance of GenAI systems. Closing the participation gap in DYMH research is both an ethical imperative and a practical necessity. Moving beyond aspirational commitments requires embedding youth participation as a standard, resourced, and accountable component of research, design, and governance. In the context of rapidly evolving digital and GenAI technologies, failure to do so risks producing interventions that are scalable but not safe, credible, or responsive to the needs of young people.
27 June 2026
Read appraisal →JMIR research protocols
Exploring Non-Embodied AI-Based Digital Companions for Older Adults in Aging and Care Contexts: Protocol for a Scoping Review
BACKGROUND: Conversational artificial intelligence (AI) technologies are increasingly positioned as a response to social isolation, loneliness, and unmet psychosocial needs across health and care contexts. Non-embodied AI-based digital companions have attracted growing attention for their potential to support companionship, social interaction, communication, and psychosocial well-being among older adults, including people living with dementia. However, the evidence base remains underexplored. Terminology is inconsistently applied, systems are variably defined, and studies are distributed across disciplinary silos, limiting critical investigation of how these technologies are conceptualized, designed, and evaluated. OBJECTIVE: This study aims to map and critically synthesize the existing literature on non-embodied AI-based digital companions for older adults in aging, health, and care contexts. The review seeks to describe digital companions, examine methodological approaches, and identify research gaps. METHODS: This protocol follows the Joanna Briggs Institute methodology for scoping reviews and will be reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines). A comprehensive search will be conducted across multiple electronic databases, including MEDLINE, APA PsycINFO, CINAHL, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library, as well as selected gray literature sources. The search strategy combined terms related to digital companions, conversational AI, aging, and care contexts, including concepts related to companionship, social interaction, communication, loneliness, and psychosocial support., Eligible studies will include empirical studies involving older adults that examine non-embodied AI-based digital companions designed primarily to support companionship, social interaction, communication, or related psychosocial support in aging, health, and care contexts. Two reviewers (YC and MG) will independently conduct study selection and data charting. Data will be synthesized using descriptive statistics and narrative analysis. RESULTS: This protocol outlines a systematic approach to identifying, selecting, and synthesizing the existing evidence on non-embodied AI-based digital companions. A preliminary PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-style search flow for the revised database searches identified 2289 records from MEDLINE, CINAHL, and APA PsycINFO. After duplicate removal and eligibility-based removals before screening, 1978 records remained available for title and abstract screening. The full scoping review will summarize study characteristics, populations, contexts, digital companion features, and methodological trends using descriptive tables and narrative synthesis. As of June 2026, the revised database searches had been completed for MEDLINE, CINAHL, and APA PsycINFO, and title and abstract screening was underway. The full scoping review results are expected to be submitted for publication after screening, data charting, and synthesis are completed. CONCLUSIONS: This scoping review is expected to clarify conceptual boundaries, map the scope of current research, and identify knowledge gaps related to non-embodied AI-based digital companions in health, aging, and care contexts. The findings will inform future research, design, and implementation of non-embodied AI-based digital companions.
26 June 2026
Read appraisal →Prehospital and disaster medicine
Non-Resuscitative Telecommunicator-Assisted First Aid: A Scoping Review
INTRODUCTION: The provision of instant telephone pre-arrival instructions (PAI) on first aid to laypeople by emergency services' telecommunicators for conditions beyond cardiac arrest, such as trauma or acute coronary syndrome, is practiced by emergency services around the world and is recognized by some prehospital systems as a standard of care. However, to the best of the authors' knowledge, no attempts have been made to date to systematically summarize the related research evidence. STUDY OBJECTIVE: The aim of this study was to systematically map and analyze published quantitative research on telecommunicator-assisted first aid (TAFA) for medical emergencies other than cardiac arrest. METHODS: Peer-reviewed journal papers reporting original quantitative data on experimental or real-world applications of TAFA were identified through PubMed, Scopus, and Google Scholar. Studies focusing solely on cardiac arrest management (cardiopulmonary resuscitation [CPR] or automated external defibrillation) were excluded. A scoping review of eligible papers was conducted in accordance with the PRISMA-ScR guidance. RESULTS: Twenty-two studies published from 2000 through 2024 met inclusion criteria. First authors represented nine high-income countries. The studies investigated hypothetical or actual application of TAFA for anaphylaxis, chest pain or myocardial infarction, stroke, trauma, bleeding, childbirth, foreign body airway obstruction, opioid overdose, and respiratory arrest; four studies did not specify the type of emergency. Eight studies (36.4%) analyzed real Emergency Medical Services (EMS) call data, another eight (36.4%) were simulation-based, five (22.7%) used surveys, and one (4.5%) represented a cost-effectiveness analysis. The studies described existing TAFA practices, evaluated expectations and perceptions of the service by the public, demonstrated the feasibility of experimental techniques, including video communication, and identified areas for future investigations and interventions. Along with some beneficial effects of TAFA, several studies have demonstrated downsides of the practice, including suboptimal dispatcher adherence to pre-arrival protocols. The relationships between the real-world application of TAFA and patient outcomes are unclear. CONCLUSION: According to the review of quantitative research, non-resuscitative TAFA remains an under-explored area of prehospital medicine. Considering the paucity and the inconclusiveness of available evidence, significantly more research is required to better understand its effects and practical implications. The evidence map generated by this scoping review may assist the professional community in advancing the development of non-resuscitative TAFA.
26 June 2026
Read appraisal →BMJ open
Caregiver experiences of social isolation and loneliness in chronic kidney disease: systematic review of qualitative studies.
