Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 14 appraisals
Journal of food science
Artificial Intelligence in Food-Nutrition-Health Research: From Multimodal Data Integration to Precision Intervention
Artificial intelligence (AI) is transforming food-nutrition-health research by enabling pattern recognition in complex, high-dimensional datasets that traditional hypothesis-driven approaches cannot address. This review systematically synthesizes research progress of AI across the food-nutrition-health continuum from 2020 to 2025. By examining 181 systematic reviews through PRISMA-guided selection, we provide a comprehensive overview and prospects across four dimensions: technical foundation, application scenarios, existing challenges, and future prospects. We propose a tripartite framework comprising (1) a data layer enabling multisource fusion of food composition, health monitoring, and individual characteristic data; (2) a technological layer of nondestructive testing (spectroscopy, nuclear magnetic resonance [NMR], imaging); and (3) an algorithmic layer progressing from machine learning to deep learning architecture. Key applications include food component analysis and safety detection; nutrition-disease association modeling; pathogen identification; and personalized dietary intervention systems. Despite rapid progress, critical challenges persist, insufficient model generalization across populations, algorithmic opacity limiting clinical trust, data privacy vulnerabilities, and lack of standardized multi-omics integration protocols. Future directions emphasize multimodal fusion models, explainable artificial intelligence (XAI), federated learning for privacy-preserving collaboration, gene-guided precision nutrition, and development of intelligent wearable devices and functional food. This review provides a roadmap for transitioning from population-averaged guidelines to dynamic, individualized health optimization through AI-enabled food system.
1 Aug 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 →ACS applied materials & interfaces
Advances and Challenges in Wearable Sensors for Health Monitoring
Analytical tools may revolutionize healthcare by enabling accessible, rapid, and decentralized testing. Wearable (bio)sensors, in particular, provide frequent or continuous patient monitoring through non- to minimally invasive measurements. This approach yields unprecedented amounts of health-related information, leading to more informed clinical decision-making and closer patient follow-up. In this mega-review article, we bring together leading researchers in the field to discuss the state of the art in wearable devices for health monitoring. We begin by providing a broad overview of the field through citation network analysis. We then review the application of chemical (bio)sensors in biofluids (e.g., sweat, saliva, tears, interstitial fluid, and cerebrospinal fluid), highlighting the challenges and advantages associated with each. Subsequently, we discuss the construction of wearable devices and their main formats (e.g., smart contact lenses, textiles, mouthguards, watches/wristbands, and implantable systems). Physical sensors are addressed in a dedicated section focusing on the assessment of heart rate, blood pressure, and body temperature. The role of soft electronics in wearable devices is also examined, as these technologies are essential for enhancing user comfort and sensor reliability, which demands advances in materials science. Furthermore, we present strategies for signal acquisition and transmission, as well as approaches for on-body energy harvesting and device self-powering. The use of artificial intelligence and machine learning is then discussed as a means of enhancing analytical performance and managing the large volumes of data generated by wearable devices. Finally, business, regulatory, and ethical considerations are examined. We expect that this review will provide an overview of sensing and biosensing technologies for health-related applications, identify promising research directions, and inspire future developments.
