Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

The leading peer-reviewed journal for digital medicine and health and health care in the internet age. 

Latest Submissions Open for Peer Review

JMIR has been a leader in applying openness, participation, collaboration and other "2.0" ideas to scholarly publishing, and since December 2009 offers open peer review articles, allowing JMIR users to sign themselves up as peer reviewers for specific articles currently considered by the Journal (in addition to author- and editor-selected reviewers).

For a complete list of all submissions across all JMIR journals as well as partner journals, see JMIR Preprints

Note that this is a not a complete list of submissions as authors can opt-out. The list below shows recently submitted articles where submitting authors have not opted-out of open peer-review and where the editor has not made a decision yet. (Note that this feature is for reviewing specific articles - if you just want to sign up as reviewer (and wait for the editor to contact you if articles match your interests), please sign up as reviewer using your profile).

To assign yourself to an article as reviewer, you must have a user account on this site (if you don't have one, register for a free account here) and be logged in (please verify that your email address in your profile is correct).

Add yourself as a peer reviewer to any article by clicking the '+Peer-review Me!+' link under each article. Full instructions on how to complete your review will be sent to you via email shortly after. Do not sign up as peer-reviewer if you have any conflicts of interest (note that we will treat any attempts by authors to sign up as reviewer under a false identity as scientific misconduct and reserve the right to promptly reject the article and inform the host institution).

The standard turnaround time for reviews is currently 2 weeks, and the general aim is to give constructive feedback to the authors and/or to prevent publication of uninteresting or fatally flawed articles. Reviewers will be acknowledged by name if the article is published, but remain anonymous if the article is declined.

The abstracts on this page are unpublished studies - please do not cite them (yet). If you wish to cite them/wish to see them published, write your opinion in the form of a peer-review!

Tip: Include the RSS feed of the JMIR submissions on this page on your homepage, blog, or desktop RSS reader to stay informed about current submissions!

JMIR Submissions under Open Peer Review

↑ Grab this Headline Animator

If you follow us on Twitter, we will also announce new submissions under open peer-review there.

Titles/Abstracts of Articles Currently Open for Review:

  • Backcasting the Trust Gap: A Strategic Roadmap for Clinician Adoption of AI Diagnostics by 2040

    Date Submitted: Feb 26, 2026
    Open Peer Review Period: Mar 19, 2026 - May 14, 2026

    Background: The integration of artificial intelligence (AI) into clinical medicine presents a paradox: diagnostic models routinely demonstrate benchmark superiority over human experts, yet bedside adoption remains low and clinician trust is fragile. Conventional approaches address this gap through forecasting—projecting model performance metrics along optimistic trend lines—but forecasting cannot account for the non-linear socio-technical transitions that separate technical capability from institutional trust. Objective: This Viewpoint applies Backcasting, a normative futures methodology with a four-decade evidence base in energy policy and public governance, to the specific problem of AI adoption in clinical medicine. Methods: Starting from a defined 2040 Vision State—a health care ecosystem in which clinician trust in AI diagnostics has reached a stable threshold—we identify three temporal Pivot Points that must be engineered, not waited for: (1) the 2030 standardization of Dual-Process AI Architectures, in which Large Language Models (LLMs) are verified in real time by locally deployed Small Language Models (SLMs); (2) the 2035 emergence of agentic orchestration workflows governed by an institutionalized Chief AI Officer (CAIO) role; and (3) the 2040 integration of Futures Literacy into medical education. Results: Key Insights: 1). The primary barrier is regulatory and institutional, not technological; 2). Proactive safeguards address automation bias, algorithmic equity, and fragmented liability; 3). Patient trust requires parallel attention through explainable outputs and CAIO-led education. Conclusions: The roadmap transforms the question from "when will AI be ready for medicine?" to "what must we build to make medicine ready for AI?"—offering structural commitments for clinicians, health systems, and policymakers to begin building today.

  • The application of artificial intelligence (AI) in medicine has dramatically changed the paradigm of clinical practice. Several studies recently published in ‘Journal of Medical Internet Research’ have witnessed this comprehensive evolution which spans from diagnosis, treatment to prevention. However, our experience as clinicians and digital health researchers suggests critical, underexplored facets in the process of using AI technology to advance the diagnosis and treatment of sexually transmitted infection (STI) diseases, including the inherent heterogeneity in real-world image acquisition, the societal and ethical ripple effects of STI, and the successful and equitable integration of advanced computational tools in diagnostic pathways. On this basis, we also envisioned a framework that clinicians should follow within the AI-assisted diagnostic process. Collectively, the above issues are worth warranting further attention to enhance the true clinical utility, interpretability, and equitable deployment of such technologies.

  • Background: Refugees frequently face language and access barriers to preventive health information, contributing to avoidable oral disease and widening health inequities. Scalable digital micro-learning interventions may offer a pragmatic strategy to reduce health information barriers in settings where conventional health education is difficult to deliver. y Objective: This randomized controlled trial aimed to evaluate whether a brief multilingual digital micro-learning intervention improves clinical oral hygiene outcomes and selected oral health literacy–related outcomes compared with delayed access control among adults living in refugee shelters. Methods: A two-arm, parallel-group randomized controlled trial was conducted among 86 adults living in two municipal refugee shelters in Germany (November 2023 - February 2024). Participants were randomized (1:1) to a four-minute animated oral-hygiene micro-learning video (“GlobeSmile”; Arabic, Kurdish, Dari, Farsi, Ukrainian) or delayed access. The intervention was viewed once at baseline on a tablet, with optional re-viewing via QR code for two months. Primary outcomes were change in Plaque Index (PI) and Gingival Index (GI) from baseline to two-month follow-up. Secondary outcomes included oral health knowledge, attitudes, perceived self-efficacy, and self-reported behaviours. Results: Eighty-three of 86 participants completed follow-up (97%). PI decreased more in the intervention group than in controls (mean change −0.21 ± 0.27 vs −0.04 ± 0.17; p = 0.002). GI decreased in both groups without significant between-group difference. Questionnaire findings indicated improvements in selected knowledge, attitude, and self-efficacy outcomes in the intervention group, while most self-reported behaviours and lifestyle factors changed little. Approximately three-quarters of intervention participants reported re-viewing the video at least once, and re-viewing was associated with larger PI reductions. Conclusions: Brief multilingual digital micro-learning may improve plaque control and selected oral health literacy outcomes in refugee shelters, supporting its potential as a scalable, low-cost strategy to reduce health information barriers in underserved settings. Clinical Trial: German Clinical Trials Register (DRKS00032017).

  • Training AI Models for Aesthetic Facial Evaluation: A Focused Review and Framework to Mitigate Homogenizing Bias

    Date Submitted: Mar 16, 2026
    Open Peer Review Period: Mar 17, 2026 - May 12, 2026

    As artificial intelligence (AI) models become increasingly integrated into facial aesthetic surgery for attractiveness prediction and surgical outcome simulation, their potential to perpetuate bias poses clinical concerns. Current models trained on limited datasets inaccurately evaluate underrepresented populations and risk promoting aesthetic homogenization that conflicts with patient goals of ethnic feature preservation. Drawing on current literature, this paper examines bias across AI development stages in aesthetic facial evaluation. Benchmark datasets such as SCUT-FBP and Chicago Face Database underrepresent elderly, non-White, and ethnically diverse populations. Training methodologies lack fairness-aware techniques, and evaluation focuses on overall rather than demographic-stratified accuracy. While individual mitigation strategies exist—including balanced datasets, adversarial debiasing, and fairness metrics—no comprehensive framework integrates these approaches across the entire development lifecycle. We propose a six-pillar framework spanning the AI development lifecycle: (1) diverse data collection with synthetic augmentation, (2) fairness-aware training techniques, (3) complementary fairness metrics with intersectional assessment, (4) explainable AI for clinical transparency, (5) stakeholder engagement, and (6) continuous monitoring. Despite the challenges of maintaining algorithmic standardization and cultural specificity, this framework provides implementation guidance for AI developers, clinicians, and institutions, with principles applicable beyond aesthetic surgery to broader facial analysis applications.

  • Pancreatic cancer (PC) is a highly lethal malignancy requiring multidisciplinary team (MDT) management for optimal care, yet MDT is constrained by resource limitations. This single-center retrospective feasibility study enrolled 125 treatment-naive PC patients to assess concordance between ChatGPT-5.2-generated treatment recommendations and real MDT consensus decisions. Results demonstrated high alignment of large language model (LLM) suggestions with MDT conclusions: 80% for resectable, 100% for borderline resectable, 85.7% for locally advanced, and 100% for metastatic disease, with full concordance in biomarker-guided therapy for BRCA1/2-mutant and MSI-H/dMMR patients. Expert scoring showed mean 3.85 for concordance, 3.97 for rationality, and 3.21 for comprehensiveness, with moderate-to-near perfect inter-rater reliability (κw=0.70–0.83). The LLM’s main shortcoming was insufficient details in perioperative and surveillance management. In conclusion, ChatGPT-5.2 presents high feasibility as an auxiliary tool for PC MDTs, matching guideline-consistent and personalized decisions, though multimodal data integration and large-scale prospective validation are needed to improve comprehensiveness and clinical utility.

  • Governing the Digital Health Commons: Ostrom's Design Principles for Strategic Planning

    Date Submitted: Mar 16, 2026
    Open Peer Review Period: Mar 17, 2026 - May 12, 2026

    Digital health transformation has absorbed tens of billions of dollars in public investment over two decades and produced a consistent pattern of failure. The United States spent $36 billion on electronic health record incentives that achieved adoption without interoperability. England's National Programme for IT was cancelled after a decade and £13 billion. Australia's My Health Record required twelve years and legislative compulsion before providers shared data. These failures have been attributed to management shortcomings, funding gaps, and political interference, yet none of these explanations accounts for why the same pattern recurs across countries with different political systems, funding models, and vendors. This paper proposes that the cross-national consistency reflects a structural governance problem. Drawing on Elinor Ostrom's polycentric governance framework, the analysis argues that digital health ecosystems function as commons governed by multiple autonomous decision centers with overlapping jurisdictions. When Ostrom's eight design principles are absent, coordination fails predictably regardless of technology or funding. The paper tests this claim against six initiatives across three countries, mapping each against the design principles and identifying which are systematically absent. A contrast case, OpenNotes, demonstrates that partial presence of these principles improves outcomes. The analysis translates these findings into a governance-first strategic planning framework specifying requirements for boundary definition, stakeholder inclusion, outcome monitoring, graduated sanctions, conflict resolution, and nested governance. For digital health strategic planning, the contribution is foundational: hierarchical planning frameworks are structurally inadequate for polycentric environments, and Ostrom's design principles offer a rigorous alternative.

  • Background: Patients with tracheal diseases often require long-term follow-up after tracheal device placement, with a risk of adverse events that may lead to emergency care and unplanned interventions. Telemedicine has been proposed as an alternative to in-person follow-up to improve access and continuity of care. Objective: The primary objective was to compare the need for emergency department visits between telemedicine and in-person groups. Secondary objectives included comparing hospital readmissions, 30-day hospital readmissions, and unplanned interventions between groups. Methods: This retrospective, single-institution study included adult patients with tracheal devices who underwent telemedicine and in-person outpatient clinic visits between 2020 and 2024. To balance the groups, we used 1:1 propensity score matching. We collected demographic and clinical data and evaluated the need for emergency department visits, hospital readmissions, 30-day hospital readmissions, and unplanned interventions. Kaplan–Meier estimation of time to first emergency department visit was performed to assess outcomes after outpatient visits. Results: A total of 483 patients (277 telemedicine and 206 in-person) underwent 2487 visits (1258 telemedicine and 1229 in-person). After propensity score matching, 336 patients remained (168 in each group). There were no significant differences in the need for emergency department visits, hospital readmissions, or unplanned interventions. Telemedicine group had significantly fewer 30-day hospital readmissions (OR = 0.38; 95% CI = 0.16–0.87; p = 0.021). Kaplan–Meier analysis indicated no statistically significant difference in emergency department–free visits. Conclusions: A telemedicine outpatient program is safe for the follow-up of adult patients with tracheal devices.

