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Latest Submissions Open for Peer Review

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

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JMIR Submissions under Open Peer Review

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Titles/Abstracts of Articles Currently Open for Review:

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

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

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

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

  • Background: Emotional cognition deficits are a core feature of autism spectrum disorder (ASD) and contribute significantly to social difficulties in affected children. Digital, app-based training may offer scalable, structured practice, but evidence from randomized pilot trials remains limited. Objective: To evaluate the feasibility, acceptability, and preliminary efficacy of the Autism Emotion Cognition Training System (AECTS), a tablet-based, parent-mediated program designed to support emotional cognition in young children with ASD. Methods: We conducted a single-center, two-arm, parallel-group randomized controlled pilot trial between April and October 2025. Children aged 4–8 years with ASD were assigned to AECTS plus treatment as usual (TAU) or TAU alone for 8 weeks. Feasibility and acceptability were assessed in the intervention group using a study-specific mixed-methods questionnaire (25 Likert items and 5 open-ended questions). Preliminary efficacy was explored using the Social Responsiveness Scale (SRS) and the Clinical Global Impression (CGI), with ANCOVA adjusting for baseline SRS scores. Results: Of 20 randomized participants, 19 completed the trial (10 in the intervention group and 9 in the control group). Caregiver-rated feasibility was high across domains (mean scores 3.92–4.70 out of 5), with the highest ratings for overall acceptability and technical feasibility. Usability showed the lowest score and greatest variability. Qualitative analysis identified four themes: (1) strong but module-specific engagement, (2) smooth operation with unclear system status, (3) variable generalization to daily life, and (4) requests for smarter personalization and realistic scenarios. On secondary outcomes, SRS scores favored the intervention group but were not statistically significant. CGI outcomes were comparable between groups. Conclusions: This pilot trial demonstrated that AECTS is a feasible and acceptable digital intervention for children with autism, with positive caregiver feedback and preliminary signals of benefit. Although clinical efficacy was not statistically significant, favorable trends in social responsiveness suggest potential value. Future large-scale trials with enhanced usability, adaptive personalization, real-life social scenarios, and caregiver support are warranted to establish the intervention’s effectiveness and scalability.

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

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

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

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

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

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

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

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

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

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

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

  • Application of Immediate Adaptive Intervention in Dietary Health Management: A Systematic Review

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

    Background: With the development of mHealth technology, Just-in-Time Adaptive Interventions (JITAI), as a new type of intervention that leverages real-time data to provide personalized support, has gradually gained attention. Relying on terminals such as smartphones and wearable devices, this intervention can collect individuals’ physiological indicators and environmental context data in real time, and dynamically adjust the type and intensity of support content. However, current research related to JITAI faces issues including inconsistent definition of core elements, high heterogeneity in intervention design, and controversial evidence on effectiveness. Additionally, there is a lack of systematic sorting out of its feasibility and generalizability, requiring evidence integration to guide its optimization. Objective: To systematically review the application effect of Just-in-Time Adaptive Interventions (JITAI) in dietary health management, comprehensively analyze its impacts on diet-related behaviors and physiological indicators, and assess the certainty of existing evidence. Methods: Databases including PubMed, Embase, Scopus, CINAHL, and Web of Science were searched from their inception to August 20, 2025. The search terms included dietary health, dietary behavior, JITAI, EMA, and others. Two researchers independently conducted literature screening, data extraction, and quality assessment, sorted out participant characteristics, JITAI features, outcome measures, and other relevant content, and summarized these elements. Results: A total of 12 studies were ultimately included in this research. The study populations covered groups such as adults with overweight/obesity, patients with hypertension, young people with low fruit and vegetable intake, patients with type 1 diabetes, patients with kidney stones, hemodialysis patients, and individuals with binge-eating spectrum disorders. Intervention types mainly included smartphone app interventions, short message service (SMS) reminders, wearable device interventions, and context-aware location-triggered interventions. Most studies reported positive effects of JITAI, such as increasing fruit and vegetable intake, reducing sodium intake, improving uncontrolled eating behaviors, enhancing the automaticity of fluid intake, and decreasing the frequency of binge eating and compensatory behaviors in patients with eating disorders. Meanwhile, implementation barriers including insufficient device adaptability, differences in digital literacy, and limitations of GPS signals were also revealed. Conclusions: Existing evidence suggests that JITAI-based dietary interventions may have potential value in promoting dietary behavior change. However, due to research heterogeneity and methodological limitations, the certainty of their effectiveness remains limited. In the future, it is necessary to design high-quality studies with more rigorous methodologies, standardized outcome measures, and sufficient follow-up periods to clarify the effectiveness of JITAI in dietary health management.

