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Latest Submissions Open for Peer Review

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JMIR Submissions under Open Peer Review

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Titles/Abstracts of Articles Currently Open for Review:

  • Background: Clinicians exhibit considerable variability in diagnosing and managing thyroid nodules. While large language models (LLMs) show promise in processing medical data, their effectiveness and reliability in standardizing the interpretation of thyroid nodule ultrasound text report have yet to be thoroughly validated. Objective: To assess two LLMs, DeepSeek-R1 and ChatGPT-4o, in interpreting thyroid nodule ultrasound text report, emphasizing the accuracy in benign-malignant differentiation, the agreement of Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) classification and management recommendation, and the stability of each task. Methods: We analyzed 1,063 ultrasound text reports from three medical centers, with 306 nodules confirmed by histopathology. Each nodule's report was processed through two LLMs using standardized prompts, repeated five times, with the final result determined by mode voting. Results: DeepSeek-R1 excelled over ChatGPT-4o in differentiating benign from malignant nodules, with superior sensitivity (0.879 vs. 0.692), accuracy (0.729 vs. 0.644), and Area Under the Curve (AUC) (0.694 vs. 0.632). However, senior radiologists achieved notably better results with higher accuracy (0.804), and AUC (0.865) compared two LLMs. In C-TIRADS classification, DeepSeek-R1 also outperformed ChatGPT-4o (κ=0.770 vs. κ=0.688, Δκ=0.083 [95% CI: 0.048, 0.122]). Both models showed substantial agreement with clinicians on management recommendation (κ=0.606 vs. κ=0.608, Δκ=-0.002 [95% CI: -0.044, 0.041]). In terms of stability, LLMs exhibited almost perfect agreement in C-TIRADS classification (α=0.864 vs. α=0.866, Δα=-0.003 [95% CI: -0.023, 0.017]) and management recommendation (κ=0.853 vs. κ=0.849, Δκ=0.004 [95% CI: -0.026, 0.033]). However, in benign-malignant discrimination, DeepSeek-R1 demonstrated significantly greater stability than ChatGPT-4o (κ=0.849 vs. κ=0.550, Δκ=0.260 [95% CI: 0.191, 0.321]). Conclusions: Our study highlights the potential of LLMs for interpreting thyroid nodule ultrasound text reports. DeepSeek-R1 outperformed in benign-malignant differentiation accuracy and classification consistency, whereas ChatGPT-4o and DeepSeek-R1 performed similarly in management recommendation.

  • Systematic literature reviews (SLRs) are critical for evidence synthesis and play a central role in supporting research, policy development, and evidence‑based decision‑making across healthcare and related disciplines. However, traditional SLRs are resource-intensive and time-consuming, often requiring months of manual effort to screen thousands of records, extract data, and maintain methodological rigor. As global scientific output continues to grow exponentially, these operational challenges have intensified, contributing to longer completion timelines, greater workforce burden, and a heightened risk that reviews become outdated shortly after publication. In response, there is growing interest in artificial intelligence (AI) approaches to enhance the efficiency and scalability of SLRs. AI tools now offer support for a broad range of review tasks, including assisting with search strategy development, identifying relevant concepts, prioritizing records for screening, and supporting data extraction and risk‑of‑bias assessments. AI can significantly accelerate labor-intensive stages, reduce human error during repetitive tasks, and enable the synthesis of evidence bases that might otherwise be impractical to review manually. However, these efficiencies must be balanced against the risks associated with AI, including bias, lack of transparency, variable outputs, and hallucinations (outputs that appear plausible but are factually incorrect). A human-in-the-loop approach is therefore essential to validate AI outputs and maintain scientific integrity. Human expertise remains critical for defining research questions, validating search strategies, confirming study eligibility, interpreting nuanced data, and making final judgments on quality and risk of bias. Clear methodological guidance is required to support teams in integrating AI tools responsibly, transparently, and reproducibly into SLR workflows. Methodological considerations include selecting appropriate tools, defining oversight strategies, and applying performance metrics such as precision and recall. This paper aims to provide methodological guidance on the effective integration of AI into each stage of the SLR process, drawing on both published literature and the authors’ real-world experience. We outline key considerations for selecting and implementing AI tools while maintaining human oversight. We also discuss how to maintain transparency, auditability, and alignment with established standards, including PRISMA‑P, PRISMA‑trAIce, and emerging guidance from regulators and health technology assessment bodies. We also present future directions for responsible AI use in SLRs. AI should complement, not replace, human judgment. When implemented within a human-in-the-loop framework, AI has the potential to accelerate evidence synthesis, enabling faster, scalable, and rigorous reviews while preserving transparency and reproducibility.

  • Background: Pregnant women with ICD-10 affective or stress-related disorders face elevated risk for perinatal depression and anxiety, yet evidence on digital non-pharmacologic interventions for this population remains limited. Objective: To evaluate the effectiveness of an 8-week digital mindfulness-based intervention (eMBI) compared with treatment as usual (TAU) among pregnant women with ICD-10 affective or stress-related disorders participating in a randomized clinical trial. Methods: This prespecified secondary analysis was conducted within a multicenter randomized controlled trial in Baden-Württemberg, Germany. Pregnant women aged ≥18 years with elevated depressive symptoms (Edinburgh Postnatal Depression Scale [EPDS] >9) and ICD-10–diagnosed affective or stress-related disorders were randomized 1:1 to eMBI or TAU. The intervention consisted of eight weekly app-based mindfulness sessions (45 minutes each) delivered during gestational weeks 29–36, with no direct therapist contact. Primary outcome was depressive symptom severity (EPDS) at 4–6 weeks postpartum. Secondary outcomes included EPDS at 6 months postpartum, generalized anxiety (STAI-S, STAI-T), and pregnancy-related anxiety (PRAQ-R). Analyses followed the intention-to-treat principle using mixed models for repeated measures and multiple imputation. Results: Of 5299 screened women, 147 met inclusion criteria for this subgroup analysis (intervention: n=73; control: n=74). Groups were comparable at baseline. The intervention group showed significantly greater reductions in EPDS scores at gestational week 34 (Δ=–2.21; P=.013), week 36 (Δ=–3.25; P=.013), and 4–6 weeks postpartum (Δ=–4.81; P=.007). Treatment effects remained robust under conservative missing-data assumptions. At 4–6 weeks postpartum, a higher proportion of participants in the intervention group achieved clinically meaningful improvement (42.5% vs 28.4%; adjusted odds ratio 1.56, 95% CI 1.19–2.05; P=.001). Anxiety outcomes followed a similar pattern, whereas pregnancy-related anxiety did not differ between groups. Conclusions: In this prespecified subgroup of pregnant women with ICD-10 affective or stress-related disorders, the use of a digital mindfulness intervention during pregnancy was associated with clinically meaningful reductions in depressive symptoms until 6 weeks postpartum. Even though effects at 6 months postpartum (T7) were smaller and statistically unstable across missing-data approaches, the digital mindfulness intervention effectively improved perinatal mental health in women with preexisting affective disorders, supporting its use as a safe, low-threshold alternative to pharmacological treatment during pregnancy and breastfeeding. Clinical Trial: DRKS00025697

  • The Digital Evolution of the Medical Black Bag

    Date Submitted: Feb 20, 2026
    Open Peer Review Period: Feb 21, 2026 - Apr 18, 2026

    Background: The medical black bag is synonymous with physicians, especially general practitioners who are expected to be ready to provide care across settings. The content of the devices they use will likely expand due to the proliferation of digital tools. As portable diagnostics diversify, guidance is increasingly needed on which tools clinicians should choose and what this shift may mean for the physical examination and point-of-care assessment. Objective: The aim of this study is to map the current, the possible, and the future content of the medical black bag using anticipatory methods, and to provide a general, practice-oriented outline of how portable diagnostic technologies may evolve in primary care. Methods: National equipment lists and the World Health Organization’s MeDevIS database were compiled and filtered to define a contemporary reference set of reusable portable diagnostic instruments relevant to generalist practice. A one-year trend analysis using major professional and medical technology news sources were conducted to identify possible additions, screening for devices with diagnostic relevance, portability, digital capability, market presence, and evidence visibility. To extend the outlook to 2035, we performed a horizon scanning exercise using the same review period. These devices got grouped into thematic categories. Results: National equipment recommendations and World Health Organization lists yielded a stable core set of diagnostic tools used in routine primary care practice. Trend analysis and horizon scanning expanded this set by identifying possible and future additions of portable medical devices that can be used at the point of care. Overall, the identified technologies were increasingly digital, diverse, connected, and in some cases, AI supported, reflecting a trajectory toward more integrated and data-enabled diagnostics. Conclusions: The medical black bag is likely to evolve from a stable set of familiar instruments toward a broader toolbox of portable and connected diagnostic devices. While these tools may expand the scope of bedside assessment and enable more reproducible and shareable clinical signs, their value depends on appropriate validation, usability, workflow integration, training, and supportive financial and organizational conditions. Regular evidence-informed updates of equipment recommendations, alongside practical implementation support, may help primary care systems adopt useful innovations while preserving the human dimensions of clinical care.

  • Resistance Training Prescriptions Generated by ChatGPT and Google Gemini: Cross-Sectional Comparative Study

    Date Submitted: Feb 20, 2026
    Open Peer Review Period: Feb 21, 2026 - Apr 18, 2026

    Background: Large language models (LLMs) have expanded the use of generative AI in exercise prescription, but the quality and safety of these recommendations in real-world practice remain uncertain. Objective: To compare resistance training prescriptions generated by ChatGPT (GPT-5.1) and Gemini (Flash 2.5) as evaluated by licensed physical education professionals. Methods: We conducted an analytical, quantitative, cross-sectional survey with 25 licensed professionals affiliated with the CREF20 council. Two 12-week resistance training programs for a 35-year-old woman with overweight were generated using a standardized prompt—one by each model—and then blindly labeled as Prescription A and Prescription B. Participants rated each prescription on a 5-point Likert scale across five dimensions (quality, clarity, relevance, safety, and usefulness). Data were analyzed in R using Shapiro–Wilk tests for normality and nonparametric comparisons (Wilcoxon and Mann–Whitney U tests). Prespecified subgroup analyses examined differences by age, professional experience, AI usage, and online coaching practices. Results: Across the 25 evaluators, no statistically significant differences were observed between ChatGPT and Gemini for the five rated dimensions (all P>.05). Gemini showed a non-significant trend toward higher perceived safety (P=.064; r≈0.37). Subgroup analyses by age, professional experience, AI usage, and online coaching practices likewise showed no significant differences between model outputs (all P>.05). Conclusions: ChatGPT and Gemini generated resistance training prescriptions perceived as moderately good and largely equivalent by licensed professionals. These findings suggest that LLMs may be useful as auxiliary tools for drafting training programs, but they do not yet demonstrate sufficient technical refinement to replace professional expertise, particularly regarding individualization, load progression, and systematic risk management.

  • Background: Individuals with infertility often experience substantial psychosocial distress. eHealth technologies have emerged as tools for delivering patient-centered care by addressing patients’ psychosocial needs. However, no systematic review has evaluated the overall impact of eHealth interventions on patient-reported outcomes and experiences among infertility patients. Objective: This study aimed to (1) examine the patient-centered care components incorporated in existing eHealth interventions for infertility care and (2) synthesize the most recent evidence regarding the effects of eHealth interventions on patient-reported outcomes and patient-reported experiences among infertility patients. Methods: A systematic review and meta-analysis were conducted by searching seven electronic databases: MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, PsycInfo, and KoreaMed. The final search was performed in August 2025. Eligible studies were randomized controlled trials that assessed the impact of eHealth interventions on infertility patients, measured patient-reported outcomes and/or patient-reported experiences, and were published in English or Korean. Patient-centered care components were identified using a conceptual framework. Risk of bias was evaluated using the Cochrane risk-of-bias tool for randomized trials (RoB 2). Using a random-effects model, meta-analyses were conducted and reported as Hedges’ g with 95% confidence intervals (CIs). Subgroup analyses were performed to explore potential sources of heterogeneity. Assessments were conducted for publication bias, sensitivity analyses, and the certainty of evidence. Results: Twenty-five studies published between 2001 and 2025 were included. eHealth technologies included internet-based websites, mobile applications, real-time interactive platforms, self-monitoring devices, virtual reality devices, videos, and telephones. The most common patient-centered care components were emotional support and quality improvement to enhance access to care. eHealth interventions had statistically significant effects on infertility patients’ patient-reported outcomes at post-intervention (g = 0.428, 95% confidence interval [CI]: 0.179–0.676, k = 18, n = 2,202) and at follow-up (g = 0.824, 95% CI: 0.024–1.623, k = 5, n = 448), with low certainty of evidence. However, no statistically significant effect was observed for patient-reported experiences (g = 0.094, 95% CI: −0.097 to 0.284, k = 9, n = 890). Subgroup analyses indicated that post-intervention patient-reported outcomes differed by outcome type, gender, and intervention type. Conclusions: eHealth interventions may support patient-centered infertility care by improving patient-reported outcomes. To date, eHealth interventions have addressed patients’ psychological needs and reduced the need for frequent clinic visits. To advance patient-centered infertility care, future studies should develop gender-specific eHealth interventions tailored to men with infertility or couples. Clinical Trial: PROSPERO (CRD42021290277)

  • Surgeons’ Perceptions of Machine-learning‑based Prognostic Tools for Spine Surgery: A Qualitative Study

    Date Submitted: Feb 20, 2026
    Open Peer Review Period: Feb 21, 2026 - Apr 18, 2026

    Background: Machine-learning-enabled prognostic models are increasingly proposed to support surgical decision-making for degenerative lumbar disorders, yet their clinical adoption remains limited. Understanding how surgeons perceive these tools is critical for effective implementation, particularly given the high-stakes nature of spine surgery where decisions carry long-term functional consequences and medico-legal implications. Objective: This study aimed to explore how consultant-level spine surgeons perceive machine-learning-based prognostic tools, including their trustworthiness, clinical utility, usability, workflow integration, and implications for patient counselling and shared decision-making. Methods: A qualitative study using semi-structured one-to-one interviews was conducted with 11 consultant-level orthopaedic and neurosurgeons practising in Singapore (response rate: 73% of 15 invited). Participants had a mean of 10.9 years (range: 8-25 years) of spine surgery experience; 82% (n=9) were male. Interviews (range: 15-57 minutes) were transcribed verbatim and analysed using Braun and Clarke's six-step reflexive thematic analysis. Data collection continued until information power was achieved, with three additional interviews completed after thematic sufficiency was reached as participants had already consented. The study was designed and reported in accordance with COREQ criteria. Results: Three overarching themes were developed. Trust contingent on data integrity revealed that surgeons' confidence depended fundamentally on data quality, local representativeness, labelling credibility, and rigorous validation, with participants consistently emphasising population representativeness as a trust prerequisite. Surgeons operationalised trust through accessible performance metrics, with most identifying AUROC as their preferred credibility heuristic. Pragmatic orientation as important for implementation demonstrated that usability and seamless electronic health record integration were non-negotiable prerequisites, with surgeons explicitly stating that manual data entry would preclude adoption. Medico-legal concerns were prominent, with participants emphasising that decision responsibility remains with the clinician. Surgical decision-making as a delicate dance between art and science reflected how prognostic outputs were positioned as adjunctive inputs to be reconciled with experiential judgement and patient heterogeneity. Surgeons emphasised that identical prognostic information would be interpreted differently based on practice philosophy, and most highlighted the inherent divergence between technical success and patient-perceived success, underscoring prognostic tools' value for expectation management rather than deterministic prediction. Conclusions: This first qualitative study of spine surgeons' perceptions reveals that adoption of machine-learning-based prognostic tools is contingent on data integrity, pragmatic workflow integration, and alignment with professional judgement and not predictive performance alone. Surgeons expressed cautious openness, viewing these tools as valuable in complex cases for clarifying outcome expectations without displacing clinical responsibility. Meaningful implementation requires robust data governance, contextually grounded validation, seamless electronic integration, and explicit positioning of machine learning as supportive rather than substitutive of surgical judgement. These findings provide empirically grounded guidance for developing clinically acceptable and implementation-ready prognostic decision support systems.

  • Background: Expenditures for physiotherapy (PT) and extended outpatient physiotherapy (EAP) are increasing within Germany’s statutory accident insurance system (Berufsgenossenschaften, BGs), placing growing pressure on rehabilitation capacity and timely access to care. Digital health applications (DiGAs) are reimbursable nationwide and represent a novel component of routine rehabilitation pathways. However, their real-world system-level and economic effects in occupational rehabilitation remain insufficiently understood. Objective: This study aimed to evaluate how integration of DiGAs into occupational rehabilitation pathways may influence costs, service capacity, and waiting times within routine care. Methods: Aggregated administrative data from five BGs covering 25.9 million insured individuals (2023–2024) were analyzed using a multi-level simulation framework. The framework combined (1) probabilistic cost–consequence modeling with Monte Carlo simulation (10,000 iterations), (2) an adherence-based adoption funnel distinguishing long-term and short-term DiGA engagement, and (3) a calibrated M/M/1 queuing model validated through discrete-event simulation to estimate effects on waiting times and system capacity. Primary outcomes included net financial impact, break-even thresholds, and changes in access-related performance metrics. Results: Combined PT/EAP expenditures reached €404 million in 2024, increasing by 10.1% year over year. Simulation results indicated mean annual net savings of €18.4 million with a 90.7% probability of cost savings. After incorporating adherence dynamics, projected mean net savings were €16.2 million (95% CI €5.0–29.8 million), corresponding to a 100% probability of positive financial impact. Cost neutrality was maintained for DiGA prices up to €617.80 per prescription. Queuing analyses demonstrated that modest reductions in therapeutic demand could decrease mean waiting times from 17.3 to 12.8 days (−26%), equivalent to approximately 120,000 cumulative patient waiting days saved annually. Conclusions: Under conservative assumptions, integrating digital therapeutics into occupational rehabilitation pathways is likely to generate both economic benefits and substantial system-level capacity gains. Beyond cost effects, DiGAs may function as scalable implementation tools that alleviate bottlenecks and improve timely access to rehabilitation services in capacity-constrained health systems.

  • Background: Diabetes mellitus affects approximately 537 million adults globally, with projections indicating an increase to 643 million by 2030. Mobile health applications (mHealth apps) offer promising support for diabetes self-management, yet adoption rates remain low. Understanding the factors influencing patients' intentions to use mHealth apps is essential for designing effective interventions. Objective: To develop and empirically validate an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model incorporating personal innovativeness and attitude to explain behavioral intention to use mHealth apps for diabetes management. Methods: A cross-sectional survey was conducted with 485 Chinese adults. The measurement and structural models were assessed using Partial Least Squares Structural Equation Modeling (PLS_SEM). Results: Performance expectancy (β = .110, t = 3.401, P < .001), effort expectancy (β = .226, t = 5.942, P < .001), social influence (β = .112, t = 2.953, P =.002), facilitating conditions (β= .095, t = 2.476, P =.007), and personal innovativeness (β = .365, t = 9.280, P < .001) significantly influenced attitudes toward mHealth apps. Performance expectancy (β = .069, t = 2.239, P =.01), effort expectancy (β = .377, t = 8.939, P < .001), social influence (β = .123, t = 3.279, P < .001), and personal innovativeness (β = .116, t = 3.459, P < .001) significantly affected behavioral intention, while facilitating conditions did not (β = .041, t = 1.418, P =.07). Attitude significantly influenced behavioral intention (β = .337, t = 8.010, P < .001). Additionally, attitude significantly and positively mediated the relationships between performance expectancy (β = .037, t = 3.128, P < .001), effort expectancy (β = 0.076, t = 4.568, P < .001), social influence (β = .038, t = 2.775, P =.003), facilitating conditions (β = .032, t = 2.433, P =.007), and personal innovativeness (β = .123, t = 5.787, P < .001) and the behavioral intention to use mHealth apps for diabetes management. The model explained 31.7% of the variance in attitude and 51.5% in behavioral intention. Conclusions: The extended UTAUT model effectively explains mHealth app adoption for diabetes management by integrating personal innovativeness and attitude. Emphasizing app utility, usability, social influence, and fostering positive attitudes can enhance adoption. These insights inform healthcare providers and developers aiming to increase mHealth engagement among patients with diabetes.