30 July 2026
Read appraisal →Journal of breast imaging
Contrast-Enhanced Mammography for Breast Cancer Screening: A Systematic Review and Meta-Analysis
OBJECTIVE: To evaluate pathway-specific cancer detection and summarise screening outcomes (recall/biopsy/PPV3) for contrast-enhanced mammography when reported, and to compare them with those for low-energy mammography and breast MRI. METHODS: This systematic review and meta-analysis followed PRISMA guidelines. PubMed (MEDLINE), Embase, Scopus, and Web of Science were searched from January 1, 2011, through August 31, 2025, for studies evaluating contrast-enhanced mammography in screening or surveillance populations (including supplemental/add-on screening and surveillance of patients with a personal history of breast cancer). Diagnostic, recall, and problem-solving studies were excluded. Screening pathways were categorized as: (1) direct comparison with low-energy mammography, (2) direct comparison with MRI, and (3) add-on CEM to mammography-based screening. Random-effects meta-analyses were performed when designs were comparable. Comparisons between CEM and MRI were summarized qualitatively because of heterogeneity and limited cancer events across studies. Single-arm screening studies were summarized descriptively. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). RESULTS: Fourteen studies met eligibility criteria for qualitative synthesis; seven screening and surveillance studies contributed to the meta-analysis. Compared with low-energy mammography, CEM detected 9.3 additional cancers per 1,000 women screened (risk difference [RD], +9.3 per 1,000; 95% CI, 4.0-14.6; P < 0.01; I2 = 0%). Three studies directly compared CEM with MRI in screening settings and were summarized qualitatively because of heterogeneity and limited cancer events. CONCLUSION: In screening and surveillance populations, contrast-enhanced mammography increases cancer detection compared with low-energy mammography. Evidence from available comparative studies suggests that cancer detection with CEM may be broadly comparable to MRI in selected screening contexts; however, the limited number of studies and heterogeneous designs preclude definitive comparative conclusions. These pathway-specific estimates may inform clinical implementation and the design of prospective screening trials.
30 July 2026
Read appraisal →Journal of medical Internet research
The Next Generation of Wearables Won't Need the Cloud
Wearable health and fitness devices have typically relied on cloud computing to deliver insights. In this News and Perspectives article, JMIR Correspondent Michelle Falci reports on the advances facilitating on-device data processing and the potential of these next generation wearables.
25 July 2026
Read appraisal →NEJM catalyst innovations in care delivery
Thinking about the Impact of Artificial Intelligence on U.S. Health Care Costs and Spending Growth
This article critically examines the potential impacts of artificial intelligence (AI) on prices, total spending, and the rate of spending growth in the areas of prescription drug innovation and expanded patient access to care - including developments in remote patient monitoring, chronic care management, direct-to-consumer health care, clinical decision support, and nonclinical administrative labor. Based on observed industry practices, economics, and public policy, the article presents a thought exercise on the most likely effects for patients, providers, payers, and the broader health care system. The authors argue that under the still-dominant fee-for-service payment model, as well as the highly consolidated hospital and insurance markets, AI is more likely to increase total costs and spending growth in the short to medium term rather than slow them, even as it delivers substantial access and clinical quality improvements for patients. The cost-bending potential of AI varies distinctly by fee-for-service versus value-based payment. Regulators need to introduce policy and reimbursement levers for AI to slow cost growth. The authors conclude that, without significant changes in the health care system's financial incentives and market structures, AI will not slow cost growth.
16 July 2026
Read appraisal →Journal of medical Internet research
User Acceptability and Adoption of AI-Generated Lifestyle Intervention Recommendations: Scoping Review and Theoretical Integration
BACKGROUND: Artificial intelligence (AI)-generated lifestyle recommendations are increasingly used to support health behavior change. However, AI advice does not necessarily mean that users will accept or adopt those recommendations. Although prior reviews have examined AI-enabled lifestyle interventions and health behavior technologies, fewer have focused on whether users accept and adopt AI-generated recommendations. OBJECTIVE: This scoping review aimed to map user acceptability and adoption of AI-generated lifestyle recommendations in user-facing systems used by end users or caregivers. Objectives were to characterize systems and evaluation contexts, clarify how recommendation-level outcomes were conceptualized and measured, synthesize shaping factors, and develop an evidence-informed framework to guide future research, evaluation, and design. METHODS: Following JBI (Joanna Briggs Institute) guidance and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews), we searched Ovid MEDLINE, Ovid Embase, APA PsycInfo via ProQuest, Web of Science Core Collection, Scopus, ACM Digital Library, and IEEE Xplore from database inception to May 5, 2026. The initial search was conducted on November 