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

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

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

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

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

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

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

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

  • Background: Academic staff and researchers experience high levels of stress, burnout, and psychological distress, driven by competitive work environments, job insecurity, heavy workloads, and performance pressures. These conditions have led to growing interest in interventions aimed at promoting mental health and well-being in higher education workplaces. Despite this expansion, existing evidence remains fragmented and has largely focused on students or healthcare professionals, with less attention to academic staff and researchers. Objective: This systematic review aimed to identify, evaluate, and synthesise interventions implemented to promote mental health and well-being among academic staff and researchers in higher education institutions. Specifically, the review examined which interventions have been applied, their effectiveness, and the opportunities and challenges associated with their implementation. Methods: Following PRISMA guidelines, systematic searches were conducted in PubMed/MEDLINE, Web of Science, and Scopus for peer-reviewed articles published between 1994 and 2024. Eligible studies targeted academic staff and/or researchers, implemented interventions aimed at improving mental health or well-being, and reported empirical outcomes. Studies focusing exclusively on students, editorials, reviews, and studies assessed as having low methodological quality using the Physiotherapy Evidence Database (PEDro) scale. Screening was conducted independently by two reviewers, with disagreements resolved by a third. The review protocol was registered in PROSPERO. Due to substantial heterogeneity in study designs, outcomes, and measurement tools, no meta-analysis was conducted; findings were synthesised narratively. Results: A total of fifty-three studies met inclusion criteria. Interventions were categorised into three groups: web-based programs (n = 14), hybrid formats combining digital and in-person components (n = 18), and in-person programs delivered on campus (n = 21). Web-based interventions commonly reported improvements in stress, anxiety, and coping but were frequently by challenges related to adherence and sustained engagement. Hybrid interventions demonstrated balanced benefits, combining flexibility with interpersonal support. In-person interventions reported more consistent improvements in stress reduction, well-being, and sense of community, although scalability and resource demands were commonly reported limitations. Across modalities, most studies reported at least one positive mental health or well-being outcome; however, the evidence base was constrained by small samples, short follow-up periods, single-site designs, and methodological heterogeneity. Conclusions: Interventions targeting mental health and well-being among academic staff and researchers show promise, with digital, hybrid, and in-person approaches each offering distinct strengths and limitations. Institutions should prioritise integrated, multimodal strategies that combine individual-level support with broader structural and cultural change. Future research should adopt more rigorous and longitudinal designs to strengthen the evidence based and clarify long-term effectiveness and sustainability. Clinical Trial: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251025454

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

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

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

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

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

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

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

  • AI-Powered Health Chatbot and Plate Recognition for Weight Loss and Health Literacy in Adults With Overweight: Quasi-Experimental Case-Control Study

    Date Submitted: Jan 16, 2026
    Open Peer Review Period: Jan 18, 2026 - Mar 15, 2026

