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Latest Submissions Open for Peer Review

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JMIR Submissions under Open Peer Review

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Titles/Abstracts of Articles Currently Open for Review:

  • The SaMD Trap: A Sociotechnical Theory of Pre-deployment Failure in Regulated Digital Health

    Date Submitted: Jul 27, 2026

    Open Peer Review Period: Jul 28, 2026 - Sep 22, 2026

    Background: Pre-deployment failure, where digital health technologies do not progress to implementation despite technical maturity, clinical validation, and user testing, remains poorly understood and undertheorised. Objective: This study introduces the Software as a Medical Device (SaMD) Trap, a sociotechnical theory that explains how regulated digital health innovations can fail before implementation and adoption Methods: The theory was developed through a qualitative case study of a digital health tool created within a funded research programme. Data comprised 11 semi-structured interviews involving seven stakeholder groups and 17 months of programme meeting minutes. A sociotechnical analysis was conducted to identify mechanisms contributing to pre-deployment failure. Results: Three interacting mechanisms were identified. Invisible Regulatory Infrastructure reflects limited regulatory literacy and insufficient access to regulatory expertise and support. Sequential Gatekeeping describes how regulatory, governance, and compliance requirements emerge progressively and cannot be addressed independently. Temporal Misalignment occurs when regulatory and implementation timelines extend beyond the duration of research funding cycles. Together, these mechanisms explain why technically viable and clinically validated digital health systems may be unable to transition into routine practice. The theory also generated a set of testable propositions and informed the development of the SaMD Readiness Scoping Tool, designed to assess regulatory feasibility during project planning. Conclusions: The SaMD Trap provides a novel sociotechnical explanation for pre-deployment failure in regulated digital health. By identifying the mechanisms that hinder implementation before deployment begins, the theory offers a foundation for future empirical testing and highlights the importance of considering regulatory readiness alongside technical and clinical development during the design and funding of digital health innovations.

  • Multi-AI Review Enhances the Clinical Quality of AI-Generated Rehabilitation Exercise Instructional Images: A Blinded Multi-Institutional Study

    Date Submitted: Jul 27, 2026

    Open Peer Review Period: Jul 28, 2026 - Sep 22, 2026

    Background: AI-generated instructional images are increasingly used in rehabilitation patient education, yet single-pass generation yields anatomical inaccuracies and unsafe postures. Objective: Whether structured multi-agent AI critique pipelines measurably improve clinician-judged image quality has not been empirically evaluated. Methods: In this double-blind comparative study, 41 reviewers from three independent hospital and university teams rated 98 paired rehabilitation exercise images on a validated 14-item instrument covering four domains: clinical accuracy, instructional utility, patient safety, and clinical adoption intent. Image A was produced by single-pass AI generation; Image B underwent a four-agent critique-and-refinement pipeline. Wilcoxon signed-rank tests with Bonferroni correction, mixed-effects models, and intraclass correlation coefficients were applied. Results: Results Across 815 paired ratings, all 14 rating dimensions favored Image B after Bonferroni correction (all p < 0.001; Rank-Biserial Correlation, r_RBC 0.48–0.73), with the largest effects in visual clarity (r_RBC 0.71–0.73) and patient safety (r_RBC 0.65–0.70). At construct level, patient safety showed the greatest absolute improvement, followed by instructional utility, clinical adoption intent, and clinical accuracy. Image B was the preferred choice in 65.3% of forced-choice judgements versus 12.4% for Image A (one-sided binomial p < 0.001). Open-text defect coding showed Image B had markedly lower rates of unclear imagery (9.7% vs. 2.6%), lack of instructions (6.7% vs. 0.1%), and missing safety notes (2.5% vs. 0.4%). Quality advantages were observed across all ten anatomical regions and were independent of professional backgrounds. Conclusions: A multiple-agent critique-and-refinement pipeline substantially improves clinician-judged quality of AI-generated rehabilitation instructional images across all measured domains, with the greatest gains in patient safety and visual clarity. These findings provide the first empirical quality benchmark for pipeline-based AI image generation in rehabilitation and support multi-AI review as a scalable minimum standard before clinical deployment, pending prospective patient-outcome evaluation.

  • Artificial Intelligence Attitude Measurement Instruments in Healthcare: A Systematic Review of Measurement Properties

    Date Submitted: Jul 27, 2026

    Open Peer Review Period: Jul 28, 2026 - Sep 22, 2026

    Background: The rapid advancement of artificial intelligence (AI) in healthcare has led to the development of numerous instruments for assessing attitudes toward AI. Although a variety of instruments have been developed for healthcare populations, the quality of their measurement properties, the certainty of the supporting evidence, and their applicability have not been systematically evaluated. Objective: Systematically evaluate the measurement properties and methodological quality of AI attitude measurement instruments in the medical field, and to provide evidence-based recommendations for healthcare administrators in selecting appropriate measurement instruments. Methods: A systematic search was conducted in PubMed, Embase, Web of Science, and CINAHL databases to identify studies assessing attitudes toward AI among healthcare populations. The search covered all records from database inception to January 28, 2026. The methodological quality and measurement properties of included instruments were assessed following the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines. The quality of each instrument was rated, and overall recommendations were formulated. Results: A total of 30 studies involving 17 artificial intelligence attitude measurement instruments were included. Most instruments demonstrated satisfactory structural validity and internal consistency; however, evidence regarding content validity, cross-cultural validity, and criterion validity remained limited. Hypothesis testing for construct validity showed generally favorable results. Based on the overall assessment of measurement properties and evidence grading, nine instruments were classified as A-level recommendations, six as B-level recommendations, and two as C-level recommendations. Conclusions: AAAW demonstrated the most favorable overall measurement properties among existing artificial intelligence attitude measurement instruments in the medical field and is recommended for current use. However, the overall methodological quality of available instruments remains limited due to insufficient reporting of measurement properties and methodological procedures, as well as heterogeneity among target populations. Future studies should adhere to standardized instrument development guidelines, enhance methodological rigor and generalizability, and promote the development of reliable measurement instruments to support the evidence-based implementation of artificial intelligence in healthcare. Clinical Trial: PROSPERO CRD420261365231；https://www.crd.york.ac.uk/PROSPERO/recorddashboard

  • Background: Video consultations have become an established component of general practice and are increasingly promoted to improve access, efficiency, and healthcare sustainability. While previous research has primarily explored the perspectives of individual patients and healthcare professionals, less is known about how organisations involved in strategic video consultation implementation consider their benefits, challenges, and future role within healthcare systems. Objective: This study aims to explore the experiences of organisational interest-holders involved in implementing video consultations in Danish general practice, focusing on the perceived strengths, weaknesses, opportunities, and threats related to their use. Methods: A qualitative interview study was conducted with 17 representatives from 13 organisations involved in the development, support, regulation, governance, or strategic implementation of video consultations in Denmark. Purposive and snowball sampling identified participants from four different organisational groups: patient organisations, professional interest organisations, technology and digital health organisations, and public health authorities. Semi-structured interviews were conducted between October and December 2023. Data were analysed using deductive-inductive framework analysis informed by the Strengths, Weaknesses, Opportunities, and Threats (SWOT) framework. Results: Four overarching tensions, competing values, and dilemmas emerged across the organisational interest-holder groups: (1) improved access versus widening inequity, (2) efficiency versus relational and clinical quality and safety, (3) digital transformation versus implementation uncertainty, and (4) strategic governance versus target-driven digitalisation. Participants recognised that video consultations may improve accessibility, convenience, and coordination of care while supporting broader digital transformation. However, concerns were raised regarding digital exclusion, limitations in relational and clinical assessment, uncertainty about appropriate use, cross-sector implementation challenges, and governance approaches that prioritise utilisation targets over clinical value, quality, and patient safety. Conclusions: Exploring the perspectives of organisational interest-holders suggested that implementing video consultations in general practice is not simply a matter of increasing technology uptake. Rather, it requires balancing competing priorities related to access and equity, efficiency and quality/safety, digital transformation and implementation uncertainty, and governance and professional autonomy. Sustainable implementation should promote equitable access, context-sensitive clinical use, interoperable digital infrastructure, and governance frameworks that prioritise quality of care and patient safety alongside digital innovation.

  • Background: Participants with β-thalassemia frequently experience repeated hospital visits and cannulations, leading to chronic pain and anxiety, which adversely affect their quality of life. Virtual Reality (VR) has shown efficacy in managing the pain and anxiety associated with medical procedures. The purpose of this study was to evaluate the effects of therapeutic VR on pain, anxiety, fatigue, boredom, and participant satisfaction during intravenous (IV) cannulation procedures Objective: This study evaluated the effectiveness of therapeutic VR in reducing pain and anxiety during IV cannulation among thalassemia participants, compared with SOC. Secondary objectives included assessing the impact of VR on fatigue, boredom, and participant satisfaction. Methods: A single-center, non-randomized crossover clinical trial was conducted at the Dubai Thalassemia Center, United Arab Emirates, between May and September 2024. Participants aged >7 years undergoing routine IV cannulation received SOC during their first visit, followed by VR-assisted cannulation during two subsequent visits. Outcomes were assessed using Visual Analogue Scale (VAS) scores for pain, anxiety, fatigue, and boredom. Physiological parameters, including heart rate and blood pressure, were also recorded. Comparisons between SOC and VR sessions were performed using paired statistical tests, with statistical significance set at P<.05 Results: A total of 115 participants completed at least one SOC session, and 111 participants completed at least one VR session. Overall, 82% of the participants were older than 18 years, and 51% were male. The mean anxiety score was significantly lower in the VR group (2.24 ± 2.6) than in the SOC group (2.92 ± 2.3; p=0.02). Similarly, the fatigue score was significantly lower in the VR group (1.67 ± 1.3) than in the SOC group (2.65 ± 2.2; p=0.01). Boredom scores (1.72±1.4 vs 2.67±2.2; P=.035) were also significantly reduced with VR. Pain scores were lower in the VR group but did not differ significantly from SOC (2.23±1.8 vs 2.89±2.1; P=.07). Participant satisfaction was numerically higher with VR (90% vs 85%; P=.237). No VR-related adverse events were reported, and the intervention was well tolerated. Conclusions: The usage of VR for the intervention is feasible, safe, and generally well-tolerated by thalassemia participants. VR effectively reduces anxiety and fatigue during routine intravenous cannulations. These findings are particularly relevant for participants who undergo lifelong, repeated procedures that cumulatively contribute to procedural distress. Clinical Trial: Clinical Trial Registration: https://clinicaltrials.gov/study/NCT07099196, identifier: NCT07099196, registered 2025-07-05. This trial was registered retrospectively.

  • Background: Natural language processing (NLP) is being increasing used to analyse unstructured patient feedback (UPF) for healthcare service quality improvement. Prior studies demonstrate the analytical potential of methods like topic modelling and sentiment analysis, but are largely descriptive or conceptual, with limited translation into tools for routine clinical practice. This leaves a critical knowledge gap between computational research and its realisation within (oral) healthcare service quality improvement, where large volumes of UPF are available but difficult to interpret at scale. Objective: Develop, implement, and evaluate a clinician-facing dashboard to translate unstructured patient feedback (UPF) into actionable insights for oral healthcare service quality improvement via a hybrid NLP Pipeline. Methods: We developed a four-stage hybrid NLP pipeline, combining topic modelling, sentiment analysis, and multi-label text classification. We then applied it to 57,794 reviews of NHS dental practices in England (2019–2024). Topic modelling using BERTopic identified 191 topics, with sentiment analysis via a fine-tuned DeBERTa model across four classes (positive, negative, neutral, and mixed). We iteratively consolidated topics into a ten-theme taxonomy through a hybrid approach integrating LLM-assisted classification with expert qualitative interpretation. The taxonomy informed a supervised multi-label text classifier, adapted for Google Maps Places reviews to assess portability, deploying it within a dashboard that processes real-time patient reviews, visualising thematic and sentiment insights. We evaluated the ten-theme taxonomy composition and dashboard useability through qualitative applied thematic analyses of reviews and ten semi-structured interviews with dental professionals. Results: Topic modelling generated 191 topics, consolidated into a ten-theme taxonomy. The sentiment classifier achieved F1=0.952 across four classes, while the multi-label theme classifier achieved micro-F1=0.765 and ROC-AUC of 0.935. Operationalised within a clinician-facing dashboard, these models enabled near real-time synthesis of patient feedback at practice level. Qualitative useability evaluation indicates the dashboard helps identify areas for service quality improvement that would otherwise be difficult to detect. Dual-axis representation of theme and sentiment enabled more nuanced interpretation, going beyond binary or single-label approaches. Overall, the dashboard helped summarise reviews for staff meetings and QI, but users tended to focus on negative feedback. Conclusions: Our study demonstrates a reproducible NLP pipeline to produce practical taxonomies both for oral healthcare and potentially other patient-feedback contexts. It addresses a critical knowledge gap in translating NLP research into clinician-facing tools for real-time service quality improvement. By integrating computational methods with domain-specific expert interpretation, we provide a way to bridge between data analysis and clinical application. Our findings highlight the need for hybrid approaches incorporating expert assessment to address error, bias, and useability. Meanwhile, our dashboard and mapping of its development pipeline offer a practical approach for embedding patient perspectives within routine digital healthcare to support data-driven, patient-centred service improvement in oral healthcare and beyond.

  • Background: Fatigue is a common, debilitating symptom across many chronic and post-acute conditions, yet it remains difficult to assess in routine care due to its subjective, fluctuating, and context-dependent nature. Conventional fatigue assessments rely primarily on retrospective self-report measures, which lack temporal resolution and ecological validity. Advances in digital health technologies create new opportunities to capture fatigue as it is experienced in daily life through continuous, remote, and patient-centred data collection. Objective: This study evaluates the feasibility and acceptability of a fully remote digital health platform that integrates wearable sensing, environmental monitoring, cardiorespiratory physiology, and ecological momentary assessment to capture the lived experience of fatigue in everyday life. Methods: The Understanding Patterns of Fatigue in Health and Disease study (NCT05622669) was a fully remote mixed-methods observational study. Participants with long COVID, myeloma, heart failure, and healthy controls completed either 2 or 4 weeks of monitoring. Data were collected using a wrist-worn wearable bracelet, in-home Bluetooth environmental beacons, a chest-worn ECG patch, and a smartphone application delivering ecological momentary assessments of fatigue. Data streams were integrated into unified visual representations combining activity, sleep, location, physiology, and self-reported symptoms. Feasibility was evaluated through recruitment, retention, adherence, and data completeness. Acceptability and interpretability were assessed through end-of-study interviews and optional participant feedback sessions. Results: Forty participants were enrolled and 37 completed study monitoring (retention rate 92.5%). Wearable bracelet data were available for 87% of study days, with adherence reaching 93% during periods of device operation. Ecological momentary assessments were completed on 83% of study days, whereas ECG patch data completeness averaged 72%. Twenty-two participants participated in feedback sessions. Participants reported high acceptability of the remote study procedures and considered the integrated visualisations to be plausible representations of their daily routines and symptom experiences. Contextual information derived from room-level location and environmental monitoring improved interpretation of activity and physiological data, enabling identification of behavioural patterns associated with work schedules, treatment cycles, and daily functioning that would not have been apparent from wearable-derived measures alone. Conclusions: A fully remote digital health platform integrating wearable, environmental, physiological, and self-reported data was feasible and acceptable across diverse populations experiencing fatigue. The integration of contextual information with behavioural and physiological monitoring enabled interpretable representations of daily life that participants recognised as meaningful reflections of their lived experience. These findings provide methodological guidance for future digital health studies and support the development of context-aware approaches to fatigue assessment and digital phenotyping in real-world settings. Clinical Trial: NCT05622669: Understanding Patterns of Fatigue in Health and Disease study (registered 17 November 2022)

  • Remote Digital Health Technologies for Reducing Time Toxicity in Cancer Patients: A Scoping Review

    Date Submitted: Jul 23, 2026

    Open Peer Review Period: Jul 24, 2026 - Sep 18, 2026

    Background: Time toxicity, defined as the cumulative burden of time spent by cancer patients during diagnosis and treatment, significantly impacts their quality of life, treatment adherence, and imposes additional stress on family caregivers. Remote digital health technologies are considered effective strategies to mitigate this burden. However, systematic evidence regarding their impact on time toxicity as a specific outcome remains fragmented, and assessment methods are not yet standardized. Objective: This scoping review aims to systematically summarize the current landscape of remote digital health technologies in reducing time toxicity among cancer patients, including their technological types, mechanisms of action, and outcome measures. The findings will provide an evidence-based foundation for clinical practice, technology development, and health policy formulation. Methods: This scoping review adhered to the Joanna Briggs Institute (JBI) scoping review framework and strictly followed the PRISMA-ScR guidelines. A comprehensive literature search was conducted across 7 English-language databases (PubMed, Web of Science, CINAHL, Embase, Scopus, Cochrane Library, IEEE Xplore) and 4 Chinese-language databases (CNKI, WanFang, VIP, SinoMed). The search spanned from database inception to April 26, 2026. Inclusion criteria comprised original research studies involving cancer patients aged ≥18 years, utilizing remote digital health technologies, and reporting time toxicity-related outcomes. Two reviewers independently screened the literature and extracted data, resolving discrepancies with a third. Quantitative data were extracted using a standardized form and descriptively synthesized. Qualitative data were processed using content analysis. Results: Fifty-nine studies were included, with most (83.1%) published since 2020. Six technology types were identified, predominantly web portals (45.8%), videoconferencing (23.7%), and WeChat (22.0%). Five mechanisms of action were found, with direct substitution (33.9%) and process compression (28.8%) being most common. Time toxicity outcomes, categorized into travel time/visits, waiting time, total outpatient/visit duration, hospital stay/postoperative recovery, and unplanned healthcare utilization, showed consistent reductions in the first two dimensions, and significant benefits in the third. However, the latter two exhibited higher heterogeneity. Analysis revealed research gaps, particularly for video conferencing in prehabilitation/remote monitoring and WeChat in remote monitoring/data-driven triage. Only 6.8% of studies applied theoretical frameworks. Qualitative findings highlighted the elimination of travel/waiting times, and downstream benefits like energy preservation and reduced caregiver burden. Conclusions: Remote digital health technologies show significant potential in reducing cancer patients' time toxicity, mainly by optimizing administrative processes and minimizing travel/waiting times through direct substitution and process compression. However, further exploration is needed for app-driven data-driven triage, wearable-assisted prehabilitation, and WeChat-based remote monitoring/triage. Future research must enhance theoretical guidance, standardize outcome measurement, and address global health equity, with nursing playing a crucial role in patient-centered digital health innovation.

