Latest Submissions Open for Peer Review

Titles/Abstracts of Articles Currently Open for Review:

• The effectiveness of digital educational interventions on glycaemic control in children and young people living with diabetes: Systematic Review & Meta-analysis

  Date Submitted: Jul 10, 2026

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

  • Peer Review Me

  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

  • Peer Review Me

  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

  • Peer Review Me

  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

  • Peer Review Me

  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

  • Peer Review Me

  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

  • Peer Review Me

  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

  • Peer Review Me

  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

  • Peer Review Me

  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.

• Exploring Patients’ Perception of Integrating an AI-Powered Clinical Intelligence Companion in a Resource-Constrained Setting: A Cross-Sectional Multi-Centre Study

  Date Submitted: Jul 8, 2026

  Open Peer Review Period: Jul 9, 2026 - Sep 3, 2026

  • Peer Review Me

  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.

• Identifying The Factors Influencing the Use and Acceptability of Artificial Intelligence in Antimicrobial Stewardship: A Systematic Review Using the Theoretical Domains Framework and Technology Acceptance Model

  Date Submitted: Jul 8, 2026

  Open Peer Review Period: Jul 9, 2026 - Sep 3, 2026

  • Peer Review Me

  Background: Artificial Intelligence (AI) offers a novel approach to enhance antimicrobial stewardship (AMS). Existing systematic reviews focus on AI’s predictive performance, overlooking the implementation gap between AI’s potential and its use. Objective: This systematic review aimed to identify and synthesise peer-reviewed literature on the factors influencing AI use and acceptability in AMS. Methods: Eight databases were searched from inception to September 2025 to identify perceived barriers and enablers to AI use in AMS. Data on factors influencing AI use and its acceptability were deductively coded into domains from the Theoretical Domains Framework (TDF) and The Technology Acceptance Model (TAM3) respectively. Inductive thematic analysis within domains provided deeper insights into the specific influences on AI use. The Theory and Techniques Tool (TaTT) and Affordability, Practicability, Effectiveness, Acceptability, Side-effects, Safety (APEASE) criteria were used to identify potential Behaviour Change Techniques (BCTs) to address these influences. Results: Twelve primary studies were included. Barriers/enablers reported were mostly from HCPs (92%), across healthcare settings and roles. Key factors influencing AI use in AMS fell within five domains: Beliefs about Consequences (100% of included studies; e.g. impact on patient safety), Environmental Context & Resources (92%; e.g. usability), Memory, Attention & Decision Processes (75%; e.g. guidance under uncertainty), Knowledge (75%; e.g. understanding of system functionality), and Social/Professional Role and Identity (67%; e.g. professional autonomy and expertise). Domain mapping to the TAM3 identified Reasonable Demonstrability, Job Relevance, and Output Quality as key determinants to Perceived Usefulness, and Perception of External Control and Computer Self-Efficacy as key determinants of Perceived Ease of Use. Potential BCTs to address these influences include: Feedback on behaviour and outcomes of behaviour, Information about health consequences, and Adding objects to the environment. Conclusions: Key influences on AI use in AMS were identified across five TDF domains, alongside five key determinants utilising the TAM3 impacting its perceived usefulness and perceived ease of use, highlighting targets for intervention. Further primary research is needed on factors influencing AI use in AMS, particularly in agriculture and veterinary care, and on the effectiveness of various intervention strategies.

• Development and validation of a pragmatic, expert-informed digital twin for the immunosuppressive state using age, biomarkers, and treatment history

  Date Submitted: Jul 6, 2026

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

  • Peer Review Me

  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

  • Peer Review Me

  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.

• Characteristics and Performance of Prediction Models for Fatigue Risk in Patients with Stroke: A Systematic Review and Meta-analysis

  Date Submitted: Jul 6, 2026

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

  • Peer Review Me

  Background: Fatigue is increasingly recognized as a clinically important syndrome among patients with stroke. Although a growing number of prediction models have been developed for this population, evidence regarding their methodological rigor, predictive performance, and generalizability remains fragmented. Objective: This study aimed to evaluate and characterize existing models designed to detect or predict fatigue in patients with stroke. Methods: We systematically searched PubMed, Embase, Web of Science, the Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, VIP Database, and the Chinese Biomedical Literature Database from inception to May 2026. Random-effects meta-analyses were performed using the Hartung–Knapp–Sidik–Jonkman method to synthesize model performance metrics, including the pooled area under the receiver operating characteristic curve. Subgroup and sensitivity analyses were conducted to explore sources of heterogeneity. The robustness of findings from small studies was assessed using funnel plots, Egger’s test, and Deeks’ funnel plot asymmetry test. Results: Twelve studies comprising 34 diagnostic models were included. In the training sets, the pooled area under the receiver operating characteristic curve was 0.84（95%CL 0.77-0.90）, with a sensitivity of 0.75（95%CL 0.63-0.84）and a specificity of 0.84（95%CL 0.76-0.89）. In the internal validation sets, the corresponding estimates were 0.83（95%CL 0.78-0.87）for the area under the curve, 0.78（95%CL 0.70-0.85）for sensitivity, and 0.80（95%CL 0.68-0.88） for specificity. In the external validation sets, the area under the curve was 0.82（95%CL 0.74-0.91）, with a sensitivity of 0.71（95%CL 0.64-0.78） and a specificity of 0.79（95%CL 0.73-0.83）. Subgroup analyses indicated that, in the training sets, models developed with sample sizes of 200 or more achieved a significantly higher area under the curve than those based on fewer than 200 participants (0.88 vs. 0.74; P < 0.001). With respect to validation strategy, models subjected to both internal and external validation also showed superior discrimination compared with those evaluated by internal validation alone (0.91 vs. 0.80; P < 0.001). No statistically significant differences were observed across modeling approaches, data sources, or study designs in the training, internal validation, or external validation sets (all P > 0.05). Conclusions: The available models showed generally favorable discriminatory performance. Their clinical applicability, however, remains constrained by a high risk of bias and the limited use of external validation. Future work should therefore prioritize rigorously designed, prospective, multicenter studies to develop and validate more robust prediction models.

• Effects of a Web-Based HIV Self-Testing Education Intervention on Testing Intention and Preventive Behaviors Among Junior High School Students in Indonesia: Controlled Quasi-Experimental Study

  Date Submitted: Jul 6, 2026

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

  • Peer Review Me

  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

  • Peer Review Me

  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

  • Peer Review Me

  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

  • Peer Review Me

  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.

• Business intelligence, data visualisation, and machine learning in mental health and wellbeing services: an umbrella review

  Date Submitted: Jul 2, 2026

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

  • Peer Review Me

  Background: Background: Mental health and wellbeing services generate growing volumes of operational and client-level data. Business intelligence, data visualisation, and machine learning are increasingly proposed to translate these data into insight that can improve service delivery and decision making. The review-level evidence base for these technologies in mental health and wellbeing services is fragmented across methodologies, populations, and technology categories, and has not been integrated. Objective: Objective: To synthesise review-level evidence on how business intelligence (BI), data visualisation, and machine learning (ML) are used within mental health and wellbeing services; to characterise the service contexts, methods, applications, benefits, and barriers reported; and to identify evidence gaps and future priorities. Methods: Methods: An umbrella review was conducted per Joanna Briggs Institute (JBI) methodology and reported following PRISMA 2020. Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and PsycINFO were searched on January 27, 2026 for peer-reviewed English-language reviews published between 2021 and 2026. Two reviewers screened records, with third-reviewer resolution of conflicts. Quality was appraised with the JBI critical appraisal checklist. The protocol was registered on the Open Science Framework, and findings were synthesised narratively around five research questions. Results: Results: Eight reviews were included (two systematic, three scoping, one umbrella, one narrative, one mini-review). Across the set, the reviews drew on several hundred primary studies and source documents spanning mental health, broader healthcare, and digital health. Reported methods were dominated by AI and ML (conversational agents, NLP, deep learning, large language models), while BI and data visualisation were thinly represented. Most reviews addressed broad healthcare contexts in which mental health was one application domain among several; only one focused specifically on mental health helpline services. Applications clustered around clinical decision support, screening and triage, conversational delivery, and patient-feedback analytics. Service-level benefits (efficiency, accessibility, satisfaction) were reported more consistently than clinical benefits, and short-term effects more reliably than sustained ones. Notably, several reviews reported no significant clinical benefit at three- to six-month follow-up, one reported chatbot use associated with increased depressive symptoms, and two flagged large language model bias or hallucination, so positive service-level signals are not matched by durable clinical effect. Cross-cutting barriers included data privacy, technology immaturity, poor integration with clinical workflows, alert fatigue, and absent design-to-evaluation frameworks. Two reviews were judged low quality on JBI criteria and retained with their limitations explicitly flagged. Conclusions: Conclusions: Review-level evidence on data-driven methods in mental health and wellbeing services has grown rapidly, but the field is uneven. AI and ML methods are well covered, while BI and operational data visualisation are not. Service-level outcomes are reported more reliably than clinical outcomes, and helpline-specific evidence is concentrated in a single review. Priorities for future work include service-delivery-focused evaluation, longer follow-up, helpline-specific implementation evidence, and explicit treatment of safety, equity, and ethics.

