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Latest Submissions Open for Peer Review

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JMIR Submissions under Open Peer Review

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Titles/Abstracts of Articles Currently Open for Review:

  • Background: Health services increasingly face decisions about how to integrate immersive technologies into routine practice. International guidance highlights the need for structured governance in digital health, yet extended reality (XR) initiatives are often launched through isolated pilots without a clear assessment of organisational readiness or implementation risk. Although factors influencing XR adoption are well documented, healthcare organisations and system-level decision makers still lack practical, governance-oriented tools to translate these determinants into structured strategic decisions made before implementation. Objective: To develop MCDA-XR, a strategic governance framework that translates behavioural, organisational, and technical implementation determinants into a structured decision-support process for healthcare organisations. Methods: The study followed a sequential mixed-methods design covering the first two phases of a three-stage framework development and validation project. Phase 1 (Identification) defined strategic criteria by integrating theoretical perspectives on organisational complexity, behaviour change, technology acceptance, and immersive safety, together with a targeted review of XR implementation evidence. Phase 2 (Construction) refined the framework through participatory sessions. A multidisciplinary group of 33 stakeholders, including professionals and managers from hospital and primary care settings and postgraduate students, evaluated the proposed criteria for strategic relevance and operational clarity. This process resulted in a final ten-criterion structure and the establishment of a dual-score assessment logic. Phase 3 (Validation), planned as a subsequent step, will examine the predictive value of the framework in longitudinal clinical settings. Results: The development process yielded a framework comprising ten operational criteria grouped into three conceptual domains (Human, Organisational, and Technical). Stakeholder ratings indicated high strategic relevance across all criteria (mean scores above 4.0 on a 5-point scale), with Safety and Comfort receiving the highest prioritisation (mean 4.6). The final instrument applies a dual-assessment approach in which each criterion is rated separately for Strategic Importance and Organisational Readiness. Mapping these dimensions enables organisations to identify priority gaps, particularly areas of high importance and low readiness, and to distinguish between manageable constraints and critical barriers requiring targeted preparatory action prior to implementation. Conclusions: MCDA-XR addresses a key governance gap in XR implementation by providing a structured way to align adoption decisions with institutional priorities and operational constraints. Rather than relying on descriptive feasibility assessments, the framework supports explicit prioritisation and action-oriented decision making at the organisational level. MCDA-XR is positioned for Phase 3 evaluation, which will examine whether its readiness profiles anticipate implementation challenges and early sustainability outcomes in real-world clinical deployments.

  • Background: Online virtual worlds are platforms that allow users, represented as avatars, to meet and interact with other users in real time within 3D virtual environments. These platforms have potential utility as vehicles to deliver/receive clinical services, especially as a preference to video-conferencing-based telehealth. However, commercial virtual worlds (e.g.,“Second Life”) are often deemed unsuitable due to privacy and safety concerns. Objective: The aim of this study was therefore to co-develop and test a bespoke virtual world platform to deliver routine youth mental health services. Methods: We undertook a participatory-design process to develop the platform (Orygen Virtual Worlds) involving 10 young people with lived experience of mental health difficulties, researchers, software designers and mental health clinicians. We then tested two types of clinic-led interventions delivered through the virtual world (a structured therapy group and an individual therapy) in a public youth mental health service setting in Australia. Participants were patients receiving treatment in the service. The main outcomes were acceptability and feasibility; we also measured symptom change, usability, presence and therapeutic alliance. We conducted qualitative interviews post-intervention with the participants and analysed these interviews using thematic analysis. Results: 15 young people were recruited to the structured group (27% consented from referred) and 8 were recruited to the individual therapy (36% consented from referred). Drop out was higher in the individual therapy than the structured group therapy (38% versus 80%). Acceptability ratings were high for both therapy approaches and there were no significant safety events attributed to using the platform. There were no significant pre-post differences in the symptom outcome measures in either the structured group intervention or individual therapy. The platform was perceived as being comfortable and safe, enjoyable, fun and interactive, and was not confusing to navigate or difficult to use. The qualitative themes included the platform being fun and engaging, making treatment more accessible, providing a safe and inclusive place, fostering connections, positively impacting wellbeing and providing a catalyst for real life functional change. Young people perceived decreased barriers, increased comfort with help-seeking and reduced social stress facilitated by the avatar, communication options (emoji, text, voice) and accessibility from home. Conclusions: Our findings indicate that online virtual world platforms, such as the one we have designed, hold considerable promise for providing interventions for young people in clinical services. Virtual worlds can provide fun and engaging experiences of therapeutic interventions for young people with mental health difficulties which are safe and inclusive, especially for harder to reach groups.

  • Background: Public awareness campaigns and testing promotion must be strengthened to eliminate infections with hepatitis B and C viruses (HBV and HCV, respectively) by 2030. Although public health campaigns using various types of advertising are widely conducted, the appropriate channels for viral hepatitis testing remain unclear. Objective: To identify web services and digital advertising channels appropriate for promoting HBV and HCV testing, segmented by prior testing history and testing intention. Methods: A nationwide cross-sectional online survey of Japanese adults aged 20–69 years was conducted. The respondents answered questions on viral hepatitis testing status, routinely used web services with 180 options, and exposure to digital advertising with 25 choices. Correspondence analysis was used to visualize the associations among the testing segments, web services, and digital advertising. The distinctiveness was quantitatively evaluated. Results: Of the 2000 respondents (1011 men, 989 women), 18.0% (359/2000) reported prior HBV and HCV testing, and 22.1% (441/2000) were unsure whether they had ever been tested. Web services characteristically associated with those who had never been tested but were willing to be tested included Lawson (convenience store) and cosme (cosmetic shopping). The corresponding digital advertising channels included in-store and storefront screens at Welcia (pharmacy chain) and Lawson (convenience store). Segment-specific patterns varied according to age group and sex. Conclusions: In Japan, the convenience store chain Lawson was a distinctively frequent touchpoint, both online and offline, among individuals who wished to undergo viral hepatitis testing. Future studies are needed to determine whether implementing awareness-raising activities through Lawson can lead to an increased uptake of testing and subsequent treatment.

  • Background: Diabetes self-management and education services can improve health outcomes, but engagement is often low. ‘Healthy Living’ is an online self-management programme for people with type 2 diabetes, based on the ‘HeLP-Diabetes’ intervention which demonstrated effectiveness in a randomised controlled trial. Healthy Living was commissioned by NHS England and rolled out nationally into routine care. The website comprises structured learning, unstructured articles (which users could access at any time), and tracking tools such as goal setting. Objective: To investigate overall usage and exposure to content of Healthy Living, including differences in usage/ exposure by user characteristics. Methods: Anonymous usage data from all people (n=27,422) who activated an account between May 2020 and September 2023 were available, including (1) which website activities were accessed, (2) when activities were accessed and (3) how long users spent on each activity. User demographic and usage information was summarised. Logistic regression evaluated the association between user demographics and usage. Results: The median length of time spent on the website in total was 7·6 minutes (IQR 0·6-27·6 minutes); 12,066 (44·0%) users spent less than five minutes on the website and 3,022 (11·0%) spent one hour or more. Of those who activated an account, 69·8% accessed some website content, 40·7% completed the first section of structured education, and 4·7% completed 60% of the structured education. Usage of the unstructured aspects of the programme was low. Female gender, lower deprivation, White ethnicity, and a shorter time since diagnosis were associated with increased usage. Conclusions: This study is one of the first to provide detailed analysis of user engagement with a national digital self-management programme for type 2 diabetes. Usage of with Healthy Living was generally low, in line with other digital self-management programmes. However, encouraging increased usage with the programme has the potential to lead to better health outcomes in people with type 2 diabetes.

  • Background: Primary care providers (PCPs) must consolidate diverse sources of data (clinical, laboratory, administrative etc.) to make clinical decisions. As these sense-making tasks become increasingly challenging, artificial intelligence (AI) offer potential to prioritise guideline-recommended tasks based on clinical benefit. Objective: To demonstrate a participatory approach to the conception, design, and development of such AI-assisted clinical decision support (CDS) systems for primary care. Methods: A mixed methods study was performed, including in-clinic observations at primary care clinics and a focus group involving 20 PCPs in Singapore. The design thinking double diamond process model was applied to define care delivery challenges and conceptualise digital tools. Participants periodically evaluated data saturation, defined as saturation ratio <5% on two consecutive occasions. Results: In-clinic observations produced a patient journey map (Figure 1) highlighting current workflows, data sources and challenges. PCPs described consolidating patients’ medical records, presenting complaint, financial and sociobehavioral considerations before formulating a management plan based on multiple guidelines and the latest literature. PCPs also reported that core challenges included rapid guideline adaptation, repeated manual entry across multiple systems, complex claims processes, and limited patient health ownership (Figure 3). Participants further conceived AI tools that could automate eligibility checks for recommended interventions (e.g. screening and vaccinations), deliver just-in-time reminders at the point-of-care, consolidate actionable sociobehavioral data, contextualise relevant literature, and develop personalised risk-based action lists (Figure 5). Conclusions: This study describes Singapore’s primary care delivery challenges and identifies parallels from international reports in the United States and Europe. Key providers’ considerations for AI-assisted CDS tools to best support care delivery are described. Additional findings include provider concerns over AI-scribes, highlighting a need for robust evaluation and privacy-preserving approaches. A blended implementation strategy for developed countries was developed using AI agents to aggregate and analyse data, suggest “next best action” lists, and prioritise recommended tasks based on AI-predicted health benefit.

  • Background: Over the past quarter-century, designers of digital behavior change tools have increasingly blended constructs from multiple theories, yet the extent to which such integration enhances intervention outcomes remains unclear. Objective: To clarify this relationship, this study systematically reviewed literature published between 1999 and 2025, focusing on IT-mediated interventions that explicitly combined at least two behavioral theories and reported intention or behavior outcomes. Methods: Following a registered protocol (PROSPERO CRD42022285741) and PRISMA guidelines, searches across seven databases identified 62 eligible studies. Results: Most investigations were quantitative (77%), featured sample sizes from 16 to 8840, and lasted under 6 months; only 9 applied randomized controlled designs. Twenty-nine theories appeared, with Self-Determination Theory (35%) and the Theory of Planned Behavior (29%) being the most prevalent, often paired with the Technology Acceptance Model or Task-Technology Fit. Integrated models consistently outperformed their single-theory counterparts. Health care and fitness interventions dominated (44%), followed by online learning (23%) and mobile commerce (11%), but long-term follow-ups and explicit mappings of theory to behavior change techniques were scarce, and overall risk-of-bias ratings were moderate. Conclusions: Findings indicate that integrated theoretical frameworks deliver measurably superior behavioral outcomes in digital environments, yet evidence remains short-term and health centric. Future research should extend evaluation horizons beyond 6 months, diversify application domains, apply more rigorous randomized designs, and articulate more transparently how theoretical constructs guide specific intervention techniques to advance replicable, theory-driven digital solutions.

  • Background: Digital health has the potential to mitigate health inequity for priority populations who are underserved or marginalised by the health system. However, there is a lack of practical guidance on how to include priority communities in the co-production of digital health technologies, particularly across the entire lifecycle of innovation including research, development, and evaluation. Objective: The aim of this scoping review was to systematically identify and assess published methods used during digital health innovation to promote equitable inclusion of priority communities at every stage of the CeHRes Roadmap for Digital Health Technologies. Methods: This review was based on the Arksey and O’Malley framework for scoping reviews. A 6-stage framework was used to execute the review. To increase the trustworthiness of the findings, an expert advisory group was consulted and their feedback incorporated into the final manuscript. The Participant, Concept and Context (PCC) framework was used to structure the inclusion criteria. Results: The review identified a total of 106 articles, 58 methods, 4 approaches, and 17 research adjustments utilised to co-produce digital health technologies with priority communities. Common methods across multiple stages included interviews, focus groups, surveys and workshops, however the most accessible way to make equity a practical reality during health technology innovation is to appoint a priority population community advisor, or advisory group, from project inception to project closure. Visual and creative methods like photovoice, home tours and body-mapping were also employed, often by priority population researchers themselves. Research adjustments that promote patient safety and comfort, enhanced literacy, peer-support and recognize socio-cultural and demographic considerations have been employed to increase the inclusion of priority populations during digital health innovation. Conclusions: Embedding equity is possible using the practical methods and research adjustments identified to promote inclusive co-production. Professionals working across healthcare, health informatics, research, digital health, and technology development can utilise these findings to centre digital health equity during technology innovation. This research also recognises that co-production must draw on epistemological frameworks, or ways of thinking, which support Indigenous and other priority population knowledge systems. A solely Western lens risks reinforcing structural barriers and overlooking essential knowledge, as demonstrated by this review when the search strategy missed key scholarly works by priority population authors themselves.