  • Redesigning hypertension care: A qualitative implementation evaluation of a multi-site, multi-pronged blood-pressure intervention

    Date Submitted: Jul 23, 2026

    Open Peer Review Period: Jul 24, 2026 - Sep 18, 2026

    Background: Hypertension control is critical to reducing cardiovascular morbidity and mortality, as well as being a key quality measure and indicator for value-based care. Multipronged interventions have been successful in public health (e.g., tobacco control), but have infrequently been intentionally used in quality improvement efforts. Furthermore, quality improvement across diverse clinic settings (academic, employer-based, community) has rarely been examined. Objective: The objective of this qualitative implementation science evaluation of a layered hypertension intervention was to identify lessons learned and recommendations to support sustainability and scale of hypertension control. Methods: We evaluated efforts to improve blood pressure control in n=6 total clinics - 2 academic, 1 employer-based, and 3 community clinics. Intervention components included: 1) implementing an electronic medical record advisory support, 2) an ambulatory office blood pressure measurement (AOBP), and 3) pre and post visit hypertension-focused care coordination delivered by medical assistants. During half-day site visits (2019-2020), we conducted 28 semi-structured interviews with clinicians (physicians and advance practice providers, n=9), medical assistants (n=11), clinic management (n=6), and other team members (n=2). We examined the following implementation outcomes for individual intervention components: feasibility within clinical workflow, acceptability to clinical teams, and facilitators and barriers to sustainability/maintenance. We also assessed themes across intervention component and clinic setting for emerging best practices for implementation. Results: Participants reflected that the EMR advisory component was: 1) acceptable with some feasibility concerns; 2) implemented with significant variation; and 3) sustainable in part due to perception that adoption provided MA training in new skills. AOBP acceptability and feasibility was also challenged by variation in usage and lack of confidence in the technology. These concerns resulted in major sustainability challenges for this component. Participants reflected that the patient care coordination component was: 1) acceptable with some modifications; 2) offered the potential for high fidelity and standardization (e.g., a single patient coordinator could theoretically support multiple clinics in a remote coordinator position); and 3) sustainable with risks related to turnover and competing priorities. Emerging best practices for implementation across clinic settings included emphasizing process, identifying champions, deliberately balancing improvement work against other priorities, and aligning incentives and rewards. Process success focused on creating multiple cues to take two blood pressure readings (e.g. visual reminders in patient rooms, verbal reminders at meetings, written e-mail reminders, etc.). BP champions (i.e., highly-engaged clinic managers and some clinicians) also supported implementation, adoption, and maintenance by motivating teams and supporting continuous learning environments. In terms of competing priorities, there was a recognition that this kind of long-term, multi-layered intervention needs to be balanced against the volume of work overall in the clinic. Lastly, alignment with internal and external rewards is needed, particularly in a value-based care context; one example of this related to benchmarks set as part of contracts with the academic health center for employer-based clinics. Conclusions: Next steps and further directions point towards strengthening implementation strategies, and innovating on technology and reimbursement. Specifically, the current billing/remote patient monitoring codes to support this work are cumbersome. A more efficient tracking-to-billing process is needed. Also, facilitation of EMR simplification (e.g., simplifying SmartSets; designating intuitive areas to house additional home monitoring data) will be key to tracking and success of multi-layered population health initiatives such as this one in the future.

  • Background: Progression is the leading cause of death among patients receiving first line treatment for diffuse large B-cell lymphoma. Some patients die from treatment-related toxicity, secondary cancers, or comorbidities, particularly cardiovascular conditions. The early detection of these events could improve quality of life and event-free survival. Objective: This study aims to describe the adverse events that have occurred and the methods used to manage them, comparing an electronic application-assisted physician approach with standard follow-up procedures. Methods: An open-label prospective, randomized, controlled phase 3 trial was conducted to compare the impact of a web-based application for monitoring events occurring during rituximab combined with cyclophosphamide, hydroxydaunorubicin (doxorubicin), oncovin (vincristine), and prednisone (R-CHOP) treatment (experimental group) with that of standard monitoring (control group). Results: A total of 62 patients were included in the study, with a median age of 62.0 years (Q1–Q3: 50.0–74.0). Thirty-one patients were randomized to each group (1:1 randomization). The study was terminated prematurely on October 17, 2024, due to bankruptcy of the unit responsible for promoting clinical trials. The median follow-up was 8.3 months (Q1–Q3: 1–24.2 months). In total, 617 events were reported (403 in the experimental group and 214 in the control group) (P<.01). About 340 events, including 238 in the experimental group and 102 in the control group, required intervention (P<.01). The median numbers of events per patient were 14 in the experimental group (Q1–Q3: 4–24), and 7 in the control group (Q1–Q3: 3–10) (P=.05). The mean times to treatment were 7.7 days in the overall population, 4.5 days in the experimental group, and 15.6 days in the control group (P=.14). Thirty-one events, including 13 in the experimental group and 18 in the control group, required hospitalization (P<.01). The event-free survival rates at the 12-months follow-up were 61.1% in the experimental group and 68.7% in the control group (P=.40). Conclusions: Event monitoring via a web application was feasible in patients with diffuse large B-cell lymphoma, with a higher number of reports in the experimental group. Compared with the control group, the experimental group had a lower proportion of events requiring hospitalization. However, considering the premature termination and the descriptive nature of the study, these results should be interpreted as exploratory Clinical Trial: ClinicalTrials.gov NCT05298293

  • Telemedicine Implementation During Prolonged Conflict: Retrospective Observational Study of Organizational Readiness

    Date Submitted: Jul 22, 2026

    Open Peer Review Period: Jul 23, 2026 - Sep 17, 2026

    Background: Armed conflicts and prolonged security crises substantially disrupt routine ambulatory healthcare delivery. Telemedicine has increasingly been recognized as an important component of healthcare system resilience during emergencies, yet evidence regarding sustained large-scale telemedicine implementation during prolonged armed conflicts remains limited. Objective: To evaluate large-scale telemedicine implementation during a prolonged regional armed conflict and identify the organizational determinants of successful telemedicine scalability within a tertiary healthcare system. Methods: This retrospective observational study evaluated ambulatory telemedicine implementation at Sheba Medical Center, Israel's largest tertiary academic medical center, during the regional conflict beginning on February 28, 2026. Outpatient encounters performed between February and April 2026 were analyzed, including both in-person and telemedicine visits. Imaging and laboratory services were excluded because they are inherently unsuitable for telemedicine delivery. Primary analyses focused on March 2026, the only complete month during the conflict period. Telemedicine utilization, implementation patterns, and temporal trends were analyzed longitudinally. Results: Despite prolonged emergency conditions and repeated missile alerts, overall institutional ambulatory activity remained at approximately 85% of pre-conflict baseline activity during March 2026. Across the study period, 21,454 of 119,632 analyzed ambulatory encounters were conducted via telemedicine, corresponding to an overall telemedicine utilization rate of 17.9% (95% CI 17.7–18.2%), compared with 5.8% (95% CI 5.7–5.9%) before the conflict (relative risk 3.09, 95% CI 3.01–3.16; P < .001). Mean daily telemedicine activity increased from 363 to 933 encounters per day. Telemedicine expansion was heterogeneous across clinical divisions, with the greatest scalability observed in services that had integrated telemedicine into routine practice before the conflict. Large-scale implementation was supported by same-day conversion of scheduled visits, clinician enablement, centralized operational oversight, and continuous monitoring of ambulatory activity. Conclusions: Large-scale telemedicine implementation was associated with sustained ambulatory care delivery during a prolonged armed conflict and functioned as a key component of a hybrid continuity-preservation strategy. Successful telemedicine scalability depended less on technology itself than on organizational readiness established before the crisis, including pre-existing clinical integration, operational governance, institutional adaptability, and clinician familiarity with telemedicine delivery. These findings suggest that healthcare systems seeking to strengthen resilience should integrate telemedicine into routine clinical practice before emergencies occur rather than relying on crisis-driven implementation.

  • Background: Large language model-based intelligent standardized patient systems offer a scalable solution for clinical skills training, yet rigorous comparisons against active pedagogical controls remain scarce, and the mechanisms underlying their effectiveness are poorly specified. This study evaluated an LLM-based intelligent SP system compared with small-group, tutor-facilitated case discussion in dental education, with the explicit objective of disentangling the role of differential individual active learning time as a potential mediator of observed effects. Objective: To evaluate an LLM-based intelligent standardized patient system compared with small-group, tutor-facilitated case discussion in dental education, and to disentangle the role of differential individual active learning time as a potential mediator of observed effects. Methods: In this single-center, parallel, open-label randomized trial conducted from May to June 2025, 60 third- and fourth-year dental students were randomized 1:1 to the AI-SP group (n=30) or the TC-SG group (n=30). The AI-SP group completed six interactive virtual patient modules powered by DeepSeek-V3 with automated feedback, while the TC-SG group discussed identical cases in groups of four to five students with a tutor. The primary outcome was the adjusted post-intervention mini-CEX Overall Score assessed by a blinded expert panel using ANCOVA with baseline adjustment. Secondary outcomes included AI-generated communication metrics and clinical self-efficacy. The study design inherently produced substantially different individual active learning time between arms—approximately 110–120 minutes per session for AI-SP versus 24–30 minutes for TC-SG—which we explicitly treated as a design-defined dose parameter. Due to significant baseline imbalances in secondary AI-generated metrics favouring the TC-SG group, causal inferences were restricted to the primary outcome, where baseline balance was confirmed. Results: Fifty-nine participants completed the trial (AI-SP=30, TC-SG=29). For the primary outcome, the AI-SP group showed significantly higher adjusted post-intervention mini-CEX Overall Score compared with TC-SG (adjusted mean difference [aMD]=0.68; 95% CI, 0.29–1.07; P=0.001; η²p=0.22). This effect size corresponded to the substantial difference in individual practice density between the two conditions. For AI-generated secondary metrics, despite significant within-group improvements in the AI-SP group (all P<0.001), no significant between-group differences were observed at post-intervention for Accuracy (P=0.059) or Interactivity (P=0.161), indicating comparable endpoint performance. Given the baseline imbalance in these metrics, we interpret these null between-group differences conservatively, without claims of catch-up or superiority. An exploratory regression analysis treating estimated individual active learning time as a continuous predictor revealed a significant association with mini-CEX improvement (β=0.34, P=0.002), supporting the dose-response interpretation. Conclusions: The AI-SP system, by affording substantially higher individual active learning density, produced superior expert-assessed clinical outcomes compared with small-group discussion. However, this advantage is confounded by unequal practice time and cannot be attributed solely to AI intelligence. Our findings suggest that AI-SP functions primarily as an effective pedagogical platform for scaling individual deliberate practice opportunities. Future research must employ dose-equated designs to isolate the unique contribution of AI-generated feedback from the general benefits of increased active learning time.

  • Remote Patient Monitoring in Orthopaedic Surgery: Leveraging Digital Health for Enhanced Patient Care

    Date Submitted: Jul 22, 2026

    Open Peer Review Period: Jul 22, 2026 - Sep 16, 2026

    Remote patient monitoring (RPM) includes a variety of technologies for evaluation of patient symptoms and joint motion beyond conventional clinical settings, with the goals of increasing access to care and possibly decreasing healthcare costs. RPM utilizes modern app-based and sensor-based technology to transmit qualitative and quantitative data to orthopaedic surgeons and their assistants for analysis and possible intervention. RPM technology includes smart phone mobile applications, telemedicine, portable wearable motion sensor devices, and a knee arthroplasty component. RPM in orthopedic surgery may improve collection of patient-reported outcomes (PROMS) and may increase physician reimbursement with new billing codes. The data collected could help individualize perioperative care, increase access to care in rural areas, and possibly empower patients to take a more active role in their care. RPM could possibly decrease healthcare costs with a reduction of unnecessary hospital readmissions or emergency room visits. It may also be used for preventive and post-procedural services. As RPM for orthopedic surgery patients may have more widespread use in the future, orthopedic surgeons should understand its use for providing musculoskeletal care and the appropriate billing codes for reimbursement.

  • Mobile-Based Telerehabilitation with Sensors for Adherence and Efficacy in Chronic Patellofemoral Pain: A Randomized Controlled Trial

    Date Submitted: Jul 21, 2026

    Open Peer Review Period: Jul 21, 2026 - Sep 15, 2026

    Background: The Internet and mobile technologies offer promising platforms for delivering rehabilitation remotely, yet the optimal preparation strategy to maximize patient adherence and clinical outcomes in digital rehabilitation programs remains unclear. Objective: To compare the effects of 1, 3, or 6 face-to-face pre-telerehabilitation tutorial sessions on adherence and clinical outcomes in patients with patellofemoral pain (PFP) participating in an 8-week digital rehabilitation program. Methods: 174 patients with PFP were randomized to one-session (OST; n=58), three-session (TST; n=58), or six-session (SST; n=58) face-to-face pre-telerehabilitation tutorial groups. All participants completed an 8-week digital health program comprising sensor-guided exercise therapy and self-care education. Primary outcome was device-recorded training adherence over 6 weeks. Secondary outcomes included self-reported adherence (Exercise Adherence Rating Scale [EARS]), pain intensity (Visual Analog Scale), quadriceps strength, Kujala Patellofemoral Score, Fatigue Severity Scale, and a closed-ended adherence survey. Primary and EARS analyses used one-way ANOVA with Tukey post-hoc tests. Secondary longitudinal analyses used linear mixed models. Results: Device-recorded total training time was greater in TST (12.4±2.3 h) and SST (12.6±2.5 h) versus OST (9.3±1.6 h; both P<.001), with no difference between TST and SST. Mean weekly training time showed a similar pattern (both P<.001). For engagement metrics, TST and SST groups had fewer Q&A visits and alert reminders than OST (all P<.001), and SST received fewer reminders than TST (P=.03). Self-reported adherence (EARS Part A) was higher in TST (18.9) and SST (19.1) versus OST (14.0; both P<.001); EARS Part B (barriers) was also higher in TST (13.1) and SST (14.2) versus OST (10.5), with SST higher than TST (P=.04). TST and SST showed greater improvements than OST in daily activity pain, squatting pain, and Kujala score (all adjusted P<.05), with no TST-SST differences. For concentric quadriceps strength, males in TST and SST had greater gains than OST; females showed TST superiority only. Eccentric strength improved more in males for TST and SST versus OST. Fatigue scores did not differ among groups (P=.63). Over 90% endorsed program features for adherence; 96.7% would recommend the program. Conclusions: Three face-to-face pre-rehabilitation sessions optimized adherence and short-term clinical outcomes for PFP telerehabilitation, with no added benefit from six sessions. This suggests a plateau effect, supporting a practical and efficient pre telerehabilitation preparation strategy. Clinical Trial: ClinicalTrials.gov NCT06651996; https://clinicaltrials.gov/ct2/show/NCT06651996

  • Test-Retest Reliability of Smartwatch-Derived Features for Longitudinal Monitoring: An Observational Cohort Study

    Date Submitted: Jul 20, 2026

    Open Peer Review Period: Jul 20, 2026 - Sep 14, 2026

    Background: Smartwatches are increasingly used for decentralized data collection in clinical research, but the everyday-life settings that make these data attractive also introduce variability. Before a smartwatch-derived feature can support clinical monitoring, its reproducibility must be established: features with low test-retest reliability weaken associations with clinical outcomes and potentially generate non-actionable signals. Reliability is expected to vary by feature type, aggregation window, and data availability, but has not been systematically screened in a clinical cohort. Objective: This study aimed to evaluate the test-retest reliability of candidate smartwatch-derived features for longitudinal monitoring in adults with advanced cancer and in healthy controls, and to determine how reliability depends on the temporal aggregation window. Methods: In a prospective single-centre observational cohort study, we analysed 8 weeks of Garmin Vivosmart 5 sensor data from 60 adults with advanced cancer, and 20 healthy controls. Out of 80 participants, 77 contributed usable smartwatch data. We examined 35 daily features across seven domains: heart rate variability, heart rate, respiration, oxygen saturation, sleep, activity, and smartwatch-derived stress. Test-retest reliability was quantified as the intraclass correlation coefficient (ICC(2,1)) across adjacent non-overlapping 1-, 3-, and 7-day windows, with 95% CIs from subject-level bootstrap resampling (10,000 resamples). Between-group and therapy-centred contrasts used permutation testing with Benjamini-Hochberg false discovery rate correction. Results: Reliability improved with longer aggregation windows in both cohorts. Between 1-day and 7-day windows, median ICC(2,1) increased from 0.53 to 0.73 in controls and from 0.66 to 0.79 in patients. The number of features reaching good-to-excellent reliability, defined as ICC(2,1)≥0.75, increased from 5 of 35 to 15 of 34 in controls and from 12 of 35 to 26 of 35 in patients. Heart rate and heart rate variability features were the most reliable, with 4 of 11 reaching weekly ICC(2,1)≥0.90 in both cohorts. Activity, respiration, sleep and oxygen-saturation features were more sensitive to aggregation window and data availability, showing larger gains from daily to weekly aggregation (e.g., step count ICC increased from 0.30 to 0.73 in controls). Weekly reliability did not differ significantly between patients and controls (median ΔICC=0.036, no feature survived FDR correction, all q>0.05). No feature showed a significant change in reliability around therapy (median ΔICC=0.016, all q>0.05). Conclusions: Weekly aggregation improved the reliability of many smartwatch-derived features, but reliability remained feature specific. Heart rate and heart rate-variability features were consistently reliable, whereas selected sleep and oxygen-saturation features displayed only moderate reliability across all aggregation windows. Reliability was comparable across cohorts and stable around therapy, indicating that feature-wise estimates are transferable across these clinical contexts. Feature-level reliability screening is a prerequisite before smartwatch-derived measures are used in clinical monitoring.

