Journal of Medical Internet Research

BackgroundIrritable bowel syndrome (IBS) contributes disproportionately to gastrointestinal-related emergency department (ED) utilization in South Korea, yet evidence on population-level interventions informed by patient-generated digital discourse remains limited. Recent social media analyses have identified dominant thematic concerns among IBS patients, including dietary triggers, symptom management, psychosocial burden, and information-seeking, suggesting actionable targets for digital self-management tools. ObjectiveTo evaluate the population-level impact of the Jang Geongang (, "Gut Health") digital self-management platform, whose content architecture was informed by topic modeling of IBS-related social media discourse, on IBS-attributed ED visits and unplanned hospitalizations, using a controlled interrupted time series (CITS) design. MethodsWe analyzed monthly aggregate claims data from South Koreas National Health Insurance Service (NHIS) spanning January 2018 to December 2024 (84 monthly observations). The Jang Geongang platform was launched in four pilot metropolitan areas (Seoul, Incheon, Daejeon, Gwangju) in July 2021, with eight non-pilot metropolitan areas serving as concurrent controls. Segmented regression with Newey-West heteroskedasticity and autocorrelation consistent (HAC) standard errors was used to estimate changes in level and trend of IBS-attributed ED visits per 100,000 insured population. Sensitivity analyses included autoregressive integrated moving average (ARIMA) transfer function models, varying pre-intervention windows, and leave-one-out control exclusion. ResultsThe CITS model estimated an immediate level change of -3.42 IBS-attributed ED visits per 100,000 (95% CI: -5.18 to -1.66, p < 0.001) following platform launch, and a change in monthly trend of -0.19 visits per 100,000 per month (95% CI: -0.31 to -0.07, p = 0.003), compared to control areas. By December 2024, the cumulative estimated reduction was 10.5 ED visits per 100,000 (23.8% relative reduction). Effects were concentrated in younger adults (19-39 years; level change: -5.14, p < 0.001) and IBS-D subtype visits (level change: -4.87, p < 0.001). ARIMA transfer function models corroborated these findings (immediate impact: -3.28, p = 0.001). Unplanned hospitalizations showed a smaller but significant reduction (level change: -0.84 per 100,000, p = 0.018). ConclusionsA digital self-management platform designed using social media derived IBS patient discourse insights was associated with sustained population-level reductions in IBS-attributed emergency utilization. Controlled interrupted time series analysis provides robust evidence for the public health impact of translating social media analytics into scalable digital health interventions.

BackgroundTimely detection of seasonal influenza outbreaks is critical for healthcare system preparedness and public health response. Although numerous studies have examined short-term influenza forecasting, fewer have operationalized prediction as a binary early-warning problem linked to actionable surveillance thresholds. This study evaluated the performance of traditional and machine learning models for detecting national influenza outbreak weeks using U.S. Centers for Disease Control and Prevention (CDC) ILINet surveillance data. MethodsWeekly national ILINet data from 2010-2025 were analyzed. Outbreak weeks were defined as those in which weighted influenza-like illness (ILIPERCENT) exceeded the 90th percentile of the 2010-2017 training distribution (threshold = 3.3932%). Predictors included three-week lags of ILIPERCENT and percent positive laboratory specimens, along with seasonal harmonic terms. Models were trained on 2010-2017 data and evaluated on a temporally held-out 2020-2025 test period. Performance metrics included area under the receiver operating characteristic curve (AUC), precision-recall area under the curve (PR-AUC), sensitivity, specificity, precision, and F1-score. FindingsOn the 2020-2025 test set, logistic regression achieved an AUC of 0.9964 and PR-AUC of 0.9868, with sensitivity of 1.0000 and specificity of 0.9516. XGBoost achieved an AUC of 0.9946 and PR-AUC of 0.9812, with sensitivity of 0.8939 and specificity of 0.9798. Both models demonstrated near-perfect discrimination between outbreak and non-outbreak weeks under strict temporal validation. InterpretationNational influenza outbreak early-warning can be implemented using publicly available CDC surveillance data with high discriminatory accuracy. Framing prediction as a threshold-based outbreak detection problem strengthens operational relevance and supports integration of predictive analytics into routine influenza surveillance and preparedness planning. Author SummarySeasonal influenza places a heavy burden on hospitals and communities each year, yet public health officials often rely on surveillance reports that describe what has already happened rather than signaling when activity is about to intensify. We examined whether routinely collected U.S. influenza surveillance data could be used to detect outbreak conditions earlier and more clearly. Using national data from the Centers for Disease Control and Prevention (CDC) covering 2010 to 2025, we compared a traditional statistical model with a machine learning approach to determine how accurately each could identify weeks when influenza activity exceeded a predefined outbreak threshold. Both approaches performed extremely well when tested on recent seasons, correctly distinguishing outbreak from non-outbreak weeks with high accuracy. Importantly, this framework translates weekly surveillance data into a practical alert signal rather than simply producing numerical forecasts. By linking model output to a clear outbreak definition, health departments and healthcare systems could use similar tools to support timely planning, communication, and resource allocation during influenza season.