OBJECTIVES: This qualitative systematic review aimed to describe the experiences and perspectives of loneliness and social isolation among informal caregivers of people with chronic kidney disease (CKD). METHODS: Terms for caregivers, qualitative research, loneliness and social isolation and CKD were entered into MEDLINE, Embase, CINAHL and PsycINFO and searched from inception to May 2025. Qualitative studies that described social isolation and loneliness among caregivers of people with CKD were included in this review. Study characteristics were extracted into Microsoft Excel. Qualitative data from each study were imported into HyperRESEARCH and analysed using thematic synthesis. RESULTS: We included 19 articles involving 598 caregivers of people with CKD from 28 countries. Four major themes with subthemes were identified: confined by the patient's needs (social withdrawal due to unrelenting demands, separated from communities due to treatment, torn between work and caregiving responsibilities, foregoing social outings with family and friends, restricted by patient's diet, feeling protective against infection risk); limited care assistance exacerbating isolation (inadequate familial support, absence of respite care, lacking accessible guidance from health professionals); disrupting relationships and social roles (sacrificing social needs and identity, family conflicts worsening isolation, withdrawing to avoid stigma and ridicule, grappling with hopelessness, reluctance to share struggles); and coping with support resources (connecting with other families, seeking assistance from support services, finding support through faith). CONCLUSIONS: Caregivers of people with CKD experience restricted social participation and loss of social roles and identity, which can exacerbate feelings of loneliness and social isolation. Support services are needed to prevent and address social isolation and loneliness in CKD caregivers. PROSPERO REGISTRATION NUMBER: CRD420250637194.
26 June 2026
Read appraisal →Cancer causes & control : CCC
HPV in breast cancer: prevalence and comparison with healthy tissue-a systematic review and meta-analysis
INTRODUCTION: Breast cancer (BC) is the most common cancer among women worldwide, with over 2.3 million new cases annually. Recent studies suggest that Human Papillomavirus (HPV), a known oncogenic virus, may be involved in BC development. This study investigates HPV prevalence in BC samples and its potential role in tumorigenesis. METHODS: Random-effects meta-analyses were conducted to estimate raw proportions and odds ratio (OR), with 95% confidence intervals (CIs). Heterogeneity was assessed using I2. Statistical significance was set at p < 0.05. Analyses were performed in R 4.5.0 RESULTS: Our meta-analysis encompassed 82 studies and evaluated 7,683 breast cancer (BC) tissue samples to assess the presence of HPV. The overall prevalence of HPV in BC specimens was estimated at 23% (95%CI: 19%-28%). When stratified by continent, Oceania exhibited the highest regional prevalence at 38%. Comparative analysis between BC tissues and healthy controls revealed a significantly increased likelihood of HPV detection in the cancer group (OR 5.06; P < 0.001). This association remained statistically robust in both case-control (OR 6.34; P < 0.001) and cross-sectional designs (OR 2.83; P < 0.001). Among continents, South America demonstrated the most pronounced association (OR 11.66; P = 0.005). Subgroup analysis based on economic classification indicated that countries with low-income settings had the highest HPV prevalence (34%; 95%CI: 9%-73%). Evaluation by BC subtype revealed that luminal B had the highest HPV-positive rate (44%; 95%CI: 27%-61%). CONCLUSION: This meta-analysis reveals a global presence of HPV in BC and suggests a possible link. Further well-designed studies are needed to confirm its role in tumorigenesis.
26 June 2026
Read appraisal →Pulmonology
Association between smoking and prognosis in idiopathic pulmonary fibrosis: A systematic review and meta-analysis
BACKGROUND: Although smoking is a well-established risk factor for idiopathic pulmonary fibrosis (IPF) development, its impact on clinical outcomes remains unclear. RESEARCH QUESTION: Is smoking associated with clinical outcomes of IPF? METHODS: A systematic search of PubMed, Embase, and the Cochrane Library was conducted to identify studies reporting associations between smoking status and IPF outcomes, including mortality, acute exacerbation (AE), lung cancer development, and baseline lung function. Summary estimates with 95% confidence intervals (CIs) were pooled using random-effects model. Subgroup analyses were conducted by study region. RESULTS: Forty-nine studies comprising 32 974 patients were included. Overall mortality did not differ by smoking status. However, significant regional differences were observed, with ever smokers demonstrating higher mortality in non - East Asian studies (Hazard ratio [HR] 1.17, 95% CI: 1.03-1.33) but lower mortality in East Asian studies (HR 0.75, 95% CI: 0.62-0.91, p-for-interaction < 0.001) compared with never smokers. Current smokers had a higher risk of developing lung cancer compared with non-current smokers (HR 1.88, 95% CI: 1.24-2.87). No significant associations were observed between smoking and the risk of AEs or baseline forced vital capacity. CONCLUSION: Smoking was not associated with overall mortality in IPF, although significant regional differences were observed.
26 June 2026
Read appraisal →Journal of medical Internet research
The Emerging Roles of AI in Self-Directed Stress Management: Systematic Review
BACKGROUND: Stress is widespread and carries substantial mental health, social, and economic burdens. Yet, access to clinician-led stress management remains constrained by service capacity, cost, and stigma. In response, artificial intelligence (AI)-enabled tools have rapidly proliferated as scalable, self-directed options. However, evidence on how these systems support stress management outside formal clinical settings remains fragmented. OBJECTIVE: This systematic review aimed to synthesize empirical evidence on how AI-enabled technologies are used for self-directed stress management. We mapped the emerging functions of these tools, the psychological frameworks informing their design, the populations and settings studied, and the outcomes reported. METHODS: We conducted a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-compliant systematic review of English-language studies published between 2000 and 2025. Six databases were searched (APA PsycINFO, PubMed, MEDLINE, Scopus, Web of Science Core Collection, ProQuest, and Google Scholar). RESULTS: Of 3008 records identified, 35 studies met the inclusion criteria. The methodological quality of included studies was critically appraised using the Mixed Methods Appraisal Tool (version 2018). Findings illustrated that AI-supported stress management can operate through 5 core functions, including psychological intervention, behavioral support, psychoeducation, companionship, and emotional support, and stress monitoring, detection, and triage. Across the reviewed studies, these functions supported self-directed stress management by helping users identify stress, regulate responses, and engage in coping outside formal clinical care. CONCLUSIONS: AI-enabled systems show preliminary promise for supporting self-directed stress management through multiple user-facing functions grounded in established psychological frameworks.
