Date Submitted: Nov 24, 2025. Open Peer Review Period: Nov 24, 2025 - Nov 9, 2026.

Background: Mindfulness-based programming (MBP) is increasingly implemented within university settings to support students’ mental health and typically includes the instruction of formal (FM) and informal (IM) mindfulness activities. However, recent evidence suggests that university students with a history of nonsuicidal self-injury (NSSI) may experience challenges in response to FM (e.g., physical/psychological discomfort), whereas the flexibility and brevity inherent in IM may be better tolerated. Objective and Methods: This randomized controlled trial thus compared the effectiveness and acceptability of four-week-long web-based FM and IM instructional programs relative to an inactive control condition among university students with (n = 127) and without (n = 100) past-year NSSI engagement. Results: Overall, results did not differ as a function of NSSI history. Three-way ANCOVAs revealed that both the FM and IM programs were effective at improving dispositional mindfulness, nonjudging, describing, well-being, and psychological need satisfaction immediately post-program, with these improvements sustained one month later. Neither program resulted in improved awareness, nonreacting, observing, stress, emotion regulation styles, or academic engagement. Moreover, three-way ANOVAs revealed high satisfaction with both the FM and IM programs, with a preference for IM immediately post-program. Conclusions: Findings underscore the effectiveness and acceptability of both approaches to MBP in the university context, as well as the potential value of offering FM and IM instruction independently of one another – an approach which may be optimally responsive to diverse needs and preferences among students.

Background: Loneliness has emerged as a global public health issue, with recent data indicating that 27.6% of adults aged 65 to 80 report feelings of loneliness despite the post-pandemic resumption of social activities. Older caregivers face unique challenges that may exacerbate feelings of loneliness due to the demanding nature of caregiving responsibilities. While Internet use has been suggested as a potential intervention to reduce loneliness, its moderating effect on the relationship between caregiving-related health effects and loneliness remains understudied. Objective: This study aims to investigate: (1) the association between caregiving-related health effects and loneliness among older informal caregivers; (2) the relationship between Internet use frequency and loneliness; and (3) whether Internet use moderates the association between caregiving-related health effects and loneliness. Methods: We analyzed cross-sectional data from the 2019-2020 California Health Interview Survey, focusing on 3,957 informal caregivers aged 65 and older. Loneliness was measured using a modified 3-item UCLA Loneliness Scale. Health effects of caregiving were assessed by self-reported physical/mental health problems due to caregiving responsibilities. Internet use frequency was measured on a 4-point scale. Multivariable linear regressions were employed to test the study aims, adjusting for socio-demographic factors, health status, and caregiving-context characteristics. Results: Among participants, 12.0% reported experiencing physical/mental health problems due to caregiving responsibilities. After adjusting for covariates, caregivers who experienced health problems related to caregiving reported higher levels of loneliness compared to those who did not (β = 0.76, SE = 0.07, p < .001). More frequent Internet use was associated with a lower level of loneliness (β = -0.11, SE = 0.03, p < .001). Additionally, Internet use significantly moderated the relationship between caregiving-related health effects and loneliness (β = -0.16, SE = 0.07, p < .05), suggesting that the negative impact of caregiving-related health effects on loneliness was attenuated among caregivers who used the Internet more frequently. Conclusions: This study provides evidence that while caregiving-related health effects are associated with increased loneliness among older informal caregivers, more frequent Internet use may both directly reduce loneliness and buffer against the adverse impact of caregiving on loneliness. These findings align with recent research highlighting the potential of technology-based interventions to combat social disconnection among older adults. Healthcare providers and policymakers should consider implementing programs that enhance Internet access among older caregivers as part of comprehensive strategies to address loneliness in this vulnerable population.

Date Submitted: Nov 22, 2025. Open Peer Review Period: Nov 24, 2025 - Jan 19, 2026.

Date Submitted: Nov 24, 2025. Open Peer Review Period: Nov 24, 2025 - Jan 19, 2026.