Patient-generated streaming data from wearable and digital technologies is increasingly promoted as a means of supporting mental health monitoring and clinical decision-making. While patient acceptance of these technologies has been reported, clinician perspectives remain underexplored despite their central role in determining whether streaming data are meaningfully integrated into routine care. This study explored clinicians experiences, as well as perceived facilitators and barriers, related to integrating patient-generated streaming data into routine mental health practice. A qualitative, exploratory interview study was conducted to examine clinicians experiences and perspectives on integrating patient-generated streaming data into mental health care. Semi-structured interviews were conducted with 33 clinicians, including family physicians (n=11), psychiatrists (n=12), and psychologists (n=10). Data were analyzed using reflexive thematic analysis guided by Braun and Clarkes six-step approach. Six themes were identified. Clinicians described variable use of digital and streaming technologies, ranging from routine engagement to deliberate non-use. Streaming data were viewed as clinically valuable when they provided longitudinal and objective insights, identified physiological and behavioural pattern changes, and supported patient engagement. However, clinicians emphasized that clinical usefulness was contingent on interpretability, contextual information, and relevance to decision-making. Major barriers included poor integration with electronic medical records, time constraints, data volume, limited organizational support, and uncertainty regarding data reliability and validity. Clinicians also expressed persistent concerns about privacy, governance, and regulatory oversight, highlighting the need for clear safeguards and accountability structures. Clinicians view patient-generated streaming data as a promising adjunct to mental health care, particularly for capturing longitudinal change between visits. However, meaningful clinical integration remains constrained by usability, workflow, organizational, and regulatory challenges, as well as limited confidence in data interpretation. Addressing these barriers through improved system integration, interpretive support, validation, and governance will be essential for translating the potential of streaming data into routine clinical practice.