26 June 2026
Read appraisal →Scientific reports
Cross-domain transfer learning strategy enhances interpretability of deep learning model explanations
Clinical decision-making increasingly relies on deep neural networks (DNNs), yet their deployment in practice requires transparent and interpretable predictions. Explainable artificial intelligence (xAI) methods can identify input regions relevant to a model's decision, but their clinical interpretability remains limited. In this study, we investigated whether inductive transfer learning (TL) can reinforce domain-specific feature separation in xECGArch, a two-branch convolutional neural network for atrial fibrillation (AF) detection from electrocardiograms (ECGs). Each branch was pre-trained on a task aligned with its designated feature domain, P wave detection for morphology and RR interval variability prediction for rhythm, then fine-tuned on binary AF classification using an iterative layer freezing schedule. Deep Taylor decomposition (DTD) was applied to analyze explanations across all configurations. Fine-tuning accuracy ranged from 85.70% to 95.23%, remaining comparable to the original xECGArch architecture and previous TL-based approaches. However, DTD analysis demonstrated that morphology pre-training directed relevance toward P waves, whereas rhythm pre-training concentrated explanations on R peaks, with domain specificity increasing as more layers were frozen. These findings suggest that inductive TL can encourage domain-specific feature attribution in DNNs, improving the alignment of post-hoc explanations with clinically meaningful ECG regions.
26 June 2026
Read appraisal →BMJ open
Effectiveness of peer support interventions to improve mental health outcomes after miscarriage: a systematic review and call for high-quality evidence.
OBJECTIVES: Peer support is being integrated into the new maternal mental health services in England to further the development of the recovery approach in relation to loss. Psychological support after miscarriage (pregnancy loss prior to viability) is often overlooked, despite significant psychological morbidity. This systematic review explored the effectiveness of peer support interventions to improve mental health outcomes after miscarriage. DESIGN: Systematic review DATA SOURCES: A comprehensive systematic search across nine databases (MEDLINE, CINAHL, APA PsycINFO, Web of Science (all databases), EMBASE, CENTRAL, LENS.org, British Nursing Index and Health Management Information Consortium) was conducted in June 2025. Grey literature was identified through website searching, contact with topic experts and a national call for evidence. ELIGIBILITY CRITERIA: Study designs with a quantitative evaluative component or mixed-methods studies reporting effectiveness were eligible if they involved women and/or partners who had experienced miscarriage and been offered a peer support intervention. Any peer support versus control (no treatment, wait list and usual care) or peer support versus another psychosocial intervention was eligible for inclusion. Studies that report any of the following broad groups of outcomes (whether validated measures or by self-report) were eligible for inclusion: (a) personal recovery, (b) mental health recovery, (c) health service use and (d) social outcomes. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers used standardised methods to search and screen for eligible studies. RESULTS: Of the 4342 titles screened, 100 potentially relevant full-text papers were retrieved and screened resulting in seven randomised controlled trials and two controlled trials identified across 10 papers. Of these, seven did not evaluate a peer-led intervention, one reported only on women who had experienced pregnancy loss later than 24 weeks gestation, and one reported a peer support intervention for those who experienced pregnancy loss at any age of gestation but did not disaggregate data for those who experienced miscarriage. Thus, no studies were eligible for inclusion. This indicates a significant gap in the current literature. The inconsistencies and limitations in existing research approaches are explored in detail. CONCLUSIONS: This systematic review has identified an evidence gap as there is currently no robust evidence for the effectiveness of peer support interventions after miscarriage. Given the drive for the inclusion of peer support in new maternal mental health services, there is therefore a need for targeted intervention research to provide reliable evidence to determine effective peer support interventions for this population. PROSPERO REGISTRATION NUMBER: CRD42024518248.
25 June 2026
Read appraisal →Journal of medical economics
From options to decisions: an innovative model for treatment sequencing in relapsing-remitting multiple sclerosis
AIMS: With the expanding range of disease-modifying therapies (DMTs) for relapsing-remitting multiple sclerosis (RRMS), clinicians face increasing complexity in defining optimal treatment sequences and timing of therapy switches. In this study, we adapted an earlier computer-assisted model to optimize therapeutic decisions and identify preferred treatment pathways, based on expert opinion and observations from clinical practice in Spain. MATERIALS AND METHODS: The original model was updated to integrate magnetic resonance imaging (MRI) activity data and define reaching an Expanded Disability Status Scale (EDSS) score of 3 - indicating moderate disability without ambulation impairment - as a criterion for switching treatment. Matrices were designed to model options when switching DMTs, triggered by either a lack of effectiveness or safety concerns. Change in DMT was based on a set of composite criteria (encompassing contributions from relapses, disability worsening, MRI activity, costs, and quality of life) according to the disease activity level. A maximum of three DMTs could be administered within the 8-year time horizon evaluated. RESULTS: The revised model identified high efficacy treatment, in particular cladribine tablets as the preferred initial DMT for patients with RRMS with mild or moderate disease activity. Of these patients with mild and moderate disease activity, most were switched to ofatumumab (79.4%) and ocrelizumab (75.9%), respectively, when disease progression occurred. Patients with high disease activity mostly received natalizumab if they were John Cunningham virus (JCV)-negative, or ocrelizumab if they were JCV-positive. LIMITATIONS: The model is informed by a Spain-based healthcare expert panel and has been evaluated using a simulated cohort of 10,000 patients with RRMS. CONCLUSION: This computational model helps to inform clinicians towards making optimal treatment decisions for RRMS, and identified high-efficacy DMTs as the preferred model-based option for all levels of disease activity, supporting early and effective control of disease activity in real-world clinical practice.
25 June 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Efficacy of Yoga in Tobacco Cessation: A Systematic Review and Meta-Analysis
INTRODUCTION: Tobacco consumption is a global epidemic with a high relapse rate. A complementary method like yoga, recognized for addressing psychological and behavioral aspects of addiction, was evaluated in this systematic review and metaanlaysis. METHODS: The objective was to comprehensively summarize the current evidence and evaluate the efficacy of yoga in tobacco cessation. This review was registered with PROSPERO (CRD42025643806). A systematic search across four databases identified randomized controlled trials (RCTs) in English up to September 2024. Eligible studies included adults (≥18 years) using any form of tobacco excluding vaping, randomized to yoga as monotherapy or adjunct therapy. The primary outcome was 7-day point prevalence abstinence (7PPA), while the secondary outcomes included quality of life, depression, anxiety, and mood states. Two reviewers independently extracted data and assessed quality using the Cochrane Risk of Bias (RoB2) tool. A meta-analysis was conducted for outcomes assessed in at least two studies. RESULTS: Seven RCTs were included, with five suitable for meta-analysis. The pooled odds ratio for 7PPA at the end of treatment was 1.50 (95% confidence interval = 0.60 to 3.73), suggesting a positive but inconsistent effect. Considerable heterogeneity was observed (I2 = 65%, p = .07; τ2 = 0.7127), indicating substantial variability across studies. Heterogeneity (I2 = 65%) markedly reduced to 16% when high-risk trials were excluded in sensitivity analysis. Studies reported improvements in depression, anxiety, and quality of life. CONCLUSION: This review consolidates the best available evidence on the effects of yoga interventions for tobacco cessation, showcasing the promising potential of yoga in smoking cessation. IMPLICATIONS: Active yoga styles like hatha, vinyasa, and Iyengar improved 7PPA by reducing stress and depression, while pranayama reduced cravings and negative affect. Future research should explore yoga-based cessation programs in diverse settings, especially low- and middle-income countries, to address unique challenges. Standardizing methodologies across populations will enable a more comprehensive evaluation of yoga's role in tobacco cessation and its potential to enhance global public health outcomes.