  • Background: Heart failure is a chronic condition which significantly impacts patients’ quality of life and increases healthcare burden. Effective self-monitoring and lifestyle modifications are essential components of management and improving health outcomes. Mobile health technologies, such as smartphone apps, are being used more widely to assist heart failure patients with self-management. However, evidence regarding patient engagement, user experience, and the effectiveness of these mobile health tools remains limited and continues to evolve. Objective: Our research aimed to explore the feasibility of a mobile health platform, MoTER-HF, which incorporates a smartphone app and a web-based clinical portal to support self-management in heart failure patients. Methods: The feasibility study utilized a single-group pretest-posttest mixed-methods design. A total of 23 participants diagnosed with heart failure were recruited to use the app and two Bluetooth-enabled devices (a blood pressure monitor and a digital weight scale) over a 12-week period. Patients’ engagement and acceptance were assessed using a satisfaction questionnaire, semi-structured interviews, and platform usage logs. Health and behavior outcomes were measured at baseline and at week 12 using validated instruments. Results: Most participants found the MoTER-HF app easy to use and aligned with their daily health monitoring routines. The frequency of use for features such as tracking blood pressure and weight daily was high. However, features such as self-reported symptom tracking and recording exercises in the app were used less frequently, reflecting individual preferences and perceived relevance. While no statistically significant changes in health and behaviour outcome were observed, trends indicated modest improvements in self-care, quality of life, and psychological well-being. Participants reported improved self-monitoring practices and valued the ability to visualize and track their data as well as the reassurance provided through nurses’ oversight. Conclusions: The MoTER-HF platform has demonstrated potential in supporting self-management among individuals with heart failure, particularly when it incorporates features that participants find engaging. Further research is needed to better understand the platform’s impact on health outcomes and to involve clinicians in developing a scalable digital model of care.

  • Young Adults' Interactions with Food and Nutrition Content on Social Media: A Qualitative Study to Inform Intervention Design

    Date Submitted: Dec 11, 2025
    Open Peer Review Period: Dec 11, 2025 - Feb 5, 2026

    Background: Young adults increasingly rely on social media for nutrition information. However, little is known about (i) which types of eating-related content they actively engage with and why, and (ii) how they interpret, evaluate, and incorporate this content into their everyday food choices and health behaviours. Objective: This qualitative study explored how UK young adults (aged 1825 years) interact with food and nutrition content across social media platforms to inform the design of future social media interventions. Methods: Semi-structured online interviews, guided by the COM-B model, were conducted with active social media users in the UK between August and October 2024. The study design was informed by Patient and Public Involvement (PPI) to ensure relevance and acceptability. Data were analysed using reflexive thematic analysis. To guide intervention development, key findings (coded as barriers and facilitators) were systematically mapped to the Theoretical Domains Framework (TDF), and the COM-B. Ethics approval was obtained from the University of Cambridge (24.368). Results: Twenty-five participants (72% female, mean age 22.2 years, ethnically diverse) were interviewed. Five key themes were identified: (1) Evolving Engagement Patterns (passive scrolling to active interaction, mixed feelings on algorithmic control); (2) Conflicted Information Seeking (frustration with contradictory advice, varied strategies to assess credibility); (3) Multifaceted Behavioural Impact (simultaneous positive impacts like cooking inspiration and negative impacts like restrictive eating triggers); (4) Shifting Goals (a movement from appearance-focused to health-centred goals, yet vulnerability to body-image issues); and (5) Intervention Preferences (demand for credible professionals, customisable content, and privacy protection). Participants demonstrated a reactive learning process, developing ‘digital nutrition literacy’ often after negative experiences. Social influences were identified as the most frequently cited domain (mapped to TDF/COM-B) shaping interactions with social media content. Conclusions: This study challenges assumptions of passive social media consumption, showing that young adults actively develop protective strategies yet remain vulnerable to misinformation. Digital interventions should leverage user agency and address diverse perceptions through customisable, credible content delivered with privacy and emotionally safe messaging. The COM-B and TDF mapping provide specific, evidence-based behavioural targets, particularly within the domain of Social Opportunity and Reflective Motivation, to guide the development of effective eHealth interventions

  • Background: Limited public understanding of randomized controlled trials (RCTs) hinders recruitment, retention, and confidence in research. Early exposure to trial concepts may strengthen health literacy and research engagement. The Kid’s Trial was a global, decentralized, child-led study that co-created and conducted an RCT to help children understand trials, their importance, and improve critical thinking. Objective: This paper presents its design, outcomes, and methodological reflections. Methods: The Kid’s Trial employed a dedicated website with study materials guiding children through each step of designing and conducting an RCT. Each step was linked to an online survey. Materials were co-developed with two patient and public involvement groups of children and parents. Any child, aged 7 to 12 years, could take part in as many or as few steps as desired. Recruitment combined online and offline strategies, and engagement and self-reported learning were descriptively analyzed. The co-created REST (Randomized Evaluation of Sleeping with a Toy or Comfort Item) trial was a two-arm, pragmatic RCT comparing one week of sleeping with versus without a comfort item. The primary outcome was sleep-related impairment, and the secondary outcome was overall sleep quality. Analyses followed an intention-to-treat approach using mixed-effects models adjusted for baseline measures. Results: Overall, 224 children from 15 countries participated in at least one step. Participation varied: 37% (n = 82) completed one step and 21% (n = 48) completed six. The REST trial randomized 139 children, with 73% (n = 101) completing outcome surveys. Adjusted mean differences (intervention–control) were −0.53 for sleep-related impairment (95% CI −3.40 to 2.34; P=.71) and 0.28 for sleep quality (95% CI 0.01 to 0.55; P=.04), a small, uncertain difference not supported with sensitivity analyses. Post-study responses (n = 20) indicated improved understanding of RCT concepts. Conclusions: The Kid’s Trial demonstrates the feasibility of a decentralized, child-led RCT co-created through participatory citizen-science methods. Children can meaningfully contribute to trial design and conduct, and experiential participation may foster early trial literacy and critical thinking. Future studies should enhance engagement through community partnerships, shorter intervals between steps, and embedded learning assessments to improve inclusivity and retention.

  • Background: The health benefits of breastfeeding for both infants and parents are well-established, yet global breastfeeding rates remain below recommended levels. Parent-targeted Digital Health Interventions (DHIs), including mobile health (mHealth) and electronic health (eHealth) strategies, offer a scalable way to support breastfeeding, but their effectiveness remains uncertain. Objective: To explore the effectiveness of parent-targeted DHIs for improving breastfeeding outcomes. Methods: Seven databases (CENTRAL, CINAHL, Education Research Complete, Embase, MEDLINE, PsycINFO and Scopus) were searched on April 15, 2024, for randomised controlled trials (RCTs) involving parents of children aged under five years. Eligible interventions aimed to promote breastfeeding and were primarily delivered via digital platforms (e.g. mobile apps, text messaging and websites). Studies were excluded if the DHI exclusively targeted breastfeeding within clinical settings or focused on non-digital content. Outcomes of interest included exclusive breastfeeding, any breastfeeding, breastfeeding duration, breastfeeding self-efficacy, cost-effectiveness and adverse events. Risk of bias of the primary outcome was assessed using the Cochrane Risk of Bias 2 (RoB2) tool. Meta-analyses were conducted in accordance with Cochrane methods and result are reported following PRISMA guidelines. Results: Thirty-one (29 RCTs and 2 cluster-RCT) studies, including 14776 participants from 17 diverse countries were included. Nineteen of the interventions focused on mHealth strategies, nine were delivered online and five were telecommunication interventions. Risk of bias was indicated with ‘some concerns’ or ‘high risk’ for 26 (84%) studies. Pooled results indicated that DHIs can significantly improve the odds of exclusive breastfeeding (OR: 2.35, 95% CI: 1.71 to 3.23, I2=81%; 26 trials, 9884 participants), however considerable heterogeneity was present. Pooled results also indicated DHIs may improve breastfeeding duration (SMD: 0.50, 95% CI: 0.30 to 0.69, I2=15%, 5 trials, 601 participants), and ‘any’ breastfeeding (OR: 1.16, 95% CI: 0.99 to 1.35, I2=7%, 14 trials, 7974 participants). Conclusions: Improvements to exclusive breastfeeding rates and breastfeeding duration are linked to major societal and health benefits for infants and mothers. Our results indicate that parent-targeted DHIs are effective for improving key breastfeeding behaviours, with evidence of their impact spanning diverse populations and contexts. Clinical Trial: PROSPERO (CRD42023492644)

  • Background: Patients with terminal illnesses often face profound challenges when making end-of-life care decisions. Digital health technologies, particularly patient decision aids (PDAs), have emerged as a promising approach to support informed, value-concordant decision-making in hospice care contexts. Objective: This systematic review aimed to evaluate the efficacy, functional characteristics, and implementation features of digital health-based PDAs designed to support hospice care decisions among patients with terminal illness. Methods: A systematic review was conducted following PRISMA guidelines, searching nine electronic databases from inception to June 2024 for randomized controlled trials (RCTs) and controlled clinical trials (CCTs). Additional sources included the Ottawa Hospital Research Institute's Decision Aid Library Inventory, professional books, reference lists, and author contacts. Two reviewers independently performed study selection, quality assessment using Cochrane RoB 2 and ROBINS-I tools, and data extraction. PDA quality was evaluated using International Patient Decision Aids Standards (IPDASi v4.0). Results were synthesized narratively without meta-analysis due to heterogeneity. Results: Nine RCT studies involving 1466 participants were included. Methodological quality varied, with four RCTs demonstrating low/some risk of bias, while five RCTs had high risk. Among them, five PDAs met IPDASi qualifying criteria (66.7%-100% compliance), though overall compliance rates of all included PDAs were modest (37.1%-57.14%). Four core digital PDA functions were identified: (1) multimedia knowledge delivery, (2) interactive values clarification, (3) facilitated decision communication, and (4) emotional normalization. PDAs significantly improved patients' decision-making knowledge (6 studies, n=965), and decision self-efficacy (1 studies, n=265). PDAs also positively influenced end-of-life care preferences (n=6) and actual transitions to hospice care (n=2) . Conclusions: Digital health-based PDAs are valuable tools for improving decision-related outcomes and facilitating hospice care transitions. However, significant gaps remain in addressing emotional dimensions and cultural sensitivity. Future research should develop rigorously designed, culturally adapted digital PDAs that comprehensively address the cognitive-emotional aspects of end-of-life decision-making. Clinical Trial: The review was registered with PROSPERO (CRD420251031554).

  • Background: Depression and anxiety are prevalent in working-age adults. Although treatment provided by health professionals can improve symptoms and functioning, many people experiencing mental-ill health do not seek help. There have been very few effective interventions to improve help seeking in adults, with none implemented across diverse workplaces through online delivery. Objective: The primary aim of this trial was to test the effectiveness of a co-designed program for increasing professional help seeking intentions in Australian employees, relative to an active control condition. Methods: A triple-blinded two-arm cluster randomized controlled trial (N=487, control workplaces=26, intervention workplaces=25) was conducted to assess the relative effectiveness of Helipad, a fully-automated co-designed single-session interactive program (intervention condition) with a standard psychoeducation program (active control condition). Workplaces (clusters) were recruited via advertising or invited directly by researchers. Participants completed a pre-test, immediate post-test, and 6-month follow-up survey sent via email assessing help-seeking intentions (primary outcome), mental illness stigma, mental health literacy, help seeking attitudes and behavior, work and activity functioning, quality of life, and symptoms of depression, anxiety, and general psychological distress. Results: A significant difference in change over time on professional help seeking intentions was found between the two conditions F(2, 185.44)=6.89, P=.001, with planned contrasts showing that the Helipad program was effective in increasing professional help seeking intentions compared with the control at the primary endpoint of immediate post-test (t(359.35)=-3.72, P<.001). This difference was not maintained at the 6-month follow-up (t(119.76)=-1.05, P=.295). Retention rates were 71.1% at post-test and 24.9% at follow-up. The Helipad program was also associated with improved mental health literacy and help seeking attitudes at post-test. Helipad was not significantly superior to the control in reducing mental illness stigma or improving help seeking behavior, functioning, quality of life, or symptoms of depression, anxiety or general psychological distress (secondary outcomes) at 6-month follow-up. Conclusions: This study demonstrated that the Helipad program was effective in improving the intentions of employees to seek help from a professional compared to an active control. The program also improved mental health literacy and help-seeking attitudes, but these changes were not sustained and did not translate into observable differences in help-seeking behaviors or mental health symptoms. Selective interventions may be needed to demonstrate behavioral outcomes, and programs may be more effective when paired with organizational interventions. Clinical Trial: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12623000270617p