We developed and validated a self-administered clinical vignette platform powered by a large language model (LLM), deployed through a SurveyCTO web survey, to measure primary health care provider competencies in Vietnam. In a pilot focus group, nine physicians rated LLM-simulated patient interactions as realistic (mean 3.78/5) and user-friendly. In the validation phase, 22 providers completed 132 vignette interactions across ten clinical scenarios in Vietnamese. Essential diagnostic checklist scores (human-coded from translated transcripts) correlated with expert clinician evaluations (Pearsons{rho} = 0.55-0.60). LLM-automated coding of checklist items from translated English transcripts correlated reasonably with human coding ({rho} = 0.53), and coding directly from Vietnamese transcripts performed comparably ({rho} = 0.51), suggesting that a separate translation step may not be necessary. The total cost of 132 chatbot interactions was under USD 2. LLM-driven conversational vignettes represent a low-cost and scalable method for assessing provider competencies in respondents local language, eliminating the need for extensive enumeration staffs while preserving the open-ended format critical to vignette validity, and additionally introducing flexible feature extraction from transcripts using grading rubrics. The platform is open-source and designed for replication in other health system contexts. Author summaryMeasuring the clinical skills of healthcare providers is essential for improving the quality of care, but current survey methods are expensive and require trained enumerators to travel to health facilities in person. We developed a new approach that uses large language models (LLMs) - the technology behind tools like ChatGPT and Claude - to simulate patients in realistic clinical conversations that healthcare providers can complete on their phones or laptops over the Internet in their own language. In Vietnam, we tested this tool with 31 physicians across ten clinical scenarios. Providers found the simulated patient conversations realistic and easy to use. We also tested whether LLMs could automatically score the conversations, which showed reasonable agreement with human scoring, and performed nearly as well when scoring directly from Vietnamese, without requiring a separate translation step. When we compared these results from our tool against holistic expert physician ratings of the same conversations, the scores agreed well, suggesting that automatic transcript grading based on rubrics produces meaningful measures of clinical skill. This tool costs less than two US dollars for over a hundred consultations and required no in-person surveyors, making it potentially transformative for routine, large-scale monitoring of healthcare quality in resource-limited settings. The platform and code are openly available for adaptation.

Eye tracking is recognized as a gold standard for measuring visual attention and cognitive engagement. In this study, it offers a useful lens for understanding how primary care providers balance patient communication with navigation of electronic health records (EHRs). We used wearable eye tracking to collect visual information processing behavior and conducted a retrospective think-aloud protocol to examine how primary care clinicians processed suiciderelated information (CAT-MH(R)) embedded in the EHR during simulated visits. Eye-movement data showed substantial visual attention directed toward the EHR, indicating added information-processing demands during communication. Retrospective think-aloud data supported the analysis of eye movement data by revealing that clinicians searched multiple record sections to verify risk indicators and often postponed suicide-related discussions until confirming relevant results. These findings illustrate how EHR-embedded screening tools shape clinical attention and encounter flow.

BackgroundObjective Structured Clinical Examination (OSCE; Clinical Performance Examination [CPX] in South Korea) is a high-stakes assessment of clinical performance, communication, and reasoning during time-limited patient encounters. As AI-enabled virtual standardized patient (VSP) simulation and automated scoring are introduced for OSCE-like training, prospective evidence is needed on how such systems perform and are perceived when embedded in real educational workflows. MethodsWe developed CPX with Medical students Assistant for Training and Evaluation (CPX-MATE), a web-based platform integrating (1) CPX with Virtual Standardized Patient (CPX-VSP), real-time voice dialogue with a VSP using speech-to-speech (STS) models, and (2) CPX with Real-Time Evaluator (CPX-RTE), automated transcription, checklist-based scoring, and feedback from encounter audio using a Speech-to-Text model and a large language model. During an emergency medicine clerkship (Nov 2025-Jan 2026), 60 senior medical students completed two 12-min CPX encounters (VSP with acute pancreatitis; HSP with ureteral stone) with immediate CPX-RTE feedback. For CPX-VSP, students were assigned to either a full-capacity or a resource-limited STS configuration (n=30 each). Dialogue fidelity was evaluated by turn-by-turn analysis of student-VSP exchanges, classifying responses into clinically meaningful error types (tangential, oversharing, role-breaking, off-script). CPX-RTE performance was assessed by agreement (Gwets AC1) with professor real-time and resident video-based ratings using a 45-item checklist. Usability of CPX-VSP and CPX-RTE, with overall system usability scale (SUS), were surveyed, and mean per-session costs for CPX-VSP and CPX-RTE were calculated. ResultsAcross 3,282 dialogue turns, overall error rates were 1.77% versus 9.43% for full-capacity versus resource-limited STS configurations (p<0.001), driven by fewer tangential and oversharing responses; no off-script errors were observed. The mean per-session cost was $0.12 for resource-limited configuration and $0.78 for full-capacity configuration. CPX-RTE showed high agreement with human ratings (AC1=0.916 vs professor; 0.916 vs resident), with slightly different levels of agreement across four sections, and high usability across all domains (mean scores, 4.65-4.92), with a per-session cost of $0.17. CPX-MATE demonstrated good overall usability (median [IQR] of 77.5 [70.0-85.0]). ConclusionsEmbedded within a prospective clinical clerkship, CPX-MATE demonstrated operational fidelity and human-level checklist agreement as an end-to-end, voice-based AI-assisted OSCE platform. This real-world deployment supports its scalable integration as a complementary assessment tool while highlighting the importance of systematic validation and context-aware implementation in medical education.