25 June 2026
Read appraisal →Journal of medical Internet research
You Can't Launch This: Trust as Infrastructure in Digital Behavioral Health
Digital behavioral health interventions frequently fail to scale, even when evidence-based and technically and operationally sound. In this News and Perspectives article, researcher, digital behavioral health platform founder, and JMIR Correspondent Trevor van Mierlo concludes a four-part series examining why this occurs, reporting on the foundational role of trust.
25 June 2026
Read appraisal →Journal of neurology
Exercise- and dietary pattern-based lifestyle interventions and neurocognitive function in adults: a Bayesian multilevel network and dose-response meta-analysis
OBJECTIVE: To systematically review the effects of lifestyle interventions focused on exercise and dietary patterns on neurocognitive function in adults, and to examine their relative effects and dose-response relationships. METHODS: PubMed, Embase, Web of Science, and the Cochrane Library were searched from database inception to March 20, 2026. A total of 23 randomized controlled trials (RCTs) involving 12,286 participants were included. Bayesian multilevel pairwise, network, and dose-response meta-analyses were conducted to evaluate overall cognition, memory, and executive function. RoB 2 and CINeMA were used to assess the credibility of the evidence. RESULTS: Combined interventions yielded small, but statistically significant cognitive benefits (overall cognition: SMD = 0.18; memory: SMD = 0.10; executive function: SMD = 0.09). These benefits were driven primarily by differences between the combined intervention and control or usual care, as well as exercise alone; compared with diet alone, the combined intervention still showed a small advantage for executive function. Network meta-analysis suggested that AE_MIND and AE_RT_MED were more beneficial for overall cognition, AE_RT_MIND and AE_RT_MED for memory, and AE_RT_CR and AE_MIND for executive function. Exploratory dose-response analyses suggested possible curvature in some spline-based models; however, comparison with simpler monotonic specifications did not provide robust evidence for a clinically interpretable non-linear or monotonic dose-response relationship. CONCLUSION: Combined lifestyle interventions may produce statistically significant but modest improvements in cognitive function in adults, with benefits varying across cognitive domains. However, current evidence remains insufficient to support a standardized precision prescription, and high-quality RCTs with long-term follow-up are needed to determine the optimal combinations and dosages.
25 June 2026
Read appraisal →BMJ global health
Lifestyle interventions for dementia prevention in low- and middle-income countries: a systematic review.
INTRODUCTION: By 2050, two-thirds of people with dementia will live in low-and-middle-income countries (LMICs). However, multimodal prevention lifestyle interventions for people at risk are being developed predominantly in higher-income countries. METHODS: We systematically reviewed randomised controlled trials (RCTs) evaluating non-pharmacological interventions in individuals with mild cognitive impairment and subjective cognitive decline in LMICs. We assessed quality using the Mixed Methods Assessment Tool, meta-analysed and synthesised evidence. RESULTS: We included 25 RCTs from six countries (most in China, n=17), involving 1304 participants. In the 15 studies with sufficient data to meta-analyse, we found significant positive effects on cognition favouring interventions (1.49 (standardised mean difference, 95% CI 1.06 to 1.93)). There was significant publication bias. We classified interventions into exercise, multidomain and arts/creative expression. Exercise (1.67, 95% CI 1.24 to 2.11, n=8) and multidomain (1.22, 95% CI 0.22 to 2.21, n=5) had replicated evidence of effectiveness. There was insufficient data to meta-analyse the arts/creative category. CONCLUSION: We propose greater consideration and investment in the development of interventions accounting for specific LMIC contexts from the outset, so they are acceptable and used by local services. PROSPERO REGISTRATION NUMBER: CRD42023403908.
24 June 2026
Read appraisal →Journal of medical Internet research
Potential of Digital Tools for Chronic Pain Management
Chronic pain is notoriously complex to manage and treat. In this News and Perspectives article, JMIR Correspondent Vanessa Nirode reports on several digital innovations that may help to bridge existing care gaps.
24 June 2026
Read appraisal →International ophthalmology clinics
Beyond the Clinic: Home-Based Monitoring Strategies in Glaucoma Care
Glaucoma is a chronic and progressive optic neuropathy, necessitating lifelong monitoring for affected individuals. Traditional surveillance of the disease relies on evaluation of intraocular pressure (IOP), optic nerve anatomy, and visual function at the physician's office, which only provides limited snapshots of a disease characterized by dynamic physiological variation. Developments in portable ophthalmological technologies and digital health platforms have enabled home monitoring of glaucoma. This review synthesizes current evidence on the home monitoring of glaucoma, with a focus on self-tonometry and home-based visual field testing. Handheld tonometers such as iCare HOME and iCare HOME2 allow repeated, anesthesia-free self-measurement of IOP in a real-world setting. These devices have demonstrated good agreement with Goldmann applanation tonometry, the gold standard. Home tonometry captures diurnal variations of IOP, thus providing relevant insights for diagnosis and therapeutic decision-making. Home-based visual field testing (eg, tablet-based platforms, virtual reality-based testing) enables frequent, unsupervised functional assessment with repeatability and sensitivity comparable to standard automated perimetry. Increased testing frequency may facilitate earlier detection of rapid progression, particularly in the early stages of disease. Collectively, evidence suggests the feasibility and clinical relevance of these home monitoring modalities. However, patient compliance with testing schedules and cost-effectiveness remain as challenges.