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

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

  • Background: Using mobile in healthcare is modernizing Patient-Reported Outcomes (PRO) for patient-centered approach. Our study introduces a mobile application that combines IoT devices as a remote patient monitoring to enhance real-time communication and management between solid malignancy and healthcare providers. Objective: To evaluate the effectiveness of this mobile application on quality of life and compare emergency room visits in solid malignancy. Methods: A pilot randomized controlled trial was conducted on 30 patients with solid malignancies, recruited from an outpatient oncology clinic. The study compared remote monitoring via a mobile application and smartwatch plus conventional care with a physician as the care provider, to conventional care alone. The primary outcome was quality of life, assessed using the Functional Assessment of Cancer Therapy – General (FACT-G). The secondary outcome was the cumulative number of emergency room visits. An additional finding was literacy of side effects. Quality of life and emergency room visits were collected and analyzed at 1, 3, and 6 months, while literacy of side effects was assessed at 3 months. Results: Of the 30 participants, 26 completed all 6 months of follow-up assessments (Intervention: 13/15, 86.6%; control: 13/15, 86.6%). At 6 months, the intervention group had higher total quality of life scores (84.23 ± 12.128) compared to the control group (77.15 ± 14.002), though this was not statistically significant (P=.073). Notably, statistically significant improvements were observed in the intervention group in physical well-being at 1 to 3 months within group (P=.010) and at 6 months between groups (P=.048), and in emotional well-being at 1 to 6 months (P=.032). Functional well-being was preserved in the intervention group, while a decline was observed in the control group, with a significant within-group decline from baseline to 3 months (P=.033). Social and family well-being did not differ between groups across time. The intervention group had no emergency room visits, compared to three in the control group (P=.070). The literacy of side effects was not significantly different (P=.318). Conclusions: This study suggests that a smartphone application with wearable IoT support has the potential to improve quality of life in cancer patients. A clinically meaningful trend toward better outcomes was observed, with significant improvements in physical and emotional well-being, along with the prevention of functional deterioration. Fewer emergency room visits in the intervention group suggest effectiveness in remote patient monitoring (RPM) for the early detection of adverse clinical outcomes, supporting a more proactive approach to cancer care. These findings warrant further evaluation in larger, adequately powered trials. Clinical Trial: Thai Clinical Trials Registry TCTR20230331002; https://www.thaiclinicaltrials.org/show/TCTR20230331002

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

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

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

  • Objective: The exponential expansion of biomedical literature has created an urgent need for efficient methods to recognize and extract PICO (Population, Intervention, Comparison, Outcome) - the foundational elements of evidence-based medicine (EBM). This study systematically evaluates two complementary approaches for automating PICO recognition and extraction in medical literature: prompt engineering optimization and parameter-efficient Fine-Tuning of large language models (LLMs). Methods: We developed a dual-phase methodological framework: (1) systematic prompt optimization incorporating In-Context Learning (ICL), Chain-of-Thought (COT), and Tree-of-Thought (TOT) reasoning strategies; and (2) parameter-efficient fine-tuning (PEFT) of the LLM architecture using Low-Rank Adaptation (LoRA), Quantized LoRA (QLoRA), and Freeze techniques. PubMed-PICO and NICTA-PIBOSO benchmark datasets are used for recognition tasks while EBM-NLP is applied for extraction tasks. Performance metrics includes precision, recall, and F1-score . F1 is adopted as the major metric as it balances precision and recall. Results: COT prompting demonstrated superior recognition accuracy, achieving F1-scores of 77.1% (Population) and 84.5% (Outcome) on PubMed-PICO. In PEFT implementations, LoRA achieved peak classification performance (91.7% F1 for Population), while QLoRA showed best ex-traction capability (79.3% F1 for Intervention). Fine-tuned models established new benchmarks across all datasets, attaining SOTA results on NICTA-PIBOSO and EBM-NLP. PEFT demonstrated marked improvements over prompt engineering. Conclusion: Our findings indicate that large language models (LLMs) can effectively automate PICO recognition and extraction through two complementary approaches. First, prompt engineering allows the model to perform tasks directly without altering its internal settings. Second, the PEFT method further unlocks their maximum performance potential by incorporating additional fine-tuning based on prompt engineering. This work made significantly advances and provides critical insights for optimizing methodological approaches in clinical applications related to or comprised of PICO extraction and recognition tasks.