  • Adaptation and Potential of Virtual Reality in Substance Use Disorders: A focus review

    Date Submitted: Feb 17, 2026
    Open Peer Review Period: Feb 19, 2026 - Apr 16, 2026

    Background: Substance use disorder (SUD) is a chronic, relapsing condition characterized by compulsive substance use and dysregulation in reward and control systems. Although effective pharmacological and psychosocial treatments are available, their impact is often limited by barriers such as stigma, poor adherence, and restricted access to care. Virtual Reality (VR) has emerged as a digital health intervention offering an adjunctive approach by providing immersive, interactive environments that may enhance engagement, simulate real-world triggers, and support therapeutic learning. Objective: This focus review aimed to map and synthesize the existing evidence for VR-based interventions in SUD treatment. We examine both therapeutic applications across established treatment frameworks and experimental approaches, identify key opportunities for future research and clinical innovation. Methods: We searched electronic databases including PubMed/MEDLINE, Science Direct and MDPI covering 2004 to 2025. Two reviewers independently screened for relevant studies and extracted study characteristics. Studies addressing VR applications for substance use disorders including peer-reviewed articles, randomized controlled trials, protocols and pilot studies published in English were selected. Any discrepancies were resolved through discussion. Results: A total of 26 studies or protocols were included in this review. Overall, the studies reviewed are broadly categorized into 6 sub-groups based on the type of the VR intervention and treatment class delivered. The reviewed literature indicates that VR-based cue exposure therapy is associated with reductions in craving and physiological reactivity for nicotine, alcohol, and cannabis use, with more limited and preliminary findings for opioid use disorder. VR relaxation and stress-management environments were linked to decreases in craving, stress, and pain among individuals with opioid and alcohol use disorders. VR-enhanced cognitive-behavioral interventions showed improvements in attention, cognitive flexibility, and emotion regulation. Motivational, social skills, and gamified VR interventions were associated with increased engagement, reduced stigma, enhanced self-efficacy, and improved treatment retention. Conclusions: This focus review contributes to the growing digital health literature by synthesizing current evidence on VR-based interventions for SUDs. The findings suggest that VR may serve as a flexible adjunct to existing treatments, with the potential to address persistent barriers to engagement and access. Further rigorously designed studies are needed to evaluate long-term effectiveness, optimize VR design, and support their integration into routine clinical practice.

  • Background: Mobile health (mHealth) app effectiveness may be limited by low engagement. Increasing understanding of factors influencing engagement may help. Paid mHealth app subscription and renewal are two metrics of particular interest to commercial app developers. Objective: Objective: To identify homogenous user subgroups (ie, behavioral phenotypes) within a paid mHealth app context and examine associations with app subscription and renewal. Methods: Methods: In this 6-month prospective cohort study, latent class analysis (LCA) was conducted with users of a paid mHealth app. Users completing a 7-day free trial between November 2023 and January 2024 were included. LCA produced phenotypes using survey responses (eg, chronic disease status), device-assessed health data (eg, daily step count), and 7-day free trial period engagement data (eg, number app opens). Odds ratios (ORs; P < .05) assessed associations between phenotypes and subscription/renewal. Results: Results: The sample included 934 users (mean age, 41.53 [SD, 9.65] years). Based on LCA fit indices five distinct phenotypes were formed: (1) Highly engaged subscribers, (2) Subscribers with multimorbidity, (3) Healthy subscribers, (4) Non-subscribers with multimorbidity, and (5) Healthy non-subscribers. Phenotypes 1–3 had greater odds of subscribing (OR = 21.31 [8.56, 53.06]; OR = 7.11 [4.04, 12.50]; OR = 8.28 [4.26, 16.08], respectively) than phenotype 4 (OR = 0.82 [0.48, 1.41]), compared to phenotype 5, the reference scenario. Additionally, renewal odds for phenotypes 1–4 were 1.06 [0.62, 1.81], 0.90 [0.54, 1.49], 0.99 [0.58, 1.69], and 0.93 [0.48, 1.80], respectively (vs. reference). Conclusions: Conclusions: Behavioral phenotypes associated with subscription likelihood were identified using data collected during the 7-day trial period. These phenotypes may be strategically targeted with future intervention to boost early engagement and long-term behavior change potential.

  • HIV prevalence and associated factors among Injecting and Non-Injecting men who have sex with men in India.

    Date Submitted: Feb 18, 2026
    Open Peer Review Period: Feb 19, 2026 - Apr 16, 2026

    Background: Men who have sex with men (MSM) and also inject drugs represent a subgroup facing compounded risks through both sexual transmission networks and parenteral exposure via contaminated injection equipment and acts as a risk factor and increase the vulnerability to Sexual Transmitted Diseases, including Human Immunodeficiency Virus. Objective: This study aims to estimate the HIV prevalence and associated factors among injecting drug MSM (ID- MSM) and non- injecting drug MSM (NID-MSM) in India. Methods: This is secondary data analysis of MSM data from the National Integrated Biological and Behavioral Surveillance (IBBS) survey. MSM-specific data collected in 2014-15 from 24 of the 36 States and Union Territories (UTs). Respondents who reported injecting drugs for non-medical reasons in the last 12 months were classified as injecting drug MSM (ID- MSM), others as non –injecting MSM (NID-MSM). Results: A total 23,081 MSM were included in the analysis. Out of which, 3.9 % MSM reported injecting drug use. Factors like increasing age (aOR = 1.70, 95% CI: 1.26–2.29 for 25–34 years), who aged ≥35 years (aOR = 2.75, 95% CI: 2.00–3.78), widowed/divorced/separated (aOR = 0.52, 95% CI: 0.29–0.93), involvement in sex work (aOR = 2.73, 95% CI: 1.68–4.42), first sex before the age of 18 years (OR = 1.42, 95% CI: 1.11–1.82) and selling sex to men ( aOR = 1.38, 95% CI: 1.10–1.72) were associated with ID- MSM. While, currently married (aOR = 2.16, 95% CI: 1.10–4.27), sex work odds (aOR = 3.41, 95% CI: 1.33–8.78), experienced physical violence (aOR = 1.57, 95% CI: 0.82–3.00), associated with NID – MSM. Conclusions: The findings of this study demonstrate ID-MSM experiencing a modest but non-significant elevation in prevalence compared with NID-MSM. More importantly, the determinants of HIV differed between these groups. While sex work and marital status were key predictors of HIV among ID-MSM; increasing age, early sexual debut, transactional sex, and inconsistent condom use were major drivers among NID-MSM. These findings highlight that targeted harm-reduction services for ID-MSM and strengthened behavior-focused interventions, including condom promotion, PrEP access, and early sexual health education for NID-MSM, are essential. Addressing structural barriers such as stigma and economic vulnerability remains critical for reducing HIV transmission within these diverse MSM populations.

  • Background: It is increasingly common that patients are given the option to receive health information using digital technology. Augmented reality (AR) is an emerging technology which may enable patients to better appreciate anatomy pertinent to their disease process. Objective: Our objective was to understand patients’ perspectives about augmented reality in the context of shared decision making (SDM) for oncoplastic breast surgery. Methods: Three focus groups with a total of 17 participants without breast cancer were recruited from general surgery out-patient clinics in a university teaching hospital. Participants interacted with a Microsoft HoloLens 2™ head-mounted display presenting an anonymised three-dimensional holographic model of a breast cancer, which was used as a stimulus to prompt discussion. Anonymised interview audio transcripts were transcribed verbatim and analysed using thematic analysis. Results: Analysis revealed four themes: 1) Seeing as believing – AR enhanced participants ability to visualise abstract anatomy and aid understanding, 2) Being in the surgeon’s shoes – the technology offered insight into the clinicians perspective, with concerns raised about the emotional impact, 3) How technology influences trust – AR reinforced confidence in the shared decision-making process when introduced by trusted clinicians, 4) Involving people in my life – shared viewing with family or friends offered support in the decision-making process. Conclusions: Participants viewed AR as a promising tool to enhance knowledge and add value in the process of SDM beyond the reach of current information giving. They also expressed caution, emphasising the need for careful introduction and ongoing clinician support to ensure meaningful use. Their varied responses highlighted the challenge inherent to introduction of digital technologies such as AR – the question of how much, and what type of technology best supports patients without causing information overload. Trust in clinicians remained central to the perceived value of the technology. These findings highlight AR’s potential to enhance SDM when thoughtfully integrated into clinical practice.   Clinical Trial: N/a

  • Background: As oncology workflows integrate increasingly autonomous artificial intelligence (AI) agents, health systems face uncertainty regarding operational impacts. Traditional linear forecasting methods fail to capture second-order effects such as governance saturation, induced demand, and bottleneck migration. To navigate this complexity, the emerging field of Medical Futures Studies requires methodologies that bridge qualitative strategic foresight with quantitative operational modeling. These system-level dynamics directly influence patient access to timely diagnosis and treatment, with direct consequences for patient access, treatment delays, and health system resilience. Objective: To develop a proof-of-concept framework for stress-testing AI adoption strategies in oncology by coupling qualitative scenario planning with computational discrete-event simulation (DES). Methods: We defined a strategic state space using two orthogonal axes, AI automation intensity and data interoperability, resulting in four distinct futures scenarios. We translated these qualitative narratives into a quantitative DES model to simulate a 3-year operational horizon. The model quantified system performance (Referral-to-Treatment Interval [RTTI], throughput), volatility, and resource constraints across different adoption trajectories. Results: The scenario planning phase yielded four operational archetypes (analog oncology, automation islands, interconnected clinicians, and AI-orchestrated care) with distinct constraints, risks and failure modes. In the simulation, the fully integrated scenario maximized capacity (1,244 patients/year) and halved the mean RTTI to 14.9 days, a magnitude comparable to major pathway redesign interventions. Isolated automation without data infrastructure led to reduced system performance, increasing RTTI by 26% (37.1 days) and reducing throughput to 647 patients/year due to administrative governance saturation. The model demonstrated a structural bottleneck migration: successful upstream AI adoption shifted binding constraints from diagnostic scanners to downstream chemotherapy infusion units, while missing data interoperability resulted in governance constraints. Pathway optimization analysis indicated that a coordinated strategy prioritizing early improvements in data interoperability reduced transition volatility compared to an automation-first approach. Conclusions: Integrating qualitative scenario planning with quantitative simulations enabled a systematic evaluation of oncology AI adoption strategies. As a proof of concept, it offers a replicable framework for health leaders to model future scenarios of digital transformation in times of high uncertainty. Subsequent work should expand this methodology to incorporate financial and health equity dimensions, establishing simulation-based scenario planning as an important tool in Medical Futures Studies.

  • Background: Health profession education students exhibit a higher rate of excessive digital technology use compared to their peers. Although the interaction of technology with student well-being has become more pronounced, the lack of awareness about digital detox among students in technology-intensive healthcare disciplines, along with the scarcity of studies exploring their practices, is concerning. Objective: This study aimed to investigate the patterns of social media usage and potential relationships between digital detox practices, mental well-being, physical health, and academic performance. Methods: A cross-sectional survey design was employed at King Saud bin Abdulaziz University for Health Sciences (KSAU-HS) in Riyadh. The sample consisted of 471 students from the health professions. Validated surveys were used, including the Social Media Disorder Scale, Digital Detoxification Awareness Questions, Kessler Psychological Distress Scale (K-6), and physical health assessments. The relationships between the study variables were analyzed using the chi-square test and ANOVA, with a significance level of 0.05. Results: A total of 471 students were included, with the majority being female (n = 291, 61.8%), single (n = 440, 93.4%), and aged between 18 and 37 years (M = 21.62, SD = 2.30). Participants reported an average daily social media usage of 7.07 ± 4.11 hours, with 31.6% of the sample classified as problematic users. Digital detox awareness was 59.7%, and 58.6% reported having experienced a digital detox. The most common strategies reported were avoiding phone use (69.1%) and muting notifications (70.3%). Participants reported eye strain (59.0%), neck pain (56.7%), and back pain (49.7%) due to the use of smartphones. Significant associations were found between social media use, gender, college affiliation, awareness of digital detox, level of physical activity, and sleep patterns (p < 0.005). A positive correlation was found between GPA and digital detoxification (p = 0.01). Social media use was significantly associated with the mental well-being of the participants (F = 214.096, p < 0.001) and with their academic performance (p = 0.04) Conclusions: The relationships between digital behavior, physical health, mental well-being, and academic performance of health profession students are complex and intertwined. The practice of digital detox, as observed, offers improvements in various aspects of students' lives; therefore, incorporating digital wellness strategies into the curriculum is vital for preparing students as professionals and enhancing student outcomes. Clinical Trial: NRR24/007/11

  • Background: Real-world gait assessment has gained momentum in populations with walking impairments, offering insights beyond standardized tests and supporting the integration of remote monitoring into clinical care. However, the full potential of wearable sensors remains limited by the lack of validated population- and context-specific digital biomarkers. Objective: The primary objective was to update the state of the art and summarize challenges in real-world gait assessment for PD and stroke. The secondary objective was to report pooled means and standard deviations of gait parameters. Methods: PubMed and Scopus were searched for English-language studies published up to December 31, 2024. Eligible studies included a minimum of five individuals with Parkinson’s disease (PD) or post-stroke and used wearable sensors to assess gait in real-world settings. Studies conducted solely in laboratory or rehabilitation environments, non-peer-reviewed articles, abstracts, or studies before 2014 were excluded. Results: Of 167 records identified, 34 studies were included, comprising 30 on PD (n=209; 812 [37%] female, 1359 [63%] male, mean age 68,59 years [SD 7,86]) and four on stroke (n=159; 77 [49%] female, 80 [51%] male, mean age 64,16 years [SD 10,51]). The meta-analysis for PD covered seven gait parameters with high heterogeneity across outcomes (I²>97%). Conclusions: Wearable sensors show strong potential for real-world gait assessment, but inconsistent methods call for standardization in sensor placement, algorithm validation, and metric definitions. Stroke populations are underrepresented, highlighting the need for targeted validation. Clinical Trial: This rapid review and meta-analysis was registered with PROSPERO, CRD42024531665.

  • Reliability, Quality, and Content of YouTube Videos on Xerostomia: A Cross-Sectional Study

    Date Submitted: Feb 17, 2026
    Open Peer Review Period: Feb 18, 2026 - Apr 15, 2026

    Background: Xerostomia is a prevalent condition that negatively affects quality of life. Patients increasingly seek health-related information through online platforms such as YouTube. Given the growing role of social media in digital health communication, evaluating the reliability and quality of publicly accessible video content is essential. Objective: This study aimed to assess the reliability, quality, and content characteristics of YouTube videos related to xerostomia. Methods: In this cross-sectional study, a YouTube search was conducted on January 10, 2025, using the keyword “dry mouth.” The first 100 videos retrieved using the relevance filter were screened. After applying inclusion and exclusion criteria, 46 videos were included in the analysis. Video reliability was evaluated using the Modified DISCERN (mDISCERN) instrument, while quality was assessed using the Global Quality Score (GQS) and the Video Information and Quality Index (VIQI). Videos were further categorized as “useful” or “misleading”. Engagement metrics, including number of likes, views, comments, interaction index, and viewing rate, were recorded. Statistical analyses were performed using SPSS version 22.0, with significance set at P < .05. Results: A substantial proportion of videos demonstrated low reliability and quality. Approximately half of the included videos were classified as misleading. Useful videos had significantly higher mDISCERN, GQS, and VIQI scores compared with misleading videos (P < .05). In addition, useful videos showed significantly higher engagement metrics, including number of likes, views, comments, and viewing rate (P < .05). Positive correlations were observed between reliability and quality scores and engagement parameters. Conclusions: A considerable portion of YouTube videos on xerostomia contains low-quality or misleading information. Although higher-quality videos tend to receive greater user engagement, the presence of inaccurate content remains concerning. Increased involvement of healthcare professionals and academic institutions in producing evidence-based digital content may improve the quality of online health information. Clinical Trial: This cross-sectional study evaluated YouTube videos related to xerostomia. As the study analyzed publicly available data on an open-access platform and did not involve human participants or identifiable personal information, ethical approval was not required, consistent with previous similar studies.

  • Background: The growing elderly population can directly impact countries’ productivity and pose significant challenges for governments, becoming a potential public health concern due to the increasing prevalence of health issues such as frailty, dementia, mobility limitations, and cardiovascular diseases. One promising approach is to integrate emerging technologies, such as wearable devices, machine learning, and smart sensors, to support older adults in their daily activities. These technologies can promote independence by enabling the safe execution of essential tasks while allowing continuous, 24-hour monitoring through mobile health systems. Objective: This research paper aims to evaluate the current use of consumer-grade wearable technologies, in combination with machine learning techniques, to promote autonomy and enhance daily activities among older adults. Methods: We conducted a systematic review in accordance with the Cochrane Handbook and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to synthesize evidence on the use of wearable technologies combined with artificial intelligence, particularly machine learning methods, to support daily living and prevent falls among older adults. The search was conducted in PubMed, MEDLINE, Scopus, the Institute of Electrical and Electronics Engineers (IEEE) Xplore, and the Association for Computing Machinery (ACM) Digital Library from their inception through April 2025, with no date or language restrictions. Results: Twenty-four studies were included. Mos studiest were observational or methodological and relied primarily on inertial sensing from wrist- or waist-mounted devices. The main application domains were activities of daily living monitoring, gait and mobility assessment, cognitive impairment, Parkinson’s disease symptoms, fall risk and detection, and frailty assessment. Classical machine learning models (e.g., support vector machines and random forests) and deep learning architectures (e.g., CNNs and LSTMs) were both widely used. However, studies were highly heterogeneous, frequently involved small samples, and rarely performed external validation or reported clinically actionable outcomes. Conclusions: Consumer-grade wearable devices, when combined with machine learning, show promise in supporting autonomy, daily activity monitoring, and fall-related safety in older adults. Nevertheless, the current evidence base is limited by methodological heterogeneity, small sample sizes, scarce external validation, and limited clinical integration. Future research should prioritize real-world evaluations, standardized reporting (e.g., TRIPOD-AI), interdisciplinary co-design, and patient-centered outcomes to enable translation into routine care. Clinical Trial: International Prospective Register of Systematic Reviews (PROSPERO) Registration: CRD420251044449

  • Background: Most studies on internet use and health outcomes among older adults rely on cross-sectional designs and binary exposure measures. It is usually difficult for time to capture multidimensional health-related digital engagement. The high collinearity between digital engagement and socioeconomic factors makes it challenging to disentangle independent effects from marker effects. Currently, longitudinal evidence linking health-related digital engagement to incident stroke remains limited. Objective: This study aimed to examine the longitudinal association between a composite Health-Related Digital Engagement Index (HDEI) and incident stroke among community-dwelling older adults. Bisides, it sought to quantify the extent to which socioeconomic factors account for this association. Methods: This prospective cohort study used data from the National Health and Aging Trends Study (NHATS), Waves 1-10 (2011-2020). The HDEI (range 0-4) was constructed from 4 health-related internet behaviors at baseline. The primary outcome was incident stroke ascertained by self- or proxy-reported physician diagnosis. Discrete-time hazard models with a complementary log-log link were fitted with 4 nested models progressively adjusting for demographics, socioeconomic factors, chronic disease burden, disability, and social isolation. Results: Among 5,384 participants (81.6% HDEI=0; 10.5% HDEI=1; 7.9% HDEI≥2) followed for a median of 5 years (IQR 2-9), 470 incident stroke events occurred. In the unadjusted model, each 1-point HDEI increase was associated with 24% lower stroke risk (hazard ratio [HR] 0.76, 95% CI 0.66-0.88; P<.001). After adjustment for age and sex, the association attenuated but remained significant (HR 0.82, 95% CI 0.71-0.94; P=.006). Upon further adjustment for race or ethnicity, education, and income, the association was no longer significant (HR 0.91, 95% CI 0.78-1.05; P=.18); full adjustment yielded similar results (HR 0.91, 95% CI 0.79-1.04; P=.18). Subgroup analyses showed a stronger association among men (HR 0.70, 95% CI 0.55-0.89; P=.003), though no interaction terms reached significance. Sensitivity analyses excluding early events and substituting cellphone use as an alternative exposure yielded consistent attenuation patterns. Sensitivity analyses excluding early events and using cell phone instead as a alternative exposure variable showed a similar attenuation patterns. Conclusions: In unadjusted and sociodemographic-adjusted models, higher health-related digital engagement was associated with lower stroke incidence. However, after adjusting for socioeconomic factors, this relationship was reduced. The observed association between digital engagement and stroke risk seems to be predominantly confounded by socioeconomic advantage. Therefore, digital health interventions those aiming at stroke prevention should address both the digital divide and the underlying socioeconomic determinants of cerebro-cardiovascular risk.