  • Background: Background: Exercise rehabilitation is a core component of knee osteoarthritis management, but sustained delivery may be constrained by time, geography, and limited health care resources. Digital technologies may improve access to prescribed exercise and support its delivery and monitoring; however, their overall effectiveness and the factors influencing treatment response remain uncertain. Objective: Objective:To systematically evaluate the effects of digital technology–supported exercise rehabilitation on pain, patient-reported function, performance-based physical function, and quality of life in people with knee osteoarthritis, and to investigate potential moderators of treatment effects. Methods: Methods: PubMed, Web of Science, Embase, EBSCO, the Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data were systematically searched from inception to May 20, 2026. Randomized controlled trials were eligible if they evaluated exercise-based rehabilitation supported by digital technologies, including telerehabilitation, mobile applications, or virtual reality. Three-level random-effects models were used to account for dependencies among multiple effect sizes arising from different outcomes, measurement instruments, or assessment time points within the same study. Statistical inference was further supported by cluster-robust variance estimation with CR2 small-sample correction. Subgroup analyses and meta-regression were conducted to examine the potential moderating effects of knee osteoarthritis severity, type of digital technology, comparator condition, sensor use, intervention duration, and weekly intervention frequency. The certainty of evidence was assessed using the GRADE approach. Results: Results: Twenty randomized controlled trials involving 1,222 participants with knee osteoarthritis were included. Three-level meta-analyses showed that digital technology–supported exercise rehabilitation significantly reduced pain (Hedges’ g = 0.60, 95% CI [0.15, 1.06], 95% PI [−1.29, 2.50]) and improved patient-reported function (Hedges’ g = 0.72, 95% CI [0.24, 1.20], 95% PI [−1.42, 2.87]), performance-based physical function (Hedges’ g = 0.54, 95% CI [0.11, 0.97], 95% PI [−0.99, 2.07]), and quality of life (Hedges’ g = 0.51, 95% CI [0.16, 0.85], 95% PI [−0.46, 1.48]). These effects remained statistically significant after CR2 small-sample correction. Subgroup analyses indicated greater improvements in quality of life among participants with mild-to-moderate knee osteoarthritis. Meta-regression further showed that longer intervention duration was associated with greater pain reduction and improvements in quality of life, whereas a higher weekly intervention frequency was associated with greater improvements in patient-reported function. Sensitivity analyses supported the robustness of the findings for patient-reported function and quality of life. Although the pooled estimates consistently favored digital technology–supported exercise rehabilitation, all 95% PI crossed the line of no effect, indicating substantial variability in the treatment effects that may be observed across future clinical settings. Conclusions: Conclusions: Digital technology–supported exercise rehabilitation may improve pain, function, and quality of life in people with knee osteoarthritis. Its principal clinical value may lie in expanding access to exercise rehabilitation and supporting the implementation of prescribed exercise. however, the optimal mode of delivery remains to be established through further high-quality research. Clinical Trial: Trial Registration: PROSPERO CRD420261404719; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261404719

  • Ready to Play? A systematic review and meta-analysis of digital gamified interventions for children and adolescents with internalizing symptoms

    Date Submitted: Jul 17, 2026

    Open Peer Review Period: Jul 18, 2026 - Sep 12, 2026

    Background: Gamified digital mental health interventions have been proposed as a scalable and engaging approach to addressing the substantial treatment gap in child and adolescent mental health. However, the evidence remains fragmented, and it is unclear which specific gamification elements contribute to clinical effectiveness and sustained engagement. Objective: This review aimed to (1) characterize the gamification elements implemented in digital mental health interventions for children and adolescents, (2) evaluate their effects on internalizing mental health outcomes, (3) assess engagement, adherence, and user experience outcomes, and (4) descriptively explore whether specific gamification elements are associated with differences in clinical or engagement outcomes. Methods: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines. Six electronic databases were searched for studies published between January 2018 and March 2026. Eligible studies examined gamified digital interventions targeting internalizing symptoms in children and adolescents aged 4–18 years. Gamification elements were systematically coded across ten categories using a predefined coding framework. A three-level random-effects meta-analysis was conducted for controlled studies reporting validated mental health outcomes. Results: Twenty-seven studies (N=28,777; mean age range: 5.05–18.78 years) were included. Feedback, challenges, and progress tracking were the most frequently implemented gamification elements, social interaction and competition were rare. The meta-analysis of five eligible controlled studies yielded a small overall effect in favor of gamified interventions (g=−0.28, 95% CI −0.43 to −0.14, P<.01). Narrative synthesis indicated more consistent effects for depression and anxiety outcomes in clinical or at-risk samples, particularly for CBT-based serious games with some degree of human support. Engagement and adherence limitations were frequently reported across studies. Conclusions: Gamified digital interventions show promising improvements for internalizing outcomes in children and adolescents, particularly in clinical contexts with structured therapeutic content and human guidance. However, gamification alone does not appear sufficient to ensure therapeutic effectiveness or sustained engagement. Future research should determine which gamification elements enhance clinical outcomes and engagement, for whom they are most effective, and under which conditions.

  • Background: Digital health has the potential to improve maternal and child health (MCH), particularly during the first 1,000 days of life. However, its successful implementation and adoption depend not only on the availability of technology but also on organizational readiness, governance, and the broader sociotechnical context. Evidence on how these factors interact in low- and low-middle income settings remains limited. Objective: This study examined the interplay of sociodemographic context, facility governance, and ICT readiness in shaping the adoption of a digital health tool for MCH in Manila, Philippines. Methods: A qualitative descriptive study underpinned by a constructivist paradigm was conducted in Manila, Philippines. The study sites included two outpatient departments in the Philippine General Hospital (PGH) and government primary healthcare facilities. Participants were selected through purposive sampling. Data were collected through a desk review, facility observations, and semi-structured in-depth interviews and focus group discussions. Qualitative data were analyzed using a two-phase approach combining rapid qualitative analysis and reflexive thematic analysis, guided by the Network of Influence Framework. Results: Readiness for digital health adoption was shaped by the interaction of favorable user-level factors and persistent organizational and governance constraints. Manila demonstrated high levels of digital engagement, with widespread smartphone ownership and internet access among potential users. However, fragmented health information systems and limited interoperability continue to constrain digital transformation across healthcare facilities. Six themes emerged from the qualitative analysis. Barriers included limited internet connectivity, inadequate availability of digital devices, usability challenges, and the lack of localized and integrated digital health tools. Enablers included interoperability across health information systems and positive attitudes toward technology adoption among mothers and healthcare providers. Although participants expressed strong willingness to adopt digital health innovations, structural and governance constraints limited their routine implementation. Overall, the findings indicate that the study sites currently exhibit more barriers than enablers, reflecting an imbalance in readiness to support the implementation of the Kalinga application. These findings provide important considerations for prioritizing context-responsive features and developing implementation strategies to address identified readiness gaps. Conclusions: Despite high levels of digital engagement among potential end users, structural and governance factors continue to impede digital health adoption for MCH in Manila. Bridging the gap between user readiness and system capacity requires investments in digital infrastructure, interoperability, localized digital solutions, and integrated health information systems.

  • Background: Childhood cancer remains the leading cause of disease-related death among children and adolescents worldwide, despite increasing survival rates. Survivors often face long-term physical and psychological side effects. In response, mobile health (mHealth) has emerged as a promising tool to support pediatric oncology patients. However, adoption and sustained use of mHealth interventions vary, often due to usability, accessibility, acceptability, feasibility, and user satisfaction challenges. Objective: This systematic review aims to synthesize existing literature on the usability, accessibility, acceptability, feasibility, user satisfaction, and overall user experience of mHealth interventions in pediatric oncology. Methods: This systematic review was conducted according to ENTREQ and PRISMA guidelines. Studies were identified through a comprehensive database search (Medline, Embase, Web of Science, Cochrane CENTRAL, and Google Scholar) performed by a medical information specialist. Screening and selection were independently performed by three reviewers using Rayyan. Data extraction included intervention characteristics, participant demographics, and reported outcomes. Thematic analysis was used to synthesize the reported outcomes across the included studies. The methodological quality of the included studies was assessed using the Critical Appraisal Skills Programme (CASP) checklist. Results: Of 12,620 studies identified, 13 were included in this systematic review. Thematic analysis of the included studies revealed four themes, each encompassing multiple subthemes. These were: Empowerment and Participation in Care, Engagement Through Design and Motivation, Usability and User Experience, Informational Support and Peer Connection, and System-Level Limitations and Disconnects. Children and adolescents were found to play an active role in their care, using mHealth applications to track symptoms, support self-management, and enhance adherence. Design and personalization features—such as narrative elements, gamification, and visual appeal—played a key role in sustaining engagement. Usability was influenced by factors including digital literacy, clarity of instructions, and intuitive navigation, with notable differences across age groups. mHealth tools were also valued for their capacity to deliver trusted, peer-mediated information and foster meaningful connections with others facing similar health journeys. Conclusions: mHealth interventions show a promising role in supporting pediatric cancer care, particularly by enhancing engagement, empowerment, and overall user experience. However, their effectiveness depends on user-centered, developmentally appropriate design and more diverse, long-term research to ensure they align with the real-world needs of pediatric oncology patients.

  • A scoping review of dashboards to measure and improve quality of care in oncology

    Date Submitted: Jul 16, 2026

    Open Peer Review Period: Jul 17, 2026 - Sep 11, 2026

    Background: Quality dashboards are increasingly being used in oncology to support monitoring and standardisation of quality of care against defined benchmarks, yet there is limited consensus or guidelines on effective dashboard design. Objective: This scoping review aims to summarise the evidence supporting the use of quality dashboards in improving cancer care and to identify key design features to inform future development. Methods: A comprehensive literature search of MEDLINE (PubMed) and EMBASE (Ovid) was conducted on June 12, 2026, using keywords including “performance,” “cancer,” “quality,” and “dashboard.” Reference lists of included studies were screened, and updates to previously published studies were searched to identify additional relevant articles. Eligible studies were full-text English-language publications involving adult patients undergoing work-up, treatment, or follow-up for solid organ malignancies in the outpatient setting that described the design and utilisation of digital dashboards capturing data on quality indicators. Results: Of 181 abstracts retrieved, 16 met inclusion criteria. An additional 18 papers were identified through reference list screening and searches for updated publications, resulting in 34 included studies. Across a range of cancer types, there was marked heterogeneity in the quality indicators, outcome measures, and reporting frequency. Despite this, there is evidence that implementing quality dashboards can improve cancer care processes, outcomes, and adherence to best practice guidelines. However, notable underexplored areas include the impact on recurrent disease management, end-of-life care, and survival outcomes. Effective design features include a single overview page with more information provided on click-through, colour coding to indicate performance levels, and funnel plots to identify outliers. Conclusions: Quality dashboards can improve guideline-recommended care processes across various stages of cancer care, with considerable scope for improvement, including through standardisation. Further research is needed to assess their impact in underexplored but clinically important areas, such as recurrent disease or end-of-life care settings. Clinical Trial: N/A

  • Using Design Thinking and Co-Design Methods to Align Diverse User Needs in Care Transitions: Designing the Digital Bridge.

    Date Submitted: Jul 15, 2026

    Open Peer Review Period: Jul 16, 2026 - Sep 10, 2026

    Background: Developing digital health solutions for complex health service environments poses a unique design challenge. Service models, like transitions from hospital-to-home for older adults with complex care needs, represent dynamic contexts in which multiple user types are working across diverse settings and workflows. Designing digital health solutions for these types of environments requires advancing traditional methods intended to design for the needs of single user groups working in more bounded settings. Objective: This study addresses this design challenge by combing Design Thinking and co-design methods to develop a digitally enabled hospital-to-home communication platform to meet the needs of older adult patients with complex care needs, their caregivers and hospital and primary care clinicians who are involved in the transition process. To co-design the Digital Bridge tool, the study was guided by two questions addressed in this paper: 1) How can we translate diverse user groups’ needs into technology features? And 2) Can incorporating a Design Thinking-driven process as part of user-centred co-design help to manage tensions with diverse user groups perspectives? Methods: The Institute of Design at Stanford’s Design Thinking approach (empathize, define, ideate, prototype and test) was applied to guide co-design of the tool; leveraging multiple virtual platforms (e.g. Zoom, Jamboards, journey maps), research methods (e.g. interviews, working groups, asynchronous feedback, surveys), and informatics tools (e.g. information flow and business process maps). Working groups consisting of patients and caregivers, hospital and primary care clinicians, and the project’s Citizen Advisory Committee. Working groups engaged in multiple-iterative design phases with the research and design team to adapt two existing technologies into the new Digital Bridge platform. Results: Pre-design work involving interviews with patients and caregivers identified nine challenges in the hospital-to-home transition process, including communication barriers, feeling rushed and invisible, and not knowing where to go when help was needed. Challenges acted as design anchors, guiding a series of five working group sessions with patients and caregivers (n=9), five working group sessions with hospital (acute and rehab sites) and primary care clinicians (n=28) and a round of surveys. Needs were mapped onto six tangible design functions and integrated in two separate technology platforms to align to the local digital ecosystems and workflows at two hospitals networks in Canada. Working across user groups, tensions around language and workflow surfaced and were addressed by prioritizing the patient- and caregiver-identified challenges, while maintaining a person-centred lens. Conclusions: The Design Thinking approach was useful in guiding the co-design process, however, effective collaboration across diverse user groups required iterative rather than linear movement between stages of the Design Thinking approach. Future co-design projects working across diverse groups should consider embedding shared-empathy activities to manage tensions and develop true person-centred solutions. Clinical Trial: ClinicalTrials.gov NCT04287192; https://clinicaltrials.gov/ct2/show/NCT04287192

  • Background: Excessive gestational weight gain (GWG) is associated with a range of adverse maternal and neonatal outcomes. Digital health interventions (DHIs) are increasingly used in prenatal care, but their effectiveness for GWG management remains uncertain, and the factors that may influence intervention effects have not been fully clarified. Objective: This systematic review and meta-analysis aimed to examine the effectiveness of DHIs on GWG outcomes during pregnancy. Methods: We conducted a systematic review and meta-analysis of randomized controlled trials evaluating DHIs for GWG management during pregnancy. PubMed, Embase, Medline, Cochrane CENTRAL, and ClinicalTrials.gov were searched from inception to January 16, 2026. Primary outcomes included total GWG, weekly GWG, and excessive GWG according to Institute of Medicine recommendations. Random-effects models were used to pool mean differences (MDs) and risk ratios (RRs) with 95% confidence intervals (CIs). Prespecified subgroup analyses explored potential effect modifiers, including pre-pregnancy body mass index (BMI), gestational age at intervention initiation, geographic region, and gestational diabetes mellitus (GDM) status. Results: Forty randomized controlled trials involving 8,178 pregnant women were included. Compared with usual care, DHIs significantly reduced total GWG (MD = −0.68 kg, 95%CI = −1.11 to −0.25) and weekly GWG (MD = −0.05 kg/week, 95%CI = −0.09 to −0.02). DHIs also reduced the risk of excessive GWG (RR = 0.85, 95%CI = 0.77 to 0.94). Subgroup analyses showed that DHIs reduced total GWG by −1.34 kg (95%CI = −1.69 to −1.02) among women with pre-pregnancy overweight or obesity, whereas no significant reduction was observed among mixed BMI women. Regarding intervention timing, HDIs initiated at or before 20 weeks’ gestation yielded significant reductions in weekly GWG and risk of excessive GWG. Conversely, initiating DHIs after 20 weeks showed greater reductions in total GWG. Geographically, DHIs significantly lowered total GWG in Europe and Asia, but no such effect was observed in North America. Meta-regression analyses did not identify significant linear associations between pre-pregnancy BMI and GWG outcomes. Sensitivity analyses supported the robustness of the findings, although publication bias was detected. Conclusions: DHIs can achieve modest but significant improvements in GWG management during pregnancy and may help reduce the likelihood of excessive GWG. The benefits appear more evident among women with pre-pregnancy overweight or obesity and in interventions initiated earlier in pregnancy. Given their accessibility and scalability, DHIs may represent a useful complement to routine prenatal care, although further high-quality large-scale trials are still needed to refine intervention strategies and determine the most effective implementation approaches.

  • The Utility of Google's AI Overview in Answering Anatomy-related Search Queries: Cross-sectional Quantitative Analysis

    Date Submitted: Jul 15, 2026

    Open Peer Review Period: Jul 16, 2026 - Sep 10, 2026

    Background: This study provides an understanding of Google’s new search engine feature AI (Artificial Intelligence) Overview and its usefulness in answering anatomy search queries. The study outlines the function, utility and limitations of AI Overview in this context. Objective: The study aims to evaluate the utility of the tool by investigating the appearance rate, readability and substantiation of AI Overview responses to anatomical queries. Methods: A list of 120 anatomical prompts of different obscurities, anatomical regions, and sex were curated from a standard anatomy textbook. The prompts were then entered into Google’s search engine in two separate rounds of searching. Search results were analysed in Python for readability and recency of cited literature. Results: The preliminary search found that AI Overview responses were returned for 70.8% of all anatomical queries searched. The secondary search found that for 120 AI Overview responses, 2731 citations across 1327 sources were returned. Flesch-Kincaid readability analysis of AI Overview responses found the readability of AI Overview to be at a level equivalent to that obtained at college (US). AI Overviews were strongly substantiated with most cited sources being recently published and of reputable background. AI Overviews favoured highly ranked sources, with the top 5 AI Overview sources accounting for more than 46% of all citations. Conclusions: Overall, the functional capabilities of AI Overview make it suitable for use in self-study of anatomy. However, users should be aware of the functional pitfalls of AI Overview. Selective presentation of corroborating sources and biases in training data were considered limitations of AI Overview. Future research should investigate potential algorithmic biases.

  • Background: Multicomponent digital health interventions are increasingly used to support type 1 diabetes (T1D) self-management and are generally acceptable to patients. However, existing evaluations primarily report average effects at the group level, with limited understanding of how outcomes arise across individuals, intervention components, and engagement patterns. Objective: To identify the contextual profiles under which favourable self-efficacy was observed within a multicomponent digital health intervention for emerging adults living with T1D. Methods: We conducted an embedded evaluation using Coincidence Analysis, a configurational method that examines how combinations of factors are associated with outcomes in complex interventions. The analysis was embedded within a randomised controlled trial of Keeping in Touch (KiT), a 12-month personalised text message-based intervention for emerging adults living with T1D in Canada. Self-efficacy was dichotomised as favourable or unfavourable based on baseline level and change over 12 months. We first examined contextual profiles associated with outcomes in the full sample, including the role of overall intervention exposure within these profiles. Among intervention-group participants, we further examined how patterns of exposure to intervention components and participant engagement were associated with outcomes. Results: Among 168 participants in the full-sample analysis and 89 intervention-group participants in the engagement analysis, multiple distinct profiles were associated with favourable and unfavourable outcomes. Exposure to the intervention was associated with favourable outcomes only within a specific profile characterized by shorter diabetes duration (<10 years) and lower baseline HbA1c (<9.0%). Higher engagement, characterised by a higher prompt response rate (≥Q2) and use of optional features, featured in favourable profiles, whereas lower engagement featured in unfavourable profiles. However, engagement alone was neither necessary nor sufficient for benefit. Instead, its association with outcomes depended on personal context, including diabetes duration, sex, and exposure to specific educational topics. Conclusions: For emerging adults with T1D, a one-size-fits-all approach may be insufficient, and evaluations focus solely on average intervention effect may obscure important differences in effectiveness across subgroups. By identifying the multiple context-dependent pathways associated with both outcomes, the findings underscore the need for context‑responsive, person‑centred approaches that tailor intervention content and engagement strategies to diverse population.