  • Care Robots as Emerging Health Technologies: A Systematic Review and Meta-Analysis

    Date Submitted: Mar 13, 2026
    Open Peer Review Period: Mar 16, 2026 - May 11, 2026

    Background: The rapid growth of aging populations worldwide is placing mounting pressure on long-term care systems already facing critical nursing workforce shortages. Care robots—including socially assistive robots (eg, PARO, NAO, Pepper), companion robots, and therapeutic robots—have emerged as a promising category of digital health technology designed to complement professional caregiving in older adult populations. Despite increasing deployment across clinical and community care settings, a comprehensive quantitative synthesis of their clinical effectiveness across diverse patient populations and outcome domains has not been established. Objective: This systematic review and meta-analysis aimed to quantify the pooled effects of care robot interventions across key patient outcome domains, identify moderating factors including robot type and target population characteristics, appraise study methodological quality, and evaluate the overall certainty of evidence to inform clinical implementation decisions. Methods: We searched PubMed, CINAHL, Cochrane Library, and Embase from inception through December 2024 following PRISMA 2020 guidelines. Eligible studies were randomized controlled trials (RCTs) or quasi-experimental studies comparing care robot interventions with standard care, active controls, or waitlist controls in any clinical or community setting. Methodological quality was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool; certainty of evidence was evaluated using the GRADE framework. Pooled standardized mean differences (SMDs; Hedges' g) with 95% CIs were calculated using random-effects models. Results: Thirty-four studies (N = 2,476 participants; 17 countries; 2015–2024) met inclusion criteria; 28 were included in meta-analyses. Care robot interventions yielded statistically significant effects compared with controls for neuropsychiatric symptoms (k = 8; SMD = −0.34; 95% CI −0.62 to −0.06; I² = 64%), quality of life (k = 6; SMD = 0.27; 95% CI 0.03 to 0.51; I² = 52%), agitation (k = 5; SMD = −0.31; 95% CI −0.55 to −0.07; I² = 48%), stress and pain (k = 6; SMD = −0.38; 95% CI −0.68 to −0.08; I² = 72%), and social-communicative skills (k = 6; SMD = 0.45; 95% CI 0.14 to 0.76; I² = 54%). Effects on depression and cognitive function were not statistically significant. Subgroup analyses indicated that PARO demonstrated the strongest effects in older adults with dementia, whereas humanoid robots (NAO) yielded the largest effect sizes for children with autism spectrum disorder (ASD). GRADE certainty of evidence ranged from moderate (neuropsychiatric symptoms, agitation) to very low (depression, cognitive function). Conclusions: Care robots represent a viable and evidence-supported category of digital health technology with significant effects across multiple patient outcome domains relevant to aging care. These findings support integration of care robots—particularly PARO in dementia and long-term care settings—as complementary digital health interventions. Successful implementation requires attention to technology acceptance, facilitator training, and interoperability with existing health information systems. Clinical Trial: Not applicable

  • Co-developing an implementation plan for a digital health intervention: an example in rural Missouri clinics

    Date Submitted: Mar 13, 2026
    Open Peer Review Period: Mar 16, 2026 - May 11, 2026

    Background: Given the rapid growth of digital healthcare, greater transparency in engaging end-users in developing implementation plans is needed to improve the sustained delivery and effectiveness of digital healthcare. PREVENT is a patient-centered digital health tool designed to engage patients with overweight or obesity in conversations about behavior change to lower their cardiovascular disease (CVD) risk. We are implementing PREVENT in a rural federally qualified health center network with eight clinics. Objective: Objective: The objective of this paper is to present a multi-method approach used to engage clinic teams, including healthcare team members and administrators, to co-develop an implementation plan for the PREVENT digital health tool. Methods: Methods: Healthcare team members (e.g., clinicians, community health workers) and administrators (e.g., informatics professionals, clinic managers, CEO) were engaged through qualitative interviews, advisory board meetings, and site visits to develop an implementation plan prior to implementation. Qualitative interviews were coded using the Consolidated Framework for Implementation Research to identify potential barriers and facilitators of implementing PREVENT and paired with learnings from previous trials of PREVENT to inform the selection and tailoring of implementation strategies. Direct clinic observations generated clinic workflow maps, roles and responsibilities of team members, clinic resources (e.g., technology, space), and an understanding of the clinic and community context and readiness for implementation. A mixed-methods evaluation plan was developed using validated measures, input from the advisory board, and implementation research logic models. Results: Results: Implementation of the PREVENT tool was facilitated by a positive clinic culture, alignment with chronic disease priorities, an easy-to-use and adaptable design with EHR integration, and CHWs’ experience addressing social needs. Key barriers included limited technology infrastructure, variable staffing across clinics, patients’ digital access and literacy challenges, and the need for additional training and support for care team members. An implementation plan that includes thirteen strategies across six implementation strategy clusters was created to leverage facilitators and address barriers. A multi-modal evaluation plan was created to examine implementation and effectiveness. Conclusions: Conclusions: This paper provides an example of how to develop implementation and evaluation plans tailored to the healthcare context that engage end users to increase the impact of a digital health intervention. This work may be replicated to support the successful implementation of other digital health tools, particularly in under-resourced, complex healthcare contexts such as rural clinics where resources are less available.

  • Artificial intelligence and mobile applications for digital psychiatry: development and case study of the NewPsy4u platform

    Date Submitted: Mar 13, 2026
    Open Peer Review Period: Mar 16, 2026 - May 11, 2026

    Background: The prevalence of mental health disorders is steadily increasing, while informative biomarkers remain lacking. Although artificial (AI) intelligence shows promise for revealing latent patterns in data, available datasets in the computational psychiatry community are still insufficient. Digital technologies and informatics tools could fill this gap, offering new strategies for collecting large real-world data. Furthermore, computational infrastructures provide scientists with access to e-services and powerful computational resources. Objective: We present the NewPsy4U web platform, which integrates data, AI pipelines, and mobile applications into an efficient environment from the end-user’s perspective. Methods: NewPsy4u is built on a LAMP architecture (Linux, Apache, MySQL, PHP) and is hosted at the IRCCS-FBF High Performance Computing (HPC) Center. These resources enable the execution of classical and generative AI algorithms required for computational psychiatry. The platform implements the highest standard protocols, while access is granted following registration and approval. The data repository complies with the FAIR principles (Findable, Accessible, Interoperable, Reusable), and the datasets provided are structured according to the OMOP Common Data Model or BIDS standard, enabling standardized data storage and interoperability. Mobile technology is integrated into NewPsy4u, allowing users to collect patient data using the experience sampling method (ESM). All mobile data are synchronized within the NewPsy4u web-based portal, where they can be managed for research purposes. Results: NewPsy4u is designed to provide access to pipelines and multiple datasets. The platform is freely accessible and its AI algorithms are released as open-source tools to promote transparency, reproducibility, and collaborative development. All AI algorithms available on the platform are offered as group or single-case second opinion tools. Multimodal patient records can be hosted in the web-platform with either open or restricted access. NewPsy4u currently includes data from 1900 patients diagnosed with various psychiatric conditions, collected at multiple time points. The platform architecture supports integration of multimodal data (sociodemographic, clinical, imaging and digital information captured by a mobile app) and serves as both a data-sharing solution and a hypothesis testing environment. Conclusions: NewPsy4u is a platform developed to support both research and clinical settings, offering an integrated suite of digital tools for psychiatry.

  • The Digital Twin Paradigm in Head and Neck Cancer: Clinical Opportunities and Challenges

    Date Submitted: Mar 13, 2026
    Open Peer Review Period: Mar 16, 2026 - May 11, 2026

    Head and neck squamous cell carcinoma (HNSCC) represents a significant public health challenge. its treatment is characterized by high anatomical complexity and a critical need for functional preservation of speech, swallowing, and respiration. While multimodality care, including surgery, radiotherapy, and systemic therapy, has substantially improved patients’ survival, there are still high rates of life-altering toxicities and low response rates to immunotherapy. Current clinical decision-making largely relies on "digital snapshots"—static representations derived from population-based statistics and time-point-specific imaging—which usually fail to account for the rapid anatomical and biological change during treatment course. Digital twins hold great promise in accelerating scientific breakthroughs and transforming oncology treatment and precision medicine by the creations of high‑fidelity virtual patient representations that integrate real‑time biological, anatomical, and clinical data. In this viewpoint, we propose three interconnected digital twins to establish a conceptual framework for the HNSCC digital twin. Those digital twins include the anatomical twin, utilizing virtual surgical planning and augmented reality for geometric precision; the dosimetric twin, employing daily imaging and synthetic CT generation for automated adaptive radiotherapy; and the biological twin, integrating deep phenotyping, radiomics, and mechanistic omics to simulate individualized therapeutic trajectories. Beyond acute care, we explore the clinical utility of digital twins in navigating the "de-escalation dilemma" for HPV-associated disease and improving survive through digital phenotyping. With the advent of longitudinal data from medical-purpose Internet of Things (IoT) sensors and circulating tumor DNA (ctDNA) kinetics, digital twins can serve as active sentinels, detecting functional decline or subclinical events months before clinical manifestation. Furthermore, the implementation of synthetic control arms via virtual populations offers a venue to accelerate clinical trials and address ethical issues in rare molecular subtypes. Despite this potential, substantial barriers to implementation remain, including profound anatomical instability during treatment, the "black box" nature of deep learning algorithms, and the challenges of multiscale data integration. We provide a technical roadmap for the development of "morphing" digital twins—dynamic systems capable of continuous geometric and biological auditing. By bridging the gap between the macroscopic world of radiation physics and the microscopic world of cellular evolution, digital twins promise to shift the management of HNSCC from statistical probability toward personalized biological determinism, optimizing HNSCC management while maintaining the essential human functions of the patients.