21, 2025, and an updated search was conducted on May 5, 2026. Eligible studies reported empirical end-user or caregiver data, evaluated AI-generated lifestyle recommendation content delivered without manual review or editing, and reported an acceptability or adoption outcome linked to recommendations. English empirical papers and conference papers were included. Data were charted on study, system, outcome, measurement, factor, and theoretical characteristics. Quality was assessed with the Mixed Methods Appraisal Tool. Findings were synthesized descriptively and through evidence mapping. RESULTS: Searches yielded 12,997 records; 8570 unique records were screened, and 21 studies were included. Most were published in 2025 or 2026 (17/21, 81%). Large language model-centered systems were the most common format (12/21, 57.1%). Outcomes were concentrated in acceptability-related perceptions, such as satisfaction or enjoyment, perceived quality or fit, and persuasiveness, whereas adoption-related outcomes were assessed less often and mainly reflected intention, in-study uptake, or short-term enactment. Factors clustered across system capabilities, content properties, individual states and capacities, and contextual constraints. Findings informed an integrative perception-intention-enactment framework positioning acceptability and adoption as a system-content-user-context process. CONCLUSIONS: This review extends prior AI and digital health reviews by shifting attention toward how users perceive, intend to follow, and enact AI-generated lifestyle recommendations. Acceptability and adoption appear to depend on systems eliciting and adapting to context, content being actionable and credible, users having the capacity to interpret, trust, and engage with recommendations while retaining control, and resources, routines, and social contexts allowing enactment. The framework can guide theory-driven evaluation, outcome selection, and system design by identifying where recommendation processes may succeed or fail, but should be interpreted as preliminary and evidence-informed rather than causal. By integrating implementation, behavioral, and human-AI perspectives, this review provides a foundation for moving AI-generated lifestyle recommendations from technically plausible outputs toward user-centered, context-sensitive, and behaviorally actionable support.
16 July 2026
Read appraisal →Analytical chemistry
Large Language Model-Generated Dietary Metabolite Biomarker Database Drives Deep Annotation of the Human Diet Metabolome
The annotation of dietary biomarkers is crucial for nutritional epidemiology. While untargeted liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful analytical approach, the annotation of dietary biomarkers is hampered by the low specificity of existing public databases, which limits annotation coverage and accuracy. To address this limitation, we developed a novel database construction strategy and a dual-annotation workflow. We first employed an automated, large language model (LLM)-based text-mining pipeline to parse 7339 scientific articles and supplementary materials, creating the Dietary Metabolite Biomarker Database (DMBDB), which contains 4983 nonredundant biomarkers. The LLMs workflow demonstrated high performance, achieving an F1 score of 0.9269 for biomarker name recognition. Subsequently, two complementary annotation strategies were designed: (i) a specialized LC-MS database derived from DMBDB, incorporating predicted retention times and experimental MS/MS spectra for high-confidence matching, and (ii) a structure-guided molecular networking strategy (SGMNS) that uses DMBDB as background knowledge to annotate dietary biomarkers and their metabolites lacking spectral evidence. The framework was validated using untargeted LC-HRMS analysis of urine samples. LC-MS database directly annotated 566 metabolites, and the integration with SGMNS expanded the total number of annotations to 2078. The LLM-driven database construction combined with the dual-strategy annotation framework provides a powerful paradigm for achieving high-coverage and high-accuracy dietary metabolomics.
15 July 2026
Read appraisal →Current diabetes reports
Digital Management of Early-Onset Type 2 Diabetes: Empowerment, Challenges, and Future Outlook
PURPOSE OF REVIEW: Early-onset type 2 diabetes (EOT2D), defined as a diabetes diagnosis before 40 years of age, is rising globally and associated with an aggressive disease course and early complications. This review examines the role of digital health technologies (DHT) in addressing the unique clinical and life-course challenges of EOT2D. RECENT FINDINGS: DHT, including continuous glucose monitoring, mobile health applications, digital therapeutics, telemedicine, remote patient monitoring, wearable devices, and artificial intelligence-based analytics, have demonstrated modest improvements in glycemic control, weight management, and patient engagement in people with type 2 diabetes. However, evidence in adults with EOT2D remains limited. Compared with usual-onset T2D, people with EOT2D may derive particular benefits due to higher digital literacy, greater lifestyle variability, and longer anticipated disease duration. Although DHT shows promise for improving empowerment and care integration in EOT2D, important gaps persist, including a lack of EOT2D-specific trials, digital divide-related inequities, interoperability challenges, and reimbursement barriers. Future research should prioritize tailored interventions and hybrid care models to optimize long-term outcomes in this high-risk population.