  • Background: Traditional conversational agents have emerged as potential psychological tools in mental health field. While the text-only interactions limit compliance and efficacy. Virtual agents (VAs) show great potential to solve this problem. Objective: This study aimed to assess whether the combination of Echo appV2.0, a VA-based digital psychological intervention and TAU (Treatment as usual) yield greater efficacy compared to TAU alone. Methods: 93 participants were randomized to 4-week Echo-app-v2.0 intervention combined with TAU compared to TAU alone. The primary outcome was change of craving. Secondary outcomes were relapse and change of emotional state, sleep quality, and treatment motivation. Results: The intervention group showed significant lower craving at week 8(β = -1.81, 95% CI: [-3.45, -0.16], p = 0.03) and week 16 (β = -1.97, 95% CI: [-3.61, -0.32], p = 0.02). A significant difference in relapse between the two groups at the week 8 follow-up (χ2=4.09, P=0.04). Statistically significant larger improvement in sleep quality was found in the intervention group (β = -3.28, 95% CI: [-5.08, -1.49], p <0.001). Perceived stress decreased significantly over time (β = -2.91, 95% CI: [-5.27, -0.56], p = 0.02). Linear regression showed that group and change in PSS significantly predicted Craving change at week 8(intervention: β = -2.13, 95% CI: [-4.03, -0.24], p = 0.03 ;PSS: β = 0.15, 95% CI: [0.03, 0.26],p = 0.02) and week 16(intervention:β= -2.24, 95% CI: [-4.07, -0.42], p = 0.02；PSS: p = 0.03).Both group (β = 0.12, 95% CI: [0.01, 0.24], p = 0.03) and abstinence frequency (β = 0.27, OR = 1.31, 95% CI: [1.06, 1.63], p = 0.01) significantly predict relapse at week 8. Conclusions: Echo app V2.0 has certain therapeutic potential in treating AUD, but further adjustments to the intervention are needed to enhance its long-term efficacy. Clinical Trial: ClinicalTrials.gov Identifier: NCT05675553

  • Background: Vessels encapsulating tumor clusters (VETC) are a distinct vascular pattern associated with aggressive behavior and poor prognosis in hepatocellular carcinoma (HCC). Preoperative identification of VETC is crucial for treatment planning but currently relies on invasive pathological examination. Radiomics-based artificial intelligence (AI) offers a potential noninvasive solution, yet evidence regarding its diagnostic and prognostic accuracy remains synthesized. Objective: We aimed to systematically evaluate the diagnostic performance and prognostic value of radiomics-based AI models for noninvasively predicting VETC status in patients with HCC. Methods: We systematically searched PubMed, Embase, Web of Science, and the Cochrane Library for studies published up to July 11, 2025. Studies developing or validating AI models using medical imaging (contrast-enhanced MRI [CEMRI], contrast-enhanced CT [CECT], contrast-enhanced ultrasound [CEUS], or [18F]FDG PET/CT) to predict pathologically confirmed VETC status in HCC patients were included. Study quality was assessed using the PROBAST+AI tool. Diagnostic accuracy (sensitivity, specificity, AUC) and prognostic value for early recurrence (hazard ratio [HR]) were pooled using random-effects models. Results: Fourteen studies involving 729 patients in internal and 581 in external validation cohorts were analyzed. AI models based on CEMRI demonstrated the highest diagnostic accuracy, with a pooled AUC of 0.87 (95% CI 0.84-0.90), sensitivity of 0.82 (95% CI 0.75-0.88), and specificity of 0.77 (95% CI 0.71-0.82). Models using other modalities (CECT, PET/CT, CEUS) showed moderate to good performance. Prognostically, HCC patients classified as VETC-positive by AI had a significantly higher risk of early recurrence (pooled HR 2.34, 95% CI 1.93-2.84). Conclusions: Radiomics-based AI models, particularly those using CEMRI, are promising for the noninvasive prediction of VETC and offer valuable prognostic stratification for early recurrence risk in HCC. However, significant heterogeneity and the retrospective nature of current studies limit the strength of evidence. Prospective, multicenter validation is required to confirm clinical utility. Clinical Trial: PROSPERO CRD420251167155

  • The shortcomings of digital technologies and artificial intelligence have transformed the landscape of health education, creating opportunities for better health education techniques and accessibility to information. These developments have accelerated the spread of a one-size-fits-all approaches which may risk disengagement, and poor adherence to care plans and may compromise the irreplaceable role of human connection in facilitating behavior change. This paper introduces a human-centered framework for patient health education that integrates theoretical insights and empirical evidence to counter the limitations of AI-driven and generalized approaches. Specifically, it presents two innovative tools—the Empathy Map and the Persuasive Pattern framework. As a theoretical paper, proposes a structured framework to align with patient-centered care principles within a proper use of technology that integrates the humanistic approach. The proposed framework is built around three pillars: (1) empathy-driven needs assessment, operationalized through the Empathy Map to capture patient perspectives, barriers, and motivations; (2) metacognitive empowerment to build reflective, self-directed learning skills; and (3) persuasive psychological strategies, organized into a Persuasive Pattern framework that enhances motivation, sustains engagement, and supports long-term behavior change.. This model reframes health education as a collaborative and empowering process rather than a passive transfer of information. A human-centered framework—with its Empathy Map and Persuasive Pattern model—offers a pathway to more effective, ethical, and equitable patient education. Integrating the framework components will ensure that Artificial IntelligenceI tools are applied as supportive complements rather than replacements for human empathy and relational care.