  • Background: The COVID-19 pandemic triggered an abrupt transition to virtual rehabilitation across physiotherapy, occupational therapy, and respiratory therapy. While telerehabilitation research has documented feasibility and patient satisfaction, less is known about how professionals navigated the destabilization and reassembly of care practices during this transformation. Existing literature frames virtual care as a technical substitution for in-person services, overlooking the deeper reconfiguration of the socio-technical networks that organize therapeutic work. Objective: Applying actor-network theory (ANT), we examined how rehabilitation professionals reconfigured their practices through technology during the first year of the pandemic. We explored how digital tools, domestic spaces, and new sensory practices reshaped therapeutic presence, professional identity, and the environments in which care was enacted. Methods: We conducted a secondary analysis of longitudinal diary-interview data collected from 16 Canadian rehabilitation professionals (occupational therapists, physiotherapists, and respiratory therapists) working in community-based primary care in Ontario and Manitoba (2020-2021). Participants recorded audio diaries over 12 weeks and completed two follow-up interviews. Analysis followed an interpretive approach informed by Science and Technology Studies, tracing how human and technological actors were enrolled, adapted, and redefined within emerging care assemblages. Results: Three interconnected processes characterized the reconfiguration of rehabilitation: (1) technology as active participant, where digital platforms mediated rather than merely transmitted therapeutic reasoning and clinical decision-making; (2) reconfiguration of therapeutic presence, as sensory attention and embodiment were redistributed across screens, sounds, and new forms of spatial choreography; and (3) enrollment of domestic spaces as clinical environments, as clinicians' and patients' homes became sites of care shaped by new ethical, material, and relational dynamics. These processes reveal that virtual rehabilitation constituted a new form of care co-produced by humans, technologies, and spaces rather than a digitized replication of traditional practice. Conclusions: The pandemic exposed rehabilitation as a socio-technical practice sustained through the coordination of multiple actors rather than professional expertise alone. Virtual care redefined therapeutic presence when traditional boundaries between clinical and domestic, human and technological, were blurred. Recognizing virtual care as a distinct modality underscores the need to integrate technology-mediated competencies into rehabilitation education and practice. Future research should incorporate patient perspectives and direct observation to trace how these care networks evolve.

  • Background: eHealth literacy is widely assumed to drive how people seek health information online—yet this assumption rests on a body of evidence that has never been quantitatively synthesized across population groups or examined against the algorithmically mediated environments in which today's users actually navigate health content. Contemporary measurement of eHealth literacy relies heavily on the eHealth Literacy Scale (eHEALS), a tool calibrated to Web 1.0-era search behaviors. Whether eHEALS retains predictive validity for online health information seeking behavior (OHIS) across generationally distinct cohorts—particularly digital natives who acquire health knowledge through curated feeds and short-video platforms rather than deliberate search—remains an open and consequential question. Objective: This study aims to quantify the strength and heterogeneity of the association between eHealth literacy and OHIS, and to identify boundary conditions across generation, morbidity status, and information source credibility. Methods: Following PRISMA guidelines, we searched PubMed, Embase, Web of Science Core Collection, PsycINFO, and Library, Information Science & Technology Abstracts for studies published through April 28, 2025. Eligible studies enrolled individual-level participants, assessed eHealth literacy with validated instruments, and measured active OHIS. Two independent reviewers extracted data and appraised study quality using the modified Newcastle-Ottawa Scale. Pearson r values were transformed to Fisher's z and pooled under a random-effects model; moderator analyses were performed for the three prespecified subgroups. Results: Of 8,090 nonduplicate records, 30 studies entered the qualitative synthesis and 18 (19 independent effect sizes) the meta-analysis. The overall pooled correlation was r = 0.30 (95% CI 0.18–0.41; P < .001), indicating a small-to-moderate association. Subgroup analyses revealed a strikingly uneven pattern: among non-Gen Z participants the correlation was r = 0.39, whereas in Gen Z it was near zero (r = 0.08)—suggesting that eHEALS-measured literacy is largely disconnected from how this cohort seeks health information. The association was substantially stronger among patients than nonpatients (r = 0.56 vs. r = 0.23) and for professional versus nonprofessional sources (r = 0.38 vs. r = 0.26). No significant publication bias was detected (Egger's test, P = .38). Conclusions: The near-zero eHealth literacy–OHIS association in Gen Z is the study's most consequential finding: it indicates that eHEALS has limited predictive validity for a generation that navigates health content through algorithmically curated feeds, short-video platforms, and AI-assisted interfaces rather than deliberate keyword search. Interpreted through a Motivation-Ability-Opportunity lens, perceived ability no longer constrains seeking behavior in digital natives—motivational activation and platform affordances do. These findings challenge the field to move beyond self-report confidence measures toward platform-sensitive, performance-based instruments, and call for intervention designs that pair literacy skills with motivational and environmental cues rather than treating literacy as a standalone determinant of health information behavior.

  • Background: Herpes zoster (HZ) imposes a substantial disease burden, yet vaccine uptake remains suboptimal in China. While eHealth literacy is a known determinant of health behaviors, its role in bridging socioeconomic disparities and its varying impact across different age groups of vaccine-eligible adults remain understudied. Specifically, it is unclear whether eHealth literacy acts as a "compensatory resource" for disadvantaged populations and if the digital skills required to reduce hesitancy differ between middle-aged and older adults. Objective: This study aimed to examine the association between eHealth literacy and HZ vaccine hesitancy among adults aged 40 years and older in Shanghai, China, with a specific focus on identifying age-dependent "digital thresholds" and the compensatory effect of literacy on socioeconomic status (SES). Methods: A community-based cross-sectional study was conducted from October to December 2022 across three districts in Shanghai. A total of 1302 adults aged ≥40 years were recruited via convenience sampling. eHealth literacy was assessed using the eHealth Literacy Scale (eHEALS). Multivariable logistic regression models were used to analyze the associations, adjusting for sociodemographic characteristics, health status, and behaviors. Stratified analyses were performed to evaluate interactions among literacy, age, and SES. Results: The prevalence of HZ vaccine hesitancy was 88.2% (1149/1302). In the fully adjusted model, participants with medium (odds ratio [OR] 0.538, 95% CI 0.326-0.886; P=.015) and high (OR 0.472, 95% CI 0.264-0.844; P=.011) eHealth literacy demonstrated significantly lower odds of hesitancy compared to those with low literacy. Age-stratified analyses revealed a distinct "digital threshold" effect: for middle-aged adults (40–59 years), medium literacy was sufficient to significantly reduce hesitancy (OR 0.501, 95% CI 0.265-0.949; P=.034), whereas older adults (≥60 years) required high literacy to achieve a significant protective effect (OR 0.347, 95% CI 0.136-0.882; P=.026). Crucially, eHealth literacy exhibited a strong compensatory effect for socioeconomic disadvantage. Among participants with low SES, high eHealth literacy was associated with an 83.1% reduction in the odds of hesitancy (OR 0.169, 95% CI 0.054-0.528; P=.002), a magnitude of effect not observed in higher SES groups. Additionally, a history of HZ infection was identified as a robust protective factor (OR 0.473, 95% CI 0.309-0.724; P=.001). Conclusions: eHealth literacy serves as a critical compensatory resource that can mitigate the disadvantage of low socioeconomic status in HZ vaccine acceptance. However, the protective mechanism is age-dependent, indicating a higher "digital threshold" for older adults (≥60 years) compared to their middle-aged counterparts. Public health interventions should prioritize digital empowerment for low-SES populations and tailor educational strategies to meet the higher digital competency needs of older adults. Clinical Trial: Not available

  • Background: Depression and anxiety are common mental disorders across all age groups. Digital intelligent interventions have not only overcome the time and space limitations of traditional psychotherapy but also provided innovative pathways for treating these conditions. However, the specific effectiveness of such interventions among groups with different demographic characteristics remains to be further clarified. Objective: To evaluate the effectiveness of digital intelligence interventions on symptoms of depression and anxiety using meta-analytic methods Methods: We searched the PubMed, Embase, Cochrane Library, Web of Science, and BIOSIS databases from inception through June 2025 for randomized controlled trials (RCTs) of digital interventions targeting depression or anxiety. Two reviewers independently screened the studies, extracted the data, and assessed the risk of bias using the Cochrane Risk of Bias tool. Meta-analyses were performed using RevMan 5.4 and Stata 15.0. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were used to assess continuous outcomes. Heterogeneity and subgroup analyses were performed. Results: Nineteen RCTs involving 4,679 participants were included. Compared with controls, digital interventions significantly reduced depressive symptoms (SMD = −0.25; 95% CI, −0.41 to −0.09; P = .002) and anxiety symptoms (SMD = −0.20; 95% CI, −0.32 to −0.08; P = .0009). Subgroup analysis by intervention duration indicated the largest effect for depressive symptoms at approximately 4 weeks (SMD = −0.26; 95% CI, −0.40 to −0.11; P = .0006). The greatest reduction in anxiety symptoms was observed at 5–8 weeks (SMD = −0.22; 95% CI, −0.47 to 0.03; P = .08), though this did not reach statistical significance.App-based interventions demonstrated the most significant effects on depression and anxiety, with effect sizes of (SMD=-0.44 (95% CI: -0.82, -0.06; P = .02) ;SMD= -0.36 (95% CI: -0.59, -0.12; P = .003), respectively. Furthermore, therapeutic efficacy was superior among the older adult population, showing values of SMD= -0.37 (95% CI: -0.64, -0.09; P = .009) ;SMD= -0.51 (95% CI: -0.87, -0.14; P = .006). Conclusions: Nineteen RCTs involving 4,679 participants were included. Compared with controls, digital interventions significantly reduced depressive symptoms (SMD = −0.25; 95% CI, −0.41 to −0.09; P = .002) and anxiety symptoms (SMD = −0.20; 95% CI, −0.32 to −0.08; P = .0009). Subgroup analysis by intervention duration indicated the largest effect for depressive symptoms at approximately 4 weeks (SMD = −0.26; 95% CI, −0.40 to −0.11; P = .0006). The greatest reduction in anxiety symptoms was observed at 5–8 weeks (SMD = −0.22; 95% CI, −0.47 to 0.03; P = .08), though this did not reach statistical significance.App-based interventions demonstrated the most significant effects on depression and anxiety, with effect sizes of (SMD=-0.44 (95% CI: -0.82, -0.06; P = .02) ;SMD= -0.36 (95% CI: -0.59, -0.12; P = .003), respectively. Furthermore, therapeutic efficacy was superior among the older adult population, showing values of SMD= -0.37 (95% CI: -0.64, -0.09; P = .009) ;SMD= -0.51 (95% CI: -0.87, -0.14; P = .006). Clinical Trial: CRD420251069160

  • Cutaneous Manifestation in Neurological Disorders: A Systematic Review of Observational Studies

    Date Submitted: Feb 14, 2026
    Open Peer Review Period: Feb 15, 2026 - Apr 12, 2026

    Background: Cutaneous manifestations are increasingly recognized as serving as important indicators of several neurological disorders because of the shared mechanisms between the two conditions. This systematic review aims to synthesize the available observational evidence on cutaneous manifestations related to neurological disorders. Objective: To systematically review and synthesize observational evidence on the relationship between cutaneous manifestations and neurological disorders, highlighting their role as early indicators of underlying neurological pathology. Methods: This is a systematic review conducted in accordance with PRISMA guidelines across different electronic databases from inception up to 2023. Study selection, extraction of data, and quality assessment were performed independently by two reviewers. Results: The review included 30 studies published between 1964 and 2023, including a total sample of 9472 (1172 cases and 8300 controls), mostly as case reports, and representing diverse geographic regions. Pigmentary disorders were the most common reported cutaneous manifestations (N=17 studies), particularly among patients with congenital neurocutaneous disorders. Most of the cutaneous symptoms were reported before or at the onset of neurological symptoms. The shared embryological origins, neural crest cell migration defects, immune-mediated processes, or chronic inflammatory pathways were the most proposed pathophysiological mechanisms related to the two conditions. Conclusions: This review indicated that cutaneous manifestations are common and clinically relevant across different neurological disorders and frequently serve as early markers of underlying neurological pathology.

  • Background: Home spirometry has been widely adopted in the delivery of cystic fibrosis (CF) care. While existing literature largely supports its feasibility and positive outcomes, behaviour around home disease monitoring remains poorly understood. Objective: This study aimed to evaluate healthcare professionals’ (HCPs') ability to estimate home spirometry usage pwCF and compare these with actual recorded data. Methods: Home spirometry data, from a single large adult CF centre, for the year 2024, was obtained from NuvoAir. HCPs (doctors, nurses, and physiotherapists) rated their familiarity with each pwCF and categorised them as infrequent, expected, or highly frequent spirometry users. They were also asked to estimate spirometry usage as an open-ended numerical response. CF experience was defined by the number of years the HCP had worked at the centre. Estimation accuracy was assessed using mean bias and mean absolute error (MAE). Results: 10 doctors (35.7%), 6 nurses (21.4%), and 12 physiotherapists (42.9%) responded to the survey, with an overall response rate of 96.6%. There were 790 completed categorical estimates and 794 numerical estimates. The mean (±SD) CF experience was 15.7 (±8.2) years. Across all roles, HCPs systematically underestimated home spirometry usage (mean bias -4.9; MAE 6.32). No significant differences in estimation accuracy were observed based on professional role, reported familiarity or CF experience. Conclusions: This study found that CF caregivers tend to underestimate home spirometry usage, in contrast to other studies showing they often overestimate treatment adherence. This highlights gaps in understanding behaviour in pwCF and the need for CF teams to adapt to evolving models of remote monitoring.

  • Background: People living with chronic diseases increasingly rely on online sources to support ongoing self-management. While digital environments expand access to health information, they also expose patients to misinformation of varying credibility. Prior studies have largely described information-seeking behaviours or misinformation exposure separately, with limited integration of verification processes. Objective: This study examined the interrelationships between online health information seeking (HIS), verification behaviour (VER), and misinformation-related perceptions (MIS) among individuals with chronic diseases using a behaviourally integrated framework. Methods: A cross-sectional online survey was conducted among adults with self-reported chronic diseases. The questionnaire assessed health information seeking, verification practices, and perceptions of health misinformation using Likert-scale measures. Data were analysed using descriptive statistics, visual analytics, and structural equation modelling (SEM) to evaluate direct and indirect associations between constructs. Results: Participants reported frequent engagement with online health information and widespread use of verification strategies. SEM analysis revealed a strong positive association between HIS and VER (β = 0.81), indicating that active information seeking was closely linked to credibility assessment behaviours. HIS was positively associated with MIS (β = 0.41), suggesting that greater engagement increased awareness of misleading content. VER demonstrated a modest negative association with MIS (β = −0.29), consistent with a buffering effect whereby verification practices partially attenuated misinformation-related perceptions. Conclusions: Findings support a mechanistic interpretation in which online health information seeking promotes verification behaviour, and verification practices may mitigate the perceived impact of misinformation. These results extend beyond descriptive accounts by demonstrating how information-seeking and evaluative behaviours interact within misinformation-rich digital environments. Interventions that reinforce verification strategies and embed credibility cues within commonly used platforms may strengthen safe digital health engagement among chronic disease populations

  • In February 2025, Andrej Karpathy introduced vibe coding—building software by describing intent in natural language rather than writing precise code. This concept captured a broader paradigm shift: from prompt engineering toward context engineering, where the richness of context supplied to artificial intelligence (AI) determines output quality more than the precision of commands. We propose that the same principle applies to health. Vibe Health is a dual-sided paradigm in which individuals reach actionable health decisions through honest, iterative conversations with AI—without requiring medical knowledge or prompt expertise. The term deliberately extends Karpathy’s metaphor: just as vibe coding showed that programming skill matters less than the ability to articulate intent, Vibe Health posits that medical knowledge matters less than the ability to describe what is happening in one’s body. On the patient side, the core principle is that an honest prompt outperforms a perfect prompt: candid descriptions of symptoms, emotions, and lived context generate more useful AI responses than technically polished queries. On the physician side (Vibe Clinical), we reframe doctors not as novice prompt engineers but as the most experienced context engineers in any professional domain—their history-taking, physical examination, and clinical reasoning skills are precisely the context injection capabilities that enable high-quality AI interaction. We introduce the FTCAV model (Feel–Tell–Converse–Act–Verify) as an integrated behavioral framework that operationalizes Vibe Health for both patients and physicians. The model is grounded in interoception research—specifically the distinction between interoceptive accuracy (detecting bodily signals) and interoceptive awareness (reporting them)—and extends health behavior theory (the Capability–Opportunity–Motivation–Behavior model) to the conversational dynamics of AI-mediated health interactions. Each stage represents a discrete behavioral step: sensing a bodily signal or clinical cue (Feel), expressing it in natural language or structured clinical terms (Tell), refining understanding through multiturn AI dialogue (Converse), converting insight into executable action (Act), and confirming with appropriate authority (Verify). We examine the emerging medicolegal implications of AI-mediated health conversations, arguing that patients’ timestamped, contextualized AI dialogue logs carry evidentiary weight that physicians cannot safely ignore. We call for three specific actions: incorporation of Vibe Health principles into patient-facing AI platforms and health education programs, piloting of Vibe Clinical modules in medical school curricula, and development of professional guidelines for the documentation and clinical integration of patients’ AI conversation records.

  • Background: Perioperative respiratory care is a multidisciplinary process that includes several sequential steps and handoffs. Variability and inefficiencies within this workflow may delay care delivery and increase the workload of clinical staff. Quality Control Circles (QCCs) have been widely used in health care as a practical approach to addressing process-related quality problems identified in clinical practice. Objective: The aim of this study was to assess whether a Quality Control Circle–based intervention could improve workflow efficiency and care consistency in perioperative respiratory care. Methods: We conducted a before-and-after time–motion study on 30 perioperative respiratory care episodes to compare workflow before and after the implementation of a quality control circle. Recorded variables were: (1) total time from physician order to completion of respiratory care, (2) patient waiting time for incentive spirometry preparation, and (3) time clinical staff spent on patient education and respiratory training. Eligible patients were those prescribed perioperative respiratory care before or after surgery; those with prior exposure to the intervention or with hearing impairment were excluded. Guided by the Plan–Do–Check–Act cycle, improvement strategies included standardizing the provision of incentive spirometry, pre-positioning equipment at nursing stations, unifying education content, and delivering multimedia educational materials via quick response codes. Results: Before quality control circle implementation, the total process time was 266.65 minutes (240 minutes for equipment preparation and 17.5 minutes for patient education). After implementation, it dropped to 28.75 minutes (8.7 minutes for preparation, 10.5 minutes for schooling), improving overall efficiency by 89.2% and significantly reducing workflow time. Conclusions: A quality control circle framework not only optimized perioperative respiratory care but also engaged frontline staff, fostering a sense of teamwork and shared purpose. Multimedia patient education improved understanding and engagement, and cross-disciplinary collaboration reduced clinical workload. This strategy may reduce postoperative pulmonary complications and can be applied to other respiratory care workflows.