11 July 2026
Read appraisal →Journal of epidemiology and community health
Analysis of the potential association between physical activity and skin autofluorescence: a systematic review.
INTRODUCTION: Accumulation of advanced glycation end products, measured by skin autofluorescence (SAF), has been shown to be associated with several chronic non-communicable diseases, particularly cardiovascular diseases (CVDs). The promotion of physical activity (PA) as a strategy for the prevention of CVD by modifying healthy habits has been widely studied. AIM: To assess the evidence for the association between PA and SAF in the general adult population. METHODS: A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and the Synthesis Without Meta-analysis framework. A search was performed in MEDLINE (via PubMed), Web of Science, Scopus, Cochrane Library and SportDiscuss (via EBSCOhost), from inception to September 2024. Study quality was assessed using the National Heart, Lung and Blood Institute tools and the certainty of evidence was evaluated with Grading of Recommendations, Assessment Development and Evaluation. Vote counting based on the direction of effect was used as the standardised synthesis metric. RESULTS: In the systematic review, 17 studies were included. The qualitative synthesis showed a predominant consistency in favour of a beneficial association. Specifically, 58.8% of the studies reported a statistically significant inverse association, indicating that higher levels of PA or exercise frequency are correlated with lower SAF levels. The remaining studies (41.2%) reported non-significant results, though several showed favourable trends. No studies reported a positive association between PA and SAF. The quality of the studies was generally fair, and the certainty of evidence low. CONCLUSIONS: PA is inversely associated with SAF. Therefore, while causality cannot be proven, it is hypothesised that PA may reduce SAF and thus have a positive impact on health.
11 July 2026
Read appraisal →BMC public health
Motivating factors and possible barriers to participation in digital prevention courses of two statutory health insurance funds in Germany: a qualitative interview study
BACKGROUND: Digital prevention courses offered by statutory health insurance funds in Germany represent a low-threshold opportunity to support health-promoting behaviors through pre-recorded, video-based programs focusing on exercise, nutrition, or stress management. However, little is known about insured individuals' motivations and barriers to participation. This study aimed to identify relevant motivating and obstructive factors for participation in digital prevention courses and to explore differences compared to on-site formats. METHODS: We conducted semi-structured telephone interviews with 20 insured members of two large regional statutory health insurance funds in Germany (AOK North-East and AOK North-West) who had recently completed digital prevention courses. Participants were recruited via e-mail invitations sent by the health insurance funds following course participation. Five additional interviews were conducted with participants of on-site courses for contextual comparison. Interviews were conducted between February and December 2023, transcribed verbatim, and analyzed using qualitative content analysis with a deductive-inductive coding approach based on the study objectives and interview guide. RESULTS: The sample included 20 participants (mean age 41 years, range 25-72; 19 women and 1 man) who had completed digital prevention courses. Participants emphasized temporal flexibility, home-based access, and low preparation effort as key facilitators of participation. Additional motivators included health-related goals, free access, practical content, reminders, and the possibility to repeat sessions. Reported barriers included a lack of discipline, reduced social interaction and commitment compared to on-site formats, and work- and family-related demands. The availability of recorded sessions was perceived as a major advantage that facilitated course completion. Participants also valued competent instructors, but called for clearer differentiation of difficulty levels, broader course options, continued access to materials, and follow-up opportunities. The contextual comparison showed that on-site course participants particularly valued the sense of community and the possibility of individual corrections by instructors. CONCLUSIONS: Digital prevention courses are well accepted and can promote participation due to their flexibility and accessibility. However, challenges such as reduced social interaction and the need for self-discipline limit their full potential. To enhance their effectiveness and reach, digital prevention programs should integrate more tailored content, structured follow-up offers, and interactive or hybrid elements that foster engagement and social connectedness. TRIAL REGISTRATION: German Clinical Trial Register DRKS00029761; Registration date 27.07.2022; https://drks.de/search/de/trial/DRKS00029761 .