  • Psychotherapists’ Trust, Distrust, and Generative AI Practices in Psychotherapy: Qualitative Study

    Date Submitted: Dec 4, 2025
    Open Peer Review Period: Dec 8, 2025 - Feb 2, 2026

    Background: Generative artificial intelligence (GenAI) is rapidly entering mental health care, supporting both client-facing tools (e.g., chatbots for support and self-management) and clinician-facing systems (e.g., documentation and assessment aids). Whether these tools ultimately help or harm psychotherapists and their clients depends not only on their technical performance but on how psychotherapists trust and distrust them in practice—that is, when they are willing to rely on GenAI, when they withhold reliance, and how they manage clients’ own GenAI use. Understanding how psychotherapists negotiate their trust and distrust is essential for future responsible and ethical integration of GenAI in mental health care, where GenAI’s promising benefits, such as reducing administration burden or enhancing client’ accessibility, must be balanced against risk that requires professional judgement rather than blanket adoption or rejection. Yet little empirical work has examined how practicing psychotherapists actively calibrate trust and distrust in GenAI across tasks and contexts, or how these judgments shape the evolving psychotherapist–client–GenAI relationship. Objective: This study aims to examine (1) what are psychotherapists' experiences with, perceptions of, and trust/distrust in GenAI in therapeutic contexts? and (2) how do psychotherapists perceive the role of GenAI within the therapeutic relationship, and how do their perceptions shape their trust and distrust in GenAI? Methods: Between January 2025 and May 2025, we conducted an interview study with 18 psychotherapists in the United States. Psychotherapists were recruited. Results: Our findings show that psychotherapists' adoption of GenAI was highly individualized and underpinned by “conditional'' trust—confidence that depended on maintaining professional control, aligning GenAI use with specific tasks, and considering who was using the GenAI tools. Trust was sustained when GenAI operated in clinician-supervised, supportive roles, but diminished when control shifted, tasks became high-stakes, or GenAI appeared to encroach on the therapeutic relationship (e.g., forming emotional bonds with clients or replacing core psychotherapist functions). Additionally, participants also voiced distrust towards the broader sociotechnical ecosystem, including developers, commercial incentives, and the absence of clear organizational guidelines. Conclusions: Psychotherapists’ perspectives offer critical insights into GenAI's current usages in their professional practices and the conditions under which they are willing to trust and distrust GenAI tools. Their experiences highlight the importance of maintaining clinician control, ensuring contextual appropriateness, and preserving the human connection central to psychotherapy. Future work should further examine how therapeutic orientation, professional experience, and client characteristics shape trust and distrust in GenAI. As GenAI becomes more embedded in mental-health care, research is also needed to explore how specific GenAI system features can be responsibly designed to support clinical workflows and enhance therapeutic relationships. Organizational and policy frameworks will be essential to ensure responsible, ethically aligned, and human-centered GenAI deployment in psychotherapy.

  • Simulating the Patient's Perspective: Promise and Pitfalls of LLMs in Patient-Centric Communication

    Date Submitted: Dec 8, 2025
    Open Peer Review Period: Dec 8, 2025 - Feb 2, 2026

    Background: Large Language Models (LLMs) have shown broad applicability in medicine, including the generation of clinical documents. Beyond content creation, LLMs can also be used to evaluate the quality of medical documents. Because of LLMs' ability to simulate (or impersonate) specific personas, they can offer diverse perspectives (such as those of healthcare professionals versus patients with lower health literacy) on the clarity of medical texts. Objective: The primary objective of this research was to evaluate the ability of LLMs to simulate diverse user personas, varying by demographic profiles including educational background, gender, visit frequency, for the task of interpreting ICU discharge summaries. The study aimed to benchmark the clarity assessments generated by these LLM personas against a baseline established by human participants with corresponding backgrounds, in order to highlight the potential and limitations of using current LLMs to create personalized health information. Methods: We evaluated the ability of LLMs to simulate diverse user personas for the task of interpreting ICU discharge summaries. LLMs were prompted to adopt personas with varied demographic profiles, including different educational backgrounds. The resulting LLM-generated assessments of the summaries’ clarity were then benchmarked against a baseline established by human participants with corresponding backgrounds. Results: LLMs demonstrated a strong ability to simulate personas based on educational attainment, accurately interpreting key medical information in 88% of cases. However, the models’ performance varied widely when other demographic variables were introduced. For instance, persona performance was highly erratic based on gender, with simulated male personas achieving 97% accuracy while female personas achieved only 44%. The inclusion of additional details, such as the frequency of prior emergency room visits, further degraded the models' performance. Conclusions: This research highlights both the potential and the significant limitations of using LLMs to create personalized health information. While LLMs are promising for simulating user perspectives based on education, the current models exhibit unpredictable performance when tasked with incorporating other fundamental demographic traits like gender.

  • Background: Digital health literacy (DHL), the ability to seek, understand, and apply digital health information, has become increasingly important in the United Kingdom (UK), with a focus on digital transformation within the health service. While digital tools offer the potential to improve access and equity, they may also exacerbate existing health inequities if segments of the population are unable to engage with them effectively. Understanding the determinants of DHL is essential to designing inclusive digital health services. Objective: To measure DHL among adults in the UK and identify its sociodemographic, economic, and social determinants. Methods: A cross-sectional online survey was disseminated to adults in the UK in December 2024. DHL was self-reported using the validated eHealth Literacy Scale (eHEALS), which ranges from eight to 40. eHEALS score was dichotomized into high and low DHL based on a cut-off of 26. A multivariable logistic regression model was built to identify sociodemographic, economic, and social determinants of DHL. Results: The median eHEALS score was 31; 21% of participants had a low level of DHL, while 79% had a high level of DHL. Those aged 45–64 and 65 years and older, compared to the 18–45 age group, had 1.61 and 1.98 times the odds of low DHL, respectively (45–64 years odds ratio [OR]: 1.61, 95% confidence interval [CI]: 1.13 to 2.31, P=.01; 65 years and older OR: 1.98, 95% CI: 1.36 to 2.91, P<.001). Females had 0.55 times the odds of low DHL (OR: 0.55, 95% CI: 0.42 to 0.74, P<.001), and those with an undergraduate or postgraduate degree or higher had lower odds of low DHL, compared to those educated to below degree level (undergraduate degree OR: 0.49, 95% CI: 0.33 to 0.71, P<.001; postgraduate degree or higher: OR: 0.48, 95% CI: 0.32 to 0.71, P<.001). Conclusions: Among the UK population, male sex, lower educational attainment, and older age were significant predictors of low DHL. Inclusive educational interventions and digital health solutions, tailored towards individuals with low DHL, are needed to ensure that digital transformation in healthcare helps to narrow health inequities.

  • Multimodal Intelligent Monitoring of Parkinson' s Disease: Progress and Translational Challenges

    Date Submitted: Dec 6, 2025
    Open Peer Review Period: Dec 6, 2025 - Jan 31, 2026

    Background: Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder with a rapidly growing global prevalence. Current clinical assessments, such as the Unified Parkinson’s Disease Rating Scale (UPDRS), are limited by subjectivity and episodic application, creating a need for continuous, objective monitoring solutions. Objective: Objective: This review synthesizes progress (2019–2024) in multimodal intelligent monitoring systems for PD, focusing on the quantification of motor and non-motor symptoms, algorithm development, and the clinical translation of remote monitoring platforms. Methods: Methods: A targeted literature search was conducted in PubMed, Web of Science, and CNKI. Eligible studies were thematically analyzed across three domains: sensor-based symptom assessment, multimodal algorithms, and remote monitoring platforms. Methodological reporting followed PRISMA-ScR guidelines. Results: Results: Wearable sensors demonstrated high concordance with clinical scores (e.g., 89% for tremor detection). Computer vision achieved moderate agreement with clinician ratings (ICC=0.74 for bradykinesia). Remote platforms improved medication adherence (up to 85%) and reduced outpatient visits (by 29% in one study). A heuristic CPT-PD framework was proposed to integrate key components of diagnosis, treatment, and management. Conclusions: Conclusions: Multimodal intelligent monitoring enhances objectivity and continuity in PD assessment and shows promising clinical utility. Persistent challenges include fragmented symptom focus, algorithmic heterogeneity, and barriers to adoption among older adults. Future efforts should prioritize integrated, patient-centered ecosystems to enable precision management of PD.

  • Background: The use of large language models (LLMs) for medical literature retrieval is gaining traction due to its potential to enhance efficiency. However, concerns regarding the accuracy and reliability of citations generated by LLMs remain inadequately addressed, with significant variations observed across different models. Objective: This study aimed to evaluate and compare the performance of four popular LLMs (Grok, Gemini, ChatGPT, DeepSeek) against manual PubMed retrieval in the context of chronic obstructive pulmonary disease (COPD) inhalation therapy, focusing on citation accuracy and relevance, and to preliminarily analyse the mechanisms behind performance disparities. Methods: We prompted each LLM to retrieve 150 distinct English references on COPD inhalation therapy, extracting key information including titles, authors, journals, publication dates, PMIDs, and DOIs. A parallel manual search was conducted on PubMed. All retrieved references were verified against PubMed, Google Scholar, and Web of Science. Verification results were categorized into five types: (1) Accurate, (2) Incomplete, (3) Incorrect, (4) Fabricated, and (5) Irrelevant. A chi-squared test was employed to assess significant differences in performance. Then, we attempted to analyze the mechanisms behind the differences and put forward suggestions. Results: Results showed significant performance variations. Gemini achieved 124 (82.67%) accurate references, 1 (0.67%) incomplete, 7 (4.67%) incorrect, 1 (0.67%) fabricated, and 17 (11.33%) irrelevant, which demonstrated superior accuracy, while DeepSeek showed a high fabrication rate. For all models, incomplete information was primarily limited to titles, whereas errors were concentrated in PMIDs. Conclusions: Gemini 2.5 Pro had a significant advantage in literature retrieval for COPD inhalation therapy. Although artificial intelligence (AI) had shown potential to assist in medical literature retrieval, there were still significant performance gaps among models. The reasons for the differences were likely multifactorial, involving the model architecture, user interaction and semantic bias. Therefore, manual verification of citations generated by AI remained crucial for medical research. We recommend prioritizing the verification of PMID and title, referring to Medical Subject Heading (MeSH), and choosing models with an agent system and advanced context management.

  • Background: The global prevalence of overweight and obesity among children and adolescents has tripled since 1990. Currently, approximately 390 million individuals are affected, including 160 million with obesity, and an estimated 80% are projected to remain obese into adulthood. Concurrently, over 81% of 11- to 17-year-olds worldwide fail to meet recommended physical activity guidelines. Virtual reality (VR) exergaming—categorized into non-immersive, semi-immersive, and fully immersive modalities—has emerged as a promising intervention to enhance energy expenditure, improve motivation, and reduce body mass index (BMI). However, evidence regarding its application among children and adolescents remains fragmented. Following the methodological framework established by Arksey and O’Malley, this scoping review aims to systematically map the characteristics of existing interventions in order to inform future research and clinical practice. Objective: This scoping review aimed to explore the application of virtual reality (VR) exergaming in overweight or obese school-aged children and adolescents by identifying the intervention content, outcome indicators, evaluation tools, and application effects of VR exergaming and to provide a reference for future research and clinical practice in this field. Methods: Following the Arksey and O'Malley framework, a systematic search was conducted in PubMed, Embase, Web of Science, CINAHL, the Cochrane Library, CNKI, Wanfang, and VIP databases from their inception to April 12, 2025. Two reviewers independently screened studies and extracted data using a standardized template. Results: This study included 24 research projects from nine countries. Regarding the technical type of VR exergaming, three studies (12.5%) employed IVR, while the remaining 21 (87.5%) utilized NIVR. The intervention settings exhibited diverse characteristics, with half of the studies conducted in schools, homes, or communities. Furthermore, most intervention cycles lasted between 6 and 20 weeks, emphasizing high-frequency training to achieve significant health promotion outcomes. The outcome measures broadly encompassed three dimensions: physiological, psychological, and behavioral. Conclusions: VR exergaming improves engagement and adherence in overweight/obese youth through enjoyable, interactive features. However, research shows regional disparities and non-standardized outcomes. Future efforts should foster multisector collaboration to enhance its role in obesity prevention.