• Effect of digital cognitive behavioral therapy combined with transcutaneous auriculotemporal vagus nerve stimulation for depression and obesity after laparoscopic sleeve gastrectomy: a randomized controlled trial

  Date Submitted: Jul 3, 2026

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

  • Peer Review Me

  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.

• Large Language Models in Gastrointestinal Endoscopy: From Data Structuring to Clinical Decision-Making and Communication

  Date Submitted: Jul 1, 2026

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

  • Peer Review Me

  Large language models (LLMs) are rapidly being adopted to augment clinical workflows in gastrointestinal (GI) endoscopy, where vast multimodal data must be interpreted, documented, and translated into guideline-concordant management and patient communication. Early prototypes look promising, but the evidence comes from disparate study designs and evaluation methods that are hard to compare, leaving the real-world value of these systems unclear. In this Viewpoint, we argue that evaluating LLMs task by task obscures how they behave once embedded in the endoscopic process, and that a systems-level perspective is needed. We propose a pipeline-based conceptual framework that organizes LLM applications into four interconnected layers—data structuring, perception and interpretation, clinical decision-making, and patient communication—spanning the full path from raw data to patient interaction. Our key message is that performance is uneven across this pipeline: it is generally higher in text-centric tasks and degrades in complex multimodal reasoning and individualized decision support, and, critically, errors introduced upstream can propagate downstream to compromise clinical decisions and patient-facing outputs. Reading the pipeline as a whole, we surface the cross-layer risks and key barriers that isolated evaluations miss, and outline directions for integrated end-to-end evaluation, prospective real-world validation, stronger multimodal reasoning, and knowledge-grounded architectures. We advance this framework to guide rigorous assessment and the responsible translation of LLMs into routine GI endoscopic care.

• Multimodal Artificial Intelligence for Prostate Cancer Diagnosis and Risk Stratification: A Scoping Review

  Date Submitted: Jul 1, 2026

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

  • Peer Review Me

  Background: Multimodal artificial intelligence (AI) may improve clinically significant prostate cancer (csPCa) detection and risk stratification by integrating imaging, histopathology, molecular, radiomic, and structured clinical data. Objective: This scoping review mapped recent evidence on multimodal AI for prostate cancer diagnosis, staging, prognosis, and treatment personalisation, with emphasis on data modalities, fusion strategies, validation, reproducibility, and clinical translation. Methods: A PRISMA-ScR-guided search of IEEE Xplore, Scopus, Web of Science, PubMed, and SpringerLink identified English-language peer-reviewed studies published from January 2021 to December 2025. Eligible studies applied AI to at least two data sources for prostate cancer detection, grading, staging, prognosis, recurrence prediction, or treatment selection. Two reviewers screened records and charted clinical task, modalities, fusion strategy, validation design, performance, and reproducibility indicators. Results: Twenty-six studies were included. Clinical variables were incorporated in 24 studies, multiparametric magnetic resonance imaging in 18, and whole-slide histopathology in six. Multimodal models usually outperformed unimodal baselines in paired comparisons, including PI-CAI performance above the median radiologist AUROC (0.91 vs 0.86) and trimodal PET/MRI/clinical models reporting AUC values up to 0.955. Evidence was limited by retrospective designs, small cohorts, geographic concentration, scarce public code, inconsistent calibration reporting, and no prospective workflow validation. Conclusions: Multimodal AI is promising for biopsy triage, grading, staging, prognostication, and treatment selection, but current evidence supports research prioritisation rather than routine deployment. Prospective, diverse, transparent, and clinically embedded validation is required before multimodal AI can guide routine prostate cancer decisions.

• 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

  • Peer Review Me

  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

  • Peer Review Me

  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.

• Diagnostic performance of artificial intelligence based on cardiovascular magnetic resonance imaging for myocarditis: a systematic review and meta-analysis

  Date Submitted: Jun 30, 2026

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

  • Peer Review Me

  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.

• Structural Shifts in Adolescent Violence and Evolving Risk Factors in South Korea (2012–2024): A Nationwide Big Data Study of Over 800,000 Adolescents

  Date Submitted: Jun 30, 2026

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

  • Peer Review Me

  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

  • Peer Review Me

  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

  • Peer Review Me

  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

  • Peer Review Me

  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

• Prognostic Value of Artificial Intelligence–Enabled Electrocardiography for All-Cause Mortality and Major Adverse Cardiovascular Events: A Systematic Review and Meta-Analysis

  Date Submitted: Jun 27, 2026

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

  • Peer Review Me

  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

  • Peer Review Me

  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

• Peer‑to‑Peer Referral Networks and Six‑Month Outcomes in a Tirzepatide‑Supported Digital Weight‑Loss Service: A Retrospective Cohort Study

  Date Submitted: Jun 25, 2026

  Open Peer Review Period: Jun 25, 2026 - Aug 20, 2026

  • Peer Review Me

  In a retrospective analysis of 34,449 adults using an unsubsidised, tirzepatide‑supported digital weight‑loss service in Australia, patients entering via peer referral showed higher six‑month program adherence and greater percentage weight loss than propensity score–matched non‑referred patients, suggesting that peer‑to‑peer networks may support retention and effectiveness in medicated obesity care.

• Sovereign Language Models for Regional Health Research under the European Health Data Space

  Date Submitted: Jun 24, 2026

  Open Peer Review Period: Jun 25, 2026 - Aug 20, 2026

  • Peer Review Me

  Several major artificial intelligence (AI) policy frameworks have come into effect across Europe. The European Health Data Space (EHDS) Regulation is being implemented across member states, the EU AI Act is moving from text to enforcement, and national strategies on AI in healthcare are emerging alongside them. Each raises the same two questions: where does health data sit, and who controls it. Most published work on large language models (LLMs) in clinical research describes systems built inside well-resourced academic medical centres on commercial cloud infrastructure. Regional health services and resource-constrained academic settings are underrepresented in this literature, even though they hold the majority of European longitudinal clinical data, much of it unstructured text on which little patient-level analysis has been done. A further vulnerability has recently become visible. Reliance on externally controlled frontier models means that access to capability can be constrained or withdrawn by commercial and political decisions taken outside the institution. We argue that sovereign, on-premise LLM infrastructure offers a practical and realistic alternative. Sovereignty is defined not by a vendor or model, but by deployment characteristics: inference runs under institutional control, patient-level data remains within the originating organisation, and participation in wider research occurs through federation rather than data transfer. We describe an architecture combining local inference, OMOP standardisation, federated analytics through OHDSI and EHDEN, and a tiered governance framework. We examine the concerns commonly raised about LLM-assisted research, distinguishing those that sovereign deployment addresses directly, those it partially mitigates, and those it does not solve. We argue that the convergence of open-weight models, maturing federated research ecosystems, and European policy frameworks creates a distinctive opportunity for regional institutions to participate in modern AI-enabled research while preserving data sovereignty and continuity of access. The central question is no longer whether such systems can be built, but whether institutions, funders, and research networks are prepared to support their adoption.

• 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

  • Peer Review Me

  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).

• Toward Explainable AI for Clinical Decision Support: A Qualitative Study of Final-Year Medical Students’ Diagnostic Reasoning and AI Support Preferences

  Date Submitted: Jun 23, 2026

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

  • Peer Review Me

  Background: Medical students increasingly encounter artificial intelligence (AI) and explainable AI (XAI) in clinical training, yet their mental models of diagnostic decision-making and expectations for AI support remain poorly understood. Understanding these expectations is crucial for designing human-centered clinical decision support systems that are pedagogically effective and safe. Objective: This study explores medical students' diagnostic processes in their final year of undergraduate training and AI support preferences across four research questions: (1) What information do medical students use to make a diagnosis? (2) In what form would they like (X)AI to support them in this process? (3) What questions do they have for such (X)AI? (4) What form of presentation would they prefer for XAI? Methods: Semi-structured interviews (N=14 medical students, ages 24-31) were analyzed using reflexive thematic analysis, following information power principles. Participants worked through a standardized interactive online CASUS™ case based on a real multimorbid patient. During the CASUS™ case, participants used think-aloud techniques, followed by semi-structured interviews and a drawing task in which they sketched desired XAI interfaces for four modalities (laboratory results, electrocardiograms, radiographs, patient photographs). Interviews were audio-recorded, transcribed, and analyzed using an iterative, mixed deductive–inductive content analysis with a collaboratively developed codebook. Results: (1) Students prioritized patient-led symptom narratives, progression patterns, comorbidities, and physical signs during the diagnostic process. (2) They sought diagnostic-specific AI support: guideline prompts, ranked differentials (including rare diseases), test checklists, multimodal interpretation, and therapy verification. (3) Trust in AI prerequisites included data origins and specificity metrics. Fears regarding AI centered on loss of control, while benefits included time savings. (4) Preferred XAI featured step-by-step mentor guidance, counterfactuals, similar cases, and chat-based learning triggered by AI actions, surprises, or knowledge gaps. Conclusions: Final-year medical students conceive AI not only as a diagnostic assistant but also as a teaching partner that should scaffold reasoning, broaden differentials, and support confidence calibration. Their preferences yield concrete design requirements for trainee-oriented clinical AI/XAI: longitudinal workflow support, layered and multimodal explanations, and explicit communication of uncertainty and limitations to mitigate automation bias. While limited by its single-center, small-sample design and simulated case setting, this study offers actionable insights for the design and evaluation of human-centered AI tools in medical education and motivates prospective studies in authentic clinical learning environments.

• Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies

  Date Submitted: Jun 23, 2026

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

  • Peer Review Me

  Background: Predictive modelling is important for health data analysis and data-driven clinical decision-making. However, predictive studies are challenging to design optimally by hand when tens or even hundreds of features require selection, transformation, or interaction modelling. While complex machine learning models offer high performance, their "black-box" nature limits the clinical trust, transparency, and interpretability required for decision-making. Objective: The objective of our study was to develop an explainable Artificial Intelligence (AI)-based framework that provides data-driven feature-related recommendations, which, once incorporated, improve predictive performance of existing interpretable statistical models on high-dimensional data. Methods: We developed and evaluated an Exploratory AI Recommender that provides data-driven recommendations to improve predictive performance of existing interpretable statistical models. The developed framework uses flexible AI modelling to capture complex data patterns and explainable AI techniques to translate the patterns into three recommendation types: feature exclusion, non-linear terms, and feature interactions. We evaluate the framework by comparing predictive performance of a baseline (i.e., no interactions or non-linear terms) Cox Proportional Hazards (CPH) model against an augmented CPH incorporating recommendations suggested by our method. Results: The primary analysis predicts the time to the first occurrence of a fall or related injury in 245,614 patients (mean age 67 ± 12 years). Our method recommended excluding 23 features, including non-linear terms for two features, and including 221 suggested feature interactions. The C-index improved from 0.805 (95% CI 0.798-0.812) to 0.815 (95% CI 0.809-0.822), and so did calibration (intercept: -0.006 to 0.003; slope: 1.063 to 0.950). All recommendations were supported by existing literature. The method also proved effective on two additional public datasets, demonstrating wider applicability. Conclusions: The proposed Exploratory AI Recommender demonstrates the potential of explainable AI and data-driven study design to improve the process of developing, and the performance of high-dimensional transparent predictive models.

• 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

  • Peer Review Me

  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

  • Peer Review Me

  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.

• Equity in Access to Internet of Medical Things (IoMT)–Supported Home-Based Care for Chronic Obstructive Pulmonary Disease: Systematic Review of Clinical, Economic, Engagement, and Implementation Evidence

  Date Submitted: Jun 18, 2026

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

  • Peer Review Me

  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.

• Bidirectional Relationship Between eHealth Literacy and Illness Cognitions After Urolithiasis Surgery: A Cross-Lagged Panel Analysis

  Date Submitted: Jun 18, 2026

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

  • Peer Review Me

  Background: Urolithiasis is a highly prevalent urological condition with recurrence rates exceeding 50% within five years. While eHealth literacy has been associated with better health outcomes, its dynamic interplay with illness cognitions, particularly illness-related helplessness, acceptance, and perceived benefits—remains poorly understood. Guided by Leventhal's Common-Sense Model, this study examined the bidirectional relationships between eHealth literacy and illness cognitions over a six-month period following urolithiasis surgery. Objective: To investigate the directionality and magnitude of longitudinal associations between eHealth literacy and three dimensions of illness cognitions (helplessness, acceptance, and perceived benefits) in postoperative urolithiasis patients. Methods: A three-wave longitudinal design was employed with 368 patients who underwent urolithiasis surgery. Data were collected at 1 month (T1), 3 months (T2), and 6 months (T3) postoperatively. eHealth literacy was measured using the eHealth Literacy Scale (eHEALS), and illness cognitions were assessed with the Illness Cognition Questionnaire (ICQ). Cross-lagged panel models (CLPMs) were estimated to examine bidirectional effects while controlling for autoregressive stability. Results: The primary CLPM (eHEALS–Helplessness) demonstrated good fit (χ²=9.94, df=6, P=0.127; CFI=0.988; TLI=0.967; RMSEA=0.050). Significant bidirectional cross-lagged effects were identified: higher eHEALS predicted lower Helplessness (T1→T2: β=−0.096, P=0.003; T2→T3: β=−0.245, P<0.001), while Helplessness also predicted lower eHEALS (T1→T2: β=−0.099, P=0.017; T2→T3: β=−0.139, P=0.010), revealing a bidirectional negative spiral. Supplementary models revealed that eHEALS predicted increased Acceptance (β=0.098 at T2→T3, P=0.010) and Perceived Benefits (β=0.083–0.092, P<0.01), with reverse effects being non-significant. Conclusions: eHealth literacy and illness helplessness are reciprocally related in a bidirectional negative spiral, while eHealth literacy exerts unidirectional effects on promoting acceptance and perceived benefits. These findings delineate a public health causal chain—eHealth literacy → adaptive illness cognitions → improved self-management → reduced recurrence risk—that supports the integration of eHealth literacy interventions into postoperative care and tiered healthcare systems to facilitate cognitive adaptation and recurrence prevention in urolithiasis.

• Artificial Intelligence–Integrated Diabetes Self-Management Education and Its Impact on Glycemic Control, Self-Efficacy, and Hospital Readmission Among Adults with Type 2 Diabetes Mellitus.

  Date Submitted: May 28, 2026

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

  • Peer Review Me

  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.

• The Application of Mobile Health in Cognitive Management Among Children with Cancer: A Scoping Review

  Date Submitted: Jun 17, 2026

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

  • Peer Review Me

  Background: The incidence of cognitive late effects among children with cancer has been increasing. Mobile health (mHealth), which delivers healthcare services through portable devices, may represent an innovative and scalable approach to optimize cognitive management in this population. However, evidence regarding its effectiveness and acceptability remains limited. Objective: This study aimed to systematically explore the evidence on mHealth for cognitive management in pediatric cancer patients and to characterize its key features, including feasibility, acceptability, functionality, and cost-effectiveness. Methods: This scoping review was conducted in accordance with the Arksey and O’Malley framework and the PRISMA-ScR(Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Six academic databases and grey literature were searched to identify relevant studies published between January 2010 and April 2026. Reference lists of included studies were also manually screened. A random sample of abstracts and full texts was independently screened by a second reviewer to ensure consistency. Data were charted based on key study characteristics, including study design, participants, intervention content, outcome measures, and main functional components. The results were collated and synthesized using a structured spreadsheet. Results: A total of 3,120 records were identified through the literature search, of which 19 studies met the eligibility criteria. Among the included studies, app-based cognitive training was the predominant form of intervention, while other modalities included exergaming, interactive websites, and newly developed mHealth applications. Nearly all studies incorporated cognitive training as a core component; however, only a limited number included coaching support, telephone communication, or educational features, and most interventions primarily focused on training a single cognitive domain. Conclusions: Conclusion: Although mHealth shows promise for improving cognitive function in children with cancer, substantial room for improvement remains. Future research should focus on developing multidimensional integrated intervention models, addressing the specific needs of pediatric patients, incorporating multidimensional clinical outcomes, and evaluating the cost-effectiveness of interventions. Our findings provide recommendations for optimizing mHealth-based cognitive management in children with cancer and offer targeted guidance and practical insights for the development of future interventions. Clinical Trial: https://osf.io/mkbaj/overview

• Enhancing Early HIV Detection Among Men Who Have Sex with Men: A Real-World Evaluation of a Digital Behavioral Risk Stratification Tool Integrated with Online Self-Testing