  • Machine learning biological age from routine blood tests: a systematic review and quality assessment

    Date Submitted: Jul 15, 2026

    Open Peer Review Period: Jul 15, 2026 - Sep 9, 2026

    Background: The human body ages at different rates across individuals—a reality that chronological age alone fails to capture. Biological age (BA), estimated from measurable physiological parameters, offers a quantitative lens on this variability and has emerged as a promising predictor of morbidity and mortality. Among the data modalities used to estimate BA, routine blood-based laboratory tests stand apart: they are performed billions of times annually, cost virtually nothing beyond the standard clinical encounter, and are already archived in electronic health records worldwide. Machine learning (ML) and deep learning (DL) methods have been increasingly applied to derive BA from these ubiquitous measurements, yet no systematic evaluation of this rapidly growing evidence base has been conducted. Objective: We aimed to conduct the first PRISMA-compliant systematic review of ML/DL models for BA estimation from routine blood biomarkers, characterizing the algorithms and biomarker panels used, quantifying predictive performance, and rigorously assessing methodological quality using the latest AI-specific appraisal tools. Methods: We systematically searched PubMed, Scopus, and Web of Science (April 2026) for studies developing or validating ML/DL models that estimate BA from routine blood biomarkers constituting more than 50% of input features. Two reviewers independently screened records, extracted data, and assessed risk of bias using PROBAST+AI (2025) and reporting quality using TRIPOD+AI (2024). The protocol was prospectively registered (Open Science Framework). Narrative synthesis was used given anticipated heterogeneity. Results: Thirty studies encompassing 3.78 million participants across 15 countries met eligibility criteria. Deep neural networks (43%) and gradient boosting methods (30%) were the dominant algorithm classes, achieving comparable accuracy (median mean absolute error 6.0 years). Despite striking heterogeneity, a core panel of approximately 10 blood parameters—albumin, creatinine, glucose, HbA1c, total cholesterol, HDL cholesterol, mean corpuscular volume, platelet count, blood urea nitrogen, and hemoglobin—converged as the most age-predictive features across 17 independent cohort clusters. This convergence maps onto multi-system aging biology: inflammaging-driven hepatic reprioritization (albumin), glycation through the Maillard reaction (HbA1c), nephron loss (creatinine, blood urea nitrogen), and clonal hematopoiesis (mean corpuscular volume, platelet count, hemoglobin). The age gap was consistently associated with all-cause mortality (hazard ratio 1.02-1.09 per year) across all 14 studies assessing this outcome, including those with low risk of bias. However, 63% of studies were rated high risk of bias, driven by absent calibration (90%), lack of regression-to-mean correction (80% of chronological-age-prediction models), and insufficient fairness evaluation (90%). Conclusions: Routine blood biomarkers analyzed through ML carry robust, clinically relevant information about the pace of biological aging, positioning blood-based BA as the most scalable aging biomarker for population health. However, critical methodological gaps—particularly the widespread absence of regression-to-mean correction and calibration—must be addressed before clinical implementation. We provide specific standardization recommendations and propose an open-source reference model built on the empirically identified core panel.

  • From scroll to screening: a case study of social media recruitment for a mobile-based depression screening trial in the Netherlands

    Date Submitted: Jul 14, 2026

    Open Peer Review Period: Jul 14, 2026 - Sep 8, 2026

    Background: Recruitment is a major challenge in randomized controlled trials, especially in mental health research, where barriers such as stigma can discourage participation. Social media offers a scalable way to reach potential participants. However, there are limited reports on its practical application as a recruitment strategy in community-based research trials. Objective: This study aims to present a case study of social media recruitment in a Dutch mobile-based depression screening trial and to generate evidence and practical insights for future trials. Methods: Advertisements were designed for this study by combining stock photos and a video with six different behavioural messaging strategies: social importance, autonomy, social proof, commitment, altercasting, and shortage. Paid advertisements were deployed on Facebook and Instagram from December 2024 to March 2026 in nine campaign flights. Campaigns were continuously optimized via A/B testing, phased budget allocation, geographic targeting, and Meta Pixel tracking. Outcomes included number of impressions, reach, clicks, eligible participants, consenting participants, completed baseline assessments, and costs. Results: The campaign generated 2,682,854 impressions, reached 1,078,976 individuals, and resulted in 44,893 clicks (Clicks through rate = 1.67%; Cost per click = €0.33). Of 3,174 eligible potential participants, 2,013 signed consent and 1,504 completed baseline assessment (47.4% success conversion). Total advertisement costs were €14,808.73 (€9.85 per participant), and total recruitment costs including campaign management were €27,063.45 (€17.99 per participant). Recruitment improved after national expansion and Meta Pixel implementation. Autonomy-based advertisements consistently performed best across campaign phases. Conclusions: This study shows that social media advertisements can be a promising and scalable strategy for community-based mental health research recruitment. Phased campaigns, continuous optimization, conversion tracking, and autonomy-focused messaging supported recruitment of over 1,500 participants and offer practical guidance for future online trials.

  • Patients’ Expectations of Physician Use of Artificial Intelligence: Systematic Review

    Date Submitted: Jul 14, 2026

    Open Peer Review Period: Jul 14, 2026 - Sep 8, 2026

    Background: As artificial intelligence (AI) is increasingly integrated into clinical workflows, traditional models of the patient-physician relationship are being redefined. Understanding how AI shifts patient expectations of physicians is critical for maintaining trust, ensuring accountability, and guiding medical education. Objective: To systematically review and synthesize empirical evidence on patients’ expectations of physicians who use AI in clinical decision-making. Methods: A systematic search was conducted in PubMed and Web of Science for empirical studies published between January 1, 2000, and January 31, 2026. Eligible studies examined triadic patient-physician-AI contexts and reported on patient perspectives regarding physician roles, competence, communication, or responsibility. Data were synthesized using thematic analysis and mapped onto established theoretical perspectives, including role, trust, attribution, and agency based-perspectives. Results: A total of 16 studies met the inclusion criteria, spanning diverse clinical contexts such as oncology, radiology, and primary care. Mapping findings to role-based perspectives, patients viewed clinical judgment as non-delegable (Theme 1), expecting physicians to maintain diagnostic oversight and take dual responsibility for algorithmic outputs. Final accountability remained anchored in the clinician (Theme 3), as patients continue to place the moral and legal responsibility for medical outcomes within human agency. AI literacy was also recognized as an emerging component of professionalism (Theme 7). From trust-based perspectives, confidence in AI was mediated and context-dependent (Theme 4), often grounded in the existing patient-physician relationship, provided that humanistic care was preserved (Theme 5). Patients also identified ethical responsibilities, including regulatory approval of AI use, data privacy, informed consent, patient safety, and fairness (Theme 6), as non-transferable duties of physicians. From agency-based perspectives, explaining AI was viewed as a fundamental clinical responsibility (Theme 2), requiring physicians to interpret complex AI-generated outputs for patients. Despite its perceived benefits, AI was consistently positioned as a supportive third-party in decision-making (Theme 8), with physicians expected to act as the primary mediator who contextualizes technical evidence within individual value systems. Furthermore, physicians were expected to preserve patient autonomy and facilitate shared decision-making, advancing the core principles of patient-centered care (Theme 9). Although not a primary perspective, attribution-based considerations also shaped expectations regarding physicians’ professional competence, informed consent, and patient autonomy (Theme 1, 6, and 9). Conclusions: In the context of AI-assisted care, patients articulate expanded expectations of physician responsibility, encompassing non-delegable clinical judgment, ultimate accountability, and the preservation of humanistic care. Medical education and health system governance must therefore prioritize the cultivation of augmented professionalism, ensuring that AI integration enhances rather than undermines the relational core of the physician-patient relationship. Clinical Trial: PROSPERO CRD420261295341

  • Background: Digital technology can improve diabetes treatment and management. However, the effectiveness of using digital education interventions in improving the glycaemic control of children and young people is unknown. Objective: To explore the evidence-based literature on the effectiveness of digital educational interventions in children and young people living with diabetes. The review aimed to identify online resources and technology and synthesise the effect size of interventions on glycated haemoglobin (HbA1c), in addition to other outcome measures used to assess the efficacy of the intervention. Methods: A systematic review and meta-analysis were conducted using the Joanna Briggs Institute (JBI) Methodology. A database search was completed using MEDLINE, CINAHL, Cochrane Library, Embase, ClinicalTrials.gov website, the International Clinical Trials Registry Platform, and ProQuest Dissertations. Only studies published in English and published during the last 20 years were included. An a priori protocol was developed and made available on the Open Science Framework and was registered in PROSPERO (CRD42024599125). Results: A total of 14 studies, comprising 1330 participants from 9 countries, were included. A statistically significant reduction in HbA1c levels in children and young people diagnosed with type 1 diabetes was found (MD= ꟷ0.17, 95% CIꟷ 0.29,ꟷ0.05, P = 0.006, I2 = 38%). The use of telemedicine platforms, including the transmission of blood glucose data with feedback or wearable devices, was the most common platform and form of data collection. In the subgroup analysis, the fixed effects model showed positive outcomes for diabetes related worry (MD = 2.59, 95% CI 0.77, 4.42, P = .005, I2 = 87%) and treatment satisfaction (MD= 1.92, 95% CI 0.78, 3.05, P = .001, I2 = 0%), favouring the use of 'digital educational interventions. Subgroup analyses included the duration of interventions as well as the types and content of digital educational interventions. However, the effect of these interventions according to age group and digital platform remains uncertain. Conclusions: Engagement with interventions using 'digital educational interventions' demonstrated improved HbA1c levels in children and young people with diabetes. Since we were unable to locate studies among children and young people with type 2 diabetes or prediabetes, our findings are limited to type 1 diabetes. Establishing guidelines for the design of digitally interactive interventions informed by motivational theory, the inclusion of longer follow-up times, the inclusion of low- and middle-income countries and the development of interventions for culturally and linguistically diverse populations would improve study quality, consistency of reporting and development in this emerging field.

  • Theory-Guided Development and Usability Evaluation of a Web-Based Self-Management Module for Older Adults with COPD: A Mixed-Methods Study

    Date Submitted: Jul 13, 2026

    Open Peer Review Period: Jul 13, 2026 - Sep 7, 2026

    Background: Chronic obstructive pulmonary disease (COPD) imposes a substantial burden on older adults, yet existing digital self-management interventions often fail to address age-specific usability barriers and lack integration of established behavioral and technology acceptance theories. While web-based platforms hold promise for supporting COPD management, few have been systematically developed with direct input from older patients and validated through rigorous mixed-methods usability evaluation in the Chinese healthcare context. Objective: To describe the theory-guided development process and evaluate the usability of a web-based self-management module embedded within the SLH-COPD platform, specifically designed for older adults with COPD in China. Methods: This study employed a sequential exploratory mixed-methods design comprising three phases. In Phase 1 (Module Development), the COPD digital health intervention module was developed and finalized based on findings from prior research, a systematic literature review, and two rounds of Delphi expert consultation. Phase 2 involved the technical configuration and integration of the module into the SLH-COPD platform. In Phase 3 (Validation), alpha and beta testing were conducted with older adults with COPD; usability was assessed using the UMUX alongside objective behavioral data to inform iterative refinement of the module. Results: Using a mixed-methods design, this study successfully constructed and optimized a web-based self-management module for older adults with COPD, embedded within the SLH-COPD platform. A cross-sectional survey (n=199) identified five core domains of user needs, including symptom monitoring, medication management, and rehabilitation exercise. Following two rounds of Delphi Method (n=17, authority coefficient Cr=0.88), expert consensus was satisfactory, with Kendall’ s W values of 0.230, 0.321, and 0.285 for first-, second-, and third-level indicators, respectively (P<0.05). The final intervention framework comprised 5 first-level, 14 second-level, and 34 third-level indicators, demonstrating excellent content validity (S-CVI/Ave=0.988) and item-level CVIs (I-CVI) ranging from 0.80-1.00. Consistency testing using the AHP yielded a random CR of 0.009 (<0.1), indicating a scientifically sound weight allocation. Alpha testing resolved technical issues such as medication reminder delays and insufficient Shaanxi-localized content. Subsequent Beta testing revealed a mean UMUX score of 77.25 (SD=2.06), significantly exceeding the accepted usability threshold, with a task completion rate > 85%. Qualitative feedback confirmed that senior-friendly designs and plain-language, localized content effectively improved the user experience among low-literacy older adults. Conclusions: This study confirms that the theory-guided web-based self-management module for older COPD patients demonstrates adequate content validity and usability, with senior-friendly design effectively meeting user needs. It provides a replicable development and validation paradigm for digital chronic disease interventions in older adults. Future randomized controlled trials are needed to evaluate its clinical effectiveness and long-term adherence. Clinical Trial: Chinese Clinical Trial Registry (ChiCTR number)：PID331832

  • Trauma-Informed Language as a Safety Standard for AI and Digital Health: Lessons From Intimate Partner Violence Survivor Support

    Date Submitted: Jul 13, 2026

    Open Peer Review Period: Jul 13, 2026 - Sep 7, 2026

    Technology-mediated services, including chat platforms, social media, mobile applications, and emerging artificial intelligence (AI) tools, are increasingly used to support survivors of intimate partner violence (IPV). These tools can expand access to information and support, particularly for survivors who face barriers to in-person services, such as a partner’s controlling behaviors, geographic distance, transportation, childcare, or concerns about privacy and safety. However, safety in technology-mediated services is not limited to protecting survivors’ privacy and collected data. It also depends on how technologies communicate with survivors. Although risks related to privacy and confidentiality are widely recognized, this Viewpoint draws attention to an underrecognized safety concern: the potential for language used or generated by technology to cause distress, reinforce bias, stereotype, and stigma, or re-traumatize survivors. Language is not neutral. It reflects dominant social norms, power structures, and the perspectives of those with greater access to power, privilege, and resources. As a result, even language that appears respectful or objective may carry bias, stereotypes, victim-blaming narratives, or assumptions that marginalize IPV survivors. Explicitly discriminatory, bigoted, or hateful language may be more readily recognized. More difficult to identify is language that appears neutral but minimizes survivors’ concerns, misinterprets their experiences/thoughts/feelings, implies responsibility for the violence they experienced, or excludes the experiences of male, nonbinary, disabled, racialized, immigrant, or otherwise marginalized survivors. These risks are heightened in digital interactions that rely primarily on written communication because they lack tone, facial expression, body language, and other contextual cues. This concern applies across technology-mediated services but becomes especially urgent with generative AI. Because AI systems are trained on large bodies of historical language data, they may reproduce and amplify entrenched social inequities. Without intentional trauma-informed design and evaluation, AI-generated responses may threaten survivors’ perceived safety and trust in technology, disempower them, and potentially discourage future help-seeking. Drawing on the six principles of a trauma-informed approach, this Viewpoint introduces trauma-informed language as communication that recognizes the widespread impact of trauma, acknowledges that language itself can cause or exacerbate harm, and actively resists re-traumatization through language that promotes safety, trustworthiness and transparency, support, collaboration, empowerment, and attention to cultural, historical, and gendered contexts. This perspective shifts the field from reactive approaches that detect and mitigate harmful outputs after they occur toward proactive prevention. It also reframes language not as a stylistic concern, but as a core design, safety, and equity standard for digital technologies. Future work should develop and test trauma-informed language frameworks, dictionaries, and evaluation criteria across diverse survivor populations and technology contexts to ensure that digital innovation advances not only access, but also safety, dignity, equity, and healing.

  • Exploring personalized pressure injury prevention enabled by smart sensing and artificial intelligence: a scoping review

    Date Submitted: Jul 12, 2026

    Open Peer Review Period: Jul 12, 2026 - Sep 6, 2026

    Background: Over the past decade, research on sensor applications for pressure injury prevention has increased steadily, demonstrating significant advantages in the real-time and dynamic monitoring of pressure injuries related risk factors. Meanwhile, the rapid development of artificial intelligence has accelerated interest in intelligent smart sensors with advanced computational techniques. By combining the strengths of artificial intelligence and sensors technologies, these systems can achieve superior pattern recognition and predictive capabilities compared with traditional approaches, offering a new paradigm for personalized pressure injury prevention. However, systematic evidence summarizing the current applications of smart sensors in personalized pressure injury prevention remains lacking. Objective: To summarize current evidence regarding the application, technical maturity, and clinical translation of smart sensors in personalized pressure injury prevention. Methods: PubMed, Web of Science, Embase, Cochrane Library, CNKI, WEIPU, WANFANG, and SINOMED were searched for relevant studies using terms related to pressure injuries, sensors, artificial intelligence, and machine learning. Studies focusing on the development or validation of smart sensors for pressure injury prevention were included, while studies unrelated to personalized pressure injury prevention or treating pressure injury prevention as a secondary outcome were excluded. Results: From January 2015 to October 2025, a total of 2158 articles were identified, of which 78 studies were ultimately included in this review. Current applications mainly involve pressure monitoring, posture recognition, moisture and temperature sensing, and multimodal monitoring systems combined with machine learning algorithms. Most studies remained at the prototype development or preliminary validation stage, with limited large-scale clinical implementation. Conclusions: Although smart sensors demonstrate considerable potential for improving pressure injury prevention, current research is still largely limited to early-stage technological development and descriptive investigations. Several barriers hinder bedside translation, including the lack of high-quality clinical trials, insufficient involvement of nursing professionals during device development, poor device stability in complex clinical environments, and the black-box nature of machine learning algorithms. Future research should prioritize the development of explainable artificial intelligence systems to enhance clinical trust and facilitate adoption. Furthermore, future efforts should move beyond passive data collection toward closed-loop intervention systems capable of automatically delivering precise pressure-relief strategies based on individual tissue tolerance thresholds. Clinical Trial: PROSPERO CRD420251183646, registered 4 November 2025

  • Real-world Implementation and Outcomes of Artificial Intelligence in Healthcare Supply Chains: A Systematic Review

    Date Submitted: Jul 10, 2026

    Open Peer Review Period: Jul 12, 2026 - Sep 6, 2026

    Background: Healthcare supply chains face persistent challenges such as information asymmetry, fragmented coordination, and limited technological integration, vulnerabilities starkly exposed during the COVID-19 pandemic. While artificial intelligence (AI) has shown promise in clinical applications, its use in healthcare supply chain management remains understudied. Objective: This systematic review examined the extent to which AI implementation in healthcare supply chain management (SCM) has been evaluated in the peer-reviewed literature, focusing on: (1) types of AI tools reported as implemented in real-world settings, (2) the degree to which implementation science frameworks were applied in these deployments, and (3) operational outcomes reported following implementation. Methods: Following PRISMA 2020 guidelines, three electronic databases (PubMed, Scopus, and Web of Science) were searched on April 9, 2024, using keywords related to implementation science, artificial intelligence, healthcare, and supply chain management. Searches were limited to English-language, peer-reviewed articles published between 2009 and 2024. Six reviewers independently screened 5,499 unique records using predefined inclusion and exclusion criteria. Studies were included only if they documented actual AI implementation beyond pre-implementation modeling or simulation. Data extraction focused on study characteristics, AI implementation contexts, supply chain domains, implementation science framework use, and reported outcomes. Results: From 5,499 initial unique records, 54 proceeded to full-text review; only 3 met final inclusion criteria, a 99.95% exclusion rate, revealing a fundamental gap between widespread industry AI adoption and rigorous research on the implementation of AI across supply chain functions. The three included studies reported on AI implementation across distinct supply chain functions: laboratory specimen transport optimization (genetic algorithms achieving 20-30% cost savings while maintaining ISO quality standards), acute stroke care coordination (machine learning-enabled platform reducing door-to-treatment times by 32-39% and communication burden by 30%), and integrated smart hospital operations (comprehensive AI platform supporting >12,000 daily uses with sub-second response times). While all implementations demonstrated measurable operational improvements, none employed formal implementation science frameworks (e.g., CFIR, RE-AIM, NASSS) to guide planning or evaluation, and follow-up periods were limited to six months or less in the studies that reported them. Conclusions: This review reveals a critical paradox: despite widespread industry AI adoption in the healthcare supply chain, the implementation evidence is absent. The lack of implementation research shows more than a methodological gap; it signals substantial risk for healthcare organizations looking to implement AI without evidence-based guidance on the implementation process, organizational prerequisites, or sustainability factors. Future research must prioritize implementation science approaches, longitudinal sustainability assessment, and evaluation of downstream patient outcomes. Interdisciplinary collaboration between engineers, healthcare managers, and implementation scientists is essential to transform AI from a promising concept into an equitable, sustainable component of healthcare supply chain operations.