BackgroundHwa-byung (HB) is a Korean culture-bound syndrome characterised by prolonged suppression of anger and somatic complaints. No evidence-based digital therapeutic (DTx) has been developed for HB. We evaluated the feasibility, user experience (UX), and preliminary clinical effect of an acceptance and commitment therapy (ACT)-based DTx application, Hwa-free, for HB. MethodsAdults aged 19-80 years diagnosed with HB were enrolled in a four-week app-based intervention with assessment at baseline (Week 0), Week 2, Week 4, and Week 8 follow-up. The primary outcome was UX assessed via a 22-item survey at Week 4. Secondary outcomes included HB-related symptom and personality scales, depression, anxiety, anger expression, psychological flexibility, health-related quality of life, and heart rate variability. ResultsOf 45 screened, 30 were enrolled and 28 constituted the modified intention-to-treat population. Mean app use was 19.9 {+/-} 7.9 days (71.2% adherence over 28 days). Adverse events were infrequent and unrelated to the intervention. Positive response rates exceeded 80% for video content (items 2-4: 82.8-89.7%), HB self-assessment (86.2%), meditation therapy (86.2%), and in-app guidance (85.7%). Pre-post improvements from baseline to Week 4 were observed in 11 of 18 clinical scales, including HB Symptom Scale ({Delta} = -9.8, Cohens d = -0.92), Beck Depression Inventory-II ({Delta} = -13.3, d = -1.11), and state anger ({Delta} = -7.8, d = -0.96). The HB screening-positive rate declined from 100% at baseline to 55.6% at Week 8. ConclusionsHwa-free demonstrated adequate feasibility, acceptable UX, and preliminary evidence of clinically meaningful improvement in HB-related symptoms. Future randomised controlled trial is warranted. Trial registrationCRIS, KCT0011105

BackgroundTyping in the electronic health record (EHR) takes up healthcare providers time and cognitive space and constitutes a substantial administrative burden contributing to high burnout rates in healthcare. Ambient digital scribes may improve this problem. ObjectiveTo investigate the effect of the use of Autoscriber, an ambient digital scribe, on healthcare providers administrative workload and the quality of medical notes in the EHR. MethodsA study period of 26 weeks was randomized into weeks when healthcare providers were allowed to use Autoscriber (intervention weeks) and weeks when they were not (control weeks) in a 2:1 ratio. Workload was assessed by comparing the number of characters typed in the medical note during control weeks with the number of modifications that were made to the summary produced by Autoscriber during intervention weeks. Quality of the medical note was measured by having a large language model (LLM) count the number of hallucinations, incorrect negations, context conflation errors, speculations, other inaccuracies, omissions, succinctness errors, organization errors and terminology errors per medical note. ResultsBetween 1 November 2024 and 30 April 2025, 35 healthcare providers from 14 different specialties recorded 387 consultations in intervention weeks, and 142 in control weeks. The median number of characters typed per medical note was 1079 in control weeks and the median number of modifications necessary to produce the medical note was 351 in intervention weeks, compatible with a lower workload. All types of errors occurred significantly less frequently in notes made with the support of Autoscriber than in those without, except for speculations, where the difference did not reach statistical significance. ConclusionsThe use of Autoscriber resulted in a lower workload and a higher quality of the medical note.