  Date Submitted: Jun 17, 2026

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

  • Peer Review Me

  Background: Due to concerns about privacy breaches and fear of social stigma, some individuals engaging in high-risk behaviors—particularly men who have sex with men (MSM)—may avoid in-person HIV testing services and instead choose to purchase HIV self-test kits online for self-testing. Unlike traditional e-commerce platforms that directly provide self-test kits without conducting risk assessments, the “Easy Test Know” platform integrates a structured behavioral risk assessment prior to HIVST kit purchase, allowing users to be categorized into different HIV risk groups. Objective: This study aimed to evaluate the performance of a digital HIV behavioral risk stratification tool integrated with online self-testing among MSM, and to determine whether incorporating multi-dimensional behavioral indicators—particularly high-risk venue diversity score—could improve the prediction of HIV positivity compared with the platform’s existing rule-based risk scoring algorithm. Methods: This retrospective observational study utilized data from the “Easy Test Know” platform between November 2023 and February 2025. The study included MSM classified as medium or high risk by the platform algorithm. Firth logistic regression models were developed to predict HIV positivity using behavioral variables, including high-risk venue diversity score. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and calibration analyses. Results: A total of 9,961 participants were included, of whom 90.8% were classified as high risk by the platform. The overall HIV positivity rate was 0.59%, with a higher rate in the high-risk group. The platform’s baseline risk score demonstrated moderate discriminatory power for predicting HIV positivity (AUC = 0.646). High-risk venue diversity score showed a dose–response association with HIV positivity. The data-driven model incorporating the platform’s existing variables together with the newly derived high-risk venue diversity score improved discrimination compared with the rule-based platform algorithm (ΔAUC = +0.082). The fully adjusted model achieved the highest predictive performance (AUC = 0.752). Conclusions: Digital behavioral risk stratification demonstrated moderate ability to differentiate HIV risk among MSM using online HIV self-testing services. Compared with the platform’s original rule-based scoring system, data-driven behavioral models incorporating high-risk venue diversity score showed improved predictive performance. These findings support the potential value of data-driven approaches for optimizing digital HIV risk assessment tools.

• 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

  • Peer Review Me

  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.

• Wearable Thermal Sensing for Real-Time Smoking and Eating Activity Detection: A Confirm-Refute Study

  Date Submitted: Jun 16, 2026

  Open Peer Review Period: Jun 17, 2026 - Aug 12, 2026

  • Peer Review Me

  Background: Smoking and overeating are repetitive hand-to-mouth behaviors that contribute to highly prevalent yet preventable diseases. Most existing wearable systems have not been validated in free-living conditions to detect these behaviors in real-time. Objective: Leveraging shared behavioral patterns of eating and smoking, we developed HabitSense, a wearable system that integrates thermal sensors, a privacy conscious camera, and on-device algorithms, to detect smoking and eating events in real time and trigger a paired smartwatch to collect contextual data using ecological momentary assessment (EMA). We evaluated the detection accuracy of HabitSense in a free-living user study. Methods: Seventeen participants (9 in the smoking cohort and 8 in the eating cohort) were instructed to wear HabitSense, a custom necklace paired with a smartwatch, during waking hours for 7 consecutive days. Two separate machine-learned algorithms processed data from the thermal sensor array and camera on-device. When HabitSense predicted a smoking or eating event, the smartwatch prompted a micro- Ecological Momentary Assessment (micro-EMA) asking the participant to confirm or refute the prediction (“Are you smoking?” yes/no; “Are you eating?” yes/no). Additionally, an integrated camera recorded video to enable visual confirmation of each predicted smoking and eating event. Results: In total, 780.6 hours of sensor data were collected, capturing 217 smoking episodes and 87 eating episodes. The necklace generated 229 smoking-event predictions, of which 209 (91%) were true positives and 20 (9%) were false positives. 8 undetected smoking episodes were identified through manual review of the video footage (3% of total episodes). Participants responded to 212 EMA smoking-event prompts (92.6%); of these responses, 206 (97.2%) were correct (i.e., participants responded “yes” during actual smoking events and vice-versa). The necklace also generated 84 eating-event predictions, of which 67 (79.8%) were true positives and 17 (20.2%) were false positives. 20 undetected meals were identified in video footage (23% of total meals). Conclusions: The findings suggest that the proposed system is feasible for automated and objective monitoring of contextual triggers associated with smoking relapse. HabitSense demonstrated high accuracy in smoking detection and strong response rates to smoking-triggered EMAs, supporting its potential for real-time behavioral assessment in free-living settings. For eating detection, the variability and complexity of food-related behaviors indicate that more advanced machine-learning approaches may be required, particularly for deployment on highly resource-constrained wearable devices. Future work will expand EMA queries to capture contextual factors surrounding smoking and eating episodes, leverage these data to develop just-in-time smartwatch-based interventions. Ultimately, this work aims to enable a personalized, adaptive intervention system that accounts for individual differences in behavior, a dimension often insufficiently addressed in current smoking cessation strategies.

• Trust in Generative Artificial Intelligence Chatbots for Mental Health Support: A Systematic Review

  Date Submitted: Jun 15, 2026

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

  • Peer Review Me

  Background: Generative artificial intelligence (GenAI) chatbots are increasingly used for mental health support, but trust in these emotionally vulnerable and relationally sensitive interactions remains poorly understood. Objective: This study aims to synthesize empirical evidence on how trust is conceptualized, shaped, and associated with outcomes in GenAI-based mental health support, with attention to differences across AI roles. Methods: This systematic review was conducted in accordance with the PRISMA 2020 guidelines. Peer-reviewed empirical studies were identified by searching five electronic databases. Two reviewers independently screened records, selected eligible studies, extracted data, and assessed methodological quality using the Mixed Methods Appraisal Tool. Data were synthesized descriptively and thematically. Results: Of 1180 citations retrieved, 28 studies were included. Trust was rarely explicitly defined and was most often operationalized through affective and relational indicators, including emotional comfort, perceived empathy, and psychological safety, rather than technical competence alone. Trust-related antecedents involved user vulnerability and attitudes, system reliability and emotional responsiveness, interactional continuity, and contextual constraints. Trust was associated with engagement and emotional relief, but also with relational and safety concerns, including excessive reliance on AI support, blurred role boundaries, and inadequate responses to crisis-related disclosures. Role-based synthesis suggested that lower-engagement roles (eg, functional assistants and structured facilitators) mainly involved cognitive trust, whereas more emotionally engaging or autonomous roles (eg, empathetic co-therapists and therapeutic companions) involved broader affective and alliance-like trust, together with greater relational and safety risks. Conclusions: Trust in GenAI-based mental health support should not be treated as a uniform or inherently desirable outcome. Role-sensitive evaluation and governance are needed to align user trust with system capability, safety boundaries, and responsibility.

• 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

  • Peer Review Me

  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.

• Impact of Graphics Rendering Latency on the Precision of Web-Based Cognitive Assessments on Healthy Adults: Technical Evaluation and Validation Study

  Date Submitted: Jun 12, 2026

  Open Peer Review Period: Jun 15, 2026 - Aug 10, 2026

  • Peer Review Me

  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.

• Enrollment and Adherence in an Online Parenting Intervention for Caregivers of Young Children: Secondary Analysis of a Randomized Controlled Trial

  Date Submitted: Jun 11, 2026

  Open Peer Review Period: Jun 12, 2026 - Aug 7, 2026

  • Peer Review Me

  Background: Online parenting interventions provide a unique opportunity to increase access and scalability to evidence-based parenting information that can enhance parenting practices, caregiver well-being, and child developmental outcomes. While online parenting programs can reduce access barriers, less is known about who enrolls in such programs, how participants engage with them, and whether engagement is sustained over time. This knowledge could help inform parenting program usability and engagement strategies. Objective: This study explored the demographic characteristics of caregivers who enrolled (registered) and adhered (completed) to an online parenting intervention protocol and examined sociodemographic factors associated with engagement. Methods: Data were drawn from a larger pragmatic randomized controlled trial evaluating the online Make the Connection® (MTC) program, which aims to promote healthy parent-child relationships. This secondary analysis focused on participants assigned to the intervention group (N = 215). Baseline sociodemographic characteristics were collected using an online survey prior to enrolling in the intervention. Descriptive statistics and logistic regression models were used to examine correlates of enrollment and adherence. Results: Participants were predominantly women (91.6%, 197/215), with a mean age of 35.6 years (SD = 5.19), and their children were a mean age of 13.9 months (SD = 10.7). Of the 215 participants assigned to the intervention, 107 (49.7%) enrolled in the program, while 108 (50.2%) did not. Younger child age was associated with a higher likelihood of enrollment (OR= 0.96, 95% CI 0.93-0.98, P =.002). Older caregiver age was associated with greater likelihood of enrollment (OR= 1.07, 95% CI 1.01-1.14, P =.017) and adherence (OR= 1.11, 95% CI 1.02-1.22, P=.020). Caregiver social isolation was associated with a lower likelihood of adherence (OR= 0.30, 95% CI 0.11-0.84, P =.022), but not enrollment (OR = 1.12, 95% CI 0.62-2.03, P = .706). Depressive symptoms were not significantly associated with program adherence (OR = 1.21, 95% CI 0.45-3.28, P =.708) or enrollment (OR = 0.97, 95% CI 0.90-1.05, P =.457). Conclusions: Results suggest that different factors influence enrollment and adherence in online parenting programs. Although digital delivery may reduce barriers to access, additional strategies, such as goal setting tools, personalised feedback, tailored reminders, and opportunity for peer-connection may be needed to support sustained engagement, particularly among caregivers at risk of disengagement. In this study, sociodemographic factors have been identified that can inform strategic interventions to improve engagement from caregivers most vulnerable to disengagement. Clinical Trial: NCT05770414