  • Long-term Cost and Health Impact of a Digital Obesity Intervention in Germany: An Economic Modeling Study

    Date Submitted: Nov 28, 2025
    Open Peer Review Period: Nov 28, 2025 - Jan 23, 2026

    Background: Obesity imposes a substantial economic burden, accounting for an estimated 10% of total healthcare expenditures in Germany. Digital health applications have demonstrated effectiveness in supporting weight management among individuals with obesity; however, evidence regarding their long-term economic impact remains limited. Objective: The objective of this study was to evaluate the long-term cost-effectiveness of the Oviva Direkt app for obesity treatment in the German healthcare context. Methods: A cohort-based Markov model was developed, informed from the Core Obesity Model, to evaluate the cost-effectiveness of a digital health application for weight management versus care as usual over a 10-year time horizon from a societal perspective. The model simulates disease progression using 6-month and annual cycles after an initial monthly phase and includes health states for key obesity-related comorbidities such as type-2-diabetes (T2D), acute coronary syndrome (ACS), stroke, cancer, and obstructive sleep apnoea. Patients enter the model at age 46 with a BMI of 30–45 kg/m², based on trial data. The analysis considered direct and indirect costs, life-years, and quality-adjusted life-years (QALYs). The primary outcome was the incremental cost-effectiveness ratio (ICER), complemented by net monetary benefit (NMB) analysis. Weight trajectories were extrapolated based on trial results using three scenarios (base case decay model, weight maintenance, full regain). Sensitivity analyses were conducted to assess uncertainty. Results: Under base case assumptions, the digital health application dominated care as usual, yielding cost savings of 3,511.85 € and a QALY gain of 0.0683 (≈3.6 weeks in perfect health). Direct medical costs were reduced by nearly 520 €. T2D prevalence was 1.6 percentage points lower, reducing time lived with diabetes by 8 months. Scenario analyses confirmed consistent cost-effectiveness. Conclusions: Digital health applications for weight management are cost-effective and clinically beneficial for individuals with obesity in Germany. These results add to growing evidence for digital health solutions, aligning with findings from applications for other conditions such as depression and multiple sclerosis.

  • Background: e-cohorts are susceptible to low participation rates, undermining representativeness. Frequent reminders can be a cost-effective strategy to increase response rate. However, their effectiveness may vary depending on the delivery mode, the content, and the formatting of messages. Objective: To compare different reminder strategies (i.e., emails and/or text messages with standard and/or institutional formatting) and evaluate their effectiveness in terms of response rate, in the context of a population-based e-cohort of healthy adults. Methods: We conducted a 4-arm randomized-controlled trial nested in Le French Gut e-cohort (registration number 2021-A01439-32). In November 2024, we included participants who were enrolled online (SKEZIA plateform) but not yet active participants (i.e., eligibility, consent form, and/or personal information questionnaire not completed). Randomization was stratified on time since enrolment. We sent three reminders, 72-h apart, following four experimental designs: (Group 1) standard emails only; (Group 2) text messages only; (Group 3) institutional emails only; and (Group 4) standard email, followed by text message and institutional email. Our primary outcome was the completion rate of the personal information questionnaire. We also measured the completion time (i.e. time between reminder received and questionnaire fully completed), online login rate, login time, email opening, and click-through rates. Results: At the end of the trial, out of 20,487 eligible participants, 19,525 received at least one reminder. The per-protocol completion rate was 8.4%, with a higher rate in Group 4 (9.4% vs 7.4%, 8.4% and 8.3% for Groups 1, 2, and 3, respectively; P <.001). Completion time was faster in Group 2 (mean ± SD, 6.3 ± 4.3 days) compared to Groups 3 and 4 (7.0 ± 3.5 and 7.2 ± 3.8 days; P = .003). Online login rate was higher and login time faster in Group 4 (rate: 15.6% vs 12.1%, 14.3% and 15.3% for Groups 1, 2, and 3, respectively; P <.001; and time: 7.1 ± 4.3 days vs 6.7 ± 4.3, 6.2 ± 4.6, 6.8 ± 3.9 for Groups 1, 2, and 3, respectively; P = .002). For emails, opening rates were similar (P = .87) but click-through rate was higher for institutional emails (23.1% vs 19.6% for standard formatting; P <.001). Conclusions: A mixed-delivery mode strategy, combining emails and text messages, effectively increases response rate by 27% compared to other strategies. Institutional emails with plain design and signed by study coordinators, seemed more appealing to participants than a more elaborate design. Clinical Trial: registration number 2021-A01439-32

  • Channel Allocation and Equity in Preventive Campaigns for Older Adults: Agent-Based Simulation Study

    Date Submitted: Nov 25, 2025
    Open Peer Review Period: Nov 26, 2025 - Jan 21, 2026

    Background: Preventive campaigns for older adults must decide how to allocate limited resources across media channels. However, these channel allocation and budget decisions rarely use explicit criteria for distributional equity or digital health strategic planning. As a result, health systems may optimize average uptake while leaving large gaps across socioeconomic groups and media-use profiles. Objective: This study aimed to develop and apply a data-driven agent-based model as a strategic planning tool for older-adult preventive campaigns, comparing channel allocation, personalization, and loss framing options under explicit budget and equity constraints. Methods: We built an agent-based simulation calibrated to national survey data on influenza vaccination and routine health screening among older adults in South Korea. Fifteen prespecified campaign scenarios varied channel allocation across television (TV), digital, and print; total exposure budgets; two equity-focused personalization strategies; and graded loss framing. Primary outcomes were final adoption and time to adoption. Equity outcomes included the minimum class-level adoption and the 90–10 gap across latent classes. Each scenario was simulated over 12 monthly steps with 100 Monte Carlo replications. We also compared scenario portfolios using logistic and clipped-linear link functions and varied the balance of media versus social reinforcement weights, the social reinforcement threshold, and network realizations in sensitivity analyses. Results: TV-only and high-budget strategies produced some of the highest mean adoption rates for both vaccination and screening but often failed to meet equity guardrails for minimum class coverage and between-class gaps. In contrast, personalization strategies that modestly reweighted exposure toward the lowest-uptake class or assigned class-tailored channel portfolios maintained or improved mean adoption. These strategies also substantially raised minimum class-level coverage and narrowed disparities. When efficiency and distributional equity were considered jointly, these personalized portfolios emerged as the most attractive options under fixed budget constraints. Loss framing acted as a secondary tuning lever: within the tested range, stronger loss framing yielded small, monotonic gains in adoption and shorter time to adoption without worsening equity metrics. Scenario rankings were stable across sensitivity analyses, suggesting that the main patterns reflected underlying diffusion dynamics rather than any single modeling choice. Conclusions: This agent-based simulation shows how ex ante planning for preventive campaigns can move beyond intuition by comparing channel allocation and personalization options under explicit equity and budget criteria. For campaigns targeting older adults, modest equity-oriented personalization of TV and digital exposure improved or preserved mean uptake. It also consistently improved distributional equity, whereas diversified channel mixes without personalization were less efficient and less equitable. These findings support integrating equity guardrails and channel-allocation guardrails into early-stage campaign design and prioritizing targeted personalization over simple channel diversification. Future work should validate these patterns in other populations and health systems and link simulated diffusion trajectories with observed exposure and engagement in real-world digital and traditional-media campaigns.

  • “PrEP Saves Lives!”: A Content Analysis of PrEP-Related Messages Across Facebook, Instagram and Twitter

    Date Submitted: Nov 11, 2025
    Open Peer Review Period: Nov 25, 2025 - Jan 20, 2026

    Interventions are sorely needed to address the lack of PrEP awareness and mitigate barriers related to PrEP use. One such intervention modality is social media, as PrEP awareness and communicating issues, such as access and cost, are easily addressable via clear social media messages on platforms PrEP-eligible people, and especially young people, use frequently. This study seeks to extend understanding of PrEP awareness and usage by examining PrEP-related communication across 3 popular social media platforms (Facebook, Instagram, and Twitter), and identifying message and source characteristics. In February 2023, we used CrowdTangle (a public-insights tool owned by Facebook, now known as Meta) to gather a total of 39,790 Facebook posts and 5,628 Instagram posts. We also used Twitter’s public API to collect 14,061 Twitter posts during the same time frame. Of these, we drew a random sample of social media posts from each platform [Facebook (N = 1,000), Instagram (N = 1,000), and Twitter (N = 811)] in February 2023 and analyzed them using a quantitative content analysis. Our findings showed some differences in the type of text-based content most likely to appear on each platform. We also uncovered similar patterns across all 3 platforms. Across all platforms, we observed that definitions of and indications for PrEP were the most common type of text-based content in posts likely to be shared, information about PrEP appearing in social media posts did not seem to draw from traditional sources, and men who have sex with men (MSM) represented the most frequently mentioned target population. Although our study did not detect a large presence of theory-based concepts from behavior change theory such as the reasoned action approach (RAA), across all platforms, attitude emerged most frequently, followed by self-efficacy. These findings shed light on the PrEP-related beliefs shaping young people’s perceptions and engagement. Such insights can guide the design of future social media–based messages, targeting the most influential beliefs to strengthen HIV prevention efforts. They also provide a foundation for advanced machine learning models capable of predicting and explaining the diffusion potential of PrEP-related content.

  • Descriptive Validation Study of NLP Methods for Automating Clinical Communication Analysis in Cancer Care

    Date Submitted: Nov 24, 2025
    Open Peer Review Period: Nov 25, 2025 - Jan 20, 2026

    Background: Qualitative research methods offer vital insights into how patients make treatment decisions, but these approaches are labor-intensive, limited by small samples, and difficult to scale. Natural Language Processing (NLP) provides a promising solution by automating the analysis of large volumes of unstructured clinical text, improving efficiency and enabling deeper understanding of complex interactions in cancer care. Objective: The objective of this study was to develop, test, and validate a Natural Language Processing (NLP) application capable of transforming large-scale qualitative clinical communication data into structured formats, thereby reducing the need for manual coding. Methods: Using 434 transcripts of physician–patient encounters collected from a prior study, we evaluated the feasibility of advanced NLP methods to analyze cancer care communication. Results: Transformer-based models demonstrated strong performance in extracting clinically relevant information, with RoBERTa achieving the highest F1 score (76%), outperforming both BERT (71%) and the rule-based SpaCy baseline (36%). Conclusions: These findings underscore the advantages of context-aware transformer architectures, which are better suited to capturing the complexity of medical dialogues than traditional rule-based approaches. Notably, while transformers provided the greatest accuracy, results also suggest the value of hybrid systems that integrate rule-based precision with the contextual depth of transformer models. Such approaches may be especially useful for capturing longer conversational sequences, such as emotional expressions, question–answer exchanges, and multi-topic utterances. Overall, this study demonstrates the potential of NLP to improve the efficiency and scalability of clinical communication analysis, expand institutional capacity to deliver standardized feedback, and enable large-scale, multi-site research on communication processes in cancer care. Clinical Trial: N/A

  • Background: Cognitive frailty—typically described as the co-occurrence of physical frailty and mild cognitive impairment in the absence of dementia—has increasingly been viewed as a potentially reversible geriatric condition. It is closely tied to functional decline, disability, and later development of dementia. Although conventional motor–cognitive or physical training programs can offer benefits, they often struggle with poor engagement and limited relevance to daily life. In recent years, immersive virtual reality (VR) systems have emerged as a promising approach because they provide interactive environments that may stimulate both cognitive and motor processes in ways traditional programs cannot. Several trials have begun to test VR in older adults with cognitive frailty, but the overall effect remains uncertain, and existing reviews have generally been broad, including mixed populations or non-immersive VR. A focused evaluation of immersive motor–cognitive VR specifically in individuals diagnosed with cognitive frailty is still lacking. Objective: To determine whether immersive motor–cognitive VR training improves cognitive performance and physical frailty among adults with cognitive frailty Methods: We searched five major databases through November 14, 2025, for randomized controlled trials involving immersive or semi-immersive VR motor–cognitive interventions in adults diagnosed with cognitive frailty. The primary outcome was global cognitive function; physical frailty was examined as a secondary outcome. Effect sizes were synthesized using standardized mean differences (SMDs) or mean differences (MDs) with 95% confidence intervals. Risk of bias was evaluated using Cochrane criteria, and certainty of evidence was graded using GRADE. Results: Three studies involving 344 participants were included in this meta-analysis comparing VR-based intervention versus non-VR (standard care) in older people. VR intervention was associated with a significant improvement in the global cognitive function compared with non-VR (SMD = 0.42; 95% CI 0.21 to 0.64; p = 0.0001; I² = 39%; moderate heterogeneity). Only two trials reported physical frailty outcomes and involved whole-body motor–cognitive VR; therefore, the third trial was excluded from this analysis. Two studies involving 278 participants were included in comparing VRMCT (Virtual Reality Motor-Cognitive Training) versus MCT (Motor-Cognitive Training) in older people. The VRMCT showed a significant improvement in the physical frailty (Mean Difference = –0.26; 95% CI –0.47 to –0.04; p = 0.02; I² = 0%; very low heterogeneity). Conclusions: In adults with cognitive frailty, immersive VR-based motor–cognitive rehabilitation appears to provide benefits for both cognitive performance and frailty severity compared with conventional training approaches. Further high-quality trials are still needed, but current evidence supports the growing role of immersive VR in geriatric rehabilitation. Clinical Trial: CRD420251234169