Background: #patients with acute coronary syndrome (ACS) who undergo percutaneous coronary intervention (PCI) remain at high risk for major adverse cardiovascular events (MACE). Conventional risk scores may not capture dynamic or nonlinear changes in postdischarge MACE risk, whereas machine learning (ML) approaches can improve predictive performance. However, few ML models have incorporated time-to-event analysis to reflect changes in MACE risk over time. Objective: This study aimed to develop a time-to-event ML model for predicting MACE after PCI in #patients with ACS and to identify the risk factors with time-varying contributions. Methods: We analyzed #ehrs of 3159 #patients with ACS who underwent PCI at a tertiary hospital in South Korea between 2008 and 2020. Six time-to-event ML models were developed using 54 variables. Model performance was evaluated using the time-dependent concordance index and Brier score. Variable importance was assessed using permutation importance and visualized with partial dependence plots to identify variables contributing to MACE risk over time. Results: During a median follow-up of 3.8 years, 626 (19.8%) #patients experienced MACE. The best-performing model achieved a time-dependent concordance index of 0.743 at day 30 and 0.616 at 1 year. Time-dependent Brier scores increased and remained stable across all ML models. Key predictors included contrast volume, age, medication adherence, coronary artery disease severity, and glomerular filtration rate. Contrast volume ≥300 mL, age ≥60 years, and medication adherence score ≥30 were associated with early postdischarge risk, whereas coronary artery disease severity and glomerular filtration rate became more influential beyond 60 days. Conclusions: The proposed time-to-event ML model effectively captured dynamic risk patterns after PCI and identified key predictors with time-varying effects. These findings may support individualized postdischarge management and early intervention strategies to prevent MACE in high-risk #patients. Trial Registration:

Background: Patient-reported outcomes, including ecological momentary assessments (EMAs), are acquired from patients via repeated self-reports of their perceived momentary physical and emotional states before and after medical procedures. Patient-reported outcomes are used to measure health outcomes and quality of care. However, certain observable states or behaviors (eg, moods such as fatigue, hope, or medication adherence), or behaviors suggestive of health decline (eg, depression, cognitive decline), are not easily measured via self-reports in certain situations (eg, patients undergoing certain medical procedures, patients with dementia, and others). The peer-ceived momentary assessment (PeerMA) method involves support persons or peers (eg, family members and friends) to report their perception of a patient’s subjective physical and emotional states and has been validated in healthy populations. Objective: We examined the value of the PeerMA method in assessing the disease progression and recovery pathways of patients undergoing liver transplantation. Herein, the PeerMA method is operationalized via the patient’s informal caregivers and the patient-based EMA, and wearable-based physical activity datasets from the patients. We report the #feasibility results and human factors influencing the acceptance and reliability of the PeerMA method in a small study comprising 8 patients and support persons. Methods: We conducted a longitudinal observational study of 6 months (autumn 2019 to spring 2020), collecting EMA/self-reports from 8 patients (at the liver transplant clinic at Stanford University Hospital, California) about their perceived levels of hope, sleep, fatigue, depression, and pain in addition to PeerMA-based reports of the same aspects from 7 caregivers. We collected physical activity records from 5 patients using a Fitbit bracelet. Participants completed pre- and poststudy surveys, contributing qualitative data. We implemented the PeerMA method using a smartphone app, making it easy to use by both patients and support persons. Results: We collected 1142 patient-days and 976 support person–days. On average, each patient received 103 EMAs and responded to 64 (63%) of them, while support persons received 87 PeerMAs and responded to 64 (74%) of them. We report empirical evidence about the methodological #feasibility of PeerMA, showing its dual and unique information streams unavailable by EMA alone. We show examples where support person assessments and physical activity data can inform health professionals about the actual state of a patient regarding outcomes such as hope, sleep quality, fatigue, pain, and depression. We discuss human factors influencing the acceptance of the method and make methodological recommendations. Conclusions: It is possible to leverage data acquired via the PeerMA method and a wearable activity monitor to complement EMA. The PeerMA method incorporates frequent observations from support persons in patients’ daily lives, which can be compared and analyzed next to the patient’s self-reports. Such data may help to study and assist patients during disease recovery, which is beneficial for patients recovering from an organ transplant. Trial Registration:

Background: Many institutions are in various stages of deploying an artificial intelligence (#AI) (AI) scribe system for clinic #ehr (EHR) documentation. In anticipation of the University of California, Davis #health’s deployment of an AI scribe program, we surveyed current #patients about their perceptions of this technology to inform a #patient-centered implementation. Objective: We assessed #patient perceptions about current clinician EHR documentation practices before implementation of the AI scribe program, and preconceptions regarding the AI scribe’s introduction. Methods: We conducted a descriptive preimplementation survey as a quality improvement study. A convenience sample of 9171 #patients (aged ≥18 years) who had a clinic visit within the previous year, was recruited via an email postvisit survey. #patient-identified demographics (age, gender, and race and ethnicity) were collected. The survey included rating scales on questions related to the #patient perception of the AI scribe program, plus open-ended comments. Data were collated to analyze #patient perceptions of including AI Scribe technology in a clinician visit. Results: In total, 1893 #patients completed the survey (20% response rate), with partial responses from another 549. Sixty-three percent (n=1205) of the respondents were female, and most were 51 years and older (87%, n=1649). Most #patients identified themselves as White (69%, n=1312), multirace (8%, n=154), Latinx (7%, n=130), and Black (2%, n=42). The respondents were not representative of the overall clinic populations and skewed more toward being female, ages 50 years and older, and White in comparison. #patients reacted to the current EHR documentation system, with 71% (n=1349) feeling heard or sometimes heard, but 23% (n=416) expressed frustrations that their physician focused too much on typing into the computer. When asked about their anticipated response to the use of an AI scribe, 48% (n=904) were favorable, 33% (n=630) were neutral, and 19% (n=359) were unfavorable. Younger #patients (ages 18-30 years) expressed more skepticism than those aged 51 years and older. Further, 42% (655/1567) of positive comments received indicated this technology could improve human interaction during their visits. Comments supported that the use of an AI scribe would enhance #patient experience by allowing the clinician to focus on the #patient. However, when asked about concerns regarding the AI scribe, 39% (515/1330) and 15% (203/1330) of comments expressed concerns about documentation accuracy and privacy, respectively. Providing previsit #patient education and obtaining permission were viewed as very important. Conclusions: This #patient survey showed that respondents are generally open to the use of an AI scribe program for EHR documentation to allow the clinician to focus on the #patient during the actual encounter rather than the computer. Providing #patient education and obtaining consent before using AI are important components to gain #patient trust. Caution about the results is appropriate, given the low response rate and nonrepresentative profile.

Background: Virtual patient (VP) simulations can be used to practice clinical reasoning (CR) in controlled learning environments. Traditional computer-based VP platforms often lack the authenticity and interactivity required for effective CR training. Artificial intelligence (AI)–enhanced social robotic VPs can enhance realism and engagement; however, quantitative evidence comparing them with conventional VP platforms remains limited. Objective: We compared medical students’ experience of an AI-enhanced social robotic versus a conventional computer-based VP platform regarding the extent to which the design characteristics of the respective platform facilitate CR skill training. Methods: This observational crossover cohort study involved 178 sixth-semester medical students at Karolinska Institutet, Stockholm, Sweden (response rate: 42.3%; 178 of 421 invited students; Spring 2024-Spring 2025), who experienced both a large language model–enhanced social robotic VP platform supporting dialogue (social artificial intelligence–enhanced robotic interface [SARI]) and a conventional computer-based VP platform (virtual interactive case [VIC]) during their clinical rotation within rheumatology. Platform order was determined by clinical rotation scheduling. VP design was evaluated using a validated questionnaire across 5 domains: authenticity, professional approach, coaching quality, learning effects, and overall judgment. Students’ CR training preferences were assessed using categorical responses and a Visual Analogue Scale, where a lower score favored SARI and a score of 5 indicated equal preference between platforms. Results: SARI outperformed VIC across all 5 VP design domains. Students rated SARI higher for authenticity (median 4.0, IQR 3.5-4.5 vs 3.0, IQR 2.5-3.5; P