Wearable devices can collect changes in human behaviors related to mental health including depression and anxiety. Here, we examined whether and how digital metrics from a consumer-grade wearable smart ring (Oura Ring) differed by severity of depression and anxiety symptoms using data from a large-scale population-based sample of young adults (n=1,290, age range: 33-35). Participants wore the ring for two weeks, assessing sleep architecture, nocturnal heart rate (HR), heart rate variability (HRV), and movement intensity. Mental health symptoms were assessed using the Generalized Anxiety Disorder 7-item and Hopkins Symptom Checklist-25 scales. On average, participants with higher depression and/or anxiety symptoms had lower levels of rapid eye movement and had higher levels of deep and light sleep, elevated nocturnal HR, reduced HRV, and lower daytime movement compared to non-symptom individuals. Findings suggest that symptoms of depression and anxiety may manifest in physiological and behavioral metrics collected by consumer-grade wearable devices.

BackgroundTinnitus affects a substantial proportion of the global population and can severely disrupt sleep, mood, and daily functioning, yet the quality of mobile health apps designed for tinnitus management remains highly variable. Traditional evaluation methods, including clinical trials, expert rating scales, and small-scale surveys, rarely capture large-scale, feature-level feedback from real-world users, leaving a gap in understanding which app characteristics drive sustained engagement and satisfaction. MethodsThis study analysed 342,520 English-language reviews from 84 tinnitus-related apps on iOS and Android collected between 2015 and 2025. A pipeline first applied VADER-based preprocessing and sentiment assignment, then trained a graph neural network aspect-based sentiment analysis (GNN-ABSA) model operating on sentence-level dependency graphs to infer feature-level sentiment for domains such as sound therapy, sleep support, pricing, advertisements, stability, and user interface. ResultsThe GNN-ABSA model achieved an accuracy of 84.4% and a macro F1 score of 0.829 on unseen aspect-level test data, indicating stable performance across sentiment classes. Therapeutic features like sound masking and sleep support were associated with predominantly positive sentiment, whereas pricing, advertisements, background playback, and technical stability attracted more neutral or negative feedback over the ten-year period. ConclusionsLarge-scale, graph-based feature-level sentiment analysis provides a user-cantered perspective that complements clinical trials and expert app quality ratings, offering actionable guidance for developers seeking to prioritize design improvements, supporting clinicians in recommending suitable apps to patients, and informing the design of more explainable and user-driven digital health tools. Trial RegistrationNot applicable. This study analysed publicly available app store reviews and did not involve human participants.

BackgroundThe rapid proliferation of digital platforms has transformed health information access but has also led to increased exposure to misinformation. Existing research lacks standardized tools to quantify individual-level exposure to health misinformation in a comprehensive manner. ObjectiveTo develop a novel composite index--the Misinformation Exposure Index (MEI)--to measure multidimensional exposure to health misinformation among social media users. MethodsA questionnaire-based pilot study was conducted among a young adult population to assess patterns of health information exposure, source utilization, trust, and behavioural responses. The MEI was developed using a multi-domain framework comprising Exposure Frequency, Source Diversity and Risk, Trust in Information, and Behavioural Response. Responses were scored using Likert scales and weighted domain contributions to generate a composite score ranging from 0 to 100. ResultsParticipants demonstrated moderate to high engagement with digital platforms for health information, with reliance on both formal and informal sources. Variability in trust and verification behaviours was observed, with a proportion of participants reporting adoption of health-related practices without professional consultation. Composite MEI scores indicated that most individuals fell within the moderate exposure category, with a subset exhibiting high exposure characterized by frequent engagement with high-risk sources and behavioural influence. ConclusionThe MEI provides a novel and comprehensive framework for quantifying health misinformation exposure by integrating exposure patterns, source characteristics, trust, and behavioural outcomes. The index has potential applications in public health surveillance and intervention design. Further validation through large-scale studies is warranted to establish its reliability and generalizability.