  • Patterns, Perceptions, and Professional Use of Social Media Among Healthcare Professionals: A Mixed-Methods Study

    Date Submitted: Mar 15, 2026
    Open Peer Review Period: Mar 15, 2026 - May 10, 2026

    Background: Social media is increasingly used by healthcare professionals for professional communication, information sharing, and education. However, how clinicians integrate social media into their scientific information practices, and how they perceive its risks and benefits, remains incompletely understood across diverse settings. Objective: This study aimed to examine patterns of social media use among healthcare professionals, identify factors associated with scientific information seeking and medical content posting, and explore perceptions of social media through integrated quantitative and qualitative analyses. Methods: During the month of May 2025, we conducted an international, cross-sectional, mixed-methods survey of healthcare professionals, including physicians and paramedical staff. The questionnaire was administered electronically using Google Forms and disseminated via newsletters and targeted email invitations to healthcare professionals through established professional and academic networks. Multivariable logistic regressions were performed to identify factors associated with social media use for scientific information and having ever posted medical information on social media. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were reported. Model estimates were evaluated for plausibility and precision. A multinomial logistic regression was performed to identify factors associated with the risk perception of social media. Outcomes included use of social media for scientific information, posting of medical content, and perceptions of social media as an opportunity, risk, or both. Free-text responses were analysed using inductive thematic analysis, and findings were triangulated with quantitative results. Results were reported as relative risk ratios (RRRs) with 95% confidence intervals. All quantitative analyses were conducted using Stata (15, StataCorp LLC, College Station, TX), and statistical significance was assessed at a two-sided α level of 0.05. Results: A total of 1,204 respondents participated. Social media use was frequent, with 67.5% reporting daily use and 87.2% reporting professional use. Bibliographic databases (47.6%) and scientific journals (22.1%) remained the primary sources of scientific information, while 8.6% identified social media as their primary source. Use of social media for scientific information was strongly associated with professional social media use (adjusted OR 7.40, 95% CI 4.87–11.25). Posting medical information was more common among male respondents and frequent social media users, and less common among older professionals and nursing or allied health staff. Most respondents perceived social media as both an opportunity and a risk (60.8%). Qualitative findings highlighted accessibility, time efficiency, and habit as drivers of engagement, alongside concerns about misinformation, professionalism, and content quality. Conclusions: Social media is primarily viewed as a complementary tool whose value depends on critical use and professional norms, whereas healthcare professionals continue to rely on traditional scientific sources for core knowledge acquisition. Strengthening guidance, digital literacy, and institutional support may help maximise benefits while mitigating risks.

  • Background: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental condition in children and adolescents, for which conventional treatments present certain limitations. While digital therapeutics (DTx) have developed rapidly, the relative efficacy of different DTx modalities for this population remains to be established. Objective: To systematically compare the efficacy of four digital therapeutics (DTx) modalities (single-task, cognitive-motor dual-task, AI-integrated single-task, and AI-integrated cognitive-motor dual-task) on core symptoms and executive functions in children and adolescents with ADHD within the dual framework of task design and AI empowerment. Methods: We systematically searched PubMed/MEDLINE, PsycINFO, Web of Science, EMBASE, Scopus, ProQuest Dissertations and Theses, the Cochrane Library, and grey literature from ClinicalTrials.gov for randomized controlled trials published from January 2000 to February 2026, without language restrictions. A snowballing method was also employed. Risk of bias was assessed using the Cochrane RoB 2.0 tool. Data were analyzed using Bayesian network meta-analysis in R software (version 4.2.3). Heterogeneity was assessed using I² statistics, and publication bias was evaluated using Egger's test. Subgroup analyses, meta-regression, and sensitivity analyses were performed to explore sources of heterogeneity. Results: A total of 32 studies involving 2,819 patients were included. Network meta-analysis showed that AI-integrated cognitive-motor dual-task DTx appeared to be the most effective modality for improving core symptoms and executive functions, demonstrating the highest probability of being the best treatment on the Attention Deficit/Hyperactivity Disorder-Rating Scale (ADHD-RS) [Surface Under the Cumulative Ranking Curve (SUCRA): 57.5%; Mean Difference (MD): -3.03, 95% Confidence Interval (95% CI): -5.59 to -0.47], the Swanson, Nolan, and Pelham Rating Scale, Version IV - Inattention subscale (SNAP-IV-PI) [SUCRA: 58%; MD: -5.58, 95% CI: -8.76 to -2.39], the Swanson, Nolan, and Pelham Rating Scale, Version IV - Hyperactivity-Impulsivity subscale (SNAP-IV-PHI) [SUCRA: 81.6%; MD: -6.84, 95% CI: -10.37 to -3.31], and the Behavior Rating Inventory of Executive Function (BRIEF) [SUCRA: 67.4%; MD: -7.75, 95% CI: -10.06 to -5.43]. Moreover, this modality significantly outperformed conventional pharmacotherapy across all outcomes. Subgroup analyses revealed that intervention duration emerged as a potential source of heterogeneity for the SNAP-IV (both PI and PHI subscales) and BRIEF, while mean participant age was identified as a potential source of heterogeneity for the SNAP-IV-PI and BRIEF (all P < 0.05). Sensitivity analyses indicated that individual studies influenced heterogeneity. Of note, all outcome measures reported were based on parent versions of the scales. Conclusions: AI-integrated cognitive-motor dual-task DTx may be the most effective intervention for improving core symptoms and executive functions in children and adolescents with ADHD. Subgroup analyses suggested that Intervention duration and age emerged as moderators of treatment outcomes, warranting consideration in clinical practice. Clinical Trial: CRD420261304236

  • Background: Health communicators and researchers are increasingly exploring partnerships with social media influencers as a strategy to improve the reach and engagement of health messaging. However, practical guidance on how health communicators can identify, recruit, and collaborate with influencers is limited. Objective: The aim of this paper is to provide a detailed description of how to work with social media influencers to disseminate health messages and to highlight lessons learned that may help others overcome challenges associated with this communication channel. Methods: We conducted a process evaluation of an Instagram influencer campaign promoting colorectal cancer screening between March and July 2025. We reviewed publicly available guidance on collaborating with social media influencers for health promotion and summarized key recommendations. Using a paid influencer marketing platform, we identified and contacted nano- and micro-influencers (1,000–50,000 followers). Participating influencers created and posted an Instagram Reel and shared it to their Instagram Story. We documented the recruitment process, vetting criteria, negotiations, content review procedures, and engagement metrics and examined associations between influencer characteristics and engagement outcomes. Results: We sent 1,907 outreach emails to potential influencers; 72 expressed interest and we negotiated terms with 52 before finalizing agreements with 22 and receiving Reels from 16 (0.84%). Outreach emails that specified compensation and project details upfront were the most effective strategy (completed posts from 2.0% of outreach emails compared with 0.7% for emails that did not include compensation and 0% for emails sent only after pre-vetting influencers). Recruiting these influencers required approximately 2.5 months of outreach and a paid influencer marketing platform subscription costing $1,647. Influencer payments ranged from $200 to $500 (mean $389). The 16 influencer videos generated 89,764 total views (mean 5,610 per video) and approximately 232 visits to the campaign website. We found no significant associations between influencer payment or follower count and video views or engagement rates. Conclusions: Partnering with influencers to disseminate health messages on social media can result in relatively high engagement with health messages, including among audiences who may not actively seek health information. However, implementing influencer campaigns using a commercial influencer marketing platform required substantial recruitment effort, including large volumes of outreach and lengthy negotiation timelines. In our campaign, fewer than 1% of outreach emails resulted in completed posts. Providing compensation and project expectations in the initial outreach email substantially improved recruitment success. Influencers with relatively fewer followers may generate similar reach and engagement at lower cost, but may be less experienced and require more clarifications in the negotiation process. Establishing clear expectations for deliverables and revisions may help prevent delays and improve content quality. The guidance in this study can help health communicators develop more realistic implementation plans and budgets when considering influencer-based health communication campaigns.

  • Background: Chronic primary pain is a complex condition involving biological, psychological, and behavioral mechanisms and is commonly associated with emotional distress and reduced quality of life (QoL). Digital mental health interventions (DMHIs) offer scalable and accessible solutions for delivering psychological care in chronic pain management; however, evidence regarding their effectiveness across delivery modalities and outcome domains remains heterogeneous. Objective: This systematic review aimed to (1) evaluate the effectiveness of DMHIs on clinical (pain intensity, disability) and psychological outcomes (QoL, anxiety, depression, catastrophizing, and self-efficacy) in adults with chronic primary pain; (2) examine whether specific digital delivery modalities are differentially associated with particular outcomes; and (3) identify methodological gaps to inform future research and implementation. Methods: A systematic literature search was conducted in PubMed, Scopus, PsycINFO, Cochrane Library, Web of Science, and Google Scholar following PRISMA guidelines. Two independent reviewers screened randomized controlled trials (RCTs) and assessed risk of bias using the Cochrane Risk of Bias 2.0 tool. Given substantial heterogeneity in study designs, interventions, and outcome measures, a narrative synthesis was performed. Results: Twenty-two RCTs were included. DMHIs were effective in improving psychological functioning and pain-related disability, often independently of changes in pain intensity, particularly when grounded in evidence-based psychotherapeutic frameworks such as cognitive behavioral therapy and acceptance and commitment therapy. Guided web-based interventions demonstrated the most consistent benefits, whereas unguided interventions showed smaller effects. Mobile applications and virtual reality–based interventions also showed positive effects on emotional functioning, self-management, and pain interference. Interventions incorporating some form of human guidance were generally associated with superior outcomes. Conclusions: DMHIs represent a promising, scalable, and person-centered approach to improving psychological well-being and functional outcomes in adults with chronic primary pain, particularly when integrated into stepped-care or hybrid care models. Clinical Trial: CRD420251010767

  • Background: Online patient reviews are widely used by consumers to assess the quality of direct-to-consumer teleconsultation (DTCT) services, particularly in settings where objective quality information is limited. However, whether these reviews validly reflect actual clinical and patient-centered care quality remains unclear. Objective: This study aimed to evaluate the validity of online physician reviews in reflecting the quality of care delivered on China’s three largest DTCT platforms. Methods: We conducted a cross-sectional study using unannounced standardized patients (USPs) to objectively assess the quality of DTCT services. Thirty-three USPs were trained to present 11 standardized clinical cases and completed 542 DTCT consultations between physicians on three major Chinese platforms. Technical quality was assessed using a clinical guideline adherence checklist, and patient-centered quality was measured using the Patient–Patient-Centered Care Chinese version (PPPC-CN) scale. Online review quality was defined as the positive review rate displayed on each physician’s profile. Agreement between online reviews and measured quality was evaluated using Intraclass Correlation Coefficients (ICCs), with additional rank correlation analyses. Results: Of the 542 consultations initiated, 530 were completed and 404 physicians had publicly available review data. Among all encounters, 53.14% (288/542) were phone-based and 46.86% (254/542) were text-based consultations. The median positive review rate was 99.9% (interquartile range [IQR], 99.4%–100%). Median guideline adherence was low (0.16; IQR, 0.08–0.26), and median patient-centered quality was modest (PPPC-CN score 2.1; IQR, 1.98–2.79). Diagnoses were completely correct in 40.92% (196/530) of consultations. Unnecessary examinations occurred in 1.7% of encounters, and medication prescribing was appropriate in 79.04%. The median consultation time was 13 minutes (IQR, 7–64.69), and the median registration fee was 29.9 yuan (IQR, 26.1–39.9). Agreement between positive review rate and guideline adherence (ICC= 0.002; 95% CI, −0.006 to 0.013) and between positive review rate and patient-centered quality (ICC= 0.014; 95% CI, −0.043 to 0.083) was negligible and far below accepted validity thresholds. Correlations between positive review rate and diagnostic accuracy were weak but statistically significant (Spearman ρ = 0.168; Kendall τ = 0.141; both P < 0.05). Limitations include the use of standardized cases rather than real patients and the focus on publicly visible review metrics. Conclusions: Online reviews on major platforms were overwhelmingly positive but showed almost no alignment with actual provider performance. DTCT providers demonstrated low guideline adherence and modest patient-centered quality. More research on improving the review frameworks is urgently needed to fill the gap between patient feedback and service quality. Clinical Trial: The study has been approved by the Southern Medical University Ethics Committee ([2022] No. 013) and registered with the China Clinical Trial Registry (ChiCTR2200062975).