24 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 →Proceedings of the National Academy of Sciences of the United States of America
AI agents are sensitive to nudges.
Large language models (LLMs) are increasingly deployed as autonomous agents that make choices and use tools on behalf of users. Yet, we have limited evidence about how their decisions are shaped by their environment. We adapt a human decision-making task to test leading LLMs under four forms of choice architecture: defaults, suggestions, information highlighting, and "optimal" nudges derived from a resource-rational model of human choice. We treat human behavior as a baseline for predictable sensitivity to such interventions. Across models and prompting strategies, LLMs often depart substantially from this baseline. They sometimes pay excessive costs to acquire information, sometimes ignore available information, and, most crucially, are far more responsive to nudges than humans, such that weak cues that slightly shift human behavior have larger effects on model choices, toward both better and worse payoff outcomes. Chain-of-thought prompting and in-context human data do not reliably stabilize behavior. Recent reasoning-optimized LLMs can, in some configurations, restore more human-level sensitivity to nudges, but do so inconsistently and at substantial computational cost. These results point to an important and largely neglected safety concern: LLM agents can be behaviorally brittle under subtle changes in choice architecture, even in the absence of adversarial settings.
23 June 2026
Read appraisal →PloS one
Artificial intelligence in the diagnosis of deep vein thrombosis: A scoping review
Deep vein thrombosis (DVT) is the formation of thrombi in the deep venous system, most often in the lower extremities. Although usually not life-threatening, DVT requires timely diagnosis to prevent complications such as pulmonary embolism and post-thrombotic syndrome. The growing demand for image interpretation has generated interest in applying artificial intelligence (AI) to automated DVT detection. This scoping review analyzes the performance of artificial intelligence in diagnosing DVT using computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US). We conducted a search across seven databases from inception to May 2025 using terms related to deep vein thrombosis, artificial intelligence, and machine learning. Eligible studies were limited to those evaluating DVT diagnosis using CT, MRI, or ultrasound. Two independent reviewers selected eligible studies, and quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Eleven studies published between 2021 and 2025 met the inclusion criteria. Some of the AI algorithms included RetinaNet, Deep R-Belief Neural Networks, and Sooty Tern Optimization. US-based models were the most studied algorithms, with sensitivities and specificities ranging from 68 to 100% and 70-100%, respectively. The MRI-based model achieved sensitivities, specificities, and accuracies of 95% to 97%. One CT-based model demonstrated a sensitivity of 83%. Studies evaluated across multiple imaging datasets showed high sensitivities, specificities, and precision of 96% or higher. Future research should prioritize multicenter validation and integration of clinical factors. In addition, explainable frameworks capable of integrating multiple imaging datasets must be developed with attention to workflow efficiency and cost-effectiveness to support clinical translation. The results indicate that AI is best situated as a supplementary tool rather than a replacement for expert interpretation in DVT diagnosis.
23 June 2026
Read appraisal →Acta orthopaedica
Predicting persistent pain after total knee arthroplasty using different machine learning algorithms
BACKGROUND AND PURPOSE: After total knee arthroplasty (TKA), 10-20% of patients remain unsatisfied. Well-performing clinical prediction models can provide individualized risk estimates and stratification in terms of poor outcomes, resulting in unnecessary surgeries being avoided and patients being counseled preoperatively. We aimed to create a precise, well-performing prediction model for clinical application using different machine learning algorithms to predict those patients who will have residual pain, a low total Oxford Knee Score (OKS) and the patient group who do not achieve minimally clinical important difference (MCID) in OKS 1 year after TKA. METHODS: We conducted a retrospective cohort study based on patients who had undergone primary TKA at our institution combined with 751 patient-related variables. The multivariable models used were based on the results of univariate analysis. We used the machine learning method Extreme Gradient Boosting (XGBoost). The discrimination capability of the models was measured with the area under the curve (AUC). RESULTS: 11,755 patients were included in this study. There were 850 (7.2%) patients who experienced persistent pain 1 year after TKA. The AUC was 0.67. For the secondary outcomes, the AUC values were similar. The most important variables in the model were lower preoperative OKS, younger age, valgus malalignment, lower preoperative pain OKS, use of mild opioid, neuropathic pain medicine and thyroxine, and higher body mass index. CONCLUSION: The prediction models achieved poor AUCs. It seems clear that the prediction of pain and functional outcome after TKA is difficult, even with a large patient cohort combined with 751 patient-related variables and sophisticated machine-learning algorithms.
23 June 2026
Read appraisal →Medical science monitor : international medical journal of experimental and clinical research
Development and Validation of Machine-Learning-Based Prediction Models for Thyroid Diseases During Pregnancy
BACKGROUND International guidelines recommend early screening based on targeted risk factors to identify thyroid disease during pregnancy. The complexity of these risk factors makes accurate prediction challenging. This study aimed to develop and compare multiple machine-learning-based predictive models for thyroid disease during pregnancy. MATERIAL AND METHODS This retrospective study analyzed the clinical characteristics of 5461 women who gave birth at a single center. The dataset was divided into training and test sets. In the training set, feature variables associated with thyroid disease during pregnancy were selected using the Boruta algorithm. Eight models were developed: logistic regression, Bayesian approach, k-nearest neighbors, support vector machine, neural network, classification and regression tree, extreme gradient boosting, and random forest (RF). Model performance was evaluated using the receiver operating characteristic (ROC) curve, precision-recall curve (PRC), calibration curve, and decision curve analysis. RESULTS Nine feature variables were identified: age, height, pre-pregnancy weight, gravidity, parity, primiparity or multiparity, hypertensive disorders of pregnancy, scarred uterus, and autoimmune disease. The RF model demonstrated the best performance, with accuracy of 0.98387819 and 0.99597990, Matthews correlation coefficient of 0.96794139 and 0.97781292, log loss of 0.12670703 and 0.09442025, Brier score of 0.02495798 and 0.01921069, area under the ROC curve of 0.99877170 and 0.99991140, and area under the PRC of 0.99864486 and 0.99922572 in the training and test sets, respectively. CONCLUSIONS The RF model demonstrates excellent discriminative performance, accuracy, consistency, and generalizability in predicting thyroid disease during pregnancy.