Background: Preoperative endometrial cancer (EC) diagnosis often depends on radiologists’ expertise, which introduces subjectivity. Recent studies have explored radiomics-based machine learning (ML) models for detecting myometrial invasion (MI), but a comprehensive evaluation of their diagnostic performance is lacking. Therefore, our study systematically assessed the diagnostic performance of radiomics-based ML approaches for identifying MI in EC, thereby providing evidence to guide the development or improvement of noninvasive diagnostic tools. Objective: This study aims to systematically assess the diagnostic performance of radiomics-based ML approaches for identifying MI in EC and compare the diagnostic efficacy of conventional ML (CML) and deep learning (DL) models based on differences in data processing methods via subgroup analyses, thereby providing evidence to guide the development or improvement of noninvasive diagnostic tools. Methods: PubMed, Cochrane Library, Embase, and Web of Science were searched through November 26, 2024, for studies evaluating radiomics-based ML for detecting MI in patients with EC. Study quality was appraised using the radiomics quality score. Pooled diagnostic metrics were estimated using a bivariate random-effects model. Subgroup analyses compared CML and DL models. Results: We included 19 studies comprising 4373 patients with EC. Of these 19 studies, 18 (95%) used magnetic resonance imaging–based radiomics, and 1 (5%) used ultrasound imaging. The pooled estimates from the meta-analysis demonstrated a sensitivity of 0.79 (95% CI 0.73-0.83), a specificity of 0.83 (95% CI 0.79-0.86), a positive likelihood ratio (PLR) of 4.5 (95% CI 3.5-5.8), a negative likelihood ratio (NLR) of 0.26 (95% CI 0.20-0.34), a diagnostic odds ratio (DOR) of 17 (95% CI 11-28), and an area under the summary receiver operating characteristic curve (AUSROC) of 0.89 (95% CI 0.00-1.00). Subgroup analyses revealed that the DL models achieved a sensitivity of 0.81 (95% CI 0.71-0.88) and a specificity of 0.86 (95% CI 0.76-0.92). The PLR, NLR, DOR, and AUSROC were 5.6 (95% CI 3.2-9.8), 0.22 (95% CI 0.14-0.36), 25 (95% CI 10-64), and 0.89 (95% CI 0.00-1.00), respectively. By contrast, the CML models exhibited a sensitivity of 0.77 (95% CI 0.69-0.83) and a specificity of 0.81 (95% CI 0.77-0.85). The PLR, NLR, DOR, and AUSROC were 4.1 (95% CI 3.2-5.4), 0.28 (95% CI 0.20-0.39), 15 (95% CI 9-25), and 0.86 (95% CI 0.00-1.00), respectively. Conclusions: Radiomics-based ML shows strong potential for noninvasive prediction of MI in EC, with DL outperforming CML. However, current evidence is limited and relies mainly on internal validation. Larger-scale, multicenter studies are needed to establish robust artificial intelligence–based diagnostic tools. Clinical Trial: PROSPERO CRD420250625797; https://www.crd.york.ac.uk/PROSPERO/view/CRD420250625797

Background: Developing computable phenotypes (CP) based on #ehrs (EHR) data requires "gold-standard" labels of #patient charts obtained from clinicians. Charts are most often sampled randomly, but random sampling may fail to capture the diversity of a given #patient population, which may lead to bias of the CP. Objective: We proposed an unsupervised sampling approach designed to better capture a diverse #patient cohort and improve the information coverage of chart review samples. Methods: Our coverage sampling method utilizes clustering and stratified sampling to ensure diverse representation in chart review samples. We use simulations and a real-world data example to compare the performance of our method with random sampling. The performance of the samples was evaluated based on the information coverage and area under the receiver operator characteristic curve (AUROC). Results: Our simulation studies demonstrate that our unsupervised approach provided better coverage of #patient populations and equal or improved CP performance compared to random samples, especially in scenarios where minority sub-groups were present. In the real-world application, the method also outperformed random sampling, yielding more representative samples and enhancing CP performance. Conclusions: The proposed coverage sampling method enhances the coverage of chart review samples, leading to the development of CPs that can capture outcomes of interest in a diverse #patient population. This approach is particularly beneficial in cohorts with complex or minority sub-groups, providing a robust alternative to random sampling in EHR-based #research.

Background: Staff working in residential care homes (RCHs) have played a significant role in preventing the spread of infection among residents, visitors, and staff. Providing continuous professional training to the staff is essential. Current infection control training mostly rests on short educational talks or one-to-one reminders in the RCHs. A blended mode of online interactive #Games and face-to-face consultations was now proposed as a new way to conduct infection control training in the RCHs. Objective: This study aims to assess the efficacy of the Blended Gaming #covid19 Training System (BGCTS) on infection control practices and self-reported knowledge, attitude, and practices of standard precautions among health care workers in RCHs. Methods: A 2-arm, single-blinded, parallel cluster randomized controlled trial was designed, and 30 RCHs were recruited and randomized into an intervention group to receive the BGCTS and a control group to receive usual care on infection control training. Due to the #covid19 pandemic and infected cases in the homes, 17 RCHs refused or delayed the on-site observations. The BGCTS intervention, developed based on “The #covid19 Risk Communication Package for Healthcare Facilities” of the World Health Organization, consists of two parts: (1) an eHealth mode of a 120-minute web-based training system covering 8 topics, delivered in short-clip videos and #Games, and (2) two 30-minute face-to-face interactive sessions for concept clarification. The 2 infection control practices, “use of gloves and personal protective equipment (PPE) and performing respiratory hygiene” and “hand rub,” were assessed by on-site unobtrusive observations, and self-reported infection control practices and knowledge and attitude toward infection control were measured via online survey post intervention. Results: A total of 212 staff from 13 RCHs were involved in the analysis, with 7 RCHs from the intervention group (n=114) and 6 RCHs from the control group (n=98). A significantly greater increase in the proportions of proper use of gloves and PPE and respiratory hygiene performance (β=.195, 95% CI 0.046-0.344; P=.02) and properly performed hand rub (β=.068, 95% CI 0.005-0.132; P=.04) was observed in the intervention group. The changes in the self-reported outcomes were not statistically significant. Conclusions: BGCTS improved RCH staff’s performance in 2 infection control practices by objective measurement, “gloves and PPE use and performance in respiratory hygiene” and “hand rub.” BGCTS was shown to be an effective training, although it was a 2-week intervention. The BGCTS did not perform better than infection control briefing sessions in self-reported infection control knowledge, attitude, and practices. This electronic-based infection control training with 2 intensive interactive sessions has good potential to be adopted as regular training in RCHs. Trial Registration: Clinicaltrials.gov NCT04783025; http://clinicaltrials.gov/ct2/show/NCT04783025