BackgroundStroke survivors often describe problems of insufficient access to information post-discharge. Traditional resources may not meet their information needs, but Artificial Intelligence (AI) could play a role. AimsTo compare user perceptions of stroke information from third sector stroke websites with that generated by AI and summarize the attributes of the preferred stroke information formats. MethodsUK third sector stroke websites were searched for materials relevant to 15 questions commonly asked by stroke survivors. ChatGPT(-4o) was used to generate responses to these questions. Stroke professionals (clinicians, researchers), stroke survivors, and their caregivers reviewed third sector and AI responses, indicating the source of the response and their preferred text. Participants also rated responses on scales of empathy, trustworthiness, reliability, comprehensibility and usefulness, and provided free text comments. Proportions of preference and correctly guessed responses, as well as mean ratings, were compared between the groups. Framework analysis was used to identify the attributes of response formats preferred by stroke survivors. ResultsRelevant responses were found for 13 (87%) out of 15 questions. Across groups, 60 participants with mean age of 44 (SD=14) and 57% females, correctly identified 184/300(61%) of AI responses, and preferred AI response in 123/300(46%) of the cases. The groups differed in their preference with clinicians being least likely to choose AI (34%), followed by stroke survivors (49%) and researchers (54%). All groups viewed third sector responses as more empathetic in tone. The themes of content, structure, and tone of responses were described by stroke survivors with the emphasis on clarity, conciseness, and approachable tone. ConclusionsAI-generated responses to stroke questions were rated positively by stroke survivors and researchers, whereas stroke clinicians were more sceptical. Given that stroke information materials are intended for people with lived experience of stroke, their input should be prioritized to inform development of new resources.

Support from peers has long been considered an alternative support resource than professional healthcare ones. Despite the inconclusive findings of previous studies regarding the effects of peer support, the integration of Peer Support Networks (PSNs) for youth and adolescents appears to offer promising outcomes. However, many existing digital peer support systems operate as proprietary platforms, lacking transparency in monitoring the efficacy of support and in understanding how personality traits influence outcomes. However, many existing digital peer support systems operate as proprietary platforms lacking transparency in monitoring the effect of peer support and understand the influence of personality traits on its outcomes. To address these limitations, we utilized our research platform, Peer2S, a digital PSN designed to facilitate connections based on shared lived experiences while simultaneously monitoring users mental well-being and personality traits. We conducted a four-week within-subjects experiment with 28 Japanese university students to examine the PSN systems impact on anxiety and perceived social support. Following a two-week baseline control period, participants interacted with the system for two weeks. Pre- and post-intervention assessments utilized generalized anxiety and multidimensional social support measures, alongside personality evaluations. The results indicated that participants experienced a significant reduction in anxiety after using the system, whereas no significant changes occurred during the control period. Perceived general social support showed a borderline significant increase, though specific college-context support dimensions remained unchanged. Furthermore, multiple regression analysis revealed that personality traits moderated anxiety outcomes. Contrary to typical protective associations, higher agreeableness significantly predicted increased anxiety during the intervention, which may reflect cultural tendencies toward conflict avoidance and over-accommodation in Japan. Conscientiousness demonstrated a marginally significant protective effect against anxiety, while personality traits did not predict changes in perceived social support. These findings suggest that short-term, algorithmically mediated peer support can yield measurable improvements in mental well-being, particularly in reducing anxiety. Moreover, the varying impacts of personality traits highlight the necessity of considering sociocultural contexts when designing and deploying digital mental health interventions. Authors summaryThe formation of social bonds is often selective, established through shared values, cultural interests, or significant life experiences among "peers." In some populations such as adolescents and young adults, peer support is regarded as a promising source of empathy, understanding, and psychological support. We report a study conducted using our customized digital peer matchmaking system with Japanese university students to examine if this novel approach to peer support impacts mental well-being. We found that after just two weeks of using the system, participants experienced a significant reduction in their anxiety levels. We also dove deeper to look at if individual personality traits influence their use outcomes. Interestingly, our results revealed that highly agreeable individuals actually experienced increased anxiety while using the system. In a Japanese cultural context, this may occur because agreeable users tend to avoid conflict and over-accommodate others at their own expense. Ultimately, our research demonstrates that matchmaking algorithms can effectively facilitate digital peer support to improve mental well-being, provided we carefully consider how different personality traits and cultural backgrounds shape user experiences.