  • Search interest in alleged COVID-19 treatments during the pandemic and the impact of mass news media

    Date Submitted: Feb 13, 2026
    Open Peer Review Period: Feb 14, 2026 - Apr 11, 2026

    Background: During a public health emergency, interest in unsafe or illegitimate medications can delay appropriate treatment and foster medical mistrust. Methods: We obtained daily US-based Google Search Trends and Media Cloud data from 2019-2022 to assess the relationship between search interest and media coverage in three purported COVID-19 treatments: hydroxychloroquine, ivermectin, and remdesivir. Results: Search interest and media coverage of all COVID-19 treatments were significantly elevated during the study period; search interest was highest for ivermectin (6.0 out of 100; interquartile range [IQR]: 1.9-9.9), while media covered hydroxychloroquine most frequently (0.05% of all articles published; IQR: 0.02-0.13%). Anomaly detection of both data sources identified several points of higher-than-expected activity; anomalies in search interest and media coverage showed similar patterns within treatments. There were distinct patterns of media coverage – while the plurality of sources for all treatments were considered “Left” or “Left Leaning”, ivermectin was covered by the highest number of “Right”-biased sources and remdesivir had the highest coverage by “Center” or “unbiased” sources. When assessing the co-occurrence of words and phrases in media sources covering each of the treatments, there were distinct qualitative difference in the categories of words appearing alongside the drugs. Specifically, ivermectin appeared to be reported more frequently in association with specific individuals than media mentioning the other drugs. In google searches, people seemed most interested in understanding what hydroxychloroquine is and the uses of ivermectin (e.g., “for humans”, “for dogs”). We found significant associations between media coverage and search interest for all three treatments. Media coverage had the strongest impact on same-day search interest for remdesivir (199.7% increase, 95% CI: 179.2, 221.6) and hydroxychloroquine (182.6% increase, 95% CI: 172.8, 192.7); interest dropped significantly 1 and 2 days after media coverage of these treatments. Interest in ivermectin was lower overall (105.0% increase, 95% CI: 97.9, 112.3) but stayed elevated even 2 days after media coverage. When evaluating the separate impact of search interest on media coverage, the associations were much weaker for all three treatments and all lagged conditions. Conclusions: During a public health emergency, the information that populations access can directly influence health-seeking behaviors, with potentially life-threatening consequences. More broadly, positive media coverage of unsafe or unapproved medications can deter individuals from trusting and accessing safe alternatives that are more likely to be efficacious in preventing disease progression. Given the strong association between treatment-related news media coverage and public interest in said treatments, our results suggest that news media serve as a powerful mechanism for experts to inform the landscape of public opinion and to reach audiences during future public health emergencies.

  • From Performance to Governance: An Adoption-Phase Ethical Perspective on Large Language Models in Health Care

    Date Submitted: Feb 13, 2026
    Open Peer Review Period: Feb 14, 2026 - Apr 11, 2026

    Large language models (LLMs) are increasingly integrated into everyday health-care communication, moving beyond experimental evaluation into routine clinical and informational use. Early research has primarily focused on technical performance, including accuracy, validation, and bias mitigation. While these remain essential, the transition to sustained real-world integration raises additional ethical questions that cannot be addressed by model-level evaluation alone. This Viewpoint proposes an adoption-phase ethics perspective, emphasizing how ethical risks shift as LLMs become embedded within institutional workflows, professional practices, and relationships of care. Drawing on normative analysis informed by existing empirical discussions, we examine three interrelated domains: trust, responsibility, and equity. During routine use, trust becomes shaped not only by perceptions of accuracy but also by expectations regarding accountability, transparency, and institutional protection. Responsibility may become diffused or ambiguous when LLM-mediated information influences clinical communication without clearly specified oversight. At the same time, differential digital literacy and access to institutional support may create uneven capacity to interpret and benefit from AI-generated information. We argue that ethical governance must therefore extend beyond pre-deployment technical safeguards toward sustained, system-level oversight. Adoption should be understood as a dynamic ethical process requiring role-sensitive design, clear accountability structures, and equity-oriented implementation. By reframing ethical attention from experimental validation to governance during routine integration, health-care systems can better ensure that the growing presence of LLMs supports fairness, responsibility, and patient trust alongside technical advancement.

  • Background: Fitness and physical activity patterns are key predictors of cardiovascular disease. Traditionally, these factors have been assessed through participant self-report, which is prone to recall bias and inaccuracy. Smartphone-based monitoring provides a scalable and objective alternative for measuring physical activity, offering improved accuracy over conventional assessment methods. Objective: To evaluated the feasibility of smartphone-based cardiovascular research in the Netherlands and to examine associations between objectively measured physical activity, perceived activity, functional capacity, life satisfaction, and cardiovascular risk. Methods: Adults in the Netherlands were recruited via the MyHeart Counts iPhone app between August 2022 and December 2023. Within the app, participants completed surveys, passively shared motion sensor data, and were invited to perform a smartphone-based 6-minute walk test (6MWT). Perceived activity was compared with sensor-measured activity and actual activity (sensor-measured with supplemented self-reported unrecorded activity). Multivariable linear regression assessed associations between activity and 6MWT performance and between activity and life satisfaction. Perceived cardiovascular risk was compared with the difference between heart age and actual age. Results: Of 518 enrolled participants (median age 58 years; 72% female), 93% shared data beyond demographics. Median engagement duration was 27 days, and 58% completed at least one full consecutive week of motion tracking. Perceived activity weakly correlated with both sensor-measured activity (ρ = 0.15, P = .01) and with actual activity (ρ = 0.15, P = .01). Median perceived activity was 3.5 hours/week, significantly higher than sensor-measured activity (0.9 hours/week; mean difference 2.9 hours, 95% CI 2.2–3.7; P < .001). In contrast, median actual activity was 3.2 hours/week and did not differ significantly from perceived activity (mean difference 0.7 hours, 95% CI −0.2 to 1.6; P = .11), indicating no significant over- or underestimation when unrecorded activity was accounted for. Sensor-measured physical activity was associated with longer 6MWT distance (+10.1 m per hour; 95% CI 3.9-16.4, P = 0.002). No association was observed between sensor-measured activity and life satisfaction. Perceived cardiovascular risk correlated with the difference between heart age and actual age (ρ = 0.41; P < 0.001). Conclusions: Smartphone-based cardiovascular monitoring is feasible in a European adult population and yields valid functional correlates of physical activity. However, incomplete phone carriage substantially limits sensor-only activity estimates, underscoring the need for hybrid measurement strategies. These findings support the use of smartphone platforms for scalable cardiovascular research, while highlighting persistent challenges in engagement and measurement completeness.

  • Background: For patients with advanced cancer in the emergency department (ED), decisions regarding life-sustaining treatments (LST) are critical and hinge on clear communication of complex prognoses. While large language models (LLMs) can synthesize clinical information, their comparative effectiveness against clinicians in shaping real patient preferences, and the readability of their outputs, remain unproven. Objective: This study aimed to determine if LLM-generated advice is non-inferior to clinician-generated advice in changing patient resuscitation preferences. Secondarily, we compared the Chinese-language readability of the advice using a validated formula with a clinical cutoff and assessed patient satisfaction. Methods: We conducted a three-arm, parallel, randomized controlled non-inferiority trial. 189 adult patients with advanced cancer in the ED were assigned to review structured advice generated by: (1) a senior clinician, (2) ChatGPT-5.0 Mini, or (3) DeepSeek. The primary outcome was the change in score on the Cancer Advanced Care Preferences Scale. Secondary outcomes included text readability score (assessed by a validated Chinese health literacy formula) and patient satisfaction. Results: A total of 189 participants were enrolled and completed the study. In the primary non-inferiority analysis, the change in resuscitation preference scores for the DeepSeek group was non-inferior to that of the clinician group (mean difference: -0.095 points, 95% CI: -0.750 to 0.560; lower limit > -1.7 margin). Similarly, ChatGPT-5.0 Mini was also non-inferior to the clinician group (mean difference: 0.349 points, 95% CI: -0.237 to 0.935; lower limit > -1.7 margin). Regarding secondary outcomes, a significant difference in readability was found among the three groups (Kruskal-Wallis H(2)=129.36, p<0.001). Post-hoc comparisons indicated that texts from DeepSeek had the highest median readability score (7.53, IQR: 7.39-7.62), followed by ChatGPT-5.0 Mini (5.93, IQR: 5.60-6.23), and clinician-generated texts (5.51, IQR: 5.29-5.74), with all pairwise differences being significant (p<0.001). However, no significant difference in patient satisfaction was observed across the groups (H(2)=1.10, p=.578). Conclusions: LLM-generated advice was non-inferior to clinician advice in influencing resuscitation preferences. Its superior readability and higher patient satisfaction highlight the potential of LLMs as a scalable tool to support complex decision-making in time-pressured ED settings.

  • Patient-generated health data (PGHD) refers to health-related information collected by patients themselves, serving as a vital supplement to traditional clinical data. In the era of big data, the potential of PGHD in the long-term management of chronic diseases and cancer is increasingly recognised, with its clinical application becoming a key issue in the digital health field. The proliferation of smart devices and wearable technology, improvements in sensor performance, and rapid advancements in artificial intelligence have made the collection of PGHD more convenient. Existing clinical evidence preliminarily indicates that PGHD may alleviate symptom burden in lung cancer patients and enhance the quality of cancer care. However, significant challenges remain in effectively integrating PGHD with clinical data, conducting reliable analyses of vast PGHD datasets, and ultimately incorporating it into routine clinical practice. Furthermore, regulatory bodies, healthcare institutions, and device manufacturers must collaboratively establish policies and standards to safeguard patient data security and privacy. While leveraging digital tools for PGHD collection, attention must also be paid to economic costs and technical barriers to broaden coverage and promote health equity. The potential and application models of PGHD in the long-term management of lung cancer patients warrant further exploration. Against this backdrop, this paper proposes a WeChat Official Account-based model for PGHD collection and remote management, aimed at implementing sustainable symptom monitoring and health guidance for lung cancer patients. This approach seeks to advance the widespread clinical application of PGHD and further explore its potential value in promoting patient self-management and improving quality of life.

  • Background: While AI's transformative potential in healthcare is widely acknowledged, its application in highly sensitive, humanistic domains like PPC remains largely unexplored. Objective: To explore the attitudes and needs of healthcare providers on the pediatric palliative care (PPC) assisted by artificial intelligence (AI), with the goal of informing future development and implementation of AI systems in this field. Methods: This was an explanatory sequential mixed-methods study consisting of a nationwide cross-sectional questionnaire survey (March–April 2025) followed by qualitative semi-structured interviews (August–October 2025). The quantitative study aimed to investigate PPC healthcare providers' experiences, attitudes, and needs for the application of AI. Participants included team members of all recognized PPC teams in mainland China. The qualitative study aimed to explore in greater depth the potential future roles of AI in this field, as well as the features of an ideal AI-assisted tool for PPC. Potential interviewees were recruited from the pool of quantitative survey respondents. Results: Among 352 survey respondents, most (58.24%) reported moderate familiarity with AI, with large language models being the most commonly used (79.55%). Among large language model users, over half (57.50%) reported using them for clinical purposes. Attitudes were generally positive: 67.05% believed AI's benefits would outweigh drawbacks, and 78.98% considered its implementation feasible. The most desired applications were patient/family education (78.41%) and symptom management (73.01%). Interviews with 17 providers revealed three themes: (1) clinical roles and boundaries; (2) elements for clinical integration; and (3) challenges in development and deployment. Conclusions: This study reveals that PPC providers express positive attitudes and strong demand for AI-assisted clinical work. Furthermore, the research clarifies appropriate roles for AI, outlines elements for clinical integration, and highlights potential challenges in development and integration. This study provides evidence for the feasibility of AI application in PPC and offers guidance for the future development and deployment of AI tools.

  • Background: Frailty is a multidimensional clinical syndrome characterized by diminished physiologic reserve and increased vulnerability to stressors, thus putting older adults at higher risk of adverse outcomes (e.g., falls, mental and physical disability, hospitalization, mortality) in response to even minor stress events. Frailty can be reversed or at least attenuated if detected early, yet early identification remains challenging in primary care due to time- and resource-intensive assessment methods. Artificial intelligence (AI) offers promise in automating frailty identification at the point of care. Natural Language Processing (NLP) is particularly valuable for extracting frailty indicators from rich text data stored in electronic health records, but its limited interpretability has prompted growing interest in augmenting the NLP processes with the use of explainable AI (XAI) techniques. Although NLP and XAI methods have been applied for chronic disease identification, their use for frailty identification has not yet been systematically examined. Objective: This scoping review aimed to synthesize current evidence on the use of NLP and XAI methods for automating frailty identification in older adults. Methods: Peer-reviewed studies published in English between January 2015 and November 2025 were eligible if they applied AI, NLP, or XAI methods to identify frailty in adults aged ≥50 years using real-world health data from OECD or OECD-partner countries. Searches were performed in PubMed and Google Scholar and supplemented by screening bibliographies of identified studies. Data were extracted using a standardized form that captured study characteristics, sample size, data sources, and specific aspects of the AI models, and NLP and XAI methods used. Results: We identified 24 studies that satisfied the eligibility criteria. While all studies used AI approaches to identify frailty, only six used neural network-based models. Logistic regression was the most frequently used AI method (n=14), and only one study employed Bidirectional Encoder Representations from Transformers (BERT). Seven studies relied on both structured and unstructured data, two relied exclusively on structured data only, and the rest relied exclusively on unstructured data. Seven studies used NLP methods, seven used XAI methods, and only one integrated both. Only two studies reported deploying their models in real clinical settings. Conclusions: AI-based approaches show promise for automating frailty identification, yet current applications remain limited by reliance on traditional machine learning models, underuse of NLP and XAI methods, and very little real-world deployment. Future work should focus on developing explainable NLP models, facilitating access to large volumes of unstructured data, and developing standardized frameworks for the systematic evaluation of NLP and XAI methods. Coordinated efforts across clinical, technical, and regulatory domains are essential to develop scalable, transparent, and clinically meaningful AI systems for frailty identification.

  • Background: Anticoagulated patients with atrial fibrillation (AF) face significant bleeding risks, which current risk scores inadequately predict. Pulse pressure (PP), a marker of arterial stiffness, may offer additional prognostic value. Objective: This study aimed to evaluate whether elevated PP independently predicts major bleeding events. Methods: We conducted a retrospective cohort study using electronic health records from 4,935 AF patients on oral anticoagulation (2010–2019) in the REACHnet network. PP was calculated from outpatient blood pressure readings and analyzed in tertiles and as a continuous variable. Kaplan-Meier curve and log-rank test were conducted to assess the association between PP and clinical outcomes. Cox regression models further adjusted for demographics, comorbidities, systolic blood pressure, medications, and the ORBIT bleeding score. Results: Over a median 5-year follow-up, 677 patients (13.7%) experienced major bleeding. GI bleeding was significantly more frequent in the highest PP tertile (p = 0.007), while intracranial and other bleeding types showed no significant differences. Each 10 mmHg increase in PP was associated with a 15% higher risk of GI bleeding (HR: 1.014; p = 0.042), and this association remained significant after adjusting for systolic blood pressure and the ORBIT score (OR: 1.013 per mmHg; p = 0.028). PP was not significantly associated with intracranial, other, or overall bleeding. Conclusions: Pulse pressure independently predicts gastrointestinal bleeding in anticoagulated AF patients, even after accounting for traditional bleeding risk factors. These findings support the inclusion of PP in future risk stratification models and clinical monitoring strategies. Clinical Trial: N/A

  • Background: Mild cognitive impairment (MCI) is recognized as a critical stage for dementia prevention. Physical activity is an important intervention to prevent cognitive decline, but challenges still remain in improving or maintaining cognitive function in older adults with MCI through increased physical activity. Personalized mobile health (mHealth) promotion strategies based on the Behaviour Change Wheel (BCW) hold promise for enhancing physical activity levels in this population. Objective: This study aims to evaluate the feasibility and preliminary effectiveness of a personalized mobile application (App) named ActiveAide, developed based on the BCW framework, for promoting physical activity among older adults with MCI. Methods: This feasibility study employed a single‑arm, pre‑ and post‑test design. 18 participants received an 8‑week personalized intervention via ActiveAide. Feasibility measures included recruitment rate, retention rate, App usage data, App usability evaluation, and user experience with the App. Effectiveness measures encompassed physical activity level, physical fitness, physical activity self‑efficacy, and social support. Quantitative data were analyzed using paired‑sample t‑tests and Wilcoxon signed‑rank tests, while qualitative data underwent content analysis. Results: The study achieved a recruitment rate of 90.9% and a retention rate of 90%. The mean strategy completion rate was 78.5%, with the mean number of App accesses of 71. The mean System Usability Scale (SUS) score was 74.86 ± 8.81, indicating good usability. Qualitative interviews identified three themes: strengths of MotiveAide, limitations of MotiveAide, and suggestions to improve MotiveAide. Post-intervention, statistically significant improvements were observed in participants’ physical activity level (P<0.001), physical activity self-efficacy (P<0.001), VO2max (P<0.001), strength assessment score (P=0.002), and body composition measures including total physical score (P<0.001), fat mass (P=0.001), and body fat percentage (P<0.001). No significant change was found in the level of social support. Conclusions: The personalized mHealth application ActiveAide, developed based on the BCW framework, demonstrated good feasibility and preliminary effectiveness in promoting physical activity among older adults with MCI. Future research could further optimize the application’s features and employ more rigorous designs, such as randomized controlled trials, to validate its long-term efficacy and generalizability.

  • Semantic Layer in Health Care: The Art of Riding a Bicycle

    Date Submitted: Feb 10, 2026
    Open Peer Review Period: Feb 11, 2026 - Apr 8, 2026

    Health data interoperability is the central hill climb in contemporary digital health. Hospitals often accumulate data like mismatched spare parts, catalogued inconsistently, and difficult to re-use across care. The landscape of non-annotated source systems, legacy data warehouses that lack interoperable data models, the coexistence of multiple terminologies with divergent scopes, the operational turbulence of system migrations, and the persistent challenges of metadata catalogues and versioning set a starting point to a journey in building a semantic layer that makes data Findable, Accessible, Interoperable, and Reusable (FAIR), and that remains robust as terminologies evolve. Terminology updates are complex and their terms, classifications, and regulations continually change. This viewpoint article gives an exemplary historical overview at a Swiss university hospital, highlights the relevance of key decisions and projects and contrasts local conditions with the Swiss and European context. It notes perspectives of large clinical information systems and highlights organizational implications, tools and models needed, and the challenge of legacy data. It dives into project work of ontology creation. The discussion reflects on achievements and the future illustrating the cadence and resilience required to ride interoperable data “around the world”. Key Message. Achieving healthcare interoperability requires balancing diverse standards, terminologies, and data governance. The FAIR principles provide a framework. Organizational commitment to these practices is essential.