Background: Emerging adulthood is a high-risk period during which many with type 1 diabetes (T1D) demonstrate suboptimal diabetes management and glycemic control. There is a need for effective and scalable interventions designed specifically for this population. Technology-based approaches are readily accessed by this age group. Further, interventions that are consistent with self-determination theory (SDT) – which posits the fulfillment of psychological needs for autonomy, self-efficacy, and relatedness promote intrinsic motivation for change – may resonate well with emerging adults’ (EAs) developmental needs for establishing independence, autonomy, and growing their social network. Objective: To gather patient feedback on three SDT-informed mHealth interventions for EAs with T1D: a Motivational Interviewing-based counseling intervention, one-way text message reminders to complete diabetes care, and a question prompt tool to empower EAs to actively participate during medical visits. Methods: In this qualitative formative study, 20 EAs reviewed and provided feedback on the newly developed interventions via individual interviews. Interviews were analyzed using Framework Matrix Analysis, an efficient approach to inductive thematic analysis. Results: EAs provided high ratings for intervention acceptability and helpfulness. EAs appreciated the technology-based approach and the tailoring to their demographic characteristics, illness experiences, and personal preferences. They also highlighted SDT-related intervention elements that aligned with SDT. Recommendations for intervention improvement included additional tailoring to personal preferences including the frequency and duration of intervention, intervention content, and personalizing reminders with the recipient’s name. Conclusions: EA feedback supports the acceptability and utility of this intervention and will be used to refine the interventions. The unique contribution of each intervention to improvements in glycemic control will be tested in a randomized controlled trial using the multiphase optimization strategy (MOST) to build the most efficacious multicomponent intervention. Clinical Trial: N/A

#Digital #MentalHealth tools such as #Apps, virtual reality, and AI hold great promise but continue to face barriers to widespread clinical adoption. The Society of #Digital Psychiatry, in partnership with JMIR #MentalHealth, presents a three-pronged roadmap to accelerate their safe, effective, and equitable implementation. First, Education: integrate #Digital psychiatry into core training and professional development through a global webinar series, annual symposium, newsletter, and an updated open-access curriculum addressing AI and the evolving #Digital Navigator role. Second, AI Standards: develop transparent, actionable benchmarks and consensus guidance, through initiatives like MindBench.ai, to assess reasoning, safety, and representativeness across populations. Third, #Digital Navigators: expand structured, train-the-trainer programs that enhance #Digital literacy, engagement, and workflow integration across diverse care settings, including low- and middle-income countries. Together, these pillars bridge research and practice, advancing #Digital psychiatry grounded in inclusivity, accountability, and measurable clinical impact.