23 June 2026
Read appraisal →Science advances
Synchronous wearable ultrasound for early detection of coronary and carotid artery comorbidity
Coronary heart disease (CHD) and carotid artery disease (CAD) often co-occur. However, conventional diagnosis typically involves separate, site-by-site examinations after symptoms appear, leading to delayed intervention. In this work, we developed a wearable ultrasound system that enables synchronous monitoring of cardiac and carotid dynamics for comorbidity assessment. The system combines dual wearable ultrasound patches, a synchronous imaging strategy, artificial intelligence-based image processing algorithms, and human circuitry models to automatically extract and analyze key cardiac-carotid metrics, such as heart rate, pulse rate, cardiac volume, cardiac output, and carotid blood pressure. By evaluating the correlation of these metrics between modeling and measurements, we showed the feasibility of differentiating among healthy participants and patients with CAD, CHD, or CAD-CHD comorbidity. This integrated approach constitutes a promising framework for supporting the proactive assessment of coronary-carotid comorbidity.
21 June 2026
Read appraisal →Vaccine
Impact of standing orders on vaccine uptake: A systematic review.
OBJECTIVE: We sought to evaluate the impact of standing orders on vaccine coverage in clinical settings. METHODS: Two reviewers independently screened and included studies that evaluated use of standing orders either alone or in combination with other interventions and collected data on vaccine coverage. We extracted effect sizes for studies that used only standing orders as an intervention and had a comparison group. RESULTS: The search yielded 56 eligible studies, 22 of which evaluated standing orders-only interventions. Standing orders increased vaccine uptake by a median of 13 percentage points (IQR 2-20 percentage points). Eight of these studies had a comparison group including 1 randomized trial; 5 were at low risk of bias. The overall findings were similar those for seasonal influenza vaccine (median increase of 12 percentage points; IQR, 6-24; 7 studies) and pneumococcal vaccines (14 percentage points; 2-20; 5 studies), the two most studied outcomes. Multi-component studies paired standing orders with interventions for providers frequently (34%-63% of studies), patients often (20%-49%), and systems least often (3%-14%). CONCLUSIONS: Standing orders show one of the largest effects among vaccine uptake interventions. Future research should focus on randomized trials, childhood vaccination, and behavioral aspects of implementation in healthcare systems.
21 June 2026
Read appraisal →JMIR cancer
Digital Intervention for Electronic Patient-Reported Outcomes in Advanced Cancer: Mixed Methods Study
BACKGROUND: Digital health interventions are increasingly being integrated into oncological care to support patients in managing treatment-related symptoms and psychological distress. In a randomized controlled pilot trial, we investigated the feasibility and preliminary efficacy of a digital health app (SOFIA) among patients with cancer, including those in palliative care. SOFIA consists of an electronic patient-reported outcome (ePRO) assessment and coaching component. We showed good feasibility and high acceptability of SOFIA in routine clinical care. OBJECTIVE: This pilot study aimed to evaluate user experiences and behavior with SOFIA. We applied a mixed methods design, combining a qualitative exploration of patients' experiences and a quantitative analysis of app usage patterns. The integrated findings are intended to inform the refinement and further development of digital health interventions to better address the needs, preferences, and engagement behaviors of this patient population. METHODS: Patients randomly assigned to the intervention group participated in semistructured interviews at 2 time points during the 12-week intervention period: midway through the intervention (at week 6) (T1) and postintervention (at week 12) (T2). Qualitative data were analyzed using content analysis. User data were descriptively analyzed with Python and the Pandas library (version 2.2.3). RESULTS: Our qualitative analysis of 29 patients revealed benefits regarding the ePRO assessment (empowerment, support, user-friendliness, and facilitation of patient-physician communication), as well as some criticism (inflexible design and insufficient use by physicians). Benefits of the coaching tool included the availability of helpful information and design aspects such as clarity and user-friendliness. Quantitative data from 32 active users of the app showed that 16 (50%) of patients read articles, 28 (87.5%) started journeys, and 15 (46.9%) completed exercises at any point during the study. CONCLUSIONS: The results of this mixed methods study may provide important indications for digital interventions, including ePRO assessment, for patients with cancer. From the patients' perspective, key features include intuitive design, relevant symptom items, reliable reminder functions, the translation of ePROs into clinically actionable information, clear symptom visualizations, seamless integration into electronic health records, and effective physician engagement. Both our qualitative and quantitative data show the importance of therapy-specific content. These results might increase acceptance and usage, and thereby also the clinical benefit, of future digital interventions for patients with cancer.
20 June 2026
Read appraisal →Progress in neuro-psychopharmacology & biological psychiatry
Neural correlates of late talking: A systematic review of electrophysiological and neuroimaging studies.
BACKGROUND: Late talking (LT), a delay in expressive vocabulary in early childhood, affects a significant minority of toddlers and can be a precursor to persistent language and literacy impairments. While behavioral profiles are well-documented, a systematic synthesis of its underlying neurobiology is lacking. This systematic review aimed to identify, evaluate, and synthesize evidence from electrophysiological (EEG) and neuroimaging (MRI) studies on the neural correlates of late talking in children. METHODS: We conducted a systematic search of Scopus, Web of Science, and PubMed from inception to September 2025, following PRISMA guidelines. Studies investigating children formally identified as late talkers without comorbid neurological or global developmental conditions, using EEG/ERP or MRI modalities, were included. Risk of bias was assessed using the Joanna Briggs Institute (JBI) critical appraisal tool. RESULTS: Sixteen studies (8 EEG, 8 neuroimaging) comprising 231 LT children in EEG studies and 315 in neuroimaging studies were included. EEG studies revealed a neurodevelopmental cascade, beginning with atypical oscillatory activity (increased frontal gamma power) and deficits in auditory discrimination (reduced Mismatch Negativity), progressing to higher-order impairments in phonological (absent PMN) and lexical-semantic (altered N400) processing. Neuroimaging studies consistently identified structural and functional anomalies within a distributed perisylvian network, including reduced gray matter in left temporal regions, atypical right-hemispheric lateralization, reduced activation in cortical-subcortical circuits, and altered white matter connectivity. These neural markers demonstrated significant predictive value for later language outcomes. CONCLUSION: Late talking is a clear neurodevelopmental disorder with a cascading pattern of neural dysfunction. The identified neural signatures are not just findings but have strong clinical potential as biomarkers for early identification and prognosis. This evidence advocates for a shift to a precision medicine approach, opening the door to novel, neurobiologically-informed interventions like neuromodulation that target the underlying brain circuits.