  • Should Clinical Foundation Models Reason Through Disease Labels? A Falsifiable Case for Diagnosis as an Interface

    Date Submitted: Jul 11, 2026

    Open Peer Review Period: Jul 12, 2026 - Sep 6, 2026

    Clinical foundation models are increasingly trained on longitudinal electronic health records, learning continuous, high-dimensional patient representations that are not organized around the diagnostic vocabulary. Yet these systems are still built, evaluated, and governed as if the disease label were the natural unit of machine reasoning: the diagnosis is the privileged prediction target and the unit in which the model is expected to reason. In this Viewpoint we argue that this inherited assumption should be reversed. A disease label is a compressed, human-compatible abstraction whose usable resolution was bounded not by biology alone but by what clinicians and institutions could reliably name, teach, remember, and share. Foundation models relax that constraint, because the representation used to reason need no longer be human-readable: a machine can reason over a higher-dimensional latent patient state and render a named diagnosis only when a clinician, payer, regulator, or registry requires one. We therefore separate three things the label conflates—the internal representation a model reasons over, the clinical decision it is optimized against, and the human- and institution-facing code it renders—and reframe diagnosis as an external interface, a projection from that internal representation into a human-compatible code, rather than the substrate of machine reasoning. The claim is empirical, not rhetorical, and we hold it to a falsifiable test: comparing label-based against foundation-model latent representations, under matched data and compute, on outcomes defined outside the diagnostic coding system—treatment response, trajectory, dose, timing, and toxicity. We specify one such test in BCR–ABL-positive chronic myeloid leukaemia. The boundary condition is explicit: where a label is already a sufficient statistic for the decision, a richer representation buys nothing.

  • Rethinking Digital Outcome Measures for ALS Trials: Consensus Priorities for Actigraphy Harmonization, Validation, and Regulatory Readiness

    Date Submitted: Jul 9, 2026

    Open Peer Review Period: Jul 10, 2026 - Sep 4, 2026

    Amyotrophic lateral sclerosis (ALS) clinical trials need outcome measures that complement established endpoints while capturing how people function in daily life. We synthesized stakeholder perspectives, patient input, and multisite actigraphy experiences to propose a roadmap for advancing actigraphy as a digitally derived clinical outcome measure in ALS. Priorities include patient-centered protocol design, harmonized data collection, fit-for-purpose validation, and staged adoption across observational research, clinical trials, industry, and regulatory contexts.

  • Guide to Healthcare Payers Data-Driven Risk Mitigation Strategies: Illustrative Tutorial on Mitigating Social Risk Factors

    Date Submitted: Jul 9, 2026

    Open Peer Review Period: Jul 10, 2026 - Sep 4, 2026

    Healthcare payers are strategically positioned at the junction of population health, quality of care, cost, and big patient data while navigating the risky business of healthcare. Payers possess the incentives, resources, and capabilities to implement data-driven solutions and leverage advancements in artificial intelligence to improve health and financial outcomes. Rapid technological progress often fails to translate into impactful, successful realworld results. Yet, there is limited guidance for researchers and innovators seeking to develop artificial intelligence and machine learning interventions that are aligned with the operations of the fertile healthcare payer landscape. This tutorial presents a generalizable framework for the development of data-driven solutions as healthcare payer risk mitigation strategies. Using unmet social needs as an illustrative example, we demonstrate how a data-driven approach can mitigate the impact of social risk factors on healthcare payer operations while promoting health equity. This tutorial is a bridging resource to ultimately foster collaboration by facilitating the alignment of technology development and intervention design with healthcare payer processes while also providing value to policymakers, clinicians, and payers interested in the convergence of social determinants of health and data-driven risk mitigation strategies.

  • Background: The integration of artificial intelligence (AI)-powered conversational agents into healthcare has steadily progressed towards real-world deployment, despite limited patient-level evidence regarding acceptability in low-resource settings. Objective: This study evaluates patients’ perceptions, acceptance, and concerns regarding the use of an AI-powered Clinical Intelligence Companion-Hami® in outpatient clinics of a resource-constrained setting. Methods: A cross-sectional study was conducted at four hospitals in Karachi, Pakistan. A structured survey comprising demographic information and nine questions to assess perceptions was administered to 8,487 patients after they interacted with Hami®. Frequencies and percentages were reported for all responses, and binary logistic regression was performed to assess factors associated with patients’ acceptance and concerns. Adjusted Odds Ratios (aORs) were reported with 95% Confidence Intervals (CIs), and p-values <.05 were considered significant. Results: Patients were highly receptive to Hami®’s integration into healthcare settings (79·9%). Patients attending private hospitals (aOR 1·32; 95% CI: 1·14-1·53), aged 18-44 years (aOR 1·52; 95% CI: 1·29-1·78), and 45-64 years (aOR 1·32; 95% CI: 1·11-1·56) were significantly more likely to prefer Hami®’s integration into clinical care. Patients attending private hospitals also had higher odds of concerns regarding concern of medical errors (aOR 1·29; 95% CI: 1·06 – 1·56), confidentiality breaches (aOR 2·11; 95% CI: 1·57 – 2·82), reduced contact with providers (aOR 1·62; 95% CI: 1·30 – 2·01), decreased human aspects of care (aOR 2·07; 95% CI: 1·65 – 2·61) and unclear accountability (aOR 2·05; 95% CI: 1·58 – 2·65) as compared to patients attending public hospital (P <.05). Educated patients had two times higher odds of concerns regarding reduced contact with providers (aOR 1·91; 95% CI: 1·64 – 2·21) and decreased human aspects of care (aOR 2·10; 95% CI: 1·81 – 2·43) using Hami® in contrast to uneducated patients (P <.05). Conclusions: The integration of Hami® in a resource-constrained setting reveals differential readiness for AI-assisted care, with higher acceptance among younger and middle-aged patients and those visiting private hospitals and higher concerns expressed by educated patients and those visiting private hospitals. These findings underscore that patients’ sociodemographics mediate digital health acceptance and that implementation strategies should target educated and private-sector populations, as they are likely more aware of the risks associated with AI integration. By prioritizing transparent communication regarding data privacy and confidentiality, ensuring clinician oversight, and positioning AI as a supportive tool that preserves the essential human interaction, concerns can be mitigated.

  • Background: Estimating the magnitude of effect of immunosuppressive therapy in autoimmune disease and transplantation medicine as a single numeric score in tabular data is highly valuable, especially when developing statistical or machine learning models for prognosis, risk stratification, and other tasks. Objective: We aimed to derive a single continuous score that represents a patient’s overall immune status at a given time after exposure to immunosuppressive therapy. Methods: We developed an immunosuppressive intensity (ISI) score model to estimate point-in-time immunosuppressive state as a continuous cumulative score ranging from 0 to 1. Model structure and parameterization were informed by a structured expert elicitation process using a modified Delphi approach across commonly used immunosuppressive therapies. A base ISI model was implemented as a scaled and shifted sigmoidal ISI score function incorporating three parameters: A (starting intensity), n (decay rate), and d (half-life, 50% pharmacodynamic effect). Age and lymphocyte/CD19 B-cell counts were then incorporated to generate a biomarker-informed adjusted ISI score. Finally, we developed a cumulative ISI score to model the immunosuppression state when multiple medications are active contemporaneously. We evaluated the model in three international ANCA-associated vasculitis cohorts (RITA Ireland vasculitis (RIV) registry, IDIBELL registry, and Chapel Hill registry) for biological alignment, clinical plausibility across disease phases, and utility in relapse-risk modeling compared with conventional categorical treatment encoding, using a generalized estimating equation (GEE) model. The biological alignment was further evaluated by correlating the ISI score with Torque Teno virus (TTV) count, a marker of immunosuppression. Results: Following a Delphi process, we defined parameters for the base and adjusted the ISI score across a range of intravenous and continuous oral immunosuppressive medications. The adjusted cumulative ISI score showed stronger biological alignment and tracked disease stage appropriately. The median adjusted cumulative ISI scores were close to 1 during the peri-diagnosis phase (except for pre-treatment encounters), dropped to around 0.5 in remission, and around 0.3 in relapse encounters across the three AAV cohorts, thereby supporting clinical plausibility. The correlation between TTV count and cumulative ISI score was slightly stronger for the adjusted score than the base (unadjusted) (r=0.37 vs 0.35; both p<0.001). Therefore, subsequent clinical analyses focused on the adjusted score. Among the GEE models, the model including the adjusted cumulative ISI score had the lowest QIC, compared with the unadjusted ISI score model and the categorical treatment indicator model, indicating better relative model fit. Conclusions: We describe, for the first time, a pragmatic ISI score to represent a patient's overall immunosuppressive treatment status at a specific time point. This provides a reusable treatment-state variable for clinical analytics and prognostic modeling and represents a first step toward biomarker-enriched digital twins of immunosuppressive state for future decision support and translational digital medicine applications.

  • Acceptability of a Freely Available App-Delivered Cessation Treatment Among Adults 60+ Years: A Longitudinal Mixed-Methods Investigation

    Date Submitted: Jul 6, 2026

    Open Peer Review Period: Jul 7, 2026 - Sep 1, 2026

    Background: Older adults are a high priority population for tobacco cessation. Yet, this age group commonly experiences barriers (e.g., mobility impairments, lack of transportation) to in-person evidence-based cessation treatment. App-delivered cessation programs are publicly available and an accessible modality in which to widely deliver evidence-based treatment to this population. Despite promise, there has been limited research on the acceptability and efficacy of these cessation treatments within older adult populations. Objective: To (1) examine treatment acceptability and (2) identify treatment facilitators and barriers to a publicly available app-delivered cessation program among adults 60+ years who smoke cigarettes. Methods: U.S. adults 60+ years who reported past-month cigarette use and owned a smartphone were recruited via social media. At baseline, participants reported sociodemographic characteristics, cigarette smoking patterns, quitting interest/self-efficacy, and digital literacy. Personnel instructed participants how to download a National Cancer Institute freely available cessation app, with no usage guidelines imposed. At a one-month follow-up, participants completed semi-structured interviews regarding treatment acceptability. Using a deductive-inductive thematic analysis approach, themes were identified and meaningfully organized by the Technology Acceptance Model. Subsequently, qualitative and quantitative data were integrated using the Pillar Integration Technique to create “pillars” converging mixed data. Results: Participants (N=30; age range 60-83 years) were mostly (73%) women and diverse by race, education, and income. On average, this sample was highly motivated to quit (M=8.7/10), had moderate quitting self-efficacy (M=5.4), and reported high digital proficiency (M=4.7; possible range 1-5). Participants smoked an average of 13 cigarettes per day, with the majority having moderate or high nicotine dependence. At follow-up, 67% said they would use the app in the future and almost half (46%) reported daily usage. Thematic analysis identified 10 themes overall and 5 pillars converged 2 quantitative categories with 8 qualitative themes. Individuals with low to moderate dependence described the app as useful (e.g., distraction from cravings, educational); whereas those with high dependence did not. Individuals with moderate quitting interest also described the app as useful and valued its self-guided delivery format. Those highly interested in quitting wanted more instructions for optimizing treatment. Participants with moderate to high interest in quitting described the app as easy to use and motivating; whereas those with low motivation did not. Conclusions: A publicly available app-delivered program was an acceptable cessation treatment among adults 60+ years. Acceptability was highest among individuals with moderate interest in quitting and low to moderate nicotine dependence. App-delivered treatments might be optimal for adults 60+ years who are contemplating cessation but not yet ready to engage with more intensive treatment, providing an accessible opportunity to explore quitting at one’s own pace. Studies should identify app components that may enhance acceptability among individuals with low motivation to quit and high nicotine dependence.

  • Background: Adolescents may avoid HIV testing because of stigma, confidentiality concerns, low perceived risk, and limited access to youth-friendly information. A web-based HIV self-testing (HIVST) education intervention may provide private, repeatable, and standardized learning that supports testing intention and prevention behaviors. Objective: This study evaluated the effectiveness, digital engagement, and implementation of a web-based HIVST education intervention for improving HIV testing intention and HIV prevention-related outcomes among junior high school students in Bandung, Indonesia. Methods: A controlled quasi-experimental pretest-posttest study was conducted in December 2025 among 600 ninth-grade students at SMP Muhammadiyah 6 Bandung. Participants were allocated to an intervention group (n=300), which received web-based HIVST education, or a control group (n=300), which continued usual school activities during the study period. The primary outcome was HIV testing intention, operationalized as willingness to undergo HIVST. Secondary outcomes included HIV knowledge, HIV self-testing literacy, perceived HIV stigma, prevention self-efficacy, perceived HIV risk appraisal, and HIV prevention-related outcomes. Digital outcomes included module completion, time spent on the platform, quiz performance, repeated access, technical support requests, and a composite engagement score. Outcomes were assessed at baseline, immediate posttest, and 1-month follow-up. Digital engagement and implementation were assessed using platform logs, facilitator checklists, quiz completion records, and brief user feedback. Linear mixed models with fixed effects for group, time, and the group-by-time interaction were used to evaluate intervention effects across repeated measurements. Results: The intervention group improved across the primary outcome, secondary outcomes, and digital engagement indicators. Linear mixed models showed significant group-by-time effects favoring the intervention group for HIVST willingness total score (F2,1194=121.080, P<.001, partial η²=.169), behavioral control (F2,1194=116.370, P<.001, partial η²=.163), attitudes toward HIVST (F2,1194=54.080, P<.001, partial η²=.083), subjective norms and psychosocial barriers (F2,1194=16.920, P<.001, partial η²=.028), HIVST literacy (F2,1194=102.350, P<.001, partial η²=.146), HIV knowledge (F2,1194=82.470, P<.001, partial η²=.121), and perceived HIV stigma (F2,1194=64.380, P<.001, partial η²=.097). Digital engagement results indicated that 291 of 300 participants (97.0%) accessed the platform, 276 of 300 (92.0%) completed all assigned modules, median time on platform was 46 minutes (IQR 35-62), 282 of 300 (94.0%) completed the quiz with a mean score of 82.4 (SD 10.6), 36 of 300 (12.0%) requested technical support, and the mean engagement score was 84.6 (SD 11.2) of 100. Conclusions: Web-based HIVST education improved HIV testing intention and multiple HIV prevention-related domains among junior high school adolescents. The findings support a digitally delivered, adolescent-centered approach to school-based HIV prevention education, provided that implementation includes privacy protections, technical support, age-appropriate content, and clear referral pathways for confirmatory testing and counselling.

  • Voluntary Web Surveys Yield Higher Obesity Estimates Than Mandatory Screening in Chinese University Students

    Date Submitted: Jul 5, 2026

    Open Peer Review Period: Jul 7, 2026 - Sep 1, 2026

    Background: Background: Web-based surveys dominate health data collection among young adults, yet validation studies rely on mandatory participation or in-person verification, conditions absent from real-world digital surveillance. Whether voluntary web-based surveys produce systematically different estimates than mandatory objective assessment is unknown. Objective: Objective: We compared BMI from a voluntary, anonymous web-based survey with objectively measured BMI from a mandatory fitness assessment in the same university population. Methods: Methods: We paired a voluntary web-based survey (n=7,465; Wenjuanxing platform) with the mandatory Chinese National Student Physical Fitness Standards assessment (n=14,166) at a Chinese engineering university. Under full anonymity, individual matching was infeasible. We constructed six gender-by-grade strata, computed stratum-level discrepancies, and used quantile mapping and counterfactual bounding to distinguish selection from reporting effects. Bootstrap 95% CIs quantified uncertainty. Results: Results: Voluntary survey BMI exceeded mandatory assessment BMI in all six strata (+0.61 kg/m2 weighted mean). The discrepancy was driven by weight (+0.7 to +3.0 kg), not height (+0.5 to +1.0 cm). Bootstrap CIs crossed zero in the two largest strata. Self-reported obesity prevalence was 10.3% versus 8.2% measured. Treating the discrepancy as measurement error reduced obesity prevalence to 9.2%. Conclusions: Conclusions: The pattern, weight-driven, concentrated in smaller strata, indistinguishable from zero in largest strata, is consistent with heavier individuals being more likely to respond to voluntary health surveys, not with systematic reporting error. The distinction between reporting bias and selection bias determines whether the remedy is better instructions or better sampling design. Clinical Trial: no

  • Can digital storytelling enhance recovery in bipolar disorder?: A focus group study with patients and family members

    Date Submitted: Jul 6, 2026

    Open Peer Review Period: Jul 7, 2026 - Sep 1, 2026

    Background: Digital storytelling is an emerging approach in healthcare that blends narrative medicine with multimedia technology to share lived experiences of illness. While prior research has demonstrated benefits for creators of digital stories and for healthcare professionals who view them, less is known about how such stories impact patients and their families. Objective: This study explored how viewing a digital storytelling series about bipolar disorder influences patients and family members, particularly regarding personal recovery. Methods: We conducted a qualitative study using focus groups with patients diagnosed with bipolar I or II disorder and family members. Participants viewed a five-part digital storytelling series (Out of Darkness) and engaged in guided discussions. Data were analyzed using reflexive thematic analysis, with interpretation informed by the CHIME-D recovery framework (Connectedness, Hope, Identity, Meaning, Empowerment, and Difficulties/Trauma). Results: A total of 32 participants (17 patients and 15 family members) took part in 8 focus groups. These participants consistently described digital storytelling as emotionally impactful, relatable, and validating. Participant narratives reflected all CHIME domains. Participants described feelings of connectedness (“feeling seen and less alone”), hope, reduced stigma, strengthened identity, and greater empowerment in managing illness. The stories also prompted reflection on difficulties and trauma, which participants described as both challenging and healing. Family members reported enhanced empathy and understanding of their loved ones’ experiences. Conclusions: Digital storytelling appears to complement traditional psychoeducation by addressing emotional and experiential aspects of illness. It may support personal recovery in bipolar disorder by facilitating connection, hope, meaning, and agency while acknowledging the complexity of lived experience.