  • Background: Cardiovascular diseases continue to be one of the leading causes of morbidity and mortality worldwide, posing significant challenges for early diagnosis, risk stratification, and clinical follow-up. In this context, the expansion of digital health ecosystems has favored the incorporation of artificial intelligence-based tools capable of analyzing large volumes of clinical and physiological data. These technologies have the potential to support clinical decision-making, optimize preventive strategies, and personalize therapeutic management. Objective: To explore and map the available evidence on the use of artificial intelligence in the management of cardiovascular diseases, including its applications in prevention, diagnosis, and clinical management. Methods: A scoping review was conducted following the methodological framework of Arksey and O'Malley and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guideline. A systematic search was conducted in PubMed, Scopus, and BIREME for studies published between 2018 and 2025 in English and Spanish. The search strategy combined controlled vocabulary and free terms using Boolean operators. Studies evaluating the use of artificial intelligence in the prevention, diagnosis, or management of cardiovascular diseases were included. The results were summarized descriptively according to the main areas of application. Results: A total of 2,007 records were identified, of which 35 studies met the inclusion criteria after the selection process. The available evidence shows that artificial intelligence applications in cardiology are mainly focused on cardiovascular risk stratification, algorithm-assisted diagnosis, especially through electrocardiography and cardiovascular imaging, and clinical decision support. Likewise, some research explores its use in therapeutic personalization and remote monitoring using digital devices. However, most studies are retrospective designs or methodological evaluations, with limited evidence on their impact on hard clinical outcomes. Conclusions: The evidence synthesised in this scoping review indicates that artificial intelligence is emerging as a relevant tool in cardiovascular care, with applications in risk stratification, early diagnosis, therapeutic support, and patient monitoring. Although many studies report improvements in intermediate outcomes such as diagnostic accuracy and risk prediction, evidence demonstrating consistent benefits in major clinical outcomes remains limited. Future research should prioritise prospective studies and real-world evaluations to better define the role of artificial intelligence in cardiovascular practice. Clinical Trial: Not applicable

  • Artificial Intelligence for Healthcare Quality and Patient Safety: A Scoping Review of Diagnostic, Predictive, and Decision Support Applications

    Date Submitted: Mar 11, 2026
    Open Peer Review Period: Mar 12, 2026 - May 7, 2026

    Background: The past decade has seen artificial intelligence (AI) move from research curiosity to clinical challenge, with systems now matching or exceeding specialist performance on discrete diagnostic tasks. Yet for most health systems, this technical progress has translated into surprisingly little change at the bedside. Implementations stall, adoption remains uneven, and the gap between what AI can do in a paper and what it delivers in practice has become one of the more pressing questions in health informatics. Objective: This scoping review aimed to map the current evidence base for AI applications in healthcare quality and patient safety, identify patterns of clinical effectiveness and methodological limitations, and characterize structural barriers to implementation across four domains: diagnostic AI, predictive analytics, clinical decision support, and economic evaluation. Methods: A scoping review was conducted following the PRISMA extension for Scoping Reviews (PRISMA-ScR) and the Joanna Briggs Institute methodology. Systematic searches of three databases were performed covering January 2017 to March 2025. From 6,566 identified records, 80 peer-reviewed studies were included and synthesized using narrative synthesis. Study characteristics were mapped across domains, and methodological quality was assessed based on study design. Results: Included studies comprised diagnostic AI (n=28), predictive analytics (n=24), clinical decision support (n=18), and economic evaluations (n=10). Diagnostic AI demonstrated accuracies of 85–99% across imaging and pathology applications, with multiple RCTs confirming non-inferiority to specialist readers. Predictive analytics models reported mortality reductions of 12–58% for sepsis and deterioration; within the 24 predictive analytics studies included in this review, 79% (19/24) used retrospective designs, and a systematic review of machine learning for sepsis prediction reported that only a small minority of published models have undergone prospective external validation. Clinical decision support applications showed reductions in medication errors and alert fatigue, but algorithmic bias and limited generalizability were identified as recurring concerns. Economic evaluations reported returns on investment up to 451%; a systematic review of 66 health economic evaluations found that up to 91% failed to meet basic quality standards, frequently omitting implementation costs. Conclusions: This scoping review maps a consistent pattern across domains: strong technical performance in controlled settings is not reliably accompanied by prospective validation, equitable design, or transparent economic modeling. Three structural barriers were identified, including a validation gap, an implementation chasm, and infrastructure silos. These findings suggest that realizing the clinical value of healthcare AI requires a strategic shift toward rigorous prospective evaluation, standardized reporting, and equity-centered data ecosystems. Further research is needed to address generalizability and real-world implementation across diverse healthcare settings.

  • Patterns of Online Health Information Behavior among Newly Diagnosed Breast Cancer Patients in a Southern Chinese City: A Grounded Theory Study

    Date Submitted: Mar 8, 2026
    Open Peer Review Period: Mar 11, 2026 - May 6, 2026

    Background: Breast cancer has become the most common cancer worldwide, and newly diagnosed breast cancer patients are in particular need of health information. The results of this study will help clinical providers understand the complete process related to online health information behavior among newly diagnosed breast cancer patients and guide them in developing targeted information behavior intervention. Objective: The purpose of this study was to identify online health information behavior patterns in newly diagnosed breast cancer patients. The purpose of this study was to identify the specific characteristics of patients' online health information behaviors using a grounded theory approach, with the goal of ultimately identifying a behavioral pattern that depicts the trajectories of change among these behaviors. Methods: Thirty-two patients with newly diagnosed breast cancer who underwent breast surgery at The First Affiliated Hospital of Anhui Medical University were interviewed semistructurally per procedural grounded theory from August 2021 to May 2022. The data were processed through three-level coding, continuous comparison, and dimensional analysis until theoretical saturation was achieved. This manuscript adheres to the COnsolidated criteria for REporting Qualitative research (COREQ) guidelines. Results: An online health information behavior pattern was identified for newly diagnosed breast cancer patients. The complete online health information behaviors (core categories) of patients during the period from breast cancer diagnosis to treatment included the following aspects: information acquisition behavior, including active information search, avoidance of information, and encounter information; information evaluation behaviors, including information evaluation and no evaluation; information processing behaviors, including information rejection, information storage, and information utilization; and information processing outcomes, including information termination and continuous searching for information. Conclusions: This study identified a pattern of online health information behavior for newly diagnosed breast cancer patients. The findings will help clinical health care professionals understand the complete process related to online health information behavior among newly diagnosed breast cancer patients, discover entry points for interventions for online health information behavior , and develop targeted information behavioral interventions to help patients improve their e-health literacy, enhance their decision-making ability and information utilization effectiveness, and promote their recovery.

  • Effects of Telehealth on Specific Domains of Cognition in Community-Dwelling Older Adults Living Without Assistance: A Systematic Review and Meta-Analysis

    Date Submitted: Mar 5, 2026
    Open Peer Review Period: Mar 10, 2026 - May 5, 2026

    Background: The accelerated rate of global population aging underscores the critical need to mitigate cognitive decline in older adults. Telehealth offers a promising solution by providing an accessible means of cognitive intervention for older adults living independently in the community. Objective: This study aimed to synthesize current evidence to determine the effectiveness and feasibility of telehealth in improving domain-specific cognitive functions among older adults living independently in the community. Methods: Comprehensive literature search was conducted across seven electronic databases (ie, PubMed, Embase, Scopus, Web of Science, Cochrane Library, CINAHL Complete and PsycINFO) to identify randomized controlled trials (RCTs) published between January 1st, 2020, and May 28th, 2025. The included studies that investigated telehealth interventions for community-dwelling older adults living without assistance. Results: 22 RCTs were included in the systematic review, and 15 RCTs were included in the meta-analysis. The pooled effect sizes (Hedges’ g) suggested positive effects of telehealth on global cognition (g = 0.78; 95% CI = 0.10, 1.46), executive function (g = 1.06; 95% CI = 0.27, 1.85), and language (g = 0.33; 95% CI = 0.12, 0.54). There was no significant effect on memory (g = 0.17; 95% CI = -0.20, 0.54). Neither the meta-regression nor the subgroup analyses identified significant moderators to account for the substantial between-study heterogeneity observed. Conclusions: Among community-dwelling older adults who are living without assistance, telehealth may provide benefits for global cognition, executive function, and language function. These results highlighted the clinical utility and feasibility of telehealth. Future studies should explore integrating artificial intelligence in telehealth to optimize training effectiveness and maximize user engagement. Clinical Trial: PROSPERO CRD420251056647

  • From Regulation to Implementation: A Regulatory-Aligned Approach to AI-Powered Digital Platforms in the INTERCEPT Innovative Health Initiative (IHI) Project.

    Date Submitted: Mar 5, 2026
    Open Peer Review Period: Mar 10, 2026 - May 5, 2026

    Regulatory misalignment represents a critical barrier to digital health innovation. However, the Innovative Health Initiative Joint Undertaking (IHI-JU) INTERCEPT (GA n. 101194766), an initiative involving European public and private sectors aimed at intercepting Crohn’s disease prior to the manifestation of symptoms-advocates that the development of AI-powered digital platforms with compliance inherently integrated 'by design' is not only achievable but also imperative as the lack of systematic approaches for integrating regulatory requirements creates inefficiencies and delays in market access. In this perspective, we outline INTERCEPT’s strategic plan for an evolutionary digital infrastructure, coupled with AI-driven analytics, which is meticulously aligned with EU 2016/769 General Data Protection Regulation – GDPR, data privacy regulation, medical device regulation (EU 2017/745 Medical Device Regulation) and recommendations from the Medical Device Coordination Group – MDCG of the European Commission), Health Technology Assessment (HTA) criteria, and the recently enacted EU AI legislation (Regulation EU 2024/1689), which entered into force in 2024 with staggered implementation deadlines extending through 2027. Although presently in the start-up phase with clinical activities about to commence, INTERCEPT functions as a practical blueprint. Methods: Our analysis followed a multi-phase methodology comprising three sequential components. First, we conducted systematic regulatory landscape mapping to identify and categorize the most relevant EU frameworks (GDPR, Data Governance Act, EHDS, Clinical Trials Regulation, AI Act, and MDR) applicable to digital health innovation. Second, we implemented a structured stakeholder engagement through comprehensive surveys administered to all Work Package leaders (n=10), assessing both regulatory relevance and anticipated regulatory risk using high/medium/low categories, coupled with implementation timeline mapping (2025-2029). Third, we established iterative feedback cycles through structured dialogue sessions with project coordinators to identify current and anticipated regulatory challenges, implementation barriers, and mitigation strategies. Through retrospective analysis of survey responses, regulatory deliverables, and stakeholder feedback, we extracted and synthesized core principles into a generalizable regulatory-aligned innovation framework. Conclusions: The INTERCEPT regulatory-aligned framework aims to enhance digital health innovation by leveraging systematic regulatory integration to support project development and enable faster, more reliable market access for Software as Medical Device solutions, and contributes to regulatory science theory by establishing systematic principles for proactive compliance integration and offers practical guidance for digital health innovators navigating the complex EU regulatory landscape. Future research should validate the framework across diverse therapeutic areas and assess long-term impact on market success rates.