• Value Proposition of AI for Social Care Integration in Health Care and Human Services: Qualitative Insights from Social Care Providers in the Safety Net in Michigan, US

  Date Submitted: Jun 9, 2026

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

  • Peer Review Me

  Background: There are increasing interests in developing AI tools to identify and address individual-level social determinants of health in both health care and human service settings. These activities are part of social care integration, which at the individual level involves identifying individuals with social risks (awareness) and connecting them with relevant social care resources (assistance). Social care providers such as community health workers and social workers are deemed critical stakeholders in both settings. Chatbots have shown feasibility and acceptability for social risk screening in emergency departments and primary care centers. However, we do not know if a screening chatbot is worth developing for the safety net health care and human service settings, where there are visitors with greater social needs and less organizational resources. Objective: The study aims to investigate the perceived value proposition of an AI-based chatbot for social risk screening from the perspectives of social care providers in safety net health care and human service organizations. Providers’ perceived value propositions of other AI-based applications for social care integration were also examined. Methods: We conducted semi-structured interviews with 19 social care providers who have experience with awareness and/or assistance from 16 safety net health care and human service organizations in Michigan. Interview questions focused on their experiences and challenges regarding awareness and assistance when applicable. A simulated screening chatbot based on ChatGPT-4o was also used to solicit their feedback on the technology. The nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework was used to guide data analysis. Interview transcripts were first coded deductively, then inductively. Results: Social care providers perceived the screening chatbot as offering limited value. This is mainly because many participants engaged in assistance activities and they noted addressing social needs is a multi-step process requiring follow-up that screening chatbots do not provide. In addition, they valued cultivating trust as many patients/clients have high social needs and lack trust in the health care system, and felt chatbots present new challenges for maintaining essential trust and care quality with clients/patients. Instead of a screening chatbot, we identified that technologies to reduce documentation burden could improve providers’ efficiency and potentially increase time spent with patients/clients. This is because social risks and needs documentation generates administrative burden for providers. We also found that technologies to improve referral accuracy and engagement could improve providers’ effectiveness, as study participants have overall limited access to technologies that typically support referral-related activities. Conclusions: Social care providers in the safety net preferred AI-based applications for addressing documentation burden and social needs assistance rather than for social risk screening. Future strategies to develop AI tools for social care integration should align with social care providers’ professional values and focus on equity-centered care.

• 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

  • Peer Review Me

  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.

• Designing Trustworthy and Emotionally Intelligent AI for Personalized and Context-Aware Chronic Disease Self-Management: A Systematic Review

  Date Submitted: Jun 7, 2026

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

  • Peer Review Me

  Background: World Health Organization reports that chronic non-communicable diseases account for 74% of global deaths. Despite rapid advances in digital health technology, Artificial Intelligence tools for self-management remain deficient in two crucial elements: emotional connection with patients and trustworthiness. Concern around these two topics is of increasing interest and importance. With regards to trustworthiness of AI and emotional intelligence of AI however, the studies for these two concepts were developed completely separately and in an isolated manner and this in itself, is a considerable design gap. Within self-management for chronic conditions, it becomes necessary to build an approach to design with the integration of these two concerns. Objective: This systematic review aims to examine the extent to which the concepts of trustworthiness, emotional intelligence, situational awareness and personalization are integrated within artificial intelligence systems designed to facilitate self-management of chronic illness, and what the impact of integration is. Methods: From the beginning of February 2026, a thorough search was undertaken on 6 databases (PubMed, Scopus, IEEE Xplore, PsycINFO, Web of Science, and ACM Digital Library) along with connected papers, using Boolean strings linking together AI, chronic disease self-management, trust and emotional intelligence (tailored to each database's individual vernacular). Initial screening of identified articles was completed in two phases using the PRISMA 2020 criteria of pre-determined inclusion and exclusion criteria before critical appraisal using the MMAT v2018 and CASP tools. Results: After a systematic selection process of 1,486 studies, 45 studies were finally selected based on inclusion criteria. Four major theme areas emerged from the papers including: Technology in chronic illness self-management, Trust in human-AI interaction, Empathy and emotional intelligence in AI and The ethics, equity and ethical application of AI. The quality appraisal showed that 91% (41/45) of the selected studies were rated as high quality, with an average appraisal score of 93%. In addition, 73% (33/45) of the selected papers were published during 2024 and 2025 thus highlighting a high quality and contemporary compilation of literature on the subject. Conclusions: Despite advances in AI for chronic disease management and in trust-empathy theory, these fields remain siloed. We identify 5 critical research gaps; the lack of a combined trust-empathy model, the under-specification of context awareness, the absence of equity consideration, the exclusion of overtrust consideration, and lack of long-term studies demonstrating effectiveness and safety of emotional AI systems. Clinical Trial: Not registered. The review protocol was developed before the search but was not prospectively registered in PROSPERO or an equivalent database. This limitation is discussed in Section 4.4.

• 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

  • Peer Review Me

  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.

• Effects of virtual reality on pain, anxiety and fear during thyroid fine-needle aspiration biopsy: a randomized controlled trial

  Date Submitted: Jun 6, 2026

  Open Peer Review Period: Jun 8, 2026 - Aug 3, 2026

  • Peer Review Me

  Background: Thyroid fine-needle aspiration biopsy (FNAB) is a commonly used diagnostic procedure in patients with suspected thyroid cancer; however, it may induce pain, anxiety, and fear during the procedure. Objective: This randomized controlled study aimed to evaluate the effect of virtual reality (VR) on pain, anxiety, and fear of pain in patients undergoing diagnostic thyroid procedures. Methods: The study was conducted between October 15, 2024, and April 30, 2025, at Gaziantep City Hospital, Türkiye. A total of 100 patients with suspected thyroid nodules were randomly assigned to either a VR intervention group (n = 50) or a control group (n = 50). Data were collected using a Patient Information Form, the Beck Anxiety Inventory (BAI), the Fear of Pain Questionnaire III (FPQ-III), and the Visual Analog Scale (VAS). Between-group comparisons were performed using ANCOVA adjusting for relevant baseline covariates, and effect sizes were calculated using Cohen’s d with 95% confidence intervals. Results: After adjustment for baseline values and relevant covariates, no statistically significant differences were found between the VR and control groups in post-intervention VAS (P =.152), BAI (P =.501), or FPQ-III scores (P =.20). Effect size analyses indicated small between-group effects across all outcomes (Cohen’s d = −0.05 to −0.29), with 95% confidence intervals including zero. Within-group analyses indicated reductions in VAS, BAI, and FPQ-III scores over time in both groups; however, these changes were not supported by statistically significant between-group differences. Conclusions: Virtual reality was not associated with statistically significant improvements in pain, anxiety, or fear of pain when compared with standard care after adjustment for baseline differences. Although small within-group improvements were observed, these findings do not support a strong independent effect of VR on procedural discomfort in this sample. Further well-powered randomized trials are warranted. Clinical Trial: Clinical trial registration: This study was registered at ClinicalTrials.gov (NCT06792929), https://register.clinicaltrials.gov/prs/beta/studies/S000F9GN00000034/protocol/protocolSummary?fragmentId=status

• Systematic Review of Healthcare Professionals’ and Patients’ Willingness to Use mHealth and Telemedicine in Resource-Limited Settings 2025

  Date Submitted: Jun 6, 2026

  Open Peer Review Period: Jun 8, 2026 - Aug 3, 2026

  • Peer Review Me

  Background: Mobile Health (mHealth) and telemedicine can improve access to healthcare services in rural Ethiopia through education, disease monitoring, and remote consultations. Adoption depends on factors such as digital literacy, trust, training, and infrastructure, while challenges include limited smartphones and connectivity. Objective: The aim of this review was to synthesis the evidence of willingness to use mHealth and telemedicine intervention among health professionals and patients. Methods: This systematic review was conducted following the PRISMA guidelines to examine the willingness to use mHealth and telemedicine interventions and associated factors among healthcare professionals and patients in Ethiopia. A comprehensive search was carried from database such as MEDLINE, PubMed Central, CINAHL, and Africa-Wide Information using predefined searching strategies. Only full-text, peer-reviewed studies published in English were included. Two reviewers independently screened and selected studies, extracted data using a standardized form, and resolved disagreements through consensus. Study quality was assessed using Joanna Briggs Institute checklists. Results: This review consisted of 13 studies, and indicates that patients and healthcare workers are strongly willing to use mHealth and telemedicine. The highest willingness was observed among patients with chronic conditions (59.1%–96%), and they preferred simple technologies to engage (voice calls/SMS). Healthcare professionals also indicated varying but substantial willingness to engage (46.5%–83%). Conclusions: Both patients and healthcare providers have a high degree of willingness. Younger age, higher education, urban life, smartphone ownership, digital literacy, and some degree of perceived utility and simplicity of use were significant factors determining willingness. A supporting role was also provided by additional behavioral, clinical, and environmental factors. Clinical Trial: Registration: This systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD42024629424.