  • Safety of Patient-Facing Agentic AI: a Consensus Framework for Risk Assessment and Mitigation

    Date Submitted: Jun 26, 2026

    Open Peer Review Period: Jul 3, 2026 - Aug 28, 2026

    Deploying agentic AI systems without adequate plans for human supervision raises serious concerns about patient safety, privacy, and equity. To address this gap, a group of experts across industry, academic and clinical informatics interested in AI and safety convened a voice AI taskforce to discuss and develop consensus on the impact of agentic AI in healthcare. Through this collaboration, we developed a consensus framework to determine potential risks and plan mitigation efforts based on potential clinical use cases to aid health care delivery organizations assess, implement and evaluate AI agents to meet their needs. Based on five diverse use case examples, we identified common themes of risk at the level of the agent, data, patient and clinician as well as the mitigation strategies needed to address them. Agent-level risks include robust transcription validation, knowledge-grounded responses, mandatory conversation checklists, demographic bias testing, and red-teamed escalation triggers. At the data level, secure identity verification, high‑quality data, interoperable standards and rigorous governance form the foundation of safety. Patient‑level risks include equitable access, patient suitability and clear escalation paths. Finally, clinician-level risks include alert prioritization, defined liability frameworks, workflow-integrated outputs, and preserved clinical override authority. Robust symptom recognition and a thoughtful precision–recall balance are also essential aspects to consider. These guardrails, supported by multidisciplinary oversight and continuous evaluation, can enable AI agents to contribute to patient care without compromising safety, privacy or equity. This framework aims to address the uncertainties in risks to patient safety that should be considered by healthcare delivery organizations to safely apply these technologies to address healthcare needs.

  • Background: Background and Objective: While laparoscopic sleeve gastrectomy (LSG) is an effective treatment for severe obesity, many young and middle-aged patients commonly experience depression and obesity in clinical practice. Such patients often face risks such as poor improvement of depression and weight rebound after surgery, which seriously affect the long-term efficacy of surgery and quality of life. Digital cognitive behavioral therapy (dCBT) and transcutaneous auricular vagus nerve stimulation (taVNS), as emerging non-pharmacological interventions, have shown potential in improving mood and regulating metabolism, respectively. However, their combined application during the perioperative period for LSG remains unclear. Objective: this study aims to investigate the effects of combining dCBT with taVNS on depressive symptoms and weight management in young and middle-aged patients with depression and obesity who have undergone LSG. Methods: Methods: This study employed a randomized controlled trial design, recruiting 76 eligible middle-aged and young patients with depression- and obesity who were candidates for LSG. Participants were randomly assigned to either a combined intervention group or a conventional care control group. The control group received standard perioperative care and health education, while the combined intervention group additionally received dCBT with taVNS. The primary outcome of this study is the depression status of patients at the postoperative baseline and the 3-month follow-up. The secondary outcomes are patients’ anxiety, physical activity levels, diet, and quality of life, which will be assessed before and three months after surgery using the Self-Rating Anxiety Scale, the International Physical Activity Scale (IPAS), the Dutch Eating Behaviour Questionnaire (DEBQ), and the Quality of Life Scale (QLS), respectively. Results: Results： Compared with the control group, the combined intervention group showed significantly lower scores at three months postoperatively (p<0.001), indicating more effective relief of depressive and anxiety symptoms. In behavioral metrics, the combined intervention group demonstrated significantly higher IPAS scores (p<0.05) and superior DEBQ scores, particularly in the emotional and external eating dimensions (p<0.05). Meanwhile, the combined intervention group demonstrated significantly better improvement in the QLS scores than the control group (p<0.05), indicating more pronounced improvements in quality of life. Neither group reported any serious adverse events related to dCBT or taVNS. Conclusions: Conclusions: For young and middle-aged patients with depression and obesity undergoing LSG, an integrated intervention combining dCBT with taVNS can safely and effectively alleviate postoperative depression and anxiety symptoms. This approach improves physical activity levels and dietary behaviors, promotes weight loss with reduced rebound, and ultimately enhances overall quality of life. This combined intervention strategy provides an effective and feasible new paradigm for the perioperative management of physical and mental health in the LSG. Clinical Trial: Chinese Clinical Trial Registry ChiCTR2500107106. Registered on 4 August 2025.

  • Citizen Perspectives on Transparency in Communicating about Health Data Use in Research: A Qualitative Study

    Date Submitted: Jul 1, 2026

    Open Peer Review Period: Jul 2, 2026 - Aug 27, 2026

    Background: When citizens and patients are consulted, transparency emerges as a necessity in the context of secondary use of health data in research. Objective: We aimed to clarify the types of information that Quebec citizens consider most relevant regarding the use of their health data for research purposes, and to identify effective strategies for communicating this information. Methods: Eight focus groups, with a total of 53 members of the public were conducted in Quebec, Canada, in 2025. We paid attention to education levels, language spoken at home, and rural vs urban settings. We assessed which information was deemed necessary, how this information should be shared, and the impact of receiving this information on trust towards research with health data. An inductive/deductive hybrid approach was used to develop the coding framework and analyze the data. Results: Three types of individual-targeted information about the secondary use of their health data in research emerged as essential from focus groups: study objectives, data used, and study results. Types of information deemed less desirable included profits, penalties, as well as laws and regulations. Most participants favored receiving information through digital communication methods such as a secured website, but a substantial minority preferred analog methods. Participants’ opinions also converged on a set of expectations regarding the communication of information: accessibility, security, reliability, sustainability, and flexibility. Conclusions: The results from this study brought forward a potential transparency model that could be tested to meet public expectations regarding transparency in the setting of secondary use of health data for research as well as a framework for evaluating future proposals.

  • Are Nurses Prepared to Protect Patient Information? Evidence From a Nationwide Multicenter Cross-Sectional Study in China

    Date Submitted: Jul 1, 2026

    Open Peer Review Period: Jul 2, 2026 - Aug 27, 2026

    Background: In an increasingly digitalized society, information security has become a major challenge, with frequent data breaches resulting in substantial adverse consequences. Among various types of information, health-related information is particularly sensitive and vulnerable to misuse or compromise. As frontline clinical professionals, nurses have direct and frequent access to both patients and their health information. However, empirical evidence regarding information security behaviors from the perspective of nurses remains limited. Objective: To examine the current status of information security behaviors among nurses in the digital information environment. Methods: A descriptive cross-sectional study was conducted between September and November 2025. Nurses from 254 healthcare institutions across 29 provinces and seven major geographic regions in mainland China were surveyed. Convenience sampling combined with snowball sampling was used to collect data from 8,210 nurses. The primary measures included demographic characteristics, occupational characteristics, healthcare institution-related characteristics, and information security behaviors. The Information Security Behavior Scale comprised four dimensions: device protection, password management, proactive awareness, and information handling. Data cleaning and statistical analyses were performed using SPSS version 27.0. Results: After applying the inclusion and exclusion criteria, 8,041 nurses were included in the final analysis. The mean age of participants was 34.43 ± 7.23 years; 95.05% were female, and 79.21% were clinical nurses. The total score for information security behaviors was 103.85 ± 14.02. Significant differences in total information security behavior scores were observed across groups stratified by gender, age, Professional Experience, nursing professional title, nursing role, China’s Regional Divisions, hospital ownership, hospital level, hospital type, and department type (P < 0.05). After covariate balancing, gender, hospital ownership, China’s Regional Divisions, hospital type, and department type remained independent factors associated with the total information security behavior score among nurses. Conclusions: Information security behaviors among nurses in China were at a moderate-to-high level overall. However, specific behavioral domains, particularly certain aspects of device protection and proactive awareness, remained suboptimal. The findings indicate heterogeneous patterns of information security behaviors across hospital types, department type, and nursing roles in China. Future information security policies and training programs at the nursing management level should consider more tailored and context-specific intervention strategies.

  • Background: At present, the development of artificial intelligence is rapid. We have noticed that the artificial intelligence based on MRI is controversy in diagnosing myocarditis. Objective: The aim is to assess the diagnostic capability of artificial intelligence (AI) in identifying myocarditis through cardiovascular magnetic resonance imaging (MRI) Methods: A comprehensive search of studies was conducted through Web of Science, Embase and PubMed with a focus on researches published before June 7, 2026. If the studies assessment involved the application of AI models based on cardiovascular MRI in the detection of myocarditis, it will be included. The bivariate random effects model was used to ascertain the joint consideration of sensitivity and specificity. Heterogeneity across studies was assessed using the I² statistic. Employing the revised QUADAS-2 tool assesses the risk of bias. The certainty of evidence was evaluated according to GRADE framework. Results: Out of the initially identified 1,222 studies, there eventually included 17 studies. The ultimate analysis involved 93,740 patients and images. For myocarditis, AI showed that the sensitivity was 0.93 (0.88 − 0.96) and specificity was 0.94 (0.89 − 0.97), with the AUC of 0.98 (0.96 - 0.99). The asymmetry test of the Deeks' funnel plot did not indicate any significant publication bias (P = 0.46). Meta-regression and subgroup analysis revealed that there are markedly different in groups of analysis, AI method, reference standard and years (P < 0.05). Conclusions: By aggregating the data, this meta-analysis manifested that cardiovascular MRI based on AI revealed excellent ability in diagnosing myocarditis. However, this study is subject to limitations, including its retrospective design and the methodological heterogeneity across cardiovascular MRI. In the future, there is an urgent need for more forward-looking multi-center studies to prove this conclusion.

  • Background: Adolescent violence remains a major public health concern with long-term consequences. While rapid digital and social changes may have altered violence patterns in contemporary society, long-term structural shifts and the dynamic evolution of underlying risk factors remain insufficiently understood. Objective: We aimed to identify objective trend changes, statistical breakpoints, and shifting risk structures in South Korean adolescent violence over a 12-year period using nationwide population-based data. Methods: We analyzed nationally representative data from 801,050 adolescents (aged 13–18 years) from the Korea Youth Risk Behavior Web-based Survey collected between 2012 and 2024. The primary endpoint was violence victimization requiring medical treatment. Long-term trends were examined using data-driven segmented regression to identify significant temporal breakpoints without prespecifying time points. Period-stratified multivariable logistic regression assessed time-varying associations between demographic, behavioral, and socioeconomic factors and violence victimization. All analyses accounted for the complex survey design and sampling weights. Results: A significant structural breakpoint was identified around 2020. Violence prevalence declined steadily until 2019 but increased sharply and remained elevated thereafter. While sadness and smoking remained the strongest predictors (adjusted odds ratios 2.2–2.8), their relative primacy shifted: emotional distress led before 2020, but smoking emerged as the strongest predictor during 2020–2022. Crucially, while overall smoking prevalence declined, its association with violence strengthened, suggesting a 'concentration of risk' within a shrinking but increasingly vulnerable subgroup. Conversely, the impact of academic performance weakened, indicating that traditional pressures were eclipsed by pandemic-related environmental shocks. Conclusions: Adolescent violence in South Korea underwent a profound structural shift around 2020. The emergence of smoking as a primary risk indicator—despite declining overall prevalence—signals a fundamental change in the risk landscape, where health-risk behaviors now identify highly marginalized and vulnerable subgroups. Public health and digital health preventions must move beyond traditional academic-focused interventions to integrate mental health surveillance and substance use prevention with systemic efforts to rebuild protective social and institutional environments.

  • Perceptions and Attitudes of Women with Postpartum Depression: A Thematic Analysis of Comments Posted on Reddit

    Date Submitted: Jun 28, 2026

    Open Peer Review Period: Jun 30, 2026 - Aug 25, 2026

    Background: Over the past decade, postpartum depression (PPD) diagnoses have significantly risen across all ethnic and racial groups. However, PPD remains underdiagnosed due, in part, to stigmatization and misunderstanding by the public, which can result in a reluctance among women to openly discuss their symptoms with clinicians. However, social media offers supportive communities and unique platforms to share their unfiltered experiences and opinions that they may not disclose in-person. Despite the availability of new FDA-approved medical therapies for PDD over the past seven years, research investigating women’s comments about their perceptions of recommendations for and barriers to treatment of PDD have been sparse. Objective: The objective of this study is to evaluate the themes that emerged from the comments of those who responded to posts regarding PPD on Reddit to assess women’s perceptions of treatment recommendations and potential treatment barriers. Methods: A qualitative study was conducted of comments made responding to eligible Reddit posts published in English between August 2019 to August 2024, which were retrieved using the keywords “Zulresso,” “Zurzuvae,” “brexanolone,” “zuranolone,” “PPD,” and “postpartum depression.” Eligible posts had to meet the threshold of at least ≥ 5 votes and ≥ 5 comments. All comments that replied to each selected post were retrieved through the keyword search and Reddit’s default “relevance” filter. Each comment was assigned to a single theme that matched its primary focus, and these comments formed the main dataset for our thematic analysis. With our two overarching categories of interest, treatment recommendations and potential barriers to treatment, multiple specific themes were developed using inductive content analysis. Results: Of the eligible 93 identified posts, a total of 3,482 comments were evaluated for study inclusion. Of those, 1,348 comments were selected for thematic analysis based on relevance to the two topic categories of interest; 866 comments discussed treatment recommendations and 482 comments discussed perceived potential treatment barriers. In the category of treatment recommendations, the following themes were identified: support of medication use (52.0%), support for counseling (17.1%), recommendation for lifestyle changes (16.7%), and support for combination of therapies (14.2%). Comments on barriers yielded four additional themes: perceived overdiagnosis of PPD (38.8%), lack of social support and understanding within personal networks (32.0%), inadequate healthcare provider support (20.1%), and insufficient institutional support (9.1%). Conclusions: Our analysis of the Reddit comments revealed that women perceive significant unmet needs regarding PPD. Specifically, they emphasized critical areas for improvement, including the optimization of individualized treatment plans, greater public awareness about the condition, and enhanced support from both social and healthcare networks.

  • Internet-Delivered Cognitive Behavioral Therapy for Insomnia and Gut Microbiota Changes in Pregnant Women: Pilot Randomized Controlled Trial

    Date Submitted: Jun 26, 2026

    Open Peer Review Period: Jun 29, 2026 - Aug 24, 2026

    Background: Insomnia is common among pregnant women and has been associated with adverse pregnancy outcomes. Digital cognitive behavioral therapy for insomnia (dCBT-I) demonstrated efficacy in reducing insomnia. However, its potential remains to be fully uncovered in the pregnant population. Objective: To evaluate the feasibility and preliminary efficacy of internet-delivered CBT-I in pregnant women with insomnia and explore its potential effects on the gut microbiome. Methods: In this pilot randomized controlled trial, pregnant women with insomnia were recruited and randomized (1:1) to receive either internet-delivered CBT-I or sleep hygiene education. Self-report data were collected via REDCap at baseline, throughout the 5-week intervention, and at 2- and 6-week follow-ups. Feasibility outcomes included recruitment, retention, adherence, electronic sleep diary completion, actigraphy compliance, and safety. Sleep was assessed using Insomnia Severity Index (ISI), electronic sleep diary, and actigraphy. Stool samples collected at baseline and post-intervention and 16S rRNA gene sequencing were conducted. Results: Thirty-four participants were randomized, and 29 provided fecal samples. The intervention achieved high levels of engagement, including a session attendance rate of 94.1%, electronic sleep diary completion rate of 90.8%, and wearable device compliance rate of 67.1%, with no intervention-related adverse events reported. Compared with the control group, participants in the intervention group showed greater reductions in ISI and improvements in subjective sleep efficiency (SE). LEfSe identified enrichment of Ruminococcus in the control group at post-intervention, while MaAsLin2 showed a negative association between Cloacibacillus and changes in objective SE. Conclusions: Internet-delivered CBT-I was feasible, acceptable, and showed preliminary efficacy in reducing insomnia severity and improving subjective SE during pregnancy. High engagement and favorable preliminary outcomes support future large-scale interventions. This study also provided preliminary evidence linking sleep improvement to gut microbiome alterations. Clinical Trial: Chinese Clinical Trial Registry (ChiCTR), ChiCTR2500111690, https://www.chictr.org.cn/showproj.html?proj=248166

  • Internet of exoneuromusculoskeleton (Io-ENMS)-assisted poststroke lower limb telerehabilitation: pilot clinical validation