  • Background: Since 2011, Türkiye has become the primary destination for Syrian refugees. While healthcare is a fundamental human right, public discourse surrounding refugee health services can influence policy and social cohesion. Objective: The objective of our study was to examine 14 years of Turkish health-related discourse on platform X (formerly Twitter) to identify evolving sentiment, stance, and key grievances. Methods: From a dataset of 4.5 million tweets (2009-2022), 116,172 health-related posts were identified. We employed a fine-tuned Turkish BERT-based large language model to perform multi-task classification for sentiment, stance, and health topics. Tweets were categorized into five domains as Provision of Healthcare Services, Financing and Coverage, Human Resources, Public Health and Disease Prevention, and Access to Medications and Pharmaceutical Services. Lift scores and heatmaps were used to analyze the relationship between the keywords and public attitudes. Results: The fine-tuned Turkish BERT model achieved high classification performance with a weighted F1 score of 0.85 for sentiment and 0.8 for stance detection. Public discourse shifted from neutral or positive tones in 2011 to overwhelming negativity over time. By 2021, negative sentiment reached 79.9%, and anti-refugee stance peaked at 78.3%. Prominent topics evolved from Provision of Healthcare Services (47.5% in 2011) to Public Health and Disease Prevention (57.3% in 2021) and Human Resources (34.6% in 2022). High lift scores revealed that anti-refugee stances were strongly associated with keywords such as ‘appointment’, ‘vaccine’, and ‘free’. Conclusions: There is a marked and consistent rise in anti-refugee sentiment within Turkish digital health discourse, often fueled by misinformation and perceived systemic strain. Public health authorities should prioritize evidence-based communication strategies to counter digital polarization and ensure the legibility of health policies for the host population.

  • Public Online Discussions of CAR T-cell Cancer Therapy: Unpacking the Hype

    Date Submitted: Feb 9, 2026
    Open Peer Review Period: Feb 10, 2026 - Apr 7, 2026

    Background: Chimeric antigen receptor (CAR) therapy is a novel cell editing technology and innovative form of cancer immunotherapy. An individual’s immune cells (T-cells) are removed from the body, engineered to target and limit the growth of cancer cells, and reinfused into the patient’s body. The one-time treatment is expensive ($500,000 plus hospital costs), and requires specialized care to treat and manage the associated side effects, such as cytokine release syndrome (CRS), and other serious health issues including cognitive confusion, infertility, secondary malignancies, and compromised long term quality of life. At the same time, CAR T has been highly successful for patients with advanced blood cancers and no remaining treatment options. The CAR T landscape is changing rapidly, and product approvals have outpaced the capacity for researchers to collect long term evidence related to survival or predictive biomarkers that might better prioritize patients. Because CAR T is offered exclusively in urban cancer centres with access to cell manufacturing capacity, equitable access has been challenging. At the same time there is considerable demand and social hype about CAR T as a cancer cure despite the risks and uncertainty of the technology. Objective: We aimed to determine the dominant perspectives and nature of the information on CAR T-cell therapy available to the public in the online environment. Methods: In this qualitative study, we conducted a comprehensive search of websites including professional, medical, corporate, health-based, news media, and blogs to capture the diversity of online sources and their perspectives presenting information on CAR T-cell therapy. Fifty-one webpages met the study criteria and comprised the data set in this review. The content of the sites was reviewed and analyzed using a critical and interpretive descriptive lens. Results: We classified the website information into four dominant major themes characterizing CAR T-cell therapy: 1) patient stories of success, magic and hope; 2) medical science explainers; 3) economic perspectives; and 4) ethical discussions and complex arguments. With the exception of the sites that presented ethical discussions and complex information, the online environment positioned CAR T as revolutionary, curative, and the future of cancer treatment. Side effects were generally minimized, and collective dilemmas such as sustainability for the healthcare system, equitable access, and issues of prioritization were frequently sidelined or absent. Conclusions: The persuasive tone of online CAR T information combined with the increasingly blurred distinctions between research and care in genetic medical technologies suggests that obtaining informed consent or refusal may place too much onus on individual patients. In an evolving technological landscape such as CAR T, determining the acceptable risks and benefits is a question that ethically requires broader, as well as more inclusive, societal deliberation.

  • Background: Substance use disorders account for a significant portion of the disease burden attributed to mental health globally, but measurement remains suboptimal. Studies assessing substance use typically rely on retrospective recall often over long periods of time. However, the episodic, contextual and event- or time-contingent nature of substance use call into question the validity of these traditional retrospective measurement methods. One method to overcome these limitations is ecological momentary assessment (EMA). EMA methods repeatedly sample participant behaviours and experiences in real time, in the context in which they occur. Objective: This review aimed to systematically identify studies using EMA in substance use measurement, provide a comprehensive overview of the EMA methods used, and to provide a draft framework for reporting and methodological recommendations for future EMA studies in this field. Methods: Studies published between 2018 and 2023 were sourced from PubMed, Medline, Scopus, and PsycINFO via Ovid databases on 31st January 2023 using terms related to EMA, digital phenotyping, passive sensing, daily diary and specific terms for each drug type. Studies that actively or passively assessed thoughts and/or behaviour, in the participants’ natural environment/daily lives, in a repeated manner, at or close to the behaviour of interest (substance use), using either automatic prompts or notifications were included. Studies were included for all populations, any age, in any setting, any study design, including RCTs or experimental designs. This study was preregistered on PROSPERO (CRD42023400418). Results: The search identified 7053 articles of which 858 were reviewed in full, and 273 (n = 70,831 participants) were included and extracted. Most studies were conducted in the United States (80%) and focused on alcohol (78%) and cannabis use (30%) with or without the presence of other substance use. Alcohol and cannabis measurement co-occurred the most in 44 (16%) studies. Psychedelics (2%) were particularly understudied using EMA methods. PCP, bath salts, and inhalants were only measured in one study each. We found limited reporting consistency with respect to compliance, completion windows, attrition rates, survey duration and data collection technologies in EMA substance use studies. Sensing data were measured in a limited number of studies. Conclusions: While EMA is a powerful tool for capturing dynamic behaviours, inconsistencies in reporting and design transparency persist. Improving reporting practices, smart sensing and wearable integration, compliance monitoring alongside expanding EMA to underexplored substances such as psychedelics, will be critical to enhancing data quality and advancing the field.

  • Addressing the Challenges in Using Synthetic Data for Health Research: Application to Cardiology

    Date Submitted: Feb 5, 2026
    Open Peer Review Period: Feb 6, 2026 - Apr 3, 2026

    Synthetic data (SD) has emerged as a promising tool for advancing cardiology research by enabling data access, enhancing patient privacy, and supporting the development of machine learning models. By generating artificial patient records that reflect real-world distributions, SD can accelerate clinical research, improve model performance for rare cardiovascular conditions, and facilitate transnational collaborations that would otherwise be restricted by data sharing barriers. Despite these advantages, the increasing use of SD raises important ethical, regulatory, and methodological concerns that remain insufficiently addressed. Key challenges include assessing the validity and generalizability of synthetic datasets, understanding their limitations in representing complex and heterogeneous patient populations, and preventing the amplification of existing biases in cardiovascular care. Regulatory frameworks such as GDPR and HIPAA safeguard privacy but do not fully account for emerging risks such as re-identification or data leakage, leaving uncertainty regarding the use of SD in evidence generation for medical devices or therapeutic evaluation. Technical constraints, including the reliability of generative models and the difficulty of capturing nuanced clinical trajectories, further limit the clinical applicability of SD. As cardiology increasingly intersects with artificial intelligence and digital health technologies, ensuring rigorous methodological standards, transparent validation, and clear governance mechanisms is essential to harness SD responsibly. This Viewpoint highlights the opportunities and blind spots associated with SD and virtual patients in cardiology and underscores the need for harmonized regulatory guidance and ethical safeguards to support their meaningful integration into research and clinical practice.

  • Background: Primary care physicians in resource-constrained settings, particularly within low-income and middle-income countries (LMICs), frequently encounter a "diagnostic gap" when managing complex, rare, or multisystemic pathologies. While Large Language Models (LLMs) demonstrate significant potential to augment clinical reasoning, current state-of-the-art solutions rely predominantly on high-bandwidth cloud infrastructure, limiting their deployment in regions with unstable internet connectivity and strict data sovereignty regulations. Objective: The prevailing technological consensus in computer science suggests that "Agentic Workflows" or Multi-Agent Systems (MAS)—which orchestrate multiple models to simulate collective reasoning—inherently offer superior accuracy and safety compared to single models. However, the comparative efficacy, safety, and cost-effectiveness of complex MAS versus single localised models in offline, hardware-limited environments remain unproven. Methods: We conducted a prospective comparative benchmarking study using the DiagnosisArena dataset, comprising 915 complex clinical cases across 28 medical specialties. To simulate a secure, offline primary care environment, we evaluated five locally deployed single open-source LLMs (GPT-oss-20b Llama3.1-70B, Qwen3-32B, DeepSeek-R1-32B, Gemma3-27B) against two Multi-Agent architectures: a Standard voting ensemble and a novel hierarchical Adaptive Weighted System. All models were hosted on a local server (4×NVIDIA A100) using the Dify platform. Performance was adjudicated against a Reference Standard established by the consensus of three board-certified physicians using a dual-metric system: a 10-point Diagnostic Recall Scale and a comprehensive Hallucination/Safety Index. Inference latency and computational resource utilisation were recorded to assess cost-effectiveness. Results: Contrary to the hypothesis that architectural complexity yields diagnostic precision, single high-performance models significantly outperformed complex ensembles. The single GPT-oss-20b model achieved the highest Diagnostic Recall Score (mean 4.68 [SD 3.82]), statistically surpassing the Adaptive Weighted Multi-Agent System (4.13 [SD 3.43]; p<0.001) and smaller models such as Gemma3-27B (2.89 [SD 3.89]; p<0.001). The Adaptive System, despite utilising dynamic routing, failed to outperform the median score of human physicians (4.22 [SD 3.62]; p=0.432). Furthermore, the inclusion of mid-tier models in the adaptive workflow introduced an "ensemble degradation" effect, significantly lowering the Safety Score compared to the single GPT-oss-20b model (4.99 vs 5.50; p<0.001) and reducing the rate of Top-1 correct diagnoses from 51.58% to 46.89%. Crucially, the single GPT-oss-20b model demonstrated superior efficiency with an average inference time of 30 seconds per case, compared to 200 seconds for the Standard Multi-Agent System—representing an 85% reduction in latency. Conclusions: In the context of clinical diagnosis, architectural complexity does not equate to clinical utility. We identified a phenomenon of "ensemble degradation," where integrating mid-tier models into ensembles dilutes the reasoning capabilities of strong base models through the introduction of diagnostic noise. For global health equity, implementation strategies should prioritise "Lean AI"—localising a single, robust open-source model—rather than orchestrating computationally expensive agent swarms. This approach provides a safer, more accurate, and scientifically validated path for bridging the diagnostic gap in resource-constrained primary care.

  • Consensus-Based Recommendations for Optimizing Diversified TCM Data Collection during Clinical Work

    Date Submitted: Feb 5, 2026
    Open Peer Review Period: Feb 6, 2026 - Apr 3, 2026

    Background: Background: An increasing amount of TCM clinical data can be collected by software and equipment, forming diversified TCM data, which should typically be collected alongside clinical work. TCM diagnosis and treatment data collection is conducted concurrently with clinical work, typically. However, with the limited time, space, and human resources available in clinical work, collecting diversified TCM Data is difficult, which may affect the quality of the collected data. Objective: Objective: To develop recommendations for optimizing diversified traditional Chinese medicine (TCM) data collection. Methods: Method: A working group comprising 12 members was established. Based on previous survey findings regarding the burden of clinical data collection, the group developed a preliminary list of recommendations for optimizing diversified TCM data collection. A Delphi survey was conducted to investigate consensus levels(using a 5-point Likert scale for importance evaluation) on the list items, and open-ended opinions were also surveyed. If experts in the first round propose additions, deletions, or modifications, or if there is a lack of consensus on certain items, a next round of surveys will be conducted to obtain the experts' agreement rate on the related items. Results: Results: A total of 86 experts from China, the United Kingdom, and Singapore completed two rounds of surveys. Following the first Delphi survey, all items achieved agreement scores above 4, with coefficients of variation(CV) below 0.2. The working group revised 12 items based on open-ended opinions and resubmitted them for agreement assessment. All revised items achieved agreement rates of over 95%. Following the two-round survey process, the final version of the recommendations comprises 5 primary domains, 11 sub-domains, and 25 items. Conclusions: Conclusion: This study formulated recommendations for optimizing diversified TCM data collection. It is hoped that these recommendations will help clinical data collectors consider data collection in advance during the design phase

  • How pandemics have reshaped respiratory virus data landscape in Europe? A scoping review

    Date Submitted: Feb 5, 2026
    Open Peer Review Period: Feb 6, 2026 - Apr 3, 2026

    Background: Acute respiratory infections caused by influenza, respiratory syncytial virus (RSV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain a major public health challenge in Europe. Although surveillance systems for these pathogens are well established, the past two decades have seen a rapid diversification of data streams supporting surveillance and research. This expanding and increasingly complex data landscape, combined with fragmentation across institutions, sectors, and countries, may limit timely evidence synthesis and effective public health decision-making. Objective: This scoping review aimed to identify and characterize data sources used for surveillance and research on influenza, RSV, and SARS-CoV-2 across 12 European countries over the past 20 years, and to examine their evolution over time, their alignment with research objectives, and geographic variation in data availability and use. Methods: We conducted a scoping review using an objective-driven analytical framework. Empirical reports published between January 2005 and September 2025 were identified in Medline, Web of Science, and Embase. Eligible reports focused on influenza, RSV, or SARS-CoV-2 and included data from Western (France, Belgium, Germany, Netherlands), Northern (Denmark, England, Finland, Sweden), Southern (Italy, Spain), and Eastern Europe (Poland, Romania). Clinical and interventional studies were excluded. Reports were classified according to four research objectives: epidemiological monitoring; evaluation of interventions; assessment of disease burden and health outcomes; and analyses of population adherence and trust toward public health measures. Data sources were grouped into nine categories, including surveillance systems, electronic health records (EHRs), registries, claims, surveys, digital, environmental, and integrated datasets. Results: A total of 2,564 empirical reports were included. Over time, respiratory virus research relied on an increasingly diverse set of data streams. While surveillance systems remained central, particularly for epidemiological monitoring, their relative dominance declined. From 2020 onward, there was a marked expansion in the use of EHRs, registries, claims data, digital sources, and linked or integrated datasets, alongside increased use of open-access data. Data source use varied by research objective: surveillance data predominated in monitoring and intervention evaluation; EHRs in studies of risk factors and treatment effectiveness; surveys in seroprevalence and public trust analyses; and claims data in assessments of economic burden. Substantial geographic disparities were observed. Northern European countries more frequently used linked and multi-source datasets, whereas Western and Southern Europe relied more often on open-access or single-source data. Conclusions: Respiratory virus surveillance and research in Europe have expanded and diversified substantially over the past two decades, particularly after the Coronavirus disease 2019 (COVID-19) pandemic. However, access to advanced and integrated data streams remains uneven across countries. Strengthening preparedness for future respiratory virus threats will require sustained investment in interoperable data infrastructures, improved data governance, and the responsible use of artificial intelligence to integrate heterogeneous data sources.

  • Background: Talaromycosis and cryptococcosis are prevalent in Southern China and Southeast Asia and are frequently misclassified due to overlapping lesion morphology and limited access to confirmatory testing. Objective: To evaluate the zero-shot diagnostic performance of multimodal large language models in identifying and differentiating cutaneous lesions of talaromycosis and cryptococcosis Methods: Published clinical photographs of cutaneous lesions of talaromycosis and cryptococcosis were systematically retrieved up to 31 August 2025, and seven representative multimodal large language models were benchmarked under a strictly zero-shot setting using a standardized prompt template and a predefined output schema. Latency, unanswerable/invalid response rates, and diagnostic performance were evaluated using accuracy, precision, sensitivity, specificity, F1-score, and Matthews correlation coefficient. For explanation quality assessment, model-generated texts were independently rated by two clinicians across five dimensions, and hallucination events were quantified. Results: In total, 214 articles (95 for talaromycosis and 119 for cryptococcosis), including 244 talaromycosis cutaneous lesion images and 236 cryptococcosis cutaneous lesion images, were collected for zero-shot evaluation. Most models achieved acceptable performance recognition, among them, ChatGPT-5 achieved the best performance. For comprehensive performance comparison, ChatGPT-5 ranked first across six indicators but exhibited relatively lower sensitivity. Evaluation of the output text quality demonstrated that the diagnostic texts generated by GPT-5 were excellent. The EQI was 70.08, with a hallucination rate of 21.76%. Conclusions: ChatGPT-5 demonstrates feasibility in the recognition of cutaneous lesions of talaromycosis and cryptococcosis under zero-shot conditions and can serve as a potential tool for assisting in the analysis of infectious skin disease images.

  • Background: Task-oriented rehabilitation supported by exoskeletons has the potential to increase therapy intensity, personalization, and accessibility. However, to achieve fully automatic treatment, robotized systems need to analyze therapy in a more complex way than only based on reference trajectories following. Objective: This study investigates the effects of an intelligent, context-aware control algorithm for an upper-limb rehabilitation exoskeleton on patients’ musculoskeletal engagement, compared with constant-admittance robot-assisted therapy and conventional physiotherapist-guided treatment. Methods: A single-session experimental study was conducted with 34 adult participants performing six activities of daily living under three therapy modes: robot-assisted therapy with constant admittance, robot-assisted therapy with an intelligent assist-as-needed algorithm, and physiotherapist-guided therapy. Muscle activity was assessed using surface electromyography of eight upper-limb muscle groups, while joint kinematics were recorded using inertial measurement units. Metrics included EMG power, muscle activation time, joint range of motion, and burst duration similarity indices. Statistical comparisons were performed using the T-test and the Mann-Whitney U-test depending on data normality. Results: Results indicate that the intelligent control strategy engages the musculoskeletal system at least as effectively as constant-admittance control across all exercises. At the same time, more motion control is given to the patient, which is preferable for neuroplasticity training. Compared with physiotherapist-guided therapy, robot-assisted treatment with intelligent control elicited significantly higher and more consistent muscular engagement. Intelligent assistance also modified joint-level motion patterns by reducing compensatory movements, particularly in shoulder–elbow coupling, while maintaining functional task execution. Muscle activation timing patterns during intelligent robot-assisted therapy were more consistent with robotic control than with manual therapy, reflecting altered movement strategies. Conclusions: These findings demonstrate that context-aware, intelligent control in rehabilitation exoskeletons can promote active patient participation, reduce compensatory behaviors, and maintain physiologically meaningful muscle engagement. The proposed approach exceeds the results of recent similar studies, being a promising step toward effective, minimally supervised, task-oriented rehabilitation. Clinical Trial: The experiments were carried out under the KB/132/2024 approval of the Bioethical Committee of the Medical University of Warsaw (https://komisja-bioetyczna.wum.edu.pl/). Written informed consent was obtained from all of the subjects involved in this study.

  • Background: Early diagnosis, accurate severity assessment of acute pancreatitis (AP), and prediction of progression to severe acute pancreatitis (SAP) are critical. Objective: We evaluated an electronic medical record (EMR)-embedded large language model (LLM) for these tasks. Methods: Early diagnosis, accurate severity assessment of acute pancreatitis (AP), and prediction of progression to severe acute pancreatitis (SAP) are critical. We evaluated an electronic medical record (EMR)-embedded large language model (LLM) for these tasks. Results: 224 (85.8%) had mild AP (MAP), 30 (11.5%) moderately SAP (MSAP), and 7 (2.7%) SAP. The LLM diagnosed AP with 89.3% sensitivity and 100.0% positive predictive value (PPV). Severity classification was inconsistent (MAP sensitivity 49.1%, MSAP 66.7%, SAP 42.9%). For progression prediction from initial MAP, the LLM showed high sensitivity (87.5%) but low accuracy (26.8%) ; Bedside index for severity in acute pancreatitis (BISAP) had higher accuracy (95.5%) but low sensitivity (12.5%). In MSAP, the LLM sensitivity was 85.7% versus BISAP 0% . Conclusions: An EMR-embedded LLM can detect AP and identify many who progress to SAP, but specificity and severity classification require improvement.