  • Background: Healthcare service quality is inherently multidimensional, yet document-level text analysis methods such as Latent Dirichlet Allocation (LDA) force patient reviews into single dominant topics. This simplification may systematically discard evaluative information when patients discuss multiple service dimensions with varying sentiments within the same review. Objective: This study compared document-level topic modeling (LDA) with GPT-based aspect-level sentiment analysis (ABSA) to address three research questions: (1) How much information is lost when collapsing multi-aspect reviews to single topics? (2) How prevalent are mixed-sentiment reviews, and what quality tensions do they reveal—both cross-aspect trade-offs and within-aspect ambivalence? (3) Do positive and negative reviews exhibit different structural patterns in aspect co-occurrence? Methods: We analyzed 2024 Google Reviews from 24 medical centers in Taiwan. Both LDA (K=7 topics) and GPT-based ABSA were applied to the same 5,467 reviews, ensuring fair comparison on identical data. The ABSA design employed structured prompts to extract aspects from seven predefined quality dimensions. Quality validation achieved Cohen κ=.82 against human annotation. Mixed-sentiment reviews were identified as those containing both positive and negative aspect evaluations, and cross-polarity couplings were analyzed to identify recurring trade-off patterns. Rating-stratified network analysis compared aspect co-occurrence patterns between positive reviews and negative reviews using Jaccard similarity. Results: Reviews discussed an average of 2.05 distinct aspects (SD=0.97), producing 51.2% information loss under LDA's single-topic assignment. Among multi-aspect reviews, 11.0% exhibited cross-aspect mixed sentiment, with Technical–Functional Divergence—praising Professional Quality while criticizing functional dimensions—appearing in 49.9% of these mixed-sentiment cases. Network analysis revealed differential bundling: operational dimensions co-occurred more strongly in negative reviews, whereas clinical dimensions co-occurred more strongly in positive reviews. Conclusions: Document-level topic modeling discards more than half of the evaluative information patients provide. Our findings reveal that patients cognitively decouple clinical competence from service delivery—Technical–Functional Divergence appeared in half of mixed-sentiment cases—and that positive and negative reviews organize quality dimensions differently. We recommend a complementary approach: topic modeling for exploratory discovery and ABSA for diagnostic assessment. For healthcare quality improvement, hospitals should separate clinical signals from operational signals in feedback dashboards.

  • Solidarity or Segregation: ChatGPT Health and U.S. Healthcare Disparities

    Date Submitted: Mar 9, 2026
    Open Peer Review Period: Mar 9, 2026 - May 4, 2026

    A recent report published by OpenAI highlights several structural deficiencies within the United States healthcare system. These include patients’ difficulties in navigating a complex healthcare system, as well as the persistence of medical deserts all over the country. The introduction of a new Health function within the chatbot ChatGPT seeks to mitigates these issues, potentially serving as an accessible initial point of contact for individuals with limited access to traditional healthcare services. Vulnerable patients may tend to resort to ChatGPT Health to address unmet medical needs. Such overreliance can potentially lead to dangerous behaviors, such as medical rationing or self-medication. Moreover, the lack of emotional intelligence and contextual knowledge, and the potential reinforcement of confirmation bias represent hidden and insidious risks for users. To tackle both the upstream and downstream issues related to ChatGPT Health, policy solutions rooted in solidarity are needed.

  • Background: Continuous renal replacement therapy (CRRT) is a life-sustaining critical care intervention widely used for hemodynamically unstable individuals with acute kidney injury. Recent efforts, including standardized procedures, structured documentation, and quality monitory, have shown small improvements in CRRT delivery and safety. However, fragmented workflows and paper-based documentation limit the sustainable implementation of these improvements in routine practice. Objective: This study aimed to design and evaluate a CRRT information system to support standardized procedures, structured documentation, and quality monitory. Methods: A user-centered design approach, informed by Design Science Research (DSR) methodology, guided a multi-step process of identifying problems, defining objectives, and designing and evaluating the information system. The approach to design involved close collaboration within a nurse-led, 10-member multidisciplinary team comprising nephrologists, nurses, information technology specialists, and information engineers. Evaluation included six months of real-world clinical use with ongoing feedback collected through a dedicated WeChat workgroup and a System Usability Scale (SUS) survey of 27 CRRT care team members. Results: A role-based CRRT information system was developed, comprising 14 clinical modules and 6 core functions. The system embedded a continuous data-processing pipeline that enabled automated capture of treatment-related data directly from CRRT machines, creation of structured nursing documentation, and generation of quality indicators from structured data. During demonstration, workflow refinements—including dual-nurse verification and enhanced device data transmission—were incorporated following pilot testing. Over six months of clinical use, 42 user-reported issues were identified across three domains: data retrieval and calculation, fidelity of automatically generated clinical documentation, and interface appearance. Quantitative usability survey (n=27) demonstrated excellent usability (mean SUS score 95.19, SD 5.09). Conclusions: A CRRT information system integrating standardized clinical procedures, structured documentation, and ongoing quality monitoring supported complex clinical practice and management beyond simple digitization. Workflow-aligned, data-flow–enabled design may help future critical care information systems better support clinicians working in information-intensive environments.

  • Digital Health Engagement Behaviors in U.S. Family Caregivers: Trend Analysis Using HINTS 2019-2022

    Date Submitted: Mar 6, 2026
    Open Peer Review Period: Mar 9, 2026 - May 4, 2026

    Background: Digital health has provided caregivers with access to supportive resources without space-time restrictions. Caregivers’ digital health engagement behaviors can help them track their own health and that of care recipients as well as communicate with others. While digital health tools have become more prevalent since COVID-19, the trend of caregiver engagement has been less explored. Objective: This study examined the trends and factors associated with selected digital health engagement behaviors in family caregivers in the United States (U.S.), using the Health Information National Trends Survey (HINTS) datasets collected in 2019, 2020, and 2022. Methods: Our cross-sectional data analysis included 1,676 family caregivers. Dependent variables were: 1) access to online medical records (caregiver’s, care recipient’s); and 2) health-related use of social media (sharing health information, interacting with others, watching health-related videos). Independent variables were survey year, demographic, socioeconomic, caregiving, and Internet technology factors. Weighted multivariable logistic regression analyses were conducted. Results: Among 1,676 caregivers (2019: n = 570; 2020: n = 412; 2022: n = 694), access to online medical records increased from 2019 to 2022. Access to caregivers’ own records rose from 48.7% to 72.6% (P<.001), and access to care recipients’ records increased from 30.8% to 44.5% (P<.001). Health-related social media use also increased, including sharing health information (22.5% vs 39.1%, P<.001), interacting with others (16.6% vs 27.0%, P<.001), and watching health-related videos (49.5% vs 60.9%, P=.005). In adjusted analyses, higher education (college graduate vs ≤high school: OR = 2.75, 95% CI 1.56–4.85, P<.001) and having health insurance (OR = 2.40, 95% CI 1.24–4.68, P=.010) were associated with access to caregivers’ records. Female sex (OR = 1.96, 95% CI 1.36–2.84, P<.001) and spousal caregiving (OR = 2.14, 95% CI 1.26–3.65, P=.005) were associated with access to care recipients’ records. High-speed internet access was strongly associated with digital engagement outcomes (e.g., sharing health information: OR = 3.98, 95% CI 2.15–7.35, P<.001). Conclusions: Digital health engagement—including access to online medical records and the use of social media for health-related purposes—among U.S. family caregivers increased following the COVID-19 pandemic. These findings suggest that healthcare professionals and researchers should consider multifaceted factors, such as age, race/ethnicity, geography, education, insurance coverage, and digital access, when designing and implementing digital health tools and technology-based interventions. Future research should evaluate how digital technologies, automation of systems “talking” to other systems, including artificial intelligence (AI) can better support caregivers’ health information needs and care coordination. The varying learning curves for individuals and groups could further be explored for effective and efficient adoption and utilization.

  • Background: Problematic digital use among youth is associated with mental health concerns, yet the affective and behavioral mechanisms linking self-esteem to problematic digital use remain insufficiently characterized. Objective: To examine whether depressive and anxiety symptoms and objectively measured smartphone behaviors are associated with the relationship between self-esteem and problematic digital use among adolescents and young adults. Methods: This cross-sectional observational study was conducted between April 2022 and January 2023 in academic institutions in Grenoble, France. Participants were 171 adolescents and young adults aged 11 to 25 years using Android smartphones who completed self-report questionnaires alongside passive smartphone monitoring. Measures included self-esteem, depressive symptoms, anxiety symptoms, recreational smartphone time, delay to first connection in the morning, and nighttime digital disconnection (digital sleep). Problematic digital use was modeled as a latent construct encompassing excessive use, emotional regulation, and reactivity and assessed using a validated self-report scale. Results: Among 171 participants (meanage, 17.6 years, SD 3.0; 57% female), depressive symptoms mediated the association between self-esteem and problematic digital use (β = −0.33; 95% CI −0.45 to −0.21), with larger indirect effects than anxiety symptoms (β = −0.13; 95% CI −0.22 to −0.04). Recreational smartphone time was positively associated with problematic digital use (β = 0.28), whereas digital sleep was independently associated with lower problematic digital use (β = −0.24). Conclusions: Lower self-esteem was indirectly associated with problematic digital use primarily through depressive symptoms, which showed stronger associations than anxiety symptoms. Objective smartphone behaviors were independently associated with problematic digital use. Clinical Trial: This trial was registered at ClinicalTrials.gov (NCT07293208).

  • Social Determinants of Digital Health Intervention Use in Mainland China: A National Cross-Sectional Study

    Date Submitted: Mar 6, 2026
    Open Peer Review Period: Mar 9, 2026 - May 4, 2026

    Background: : Digital health interventions (DHIs), including telemedicine and artificial intelligence–enabled health tools, are increasingly integrated into health care systems worldwide. While these technologies have the potential to improve access and efficiency, unequal access to digital resources and health capabilities may create disparities in their use. Evidence on population-level determinants of digital health use remains limited in rapidly digitalizing health systems such as China. Objective: This study aimed to examine social and structural determinants of DHI use among adults in mainland China using the World Health Organization’s Social Determinants of Health (SDoH) framework. Methods: This cross-sectional study analyzed data from a nationally representative survey conducted in mainland China in 2024 among adults aged ≥18 years. The primary outcome was self-reported ever use of digital health interventions, including telemedicine, digital health applications, and AI-enabled health tools. Explanatory variables were categorized into five SDoH domains: economic stability, education and health-related capabilities, health care access and quality, neighborhood and built environment, and social and community context. Multivariable logistic regression models were used to estimate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for associations between social determinants and DHI use. Results: Among 34,672 participants, 14,565 (42.0%) reported ever using a DHI. Higher household income (≥6001 CNY vs ≤3000 CNY: aOR, 1.37; 95% CI, 1.29–1.46), higher educational attainment (bachelor’s degree or above vs junior high school or below: aOR, 1.49; 95% CI, 1.38–1.61), higher health literacy (per SD increase: aOR, 1.10; 95% CI, 1.07–1.13), and higher eHealth literacy (per SD increase: aOR, 1.20; 95% CI, 1.17–1.24) were associated with greater odds of DHI use. Health insurance coverage was associated with higher DHI use (aOR, 1.22; 95% CI, 1.11–1.34), whereas individuals aware of but not enrolled in family doctor services had lower odds (aOR, 0.65; 95% CI, 0.60–0.70). Difficulty paying medical expenses was associated with higher DHI use (aOR, 1.31; 95% CI, 1.22–1.41), while rural residence was associated with lower odds (aOR, 0.94; 95% CI, 0.89–1.00). Conclusions: DHI use in China is strongly associated with socioeconomic resources, health-related capabilities, and access to health care. These findings highlight the importance of addressing structural and social determinants to promote equitable adoption of digital health technologies in rapidly digitalizing health systems. Clinical Trial: NA

  • Ethical, Legal, and Social Implications of Automated Adverse Drug Reaction Detection: Applying the VALIDATE Framework in a Systematic Review

    Date Submitted: Mar 2, 2026
    Open Peer Review Period: Mar 6, 2026 - May 1, 2026