• Online Health Information Seeking, Consideration of Future Consequences, and Self-Control Among Adults With Chronic Diseases: Cross-Sectional Survey Study

  Date Submitted: Jun 6, 2026

  Open Peer Review Period: Jun 8, 2026 - Aug 3, 2026

  • Peer Review Me

  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.

• Digital Maturity in Integrated Care Systems: Development strategies – A Scoping Review

  Date Submitted: Jun 6, 2026

  Open Peer Review Period: Jun 8, 2026 - Aug 3, 2026

  • Peer Review Me

  Background: Digital maturity is a priority for creating efficient, patient-centered health systems, yet Integrated Care Systems (ICS) often face challenges like a lack of interoperability and weak data governance. A systematic mapping of strategies is essential to guide these organizations identify areas for improvement and define sustainable actions to ensure technology adds value to all stakeholders. Objective: To map the development strategies and interventions implemented in ICS to promote digital maturity, while identifying the associated facilitators, barriers, and recommendations described in the literature. Methods: A search was conducted on PubMed, Scopus, and Web of Science on October 17th, 2025, for English articles published since January 1st 2015. Following Joanna Briggs Institute methodology, two independent reviewers performed study selection and data extraction, and quality appraisal using the Mixed Methods Appraisal Tool, with a third reviewer resolving any conflicts, and the obtained results were synthesized through descriptive analysis and thematic grouping. Results: Eighteen articles were included, featuring mixed-methods and case study designs, predominantly set in the United States, as well as several multi-country studies set in Europe. The results identified that most strategies were technological (telehealth, electronic health records and care coordination tools) or structural (governance frameworks). Key facilitators included strong organizational leadership, pre-existing digital infrastructure, and stakeholder engagement, while significant barriers included a lack of interoperability and inadequate funding. Regulation was found to be an obstacle to digital tools development and implementation, as privacy legislation often prevents from fully achieving interoperability, making it essential to use frameworks like “Privacy by Design” to address privacy concerns during digital solutions development phase. Several frameworks surfaced, with both the Chronic Care Model and eHealth Enhanced Chronic Care Model being the most prevalent. Stakeholder engagement emerged as a pivotal enabler, yet significant resistance persists due to low digital literacy, misconceptions and an aging workforce, making it critical not only to develop formal and continuous training, but actively involving them in problem-solving though a co-creation process. Conclusions: Developing digital maturity in ICS requires a multidimensional approach that extends beyond technological adoption to include multidisciplinary governance, national eHealth policies, and value-based funding models. Addressing low digital literacy through formal training for staff and patients is critical for health care system´s sustainability. The review provides a foundational framework for healthcare managers and future research and development of digital maturity guidelines in ICS.

• Media Consumption Patterns and Public Health Knowledge: A Cross-Sectional Analysis of Climate Change, Chronic Disease, and Infectious Disease Understanding Among U.S. Adults

  Date Submitted: Jun 6, 2026

  Open Peer Review Period: Jun 8, 2026 - Aug 3, 2026

  • Peer Review Me

  Background: Media consumption is a pathway through which the public encounters health information, misinformation, and politicized interpretations of evidence, yet its relationship with knowledge across multiple health domains remains incompletely understood. Objective: We conducted a cross-sectional survey of U.S. adults recruited through CloudResearch Connect to examine associations among media source use, institutional trust, demographic characteristics, and knowledge accuracy regarding climate change, type 2 diabetes, and infectious diseases. Methods: After excluding invalid responses and a failed attention check, 509 participants were included. Knowledge was assessed with domain-specific true/false items scored as correct, incorrect, or “I don’t know,” producing climate change, chronic disease, infectious disease, and total knowledge scores. Results: Rural residence, lower income, lack of health insurance, and absence of a primary care provider were associated with lower knowledge across several domains, suggesting structural barriers to reliable health information. Trust in the CDC, physicians, and pharmacists showed the strongest and most consistent positive associations with knowledge. Political affiliation and consumption of ideologically distinct news sources were most strongly associated with climate change and infectious disease knowledge, but less so with diabetes knowledge. Conclusions: These findings suggest that public health literacy interventions should address both polarized media environments and inequitable access to trusted clinical and institutional information.

• 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

  • Peer Review Me

  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.

• Generative AI Chatbot Responses to Suicide and Self-Harm: A Systematic Review

  Date Submitted: Jun 5, 2026

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

  • Peer Review Me

  Background: A growing number of US adults and youth confide in generative artificial intelligence (AI) chatbots for mental health support, including disclosure of suicide and self-harm risk. While the quality, safety, and effectiveness of chatbot responses to risk disclosure have the potential to impact population-level rates of suicide and self-harm, there have been no systematic reviews of this burgeoning literature. Objective: We conducted a systematic review of studies evaluating generative AI chatbot responses to disclosure of suicide and self-harm risk. Methods: We searched six databases from January 2020-December 2025 and identified empirical studies involving interactions with generative AI chatbots that included discussion of suicide or self-harm. Following deduplication, studies (k = 1,042) were imported into Covidence and titles and abstracts were independently screened by two reviewers, with discrepancies resolved by a third reviewer. The same methods were used to evaluate 126 full texts. Data extraction was led by one reviewer and verified by a second. Results: We identified 29 papers (14 published; 15 preprints). Most (k = 20) were solely audit studies evaluating AI chatbot responses to suicide risk disclosure. Two developed chatbots or AI evaluation frameworks, and one was a jailbreaking study (adversarially testing AI systems or attempting to circumvent chatbot safety guardrails). The remaining studies combined approaches. Across studies, proprietary, frontier model chatbots (eg, ChatGPT, Claude) provided higher quality responses to suicide and self-harm risk than open-source chatbots (eg, LlaMA, DeepSeek), and many AI companions (eg, Replika, Character.AI). All chatbots, not just proprietary models, generally performed well on empathy, validation, and support. However, chatbot responses were often generic and lacked context. Chatbots did not proactively assess risk and performed most poorly when risk disclosure was ambiguous or moderate, frequently failing to recognize implicit risk or escalate to human-delivered services. Furthermore, responses were inconsistent between chatbots and often required multiple conversational turns before providing referrals to crisis resources and human-delivered professional support. While there were few examples of overtly harmful responses under standard conditions, jailbreaking attempts easily led to problematic responses. Finally, no chatbot proactively recommended limiting access to lethal means such as firearms, medications, or sharps. Conclusions: Chatbots provide validation and support in response to suicide and self-harm disclosure. Overall, however, their poor risk assessment, delays in referrals to crisis resources and human-delivered support, difficulty detecting jailbreaking attempts, and general lack of adherence to clinical guidelines present safety risks. While findings are limited by the rapid versioning of AI models over time, research is needed to evaluate stakeholder perspectives on AI chatbot responses to suicide and self-harm risk disclosure. Research should also examine the short- and long-term impact of these responses on clinical outcomes, utilizing follow-up assessments in real-world or clinical settings. Clinical Trial: OSF Registries osf.io/9uva3

• Digital Health Inclusion in the Global South: Healthcare Providers’ Perspectives on EMR Adoption and Health Data Quality in Nigerian Tertiary Hospitals

  Date Submitted: May 31, 2026

  Open Peer Review Period: Jun 5, 2026 - Jul 31, 2026

  • Peer Review Me

  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.