  • Background: Background: The digital transformation of healthcare is reshaping how breast cancer patients access and use information, yet little is known about how their digital information behaviours evolve across the illness trajectory. Objective: Objective: To explore stage-specific digital health information behaviours and the cognitive, emotional and social factors shaping decision-making. Methods: Design: Descriptive qualitative study informed by Uncertainty Management Theory. Setting: A tertiary hospital in Shanghai, China. Participants: Fifteen women with breast cancer. Methods: Semi-structured, face-to-face interviews were conducted with purposive sampling across diagnostic, treatment and recovery phases; data were analysed using directed and inductive content analysis within a UMT framework. Results: Results: Five themes emerged, highlighting shifts from passive reception to active screening, complementary use of search engines, social media and AI tools, and the role of trust, emotion and social context in information acceptance or rejection. Conclusions: Conclusions: Digital health information behaviours are dynamic and stage-specific, suggesting phase-tailored, nurse-led digital support.

  • Background: Digital physical exercise interventions offer a scalable solution to combat age-related cognitive decline. While various modalities exist, their comparative effectiveness across different cognitive domains remains unclear, necessitating a systematic evaluation to guide clinical practice. Objective: This study aims to evaluate and rank the comparative effectiveness of different digital physical exercise interventions—including immersive VR (IVR_E), non-immersive exergames (NI_ExG), remote exercise (RE), and VR combined with cognitive training (VR_EC)—on global cognition, executive function, and memory function in older adults. Methods: We conducted a systematic review and Bayesian network meta-analysis of randomized controlled trials (RCTs) published between January 1, 2010, and April 30, 2025. Data sources included PubMed, Embase, and Web of Science. Eligible studies involved older adults (aged ≥60 years) and compared digital physical exercise interventions against routine interventions (RI) or non-intervention (NI). The primary outcomes were global cognition, executive function, and memory function. We estimated standardized mean differences (SMDs) and ranked interventions using the surface under the cumulative ranking curve (SUCRA). Results: A total of 41 RCTs involving 2919 participants were included. For global cognition, IVR_E emerged as the most effective intervention (SUCRA=96.6%), followed by NI_ExG (SUCRA=76.4%); both modalities were significantly superior to RI. Regarding executive function, RE (SUCRA=73.8%) and NI_ExG (SUCRA=69.3%) ranked highest. Notably, NI_ExG was the only intervention to demonstrate a statistically significant improvement over RI in this domain, while IVR_E showed no significant advantage. For memory function, IVR_E was the dominant intervention (SUCRA=82.8%) and was the only modality significantly more effective than RI. Subgroup analyses further indicated that a cumulative training dose exceeding 1000 minutes is critical for observing significant improvements in memory function. Conclusions: Digital physical exercise interventions significantly enhance cognitive function in older adults, but their optimal application is domain-specific. IVR_E appears most effective for global cognition and memory, likely due to high immersion and standardization. Conversely, NI_ExG and RE are preferable for enhancing executive function, potentially offering more scalable alternatives for home-based care. Future interventions targeting memory improvement should ensure sufficient cumulative training duration. Clinical Trial: PROSPERO CRD42025103014

  • Background: Assistive technologies can support independent living among older adults, but uptake is often constrained by attitudes and confidence. The COVID‑19 lockdowns accelerated technology use across all age groups, offering a natural experiment to examine changes in adoption. Objective: This study aimed to examine changing patterns of technology use in older adults, to provide insight as to how service providers can support the use of technology to support independence and well-being. Methods: Two cross‑sectional surveys were conducted in UK retirement villages, one before the pandemic (2020) and one after lockdowns (2023), to assess technology attitudes and use. Semi‑structured interviews with eight participants in a technology trial scheme provided qualitative insights. Results: Technology adoption increased significantly between 2020 and 2023, with older adults reporting greater confidence and comfort in digital use. Self‑education and informal support from family or friends were the most common pathways to adoption. Age‑related differences in confidence observed in 2020 were no longer apparent in 2023, although gender disparities persisted. Interviewees emphasized usefulness and accessibility as key drivers of sustained engagement. Findings demonstrate that the pandemic catalyzed lasting increases in technology adoption among older adults, including increased confidence and ownership. Conclusions: Findings demonstrate that the pandemic catalyzed lasting increases in technology adoption among older adults, including increased confidence and ownership. These results provide evidence for housing providers and policymakers to embed accessible technologies and targeted support in retirement communities, thereby enhancing independence and quality of life in later life.

  • Social media influencer marketing is a digital advertisement strategy that is growing in popularity. Its use has been documented in consumer purchasing behavior but is yet to be described for clinical trial recruitment. In this tutorial, we describe the steps we followed to develop and deploy a social media influencer advertisement for the recruitment of participants into the Groceries for Residents of Southeastern USA to Stop Hypertension (GoFreshSE) trial. We also provide a preparation framework for other studies who would like to use this modality for their own clinical trial recruitment. We used Cameo Business to identify potentially relevant influencers to hire by selecting influencers who were popular in the 3 geographic areas from which GoFreshSE is recruiting. We narrowed down the list of possible influencers by selecting those with ≥100,000 followers on their respective social media platforms (for a wide reach) and charged a cost of ≤$3,000/video. We ultimately selected a former football coach, who provided a high-quality video of him reading an institutional review board-approved script 4 days later. We utilized open source, commercially available tools to edit the video and deployed the 44-second-long video on Facebook and Instagram using Meta’s Advertising platform. Social media influencer marketing through the Cameo Business platform is a rapid mechanism to develop clinical trial influencer recruitment videos.

  • Background: Sample pooling is an essential strategy for optimizing polymerase chain reaction (PCR) resources during infectious disease outbreaks, especially in the beginning. While high-dimensional hypercube pooling strategies—such as those recently highlighted in Nature—offer superior efficiency in low-prevalence settings, they are difficult to implement in practice. The human cognitive and physical limitation to three-dimensional environments makes manual execution of four- or five-dimensional sample arrays prone to significant operational error. Objective: To develop and evaluate a novel "Ternary Card Hypercube Pooling" strategy that simplifies the implementation of multidimensional pooling, making it accessible for laboratory personnel without compromising mathematical efficiency. Methods: We integrated logic from ternary card games (based on sets of three attributes) to create a visual and physical framework for hypercube pooling. This method maps high-dimensional coordinates onto a simplified "card" system, allowing laboratory technicians to organize and track samples using intuitive pattern recognition rather than complex multidimensional mapping. Results: The Ternary Card method successfully translates the efficiency of hypercube pooling into a user-friendly workflow. It maintains the high performance of traditional hypercubic algorithms—allowing for rapid identification of positive samples in a single step in the majority of cases—while significantly reducing the risk of manual pipetting errors and the need for specialized automated equipment. Conclusions: The Ternary Card Hypercube Pooling strategy bridges the gap between theoretical mathematical efficiency and practical laboratory application. By reducing the complexity of sample handling, this method provides a scalable solution for increasing PCR throughput in response to future pandemics, particularly in resource-limited settings. Clinical Trial: NA

  • Background: Co-design ensures cultural safety of health interventions for Aboriginal and/or Torres Strait Islander communities. However, an intervention developed with one Indigenous community may not be suitable for another geographically and culturally distinct community. Objective: This study aimed to culturally adapt content and features of a mobile health (mHealth) application co-created by communities in one Australian state to better meet the needs of mothers and caregivers of Aboriginal and/or Torres Strait Islander children aged 0-18 years and health professionals in another state. Methods: The study followed the stages of the cultural adaptation stepwise model by Barrera et al. Mothers/caregivers of Aboriginal and/or Torres Strait Islander children aged 0-5 years and their health professionals were recruited from multiple community sites. Data were collected through culturally appropriate yarning circles or interviews facilitated by Aboriginal research staff. Qualitative data were transcribed and inductively analysed to generate themes. The feedback was translated into practical changes that were applied to the mHealth application. Results: Data saturation was achieved after yarning circles with 21 women and seven health professionals. Nine themes were generated from mothers/caregivers’ data: 1) cultural relevance and sensitivity, 2) linking with culturally appropriate services, 3) Use of lay language and more audio-visual content , 4) concerns with mobile data usage, 5) Perceptions about the current content of the Jarjums app, 6) raising children, 7) safety, 8) health and wellbeing of mothers and caregivers, and 9) coordinating health care. Four themes were generated from data collected from health professionals: 1) favourable features of the app, 2) potential barriers to the use of the app, 3) healthcare system access issues, and 4) recommended modifications. Based on feedback received, the mHealth application changes included the addition of information on healthy relationships and raising children, more visual content, and localized service directories for different categories of care and support. Conclusions: A co-designed, culturally sensitive mHealth application is likely to support Aboriginal and/or Torres Strait Islander families facing health disparities due to disruption of Indigenous culture by a foundation for a potential clinical trial for effectiveness evaluation and wider implementation.

  • Background: Quality of Life (QoL) questionnaires are an established instrument designed to assess overall wellbeing and quality of life of patients. They are important in predicting the outcome of the disease and understanding the needs of individual patients. However, their repeated collection imposes substantial burden on both patients and clinical professionals. Many patients seek emotional support and mutual exchange in online communities for peer-support, where they frequently share detailed descriptions of symptoms and treatment experiences, addressing topics covered in QoL questionnaires. The emergence of large language models (LLMs) uncover potential for automatic extraction of relevant QoL information from patient-generated text. Objective: The aim of this study is to evaluate and compare various open-source LLMs and optimization approaches for automated extraction of QoL information from forum posts. Methods: The dataset consisted of 2,683 English-language posts from breast cancer patients recruited on Inspire.com online communities, manually annotated with sentence-level text spans indicating whether and where posts contained information relevant to 53 QoL questions from EORTC QLQ-C30 and QLQ-BR23 questionnaires. 11 open-source LLMs (8B-70B parameters) were evaluated in a zero-shot setup, generating 4,452 post-question predictions per model under two input conditions: post-only and post with additional context. For the best-performing model, additional experiments assessed the impact of chain-of-thought prompting, instruction optimization, few-shot prompting and parameter-efficient fine-tuning. For correctly classified yes/no instances, the overlap between model-generated evidence and human-annotated spans was evaluated. Results: Across 11 evaluated LLMs, GPT-OSS 20B achieved the highest macro F1-score (0.79) in the zero-shot post-only setting. Providing additional context consistently reduced performance of all models. Model size did not correlate with F1-score, with several mid-sized models (14B-30B) outperforming 70B models. For GPT-OSS 20B, chain-of-thought prompting did not improve performance (0.77). Instruction optimization produced results similar to the baseline in both zero-shot and few-shot settings (0.78-0.80). Bootstrap few-shot prompting with random search achieved the highest score overall (0.81). Parameter-efficient fine-tuning decreased performance (0.71). Most classification errors occurred in semantically broad or ambiguous terms and the fallback question. For correctly predicted yes/no answers, model-generated evidence matched or partially matched human-annotated spans in 89% of cases. Conclusions: Open-source LLMs are a promising tool for extracting QoL information that aligns with standardized questionnaire responses from online health forums. Mid-sized models achieved the highest accuracy, particularly in zero-shot, post-only settings. Few-shot prompting can further improve the results. Models were also able to generate evidence spans that closely matched human annotations. However, they consistently struggled with ambiguous and semantically overlapping terms. Overall, automated extraction of QoL information from patient-generated content may offer a faster, lower-cost and low-burden complement to traditional QoL questionnaires, given that limitations such as symptom ambiguity are addressed in future work.

  • Background: Affirming Care for lesbian, gay, bisexual, transgender, and queer (LGBTQ+) populations refers to culturally and clinically competent healthcare that recognizes specific health needs and provides respectful, inclusive, equitable, and non-discriminatory services that are supportive of diverse identities. LGBTQ+ populations face greater discrimination in healthcare, leading to higher levels of unmet health needs than the general population. Very few primary care practices in the United States have training for staff and clinicians on LGBTQ+ healthcare needs. Despite the growing needs for LGBTQ+ affirming care, there are no national standards or requirements for LGBTQ+ cultural competence training for primary-care healthcare providers in the United States. Objective: This study explores the accessibility and quality of online ‘grey literature’ providing LGBTQ+ affirming and culturally competent care information for primary care providers in the United States. Grey literature is produced by government, academic, business, and industry sources in formats not controlled by commercial publishing. Methods: We conducted a Google search of grey literature to identify readily available resources and training materials. Two thousand websites were screened. Those published in a language other than English before January 1, 2014, as well as those that were peer-reviewed literature or behind a paywall, were excluded. Fifty-four websites met the inclusion criteria for a full-text review. Results: We identified six themes from the existing academic literature: (1) affirming physical and visual environments, (2) sexual orientation and gender identity (SOGI) data collections, (3) training on LGBTQ+ health needs, (4) anti-discrimination policies, (5) appropriate, relevant services for LGBTQ+ patients, and (6) use of inclusive language. We then applied these themes as a deductive coding framework to the web-based sources and, during analysis, two additional sub-themes emerged: (1) staff diversity, (2) health inequalities and inequities. Findings revealed that not every web-based source addressed all themes. This unequal distribution of coverage across these themes means that providers must consult multiple web-based sources to obtain a comprehensive understanding. Additionally, existing grey literature resources often lacked depth, technical detail, and practical guidance, making it difficult for primary care providers to access actionable information on LGBTQ+ affirming care. ‘Training on LGBTQ+ health needs’ was the most frequently covered theme, and ‘SOGI data collection’ was the least addressed. Study limitations included geolocation biases and embedded advertisements in the Google search results. Conclusions: The study highlights that grey literature is insufficient for self-guided training. We recommend integrating formal LGBTQ+ affirming care training into medical and nursing curricula, as well as professional associations and continuing education, particularly amid growing federal and state-level restrictions on LGBTQ+ healthcare.

  • Background: The consequences of medication errors are substantial as they pose a significant threat to the high-risk population, including paediatric, neonatal and geriatric patients. Computerised Provider Order Entry (CPOE) systems and clinical decision support systems (CDSS) are increasingly implemented to reduce medical errors by automating prescribing processes and providing real-time decision support. While alerts have been shown to provide value, barriers to widespread implementation exist in the form of alert fatigue and usability problems. Objective: This systematic review and meta-analysis assessed the effectiveness of CPOE and CDSS in reducing medication errors across diverse populations and clinical environments. Methods: A systematic review was conducted following the Preferred Items for Systematic Review and meta-analyses (PRISMA guidelines), with four databases searched up to February 2025 for studies evaluating the effects of CPOE and CDSS implementation on medication error in paediatric and geriatric populations. We included only cohort and prospective studies, not restricted by language or country of publication. Single measures of continuous outcomes on medication error rates were extracted from each study. The Comprehensive Meta-analysis (CMA) was then applied to perform separate analyses to compare the outcome pre-and post-CPOE/CDSS implementation. A random-effect meta-analysis was conducted, with subgroup analyses to assess differences by population, healthcare setting, and system design. The Newcastle–Ottawa Scale was used for quality appraisal. Forest plots and funnel plots were applied for pooled results and publication bias assessment. Results: Fourteen studies met the inclusion criteria (paediatric: n = 12; geriatric: n = 2), all rated as good quality. In paediatrics, 10 of 12 studies reported significant reductions in medication errors post-implementation. Pooled analysis showed error rates were almost threefold higher pre-implementation (OR = 2.97; 95% CI 2.81–3.14), with substantial heterogeneity (I² = 94%) but consistent positive direction of effect. In geriatrics, both studies demonstrated significant reductions with no heterogeneity (I² = 0%) (OR = 2.45; 95% CI 2.29–2.62), though evidence remains limited in scope and setting due to the small number of studies. Descriptive synthesis indicated that CPOE/CDSS can intercept high severity errors, such as overdoses of high-risk medications, before reaching patients, although most studies assessed potential rather than actual harm. Meta‑regression showed study location as a significant moderator, with greater effects in North American studies compared to those conducted in Asia. No publication bias was detected, but regional variation suggests contextual factors such as healthcare infrastructure, informatics maturity and influence system effectiveness. Conclusions: CPOE/CDSS significantly reduces medication errors in special populations, with strong and consistent benefits in paediatrics and promising but limited evidence in geriatrics. Despite heterogeneity in paediatric studies, the direction of effect was uniformly positive. The systems also show potential to reduce the severity of harmful errors, although robust evidence on actual patient harm is lacking. Optimising and tailoring CPOE/CDSS to specific patient populations and healthcare settings, while addressing alert fatigue and workflow integration, are essential to maximise impact. Further research should expand the geriatric and neonatal evidence base, assess long-term outcomes and explore advanced decision support capabilities to enhance patient safety and clinical impact.

  • Application of Ecological Momentary Assessment in Maternal Health Management: A Scope review

    Date Submitted: Feb 1, 2026
    Open Peer Review Period: Feb 2, 2026 - Mar 30, 2026

    Background: Ecological momentary assessment (EMA) enables real-time, repeated evaluation of participants' emotions, thoughts, and behavioral patterns in natural settings. It effectively mitigates the retrospective bias inherent in traditional surveys and facilitates a longitudinal understanding of health status. However, its feasibility, practicality, and methodological details for monitoring and promoting maternal health remain unclear. Objective: To conduct a scoping review of studies on the application of EMA in maternal health management, providing a reference for future research and further promotion of maternal and infant health. Methods: Using the Joanna Briggs Institute (JBI) scoping review guidelines as the methodological framework, we searched the Web of Science, PubMed, CINAHL, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), China Biomedical Literature Database, Wanfang Database, and VIP Database. The search covered publications from the inception of each database to December 2025, and the included studies were subjected to a comprehensive analysis. Results: The search yielded 2,989 publications, of which 14 were ultimately included. The findings were summarized across three dimensions: study design characteristics (publication year, country, and study design features, such as sample size, study population, and outcome measure type); EMA data collection methods (EMA schedule characteristics, such as monitoring cycle, duration, and data sampling methods, such as fixed-time, random-time, or event-based sampling); and EMA response-related outcomes (participation rate and response rate). Conclusions: The EMA effectively mitigates the recall bias inherent in traditional assessment methods, offering novel approaches to enhance the quality of maternal health management. This enables longitudinal monitoring of maternal experiences in natural settings, facilitating the early identification of abnormal physiological, psychological, and behavioral issues during pregnancy and postpartum. This allows timely intervention to safeguard maternal and infant health. Future research should refine EMA study designs and implementation formats to fully leverage their potential in promoting maternal health and personalized interventions for maternal-infant wellness. Clinical Trial: Trial Registration: OSF Registries  10.17605/OSF.IO/GMFKZ

  • Background: In recent years, the field of digital health has grown exponentially, leading to notable benefits such as easier access to health-related information, but also to content saturation and misinformation. Thus, it is crucial to identify digital health tools that provide meaningful value and assess their real-world impact. Objective: This pre-registered study’s goal was to quantitively assess the LONDI platform, a German platform designed for different user groups supporting children with learning disorders. This assessment focused on user groups of mental health professionals (i.e., learning therapists and school psychologists), and was grounded on four of the five RE-AIM-framework dimensions: Reach, adoption, implementation, and maintenance. Methods: Data was collected over a 10-month period, between May first 2024 and March first 2025. The reach dimension was measured via a pop-up questionnaire (N=1324), collecting demographic and professional experience data. The adoption dimension was measured via a second pop-up questionnaire (N=160), measuring user experience (UX) and reuse intention for the platform’s help system. The implementation dimension was measured via web analytics (N= 37,133), measuring reading time for pages intended for mental health professionals. Moreover, this dimension was also assessed by comparing chatbot engagement rates with industry benchmarks. The maintenance dimension was measured via web analytics as well, comparing the usage in the previous (N= 20,496), and the current platform version (N= 37,133) in terms of number and location of users, time spent on the platform, number of actions per visit, and used devices and software. Results: 22% and 10.64% of the users that filled out the first pop-up questionnaire stated that they were learning therapists or school psychologists, respectively, exceeding their percentage in the German population (< 0.01%). The second pop-up questionnaire revealed an overall mean UX score of 1.46, surpassing the benchmark average, and UX ratings predicted intention to reuse. Time spent on the pages intended for mental health professionals was below the time needed to read them. The 0.18% rate of chatbot engagement was very low compared with industry benchmarks of 35-40%. Usage changed in the two compared time periods, and most strikingly, there was an 81.2% increase in the number of users. Conclusions: The study provides evidence to the LONDI platform’s optimal public health impact in terms of the reach, adoption, and maintenance RE-AIM-framework dimensions. Further research and endeavors and are needed to better understand and improve the platform’s impact in terms of the implementation dimension.