Introduction: Understanding the factors influencing perceptions of cancer-related information is crucial for improving public health communication. This study explores the association between perceived difficulty in understanding information related to cancer (Cancer info Hard to Understand) and concerns about the quality of cancer-related information (Concern about Cancer Info Quality) with the extent of difficulty in comprehending medical statistics information (Understanding Medical Statistics). Methods: Data came from the 2022 Health Information National Trends Survey (HINTS). The cross-sectional study included 1972 participants with a response rate of 67.36% for Cancer info Hard to Understand, and 65.31% for Concern about Cancer Info Quality. We investigated the effect of Understanding Medical Statistics on Cancer info Hard to Understand, and Concern about Cancer Info Quality using univariate and multivariable logistic regression models with survey weights. The multivariable logistic regression model was adjusted for age, gender, ethnicity, marital status, education level, employment history, confidence in internet health resources, and social media. The chi-square test was used to measure the association between the predictors and the outcome. Results: Individuals finding medical statistics hard to understand were more likely to be concerned regarding the quality of the cancer-related information (AOR=1.74, 95% CI: [1.20, 2.52]) and also found cancer-related information difficult to comprehend (AOR=1.89, 95% CI: [1.19, 3.00]). Also, the influence of social media on health information seeking was significantly associated with Concern about Cancer Info Quality (AOR=2.24; 95% CI: [1.33, 3.76]), and Cancer info Hard to Understand (AOR=2.84; 95% CI: [1.61, 5.03]). Conclusion: This study highlights the critical role of understanding medical statistics in shaping perceptions of cancer-related information. From an epidemiological perspective, enhancing statistical literacy is essential for making informed health decisions, addressing health disparities, and designing effective, targeted cancer communication strategies.

BackgroundScalable, low-burden behavioral interventions are needed to address rising subclinical mental health symptoms. However, few randomized controlled trials have evaluated ultra-brief, remotely delivered, meditation using multimodal outcome assessment under real-world conditions. MethodsWe conducted a fully remote randomized controlled trial (ClinicalTrials.gov: NCT06014281) evaluating a focused-attention meditation intervention delivered via brief instructor training and independent daily practice. A total of 299 meditation-naive adults were randomized to immediate intervention or waitlist control in a delayed-intervention design. Participants practiced [&ge;]10 minutes daily for 8 weeks within a 16-week study. Outcomes included validated self-report measures, web-based cognitive tasks, and wearable-derived physiological metrics. ResultsAcross randomized and within-participant replication phases, the intervention was associated with significant reductions in anxiety and mind wandering, with effects remaining stable during 8-week follow-up. Improvements were greatest among participants with higher baseline symptom burden. Sleep disturbance improved selectively among individuals with poorer baseline sleep. Secondary outcomes, including rumination, perceived stress, social connectedness, and quality of life, also improved. Cognitive performance showed modest improvements primarily among lower-performing participants. Resting heart rate exhibited nominal reductions. ConclusionsAn ultra-brief, fully remote meditation intervention requiring 10 minutes per day was associated with sustained improvements in psychological functioning and smaller, baseline-dependent effects on cognition in a non-clinical population. These findings support digital delivery of low-dose meditation as a scalable preventive mental health strategy.