    Background: Automated systems for detecting adverse drug reactions (ADRs) are increasingly common and carry high expectations from policymakers, researchers, healthcare professionals, and patients, yet evidence of their effectiveness and safety remains limited Objective: The aim of this systematic review was to identify the ethical, legal, organizational, social, and environmental implications of these systems. Methods: We conducted a systematic using the VALIDATE framework, we conducted a three-step approach: (1) defining scope through literature review and stakeholder consultation; (2) systematic review; (3) environmental inquiries. Results: Stakeholders prioritized research on feasibility, barriers, facilitators, alarm management, staged implementation, confidentiality, cybersecurity, and bias detection. The systematic review of ten studies revealed that leveraging new data sources and developing privacy-protection technologies is essential for upholding ethical and legal standards. Cybersecurity risks could expose patient information to unauthorized parties, while biases in training datasets can compromise fairness. Integrating ADR detection into clinical workflows and medication management systems can improve resource optimization and reporting rates. Establishing a positive reporting culture, supported by education and training for healthcare teams, is crucial to enhance ADR reporting. Conclusions: Careful planning is critical when implementing an early ADR detection system. Incorporating co-design methodologies can help align these automated systems with stakeholder needs and improve medication safety. Clinical Trial: Not requiered

  • Sexual and Reproductive Health Education Through a Mobile App-Based Approach Among Bangladeshi Adolescents: Findings from a Pilot Study

    Date Submitted: Mar 2, 2026
    Open Peer Review Period: Mar 6, 2026 - May 1, 2026

    Background: Bangladeshi adolescents, who constitute a fifth of the country's population, experience barriers in accessing sexual and reproductive health (SRH) information. Previous studies have shown that mobile health (mHealth) interventions provide adolescents with timely access to evidence-based curricula, gamified, and interactive content, sessions, and information. The widespread adoption of mHealth technologies among adolescents and their willingness to embrace emerging technologies are encouraging specialists to employ mHealth approaches to share health information. Despite the high mobile phone usage among adolescents in Bangladesh, there are a few mHealth interventions specifically targeting their SRH needs. Objective: We aimed to assess changes in SRH knowledge and awareness among adolescents in Bangladesh following exposure to "Mukhorito", an interactive mobile app-based intervention. Methods: This pilot study employing a pre-post non-randomized experimental approach was conducted in three selected secondary schools in Feni, Bangladesh, from June 2023 to March 2024. 46 students from class 9 across the three schools were recruited, with a minimum of 10 per school. Bivariate analyses were performed to assess the association between SRH knowledge and awareness scores with other covariates. Significantly associated covariates for both scores were used in building the adjusted linear regression models. Results: The adjusted models indicated a significant improvement in the end-line group compared with the baseline group for both knowledge (1.2 units; 95% CI: 0.8-1.6 units) and awareness scores (1.0 units; 95% CI: 0.3-1.5 units), indicating a high level of intervention effect. Conclusions: These findings demonstrate the potential of mobile app-based innovations to improve adolescent SRH education within a national program in resource-constrained settings, specially where conventional methods may be less effective.

  • Remotely Delivered Yoga Interventions for Pain Management: A Scoping Review

    Date Submitted: Mar 5, 2026
    Open Peer Review Period: Mar 5, 2026 - Apr 30, 2026

    Background: An expanding body of evidence suggests that yoga may be beneficial for pain management across a range of conditions. At the same time, healthcare delivery has evolved rapidly with the growth of telehealth, including advances in the remote delivery of yoga interventions. However, the literature lacks a comprehensive synthesis focused specifically on remote yoga interventions for pain. Objective: This scoping review aimed to map and characterize the extent and type of the existing evidence on remotely delivered yoga interventions for pain management. Specifically, we (i) examined general study characteristics, participant populations and intervention features, (ii) summarized reported findings on feasibility, safety and effects on pain-related outcomes, and (iii) identified research gaps to inform future investigation and practice. Methods: A systematic search was conducted in August 2025 across six databases to identify primary studies. Studies, including study protocols, conference abstracts and trial registrations, were eligible for inclusion if they reported primary data, across any study design, involving participants experiencing any type of pain, undergoing remote delivered yoga interventions (referred to also as online, virtual or tele-yoga), from any contextual setting. Eligibility was assessed through abstract and title screening and a subsequent full-text review independently by two reviewers. Results: A total of 82 sources of evidence were included, comprising 47 peer-reviewed publications, 1 preprint, 17 conference abstracts, and 17 trial registrations. Detailed data charting was conducted for peer-reviewed publications only. Overall, 3.199 participants were represented. Fewer than half of the studies examined pain as the primary condition, while the remainder assessed pain as secondary to other medical conditions or within non-clinical populations. Interventions varied considerably in duration, frequency, delivery format, and yoga style. Synchronous delivery was most common, Hatha yoga and adaptions were the most frequent style and eight- or twelve-week programs delivered twice weekly predominated. Feasibility was generally favorable, safety findings suggested a low risk of adverse events, and adherence was typically moderate to high, however reporting across these domains was inconsistent. Given the substantial methodological heterogeneity, conclusions about efficacy are limited, however reported findings indicate potential benefits for pain-related outcomes. Conclusions: Based on the current existing evidence, remotely delivered yoga for pain appears feasible and low risk, with signals of potential benefit. Future systematic reviews with formal quality appraisal and quantitative synthesis are needed to clarify effect sizes and the certainty of the evidence. Important gaps remain, including inconsistent reporting, limited comparative research on delivery formats, further investigation of intervention characteristics, underrepresentation of certain pain conditions and low- and middle-income settings.

  • Background: People living with advanced cancer experience more frequent and severe symptoms than people living with early-stage disease. Four common and distressing symptoms include sleep difficulties, worry-anxiety, fatigue, and depression. Cognitive-behavioral therapy (CBT) and acceptance and commitment therapy (ACT) interventions are effective for managing these symptoms but are often too time-intensive for people with multiple appointments, limited energy, and competing priorities. Brief, mobile health (mHealth) interventions provide an accessible alternative, particularly for those in rural communities with limited access to palliative and/or psychosocial oncology services. Objective: Based on our successful in-person/DVD-based pilot trial of a four session, integrated CBT-ACT symptom management intervention for advanced cancer patients, Finding Our Center Under Stress (FOCUS), this study tests the feasibility and acceptability of a mHealth translation of this intervention. Methods: In this single-group, feasibility trial, 11 people with advanced cancer were recruited through hospital-based oncology clinics representing four cancer types (breast, melanoma, multiple myeloma, prostate). Patients completed sociodemographic questions, initial patient-reported outcomes including sleep (ISI), anxiety (GAD-7, PSWQ), fatigue (FSI), and depression (CES-D) and a 7-day sleep diary via the mobile app. They then completed four modules focused on the self-management of sleep difficulties, worry-anxiety, fatigue, and depression. To assess feasibility, we examined recruitment, retention, and module completion. At the end of six weeks, to assess acceptability, participants completed the Internet Evaluation and Utility Scale and some participants completed a qualitative interview assessing their experience with the FOCUS app. We present quantitative and qualitative results as well as lessons learned in designing the application for this patient population. Results: Sixty-five percent entered the trial (N =11) and seventy percent completed more than half of the app. These participants gave strong ratings for FOCUS ease of use (3/4), convenience (3.7/4), utility (3.3/4), and ease of understanding (3.83/4). All participants (10/10) said they would recommend the app to other people with cancer and would return to the app with future problems. Participants’ favorite components were video recordings of other patients and the sleep and worry/uncertainty modules. Areas for improvement based on participant feedback included video quality for some components (i.e., lighting, sound), sleep diary ease of use, and a desire for professional guidance. Conclusions: The FOCUS intervention was successfully delivered via mobile technology and was feasible and acceptable per beta testing. The FOCUS mHealth app provides an evidence-based, accessible symptom management intervention for people with advanced cancer in rural communities. In accordance with participant feedback, for FOCUS 2.0 we will enhance video segments, incorporate a telehealth component to support app usage, and further develop the interactive and motivational features of the app. Future research will explore the effectiveness of this mHealth symptom management application via a randomized controlled trial.

  • Background: The rapid growth of digital technologies has generated large volumes of free-text data across healthcare, public health, and social research. These contain contextualised accounts of lived experience that are often absent from quantitative measures. Despite their value, these data remain underused because qualitative analysis is traditionally designed for in-depth work on smaller numbers. Computational methods, including topic modelling and large language models, are increasingly promoted as efficient solutions. However, concerns persist regarding interpretability, bias, hallucinations, and loss of contextual depth. Critically, there is no established human-centred framework for evaluating the quality of machine-generated outputs for qualitative analysis. Objective: 1) To develop an AI evaluation framework for assessing machine-generated outputs, 2) Evaluate different AI approached to textual data analysis Methods: We developed and applied a human-centred evaluation framework, GRACE (Grounded Review and Assessment of Computational Evidence), to assess the quality of machine-generated textual outputs. GRACE was derived from established qualitative appraisal tools and operationalised four core indicators: interpretability, actionability, nuance, and redundancy, using structured scoring and reflexive consensus. We compared classic probabilistic topic modelling (LDA), a deep learning embedding-based approach (BERTopic), and three large language models (LLMs: LLaMA-3, Copilot, DeepSeek), used alone or in combination with prior structural topic modelling (STM). These were applied to the same corpus (n = 1,044 free-text responses). LLM prompting was iteratively refined, with a single-shot STM-based configuration selected for final evaluation due to reduced hallucinations. All outputs were analysed using Machine-Assisted Topic Analysis. A rapid manual thematic analysis of a 15% subsample (n = 152) served as a pragmatic comparator. Results: Model outputs were variable, with different AI methods producing different results from the same dataset. GRACE evaluation indicated that LDA achieved the highest overall mean score (2.6/5), followed by BERTopic and topic modelling plus Copilot (2.5), topic modelling plus LLaMA-3 (2.2), and topic modelling plus DeepSeek (1.9). LDA generated broader conceptual patterns requiring interpretive refinement; while BERTopic produced narrower, more descriptive clusters with thematic overlap. LLM-only outputs were very poor. The combination of topic modelling and LLMs performed slightly better: the outputs were well structured but often superficial and repetitive. Conclusions: Computational models produced different interpretations of the same dataset, and performance did not align with technical complexity. Large language models were not suitable for thematic analysis, especially when applied to raw data, generating generalised and sometimes inaccurate outputs. Classical probabilistic modelling, particularly topic modelling + qualitative human analysis using the Machine Assisted Topic Analysis (MATA) approach provided the highest quality results. We argue that the key issue is not whether a model “works,” but whether it support meaningful, contextually grounded results. GRACE offers a simple, human-centred framework to support this assessment and build evidence base for analysis of free-text data that is useful and nuanced.