  • Background: Sleep disturbance is a common symptom of and potential risk factor for neurodegeneration. Remote sleep and cognitive assessments offer promise for monitoring symptoms and treatment response from patients’ homes, but the acceptability of remote sleep and circadian technology in older adults with and without cognitive impairment is not known. Objective: This qualitative study was designed to explore and describe the barriers, facilitators, and user experience of older adults with mild cognitive impairment and dementia and cognitively unimpaired older adults who participated in a longitudinal sleep and memory study designed around remote monitoring technologies. Methods: Patients with mild cognitive impairment or dementia due to probable Alzheimer’s disease or Lewy body disease and age-matched controls participated in a longitudinal remote study involving multimodal assessments of sleep and cognition including actigraphy, wireless electroencephalography, a smartphone app, web-based cognitive tasks, and serial saliva samples. Participants were asked for feedback via questionnaires during the study and invited to complete end-of-study interviews about their experiences. Questions were informed and thematic analysis was guided by the Capability, Opportunity, Motivation – Behaviour model of behaviour change and the extended Unified Theory of Acceptance and Use of Technology and focused on perceived barriers and facilitators. Results: The study identified six key themes. The first theme, ‘motivations to participate’, highlighted how participants felt the research could be helpful to themselves and others. The second theme, ‘navigating the user experience of devices’, identified comfort, security, privacy, ease of use, and reliability as fundamental in determining acceptability. ‘Adjusting over time to study participation’, the third theme, covered changing perceptions with increased exposure and familiarity, and the importance of convenience, flexibility, and developing a routine. The fourth theme explored ‘social support as a facilitator and barrier to research participation’, looking at the influence of both the research team and relatives supporting at home. A fifth theme of ‘adherence, accuracy, and getting it right’ was also identified, as participants were motivated to provide good quality data for the study. Finally, we identified a sixth theme surrounding participants’ ‘reflections, realities, and uncertainties around sleep’, which focused on sleep hygiene and common sleeping problems in older adults, such as snoring and nocturnal awakenings. Conclusions: Older adults with and without cognitive impairment were motivated to engage in longitudinal remote sleep research, follow remote research protocols, and produce good quality data. Acceptability was related to burden and convenience, usability, and emotional responses to study tasks. When study tasks are repeated over time, care should be taken to introduce variety where possible to avoid fatigue and frustration. Study partners offer essential support for some participants, but requiring a study partner may also be an unnecessary barrier to research participation for others. Future studies should aim to identify effective strategies for recruiting diverse populations, particularly those with limited technology experience or from underserved communities, to ensure equitable participation and representation in research. Providing education on the importance of sleep for brain health and technology use may be beneficial.

  • Background: Web-based advertisements, specifically social media advertisements, are a popular recruitment avenue among research projects involving human participants. Social media recruitment has advantages over other methods (e.g., in-person recruitment), such as aiding teams in reaching the population of interest and increasing enrollment pace at a relatively low cost. Nonetheless, social media recruitment comes with the challenge of fraudulent responses, and therefore effective identity verification procedures must be put in place in order to maintain the integrity of the final sample and data. Objective: In this paper, we outline the identity verification methods (herein referred to as “checks”) used in the recruitment process for a pilot study featuring a mobile health (mHealth) intervention app for emerging adults (EAs; aged 18-25) who regularly use cannabis. Each identity verification check is examined for its rate of passing. Methods: Participants were recruited via social media advertisements that linked directly to a study eligibility screening survey. Advertisements were posted on Meta (Facebook and Instagram), Snapchat, and TikTok. Participants were enrolled if they met study inclusion criteria (e.g., aged 18-25, reported regular cannabis use), completed the baseline consent and survey, downloaded the app, and passed all identity verification checks. Identity verification checks happened at two checkpoints: directly following screening survey completion (e.g., geolocation check, duplicative IP address check, social media check) and directly following app download and login (duplicative device ID and/or push token check). Failing an identity verification check resulted in exclusion from the study. Results: Identity checks were non-exclusive such that a single eligible screening response could undergo multiple checks. Of the 573 eligible screening responses that went through the identity verification process, a total of 3,031 identity verification checks were completed. Of these 3,031 aggregate checks, 396 failed the verification criteria (13.1%), and therefore 396 of the 573 eligible respondents were excluded from continuation in the enrollment process (69.1%). Social media checks, wherein study staff ensured the individual’s public-facing account had personally relevant information, had the highest failure rate (61.5%). The second most common failed check was due to a duplicate device ID upon logging into the app (10.0%), followed by the geolocation check (4.9%), the duplicate IP address check (4.2%), the combination check (time zone; 4.1%), and duplicate push token check (3.2%). Conclusions: This paper describes a participant identity verification process for app-based mHealth studies using social media as a recruitment source. A combination of identify verification safeguards is suggested to maintain integrity of the study sample and data. Clinical Trial: ClinicalTrials.gov NCT05824754; University of Michigan IRB: HUM00222194

  • Feasibility and Usability of a Digital Perinatal Navigator for High-Risk Pregnancies: A Mixed-Methods Study

    Date Submitted: Dec 7, 2025
    Open Peer Review Period: Nov 18, 2025 - Jan 13, 2026

    Background: The journey to parenthood involves significant physical, emotional, and psychosocial changes. Mental health challenges impact both maternal and fetal health, potentially leading to obstetric complications and developmental risks for children. Access to needed perinatal support is often limited due to individual and structural barriers. Digital health solutions can offer opportunities to provide low-threshold, personalized, and scalable support. We developed a digital navigator offering personalized guidance and connecting users to relevant support services with interactive follow-ups to self-assess their well-being. However, evidence regarding feasibility of digital solutions in high-risk patients is limited. Objective: Aim of the was assess the feasibility, usability, and preliminary effectiveness of a digital perinatal navigator app designed to provide personalized support and connect pregnant individuals to relevant health and social services. Methods: The study was conducted at the University Women’s Hospital Heidelberg to assess an app-based health service program. Using convenience sampling, eligible participants tested the perinatal guide for two weeks. A convergent mixed-methods design combined qualitative interviews (n=30) and psychometric questionnaires (n=35) to evaluate feasibility, usability and preliminary effectiveness. Statistical analysis included descriptive evaluations, paired t-tests, and Pearson correlations. Results: Participants (median age 33, median gestational age of 30 weeks) reported moderate high rates of stress, anxiety and depressive symptoms. Usability ratings were excellent (median SUS 80; MAUQ 105). Knowledge of HSPs increased significantly (mean +1.2 points, p<.01), with modest improvements in utilization. Qualitative analysis revealed key success factors such as intuitive structure, trustworthy medical content, and personalized information. Technical disruptions, navigation challenges, limited personalization, and incomplete regional integration of healthcare services were reported as barriers. Conclusions: The results indicate high feasibility and acceptance for our digital navigator in this high-risk population. The identified barriers are to be considered in the further development of the app and other perinatal digital care programs.

  • Background: The integration of Virtual Reality (VR) tools in mental healthcare, such as VR relaxation, shows promise for supporting stress reduction and mental well-being. However, implementation across healthcare settings remains complex and context-dependent, influenced by organizational capacity, stakeholder readiness, and external factors such as policy and funding. This study explores how barriers, facilitators, and evolving implementation strategies shape the use of VRelax, a VR relaxation tool, in primary, secondary, and tertiary mental healthcare settings in the Netherlands. Objective: To identify shared and context-specific barriers and facilitators, and to develop and refine tailored implementation strategies for the integration of VR relaxation in primary, secondary, and tertiary mental healthcare, with a focus on learning from the implementation approach used within the research process itself. Methods: A qualitative, comparative study was conducted using a participatory approach with 33 healthcare professionals and eight patients across primary, secondary, and tertiary mental healthcare settings in the Netherlands, involving 18 interviews and eight focus groups. Thematic analysis, guided by the Consolidated Framework for Implementation Research, was used to assess implementation barriers and facilitators. The Expert Recommendations for Implementing Change tool was applied to match found barriers with evidence-based implementation strategies. Results: Across all settings, key lessons emerged about what supports and hinders the implementation of VR relaxation in mental healthcare. While challenges such as equipment costs, limited staff capacity, technical issues, and lack of structural funding persisted, they also revealed opportunities for improvement. In primary care, collaboration with community organizations enabled low-threshold, accessible use. In secondary care, staff feedback refined strategies and strengthened team learning. In tertiary care, co-development with professionals and patients advanced person-centered care, though time constraints and fragmented organizational structures limited full adoption. Across settings, the gap between professional assumptions about patient suitability and patients’ actual enthusiasm underscores the need for shared decision-making, patient involvement, and flexible, hybrid approaches to care. Conclusions: Successful integration of VR relaxation in mental healthcare requires balancing flexibility with structured, setting-specific strategies while addressing system-wide barriers. Collaboration with community facilities, iterative refinement through staff feedback, and co-development with patients show how VR can strengthen person-centered, hybrid, and sustainable mental healthcare. These findings align with efforts to ensure accessible, appropriate, and future-ready care across all mental healthcare settings. They also underscore that effective implementation requires both localized adaptation and system-level solutions, including shared infrastructure, post-discharge continuity, and long-term funding models.

  • Background: eHealth interventions have demonstrated potential to address challenges related to health and the health care system in low- and middle-income countries. To effectively leverage eHealth in supporting health care in Ethiopia, the assessment and development of eHealth literacy of patients is essential. Objective: This study aimed to translate and culturally adapt the eHealth Literacy Questionnaire (eHLQ) to Amharic and assess its psychometric properties. Methods: We applied a systematic process of translation and cultural adaptation, including forward and backward translation, expert review, and cognitive interviews. Then we conducted a cross-sectional questionnaire-based study using a convenience sample (N=300) of patients with internet access in the primary health-care level between January and March 2025 in the capital and a larger city of Ethiopia. Internal consistency was assessed using Cronbach α and McDonald ω. Factor structure was assessed using Confirmatory Factor Analysis. Convergent and discriminant validity were examined by calculating Spearman correlations between each eHLQ scale and the total score of the eHealth Literacy Scale (eHEALS). Results: A total of 300 participants were included in the analysis. The mean age was 30.4 years (SD 6.8; range 18–55), and 69.7% (209/300) were women. Internal consistency was acceptable for all scales (Cronbach α=0.72–0.91; McDonald ω=0.79–0.96), except for Scale 4 (α=0.62; ω=0.70). The 7-factor model showed satisfactory fit, with a Comparative Fit Index of 0.97, Tucker-Lewis Index of 0.97, and Standardized Root Mean Square Residual of 0.07. Factor loadings exceeded 0.40 for all items except one. Strong correlations between Scales 1–3 and eHEALS (range r=0.69–0.74) supported convergent validity, while moderate correlations between Scales 5–7 and eHEALS (range r=0.66–0.67) indicated limited discriminant validity. Conclusions: The Amharic eHLQ demonstrated generally satisfying psychometric properties and can be considered as a valid tool for assessing eHealth literacy among patients with internet access in Ethiopia, marking the first validation of the eHLQ in Sub-Saharan Africa. Future studies could provide additional evidence to substantiate the psychometric robustness of Scale 4 (“Feeling Safe and in Control”). Overall, the Amharic eHLQ can support the development of tailored eHealth interventions in Ethiopia.