    Date Submitted: Jun 27, 2026

    Open Peer Review Period: Jun 29, 2026 - Aug 24, 2026

    Background: Persistent gait impairment after stroke limits independence and community participation. Telerehabilitation can extend rehabilitation access beyond clinical settings; however, home-based poststroke gait training remains limited by insufficient corrective assistance, limited remote supervision, and inadequate digital infrastructure for continuous monitoring and data-driven management. Objective: This study aimed to evaluate the feasibility, preliminary efficacy, and safety of an Internet of exoneuromusculoskeleton (Io-ENMS)-assisted telerehabilitation system that integrates Internet of Things (IoT) technology with a wearable ankle-foot ENMS to support self-help, home-based gait training under a hybrid remote therapist management model. Methods: A single-group, rater-blinded pilot validation trial was conducted in individuals with chronic stroke. Participants completed a 20-session ENMS-assisted gait training program combining guided preparation with remotely supervised home-based training and on-demand onsite support. Feasibility was assessed using training adherence, protocol compliance, remote management efficiency, participant experience, and satisfaction. Preliminary efficacy was evaluated using clinical outcomes, gait kinematics, plantar pressure distribution, and muscle activation before training, immediately after training, and at the 3-month follow-up. Safety was assessed based on adverse events, automated detection of protocol deviations, and therapist interventions during home-based training. Results: Sixteen participants completed the telerehabilitation program. The system demonstrated feasibility, supported by a high completion rate, consistent adherence during the program, positive usability and satisfaction ratings, and the effective operation of the hybrid remote management model that substantially reduced therapist involvement. Quantitative analysis of training logs and communication data provided detailed insights into user engagement patterns, training behaviors, and support needs throughout the program. Significant improvements were observed in lower-limb motor function, gait kinematics, plantar pressure distribution, and muscle activation profiles, with several gains maintained at the 3-month follow-up. Safety was supported through multilayered digital monitoring, automated detection of protocol deviations, and appropriate therapist intervention when needed, with no serious adverse events reported. Conclusions: The Io-ENMS-assisted telerehabilitation system demonstrated feasibility, preliminary efficacy, and acceptable safety for home-based gait rehabilitation in individuals with chronic stroke, supporting a data-driven and patient-centered model for delivering robot-assisted gait training in real-world home environments. Clinical Trial: ClinicalTrials.gov NCT04934787

  • Background: Cardiovascular disease kills approximately 20 million people each year, yet identifying who is at highest risk early enough to act remains difficult in routine practice. The 12-lead electrocardiogram (ECG) is inexpensive, universally available, and completed in minutes, but conventional physician interpretation captures only part of its prognostic signal. Artificial intelligence (AI)–enabled ECG analysis (AI-ECG) has shown promise for predicting cardiovascular outcomes, yet published estimates of its accuracy remain fragmented across different populations, AI architectures, and outcome definitions. Objective: We aimed to quantify the pooled prognostic discrimination of AI-ECG models for all-cause mortality, cardiovascular mortality, and composite major adverse cardiovascular events (MACE), and to identify key sources of between-study heterogeneity. Methods: We conducted a systematic review and meta-analysis of studies evaluating AI-based ECG analysis for cardiovascular prognostication (PubMed, Embase, Cochrane CENTRAL, Web of Science, IEEE Xplore; January 2015–June 2026). Eligible studies applied AI-based ECG models to predict all-cause mortality, cardiovascular mortality, or MACE in adults with at least six months of follow-up. Risk of bias was assessed using the PROBAST+AI tool. Pooled AUROC and hazard ratios (HRs) were estimated using logit-transformed and DerSimonian–Laird random-effects models, respectively (PROSPERO: CRD420261430251). Results: Twelve studies comprising more than 3.7 million patients across test and validation cohorts were included, published between 2020 and 2025 across seven countries. The pooled AUROC for all-cause mortality was 0.843 (95% CI 0.800–0.878; I²=99.9%; 7 studies). For cardiovascular mortality, the pooled AUROC was 0.854 (95% CI 0.796–0.897; I²=99.9%; 3 studies), and for MACE, 0.815 (95% CI 0.724–0.880; I²=96.2%; 2 studies). High-risk AI-ECG classification was associated with a pooled HR of 2.46 (95% CI 1.56–3.89; I²=97.3%; 5 studies) for long-term mortality. Sensitivity analysis restricted to externally validated cohorts yielded a pooled AUROC of 0.758 (95% CI 0.672–0.827). Nine of twelve studies were classified as low overall risk of bias. Substantial between-study heterogeneity was observed across all analyses. Conclusions: AI-ECG models discriminate cardiovascular risk with clinically meaningful accuracy across diverse populations and model architectures (pooled AUROC 0.843 for all-cause mortality; pooled HR 2.46 for high-risk classification). Performance consistently attenuates in external validation (pooled AUROC 0.758), and between-study heterogeneity is substantial, indicating that local validation is necessary before deploying any AI-ECG model in a new clinical setting. Clinical Trial: PROSPERO: CRD420261430251

  • Prediction Models for Postoperative Delirium After Hip Fracture Surgery in Older Adults: Systematic Review and Meta-Analysis

    Date Submitted: Jun 27, 2026

    Open Peer Review Period: Jun 29, 2026 - Aug 24, 2026

    Background: Postoperative delirium (POD) remains a frequent and clinically consequential complication in older adults after hip fracture surgery. Although a growing number of multivariable prediction models have been reported, it remains unclear how these models perform in hip fracture populations, how often they have been tested beyond their derivation cohorts, and how methodologically sound the supporting studies are. Objective: We aimed to review published prediction models for POD after hip fracture surgery and to quantitatively synthesize reported discrimination across development, internal-validation, and external-validation datasets. Methods: We searched PubMed, Embase, Web of Science, and the Cochrane Library from inception to October 24, 2025, for studies that developed, updated, validated, or evaluated multivariable prediction models for POD in older adults undergoing hip fracture surgery. Studies of elective arthroplasty were excluded. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). Areas under the curve (AUCs) and C-statistics were pooled separately for development, internal-validation, and external-validation data using random-effects meta-analysis on the logit scale. Exploratory subgroup analysis, meta-regression, and leave-one-out sensitivity analysis were used to examine heterogeneity and the stability of pooled estimates. Results: We included 24 studies, and 21 contributed development AUCs to the primary meta-analysis. The pooled development AUC was 0.833 (95% CI 0.774-0.879), with substantial heterogeneity (I2=93.2%) and a wide 95% prediction interval (0.483-0.964). Performance was lower in validation datasets, with pooled AUCs of 0.784 for internal validation and 0.764 for external validation. After exclusion of studies with very high development AUCs (≥ 0.95), the pooled development AUC decreased to 0.796. Exploratory subgroup analysis and meta-regression did not identify a robust study-level explanation for the remaining heterogeneity, and these analyses were interpreted cautiously because several categories included few studies and correlated study-level characteristics. Most studies were at high overall risk of bias, calibration reporting was limited, and external validation was uncommon. Age, preoperative cognitive impairment, functional dependence, blood loss or transfusion, and American Society of Anesthesiologists (ASA) grade were the predictors most frequently retained across final models. Conclusions: Prediction models for POD after hip fracture surgery show encouraging apparent discrimination, but the present evidence still warrants cautious interpretation. Validation performance was lower than derivation performance, heterogeneity remained substantial, and most studies were at high risk of bias. Future work should focus less on repeated isolated model development and more on external validation, calibration reporting, and updating of existing models. Clinical Trial: PROSPERO CRD420251167368; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251167368

  • Artificial Intelligence in Pragmatic Clinical Trials: A Viewpoint from the NIH Pragmatic Trials Collaboratory

    Date Submitted: Jun 24, 2026

    Open Peer Review Period: Jun 24, 2026 - Aug 19, 2026

    Background: Artificial intelligence (AI) tools are increasingly incorporated into clinical research, but their application within pragmatic clinical trials (PCTs) has not been systematically described. Objective: We sought to explore how AI tools are currently being used in the NIH Pragmatic Trials Collaboratory’s PCTs and to distill this experience into preliminary suggestions for the responsible use of AI in real-world health system-embedded research. Methods: We asked 35 NIH Pragmatic Trials Collaboratory teams if they used AI tools in their trials and held discussions with investigators who reported relevant experiences. Responses were qualitatively analyzed to characterize categories of AI use. Results: Among the 20 study teams that responded, six projects reported AI integration into their PCTs. AI applications included in these trials spanned from operational/analytic support or to improve a process, such as flagging potential participants or streamlining qualitative data review (n=4), participant-facing intervention delivery (n=1), and outcome ascertainment using natural language processing (n=1). Safeguards were employed in these trials, including manual review, frequent updates to AI tools, and chart-based validation. Validation strategies relied on human comparison to known data (e.g., expert chart reviews). Privacy protections included limiting AI chatbot responses via closed libraries, using secure institutional firewalls, and operating in HIPAA-compliant environments. Conclusions: Persistent human oversight, rigorous validation, and transparent reporting of AI use are needed to successfully implement AI tools in PCTs while preserving trial integrity and participant privacy. As healthcare systems increasingly use AI tools in clinical settings, PCT investigators should carefully plan for these tools and conduct ongoing monitoring and evaluation to ensure that they aid clinical research without causing harm. Clinical Trial: This project was determined to be exempt by the Duke University Health System Institutional Review Board for Clinical Investigations (Protocol ID: Pro00085360 Reference ID: Pro00085360-AMD-10.0).

  • User-Centered Design and Usability Evaluation of a Mobile Research App for Youth Mental Health Data Collection: Mixed Methods Study

    Date Submitted: Jun 22, 2026

    Open Peer Review Period: Jun 23, 2026 - Aug 18, 2026

    Background: Mobile research apps are increasingly used in mental health studies to enable multimodal data collection, including self-report, passive smartphone data, and digital behavioral assessments. However, ensuring usability and sustained engagement remains a challenge. User expectations, shaped by commercial apps, often conflict with research constraints, making it difficult to design research-driven apps that balance usability with scientific rigor. Applying user-centered design (UCD) principles can help address these challenges, but their role in optimizing research apps, especially those used in studies involving varied data protocols, requires further evaluation. Objective: This study aimed to document the UCD process used in developing a mobile research app for young people’s mental health (the UPIC app), assess its usability post-implementation, and provide insights for future research app developers and study designers. Methods: A UCD approach was applied, involving a series of design workshops with young people aged 15–29 to co-design the app’s interface, including early drafts of visual layout and wording. Iterative design modifications were made based on participant feedback. Following development, a usability test was conducted with 10 participants using iOS and Android devices. Participants completed task-based usability evaluations while using the think-aloud method, followed by semi-structured interviews, and the User Experience Plus (UEQ+) questionnaire. Qualitative data were analyzed using qualitative content analysis; quantitative data focused on task performance metrics and user experience scores. Results: User involvement contributed to improvements in interface aesthetics, navigation, accessibility, and clarity of wording. Usability testing identified remaining issues related to system feedback, survey tracking, and interaction clarity. Some user- suggested features, such as enhanced survey progress tracking and engagement elements, were only partially implemented due to feasibility constraints and concerns about data integrity. Participants evaluated the app positively in terms of trustworthiness and ease of learning, as reflected in UEQ+ scores, while ratings for engagement and novelty were lower. Conclusions: While UCD improved interface usability, findings highlight the importance of combining user involvement with early, real-world usability testing to identify persistent issues. Balancing usability best practices with research constraints requires transparent communication of study design, ethical engagement strategies, and structured usability evaluations. Future research app development studies should integrate iterative UCD to support both user experience and data quality. Clinical Trial: ClinicalTrials.gov (UU20230127; NCT: NCT06490120)

  • Examining Digital Insecurity-Related Avoidance and its Relationship with the Digital Divide in Late Middle Age and Old Age: a Cross-Sectional Survey

    Date Submitted: Jun 18, 2026

    Open Peer Review Period: Jun 19, 2026 - Aug 14, 2026

    Background: As societies digitalize, unequal access and use of technology risk creating a digital divide where older adults often are disadvantaged. While newer generations of older adults have higher levels of access and daily internet use, digital insecurities—worries and fears regarding cyberthreats and cybercrime — are suggested to be a barrier to their digital inclusion. Little is known about the characteristics of older adults who avoid digital technology due to insecurities, and there is also limited quantitative research on how insecurities affect older adults’ use and embracement of digital technology. Objective: The aim of this study was to characterize which older adults avoid digital technology due to insecurities and to examine the association between this avoidance and their levels of digital use and embracement. Methods: This cross-sectional study utilized data from the "Healthy Ageing in the Digital Society (HeADS)" survey, involving 451 participants, mean age 69 years (age range 55-92 years), in Sweden. Avoidance was assessed across five domains (e.g., e-commerce, smart devices, chatting with strangers). Digital inclusion was analyzed using measures for independent use (second-level divide), digital usage, and the Digital Living Index (DLI), which measures digital benefit and embracement (third-level divide). Multiple linear regression models were used to analyze the associations. Results: Over 70% of participants reported at least one form of avoidance due to insecurity, with chatting with unknown individuals (60.9%) and e-commerce (31.6%) being the most common. Higher levels of avoidance were significantly associated with female gender, older age, and a lower ability to use technology independently. Importantly, digital insecurity-related avoidance was found to be an independent barrier to digital embracement, even when accounting for potential confounders. Furthermore, over 50% of participants expressed a strong interest in learning more about safe digital use. Conclusions: Digital insecurity-related avoidance contribute to widening the digital divide by preventing late middle aged and older adults from fully realizing the benefits of digital services, despite having access and basic skills. To foster digital inclusion, support interventions must move beyond technical training and focus on building confidence and providing practical strategies for navigating the digital environment safely. Many late middle aged and older adults see the need of such support.

  • Background: Chronic Obstructive Pulmonary Disease (COPD) is currently the 3rd leading cause of death globally, and exacerbations are responsible for hospitalizations, cost, and mortality. The Internet of Medical Things (IoMT) is an enabling technology that could revolutionize care from reactive hospital-based care to proactive home care for those with COPD by combining sensor networks, wearables, smart inhalers, and cloud computing. Given the dynamism of evidence, no systematic review has yet to capture the clinical, economic, patient, and implementation perspectives of IoMT interventions used in COPD home care in under-resourced areas such as the Gulf Cooperation Council (GCC) region and low- and middle-income countries (LMICs). Objective: To synthesize evidence from the last five years (2020–2025) to discuss clinical, economic, engagement, and adoption outcomes of home-based COPD IoMT, highlighting inequities in LMICs. Methods: PRISMA 2020 guidelines and a PICOTS-SD framework guided a systematic review. PubMed and the Imam Abdulrahman Bin Faisal University (IAU) E-Library (which includes Embase, CINAHL, and Scopus) were searched from 2020 to 2025. Studies included were peer-reviewed empirical studies that assessed IoMT or telemonitoring systems for the home management of COPD, including randomized controlled trials, observational and cohort studies, feasibility studies, economic studies, and qualitative studies. The methodological quality of the studies was assessed independently by two reviewers using the Mixed Methods Appraisal Tool (MMAT 2018). The results were thematically synthesized into five thematic areas. Results: 655 records were identified for 28 empirical studies from 13 countries. A total of 13 (76%) of 17 clinical effectiveness studies reported significant benefits, including reduced hospitalizations and exacerbations and improved quality of life, while one large real-world study reported a survival benefit. Three economic analyses were presented: two showing cost savings and one showing increased costs, with survival benefits. Adoption was fairly high, while digital illiteracy and physical discomfort were prevalent, especially with older adults. Scepticism was not the main barrier to the uptake of healthcare providers; it was governance, infrastructural, and role ambiguity. In particular, the literature primarily focuses on high-income countries in the West, with limited information from the GCC, LMICs, and the Global South, where the burden of COPD is disproportionate and rising. Conclusions: IoMT has been shown to have clinically and economically valuable home-based COPD management benefits. Collaborative solutions are needed to address governance, infrastructure, workforce, and patient education issues to achieve success. There is a significant literature gap in the equity literature, and only scarce evidence from health systems in the GCC and LMICs. To fill this gap, it is essential that research and policy align with national digital health policies and strategies, such as Saudi Arabia's Vision 2030. Clinical Trial: A protocol has not been prospectively registered, but it is pre-designed and can be requested from the corresponding author. The project is recommended for registration in PROSPERO in the future.

  • Background: Type 2 diabetes mellitus (T2DM) remains one of the leading chronic diseases contributing to morbidity, mortality, and healthcare burden globally. Although Diabetes Self-Management Education (DSME) has demonstrated positive outcomes, evidence regarding the integration of artificial intelligence (AI)-supported nursing education in Indonesian hospital settings remains limited, particularly in multicenter contexts. Objective: This study aimed to examine the effectiveness of Artificial Intelligence–Integrated Diabetes Self-Management Education (AI-DSME) on glycemic control, diabetes self-care behavior, self-efficacy, quality of life, and hospital readmission among adults with T2DM in several Type B hospitals in South Sulawesi, Indonesia. Methods: A multicenter prospective cohort study was conducted from February 2025 to February 2026 in five Type B hospitals across South Sulawesi Province, Indonesia. A total of 630 adult patients with T2DM were recruited using stratified proportional random sampling. Participants received nurse-led AI-assisted DSME interventions incorporating personalized mobile education, automated reminders, nutritional recommendations, medication adherence monitoring, and family-centered counseling. Data were collected at baseline, 3 months, 6 months, and 12 months using the Summary of Diabetes Self-Care Activities (SDSCA), Diabetes Management Self-Efficacy Scale (DMSES), EQ-5D-5L, glycated hemoglobin (HbA1c), and hospital readmission records. Multivariate generalized estimating equation analysis was performed. Results: The mean age of participants was 56.8 ± 10.7 years, and 58.4% were female. Significant improvements were identified in self-care behavior scores (β = 1.92; p < 0.001), self-efficacy (β = 2.14; p < 0.001), and quality of life (β = 1.38; p < 0.001). Mean HbA1c decreased from 9.1% ± 1.8 at baseline to 7.3% ± 1.2 at 12 months (p < 0.001). Hospital readmission rates declined from 21.7% to 8.9% during follow-up. AI-supported individualized education demonstrated stronger effects among participants with poor baseline glycemic control and low educational attainment. Conclusions: AI-integrated DSME significantly improved glycemic outcomes, self-care practices, quality of life, and reduced readmission among adults with T2DM. Integrating digital nursing interventions into hospital-based diabetes management programs may provide scalable and sustainable solutions for chronic disease management in low- and middle-income countries.

  • Speech-Driven Reporting in Long-Term Care: A Mixed Methods Evaluation Study

    Date Submitted: Jun 17, 2026

    Open Peer Review Period: Jun 18, 2026 - Aug 13, 2026

    Background: Long-term care (LTR) faces critical challenges driven by workforce shortages, an aging population, and a growing population of people living with dementia. Administrative burdens add to this pressure, as healthcare professionals spend up to 40% of their working time on administration and documentation. Speech-driven AI reporting (SDR) may offer a technological solution to alleviate administrative reporting workload and enhance the workflow efficiency of care workers. Objective: This study aimed to empirically study the effects of SDR on documentation time, transcription accuracy measured by Word Error Rate, user experiences, and the client-caregiver interaction within nursing homes and home care settings. Methods: A mixed-methods study, involving 21 healthcare organizations, was conducted in the Netherlands between January and September of 2025. An experimental evaluation study comparing speech-driven and typed reporting under controlled conditions (n=35), complemented by a cross-sectional questionnaire study among care professionals from 14 elderly care organizations (n=293). Documentation time and Word Error Rate were analyzed using linear mixed models. Associations between system use duration and user experience were examined using correlation analyses. Results: The controlled evaluation study demonstrated a significant reduction in reporting time. SDR was found to be significantly faster than typing (p < 0.01), with a significant interaction between reporting device and method (p = 0.01), being 3.5 times faster on smartphones (34 seconds vs. 122 s) and 2.3 times faster on laptops (43 vs. 102 seconds). The SDR AI software demonstrated high transcription accuracy (Word Error Rate <0.05). SDR did change the reporting process: healthcare workers reported more directly after they provided care for their clients (19.0% vs 42.1%; p<0.001) and fewer reports were made after the end of their shift. Also, no correlations between SDR use and technology acceptance aspects, or perceived work pressure were determined. Conclusions: The current SDR technology offers time savings and high accuracy regardless of the device used (smartphone or laptop). However, the technological capability alone does not automatically translate to reduced perceived work pressure by care workers. The findings suggest that the challenge has shifted from technical feasibility to implementation strategy and behavioral change.