  • Digital Transformation in Healthcare: Are we on the right track?

    Date Submitted: Dec 26, 2025
    Open Peer Review Period: Dec 29, 2025 - Feb 23, 2026

    The healthcare digital transformation is gaining increasing notoriety, despite the observed challenges in its implementation. The envisioned benefits together with the growing need for better healthcare are motivating academia, organizations, regulatory agencies, and governments to develop more effective digital healthcare solutions. Through extensive debates among the authors and supported by a narrative literature review, this paper discusses how digital transformation is being conducted in the healthcare sector. Our discussion relies on the concepts from the sociotechnical systems theory categorizing it according to three social (people, culture, and goals) and three technical (processes/procedures, infrastructure, and technology) dimensions. Overall, we argue that both social and technical dimensions present elements that have been either encouraging or discouraging the progress of healthcare digital transformation. The identification of current trends on such (on- and off-track) elements allowed the formulation of propositions for future testing and validation. This approach can help the establishment of better government policies, foster private initiatives, and shift regulatory guidelines to support a successful digital transformation in health systems. Lastly, from a research perspective, we outline some opportunities for further interdisciplinary investigation in the field, promoting advances in the understanding of healthcare digital transformation.

  • Background: Internet search engines serve as primary gateways for cancer information, yet the commercialization of health content within organic search results remains understudied. While covert promotional content—such as native advertising and stealth marketing—has been documented in various contexts, systematic comparisons across structurally divergent search platforms are lacking. Objective: This study examined the prevalence, distribution, and information quality characteristics of covert promotional cancer-related content across Naver and Google, South Korea's two dominant search engines, which have fundamentally different platform architectures. Methods: A two-phase cross-sectional content analysis was conducted. Phase 1 employed natural language processing to identify 33 cancer-related keywords from 1,400 preliminary posts. Phase 2 systematically collected 5,848 posts in October 2023, yielding 919 unique posts (598 from Naver and 321 from Google) that covered seven major cancer types, representing over 70% of Korean cancer incidence. Two trained coders analyzed promotional status, intensity, institutional sources, and information quality indicators (citation practices, information depth, and source attribution), with inter-coder reliability exceeding κ=.80. Chi-square tests examined the associations between platform and cancer type. Results: Covert promotional content appeared in 48.6% (447/919) of analyzed posts, with significantly higher prevalence on Google (54.2%, 174/321) than Naver (45.7%, 273/598; χ²₁=5.78, p=.016). Platform differences were pronounced: Naver promotional posts predominantly originated from blogs (96.0%, 262/273) and exhibited full promotional intensity (52.1%, 126/242), while Google posts primarily came from hospital websites (81.0%, 141/174) with simple institutional identification (57.8%, 52/90). Institutional source distribution varied significantly by platform (χ²₅=215.714, P<.001): traditional medicine institutions dominated Naver (99.2%, 119/120), whereas university-affiliated hospitals predominated on Google (85.0%, 96/113). Information quality differed substantially: indirect citation was more common on Google (81.6%, 142/174) than Naver (58.6%, 160/273; χ²₁=25.653, P<.001), while comparative informational depth was higher on Google (55.7%, 97/174) versus Naver (19.4%, 53/273; χ²₂=64.683, P<.001). Conclusions: Covert promotional cancer content is pervasive in Korean search results, with platform architecture systematically shaping promotional patterns, institutional sources, and information quality rather than reflecting deliberate marketing strategies. These findings underscore the need for platform-sensitive regulation and enhanced digital health literacy to protect vulnerable cancer information seekers from commercial exploitation embedded within ostensibly neutral search environments.