  • Pallvi–Family Focused Telepalliative Care: Development of a Complex Intervention

    Date Submitted: Mar 4, 2026
    Open Peer Review Period: Mar 5, 2026 - Apr 30, 2026

    Background: Telepalliative care, the use of telehealth in palliative care, has emerged as a strategy to improve access to specialist palliative services amid growing demand, workforce shortages, and increasing digitalization of health care. Although telepalliative care has demonstrated positive outcomes for patients, families, and clinicians, its integration into standard services remains inconsistent. Existing initiatives are often operationally focused and rarely grounded in programme theory or developed collaboratively with key stakeholders, limiting sustainability and contextual alignment, particularly in Nordic health systems that emphasize home-based palliative care. Objective: This study aimed to develop a family focused model of telepalliative care for clinical practice through active involvement of key stakeholders. Methods: A co-design qualitative study grounded in interpretive description was conducted. The development followed the British Medical Research Council’s guidance for the development and evaluation of complex interventions and represents the development phase. Key stakeholders including patients, family representatives, specialized palliative care team members, community care nurses, general practitioners, voluntary representatives, IT consultants, managers, and researchers, were purposively recruited. Data were generated through four scientific workshops across two Danish sites, supplemented by participant observations of video consultations and a short questionnaire inspired by the Normalisation Measure Development (NoMAD) questionnaire. Data were analyzed using abductive thematic analysis, with qualitative and quantitative findings converged and iteratively refined through stakeholder consensus. A programme theory and logic model guided development. Results: Eighteen stakeholders participated in the workshops, with additional input from clinicians through observations (6 consultations involving 22 participants) and questionnaires (n=10). Findings highlighted both alignment and tension between the proposed model and current clinical practice, particularly regarding when and for whom telepalliative care should be used, clinician digital competencies, and family involvement. These, and insights from previous studies, informed the primary output of the study which is Pallvi – Family Focused Telepalliative Care, a comprehensive, theory-informed model comprising of a structured consultation guide and two co-designed quick guides; one for health care professionals and one for patients and families. Pallvi integrates family focused care, shared decision-making, advance care planning, and the Calgary-Cambridge Communication Guide, operationalized across seven consultation phases. Conclusions: Through systematic stakeholder involvement and theory-driven development, this study produced a contextually and culturally aligned family focused model of telepalliative care. Pallvi addresses identified gaps in telepalliative care research by providing a structured, practical guide designed to support communication, family involvement, and cross-sectoral collaboration. Future research will focus on feasibility and implementation testing to assess acceptability, fidelity, and sustainability in clinical practice and implementation.

  • Background: Psychological skills training (PST) is a core component of sport psychology, supporting athletes’ performance, well-being, and capacity to manage competitive stress. However, access to high-quality, practitioner-led PST is often constrained by time, cost, availability of trained professionals, and stigma surrounding help-seeking. In response, digital interventions such as mobile applications, biofeedback systems, and immersive technologies have been increasingly adopted to deliver PST in more scalable and flexible formats. Despite rapid growth in this area, evidence regarding the promises and challenges of digital PST remains fragmented across modalities and outcome domains. Objective: This systematic review synthesizes empirical evidence on the use of digital interventions for delivering PST in athlete populations. Specifically, it maps the digital modalities employed, the psychological skills and frameworks targeted, the populations and sporting contexts studied, and the promises and challenges reported in relation to effectiveness, feasibility, and implementation. Methods: We conducted a PRISMA-compliant systematic review of English-language studies published between 2000 and 2025. Three databases (Scopus, Web of Science Core Collection, and ProQuest Dissertations and Theses) were systematically searched, and additional records were identified through a manual search. Eligible studies examined digital or technology-based interventions deployed to support PST outcomes in athlete populations and reported empirical quantitative, qualitative, or mixed-methods findings. Two reviewers independently screened records and extracted data, resolving discrepancies through discussion. Results: Thirty-six studies met the inclusion criteria, encompassing virtual reality-based interventions, mobile applications, and biofeedback or neurofeedback systems. Across modalities, digital PST interventions targeted a range of psychological skills, including stress and anxiety regulation, attentional control, imagery ability, self-talk, and emotional regulation. Reported promises included improvements in affective, cognitive, physiological, and performance-related outcomes, enhanced accessibility, flexibility, and engagement of PST delivery, and potential for skill transfer beyond sport. However, recurring challenges were also identified, such as limited personalization, variable user engagement, technical and cost barriers, and inconsistent or weaker efficacy relative to traditional PST methods. Conclusions: Digital interventions offer a meaningful extension to traditional PST by widening access, enhancing immersion, and providing real-time feedback that supports psychological skill development. However, their effectiveness appears constrained by methodological variability, limited personalization, and implementation challenges. Future research should prioritize rigorous longitudinal designs, clearer alignment with PST theory, and hybrid delivery models in which digital tools complement practitioner expertise, to ensure digital PST enhances rather than dilutes psychological practice.

  • Digital Frailty in Ageing Societies: Introducing a Digital Health Vulnerability Index

    Date Submitted: Mar 3, 2026
    Open Peer Review Period: Mar 3, 2026 - Apr 28, 2026

    Digital health is now embedded in routine care through patient portals, teleconsultations, remote monitoring, digital triage, and other hybrid service models. While these changes can improve access and efficiency, they may also create new barriers for older adults who have limited cognitive, sensory, functional, or social capacity to engage with digitally mediated care. Current constructs such as digital literacy, digital exclusion, and conventional frailty only partly explain this problem because they do not fully capture the mismatch between the digital demands of healthcare systems and the real world capabilities and supports available to patients. This Viewpoint introduces digital frailty as a clinically relevant, multidimensional state of vulnerability that arises when a person’s intrinsic capacity and available support are insufficient to meet the digital requirements of healthcare. We argue that digital frailty should be understood not as a synonym for age, disability, or low digital confidence, but as a relational and potentially modifiable mismatch between individuals and care environments. Framing the issue in this way shifts attention from blaming patients to designing safer and more equitable systems. To operationalize this concept, we propose a Digital Health Vulnerability Index as a pragmatic framework for identifying patients at risk of digitally mediated care failure. The framework focuses on four proximal domains of vulnerability, namely access, skills, confidence or trust, and support, and is paired with brief consideration of hearing, vision, and cognition to improve clinical interpretability. Rather than functioning as a static label, the index is intended as a routable mechanism to trigger proportionate responses such as assisted digital support, proxy enabled access, simplified workflows, and analogue alternatives for safety critical steps. We further propose proportionate universalism as the most appropriate implementation principle, so that digital support is universal in reach but calibrated in intensity according to need. This approach has implications beyond individual assessment and extends to pathway design, procurement, governance, reimbursement, and digital inclusion policy. In ageing societies, digital vulnerability should be recognized as a determinant of functional access to care. A digitally inclusive health system therefore requires not only better technology, but also better identification, adaptation, and accountability for the patients most at risk of being left behind.

  • The Role of Measurement in Identifying High-Intensity Secure Message Senders: An Observational Study

    Date Submitted: Feb 27, 2026
    Open Peer Review Period: Mar 3, 2026 - Apr 28, 2026

    Background: The growing volume of secure messaging within the patient portal has imposed significant demands on clinicians and contributed to burnout. Little is known about the characteristics of patients who comprise high-volume message senders, and we lack a nuanced understanding of patient messaging intensity beyond measures accounting for sheer volume. Objective: Our objective was to characterize older adult patients (65+) with high secure messaging volume, examining both patient characteristics and other aspects of their messaging intensity such as messaging frequency, length, and messaging use relative to patient portal logins and healthcare encounters. Methods: We analyzed electronic medical record (EMR) and patient portal data from a large academic health system, encompassing 16,023 older adults who sent 199,952 messages over a 12-month period. We developed five measures to account for secure messaging intensity. Our primary measure of messaging intensity was based on message volume; high-volume message senders were identified using outlier analysis based on patients’ aggregate number of messages sent during the observation period. Additional measures of messaging intensity included identifying individuals with concentrated periods of messaging, message length (character count), a ratio of messages to portal logins and a ratio of messages to healthcare encounters. We compared sociodemographic characteristics, health status, and messaging intensity of high-volume secure messaging senders to other message senders. We also identified patients who were classified as high-intensity message senders based on all five measures of messaging intensity (‘super-senders’). Results: Of 16,023 older adult patients who sent at least one message during the observation period, 1,298 (8.1%) were classified as high-volume message senders; these patients accounted for 39.7% of total messages. High-volume message senders, compared to all other message senders, were more likely to be White (80.4% vs. 72.5%, p < 0.001), have higher comorbidity scores (2.6 vs. 1.8, p <0.001), and higher incidence of cancer (35.8% vs. 22.8%, p<0.001) and dementia (8.3% vs. 6.1%, p < 0.002). High-volume message senders were also more likely to be identified as having concentrated periods of messaging, to send longer messages, and to send more messages in relation to patient portal logins and healthcare encounters. A small subgroup of patients classified as high-volume senders were also classified as high-intensity across all four of the other measures of messaging intensity (59/1,298; 4.5%), the ‘super senders’. Conclusions: High-volume message senders represent a small but distinct group of older patients who send a disproportionate share of messages to clinicians. Triangulating multiple measures of messaging intensity can help provide additional context about patient messaging behavior and help to identify patients that may most benefit from targeted outreach while potentially easing clinicians' inbox workload.

  • Predicting Healthcare Professionals’ Use of Telehealth in China: A Cross-Sectional Study

    Date Submitted: Mar 1, 2026
    Open Peer Review Period: Mar 3, 2026 - Apr 28, 2026

    Background: While telehealth has become a transformative tool enhancing healthcare accessibility and efficiency, adoption rates in China remain low. Chinese healthcare professionals’ low telehealth adoption rates are poorly understood. Objective: Our study investigates the key factors influencing Chinese healthcare professionals’ intention to adopt and actual use of telehealth. Based on the results from estimating an integrated telehealth use framework, we also make recommendations for improving healthcare professionals’ telehealth adoption. Methods: Data on 10,372 healthcare professionals from the 2023 Xi’an Healthcare Worker Survey were analyzed, utilizing descriptive statistics (chi-square test, group differences), reliability testing (Cronbach’s α coefficients), Discriminant validity analysis (square root of average variance extracted) and fit tests. Based on our integrated telehealth use framework, structural equation modeling was employed to test hypotheses and path relationships, including multi-group analysis to examine demographic moderating effects. Results: Confirming our hypotheses on telehealth intention to use and actual use, the structural equation model showed strong fit indices. Key predictors of behavioral intention to use telehealth included effort expectancy, price value, performance expectancy, and social influence. Behavioral intention and facilitating conditions positively influenced actual use behavior, while demographic characteristics moderated specific relationships. Conclusions: Our study identifies critical factors influencing healthcare professionals’ adoption of telehealth, including performance expectancy, social influence, and facilitating conditions. It offers an integrated framework to assess behavioral intentions and provides practical insights for advancing telehealth implementation in China. Tailored strategies for diverse demographics and institutions are essential for promoting sustainable adoption. Clinical Trial: This study was reviewed and approved by the Biomedical Ethics Committee of Xi’an Jiaotong University (approval number: XJTUAE2646).