  • Digital professionally guided psychological support programs for cancer survivors: a systematic review of clinical outcomes

    Date Submitted: Jun 15, 2026

    Open Peer Review Period: Jun 16, 2026 - Aug 11, 2026

    Background: Digital psychological interventions have emerged as a promising strategy to address the growing psychosocial needs of cancer survivors. However, the specific contribution of interventions delivered with active involvement of trained mental health professionals remains insufficiently understood, particularly across different phases of cancer survivorship. Objective: This systematic review evaluates the effectiveness of professionally guided digital psychological interventions in improving psychological and symptom-related outcomes among adult cancer survivors across different phases of survivorship. Methods: Following PRISMA guidelines, a systematic search of PubMed, Scopus, and Ovid MEDLINE was conducted to identify studies published between 2013 and 2025. Eligible studies included adult cancer survivors receiving professionally guided digital psychological interventions delivered through web-based platforms or videoconferencing by trained mental health professionals. Data were extracted and synthesized narratively, and methodological quality was assessed using established risk-of-bias criteria. Results: 32 studies met the inclusion criteria, the majority of which were randomized controlled trials, with sample sizes ranging from 9 to 269 participants. Interventions included cognitive-behavioural, mindfulness-based, and supportive approaches delivered via videoconferencing or web-based platforms, with active involvement of trained mental health professionals. Most interventions were delivered synchronously (78%) and focused on acute (31%) and extended (62.5%) survivorship phases. Across studies, guided digital interventions were consistently associated with reductions in psychological distress, anxiety, and depression, as well as improvements in fear of cancer recurrence. Significant reductions were also observed in symptom burden, including fatigue, pain, and sleep disturbances; for example, one randomized trial reported a greater decrease in fatigue severity in the intervention group compared to controls (between-group difference = 0.48; p = 0.04). Improvements extended to quality of life and key psychological processes such as mindfulness, coping, and self-compassion. Overall methodological quality was fair to good. Conclusions: These findings suggest that professionally guided digital psychological interventions provide clinically meaningful benefits for cancer survivors, with their effectiveness likely linked to the preservation of structured therapeutic processes and active professional involvement, supporting their integration into stepped or blended models of survivorship care.

  • Background: Remote digital phenotyping has expanded the scalability of cognitive neuroscience studies. However, the integrity of millisecond-level response time (RT) data relies on the client-side graphics pipeline. When local Graphics Processing Units (GPUs) become unavailable, operating systems and web browsers silently transition to software-based Central Processing Unit (CPU) renderers. The extent to which these software fallbacks corrupt behavioral metrics remains unquantified. Objective: To characterize the technical constraints of software-based rendering architectures and evaluate their systemic impact on the data validity of remote, web-based cognitive tasks. Methods: We evaluated behavioral outcomes and technical paradata across two studies using our custom Adaptive Cognitive Evaluation-Explorer (ACE-X) platform. Study 1 utilized a naturalistic longitudinal sample (N = 864,702 trials; n = 277 participants) to observe real-world performance under the legacy software-based Google SwiftShader renderer. Study 2 employed a controlled, within-subjects experimental design (N = 4,089 trials; n = 74 participants) on Windows machines running Google Chrome to isolate hardware acceleration (native GPU) against software-based rendering (Windows Advanced Rasterization Platform (WARP)). Statistical profiling was conducted using stratified outlier removal and linear mixed-effects models (LMMs) with log-transformed RTs. Results: In Study 1, software rendering with SwiftShader introduced a massive, statistically significant delay, increasing baseline reaction times by 171.23% (β = 0.9978, P < .001), yielding an average hardware penalty of 515 ms (816 ms CPU vs 301 ms GPU). Study 2 experimentally validated this behavior, showing that WARP significantly inflated reaction times by 39.60% (β = 0.3336, P < .001), yielding a baseline penalty of 107 ms (377 ms CPU vs 270 ms GPU). Software rendering increased visual frame instability (FPS (frames per second) Coefficient of Variation) by over 1.5 standard deviations (P < .001). Furthermore, the integration of random slopes demonstrated that individual participant reaction times varied heterogeneously in response to this hardware-induced jitter (P < .001). Conclusions: Software-based rendering pipelines act as destructive technical artifacts in digital research, introducing profound, non-uniform delays and visual stutters that mask true psychophysiological signals. Because high individual heterogeneity renders uniform post-hoc linear corrections mathematically invalid, researchers collecting high-resolution timing data on varying hardware must actively capture graphics paradata and exclude software-rendered sessions. Ultimately, these mitigation strategies must be balanced with health equity considerations, as systematic data exclusion risks underrepresenting populations with restricted access to optimized hardware or stable device configurations.

  • Citation-Guided Sensor Metadata Enrichment: Development and Evaluation of a Large Language Model–Based Pipeline

    Date Submitted: Jun 10, 2026

    Open Peer Review Period: Jun 11, 2026 - Aug 6, 2026

    Background: Sensor metadata is critical for exposure health research because it supports accurate sensor identification, deployments, data integration, interoperability, and reproducibility. Yet it is often fragmented across multiple heterogeneous sources, such as scientific literature and manufacturer guides, where key specifications are frequently reported indirectly through citation chains, making reference tracing essential for metadata enrichment and completeness. Objective: To address this bottleneck, we developed and evaluated an LLM-based automated, citation-aware pipeline that enriches sensor metadata extracted from a primary article by identifying sensor-related citation markers and extracting additional metadata from the referenced sources. Methods: We extend our prior LLM-based metadata extraction approach by (i) detecting sensor mentions in full-text articles, (ii) capturing nearby citation markers, (iii) resolving markers to full bibliographic entries in the reference list, and (iv) retrieving cited papers to extract additional sensor metadata that may be absent from the primary document and using it to enrich and complete the base metadata. Results: Across 20 primary papers, the citation extraction component achieved 74.2% precision, 92.0% recall, 82.1% F1-score, and 69.7% accuracy, and all extracted bibliographic entries were correctly matched to their source references. This component increased sensor extraction by about 261%, yielding 94 additional sensors overall. Conclusions: The developed citation-guided pipeline improved sensor discovery and metadata completeness, thereby supporting the development of richer, more complete sensor metadata repositories.

  • A Computable Phenotype for Planned Tracheostomy Events to Characterize Outcomes and Measure Time Toxicity in Critical Care Settings

    Date Submitted: Jun 8, 2026

    Open Peer Review Period: Jun 9, 2026 - Aug 4, 2026

    Background: Tracheostomy is a frequently performed procedure in critical care settings, but procedures are often inconsistently coded in electronic health records (EHRs), with explicit designation as elective or emergency frequently absent. This coding ambiguity limits the ability to identify planned tracheostomy cohorts for observational research on outcomes and time toxicity. Common data models such as the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) enable large-scale federated research, but require validated computable phenotypes to ensure reliable cohort identification across heterogeneous data sources. Objective: To develop and validate a computable phenotype that identifies elective tracheostomy procedures from EHR data standardized to the OMOP CDM, enabling scalable and reproducible analysis of tracheostomy-related time toxicity in critically ill patients. Methods: We conducted a retrospective observational study using EHR data from the Johns Hopkins Health System from 2017 to 2024, comprising approximately 2.1 million patients with data mapped to the OMOP CDM. A series of cohort definitions were developed using standardized clinical code sets (International Classification of Diseases, 10th Revision [ICD-10] and Current Procedural Terminology [CPT]) from the Observational Health Data Sciences and Informatics (OHDSI) Standardized Vocabularies. To classify tracheostomy procedures lacking explicit urgency coding, we compared covariate prevalence and temporal relationships (e.g., intubation timing relative to tracheostomy) between explicitly coded elective and emergency cohorts. Six candidate computable phenotypes with stepwise inclusion and exclusion criteria were evaluated using PheValuator, a validated probabilistic phenotype evaluation tool. Results: Among 3552 patients with a tracheostomy procedure identified between 2017 and 2024, 2484 (69.9%) were explicitly coded as elective and 107 (3.0%) as emergency; the remaining 961 (27.1%) lacked explicit urgency classification. Covariate analysis revealed significant differences in intubation timing, drug exposures, and procedure codes between the explicitly coded groups. The best-performing computable phenotype (Cohort #202), which used inpatient visit-based attribution of planned and emergency codes, achieved a sensitivity of 0.88 (95% CI 0.84-0.91) and a positive predictive value (PPV) of 0.81 (95% CI 0.77-0.84), with an F1 score of 0.84. Conclusions: The proposed computable phenotype effectively distinguishes elective from emergency tracheostomy in structured EHR data. This approach enables large-scale, reproducible studies of tracheostomy-related time toxicity across heterogeneous OMOP-mapped data sources and provides a generalizable framework for phenotyping intent-ambiguous procedures across federated research networks.

  • Background: Effective chronic disease management requires individuals to prioritize long-term health goals over immediate temptations. As chronic patients increasingly engage with online health information, it is important to understand how such engagement may relate to future-oriented cognition and self-regulatory capacity. Objective: This study examined the association between online health information seeking behavior (HISB) and self-control among adults with chronic diseases and investigated whether consideration of future consequences (CFC) was associated with this relationship. Methods: Cross-sectional survey data were collected from 11,031 adults with chronic diseases in China. Mediation analyses were conducted using SPSS macro PROCESS with 5,000 bootstrap samples while controlling for demographic, health-related, and psychological covariates. Results: HISB was positively associated with CFC (B=.07, SE=.005, p<.001). CFC was positively associated with self-control (B=.56, SE=.008, p<.001). After CFC was entered into the model, the direct association between HISB and self-control was no longer statistically significant (B=.005, SE=.004, p=.22). Bootstrap analyses indicated a significant indirect effect of HISB on self-control through CFC (B=.041, BootSE=.003, 95% CI .0348-.0479). Conclusions: The findings suggest that consideration of future consequences may help explain the association between online health information seeking and self-control among adults with chronic diseases. More broadly, digital health environments may increase the salience of future health consequences by repeatedly rendering long-term outcomes cognitively accessible in everyday life. Longitudinal and experimental research is needed to clarify causal mechanisms underlying these associations.

  • Developing a Longitudinal Gait-Recovery Video Library for Orthopedic Patient Education at a Safety-Net Hospital: Tutorial

    Date Submitted: Jun 5, 2026

    Open Peer Review Period: Jun 6, 2026 - Aug 1, 2026

    Musculoskeletal health literacy requires patients to understand complex treatment options, postoperative precautions, and recovery timelines, which together help set realistic expectations for recovery. However, existing patient education materials are often text-heavy, exceed recommended reading levels, and fail to depict how functional recovery progresses over time, which may be especially limiting in safety-net settings serving populations with variable health literacy. In this paper, we describe methods for designing and developing a secure, institution-restricted gait-recovery video library for orthopedic patient education. Our library was built to close that gap with short, patient-perspective recovery videos centered on one of the most meaningful outcomes of lower-extremity surgery: functional mobility. Each video was built around a standardized Timed Up and Go (TUG) assessment recorded from frontal and sagittal views, paired with relevant radiographs and visually adapted patient-reported outcome measures (PROM), to create a multimodal, visually guided recovery pathway. This publication aims to detail the process of selecting a secure hosting platform; choosing the filming setup, recovery milestones, and key visual features to capture; maintaining patient privacy and data security; executing clinic-based filming and video editing; and building a personalized interface that allows videos to be filtered by procedure type, recovery stage, and patient characteristics.

  • Background: Background: Digital transformation has increasingly influenced healthcare systems globally, with Electronic Medical Records (EMRs) becoming central to improving healthcare documentation, communication and decision-making. Despite growing recognition of EMRs as tools for strengthening health data quality, healthcare institutions in many low- and middle-income countries like Nigeria continue to experience setback as regards digital inclusion, infrastructural limitations and workforce readiness. In Nigeria, public tertiary hospitals still experience inconsistent EMR implementation and persistent concerns regarding the quality of patients’ health data. Objective: Objective: This study explored healthcare providers’ perspectives on EMR adoption and health data quality in selected public tertiary hospitals in North-Central Nigeria within the broader context of digital health inclusion in the Global South. Methods: Methods: The study adopted explanatory sequential mixed-method design. The design involved quantitative phase, identification of key quantitative results, qualitative phase, integration of findings and interpretation. The quantitative data were collected using a structured clinical chart review checklist developed from internationally recognized health data quality dimensions and existing literature on EMR systems and health information management. The qualitative data were collected through semi-structured key informant interviews among physicians, nurses and Health Information Management professionals purposively selected from three public tertiary hospitals with varying levels of EMR implementation. Interviews were audio-recorded, transcribed verbatim and analyzed using thematic analysis. Results: Results: The study revealed an overall moderate level of health data quality, with high a Health Data Quality Index (HDQI) of 73%. Healthcare providers acknowledged the potential benefits of EMRs in improving accessibility, timeliness, comprehensiveness, relevancy and consistency of health data. Participants identified ease of information retrieval, reduction in missing records and improved continuity of care as major strengths of EMR systems. Several barriers to meaningful digital inclusion however emerged. These include unstable electricity supply, poor internet connectivity, inadequate training, workload pressure, dual documentation practices and limited institutional support. Providers further reported that system reliability, ease of use and user satisfaction strongly influenced their willingness to utilize EMRs consistently. Positive attitudes toward digital systems were associated with improved documentation practices and enhanced health data quality. Conclusions: Conclusion: Electronic medical records adoption in Nigerian tertiary hospitals remains shaped by complex technological, organizational and behavioural factors. Strengthening digital inclusion through reliable infrastructure, workforce capacity building and supportive institutional policies is essential for improving sustainable EMR utilization and health data quality in resource-constrained healthcare settings.

  • Background: Digital health programs in sub-Saharan Africa often assume broad mobile reach, yet population-level evidence on who can use specific technologies, and who is excluded, remains limited. Without accurate denominators, digital interventions may reinforce inequities by missing people least engaged with conventional healthcare. Objective: We assessed technology adoption, disparities, and trajectories in a high-HIV-burden rural South African population to inform equitable digital health implementation. Methods: We analyzed 309,151 person-years from the Africa Health Research Institute demographic surveillance platform in rural KwaZulu-Natal, South Africa (2017 to 2023). We measured adoption of seven technologies (calls and SMS, internet, WhatsApp, email, mobile banking, entertainment, and health tracking) and constructed a five-tier Digital Adoption Ladder from offline (T0) to digital-health ready (T4). We quantified disparities by HIV status, gender, and their intersection using logistic regression, and tracked temporal trajectories including the COVID-19 period. Results: In 2023, 61.3% of records were classified as offline (T0) under the harmonized coding rules, and only 2.9% reached digital-health readiness (T4). Among tested individuals, people living with HIV showed higher adoption across all technologies (odds ratios 1.13 to 1.57) than HIV-negative individuals, with 56.0% connected versus 44.6%. Females also showed higher adoption than males (odds ratios 1.24 to 1.80). Intersectional analysis identified HIV-positive females as the most connected group (58.1%) and HIV-negative males as the least connected (38.4%), a 20-percentage-point gap. This pattern emerged after 2019 and defines a prevention paradox: a group important for HIV testing, PrEP, and prevention outreach is also the least reachable through digital channels. Conclusions: Digital health implementation should adopt a floor-up strategy: start with SMS (reaching approximately 39%), add WhatsApp where connectivity exists, and reserve apps for the small minority able to use them. HIV-negative males require targeted outreach through non-health channels to prevent digital exclusion from weakening HIV prevention.

  • Background: Artificial intelligence research in image-guided oncology has grown exponentially, yet how far the field has progressed from diagnostic assistance toward direct therapeutic execution has never been quantified. Existing bibliometric surveys categorize studies by technical architecture or clinical domain, metrics that track publication volume but not proximity to procedural deployment. Objective: We developed a hierarchical functional classification framework to map the global landscape of therapeutic AI development across five major oncological indications. Our two specific objectives were: (1) to classify publications by clinical output function along the diagnostic-to-therapeutic continuum, and (2) to quantify the translation gap using three complementary metrics, triangulated against trial and device registries. Methods: We extracted 29,277 Web of Science publications spanning five image-guided oncologic specialties (thyroid, breast, lung, prostate, and liver) published between January 2010 and April 2026. AI-related records were classified by clinical function using a three-stage protocol: keyword categorization, contextual scoring, and rule-based filtering. Inter-rater reliability, validated on 518 independently coded publications, yielded Cohen's κ of 0.92. Our framework distinguished Diagnosis AI (disease identification) from therapeutic AI, then further stratified therapeutic AI into Bridge-support AI (treatment planning, prognosis, patient selection) and True Treatment AI. True Treatment AI was defined by concurrent satisfaction of two criteria: ≥Level 2 on the Yang Surgical Autonomy Scale and ≥Stage 1 on the IDEAL Framework. Results: Of 16,937 AI-related publications identified, 14,277 (84.3%) were categorized as Diagnosis AI and only 2,660 (15.7%) as therapeutic AI. All therapeutic publications fell exclusively within the Bridge-support tier. None satisfied the dual-framework criteria for True Treatment AI, yielding a uniform penetration rate of 0.00% across all five oncological domains. This complete execution vacuum persisted despite an 11-fold variation in inter-domain treatment-to-diagnosis ratios. The finding held under threshold relaxation, sensitivity analyses, and independent triangulation against 3,491 ClinicalTrials.gov records and 1,430 FDA device listings. Conclusions: Each specialty should periodically profile its diagnostic-to-therapeutic translational progress. The uniform absence of True Treatment AI across 15 years and five domains indicates that this gap is structural rather than cumulative, rooted in methodological inheritance from diagnostic paradigms and in regulatory category mismatches. Closing this gap requires coordinated framework development across regulatory, research, and clinical communities, rather than incremental algorithmic improvements.