9 July 2026
Read appraisal →Nutrition journal
Mindfulness and sustainable diets: a meta-analysis and CO2 emission savings scenarios
BACKGROUND: Dietary habits are a major driver of both human health outcomes and global greenhouse gas emissions, with excessive meat consumption being a key contributor. Mindfulness, understood as purposeful and non-judgmental awareness of the present moment, has been suggested as a psychological factor that can foster more sustainable food choices. However, the empirical evidence on the link between mindfulness and sustainable diets remains fragmented. MAIN BODY: This study presents the first systematic review and meta-analysis of the association between mindfulness and sustainable dietary behaviors. Twelve articles with 13 studies, published between 2018 and 2025, were included, covering both observational and interventional designs with sample sizes ranging from 16 to 560 participants. A random-effects model revealed a small but significant positive association between mindfulness and sustainable diets (d = 0.28, 95% CI [0.13, 0.43]). Subgroup analyses indicated that observational studies tended to show stronger effects than interventions, and that cultural context played an important moderating role, with larger effects reported in Asian samples. Facet-specific analyses suggested that certain mindfulness dimensions - particularly observing, describing, and non-reactivity - were more strongly associated with sustainable food choices, while others showed weaker or non-significant links. A focused analysis on meat consumption revealed that mindfulness was positively associated with reduced meat intake or vegetarian dietary intentions (d = 0.25). Scenario modelling using national Life Cycle Assessment data from Germany, the UK, and the U.S. demonstrated that the potential reductions in meat intake could translate into substantial CO2 emission savings. A full adoption of the EAT-Lancet diet was projected to result in cumulative savings of up to 11% of Germany's total emissions budget by 2040, whereas incorporating the population's willingness to reduce meat intake yielded more conservative cumulative savings of 2.8% over the same period. CONCLUSION: This meta-analysis provides systematic evidence that mindfulness is positively associated with more sustainable dietary behavior and reduced meat consumption. While effects are modest and heterogeneous, the findings suggest that mindfulness could represent a promising lever for promoting dietary change aligned with planetary health goals. Future research should further investigate cultural differences, the role of specific mindfulness facets, and the feasibility of scaling mindfulness-based interventions to support both individual well-being and climate targets.
8 July 2026
Read appraisal →JMIR research protocols
Cross-Cultural Perspectives on Metabolic Syndrome Management and Attitudes Toward Using Digital Health Tools in Two Distinct Populations: Protocol for a Qualitative Descriptive Study
BACKGROUND: Metabolic syndrome (MetS) is defined by the presence of at least 3 out of 5 clinical risk factors, including abdominal obesity, elevated blood pressure, high fasting glucose, elevated triglycerides, and low high-density lipoprotein cholesterol. Individuals with MetS face significantly increased risks of cardiovascular disease, type 2 diabetes, and all-cause mortality. OBJECTIVE: This study aims to explore the multilevel factors influencing individuals' engagement in self-management of MetS. Specifically, it seeks to identify key barriers and facilitators to effective self-management and to assess participants' attitudes toward the use of digital health tools in supporting disease management. METHODS: This qualitative descriptive study will be conducted at 2 international sites: the University of Memphis (United States) and King Saud bin Abdulaziz University for Health Sciences (Saudi Arabia), in collaboration with local primary care clinics. Participants will be recruited using purposive sampling with a maximum variation strategy, aiming for 20 to 30 individuals per site who meet eligibility criteria. Data will be collected through semi-structured, 60-minute one-on-one interviews. An abductive thematic analysis approach (integrating both inductive and deductive reasoning) will be used to analyze the data with NVivo 15 software. RESULTS: This study will begin participant enrollment in August 2026. Thematic analysis will be used to examine barriers and facilitators to MetS self-management and participants' views on digital health tools across 2 international sites. CONCLUSIONS: Findings will offer culturally grounded insights into how social, dietary, and familial factors influence chronic disease management. This knowledge can inform the design of equitable, context-sensitive digital health interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/91903.
27 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 →