  • Determinants of Malnutrition Onset in nursing home residents: a longitudinal study

    Date Submitted: Mar 2, 2026
    Open Peer Review Period: Mar 2, 2026 - Apr 27, 2026

    Background: Malnutrition is a multifactorial and chronic condition, frequently developing gradually due to a combination of biological, functional, and psychosocial factors. Objective: This study investigates the impact of general function, oral health, and nutritional factors on the time to onset of malnutrition. Methods: This is a longitudinal study utilizing interRAI data from nursing home residents for the period 2020-2025. The interRAI instruments are standardized, internationally validated, and electronically supported assessment tools to facilitate real-time data capture, analysis, and clinical decision support. Cox proportional hazard models were employed to calculate the impact of several indicators on malnutrition Results: Baseline assessments from 1,633 residents (mean age 85.68±7.82, 65.22% female) were split into assessments with malnutrition at baseline (154 residents, 9.43%) or not (1,479, 90.57%). The samples differed significantly with higher proportions in the sub-sample with malnutrition at baseline: depressive symptoms (53%vs.34.2%, p=0.000), modified mode of nutrition (26%vs.11.6%, p=0.000), loss of appetite (22.5%vs.7.5%, p=0.000), chewing difficulties (17.5%vs.7.9%, p=0.000) and dry mouth (13.7%vs.8.0%, p=0.000). Survival analysis revealed significant results for loss of appetite (1.88; 1.24-2.83), chewing difficulty (1.803; 1.12-2.90), and cognitive impairment (1.67, 1.13-2.46). Residents with an adapted mode of nutrition also had a shorter mean time to malnutrition, although this factor was not significant in the Cox model. Conclusions: Survival analysis has not been applied to the study of malnutrition in older persons, although malnutrition often appears in survival models for hospitalization or morbidity. This study highlighted the critical role of modifiable risk factors, such as loss of appetite, chewing difficulties, and mode of nutritional intake, in accelerating the progression toward malnutrition among older adults in nursing homes. As these factors are preventable, timely screening using the electronic interRAI tools may foster identification of people at risk of malnutrition and prevention or treatment. Clinical Trial: not applicable

  • Background: Patient-reported outcomes measures (PROMs) have become an important tool in measuring a patient’s health status from their own perspective; however, they are typically measured using standardized questionnaires which do not account for each patient's unique experience of health. Recent improvements in Natural Language Processing (NLP) provide new possibilities to extract PROM scores from unstructured or free-text patient narratives; however, the feasibility and minimal data requirements needed to accomplish this task remain uncertain. Objective: To assess the practicality of transformer-based models for predicting EuroQol EQ-5D-3L scores from patient narratives and to evaluate minimum data requirements, narrative length and data augmentation effects. Methods: This proof-of-concept study used synthetically generated patient narratives to evaluate methodological feasibility. Three transformer models (BERT, BioBERT, DistilBERT) were fine-tuned for regression from patient narratives representing all 243 EQ-5D-3L health states. The performance of the models in various scenarios including a range of sample sizes (n=100–850), narrative length (100–1000 words), and data augmentation conditions were compared. The performance of the models was assessed through fivefold cross-validation and additional validation on datasets created by ChatGPT and DeepSeek. Results: Each model was able to predict EQ-5D-3L scores using each of the different configurations of data (n=100-850 patients; 100-1000-word narratives). However, optimal results were obtained when training the models with 100-word narratives derived from the largest number of people (n=850), where mean squared error=0.03 (95% CI: 0.02-0.04), mean absolute error=0.13 (95% CI: 0.13-0.15), explained variance=0.77 (95% CI: 0.64-0.77), and intraclass correlation coefficient=0.85 (95% CI: 0.81-0.87). Furthermore, it was found that the shorter narratives (100 words) performed better than longer narratives (100-1000 words). Additionally, the use of data augmentation improved the predictive performance. Conclusions: Transformer models show promise in predicting EQ-5D-3L PROM scores from synthetic patient generated narratives, with a minimum of 250 patients providing around 100-word narratives required for reliable performance. The work provides both a methodological basis and empirical standards for AI-based PROM systems. However, clinical implementation will require validation using real patient-authored narratives prior to adoption. If validated, the use of this approach could provide evidence to support the inclusion of a patient's experience as a narrative into standardized outcome measures and support patient-centred healthcare evaluations.

  • Background: The COVID-19 pandemic has spurred an unprecedented collection of multi-omic and clinical data of patient cohorts. However, fragmented datasets, inconsistent terminology, and poor integration with existing knowledge of molecular pathways hinder effective analysis. Objective: The objective of our development and usability study was to develop SPOKE-C19, a web-based knowledge graph tool. Methods: We designed this tool to integrate multi-omics and clinical data from the INCOV and RECOVER cohorts with SPOKE, a knowledge graph with over 27 million nodes of biomedical entities linked by 53 million relationship edges. The platform allows mapping of empirical data to the network of known mechanistic relationships, facilitating interpretation of novel associations observed in post-acute sequelae of SARS-CoV-2 infection (PASC) studies. Results: SPOKE-C19 offers an intuitive user interface for researchers to query biomedical variables and to link empirical associations observed in clinical or experimental studies to generic mechanistic pathways and to precomputed relationships extracted from large COVID studies. Conclusions: The comprehensively documented interactive COVID-C19 platform promotes “connecting the dots” between disjoint domains and encourages cross-disciplinary collaboration, which is particularly important given the multi-faceted nature of PASC. Ultimately, we designed the application as a comprehensive platform for researchers, offering robust tools that support innovative discoveries and contribute to a deeper understanding of PASC and its implications.

  • Background: Family caregivers of children with chronic health conditions experience significant physical and mental health burdens, including burnout, anxiety, depression, fatigue, and sleep disturbances. Despite this growing need, validated digital mental health tools tailored specifically to this population remain limited. Conversational agents powered by artificial intelligence (AI) offer a promising avenue for delivering on-demand, personalized mental health support, yet evidence-based development and evaluation of such tools for family caregivers is lacking. Objective: This study aimed to systematically describe the iterative development of COCO (Caring of Caregivers Online), a conversational agent designed to deliver Problem-Solving Therapy (PST) integrated with Motivational Interviewing (MI) principles, and to evaluate its usability and preliminary effect on the emotional well-being of family caregivers of children with chronic health conditions. Methods: COCO was developed and refined across four phases. Therapeutic dialogues were grounded in PST and MI principles and informed by evidence-based caregiver personas. The Wizard-of-Oz (WOZ) method was used across phases to iteratively collect naturalistic dialogues and refine COCO's conversational design. Usability was assessed using the System Usability Scale (SUS) and the Post-Study System Usability Questionnaire (PSSUQ). Caregiver emotions were measured pre- and post-session using six subscales of the Positive and Negative Affect Schedule - Expanded Scale (PANAS-X). In the final phase, a large language model (LLM)-powered version of COCO was developed using GPT-4 with few-shot learning and evaluated using persona-based methods. Results: COCO achieved a mean SUS score of 75.6%, reflecting acceptable usability. Participants demonstrated significant improvements in negative affect, sadness, guilt, fatigue, and serenity following PST sessions (p ≤ 0.03). Analysis of MI techniques across all phases revealed progressive refinement in conversational quality, with the LLM-powered COCO achieving the highest density of MI techniques per turn (2.56) and greater balance across MI strategy types, particularly in seeking collaboration and reflection. Conclusions: COCO is a feasible, usable, and preliminarily efficacious conversational agent for supporting the mental health of family caregivers of children with chronic conditions. The iterative, human-in-the-loop development approach was instrumental in producing empathetic, therapeutically grounded responses. The systematic development and evaluation process described here can serve as a guide for similar conversational agent intervention studies. Future work will explore multi-agent architectures and retrieval-augmented generation (RAG) to further enhance personalization, controllability, and scalability toward clinical deployment.

  • Patient-Reported Experiences with Viewing and Understanding Test Results in Patient Portals: A Survey Analysis

    Date Submitted: Feb 24, 2026
    Open Peer Review Period: Feb 24, 2026 - Apr 21, 2026

    Background: The 21st Century Cures Act information blocking regulations led to many health care providers (HCPs) altering policies to electronically release test results to patients immediately upon their availability. Objective: To understand how often patients view results in the patient portal before hearing from their HCP, and whether they are given the option to decide how results are communicated. Methods: Using data from the 2024 Health Information National Trends Survey on U.S. adults who received recent test results via patient portal (N=6,045), we examined whether patients viewed test results before hearing from their HCP, were given the option to decide how test results were communicated, and understood results viewed before hearing from an HCP. Results: 70% of patients who received results viewed them in their patient portal, most of whom viewed results before hearing from their HCP (58% overall). 28% of patients and 33% of portal users reported being given the option to decide whether they wanted to receive test results before hearing from their HCP. Two-thirds of patients understood results they viewed in their patient portal before hearing from their HCP (66%). Conclusions: While most patients viewed results before discussing with their HCP, only one-third reported being given the option to decide how results would be communicated and two-thirds of patients who viewed immediately released results understood their implications. Clearly presenting the option to decide when test results are communicated and incorporating patient preferences in portal communications could help empower patients and mitigate potential worry.

  • Consumer and Patient Health Information Seeking with Generative AI Tools: A Systematic Literature Review of Facilitators and Barriers

    Date Submitted: Feb 24, 2026
    Open Peer Review Period: Feb 24, 2026 - Apr 21, 2026

    Background: Generative artificial intelligence (GenAI) tools powered by large language models (LLMs) are increasingly used by the public to seek health information. Unlike traditional web search, GenAI systems generate conversational answers, which may influence how users assess credibility, manage uncertainty, and decide whether to verify information or consult clinicians. Evidence is needed to clarify facilitators, barriers, and user practices in GenAI-supported health information seeking. Objective: This systematic review synthesizes empirical research on consumer and patient health information seeking with GenAI/LLM tools, focusing on study contexts, adoption and use outcomes, facilitators and barriers, with implications for clinician-patient interactions. Methods: We conducted a review following PRISMA-ScR. Records were identified through database searching and screened using predefined eligibility criteria. Included studies were extracted using a structured form capturing study characteristics, GenAI tool type, health context, outcomes, as well as facilitators and barriers. Findings were synthesized using structured grouping aligned to the RQs. Results: The review included 27 studies. GenAI was used for symptom appraisal, condition understanding, treatment options, and care navigation. Facilitators emphasized convenience and clarity, including efficiency and access (29.6%, n=8), comprehensibility and presentation quality (40.7%, n=11), personalization and specificity (18.5%, n=5), and affective or interpersonal comfort (18.5%, n=5). Barriers were dominated by credibility and trust concerns (48.1%, n=13), particularly when accuracy cues or citations were missing or difficult to interpret. Additional barriers included perceived unsuitability for complex, urgent, or emotionally charged situations (18.5%, n=5), privacy or data security concerns (14.8%, n=4), limited prompting skills (7.4%, n=2), and modality or interaction constraints that hindered credibility assessment and information comparison (18.5%, n=5). Literacy-related capability was reported in 22.2% of studies (n=6), and verification-supporting features (e.g., visible sourcing, transcripts, and save/revisit/share functions) were reported in 18.5% of studies (n=5). Conclusions: GenAI is used for diverse health information needs, but reliance is shaped by trust, perceived risk, and verification capacity. Future research should improve reporting of tools and prompting conditions, standardize measures of reliance and verification, and evaluate use in higher-stakes and underserved contexts to inform safer design and public guidance.

  • 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.

  • 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.

  • 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.

  • Unpacking the Role of Personal Innovativeness and Attitude in Driving mHealth Adoption for Diabetes Self-Management: Cross-Sectional Survey Study

    Date Submitted: Feb 19, 2026
    Open Peer Review Period: Feb 20, 2026 - Apr 17, 2026

    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.

  • 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: 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: 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

  • 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: 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.

  • 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: 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

  • 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.

  • 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: 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: 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: 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.

  • 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.

  • Quality of life of people living with dementia residing in nursing homes: A study using natural language processing to analyse observational data

    Date Submitted: Jan 22, 2026
    Open Peer Review Period: Jan 29, 2026 - Mar 26, 2026

    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.

  • 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.

  • We Are in control: A survey study of public perceptions of a personal data space for citizen-centered health data governance

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

    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.

  • Development of a novel musculoskeletal hypothesis in the ADVANCE cohort: application of sparse Group Factor Analysis methodology

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

    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.

  • Design Requirements for Web-Based Digital Therapeutics in Chronic Kidney Disease: A Mixed-Methods Study Integrating Patient and Clinician Perspectives

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

    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.