  • Large Language Models in Colorectal Cancer: A Systematic Review

    Date Submitted: Dec 22, 2025
    Open Peer Review Period: Dec 23, 2025 - Feb 17, 2026

    Background: The growing complexity of colorectal cancer (CRC) management requires advanced tools for integrating multimodal data and clinical knowledge. Large language models (LLMs) offer a promising approach to address these challenges through sophisticated natural language processing and reasoning capabilities. Objective: This systematic review evaluates the current applications, performance, and practical implications of LLMs across the continuum of CRC care, from screening to treatment decision support. Objective: This systematic review evaluates the current applications, performance, and practical implications of LLMs across the continuum of CRC care, from screening to treatment decision support. Methods: We searched six databases (PubMed, Embase, Web of Science, Scopus, CINAHL, Cochrane) up to November 1, 2025, following PRISMA guidelines. Included studies were original research investigating LLM applications specific to CRC, with extractable outcome data. Quality was assessed using QUADAS-2, PROBAST, and ROBINS-I tools by two independent reviewers. Results: Following the screening of 1,261 records, 34 studies met the inclusion criteria, all published between 2023 and 2025. The synthesis highlighted the utility of LLMs in automating data extraction from clinical texts, supporting patient education, aiding diagnostic processes, and assisting in clinical decision-making, with growing evidence of their emerging visual interpretation and multimodal capacities. The effectiveness of these models was significantly influenced by prompt design, which varied from basic zero-shot queries to specialized fine-tuning techniques. While the overall methodological quality of the included studies was deemed adequate, assessments identified recurring concerns regarding insufficient control of biases and inadequate reporting on data security measures. Conclusions: LLMs demonstrate tangible potential to augment CRC care, particularly in structuring unstructured data and providing clinical decision support. However, translating this potential into practice requires solutions for domain adaptation, multimodal integration, and rigorous prospective validation to ensure reliability and safety in real-world settings. Clinical Trial: PROSPERO CRD420251248261; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251248261.

  • Background: Anxiety disorders are highly prevalent among autistic adults, with 20%-65% experiencing at least one diagnosable anxiety disorder. While mindfulness-based interventions have demonstrated efficacy for anxiety reduction, treatment response varies considerably across individuals. Machine learning approaches offer potential for identifying who is most likely to benefit from smartphone-based mindfulness interventions, enabling more personalized treatment recommendations. Objective: This study aimed to develop and evaluate machine learning models to predict individual treatment response, in the form of reduced anxiety symptoms, to a smartphone-based mindfulness intervention for autistic adults. We sought to identify baseline characteristics that distinguish responders from non-responders, explore few-shot learning with large language models as a complementary approach for low-data clinical prediction, and implement a Personalized Advantage Index approach for individualized treatment recommendations. Methods: We conducted a secondary analysis of data from a randomized controlled trial comparing a 6-week smartphone-based mindfulness intervention (Healthy Minds Program) with a waitlist control condition in autistic adults. Among 73 participants who completed the intervention, we defined responders as those achieving ≥7-point reduction in State-Trait Anxiety Inventory state anxiety scores. Baseline predictors included demographic variables, autism trait measures, and self-report questionnaires assessing anxiety symptoms, perceived stress, affect, and mindfulness. We trained six machine learning models (logistic regression, Random Forest, XGBoost, TabNet, Tab-ICL, and TabPFN) using nested 10-fold cross-validation with inner 5-fold cross-validation for hyperparameter tuning. Additionally, we evaluated few-shot learning using GPT-4o models with tokenized baseline features at varying shot counts (20-70 examples). Model performance was evaluated using area under the receiver operating characteristic curve (AUC) for machine learning model and classification accuracy for few-shot learning. We examined feature importance and implemented Personalized Advantage Index analysis to estimate individualized treatment benefit. Results: Random Forest achieved the highest predictive performance for state anxiety response (AUC 0.79, 95% CI 0.66-0.91), followed by TabPFN (AUC 0.78, 95% CI 0.64-0.94) and logistic regression (AUC 0.77, 95% CI 0.73-0.81). Higher baseline state anxiety (coefficient 1.20, P<.001) predicted better treatment response, while higher trait anxiety (coefficient -0.17, P=.001), older age (coefficient -0.18, P=.02), and lower childhood pretend play scores (coefficient -0.93, P=.007) were associated with poorer response. Few-shot learning with 7-feature tokenization achieved accuracy of 0.867 (95% CI 0.81-0.92) at 70 shots, significantly outperforming Random Forest baseline (0.733, p<.001). Prediction of trait anxiety changes was substantially weaker (AUCs 0.57-0.68), likely reflecting the inherent stability of this personality dimension. The Personalized Advantage Index demonstrated significant moderation of treatment group differences (adjusted R²=0.29), with 75% of participants predicted to benefit more from the mindfulness intervention than the waitlist control. Conclusions: Machine learning models successfully identified baseline characteristics predicting treatment response to a smartphone-based mindfulness intervention in autistic adults. Few-shot learning with large language models demonstrated superior performance to traditional machine learning when provided with compact, high-signal feature representations, offering a promising approach for clinical prediction in small-sample settings. These findings demonstrate the feasibility of precision psychiatry approaches in digital mental health interventions for autistic adults. While modest sample size and limited demographic diversity warrant cautious interpretation, the stable cross-validation performance suggests robust predictive patterns within similar populations. Future research should validate these models in larger, more diverse samples and explore whether algorithm-guided treatment recommendations improve outcomes compared to standard care, through prospective randomized trials.