  • Balancing Value and Risk: Clinicians’ Perceptions and Adoption of AI-Enabled Clinical Decision Support Systems

    Date Submitted: Jan 29, 2026
    Open Peer Review Period: Jan 30, 2026 - Mar 27, 2026

    Background: The increasing adoption of Artificial Intelligence (AI) in healthcare, particularly within Clinical Decision Support Systems (CDSSs), is transforming clinical practice and decision-making. Although AI-CDSSs hold the potential to improve diagnostic accuracy, operational efficiency, and patient outcomes, their implementation also creates ethical, technical, and regulatory concerns, affecting healthcare professionals’ willingness to adopt these systems. Objective: Building on a value-based perspective, the study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) framework as determinants of perceived benefits and a risk-based perception model as determinants of perceived risks to develop a unified model exploring clinicians’ behavioural intention to adopt AI-enabled CDSSs. Methods: A self-administered cross-sectional survey was distributed to licensed healthcare professionals to examine how validated factors influence perceptions of risks and benefits. Responses were collected from 215 clinicians across Italy and the United Kingdom. Recruitment was undertaken using email invitations, attendance at academic conferences, and direct approaches within healthcare settings. Results: Perceived Benefits were found to be the strongest positive predictor of clinicians’ intentions to use AI-enabled CDSSs (β=.45, p<.001), whereas perceived risks had a significant negative effect (β=-.18, p=.002). Performance Expectancy and Facilitating Conditions significantly increased the adoption intentions, whereas Effort Expectancy and Social Influence were not significant. Among the risk antecedents, Perceived Performance Anxiety, Communication Barriers, and Liability Concerns were significant predictors of Perceived Risks. The model explained 46% of the variance in the intention to use AI-enabled CDSSs. Conclusions: The findings offer theoretical and practical insights into human factors influencing AI adoption in clinical practice, underscoring the importance of value alignment, professional accountability and institutional readiness, and highlighting the need to foster clinician trust in AI tools beyond the boundaries of technical performance.

  • Background: The COVID-19 pandemic significantly increased adoption of virtual care, including patient-to-provider secure messaging. However, this surge has heightened physician workload and burnout and has raised concerns about message appropriateness and liability among physicians. Objective: This study characterizes secure messaging use in Canadian hospital-based specialty care and explores the experiences of healthcare providers, administrative staff, and patients. Methods: We employed a convergent mixed-methods design, analyzing aggregated electronic health record (EHR) usage data and qualitative interview data. The study was conducted at Women’s College Hospital in Toronto, Canada, across four high-messaging specialty clinics: mental health, rheumatology, dermatology, and surgery. Quantitative data (Oct, 2019-Oct, 2022) detailing message volumes, response patterns, and timing. Semi-structured interviews explored messaging workflows, barriers, and facilitators. Data were analyzed separately, then converged to identify areas of convergence and divergence. Results: Message volumes surged post-pandemic, particularly in mental health. The monthly message rate per patient varied, with higher rates in mental health and rheumatology. Physicians reported negative experiences due to increased workload, lack of compensation, and inadequate integration into clinical workflows. High patient-to-physician ratios and limited nursing support for message triage were associated with a poor messaging experience. Patients and administrative staff valued messaging for its convenience, accessibility, and efficiency. A key finding was the poor engagement of all user groups in decisions regarding messaging implementation. Conclusions: The study highlights a disconnect between the high perceived value of secure messaging for patients and administrative staff and the negative experiences of physicians. Successful implementation requires thoughtful integration into care models, clear guidelines for patient use, and proper triage and "channel management" to guide patients to appropriate visit modalities. Future research should explore triaging algorithms as part of a digital front door, specialty-specific variations and the crucial role of nursing staff in message management.

  • Background: Quality of life (QoL) plays a crucial role in dementia care, yet QoL and its dynamic, context-dependent nature can be difficult to capture in people living with dementia due to challenges in memory and communication and limitations of self-reported QoL instruments. Observational tools such as the Maastricht Electronic Daily Life Observation (MEDLO) provide narrative descriptions of the daily life of people living with dementia in nursing homes. However, the MEDLO tool was not developed to assess QoL specifically, and it remains unclear to what extent its narrative descriptions reflect aspects of QoL. Analysing these narrative descriptions is labour-intensive and time-consuming. Recent advances in natural language processing (NLP), including Large Language Models, offer potential to analyse these narrative descriptions at scale. Objective: The study aims to gain insight into the QoL in people living with dementia residing in nursing homes in the Netherlands, using NLP to interpret narratives of daily life in existing MEDLO data. Methods: This study conducted a secondary analysis of existing MEDLO observational data from 151 people living with dementia residing in Dutch long-term care. Narrative data had been documented by trained observers, describing activities, interactions, settings and emotional expressions. For analysis, a local secure pipeline was developed in which GPT-4o-mini was deployed for NLP tasks. The pipeline comprised three analytical steps: (1) N-gram frequency analysis to identify common language patterns, (2) sentiment analysis of positive and negative expressions per QoL domains, and (3) topic modelling to group semantically related terms and map them to QoL domains. Outputs were iteratively refined through prompt engineering and validated through expert review for coherence and contextual relevance. Results: A total of 5,622 narratives (50,106 words) from 151 observed people living with dementia were analysed. The narratives were short, averaging 8.5 words per narrative. N-gram frequency analysis identified frequent documentation of passive activity (sits at the table) in limited indoor settings (living room). Emotional well-being was often described in positive terms (smiles, laughs), whereas explicitly negative expressions (cries, distress) occurred less frequently. Weighted sentiment analysis showed that, although fewer in number, negative expressions carried a stronger intensity, resulting in an overall predominance of negative sentiment across all QoL domains. Topic modelling identified eight coherent clusters, most of which mapped onto multiple QoL domains, underscoring QoL’s multidimensionality. Conclusions: NLP identified predominantly passive activities in little varying indoor settings, yet people living with dementia were often described with positive affect, underscoring both the complexity of QoL in dementia and the influence of documentation practices. In practice, NLP could help translate everyday care documentation into actionable information that guides more responsive, person-centred dementia care.

  • Background: Consistent physical inactivity among adults and adolescents poses a major global health challenge. Mobile health (mHealth) interventions, particularly Just-in-Time Adaptive Interventions (JITAIs), offer a promising avenue for scalable and personalized physical activity promotion. However, developing and evaluating such adaptive interventions at scale, while integrating robust behavioral science, presents methodological hurdles. Objective: The PEARL study aimed to assess the feasibility and effectiveness of a reinforcement learning (RL) algorithm, informed by health behavior change theory (COM-B), to personalize the content and timing of physical activity nudges via the Fitbit app compared to fixed and random nudging strategies, and to a control group with no nudges. Methods: We conducted a large-scale, four-arm randomized controlled trial (RCT) enrolling 13,463 Fitbit users. Participants were randomized to: (1) Control (no nudges); (2) Random (random content/timing); (3) Fixed (logic based on baseline COM-B survey); and (4) RL (adaptive algorithm). The primary outcome was the change in average daily step count from baseline to 2 months. Secondary outcomes included user engagement and survey responses regarding capability, opportunity, and motivation. Results: 7,711 participants were included in the primary analysis (mean age 42.1 years; 86.3% female). At 1 month, the RL group showed a significant increase in daily steps compared to Control (+296 steps, P<.001), Random (+218 steps, P=.005), and Fixed (+238 steps, P=.002) groups. At 2 months, the RL group sustained a significant increase against the Control (+210 steps, P=.01). Generalized estimating equation (GEE) models confirmed a sustained significant increase in the RL group (+208 steps, P=.002). In exit surveys, the RL group reported higher favorable responses regarding nudge customization (37%) compared to other groups. Conclusions: This study demonstrates the feasibility and early efficacy of using RL to personalize digital health nudges at scale. While long-term retention remains a challenge, the adaptive approach outperformed static behavioral rules, showcasing the promise of dynamic personalization in a real-world mHealth setting. Clinical Trial: doi: 10.17605/OSF.IO/TW7UP

  • An AI-Based Smart Nursing Ward Model for Enhanced Recovery After Thoracic Surgery: A Historical Controlled Trial

    Date Submitted: Jan 27, 2026
    Open Peer Review Period: Jan 28, 2026 - Mar 25, 2026

    Background: Due to surgical trauma and the impact of the disease, patients undergoing thoracic surgery often experience a series of postoperative symptom burdens, which affect their recovery. Traditional perioperative care has drawbacks. Objective: To evaluate the impact of an AI-based personalized smart nursing ward management model on postoperative recovery outcomes in patients undergoing thoracic surgery. Methods: According to patients' admission sequence, patients who met the inclusion criteria were divided into a control group (n=303) and an intervention group (n=240). The control group adopted the routine nursing mode of general wards, while the intervention group implemented the AI-based personalized smart nursing ward management model on the basis of the routine nursing provided to the control group. Results: Data from all 543 enrolled patients were analyzed. Compared with the control group (n=303) receiving routine care, the intervention group (n=240) had a significantly shorter median hospital stay (9.0 days vs 12.0 days) and chest tube indwelling time (5.0 days vs 7.0 days), as well as lower total hospitalization costs (¥61,032.87 vs ¥72,859.90) (all P < .001). The postoperative pulmonary complication rate was also significantly lower in the intervention group (3.8% vs 12.2%, P < .001). Furthermore, patient satisfaction was higher (98.53% vs 91.28%), and nurses' daily step count was reduced (12,359.52 vs 18,692.74 steps) in the intervention group (both P < .001) Conclusions: The AI-based smart nursing model effectively promotes postoperative recovery and offers an innovative management approach for thoracic surgery.

  • The Impact of AI-driven tools on Breastfeeding Outcomes: Systematic Review and Meta-Analysis

    Date Submitted: Jan 27, 2026
    Open Peer Review Period: Jan 27, 2026 - Mar 24, 2026

    Background: The current global breastfeeding landscape presents both progress and challenges. The rise of artificial intelligence (AI) has emerged as a promising new strategy to enhance breastfeeding practices. Objective: To evaluate the impact of AI-driven tools on breastfeeding practices and outcomes. Methods: We searched PubMed, Web of Science, Cochrane Library, Embase, and CINAHL from inception to October 2025 for randomized controlled trials (RCTs) and quasi-experimental studies. The risk of bias in individual studies was assessed using the Cochrane risk of bias tool for randomized controlled trials (RoB 2) and the risk of bias in non-randomized studies of interventions tool (ROBINS-I). Data were extracted independently by two reviewers and combined using Review Manager 5.4 and R-4.5.2 to obtain pooled results via random-effects models, with subgroup analyses based on intervention type, timing of implementation, population characteristics, and country income level. Results: This review included 39 studies with 10735 participants from 15 countries. AI-driven tools increased exclusive breastfeeding (EBF) rates (at <3 months: relative risk [RR] 1.21, 95% CI 1.13-1.29; P<.001, I²=56%; at 3–6 months: RR 1.54; 95% CI 1.29-1.85; P<.001, I2=69%; at ≥6 months: RR 1.47, 95% CI 1.22-1.77, P<.001, I2=78%), breastfeeding self-efficacy (BSE) (standardized mean difference [SMD] 0.41, 95% CI: 0.04-0.78; P=.03, I2=93%), and breastfeeding knowledge (SMD 1.69; 95% CI: 0.54-2.84, P=.004, I2=98%). Conclusions: AI-driven tools effectively increase exclusive breastfeeding rates, breastfeeding self-efficacy, and breastfeeding knowledge. Future studies are needed to provide stronger evidence about clinical care interventions. Clinical Trial: PROSPERO CRD420251233352; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251233352

  • ‘Carer-as-Sensor’ in Decentralized Trials: Passive Sensing Data Accuracy, Parkinson’s, and Observers

    Date Submitted: Jan 26, 2026
    Open Peer Review Period: Jan 27, 2026 - Mar 24, 2026

    Background: Parkinson's clinical trials depend on patient-reported outcomes, often overlooking the vital role of carers in collaboratively tracking symptom progression. This is a potential limitation for decentralized clinical trials aimed at measuring real-world, free-living symptoms with sensors, such as wearables and cameras in the home. Objective: The primary objective of our study was to inform the design of a multimodal sensor platform for decentralised clinical trials. Methods: A qualitative study was conducted with an inductive approach using semistructured interviews with a cohort of people with Parkinson's. Results: This study of 18 participants (14 people diagnosed with Parkinsons, 4 spouses/informal carers) found that carers, household members, and peers take a central role in helping people with Parkinson’s make sense of and manage their symptoms. Our participants relied on others to help with completing tasks and understanding their symptoms through comparison to others, using their Carer-as-Sensor. While our participants mostly viewed their relationships with others positively, this could lead to negative impacts on oneself. Participants could prioritize household needs over their health by not taking medication or risking a chance of falling, or even avoiding being around others to prevent their Parkinson's being on display to reduce carer burden. Conclusions: Our results argue that an 'outsider' and 'insider' approach to reporting symptoms can identify symptoms that are not noticed by people with Parkinson's, or withheld from carers. These form household-centred recommendations more broadly for the design of tracking and annotation strategies in the context of decentralised clinical trials and new innovations in AI to support the capture of nuanced and subtle changes in symptoms.

  • Background: Poor usability of electronic health record (EHR) systems is associated with workflow inefficiencies, patient safety risks, and burnout among health professionals. Health professionals are exposed to various work conditions, but the associations with perceived EHR usability are unknown. Objective: To examine whether medical doctors’ and nurses’ usability perceptions of an established electronic patient record (EPR) system and a newly adopted EHR system differ by work schedules, type of employment (full-time or part-time), work pace, and number of clinical settings. Methods: In the established EPR system, nurses were more likely to report low ease-of-use if they worked three-shift rotations (odds ratio [OR] 2.21, 95% CI: 1.34-3.65 vs. daytime), part-time (OR 1.63, 95% CI:1.20-2.21 vs. full-time), or faced very high work pace (OR 1.25, 95% CI: 1.42-3.58 vs. low work pace). Following EHR adoption, medical doctors and nurses reported a median (IQR) SUS score of 17.5 (7.5-32.5) and 32.5 (17.5-50.0), respectively. Both medical doctors and nurses reported lower SUS scores when they faced very high work pace compared to low work pace, with mean differences of -8.56, 95% CI (-12.60 to -4.51) and -8.43 (95% CI: -14.10 to -2.76), respectively. Part-time employed nurses reported 2.72 points (95% CI: -4.93 to -0.52) lower SUS score than full-time employed, and nurses working across 3-4 clinical settings reported 2.99 points (95% CI: -5.52 to -0.46) lower SUS score than nurses working across 1-2 settings. Results: 543 medical doctors and 1,869 nurses participated. In the established EPR system, nurses were more likely to report low ease-of-use if they worked three-shift rotations (odds ratio [OR] 2.21, 95% CI: 1.34-3.65 vs. daytime), part-time (OR 1.63, 95% CI:1.20-2.21 vs. full-time), or faced very high work pace (OR 1.25, 95% CI: 1.42-3.58 vs. low). Following EHR adoption, medical doctors and nurses reported a median (IQR) SUS score of 17.5 (7.5-32.5) and 32.5 (17.5-50.0), respectively. Both medical doctors and nurses reported lower SUS scores when they faced very high work pace compared to low work pace, with mean differences of -8.56, 95% CI (-12.60 to -4.51) and -8.43 (95% CI: -14.10 to -2.76), respectively. Part-time employed nurses reported 2.72 points (95% CI: -4.93 to -0.52) lower SUS scores than full-time employed, and nurses working across 3-4 clinical settings reported 2.99 points (95% CI: -5.52 to -0.46) lower SUS score than nurses working across 1-2 settings. Conclusions: These findings suggest that system usability perceptions differ by work conditions, particularly work pace. Although these results could guide tailored implementation strategies, ensuring adequate EHR usability architecture is likely to be as important.

  • Background: Personal Data Spaces (PDS) are increasingly promoted as digital infrastructures that enable citizen participation in health data governance by strengthening transparency and individual control over personal health data. Despite growing policy and technological attention, empirical evidence remains limited on whether citizens view PDS as acceptable and desirable governance instruments, how they evaluate different types of data and purposes of data use, and which factors shape public support. Objective: The objective of this study was to examine how citizens evaluate We Are, a proposed citizen-centered Personal Data Space model in Flanders, Belgium, and to assess overall support, reasons for endorsement, preferences for control versus transparency, acceptability of storing different types of health data, and acceptance of different purposes of data use. Methods: We conducted an online survey among adults aged 18-79 years in Flanders, Belgium (N=1,041). The sample was quota-based and representative for gender, age, education, province, and urbanization level. Participants evaluated the We Are model after reading a description. Measures included overall evaluation of the model, reasons for support, preferences for transparency and control, willingness to store medical versus lifestyle data, and willingness to share data across vignette-based scenarios varying purpose of use and recipient type. Data were analyzed using t-tests, linear regression, and mixed models with repeated measures. Results: Overall evaluations of We Are were moderately positive (Mean 2.51 on a 1-4 scale) and did not differ significantly from the scale midpoint (t(1040)=0.70, P=.24). Sociodemographic characteristics explained little variance in support, whereas understanding of the We Are model and psychographic factors substantially increased explained variance (R² increased from .03 to .24). Higher trust in technology was positively associated with support, while stronger privacy attitudes and privacy-related fears were negatively associated. Respondents valued control more strongly than transparency for both general personal data (t(1040)=-10.37, P<.001) and health data (t(1040)=-12.47, P<.001). Medical data were considered more acceptable to store than lifestyle data (Δ=0.38, P<.001). Both personal and public benefits motivated support, but commercial data use reduced willingness to share, particularly when framed around individual gain rather than collective benefit. Conclusions: Citizens view PDS as potentially valuable instruments for health data governance, but their support is conditional and shaped by understanding and psychographic factors rather than by sociodemographic factors. PDS can contribute to meaningful citizen participation only when technological features are embedded in governance arrangements that provide real agency, credible safeguards, and demonstrable public value.

  • Secondary Use of Health Data as a Core Capability in Medical Informatics

    Date Submitted: Jan 23, 2026
    Open Peer Review Period: Jan 25, 2026 - Mar 22, 2026

    The European Health Data Space represents a landmark regulatory success in enabling the secondary use of health data for research, innovation, and policy within a trusted and interoperable framework. This Viewpoint discusses how strategic alliances—such as UNINOVIS—and translational research ecosystems, with IBIMA as a driving hub, operationalize this regulation by aligning governance, infrastructure, and applied data science. Together, they illustrate how European health data policy can be translated into real-world evidence generation and sustained clinical and societal impact.