20 June 2026
Read appraisal →European radiology experimental
Digital twin technologies in prostate cancer as a frontier for precision medicine
Prostate cancer (PCa) is the most frequently diagnosed malignancy among men and presents major clinical and socioeconomic challenges worldwide. Despite advances in early detection, imaging, and therapy, managing PCa remains complex due to disease heterogeneity, risks of overdiagnosis, and overtreatment. Digital twin (DT) technologies might represent an emerging conceptual framework aimed at supporting dynamic, patient-specific virtual modeling for personalized clinical decision-making. By integrating multimodal clinical, imaging, molecular, and physiological data, DTs can simulate disease progression, predict treatment responses, and support proactive, adaptive care. This perspective explores the conceptual framework for DT ecosystems in PCa, highlighting potential clinical impacts, infrastructural requirements, and barriers to implementation. Harnessing DTs could impact PCa management into a truly predictive, personalized, and participatory approach, improving outcomes and optimizing healthcare resource utilization globally. RELEVANCE STATEMENT: DT technologies may enable personalized, predictive PCa management by integrating multimodal patient data to guide diagnosis, treatment selection, and monitoring, with the potential to improve outcomes, reduce overtreatment, and optimize healthcare resource utilization KEY POINTS: DTs create virtual patient models to personalize PCa care. They integrate imaging, clinical, and molecular data into one system. This approach may reduce overdiagnosis and unnecessary treatments.
20 June 2026
Read appraisal →The journal of headache and pain
The emerging role of the meningeal lymphatic and glymphatic systems in migraine pathophysiology: a systematic review
BACKGROUND/OBJECTIVE: Migraine is a common, debilitating neurological disorder of unclear pathophysiology. Recent evidence has implicated impaired meningeal lymphatic function and its interaction with the glymphatic system in the development of neuroinflammation and pain sensitization. This systematic review aimed to summarize existing evidence on the role of the meningeal lymphatic and glymphatic systems in migraine pathophysiology. METHODS: Following the PRISMA 2020 recommendations, we searched PubMed, Web of Science, and Scopus from inception to December 15, 2025. Inclusion criteria comprised human or animal studies examining meningeal lymphatic or glymphatic function in migraine or validated migraine animal models. SYRCLE's risk of bias tool for animal studies and the Joanna Briggs Institute (JBI) critical appraisal tools were used for quality assessment. RESULTS: The search yielded a total number of 457 records, out of which 10 articles fulfilled the eligibility criteria. These comprised six imaging studies conducted on human populations and four preclinical studies performed on animal models. Studies using dynamic contrast-enhanced (DCE)-MRI have shown changes in lymphatic enhancement characteristics in both episodic and chronic migraines. Diffusion tensor imaging along perivascular spaces (DTI-ALPS) yielded heterogeneous findings, with abnormalities more consistently observed in chronic and high-frequency migraine. Preclinical models demonstrated that cortical spreading depression, nitroglycerin exposure, and familial hemiplegic migraine mutations impaired glymphatic influx and reduced cerebrospinal fluid efflux through meningeal lymphatic vessels. DISCUSSION: Initial research has shown that altered lymphatic and glymphatic systems may be related to migraine. Nonetheless, there is no existing literature on whether these conditions can be considered causative agents for migraines. There is a need for longitudinal imaging studies in a larger population. REGISTRATION: PROSPERO ID: CRD420251266296.
20 June 2026
Read appraisal →Archives of orthopaedic and trauma surgery
Association between timing of surgery and postoperative outcomes in older adults with distal femur fractures: a systematic review and meta-analysis
INTRODUCTION: The association between surgical timing and clinical outcomes in older adults with distal femur fractures remains unclear. We conducted a systematic review and meta-analysis to evaluate whether earlier surgery is associated with improved outcomes in this population. MATERIALS AND METHODS: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two independent reviewers searched MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform from inception to October 8, 2024. The primary outcome was 30-day mortality, and secondary outcomes included longer-term mortality and postoperative complications. Risk of bias was assessed using the Quality in Prognosis Studies tool, and the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation approach. RESULTS: Of 6,101 records screened, 16 retrospective cohort studies comprising 31,213 patients met the inclusion criteria. Early surgery was not associated with reduced 30-day mortality (adjusted odds ratio [OR]: 0.77, 95% confidence interval [CI]: 0.56-1.05; low-certainty evidence) or with longer-term mortality at 90 days (crude OR: 0.91, 95% CI: 0.51-1.60), 180 days (crude OR: 0.46, 95% CI: 0.12-1.77), or 1 year (crude OR: 0.61, 95% CI: 0.34-1.09). Similar findings were observed for postoperative complications: cardiac complications (OR: 0.71, 95% CI: 0.48-1.07), pulmonary complications (OR: 1.07, 95% CI: 0.77-1.47), and pulmonary embolism (OR: 1.14, 95% CI: 0.62-2.16). Heterogeneity across outcomes was low to moderate, and definitions of early surgery varied among studies. Overall, this meta-analysis did not demonstrate a clinically meaningful association between surgical timing and short-term clinical outcomes in older adults with distal femur fractures. Sensitivity analyses consistently suggested lower mortality with earlier surgery, likely reflecting stricter definitions of early surgery and improved control of time-related bias. CONCLUSIONS: This meta-analysis did not demonstrate a clinically meaningful association between surgical timing and short-term clinical outcomes in older adults with distal femur fractures. Given the heterogeneity in definitions and study designs, high-quality prospective studies with standardized timing are warranted.