  • Social Contagion in COVID-19 Discussions within the Belgian Reddit Community: A Statistical and Modeling Study

    Date Submitted: Nov 14, 2025
    Open Peer Review Period: Nov 17, 2025 - Jan 12, 2026

    Background: Understanding how attitudes toward COVID-19 mitigation measures spread on social networks is crucial to inform infectious disease modelers and policymakers. Even though previous studies have described social media interactions during the pandemic, there remains potential to model the underlying dynamics of sentiment contagion and polarization. Objective: This study investigated the emergence and evolution of discussions on COVID-19 mitigation measures within the Belgian Reddit community (r/Belgium), focusing on how sentiments diffused among users over time. Concretely, it examined whether topic discussions exhibited patterns of social contagion and how expressed sentiments were shaped by prior interactions, contributing to homophily and polarization. Methods: We analyzed posts created on r/Belgium between 1 January 2020 and 30 June 2022. Posts were classified into three mitigation topics, lockdowns, masks, and vaccination, using a BERT-based topic model. Sentiment was assigned to English posts using a RoBERTa-based sentiment classifier. We examined temporal patterns of post volume and tested for social contagion in topic initiation using null models. Sentiment homophily was quantified by comparing observed comment-parent sentiment pairs to null distributions. We developed the Smooth Internal Expressed Bounded Confidence (SIEBC) model and tested it against two alternatives, to add mechanistic intuition to the observed homophily. Results: The analysis of 655,642 posts made by 28559 users revealed that post volume was strongly associated with external events such as policy announcements and media reports, but not with within-Reddit interactions. There was no evidence of social contagion in topic initiation. However, sentiment exhibited significant homophily, with comment sentiment correlating with parent comment sentiment. The SIEBC model reproduced observed sentiment patterns, with Kolmogorov Smirnov statistic between predicted and observed sentiment distributions ranging from 0.043 to 0.067. It slightly underestimated homophily, but still outperformed alternative models. The model revealed that expressed sentiment adapts more strongly to parent comments than internal sentiment adapts to other interactions (proportion of users showing this pattern: 0.75, 0.70, and 0.53 for lockdowns, masks, and vaccination). Conclusions: Topic discussions on r/Belgium are driven primarily by external events rather than social contagion within the platform. In contrast, for sentiment there is observed homophily. This can be explained by users adapting their expressed sentiment to match the conversational context of threads. The SIEBC model demonstrates that expressed sentiment may not reflect users’ internal attitudes, highlighting the importance of handling the former with care. These findings suggest that epidemic-social models would benefit from incorporating external information sources for topic dynamics and using complex mechanisms, such as a bounded confidence kernel, for sentiment spread.

  • Background: Digital health technologies (DHTs) are increasingly integrated into clinical practice, yet economic evaluations remain scarce, particularly in early development stages. Within the NICE Evidence Standards Framework, Tier C DHTs comprise technologies with direct clinical implications and measurable health outcomes, for which robust economic evidence is essential. Early-stage assessments are particularly important to inform subsequent development, refinement, and adoption decisions across the digital health lifecycle. Objective: This study aims to explore the feasibility of integrating a full trial-based economic evaluation within an early-stage pilot comparing a chatbot-supported remote patient monitoring (RPM) solution for anticoagulation management with standard of care (SOC). Methods: A cost-effectiveness analysis was performed alongside a pilot crossover trial among adult cardiac surgery patients receiving vitamin K antagonists. Participants were allocated to two 6-month sequences (SOC→RPM or RPM→SOC). The intervention consisted of a rule-based chatbot integrated with home-based international normalized ratio self-testing using portable coagulometers to support communication and therapy management. Effectiveness was measured as time in therapeutic range (TTR), and costs were estimated from the Portuguese National Health Service and a limited societal perspective over a 1-year horizon. The analysis (i) applied a within-patient cost-effectiveness approach to estimate incremental costs, incremental TTR, and incremental cost-effectiveness ratios (ICERs). Uncertainty was explored through non-parametric bootstrapping (5,000 replications) and deterministic sensitivity analyses. Complementary comparisons examined differences between sequences (analysis ii), between periods (analysis iii), and within each sequence (analysis iv). Results: A total of 19 patients were included in the analyses. In the analysis (i), RPM improved anticoagulation control, with a mean within-patient increase of 10.43 percentage points in time in TTR. The mean incremental costs were €198.61 from the SNS perspective and €270.05 from the limited societal perspective. The corresponding ICERs were €19.03 and €25.88 per additional percentage point of TTR gained. Sensitivity analyses produced consistent estimates across parameter variations. Complementary analyses (ii–iv) suggested that RPM tended to be more cost-effective when implemented after the initial 6-month postoperative period. Conclusions: This proof-of-concept study demonstrates that full trial-based economic evaluation can be feasibly embedded within an early-stage Tier C DHT. The intervention showed improved anticoagulation control alongside higher costs, providing initial insights on its cost-effectiveness profile. Positioned within the digital health evidence continuum, such assessments can function as a learning stage within the lifecycle. To address the persistent adoption–evidence gap, tier- and stage-aligned frameworks are needed to guide the economic evaluation of DHTs. This study contributes to that goal by providing a set of recommendations specifically for Tier C DHTs. Clinical Trial: ClinicalTrials.gov NCT06423521

  • Towards a common data model to support the FAIRification of colorectal cancer screening data.

    Date Submitted: Nov 17, 2025
    Open Peer Review Period: Nov 14, 2025 - Jan 9, 2026

    The potential to combine and analyze massive data from different colorectal cancer (CRC) screening programs across Europe is a powerful tool for improving early cancer detection. However, the current landscape of CRC screening programs is characterized by significant data heterogeneity, which makes data integration challenging. Achieving such data interoperability among different CRC screening programs is crucial to leverage the maximum benefit of the existing and future data. At EOSC4Cancer, we have worked towards optimizing the secondary use of these cancer data and contributing to its FAIRification. Starting from the harmonization of the real-world in-house data models from four different European CRC screening programs, we have created a common data model that provides an initial foundational baseline to build a new interoperable data scenario.

  • Background: Inflammatory bowel disease (IBD) requires continuous self-management, yet long-term engagement remains challenging. Digital health applications can support self-monitoring and treatment adherence, but their effectiveness often declines over time. Nurse-led interventions may complement such tools by providing emotional support and personalized feedback that sustain engagement. Objective: This randomized controlled trial (RCT) evaluated the effectiveness of WITH-Jang, a mobile self-management app, integrated with WITH-Care, a nurse-led tailored intervention, in improving digital health readiness, self-management capacity, and clinical outcomes among patients with IBD. Methods: A total of 100 adults with ulcerative colitis or Crohn’s disease were randomly assigned (1:1) to either the experimental group (app + nurse-led intervention) or the control group (app only). The 12-week intervention included motivational messages, educational content, scheduled nurse consultations, and personalized health reports, followed by a 12-week app-only follow-up. Outcomes were assessed at baseline, week 4, week 12, and week 24. Measures included digital health readiness (mDiHERS), quality of life (SIBDQ), clinical indices (Mayo Score, CDAI), and app usage logs. Focus group interviews (FGIs) were conducted with participants in the experimental group at weeks 12 and 24 to explore user experiences qualitatively. Results: No statistically significant difference was found in SIBDQ scores between groups, although the intervention group showed an overall trend toward improved quality of life. mDiHERS scores correlated positively with app usage frequency (symptom, diet, and medication logging), indicating that higher digital readiness was associated with greater engagement. Digital health equity declined in the control group but remained stable in the intervention group (p = .055), suggesting a potential protective effect of nurse involvement. App usage was strongly associated with disease activity: participants with higher CDAI scores logged symptoms more frequently (r = 0.392, p = .0137). Despite structured support, app engagement declined after week 12, reflecting the “Law of Attrition.” FGIs revealed that nurse consultations and personalized reports were key motivators that provided reassurance, contextual feedback, and emotional support beyond the app’s technical features. Conclusions: Integrating nurse-led support into digital self-management interventions may enhance long-term engagement and mitigate attrition among patients with IBD. While digital tools can improve self-management, sustained effectiveness requires ongoing human support, emotional reinforcement, and adaptive engagement strategies. Future digital health programs should incorporate booster sessions, clinician involvement, and peer support networks to ensure lasting and equitable outcomes. Clinical Trial: KCT0010068

  • Background: Ecological momentary assessment (EMA) is a tool that captures emotional states, experiences, and behaviors in real or near-real time. Using continuous glucose monitoring (CGM) data and EMA in unison may be beneficial to understand associations between psychosocial factors and momentary glucose levels. An in-depth understanding of these relationships is crucial for future interventions targeting psychosocial factors in chronic diseases such as diabetes mellitus. Objective: The goal of this scoping review was to summarize the objectives, methodologies, and outcomes of studies analyzing concurrent psychosocial EMA and CGM data. Methods: This study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. One-hundred and six studies were identified from PubMed, Embase, and EBSCOhost from May 2009-Jan 2025. Thirteen original research articles that collected and analyzed simultaneous EMA and CGM data were included. Methodological data abstracted included study characteristics, EMA protocols and outcomes, CGM outcomes, and integrated EMA and CGM study objectives. Results: Studies primarily recruited adult (92%) populations with type 1 diabetes (T1DM) (69%). EMA delivery protocols and outcomes varied significantly and included emotion, self-care behaviors, disordered eating behaviors, interpersonal interactions, cognition, sleep, workload, and impacts of hypoglycemia. Most (69%) studies analyzed blinded CGM data and CGM outcomes included both standardized and non-standardized glucose outcomes. Integrated EMA and CGM data answered study objectives including evaluating impacts of psychosocial and lifestyle factors on momentary glucose metrics; the influence of momentary glucose on emotional states, mood, personal behaviors, sleep, and cognition; and study protocol or mobile application optimization among others. Conclusions: The combination of EMA and CGM data provides an opportunity to elucidate the relationship between psychological and behavioral factors with momentary glucose. In this review, we describe a broad range of study characteristics, protocols, outcome measures, and objectives using these novel combined methodologies. Clinical Trial: Not applicable

  • Background: Parental burnout is an under-recognised syndrome characterised by emotional exhaustion, detachment from children, and reduced parental efficacy. It is associated with sleep disturbance, addictive behaviours, suicidal ideation, and increased risk of child neglect and family conflict. Despite its public-health relevance, evidence-based interventions remain limited, particularly in low- and middle-income contexts. Objective: To evaluate the efficacy and safety of a mindfulness- and compassion-based group program—Inter-Care for Parental Burnout (IBAP-PB) —designed to reduce burnout symptoms in teleworking mothers. Methods: A three-arm randomised controlled trial (IBAP-BP, active control, waitlist) was conducted across Chile (December 2022–March 2023) with nine-month follow-up. Participants (n = 593) were women ≥ 18 years teleworking ≥ 1 day/week and living with ≥ 1 child. Exclusion criteria were self-reported severe psychiatric disorders. Randomisation was computer-generated and centrally concealed; data analysts were blinded. The IBAP-BP group attended eight weekly two-hour online sessions plus daily home practice integrating mindfulness and compassion. The active control performed relaxation and reflective journaling matched for duration and structure. The primary outcome was parental burnout (Parental Burnout Assessment, PBA) at nine months; secondary outcomes were mindfulness, balance of risks/resources, and adverse effects. Modified intention-to-treat analyses and multilevel structural models assessed effects over time. Results: Of 665 enrolled participants, 343 completed follow-up. At nine months, IBAP-BP produced greater reductions in parental burnout than the waitlist (mean difference = −0.81, p < 0.05; d ≈ 0.6). No significant difference was found between IBAP-BP and the active control, which showed transient improvements up to three months. Effects remained robust in sensitivity analyses. Adverse events were rare and mild across all groups. Mediation analyses showed inconsistent associations between mindfulness facets and outcomes. Conclusions: The culturally adapted, online IBAP-BP programme is a feasible, safe, and effective approach for reducing parental burnout in working mothers, with effects sustained over nine months. Clinical Trial: ClinicalTrials.gov Identifier: NCT05833269

  • Background: Telecommunications fraud has become a salient digital-health threat, producing shame, anxiety, and other mental-health harms. However, the psychological processes through which scams progress—from initial contact to loss of behavioral control—remain underexplored. Objective: To develop and validate a time-sensitive psychological model of telecommunications fraud that traces the progression from cognition to emotion to behavior, and to identify modifiable levers for prevention. Methods: We used a mixed-methods design. In Study 1, we conducted semi-structured interviews with telecom-fraud victims and analyzed transcripts with grounded theory. In Study 2, we fielded a survey to test the qualitative model’s pathways. We measured Truth-Seeking and Cognitive Maturity, Gullibility, Difficulties in Emotion Regulation, and Brief self-control. A serial mediation was estimated using Hayes’s PROCESS Model 6 with 5,000 bootstrap resamples and 95% CIs. Results: The qualitative analysis yielded a three-stage model—Credulity Priming → Affective Manipulation → Behavioral Dyscontrol—showing how scammers (1) build trust via impersonation and scripted scenarios, (2) heighten arousal to blunt analytic judgment and push heuristic processing, and (3) trigger irrational choices through emotional reversals. We further identified two emotion mechanisms—the “desire loop” (appetitive arousal) and “fear loop” (threat arousal)—and highlighted over-expectation events as catalysts of reversal and cognitive depletion. In the survey, Truth-Seeking and Cognitive Maturity were inversely associated with Gullibility, Difficulties in Emotion Regulation, and Brief self-control. Gullibility and Difficulties in Emotion Regulation formed a serial mediation pathway from Truth-Seeking and Cognitive Maturity to Brief self-control, consistent with the three-stage model. Conclusions: Telecommunications fraud follows a dynamic progression from trust manipulation to affective manipulation and, ultimately, behavioral dyscontrol. Critical-thinking capacities protect against victimization by lowering gullibility and improving emotion regulation. The model maps stage-specific intervention points for digital-health practice and policy: (a) strengthen verification habits and critical-thinking skills to curb credulity; (b) teach emotion-regulation strategies to resist affective manipulation; and (c) deploy just-in-time frictions (eg, secondary verification/transfer holds) to interrupt dyscontrol at transaction points. This framework integrates qualitative mechanism discovery with quantitative validation and offers actionable guidance for anti-fraud campaigns, platform design, and clinical counseling.