Background: Assistive technologies (AT) are used increasingly in community settings to assist in the care of older adults. Despite a rapid increase in the capabilities and uptake of these technologies, gaps remain in understanding main barriers to their usage. Objective: This systematic review investigated the barriers and facilitators to the use of AT in the care of older adults. Methods: Six electronic databases were searched from January 2011 to March 2024. Primary studies were included if they used qualitative methods reporting findings related to barriers or facilitators to the implementation of AT (e.g. ambient and wearable sensors, alarms, tele/mhealth) for older adults (from the perspective of either carers or older adults) in community settings. All data were screened independently by two reviewers. Study quality was assessed using the Critical Appraisal Skills Programme (CASP). Data from each included study were synthesized using thematic synthesis, before barriers were mapped against the domains of the Technology Acceptance Model. Results: Ninety-five studies were included in the review. The number of studies published in the field of barriers to AT use has increased three-fold post #covid19 in comparison to the previous decade. Ten barriers - privacy, cost, insufficient knowledge, fear of misuse, usability, poor functionality, perceived lack of need, stigma and lack of human interaction were identified, as well as three facilitators –awareness of health benefits, targeted training, and user-centred design. Persistent barriers relating to all domains of the Technology Acceptance Model were identified, with the majority of these relating to the ‘behavioural intention to use’ domain (cost, privacy, stigma and fear of misuse). The majority of studies had a moderate/high risk of bias. Conclusions: There remain distinct barriers to sustained usage of AT for the care of older adults, particularly concerning adoption as defined by the Technology Acceptance Model. Further studies investigating the acceptability of ATs are needed to increase understanding of optimization strategies. Clinical Trial: PROSPERO CRD42021266656;

Background: Traditional infectious disease #Surveillance systems face significant limitations, including delayed detection, underreporting of asymptomatic cases, and inequitable healthcare access. Wastewater-based #Epidemiology (WBE), enhanced with genomic analysis, offers a non-invasive and cost-effective alternative for early pathogen detection and variant characterization, particularly valuable for monitoring international disease transmission. Objective: To implement and evaluate a genomics-enhanced WBE #Surveillance system for detecting and characterizing SARS-CoV-2 #COVID19 #coronavirus variants among international travelers at Kigali International Airport, Rwanda, and to assess its potential as an early warning system for pandemic preparedness. Methods: Between May and December 2023, we collected wastewater samples from international flights arriving at Kigali International Airport under Rwanda's National One Health strategy. Molecular detection was done using polymerase chain reaction (PCR) assays, followed by whole-genome sequencing of positive samples. Bioinformatics analysis included quality assessment with Nanoplot v.1.41.6, genome mapping using minimap2 v.2.26, and lineage identification using the Freyja tool v.1.4.5. Spatial and temporal analyses were used to identify transmission patterns and variant origins. Results: Of 630 wastewater samples collected from flights originating from nine countries, 603 were successfully processed, with 21.0% (132/617) testing positive for SARS-CoV-2 #COVID19 #coronavirus. Whole-genome sequencing was conducted on 33 samples, yielding an average viral sequence depth of 1,250 reads with 92% genome coverage (range: 78-97%). Genomic analysis identified seven SARS-CoV-2 #COVID19 #coronavirus variants, including Omicron subvariants XBB.1.5, XBB.1.16.6, EG.5.1, GE.1, and FE.1.1.1. Notably, 70% (23/33) of sequenced samples could not be assigned to existing lineages, suggesting potential novel variants. Most samples came from Qatar (21.4%, n=135), United Arab Emirates (19.5%, n=123), and the United Kingdom (19.4%, n=122). Positive samples were detected from 11 countries, with variants frequently found in flights from the United Kingdom, France, Belgium, Kenya, Tanzania, and South Africa. Sample collection capacity increased from six in week 1 to 33 by week 27. SARS-CoV-2 #COVID19 #coronavirus positivity rates showed seasonal variation, with a marked decline in June-July 2023. Conclusions: Genomics-enhanced WBE demonstrated high sensitivity for early detection of SARS-CoV-2 #COVID19 #coronavirus variants among international travelers, including potential novel variants undetectable through traditional #Surveillance. Its non-invasive and cost-effective nature, combined with the ability to generate population-level #Epidemiological insights, makes it particularly suitable for resource-limited settings. This approach supports Rwanda's National One Health strategy and offers a scalable model for advancing global health security in Sub-Saharan Africa through innovative #Surveillance tools.