• Developing a Community-Driven Framework for Harmonising Biomedical Provenance Information: A Hybrid Consensus Study

  Date Submitted: Jun 4, 2026

  Open Peer Review Period: Jun 5, 2026 - Jul 31, 2026

  • Peer Review Me

  Background: Generating Findable, Accessible, Interoperable, and Reusable (FAIR) biomedical samples, data, and tools is costly and time-consuming. Thus, transparency about their processing or evolution and reuse, particularly of health data, are highly desirable. Therefore, an appropriate fact-based decision framework to evaluate data (re)usability is required. Provenance information documents the processing or evolution of a data object, thereby providing an essential formal basis for such a (re)usability evaluation. Standardised, this provenance information facilitates better FAIR biomedical data. Objective: The MInimal Requirements for Automated Provenance Information Enrichment (MIRAPIE) project aims at defining the minimal required provenance information for harmonised documentation of a data objects processing history and to establish the MIRAPIE approach as a community standard to assure interoperability of the collected provenance information. Methods: A hybrid consensus finding method, adjusted from Nominal Group Technique (NGT) and Delphi, has been applied within an international community setting to iteratively implement a minimal data model, an ontology, and an application guideline. The data model is based on the PROV Data Model (PROV-DM), the ontology expands the PROV Ontology (PROV-O). Results: With the MIRAPIE question, we defined a harmonising framework for provenance information in biomedicine and presumably beyond. The minimal data model, a respective ontology, and an accompanying guideline facilitate means for standardised and possibly automated provenance documentation. In diverse biomedical usage scenarios their general applicability to data, workflows, models, and even samples is shown. Setting up provenance documentation from scratch is equally supported as linking alternative data schemata and mapping existing provenance documentation. Conclusions: MIRAPIE question, minimal data model, ontology, and guideline together significantly contribute to the advancement of biomedical and especially health research, setting up a basis for a contextual (re)usability evaluation. This fosters traceability of changes applied to data, workflows, tools, and samples and, in consequence, sustainable data usage and reproducibility of scientific results. The generalisation allows to overcome domain-specific differences and local, national, and international boundaries. We invite biomedical research community and health data gathering institutions to create lasting change by establishing MIRAPIE-compliant provenance information for transparent data processing and (re)usability assessment.

• Left behind by digital health. A seven-year population study of technology adoption, HIV status, and the emerging prevention paradox in rural South Africa

  Date Submitted: Jun 2, 2026

  Open Peer Review Period: Jun 4, 2026 - Jul 30, 2026

  • Peer Review Me

  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.

• Information systems’ integration for a Physical Therapy Institute in a German Medical Center: A Systematic Strategic Planning Study

  Date Submitted: Apr 28, 2026

  Open Peer Review Period: Jun 4, 2026 - Jul 30, 2026

  • Peer Review Me

  Background: There are few theoretical frameworks in the literature for the strategic planning of health information systems. Demonstrating and analyzing their use in practice can lead to a broader application and evidence-based decision making. Objective: The study aimed to analyze and assess the information systems of a university hospital’s physi-cal therapy section and a university department of physical therapy in order to plan their integra-tion following the merger of the two facilities to form an institute for physical therapy at a Ger-man medical center. Building on this, a strategic plan for the institute’s information system is proposed. Methods: We used a methodological framework for the strategic planning of information systems in hos-pitals, extended it by lean management methods and applied it at the organizational unit level. We described the organizational units’ information systems’ static view by the three-layer graph-based metamodel for health information systems (3LGM²) and the dynamic view by Business Process Model and Notation (BPMN). Information sources were interviews with per-sonnel. Results: A strategic management plan for developing the institute’s information system has been pro-posed. A migration path has been established with 23 tactical projects over the next 3 years to accomplish to attain strategic management goals. Conclusions: The method for strategic planning of information systems could successfully be adapted to the organizational unit level and should therefore be applied to other departments in hospitals as well. It helps them identify weaknesses in information logistics through a systematic approach, enabling gradual improvement as part of a long-term plan.

• A Functional Taxonomy for Quantifying the Diagnostic-to-Therapeutic AI Gap in Image-Guided Oncology: Analysis of 29,277 Publications Across Five Domains

  Date Submitted: Jun 2, 2026

  Open Peer Review Period: Jun 3, 2026 - Jul 29, 2026

  • Peer Review Me

  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.

• Comparing Digital Health Interventions for Risk Factor Control in Secondary Stroke and TIA Prevention: A Systematic Review and Network Meta-analysis

  Date Submitted: Jun 1, 2026

  Open Peer Review Period: Jun 2, 2026 - Jul 28, 2026

  • Peer Review Me

  Background: Background: Patients who experience a stroke or transient ischemic attack (TIA) face a substantial risk of future events, making optimal management of risk factors essential for secondary prevention. Digital health interventions have demonstrated promise in enhancing the control of vascular risk factors among individuals with stroke or TIA; however, the relative efficacy of different intervention modalities in achieving risk factor control remains uncertain. Objective: Objective: This study systematically assessed and compared the impact of various digital health interventions on the control of risk factors for secondary prevention among patients with stroke or TIA, aiming to determine the most effective intervention approach. Methods: Methods: A comprehensive and systematic literature search was performed across PubMed, Cochrane Library, Embase, and Web of Science databases from January 2010 to January 2026. This review included randomized controlled trials (RCTs) evaluating distinct digital health modalities among patients who experienced a stroke or TIA. Systolic blood pressure (SBP) changes served as the primary outcome, whereas alterations in diastolic blood pressure (DBP), patient medication adherence, total cholesterol (TC), and low density lipoprotein cholesterol (LDL-C) constituted the secondary outcomes. Utilizing the RoB 2 tool, two independent reviewers evaluated the risk of bias, followed by a Bayesian random-effects network meta-analysis to synthesize both direct and indirect evidence. We ranked the interventions based on their cumulative ranking curve (SUCRA) values and appraised the certainty of evidence through the GRADE approach. Crucially, the study protocol was registered prospectively in the PROSPERO database (CRD420261367782). Results: Results: A total of 25 RCTs involving 10,752 patients and six types of electronic health technologies were included. The results showed that, compared with usual care, combined digital technologies had a more pronounced benefit in reducing SBP (MD: −3.7, 95% CrI: −4.8 to −2.7; SUCRA: 71.95%); telephone follow-up demonstrated better effects on lowering DBP and LDL-C (MD: −2.4, 95% CrI: −3.7 to −1.2; SUCRA: 97.04%), (MD = −0.21, 95% CrI: −0.28 to −0.14; SUCRA = 55.95%). In addition, smartphone applications also showed certain advantages in improving medication adherence and reducing TC (MD = −0.39, 95% CrI: −0.71 to −0.068; SUCRA = 87.93%). Conclusions: Conclusions: Different digital health interventions may provide distinct benefits for secondary prevention after stroke or transient ischemic attack. Combined digital technologies appeared to be more effective for reducing SBP, telephone follow-up for improving DBP and LDL-C, and smartphone applications for enhancing medication adherence and reducing TC. However, due to the limited evidence base and small study sample size, these outcomes should be treated conservatively. Future large-scale, high-quality trials are required to verify these determinations. Clinical Trial: The study protocol was registered prospectively in the PROSPERO database (CRD420261367782).

• Designathon-based co-creation of an AI-agent workflow for COPD management in primary care: process and prototype development

  Date Submitted: May 31, 2026

  Open Peer Review Period: Jun 2, 2026 - Jul 28, 2026

  • Peer Review Me

  Background: Chronic obstructive pulmonary disease (COPD) is a major global health challenge, with the number of affected individuals projected to approach approximately 592 million by 2050. Primary healthcare institutions bear substantial responsibility for COPD screening, diagnosis, and follow-up, but often face underdiagnosis, fragmented information systems, and workforce constraints. Although digital health and artificial intelligence (AI) have shown potential in COPD management, workflow-integrated solutions tailored to primary care remain limited. Objective: To describe a designathon-based co-creation process and the subsequent development of an early-stage prototype of an AI-enabled digital workflow for COPD screening and follow-up management in primary care. Methods: This descriptive process and prototype development study followed WHO practical guidance on crowdsourcing and designathons in health research. It comprised three phases: (1) an online open call (July 22 to August 1, 2025) soliciting ideas related to AI-assisted chronic disease management and digitalized follow-up care; (2) a 3-day in-person designathon in Guangzhou involving 23 participants from five stakeholder groups (primary care physicians, implementation science scholars, AI engineers, patient representatives, and chronic disease management specialists) who worked in five interdisciplinary teams using user journey mapping and structured co-creation activities; and (3) a post-designathon translation phase in which co-created deliverables were synthesized into an early-stage WeChat Mini Program prototype named FeiChangShun. Expert rubric scoring was used to assess team deliverables generated during the designathon. Results: The online open call received 26 submissions, 25 of which met eligibility criteria. During the designathon, five priority pain points were identified: data silos and interoperability barriers, training–practice disconnect, communication barriers, human resource shortages, and low disease awareness. The five teams generated differentiated workflow concepts and corresponding user journey maps to address these challenges. Drawing on these co-created outputs, the research team developed an early-stage prototype comprising five core modules: voice interaction support, health education support, behavior management support, standardized workflow support, and draft document/report generation. Conclusions: This study reports a structured designathon-based co-creation process and the development of an early-stage, guideline-informed workflow prototype for COPD management in primary care. Future studies should evaluate the prototype with end users and assess implementation feasibility, safety, and clinical impact in real-world settings.