  • Background: Sepsis causes 48.9 million cases and 11 million deaths annually, with mortality exceeding 30% in elderly and immunocompromised patients. Each hour of delayed antimicrobial therapy increases mortality by nearly 8%; however, diagnosis relies on blood cultures requiring days and often remaining negative despite ongoing sepsis. Artificial intelligence clinical decision support system (AI-CDSS) using complete blood count with differential (CBC+DIFF) offer rapid early recognition; however, their clinical impact in real-world practice remains unclear. Objective: To evaluate the clinical impact of a workflow-integrated AI-CDSS using CBC+DIFF data for early sepsis recognition, focusing on clinician confidence, decision-making, and 28-day mortality. Methods: We conducted a single-center randomized controlled trial (ISRCTN83789437) at Tri-Service General Hospital (August–December, 2024). Adults (≥ 18 years) with suspected sepsis were randomized 1:1 to the AI-CDSS (n = 670) or standard care (n = 670) groups using concealed computer-generated sequences stratified by age and sex. The AI-CDSS generated real-time sepsis risk probabilities from the CBC+DIFF displayed within the clinical workflow. The primary outcome was clinician confidence in sepsis diagnosis. The secondary outcomes included decision-making efficiency, diagnostic satisfaction, and perceived management effectiveness (days 1, 2, 4, and 7). Mortality was assessed after 28 days. Results: Among 1340 enrolled patients (mean age 71.7 ± 16.4 years, 56.4% male), baseline characteristics were balanced (all p > 0.05). Survey completion rates were 100% on day 1 and 85.1% on day 7. AI-CDSS use significantly increased diagnostic confidence (day 1: 3.75 ± 0.78 vs. 2.93 ± 0.74; day 7: 4.17 ± 0.76 vs. 3.97 ± 0.99; both p < 0.001). Decision-making efficiency, satisfaction, and perceived effectiveness consistently favored the AI-CDSS (all p < 0.001). At 28 days, AI-CDSS group mortality was lower than that in the standard care group (21.8% vs. 27.5%; absolute difference -5.7%; 95% CI -10.3 to -1.1; p = 0.016). No AI-CDSS-related adverse events were observed. Conclusions: The AI-CDSS improved the , and satisfaction of clinicians, while reducing the 28-day mortality in patients with suspected sepsis. By providing actionable support even in culture-negative cases, the system addressed a critical gap in early sepsis care, representing a scalable strategy for improving outcomes in critical care settings. Clinical Trial: Registered as ISRCTN83789437 on 12th July 2024. https://www.isrctn.com/ISRCTN83789437

  • Quantifying the Digital Ecosystem: A Market Scan of Technologies for Decentralized Clinical Trial Operations

    Date Submitted: Nov 3, 2025
    Open Peer Review Period: Nov 4, 2025 - Dec 30, 2025

    Background: Decentralized clinical trials (DCTs) are transforming traditional research by allowing participants to remotely take part through digital technologies such as telemedicine, mobile applications, and digital platforms, overall enhancing participation, safety, accessibility, and overall trial outcomes. The COVID-19 pandemic further accelerated adoption, as there was a pressing need to minimize infection risks, delays, and disruptions. Despite growing innovation and initiatives like Trials@Home, there is limited understanding of how commercial technologies align with and support trial operations in the DCT lifecycle. Objective: This study aimed to map the landscape of commercial technologies used in DCTs and assess their availability, suitability, and alignment with clinical trial operations across the DCT lifecycle. Methods: A market scan of commercial technologies was conducted in 2024-2025 to update and add on previous work in 2020 using a structured approach. Seven peer groups were formed, each assigned to one of seven Basic Building Blocks (BBBs) representing key phases of the clinical trial lifecycle. These groups independently reviewed and categorized relevant solutions. The process included reassessment of previously identified solutions and identification of new technologies through keyword-based web searches, and categorization by the number of BBBs covered. Solutions also were categorized as Single- or Multiple-BBB based on their coverage, and a co-occurrence analysis identified strong and underrepresented pairings in BBB coverage. Results: The scan identified 312 technological solutions supporting DCTs. A similar distribution of solutions across BBBs was observed, with Set-up and Design and Patient Engagement being the most represented, while Operation and Coordination was the least covered. Most tools were specialized, with 226 single-BBB solutions, 48 covering two BBBs, and fewer than 10 addressing five or more. Only 2 solutions covered all seven BBBs. Co-occurrence analysis revealed strong overlaps between Patient Engagement, Intervention and Follow-up, and Operation and Coordination, while Set-up and Design showed minimal overlap with other BBBs. Conclusions: The rapid evolution of DCT technologies highlights the importance of structured assessments to guide technology selection. The balanced distribution of solutions per BBB suggests a broad coverage of trial operations. The stronger coverage in Set-up and Design and Patient Engagement compared with Operation and Coordination indicates areas for further development. The majority of tools were highly specialized with only a few covering multiple trial operations in an integrated manner. This reflects the maturity of specialized solutions and the potential for comprehensive systems spanning the full trial lifecycle. The strong pairs between Patient Engagement with Operation and Coordination and Intervention and Follow-up, together with the minimal overlap of Set-up and Design reveal a gap between planning and execution phases. This study provides a comprehensive catalogue of technologies, and offers practical insights to inform strategic technology selection for more efficient, inclusive, and connected clinical trial operations.

  • Background: General Practitioners (GPs) play a pivotal role in a patient’s health care journey. However, demands on general practice, including an aging population and complex patient management, workforce shortages and health system fragmentation, have been shown to adversely impact delivery of high-quality care and health outcomes. Integrated care models, particularly those that offer virtual care options, are one way to support improved access to quality care and efficiency of health care delivery across metropolitan and rural areas. The SUSTAIN model of care was created to provide an accessible option for integrated care. It consists of centralised paediatricians supporting general practitioners in their practice through virtual co-consultations, virtual case discussions and phone/email support. There is limited evaluation literature on integrated models of care being implemented in a primary care setting where the GP and family are face-to-face and the non-GP specialist is virtual. To address this gap, a comprehensive implementation evaluation of the SUSTAIN model of care was conducted. Objective: To examine what, why and how different factors impact the uptake of the SUSTAIN model of care from the perspectives of the SUSTAIN paediatricians and metropolitan and rural GPs in New South Wales (NSW), Australia. Methods: Qualitative study as part of the mixed-methods implementation evaluation of the SUSTAIN model of care. Data were collected via recorded online focus groups and interviews with general practitioners, general practice managers and paediatricians at 6- and 12-months post implementation of SUSTAIN. Data were analysed thematically using iterative thematic analysis informed by the Consolidated Framework of Implementation Research. Results: Eighteen focus groups and 13 interviews were conducted. GPs, practice managers and paediatricians found the SUSTAIN model acceptable, with the flexibility and practicality of the model highlighted. GPs valued the learning opportunities, collaboration and support they gained working alongside the paediatricians. Virtual delivery through telehealth was viewed as a positive means of receiving specialist support that would otherwise be inaccessible to many practices. Increased efficiency in workflow and working at the top of scope in paediatric care as well as opportunities for meaningful professional relationships and increased family trust in GP-delivered care were recognised as key benefits that enhanced uptake. The current landscape of Australian general practice, with fee-for-service billing, limited time and workflow pressures, were all recognised as barriers to engagement with the SUSTAIN model of care. GPs and paediatricians recognised the need for more appropriate remuneration to support co-consultation as vital to the sustainability and scalability of the SUSTAIN model. Conclusions: The SUSTAIN model of care expands on our understanding of the benefits of integrated GP-paediatrician models of care in general practice by demonstrating the utility of a paediatrician supporting a GP in their practice via telehealth across metropolitan and rural environments in NSW, Australia. Clinical Trial: Australian New Zealand Clinical Trials Registry ACTRN12623000543684

  • Background: Healthcare workers (HCWs) face sustained psychological demands that place them at heightened risk for burnout and posttraumatic stress disorder (PTSD). Yet, assessing psychological distress in this population remains challenging due to stigma, underreporting, and the limitations of self-report tools. Although nonverbal behaviors hold diagnostic promise, most approaches overlook the fine-grained, temporal fluctuations in these signals. In this study, we focused on micro-behavior intervals—brief, involuntary changes in multimodal nonverbal signals—that emerge during emotion-eliciting interviews. Objective: To determine whether micro-behavior intervals improve discrimination of psychological distress profiles among HCWs with symptoms of burnout and PTSD. Methods: HCWs participated in a semi-structured interview that included five work-related, emotionally charged questions and was recorded via Webex (online video platform). Participants also completed validated questionnaires for burnout (MBI-GS-9) and PTSD (PCL-5). Recordings were analyzed with computer vision models to generate time series of facial expressions, head movement, gaze, body posture, and hand gestures. An unsupervised anomaly detection model (MOMENT) isolated micro-behavior intervals without the need for manual labels. Features derived from these intervals were used to train a deep learning classifier that predicted four symptom classes of psychological distress: ‘Moderate-Severe Burnout’, ‘Subthreshold-Provisional PTSD’, ‘Burnout + PTSD’, and ‘Resilient’. We conducted an ablation study by systematically removing one behavioral data stream at a time. Finally, we conducted an explainability analysis to characterize the features driving model predictions. Results: We analyzed 258 interview recordings from N=151 HCWs. Per interview, 19.65±6.01 micro-behavior intervals were detected, each lasting 1.31±1.10 seconds. The classifier demonstrated robust performance across classes, achieving a macro F1 = 0.75 and a macro ROC-AUC = 0.80 on held-out data. Ablation showed that excluding gaze or arousal-valence signals caused the largest performance declines, particularly in recall and F1 score. Explainability analysis revealed distinct temporal patterns across symptom classes, with irregularity and variability in micro-behaviors emerging as key predictors. Conclusions: Focusing on micro-behavior intervals yields a scalable, interpretable, and annotation-free framework for detecting psychological distress from nonverbal signals. By moving from whole-video features to fine-grained multimodal temporal modeling, we successfully captured subtle, involuntary fluctuations in nonverbal responses to emotion-eliciting questions. This multimodal approach enables objective, robust, and explainable assessment of psychological distress, offering a promising complement to conventional psychometric assessments.

  • Background: Health apps, which comprise both medical and wellness apps, hold potential to improve prevention, diagnosis, treatment, and management of disease. Yet, adoption by health care professionals (HCP) including recommendation and prescription rates are low, even in countries where these apps are reimbursed. Professional guidelines from medical societies and trusted standardized health app quality assessment reports providing the information HCPs need to recommend or prescribe a specific app for an individual patient are crucial to enable this digital transformation of medicine. The CEN-ISO/TS 82304-2 (hereinafter “82304-2”) Technical Specification, an initiative of the European Commission, includes a research-based health app quality assessment framework comprising 81 quality requirements. Results of 82304-2 app assessments are summarized in a “health app quality label”. The 82304-2 label’s potential to increase willingness to recommend health apps was recently confirmed. However, to adequately inform HCPs for recommending and prescribing high-quality apps a more detailed “health app quality report” is required in addition to the 82304-2 label. Objective: To codesign the 82304-2 health app quality report by including the information detail that satisfies the information needs of individual HCPs in decision-making on a health app. Methods: A Participatory Design approach was applied to generate the report. In an 18-month process of participatory prototyping with 9 HCPs with digital health expertise the 82304-2 health app quality report was iteratively developed, designed, and validated. A convenience sample of 31 HCPs indicated the priority 82304-2 health app quality requirements that would inform their decision-making on recommending an app and as such need detailed quality information. Final feedback meetings with the 9 HCPs with digital health expertise and 8 medical societies were used to finalize the 82304-2 report design. Web-based questions in these meetings, as well as a comparative content analysis and the Health Education Materials Assessment Tool (HEMAT) were used to indicatively evaluate the 82304-2 report design. Results: In total 30/81 (37%) of the 82304-2 quality requirements were prioritized by >50% of the HCPs. The final 82304-2 report design provides detailed information for 27/30 (90%) of the prioritized 82304-2 quality requirements. The reporting detail for two 82304-2 quality requirements requires more research. The final feedback meetings, comparative content analysis and HEMAT provide indicative proof of the usefulness and usability of the 82304-2 report design. Conclusions: We succeeded in our aim to design the 82304-2 health app quality report and found promising potential for its distinctive usefulness for HCPs and medical societies. Further efforts are needed to test and fine-tune its multi-stakeholder and intercontinental usefulness and usability, to support medical societies in providing guidance and potentially training on recommending health apps, and to advance from the current design to a scalable fully-functional version of the 82304-2 report and associated open access database.