Background: Generative artificial intelligence (GenAI) models have emerged as a promising yet controversial tool for #MentalHealth. Objective: The purpose of this study is to understand the experiences of individuals who repeatedly used ChatGPT for emotional and #MentalHealth support (EMS). Methods: We recruited 270 adult participants across 29 countries who regularly used ChatGPT for EMS during April 2024. Participants responded to quantitative survey questions on the frequency and helpfulness of using ChatGPT for EMS, and qualitative questions regarding their therapeutic purposes, emotional experiences of using, and perceived helpfulness and rationales. Thematic analysis was used to analyze qualitative data. Results: Most participants reported using ChatGPT for EMS at least 1-2 times per month for purposes spanning traditional #MentalHealth needs (diagnosis, treatment, psychoeducation) and general psychosocial needs (companionship, relational guidance, well-being improvement, decision-making). Users reported various emotional experiences during and after use for EMS (e.g., connected, relieved, curious, embarrassed, or dis#Appointed). Almost all users found it at least somewhat helpful. The rationales for helpfulness include perceived changes after use, emotional support, professionalism, information quality, and free expression, whereas the unhelpful aspects include superficial emotional engagement, limited information quality, and lack of professionalism. Conclusions: Despite lacking ethical regulations for EMS use, GenAI has become an increasingly popular self-help tool for #MentalHealth. These results highlight the urgent need to promote AI literacy and ethical awareness among community users and healthcare providers, to examine its effectiveness and mechanisms experimentally, and to identify who may benefit or be harmed.

Background: Bladder cancer is a disease with complex perturbations in gene networks and heterogeneous in terms of histology, mutations, and prognosis. Advances in high-throughput sequencing technologies, genome-wide association studies, and bioinformatics methods have revealed greater insights into the pathogenesis of complex diseases. Network biology-based approaches have been used to demonstrate the complex physical or functional interactions between molecules which can lead to potential drug targets. Objective: There is a need to better understand gene networks and protein-protein interactions (PPI) specific to urothelial carcinoma. Methods: We performed a multi-sample PPI study comparing two urothelial carcinoma architectures: papillary and non-papillary. We used a novel PPI analysis tool, Proteinarium to identify clusters of patients with shared PPI networks in each architecture. The feature of this tool is to analyze the PPI networks of patients and visualize them in clusters based on their network similarities from any genomic data including Next Generation Sequencing (NGS). Results: We observed distinct networks for the papillary and non-papillary groups. Proteins unique to the papillary urothelial carcinoma detected in two separate datasets included UBA52, RPS27A, UBR4, CUL1, UBE2K, and CDC5L. Proteins found in the non-papillary urothelial carcinoma specific PPI network were GNB1, UBC, RHOA, FPR2, GNGT1, PIK3CA, PIK3CG, HSP90AA1, SLC11A1, CCT7, ARHGEF1, PAK1, PAK2, PSMA7, and TRIO. Conclusions: We identified distinct PPI networks specific to papillary and non-papillary urothelial carcinomas presenting unique molecular entities. Clinical Trial: N/A

Date Submitted: Nov 19, 2025. Open Peer Review Period: Nov 24, 2025 - Jan 19, 2026.

Background: Many individuals in urban low-income settings face barriers to engaging in primary care. The advancement of #Mobile #Health (#mHealth) expands options for facilitating communication between primary care providers and clients. Objective: We conducted a pilot project that provided primary care clients in a low-income neighbourhood in Vancouver, Canada with a #Mobile phone and access to WelTel, an #mHealth tool that uses a two-way texting approach and sends weekly automated check-in messages asking clients “Are you okay? If clients respond that they are not okay, clinic staff call them back to try to sort out their problem. Our study measured phone retention over a six-month period and explored the acceptability and feasibility of WelTel among a cohort of clients with complex #Health challenges. Methods: Participants completed three surveys over a six-month period, and clients who had access to a functional #Mobile phone at the end of the follow-up period were invited to complete a qualitative interview. Results: We enrolled 49 clients (median age of 48, 53% women) and interviewed 16 participants. The WelTel intervention was well-received by participants and was found to strengthen client-provider relationships, create opportunities for self-reflection, and promoted pathways for receiving care. Some participants reported that the WelTel intervention made them feel cared for. Approximately 1% of weekly messages received an “I am not okay” reply and participants reported that 75% of these problems were fully addressed by clinic staff. However, only 14 (29%) retained the #Mobile device supplied by the study at the end of six months. Conclusions: Overall, the pilot study found this intervention feasible and acceptable to clients. Expanded enrollment in the WelTel service will allow us to examine whether it also facilitates engagement in primary care among marginalized urban populations. Clinical Trial: This is not a clinical trial.