• Identification of MCI and Dementia Cohorts with a Brief Digital Cognitive Assessment via Semi-Supervised Clustering: Model Development and Validation

  Date Submitted: May 27, 2026

  Open Peer Review Period: May 31, 2026 - Jul 26, 2026

  • Peer Review Me

  Background: Traditional neuropsychological assessments for cognitive decline are lengthy in-clinic evaluations by a specialist, with typical wait times of 6-8 months. This creates a substantial patient burden and prolonged diagnostic and treatment timelines. Digital cognitive assessments (DCA) offer a scalable solution to these challenges, but their validation is challenged by the scarcity of large, high-quality datasets with established ground truth. Objective: To develop a model to identify mild cognitive impairment (MCI) and probable dementia using metrics from the Digital Assessment of Cognition (DAC), a brief, remote-capable DCA. A secondary objective was to conduct a preliminary assessment of the model's validity. Methods: We applied a semi-supervised model-based clustering method to combine a large dataset (N=1189) of DAC assessments alone, with a smaller dataset pairing DAC assessments with ground-truth neuropsychological diagnoses (N=248). We examined the model's predictive validity by comparing its predictions with diagnoses on a held-out test set. We examined congruent validity by testing associations with traditional analog assessments and demographic variables. Results: We identified a 6-cluster model with 3 MCI clusters and 2 probable dementia clusters. The model identified cognitively unimpaired, MCI, and dementia groups with high accuracy (78.7%) on the held-out test dataset, and showed excellent ability to identify cognitive impairment (AUROC=0.985) and dementia (AUROC=0.932). We identified strong associations with traditional analog assessments and demographic variables. An exploratory analysis showed evidence that clusters correspond to clinically meaningful subtypes of MCI. Conclusions: These results validate prior exploratory work and demonstrate the potential for more nuanced, holistic, and scalable cognitive assessments in non-specialist settings.

• Evaluating a Web-Based Intervention for Digital Health Measurement: a Mixed-methods Study

  Date Submitted: May 28, 2026

  Open Peer Review Period: May 31, 2026 - Jul 26, 2026

  • Peer Review Me

  Background: Despite its potential to address key challenges in primary health care, digital health measurement faces substantial implementation barriers for health care professionals. To address these barriers, professionals from 4 disciplines - physical therapy, occupational therapy, speech and language therapy, and general practitioner practice assistance – collaborated with researchers to develop an intervention. The intervention comprised a website supported by coaching on the job as a temporary implementation strategy during development. Objective: This study explored whether and how the intervention facilitates optimized use of digital health measurement in patient care to inform further intervention refinement. Methods: A mixed-methods formative process evaluation was conducted using a predominantly qualitative approach. 18 health care professionals tested the intervention in daily practice. Data collection was guided by the Medical Research Council framework, a predefined process evaluation plan, and the intervention’s initial program theory. Quantitative data (questionnaires, 7-point Global Perceived Effect measures, and monitoring lists) informed semi-structured interviews and focus groups. Data were analyzed using descriptive statistics and directed content analysis. Results: The intervention was largely implemented as intended and improved digital health measurement in patient care by enhancing participants’ capability, opportunity, and motivation. Consistent with the initial program theory, these changes triggered implementation activity at the organizational level, strengthening implementation readiness through bottom-up change processes. Intervention strategies included collaborative learning, modelling and prompting action. These strategies operated through mechanisms such as experiential learning, in which professionals experienced the benefits and feasibility of digital health measurement, reinforcing motivation for its continued use. During intervention use, additional processes emerged, including champions facilitating organizational-level adoption of digital measurement by sharing knowledge and enthusiasm with colleagues. Coaching particularly supported initial intervention engagement by contextualizing generic information, stimulating interaction, and prompting action. Individual, organizational, instrumental, temporal, policy, and societal factors interacted with intervention components, strategies, and mechanisms to facilitate or constrain outcomes. Conclusions: Future refinement should strengthen key mechanisms and processes, integrate mechanisms previously supported by coaching, and develop scalable implementation strategies. As no single approach will fit all contexts, practices should tailor implementation to their local needs. The intervention’s generic framework and flexible use of core components support local adaptations.

• Digital Health Technologies for Psychotic Disorders: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

  Date Submitted: May 26, 2026

  Open Peer Review Period: May 28, 2026 - Jul 23, 2026

  • Peer Review Me

  Background: Digital health technologies (DHTs) for psychosis may help address the substantial gap in access to psychological services, yet prior syntheses are limited by heterogeneous designs and populations. T Objective: This systematic review and meta-analysis aimed to synthesize evidence from randomized controlled trials (RCTs) to estimate the relative effectiveness of DHTs in individuals with confirmed psychotic disorders. Methods: Web of Science, PubMed, Embase, Scopus, PsycINFO, and CENTRAL were searched from inception to January 2026. Eligible studies were RCTs enrolling adults with psychotic disorders that evaluated DHT-delivered psychological interventions targeting psychotic symptoms. Comparators included passive and active controls. Primary outcomes were positive, negative, and overall symptoms. Secondary outcomes included depression, anxiety, functioning, quality of life, dropout, and adverse events. Results: Forty-one RCTs (N = 4139) were included. Compared with passive controls, DHTs showed small to moderate significant reductions in positive (g = -0.18, 95% CI: -0.33 to -0.03; I2= 60%), negative (g = -0.32, 95% CI: -0.56 to -0.07; I2= 63%), and overall symptoms (g = -0.41, 95% CI: -0.71 to -0.10; I2= 78%) at posttreatment, with effects for positive symptoms also at follow-up. No significant effects were observed when compared with active controls. Subgroup analyses indicated significant effects for delusions but not auditory hallucinations, and stronger effects for therapist-supported versus interventions delivered fully automated. Secondary outcomes showed small improvements both posttreatment and follow-up in depression, anxiety, and general functioning, but not for quality of life. Heterogeneity was moderate to high in some of the analyses. Dropout rates were comparable across groups, with no consistent pattern of serious adverse events identified, although safety reporting was inconsistent. Conclusions: DHTs represent a promising approach, with outcomes that appear broadly comparable to face-to-face interventions, while offering potential advantages in accessibility, scalability, and flexibility. Further high-quality RCTs with active comparators and standardized safety monitoring are needed. Clinical Trial: CRD42021251108

• Designing agentic speech assistance for PROM collection: a qualitative interview study with patients on assistance functions

  Date Submitted: May 22, 2026

  Open Peer Review Period: May 25, 2026 - Jul 20, 2026

  • Peer Review Me

  Background: Patient-reported outcome measure (PROM) completion is hindered by patient-level barriers—including motor, sensory, cognitive, and motivational constraints—that risk insufficient participation and non-response bias. While technology-enabled approaches such as multimodal speech assistance hold promise for reducing these barriers, assistance is a complex interaction: it can both alleviate and introduce barriers depending on how well it aligns with patients’ routines and needs. Objective: This qualitative study explores how patients perceive the advantages and disadvantages of AI-based speech assistance for PROM collection, focusing on how assistance functionalities interact with individual barriers and completion practices. Methods: We conducted semi-structured qualitative interviews with 96 psychosomatic and neurological rehabilitation outpatients, embedded in a pragmatic cross-randomised controlled trial. Participants completed PROMs with and without an AI-based speech assistance system offering speech output, speech input, and guidance by a socially interactive agent (SIA) that was physically, virtually, or voice-only embodied. The system was iteratively refined during data collection to address usability and performance issues. We included a broad sample to reflect real-world care settings, including patients without reported barriers. Using inductive content analysis (61 codes, grouped into 4 overarching and 9 subthemes), we examined perceived advantages and disadvantages of the three main assistance functionalities and multimodal interaction. Reporting followed the COREQ guideline. Results: The speech output function emerged as the most widely valued assistance feature, with many patients reporting improved concentration, question comprehension, and deeper engagement with item content. The social agent was described as making the interaction more engaging and less monotonous, by at the same time not evoking social pressure. Speech input was perceived as helpful by some, especially for those with motor impairments or a preference for verbal expression. However, each function also introduced challenges: speech output disrupted reading routines for some, the social agent was perceived as distracting or unnecessary by others, and speech input was criticised for recognition errors, inefficiency, and privacy concerns. Conclusions: AI-based speech assistance for PROM collection offers significant potential to reduce barriers and enhance patient engagement, but its effectiveness depends on alignment with individual needs, preferences and routines. While speech output proved broadly beneficial, speech input and socially interactive agents require careful design to avoid introducing new barriers, particularly for marginalised groups. Configurable, modular assistance systems that adapt to diverse user preferences and impairments are essential for equitable implementation. Future research should focus on inclusive co-design and longitudinal studies to refine these technologies for real-world clinical use. Clinical Trial: German Clinical Trail Register-ID: DRKS00035213