  • Background: Asynchronous telemedicine has emerged as a crucial component of multi-channel healthcare delivery. However, how communication modalities within these visits influence downstream patient behavioural outcomes, such as loyalty and satisfaction, remains poorly understood. Objective: This study aimed to investigate the impact of physicians’ use of text and audio responses on patient repurchase behaviour and review scores. Methods: This cross-sectional study analyzed a unique dataset of 304,337 virtual visits from a Chinese academic medical center between 2021 and 2023, which included 823,135 physician responses. The key exposure variables were the modality of physician responses (text-only, audio-only, or hybrid use of both). We used probit regression to assess the influence of communication modalities on patient repurchase behaviour and ordinary least squares (OLS) regression for review scores. Results: On average, each text response contained 40.93 Chinese characters, and each audio response lasted 25.68 seconds. Among all visits, audio-only visits were associated with the lowest follow-up visit rates, with an average marginal effect (AME) of -0.030 (P<.01), which translated to 16.40%-30.43% reduction compared to text-only visits in patient loyalty. Audio messages significantly increased the likelihood of a patient providing a review (AME of 0.04, P<.05), but it did not affect review scores after adjusting for inverse Mills ratios. An increase in the number of text and audio replies was associated with improved follow-up visit rates, with AMEs of 0.009 (P<.01) and 0.007 (P=0.058), respectively. Visits began with a sub-5-second audio response and ended with text had significantly higher follow-up visit rates than text-only visits (AME of 0.07, P<.05). Conclusions: Physicians’ communication practices in asynchronous telemedicine visits significantly influence patient loyalty and satisfaction. Our work illustrates specific behavioural patterns and identifies an optimal hybrid strategy that balances human connection with the clarity of text. These findings provide critical, data-driven evidence to guide clinicians and policymakers in designing high-quality digital health services.

  • Background: Telemedicine offers promising solutions for improving access to care among older adults with chronic conditions, but there is limited evidence on how older patients navigate and engage with video telehealth in their home environments. Objective: To assess older adults’ acceptability, useability, comfort, and engagement with pharmacist-led telemedicine visits conducted at home, using behavioral observation separate from the telehealth platform. Methods: This descriptive pilot study included 20 older adult veterans (aged ≥65, ≥2 chronic conditions, ≥5 medications) recruited from a Veterans Affairs (VA) healthcare system. A project manager observed and video-recorded participant preparation for and conduct during in-home telemedicine visits with a clinical pharmacist. Recordings were coded using structured protocols to assess verbal and nonverbal behavior, technological challenges, and environmental factors. Trained and reliable coders rated verbal and nonverbal comfort, frustration, and engagement during technology setup and the telemedicine appointment using Likert-type impression scales. Video data were also transcribed and coded for behavioral events (e.g., troubleshooting, verbal expressions of age or technology ability, nonverbal adaptation) and analyzed using rapid qualitative analysis. Results: Participants expressed moderate engagement and comfort with technology overall. Verbal and nonverbal engagement significantly increased from setup to appointment (t = 3.33, p = .004, Cohen’s d = .74). Technology-related challenges (e.g., audio/video lag, troubleshooting) occurred in over half the visits but were often resolved through participant adaptation or support from the research staff. Participants displayed both frustration (e.g., sighing, leaning away) and adaptability (e.g., propping up tablets, retrieving medications). Verbal expressions reflected a mix of technology confidence and age-related limitations. Environmental distractions were present in some visits (e.g., dogs barking or phones ringing), but also allowed for rich clinical engagement (e.g., home tours, direct observation of medications). Conclusions: Older adults showed high levels of engagement and adaptability during in-home telemedicine visits despite technological and contextual barriers. Addressing technical support, home environment considerations, and age-related perceptions of technology may increase telehealth usability and satisfaction among aging populations. Clinical Trial: n/a

  • Large Language Models as Patient Education Tools in Obstetrics: A Comparative Study on Prenatal Testing

    Date Submitted: Oct 30, 2025
    Open Peer Review Period: Oct 30, 2025 - Dec 25, 2025

    Background: Prenatal screening and diagnostic testing can be difficult for expectant patients to navigate. While many turn to clinicians, large language models, (LLMs) such as ChatGPT, have grown to become a common supplemental source of health information. This study addresses how a popular large language model addresses patient-level questions on prenatal testing. Objective: To compare the readability and accuracy of ChatGPT-generated responses to frequently asked questions (FAQs) about prenatal testing with clinician-authored patient education materials published by the American College of Obstetricians and Gynecologists (ACOG). Methods: A total of 362 unique question–answer pairs were collected from ACOG’s publicly available prenatal testing FAQs and queried into ChatGPT-o1 (December 2024 release). Answers from both sources were analyzed using the Flesch Kincaid Reading Ease, Flesch Kincaid Grade Level, Gunning Fog Score, SMOG Index, Coleman Liau Index, and Automated Readability Index. Two blinded, board-certified maternal–fetal medicine specialists independently rated each answer on a 4-point Likert scale of accuracy and comprehensiveness. Inter-rater reliability was assessed using Cohen’s kappa. Statistical comparisons were conducted using two-sample t-tests and Wilcoxon Rank Tests with significance defined as p < 0.05. Results: Across all readability measures, ACOG responses were significantly more readable (p < 0.0001 for each test), with a mean Flesch Reading Ease score of 58.1 (SD 18.9) compared to 36.6 (SD 8.8) for ChatGPT. Conversely, ChatGPT responses were rated as more accurate and comprehensive, with a mean score of 3.54 (SE 0.0365) versus 3.40 (SE 0.0366) for ACOG (Wilcoxon p = 0.0012). Inter-rater agreement was poor for both sources (weighted κ ≈ 0.05), reflecting variability in physician interpretation of adequacy. Conclusions: ChatGPT-o1 produced more accurate and comprehensive responses to prenatal testing questions than ACOG FAQs. However, these responses were written at a significantly higher reading level, limiting accessibility for the average patient. While clinician-authored materials remain more reader friendly, LLMs show promise as supplementary tools for prenatal education if tailored to appropriate literacy levels.

  • Background: In burn care one of the most debated topics is the optimal treatment of patients with deep partial-thickness burns. With these patients the decision must be made to perform early surgery or to wait and potentially limit or even avoid surgery. Both options are available in Dutch burn care, and the best treatment option is decided on clinical outcomes as well as patients’ preferences. This complexity highlights the need for shared decision-making between patients with deep partial-thickness burns and healthcare professionals and a decision aid to facilitate this process. Objective: The aim of this study was to support patients with deep partial-thickness burns and healthcare professionals in the process of shared decision-making regarding the decision between early surgery or late/no surgery by developing and implementing a decision aid to facilitate this process. Methods: The decision aid was developed using a user-centered mixed-methods design consisting of three phases. Phase 1) The needs of patients and healthcare professionals regarding current information provision, treatment choices, and collaborative decision-making were assessed (needs assessment). Phase 2) The scope, content and design of the decision aid were developed in five co-design sessions with patients, healthcare professionals, and researchers. Usability testing of the concept version of the decision aid was conducted with patients using the think-aloud method. This was followed by acceptability testing with healthcare professionals, after which final adjustments were made. Phase 3) The decision aid was implemented by a six-month pilot period. Evaluation included patient interviews, focus groups with HPs, and the Normalization Measure Development questionnaire to assess the level of normalization. Results: The final decision aid consisted of three components: 1) a paper handout sheet; 2) an interactive website; 3) a summary sheet summarizing patient’s values and preferences that were filled in during the decision aid process. During the pilot period, the decision aid was distributed 42 times and used by 28 patients, resulting in a participation rate of 67%. Overall, both patients and healthcare professionals reported the decision aid as helpful and were positive on its use. The decision aid was easily integrated into the care process and there was a positive attitude towards future use of the decision aid. Conclusions: Using a comprehensive approach, a decision aid for patients with deep partial-thickness burns was successfully developed and implemented. The decision aid facilitates shared decision-making, improves patient-centered care by providing tailored information, and empowers patients to actively participate in their treatment decisions.

  • Background: The digital transformation of the health care sector requires full readiness of institutions to ensure they are capable of responding to all relevant and evolving challenges. This readiness depends on the implementation of validated tools with which to assess the level of institutional readiness in the adoption of information systems and technologies. Despite the range of international instruments available, there is a clear need to develop and validate tools that are fully adapted to the Latin American context, and which integrate aspects of institutional management and innovation to obtain optimal results. Objective: To describe the process of development and validation of the B-PRACSIS (Best Practices in Health Information Systems) Instrument, a tool designed to comprehensively assess the level of institutional maturity regarding the adoption of health information systems. Methods: A test to validate the qualitative method was performed in three stages: 1) adapting the new instrument to the Community Clinic EHR Readiness Assessment and incorporating the ISO 56000 series of standards therein; 2) pursuing validity testing by clinical informatics experts by means of construct, criterion and content validity analysis; and 3) applying pilot schemes in 31 health care institutions. The instrument evaluates the following six dimensions: organizational alignment, management capability, operational capability, technical capability, innovation capability and human capital. Results: The validity analysis showed high rates of internal consistency between dimensions (average >3.5/4.0 in evaluation criteria) and adequate specificity among items. The pilot application scheme showed heterogeneous levels of maturity, with averages ranging between 2.51 and 3.25 points on a 6-level scale (minimum possible: 1.0, maximum possible: 6.0). Technical capability (3.21) and organizational alignment (3.25) produced the highest scores, while innovation capability displayed the lowest level of development (2.51). Conclusions: The B-PRACSIS Instrument was shown to be a useful tool for evaluating technological maturity in health care institutions, particularly in terms of its focus on change management and innovation. The results suggest that the institutions evaluated are at an intermediate stage of maturity, with specific needs related to strengthening their organizational alignment for innovation. It is recommended that a quantitative validation be performed using representative samples.

  • Background: Mental health problems among university students are a growing global concern, yet limited resources and inadequate understanding of counseling procedures often delay support. Informed consent forms (ICFs) are vital for protecting rights and autonomy, but many are incomplete, ambiguous, or overly technical, and few institutions can effectively optimize them. Large language models (LLMs) offer scalable, low-cost solutions to enhance clarity and accessibility. Objective: This study aimed to evaluate whether LLM-based optimization could improve the structure, readability, content quality, and comprehensibility of university counseling ICFs, and to compare the performance of two advanced models—ChatGPT-5 and Grok-4. Methods: Counseling ICFs from 33 Chinese universities were collected and optimized using two advanced LLMs, ChatGPT-5 and Grok-4. A multidimensional framework assessed textual structure and readability, content quality from counselors’ perspectives, and comprehension from clients’ perspectives. Evaluations were conducted by mental health professionals and student volunteers. Wilcoxon signed-rank tests and linear mixed-effects models were applied for comparison and validation. Results: Compared with the originals, LLM-optimized ICFs demonstrated significant gains across all dimensions. The Lee–Yang readability index decreased from 28.68(5.69) to 22.39(2.13) with ChatGPT-5 and 24.37(2.32) with Grok-4 (both P<.001), while tone friendliness increased from 2.57(0.29) to 2.67(0.12) and 2.67(0.13), respectively. Expert-rated content quality improved from 45.33(8.74) to 52.54(7.92) and 55.49(7.81) (P<.001), primarily through enhanced specificity and existence of key items. Client comprehension scores rose from 19.02(1.32) to 22.33(0.81) and 22.05(0.90) (P<.001), reflecting higher clarity, readability, and acceptability. Linear mixed-effects models confirmed these findings. Conclusions: LLM-based rewriting markedly improved the clarity, completeness, and readability of counseling consent forms. By enhancing linguistic accessibility and professional precision, these models can support clearer communication and stronger counselor–client understanding. For universities with limited counseling resources, integrating LLM-assisted optimization may represent a practical step toward standardized, comprehensible, and client-centered counseling documentation. Clinical Trial: Not applicable.