Background: Facial palsy is the most common single nerve disorder world-wide. Incidence rates are rising globally, with incomplete recovery producing long-term reductions in quality of life for one in three cases. Neuromuscular retraining (NMR) to restore balanced facial function is the most widely evaluated effective non-drug therapy. There are currently no estimates of the likely economic impact of telerehabilitation introduced into the facial NMR therapy pathway. Objective: To undertake an analysis of the economic burden associated with facial palsy in the UK. To model the cost-effectiveness of digital rehabilitation (tracking sensors in ‘smart specs’) added into the NMR therapy pathway. Methods: The national burden associated with facial palsy included all treatment costs and economic consequences of unresolved cases. Estimates were based on annual incidence, clinical treatment patterns, recovery profiles, and impact on health-related quality of life. The monetary value placed on different levels of clinical recovery (House-Brackmann (HB) grade) was identified in a national Delphi exercise. An economic model was developed to estimate the costs and benefits of telerehabilitation from a healthcare perspective, and to calculate the incremental cost-effectiveness ratio. Results: The direct healthcare cost of facial palsy treatment for all patients diagnosed each year in the UK is estimated at £86.3 million. Long-term morbidity costs associated with these cases total £351-£584 million. Inclusion of societal costs, such as changes in employment, increases this figure to over £1.27 billion. The value placed on recovery from HB grades 5 and 6 is >£19,400, and £8,600 for HB grades 3 and 4. The economic model predicts that telerehabilitation will reduce healthcare costs and improve outcomes; conservative estimates are £468 saving per patient and health gain of 0.14 HB grade. Implementation of telerehabilitation for patients with incomplete recovery is predicted to produce a national saving of up to £3.08 million in healthcare costs per cohort, with associated HB grade recovery valued at up to £17.8 million. Inclusion of wider societal impact (e.g. on employment) increases cost savings significantly. Conclusions: Introduction of digital rehabilitation into facial palsy therapy is predicted to reduce costs and improve patient outcomes. Further trials with integral economic evaluations are now needed to establish cost-effectiveness in real-world settings. Clinical Trial: NA

Background: Digital platforms, particularly social media including Instagram, present unique opportunities for health promotion among adolescents due to their widespread use with interactive features supporting high user engagement. However, the #feasibility of effectively utilizing platforms like Instagram for health interventions requires careful consideration of adolescent engagement patterns. Objective: This pilot study evaluated the leduin program – designed to foster essential life skills and functional social media use among adolescents – while also exploring the broader #feasibility of using Instagram to deliver complex social and psychological interventions in this population. Methods: The study adapted Bowen’s #feasibility framework and used a mixed-methods approach. Quantitatively, Instagram interaction metrics of 99 participants (62 women (62.6%) and 37 men (37.4%), aged 14–18; mean = 15.2, SD = 0.74) were analyzed descriptively (means, medians, SDs) and inferentially (Welch’s ANOVA, Kruskal-Wallis, Pearson and Spearman correlations, linear and segmented regressions) using RStudio. Metrics included story views, retention rates, feature engagement (e.g., polls, question stickers, quizzes), and drop-off rates. Recruitment efforts were also analyzed descriptively. Qualitatively, 13 post-program semi-structured interviews were conducted with 11 women (64.7%) and 6 men (35.3%) (mean age = 15.29, SD = 0.99). Participants were sampled to reflect varying engagement levels (six high, five medium, six low). The mean interview duration was 25:11 minutes (SD = 6:34). Content analysis, with high inter-coder reliability (κ = .90), comprehensively explored participants’ experiences and the program’s impact. Results: Quantitative results indicate that the recruitment process was challenging, with 101 schools and 10 youth centers contacted, resulting in a participation rate of 12.8% (99 out of 775 students). On Instagram, story views ranged from 34 to 81 per post, with an average daily retention rate of 87.7% (SD = 7.8%). By week 4, 76.0% of the total drop in views had occurred (mean views declined from 66.1 to 53.4); by week 6, 97.3% of the drhad been reached (49.9 views), indicating sustained viewer interest over the 14-week program. Features requiring minimal user effort including polls (56.8%-54.4%), quizzes (56.6%), and sliders (51.2%) showed significantly higher interaction rates than more demanding features such as challenges (21.7%) and question stickers (20.6%) (P

This viewpoint explores the role of conversational AI in educating the public on breast self-examination, using an interaction with DeepSeek AI as an example. While the AI provided clear, accessible, and evidence-aligned responses consistent with professional guidance, limitations included oversimplification and absence of multimedia resources. These findings suggest that AI can support public health education but should be complemented by physician oversight and evidence-based resources for responsible use.

Journal of Medical Internet Research · Journal of Medical Internet Research 10655 articles · JMIR Research Protocols 5166 articles · JMIR Formative ...

Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.Nov.2025. Citation. Please cite as: Crawford M What's ...