20 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 →Journal of medical Internet research
Extracting and Classifying Drug Discontinuations From Estonian Electronic Health Records: Development and Validation Study
BACKGROUND: Drug adherence is crucial for chronic disease management, yet treatment discontinuation remains common due to factors such as side effects, inefficacy, or cost. These reasons are often recorded only in free-text clinical notes, making large-scale analysis difficult. While large language models (LLMs) can interpret such unstructured data more effectively than traditional natural language processing methods, few studies have systematically categorized reasons for discontinuation or identified whether the decision was initiated by the patient or the clinician, especially in low-resource languages such as Estonian. OBJECTIVE: This study aimed to assess the ability of LLMs to extract and classify reasons for drug discontinuation and identify who initiated it using Estonian electronic health records and characterize the observed discontinuation patterns and initiators for statins and antidiabetic medications. METHODS: We combined prescription data with free-text anamneses from a 10% sample of the Estonian population (2012-2019). LLMs (Llama 3.1-70B and GPT-4o) were applied to extract discontinuation phrases and reasons, classify them into a clinician-developed taxonomy, and identify who discontinued the treatment. Performance was evaluated on 100 randomly chosen cases per drug group. RESULTS: Extraction yielded 625 antidiabetic drug and 233 statin discontinuation cases. Validation confirmed a precision of 0.93 to 0.98 for extracting phrases and 0.95 to 0.96 for extracting reasons. Classification of discontinuation reasons achieved weighted F1-scores of 0.81 to 0.84, whereas classification of who initiated discontinuation achieved weighted F1-scores of 0.64 to 0.78. Adverse reactions were the most frequent reason overall, accounting for 70% (163/233) of statin discontinuations and 44.8% (280/625) of antidiabetic drug discontinuations. Regarding antidiabetic drugs, treatment inefficacy and contraindications were more common. Patients more often stopped due to adverse reactions or nonmedical reasons, whereas physicians more often initiated discontinuation for contraindications. CONCLUSIONS: LLMs can accurately extract and classify medication discontinuation reasons and show variable performance in identifying discontinuation initiators in Estonian clinical narratives. Both local and proprietary models showed promising results, enabling scalable analyses that complement structured health records. This demonstrates the potential of LLMs to unlock information from clinical notes, turning this underused electronic health record component into a valuable resource for monitoring treatment patterns and detecting adverse event signals.
19 June 2026
Read appraisal →Current psychiatry reports
The Digital Mirror: Clinical Potentials and Relational Risks of Generative AI in Mental Health Interventions
PURPOSE OF REVIEW: This review explores the rapidly evolving integration of Generative Artificial Intelligence (GenAI) in mental health care. It aims to evaluate current applications in assessment, treatment planning, and psychotherapeutic interventions, while critically examining the clinical risks, ethical dilemmas, and the future potential of GenAI as an adjunctive tool rather than a replacement for human-delivered therapy. RECENT FINDINGS: Recent studies indicate that AI models can effectively assist in diagnostic reasoning, biomarker identification via EEG, and the prediction of symptom trajectories from session transcripts. Randomized controlled trials (RCTs) suggest that GenAI chatbots significantly reduce anxiety and depressive symptoms in the short term, particularly in settings with limited access to clinicians. However, human-led therapy remains superior in fostering deep emotional engagement and clinical impact. Significant risks identified include the potential for GenAI to foster dependency, reinforce maladaptive schemas or delusional ideation through "sycophantic" mirroring, and raise complex ethical-legal challenges regarding the reporting of criminal disclosures. AI represents a transformative adjunctive layer in mental health, offering scalable support for assessment, training, and between-session monitoring. While technological advances in personalization, multimodality, and immersive virtual reality enhance its clinical utility, GenAI lacks the authentic relational depth and"calibrated mismatches" essential for autonomy and transformative change. Future integration must prioritize a human-centered, blended approach, where GenAI is strictly supervised by clinicians within a robust ethical and regulatory framework to preserve the essential heart of the therapeutic connection. Research priorities, interim clinical safeguards, and recommendations for navigating the gap between current evidence and real-world adoption need to be defined and implemented.
19 June 2026
Read appraisal →Current psychiatry reports
Digital Tools to Support Mental Health in Later Life: Scoping Review of Systematic Reviews
PURPOSE OF REVIEW: This scoping review synthesises existing evidence from systematic reviews on the effectiveness and implementation of digital mental health interventions among community-dwelling older adults. RECENT FINDINGS: Twenty-one systematic reviews were included. Results showed that a range of digital tools demonstrate potential to improve common mental health and psychosocial symptoms among older adults, with most evidence concentrating on digital tools to improve depressive symptoms. However, reviews' findings were frequently mixed and accompanied with cautions that primary evidence under-reported key elements such as theoretical underpinnings, intervention design process, participant demographics, intervention acceptability and usability, participant retention, adverse events, and long-term outcomes. More rigorous research and reporting are needed to understand the mechanisms underpinning effective digital mental health interventions for older adults and how they might mitigate the age-related digital divide in mental health services.
19 June 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
Diabetes technology: an update.
Diabetes is one of the most prevalent chronic diseases worldwide, with rates that continue to increase. More than 30 years ago, the Diabetes Control and Complications Trial demonstrated that intensive glucose management decreased long-term vascular complications. Unfortunately, many people with diabetes still struggle to meet glycemic goals. In this mini-review, we highlight advances in diabetes technology that are associated with improvements in glycemic management. Continuous glucose monitoring (CGM) and automated insulin delivery systems are associated with significant improvements in glycated hemoglobin (HbA1c), time in range (70-180 mg/dL), and decreases in severe hypoglycemia and diabetic ketoacidosis in people with both type 1 and type 2 diabetes. Recent data show that these technologies improve outcomes early in the course of type 1 diabetes. CGM is also being explored as a tool to monitor progression through early-stage type 1 diabetes (2 antibodies positive) to identify individuals who may benefit from disease-modifying therapies as well as to prevent the onset of diabetic ketoacidosis at onset of stage 3 (insulin-requiring) type 1 diabetes. Adjunctive pharmacologic therapies and artificial intelligence may further expand and improve therapies, offering potential synergistic benefits. However, there continue to be significant disparities in access to diabetes technologies and access to insulin worldwide. This mini-review summarizes recently published data, highlights emerging applications, and underscores the need to pair technological innovation with strategies that promote equitable access and support for diabetes care to improve outcomes for all people with diabetes.
19 June 2026
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