  • Background: The integration of large language models (LLMs) into healthcare holds promise to enhance clinical decision-making, yet their susceptibility to biases remains a critical concern. Gender has long influenced physician behaviors and patient outcomes, raising concerns that LLMs assuming human-like roles, such as clinicians or medical educators, may replicate or amplify gender-related biases. Objective: To evaluate the consistency of LLM responses across different assigned genders (personas) regarding both diagnostic outputs and model judgments on the clinical relevance or necessity of patient gender. Methods: Using case studies from the New England Journal of Medicine Challenge (NEJM), we assigned genders (female, male, or unspecified) to multiple open-source and proprietary LLMs. We evaluated their response consistency across LLM-gender assignments regarding both LLM-based diagnosis and models’ judgments on the clinical relevance or necessity of patient gender. For representative models with high diagnostic accuracy, we further evaluated consistency across question difficulty tiers and clinical specialties. Results: All models showed high diagnostic consistency across assigned LLM genders (range of consistency rates: 91.45%–97.44%), though this did not always correspond to diagnostic accuracy (e.g., GPT-4.1: 97.44% consistency, 0.943 accuracy; Gemma-2B: 97.44% consistency, 0.478 accuracy). In contrast, judgments on the clinical importance of patient gender showed marked inconsistency: consistency rates ranged from 58.97% to 90.6% for relevance judgements, 78.63% to 98.29% for necessity judgements. Stratified by difficulty tier and specialty, the open-source model (LLaMA-3.1-8B) particularly showed statistically significant differences across LLM genders regarding both relevance and necessity judgements. Conclusions: Despite stable diagnostic outputs, LLMs varied substantially in their assessments of patient gender’s clinical importance across gendered personas. These findings present an underexplored bias that could undermine the reliability of LLMs in clinical practice, underscoring the need for routine checks of identity-assignment consistency when interacting with LLMs to ensure reliable and equitable AI-supported clinical care. Clinical Trial: not applicable