  • Background: Musculoskeletal conditions are a leading global cause of disability, yet the factors influencing long-term musculoskeletal health, particularly following trauma, remain incompletely understood. Machine learning could be applied to identify previously unknown patterns in large-scale multimodal datasets. Objective: Test the ability of a new sparse Group Factor Analysis method to uncover hidden patterns in large-scale multi-modal datasets and generate testable, clinically relevant hypotheses. Methods: This study applies sparse Group Factor Analysis, a hierarchical unsupervised machine learning method, to the ADVANCE cohort—a longitudinal dataset of 1445 UK Afghanistan War servicemen—to identify latent structures in multimodal clinical data. Study 1 validated the approach by rediscovering known group-level patterns between combat-injured and non-injured participants, including poorer outcomes in pain, mobility, and bone health among those with lower limb loss. Study 2 explored the Injured, non-amputee subgroup without prespecified labels to identify new hypothesis-generating clusters that could subsequently be tested using standard hypothesis testing methods. Results: A subgroup of 125 individuals with worse musculoskeletal outcomes was uncovered. This group had greater body mass, higher injury severity, and a higher prevalence of head injury. These findings led to a novel hypothesis: that head injury, including potential traumatic brain injury, is associated with long-term musculoskeletal deterioration. This hypothesis is supported by literature in both athletic and military populations and will be tested in follow-up analyses. Conclusions: Our findings demonstrate how sparse Group Factor Analysis, combined with clinical insight, can uncover hidden patterns in large-scale datasets and generate testable, clinically relevant hypotheses that inform prevention, treatment, and rehabilitation strategies.

  • Background: Chronic kidney disease (CKD) requires sustained self-management involving complex medication regimens, dietary restrictions, and symptom monitoring. These demands pose substantial challenges to medication adherence and daily disease management. Digital therapeutics (DTx) have the potential to support CKD self-management; however, CKD-specific design requirements informed by both patient and clinician perspectives remain insufficiently explored. Objective: This study aimed to identify key design requirements for CKD-specific digital therapeutics by integrating patient-reported self-management challenges with nephrologist perspectives on clinical needs and implementation considerations. Methods: A convergent mixed-methods study was conducted at a tertiary academic hospital. Quantitative data were collected through a structured survey of 60 adults with non–dialysis-dependent CKD to assess medication adherence challenges, digital health needs, and age-related differences. Qualitative data were obtained through focus group interviews with 19 nephrologists and analyzed using thematic analysis. Quantitative and qualitative findings were integrated to identify convergent priorities and design implications for CKD-specific DTx. Results: None of the patients reported prior experience with CKD-specific digital health applications, although 70% perceived a need for such tools. Younger patients (<60 years) expressed significantly greater interest in digital therapeutics than older patients (83.9% vs 55.2%, P=.015). Common patient-reported challenges included managing multiple medications (36.7%), irregular medication schedules (30.0%), and difficulty understanding medication timing relative to meals (28.3%). Nephrologists emphasized the importance of personalized medication reminders, comprehensive medication information (including adverse effects and nephrotoxic risks), symptom-monitoring systems, and features supporting dietary and lifestyle management. Integration findings highlighted the need for user-friendly, age-sensitive interfaces, data security, and clinically actionable feedback mechanisms. Conclusions: By integrating patient and nephrologist perspectives, this mixed-methods study identifies key design considerations for CKD-specific digital therapeutics. These findings provide formative, design-informed evidence to guide the early development of patient-centered and clinically relevant digital therapeutics for CKD.

  • Background: Digital multidomain interventions hold promise for dementia risk reduction; however, populations at higher dementia risk, including those experiencing socioeconomic and educational disadvantage, remain underrepresented in trials, and engagement with digital interventions often declines over time. Co-production and blended models that combine digital tools with human support may improve reach, acceptability, usability, and sustained engagement. Designing interventions that are usable and acceptable for individuals facing structural, educational, or digital barriers (underserved groups) is therefore likely to produce solutions that are both accessible and scalable for the wider older adult population. Objective: To describe the co-production process used to develop ENHANCE—a coach-supported digital intervention targeting ten modifiable dementia risk factors in older adults from underserved groups—and report key outputs and lessons learned for equitable digital prevention design. Methods: We co-produced ENHANCE between July 2023 and February 2025 using a multi-stage development process guided by the Medical Research Council framework for complex interventions and the Double Diamond design model. The Person-Based Approach informed user-centred guiding principles (key design objectives), while behaviour change content was operationalised using behavioural change theories. Co-production followed four phases. The Discovery phase explored barriers to engagement with existing digital materials and identified candidate components for each dementia risk-factor module. The Define phase translated these insights into guiding principles and blueprints of each risk-factor module integrated with behavioural change components. The Design phase involved iterative co-production and usability testing of prototypes. The Delivery phase evaluated a high-fidelity prototype through a one-week usability study with coaching support. Contributors included 162 research participants recruited from underserved community settings, 33 patient and public involvement contributors, and 4 human–computer interaction experts. Throughout development, co-production focused on reducing literacy, digital confidence, and cultural barriers to maximise usability across diverse older adult populations. Results: Co-production produced (1) evidence-informed module strategies for targeted dementia risk factors; (2) a set of guiding principles to ensure low-literacy, culturally relevant, and accessible content, supporting both equity of access and wider population usability; (3) a meadow-themed app integrating tailored check-ins, educational videos, cognitive training games, and in-app messaging; and (4) a structured coaching model, including onboarding, brief follow-up, and accompanying coaching manuals. Iterative testing and refinement improved navigation, simplified language, reduced text burden, and ensured the use of familiar and accessible game formats, resulting in a feasibility-ready prototype. Conclusions: : ENHANCE is a co-produced, coach-supported digital intervention designed to be accessible for underserved older adults at increased dementia risk, with design features intended to support accessibility, engagement, and scalability across the wider ageing population. The development process illustrates how integrating co-production with behavioural science and usability methods can support principled intervention design for equitable digital dementia prevention. Clinical Trial: ISRCTN17060879

  • Background: Clinical natural language processing (NLP) refers to computational methods for extracting, processing, and analyzing unstructured clinical text data, and holds a huge potential to transform healthcare. The advancement of deep learning, augmented by the recent emergence of transformers, has been pivotal to the success of NLP across various domains. This success is largely attributed to the end-to-end training capabilities of deep learning systems. Further, advances in instruction tuning have enabled Large Language Models (LLMs) like OpenAI’s GPT to perform tasks described in natural language. While these advancements have dramatically improved capabilities in processing languages like English, these benefits are not always equally transferable to under-resourced languages. In this regard, this review aims to provide a comprehensive assessment of the state-of-the-art NLP methods for the mainland Scandinavian clinical text, thereby providing an insightful overview of the landscape for clinical NLP within the region. Objective: The study aims to perform a systematic review to comprehensively assess and analyze the state-of-the-art NLP methods for the Scandinavian clinical domain, thereby providing an overview of the landscape for clinical language processing within the Scandinavian languages across Norway, Denmark, and Sweden. Generally, the review aims to provide a practical outline of various modeling options, opportunities, and challenges or limitations, thereby providing a clear overview of existing methodologies and potential avenues for future research and development. Methods: A literature search was conducted in various online databases, including PubMed, ScienceDirect, Google Scholar, ACM Digital Library, and IEEE Xplore between December 2022 and March 2024. The search considers peer-reviewed journal articles, preprints, and conference proceedings. Relevant articles were initially identified by scanning titles, abstracts, and keywords, which served as a preliminary filter in conjunction with inclusion and exclusion criteria, and were further screened through a full-text eligibility assessment. Data was extracted according to predefined categories, established from prior studies and further refined through brainstorming sessions among the authors. Results: The initial search yielded 217 articles. The full-text eligibility assessment was independently carried out by five of the authors and resulted in 118 studies, which were critically analyzed. Any disagreements among the authors were resolved through discussion. Out of the 118 articles, 17.9% (n=21) focus on Norwegian clinical text, 61% (n=72) on Swedish, 13.5% (n=16) on Danish, and 7.6% (n=9) focus on more than one language. Generally, the review identified positive developments across the region despite some observable gaps and disparities between the languages. There are substantial disparities in the level of adoption of transformer-based models. In essential tasks such as de-identification, there is significantly less research activity focusing on Norwegian and Danish compared to Swedish text. Further, the review identified a low level of sharing resources such as data, experimentation code, pre-trained models, and the rate of adaptation and transfer learning in the region. Conclusions: The review presented a comprehensive assessment of the state-of-the-art Clinical NLP in mainland Scandinavian languages and shed light on potential barriers and challenges. The review identified a lack of shared resources, e.g., datasets and pre-trained models, inadequate research infrastructure, and insufficient collaboration as the most significant barriers that require careful consideration in future research endeavors. The review highlights the need for future research in resource development, core NLP tasks, and de-identification. Generally, we foresee that the findings presented will help shape future research directions by shedding some light on areas that require further attention for the rapid advancement of the field in the region

  • Photoplethysmography in Healthcare: An Umbrella Review of Clinical Applications, Validation, and Evidence Gaps

    Date Submitted: Jan 20, 2026
    Open Peer Review Period: Jan 21, 2026 - Mar 18, 2026

    Background: Photoplethysmography (PPG) is widely used in consumer and clinical devices for heart rate, rhythm, sleep, respiratory, and hemodynamic monitoring. However, rapid expansion of applications has produced a fragmented evidence base with heterogeneous methods and variable validation quality. Objective: To synthesize and critically appraise systematic reviews evaluating PPG-based applications in healthcare, map major clinical domains and methodological practices, and identify limitations and priorities for future research. Methods: A protocolized umbrella review (PROSPERO CRD420251015845) was conducted across six databases. Systematic reviews and meta-analyses involving human PPG applications were included. Screening, extraction, and AMSTAR-2 quality assessment were performed in duplicate following PRISMA-S and PRIOR guidelines. Results: Fifty-nine systematic reviews were included. PPG showed consistent accuracy for resting heart-rate monitoring and strong performance for opportunistic atrial fibrillation screening when paired with confirmatory ECG. HRV estimation, stress monitoring, sleep assessment, neonatal and maternal monitoring, and metabolic applications showed emerging but heterogeneous evidence. Cuffless blood pressure estimation remains limited by calibration dependence, motion sensitivity, and poor generalizability. Remote PPG (rPPG) achieves good accuracy under controlled lighting but degrades with motion, light variability, and darker skin pigmentation. Across domains, performance was typically higher in controlled environments and attenuated in free-living settings. Common methodological limitations included small samples, inconsistent reporting of device and preprocessing details, lack of external validation, algorithm opacity, and underrepresentation of diverse populations. Conclusions: PPG is approaching clinical maturity for atrial fibrillation screening and resting heart-rate monitoring, while other applications remain earlier in development. Safe integration into practice requires confirmatory ECG for rhythm abnormalities, awareness of bias sources, and adherence to transparent reporting. Future progress depends on multicenter longitudinal studies, real-world validation, diverse benchmark datasets, standardized metrics, and improved reproducibility across devices and algorithms. PPG holds promise as a scalable component of digital health infrastructure when developed and evaluated with methodological rigor. Clinical Trial: PROSPERO Registration: CRD420251015845

  • Adolescent’s Perspectives and Experiences with Dietary Mobile Health Apps: A Scoping Review

    Date Submitted: Jan 19, 2026
    Open Peer Review Period: Jan 20, 2026 - Mar 17, 2026

    Background: Smartphones play a central role in adolescents’ daily lives, making dietary mobile health (mHealth) apps—tools that provide nutrition education and tracking eating behaviors—a promising avenue for influencing dietary habits. While numerous studies have examined the impact of mHealth apps on diet, few have investigated adolescents’ perspectives and experiences with these tools. Objective: This scoping review aimed to synthesize the evidence and map the research gaps on adolescents’ perspectives (positive or negative) and experiences (attitudes, barriers, and facilitators) of using dietary mHealth apps on their smartphones. Methods: A systematic scoping review was conducted according to the 5-stage framework by Arksey and O’Malley. Articles that included mixed-methods studies that focused on adolescents (10-19 years of age) reporting perspectives (positive or negative) and experiences (attitudes, barriers, and facilitators) related to dietary apps use were searched across: PsycINFO, Embase, Medline, Web of Science and CINAHL for studies that were published from 2012 until 2023. Articles that were not specific to diet, not research studies, and not written in English were omitted. Results: Of the 590 abstracts screened, 17 studies met the eligibility criteria. Ten studies assessed the usability, feasibility and acceptability of standalone or multi-component dietary mHealth apps, while nine examined app likability and effectiveness. Thematic analysis revealed seven overarching themes: (1) Technical Functionality and Usability; (2) Appreciation of Nutritional Education and Content Depth; 3) Importance of Social Connection, Feedback and Support; (4) Values of Entertainment and Gamification; (5) Significance of Personal Goals, Motivation and Tracking; (6) Interest for Simple Design and Interface; and (7) Perceived Effectiveness of Dietary mHealth Apps. Positively perceived features included food identification, tracking and gamification elements. Commonly barriers included technical difficulties, tracking inaccuracies, complex information delivery and limited social engagement. Facilitators to app use were ease of navigation, targeted information, social interaction, rewards and goal setting. Suggested improvements focused on tracking accuracy, interface design, feedback mechanisms and notification options. Overall, adolescents perceived effective apps to as those that raised awareness of eating habits and support improvements in dietary intake. Conclusions: This scoping review highlights that adolescents’ experiences with dietary mHealth apps are shaped by technical functionality, usability, social engagement, personalization, and gamification. While these features can enhance engagement, barriers such as tracking inaccuracies, technical issues, and limited social interaction reduce app effectiveness. Understanding these perspectives is critical for designing apps that are not only informative but also appealing and sustainable for adolescent users.

  • Background: Obesity remains a pressing global health issue. Research suggests that better health literacy can support obesity management. This study tested digital interventions combining healthy eating guidelines with AI and mobile tools, including a ChatGPT-powered Line chatbot for daily education and an AI food plate recognition system for calorie tracking and meal suggestions. Objective: This study aims to evaluate the efficacy of an integrated digital intervention, combining YOLOv5-based AI food plate recognition and a ChatGPT-powered LINE chatbot, on weight reduction (BMI) and health literacy among overweight and obese adults. Methods: The study used a quasi-experimental design-intervention case-control design. Both the case and intervention groups received basic health education through app notifications and used an AI food plate recognition tool to estimate their nutritional intake. Only the intervention group could access an AI weight-loss chatbot for timely suggestions. Questionnaire data were collected from users at several points during the intervention. Results: Eighty participants were enrolled. The intervention group demonstrated significantly greater reductions in BMI (β = −1.32; 95% CI, −1.56 to −1.09; P < .001) and improvements in health literacy (β = 4.71; 95% CI, 3.86 to 5.56; P < .001) versus controls. Physical activity (step count β = 1,926.5; 95% CI, 1,209.3 to 2,643.7; P < .001) and weekly exercise time (β = 0.56; 95% CI, 0.21 to 0.92; P = .002) also increased, while late-night snacking decreased (β = −0.45; 95% CI, −0.81 to −0.08; P = .017). The intervention group consistently outperformed the control group across key health measures. However, the AI chatbot alone lacked significant effects on primary outcomes. Conclusions: This integrated digital intervention effectively promotes weight loss and health literacy. Given the strong short-term efficacy, future research should employ randomized designs, larger sample sizes, and longer follow-ups to establish long-term weight maintenance and address potential influences such as the Hawthorne effect. It also highlights the need to further develop interactive, personalized health education tools and optimize AI food plate recognition systems to improve health literacy and weight management.

  • Background: During crisis, individuals increasingly rely on digital platforms for information, communication, and emotional support. Cyber behavior - which encompasses online engagement, security practices, and information sharing is shaped by cognitive and emotional factors such as awareness, knowledge, and anxiety. Understanding these relationships is crucial for promoting digital resilience and well-being during wartime and other large-scale emergencies. Objective: This study sought to examine how cybersecurity awareness, knowledge, and crisis-related anxiety influence cyber behavior and well-being during a national crisis. Drawing on the Protection Motivation Theory (PMT), the study further explored how cognitive and affective responses interact to shape individuals’ online engagement patterns and subsequent psychological outcomes. Methods: A cross-sectional online survey was conducted among 512 Israeli adults aged 18-65 during the ongoing war period (January 2024). Standardized psychometric instruments were used, including the WHO Well-Being Index, DASS-21 Stress subscale, and the Connor-Davidson Resilience Scale (CD-RISC-10). Media engagement was assessed across ten distinct digital activities. Data analysis employed a comprehensive approach, including cluster analysis, exploratory factor analysis (EFA), regression modeling, and path analysis. Results: Cluster analysis yielded two distinct segments: a high media engagement cluster and a low media engagement cluster. Participants in the high-engagement group reported significantly higher stress levels and greater utilization of digital media for news consumption, social networking, and charitable donations (p < .001). Furthermore, exploratory factor analysis revealed three salient dimensions of media usage: active, passive, and institutional. Path analysis indicated that stress was a positive predictor of all forms of media engagement. In predicting well-being, active media use (β = .12, p = .006) and resilience (β = .30, p < .001) were positively associated, whereas passive media use demonstrated a marginally negative association (β = -.08, p = .078). Conclusions: Cyber behavior during wartime is demonstrably influenced by both cognitive awareness and emotional stress. Specifically, while anxiety and stress tend to increase online engagement, overexposure to digital media may simultaneously well-being. Therefore, enhancing cyber literacy, cultivating emotional resilience, and promoting balanced media consumption are crucial strategies that can mitigate psychological distress and significantly strengthen digital resilience during crises.

  • Intelligent Identification of Pressure Injuries Using Multi-modal Deep Learning: A Scoping Review

    Date Submitted: Jan 13, 2026
    Open Peer Review Period: Jan 14, 2026 - Mar 11, 2026

    Background: The global prevalence of pressure injuries is high and can cause severe infections, or death. Accurate staging is vital for effective intervention. Deep learning streamlines pressure injury assessment, enhances efficiency, and yields practical, accurate results. This scoping review summarized research on multi-modal deep learning for intelligent pressure ulcer recognition. Objective: It systematized models, training methods, and outcomes to identify the best systems for rapid detection and automated staging of pressure ulcers. Enhancing the timeliness, accuracy, and objectivity of diagnosis is the goal. Methods: We searched the following databases and sources: PubMed, the Cochrane Library, IEEE Xplore, and Web of Science. The scoping review was conducted in accordance with the JBI Scoping Review Methodology Group’s guidance and reported following Preferred Reporting Items for Systematic Reviews and Meta-Analyses—Extension for Scoping Reviews guidelines. The study protocol was registered with the International Prospective Registry of Systematic Reviews (PROSPERO) on 12 December 2025 (registration number: CRD420251251573). Results: 15 articles were included: 26 models were involved, including AlexNet; VGG16; ResNet18; DenseNet121; SE-Swin Transformer; Cascade R-CNN; vision transformer (ViT); ConvNextV2; EfficientNetV2; Meta Former; TinyViT; CCM; BCM; ResNext + wFPN; SE-Inception; Mask-R-CNN; SE-ResNext101; Faster R-CNN; ResNet50; ResNet152; DenseNet201; EfficientNet-B4; YOLOv5; Inception-ResNet-v2; InceptionV3; MobilNetV2. The training methodology for intelligent pressure ulcer recognition models involves establishing an image database, processing images, and constructing the recognition model. Different models exhibit varying accuracy rates in staging pressure ulcers, with overall accuracy fluctuating between 54.84% and 93.71%. The DenseNet121 model achieved the highest recognition accuracy of 93.71%, while VGG16 was the most widely applied. The same model demonstrated significant variations in recognition accuracy across different studies. Conclusions: The multi-modal and deep learning-based intelligent recognition model for pressure injuries demonstrates high overall accuracy, enabling rapid automated staging of such injuries. Future research may explore optimized intelligent assistance systems to enhance the accuracy, objectivity, and efficiency of pressure injury diagnosis.