  • Background: Clinicians spend a substantial share of their working hours on documentation, contributing to workflow inefficiencies, reduced patient-facing time, and increased burnout. AI medical scribes have emerged as a promising solution to reduce this burden, yet real-world evidence remains limited and heterogeneous. Data from European health systems are especially scarce, despite growing interest in AI-enabled documentation support. Reducing clinicians’ documentation burden is a critical priority in modern health care, as excessive administrative work consumes substantial clinician time, contributes to burnout, and limits time available for direct patient care. Objective: To quantify the impact of an AI medical scribe on documentation time and clinician experience. Methods: This observational real-world evaluation was conducted between April 26th 2024 and October 27th 2025 to assess the impact of an AI medical scribe on documentation time and clinician experience using retrospective paired ratings. The study was carried out across multiple specialties in primary, secondary and hospital care within Capio Ramsay Santé, a large integrated health care provider operating in Sweden. The target population consisted of licensed clinicians actively using the AI medical scribe in routine clinical practice. Eligibility was limited to “fully onboarded” users, defined as clinicians who had used the scribe for at least 3 months, created more than 100 notes, generated at least one document or certificate, and used the conversational edit (“Add or adjust”) feature at least once. Results: With the introduction of the AI medical scribe, the estimated time spent on documentation per note decreased from 6.69 minutes to 4.72 minutes (-29%, p = 1.70e-11). On a five-point Likert scale, the ability to work without stress related to administrative tasks increased from a mean of 2.41 to 3.14 (p = 2.46e-8), and perceived presence with patients increased from 3.73 to 4.33 (p = 2.47e-8). The median editing time was 93 seconds, and it did not decrease significantly over continued use. Conclusions: This study shows that the clinician time savings and reductions in cognitive load and stress reported in prior US-based studies can also be achieved in a European health care system using an AI scribe. Clinical Trial: The study adhered to the Standards for Quality Improvement Reporting Excellence (SQUIRE) guideline and was preregistered on the Open Science Framework on 7 October 2025 (DOI: 10.17605/OSF.IO/YPD9E)

  • Background: Despite the promising potential of artificial intelligence (AI) in the perioperative context, the rapid pace of development and diverse implementation warrants a systematic review to consolidate existing knowledge, identify gaps, and assess the utilization of trustworthiness principles of AI integration into the perioperative period for patients with serious illness. Objective: The purpose of this study was to address deficiencies in perioperative AI literature by elucidating the extent to which equity, ethics, and safety discussions are incorporated, thereby establishing a foundation for developing robust ethical guidelines for the safe and effective integration of AI in healthcare. This study also examined the utilization of AI enabled team augmentation in perioperative serious illness care. Methods: We searched PubMed, Embase, CENTRAL, and Scopus for studies published between 2010 to July 2024. We included studies that reported patient functional outcomes, occurred in the perioperative period (30 days before and up to 90 days after surgery), included AI integration, and included patients with serious illness (defined as: malignancy, advanced organ failure, frailty, dementia/neurodegenerative disease, or stroke). To ensure reliability and minimize bias, two independent reviewers screened all studies through the title/ abstract and full-text stage; conflicts were resolved through team consensus. The abstraction form was developed iteratively and was tested through pilot abstractions. Any discrepancies identified during data extraction were resolved through discussion and consensus among the reviewers. The ROBINS-I risk of bias tool in non-randomized studies was used to assess quality. Abstraction and risk assessment occurred through a blinded, independent dual review. A narrative review was compiled with the identified studies. Results: Of the 10,980 articles identified through the database searches, this review yielded 81 articles that met inclusion criteria. A majority of the studies were published in China (35), with the United States (9) and South Korea (7) having the subsequent most publications, and 80 out of 81 (98.8%) articles focused on patients with malignancy. Analysis of AI implementation strategies revealed foundational efforts toward equitable access, with six studies providing open-access tools and several more designing models with simple inputs suitable for low-resource settings (17). Seven studies mentioned their commitment to transparency (e.g., publishing code) to enhance safety and trust. However, significant ethical deficiencies persist, particularly around input data, as only two studies explicitly addressed racial or ethnic disparities, and concerns about lack of sample diversity (16) and the omission of socially relevant features (5) were frequently noted as limitations. Although no current studies considered AI enabled team augmentation, a majority of articles described how AI could be used to prompt a team member to make a tangible action. Conclusions: Machine learning for predictive analytics and other types of AI tools in surgical outcomes offers significant potential but requires adherence to trustworthiness and safety principles to be clinically viable. By leveraging longitudinal data and continuous performance tracking, these models have the potential positive impact on diverse patient needs and healthcare systems. Future research should prioritize adhering to guidelines for equity, ethics, and safety, conduct prospective studies, incorporate more external validation of AI models, and facilitate transparent monitoring and reporting of model performance to build clinician and patient trust and to encourage broader healthcare system adoption. Clinical Trial: PROSPERO CRD42024608387

  • 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: 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: 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: 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