Background HIV remains a major public health challenge in South Africa, with gaps in early diagnosis and linkage to care driving onward transmission. Adolescent girls and young women face barriers to timely care, including stigma, privacy concerns, and limited clinic access, while healthcare providers work in resource-constrained settings with high client volumes. We evaluated the Self-Care from Anywhere (SCFA) toolkit, an AI-enabled intervention comprising an AI Companion for AGYW and a provider-facing Clinical Portal to support HIV prevention, testing, and linkage to care. The AI Companion is designed to complement and extend human-delivered services, particularly in resource constrained settings, rather than replace in-person counselling. Methods We conducted an exploratory study to assess the usability, feasibility, and acceptability of the SCFA toolkit in Gauteng Province (November 2024-May 2025). AGYW engaged with the AI Companion, and a subset completed a simulated HIV self-testing activity with AI-delivered counselling. Pre and post-intervention surveys, including the System Usability Scale (SUS), were administered. Usability testing of the Clinical Portal involved healthcare providers using the toolkit without formal training to capture first impressions. A subset of AGYW and healthcare providers participated in separate focus group discussions or in-depth interviews. Quantitative data were analysed using descriptive statistics, and qualitative data were analysed thematically. Results A total of 97 AGYW were enrolled; 75.3% had completed high school and 91.8% were unemployed or full time students. Most participants (85.6%) self-reported HIV-negative status, and 63.9% reported sexual activity in the past 12 months. The AI Companion demonstrated high usability (mean SUS 87.7, SD 12.7) and was perceived as acceptable and useful, particularly for its personalisation and confidentiality features. Healthcare providers had a mean age of 34 years (SD 6.5), with about half serving as HIV testing and screening counsellors. Most providers rated the Clinical Portal ease of use, comprehension, and client support as positive to very positive, though 23% expressed concerns regarding workflow efficiency and their ability to manage additional client volume. Providers also highlighted the Clinical Portal value for case management. Conclusion AI-powered digital health tools, such as the SCFA toolkit, show potential to enhance user engagement and support care delivery, with high usability and acceptability demonstrated among AGYW and healthcare providers. Continued user-centred refinement is essential to ensure these tools remain responsive to the evolving needs and care contexts of diverse user groups.

Introduction: Modeling when influenza epidemics typically occur can help countries optimize surveillance, time clinical and public health interventions, and reduce the burden of influenza. Methods: We used influenza virus detections reported during 2011-2024 by 180 countries to the Global Influenza Surveillance and Response System, excluding COVID-19 pandemic impacted years (2020-2023). We analyzed data by calendar year (week 1-52) or shifted year (week 30-29) time windows, based on when most influenza detections occurred in each country. For countries with sufficient data, we computed generalized additive models (GAMs) of each country's weekly influenza-positive tests to smooth and impute time series distributions. From these GAMs, we calculated each country's normalized weekly influenza burden. Country-specific normalized time series were grouped using hierarchical k-means clustering reducing the Euclidean distance between time series within clusters. We calculated cluster-specific GAMs to estimate average seasonal timing. Countries without sufficient data were assigned to a cluster based on population-weighted latitudinal distance to a cluster's mean latitude. Results: We identified five clusters, or epidemic zones, from 111 countries with sufficient data. The influenza burden in epidemic zones A and B was consistent with a northern hemisphere pattern, with most influenza detections occurring during October-April (A) and September-March (B), while epidemic zones D and E were characterized by southern hemisphere-like seasonal timing, with most influenza burden occurring during May-November. Epidemic zone C had most influenza burden occurring during September-March; most countries assigned to this cluster were in the tropics. Conclusion: Epidemic zones may serve as a useful tool to strengthen and optimize influenza surveillance for global health decision-making (e.g., during vaccine strain composition discussions) and to guide country preparedness efforts for seasonal influenza epidemics, including the timing of enhanced surveillance, as well as the procurement and delivery of vaccines and antivirals.

BackgroundDigital health dashboards can enhance health system performance by transforming routinely collected data into actionable insights for decision-making. In low-resource settings, however, their effectiveness depends not only on the relevance of indicators but also on system reliability within constrained digital infrastructure. Neonatal mortality remains a major global health challenge, with the highest burden in low- and middle-income countries, where many deaths are preventable through timely, evidence-based interventions. Continuous monitoring of care processes and outcomes is therefore essential. To support this need, we developed the NEST360 Implementation Tracker (NEST-IT) using R Shiny to support quality improvement across more than 100 hospitals in sub-Saharan Africa. As the platform scaled to over half a million records and increasing concurrent users, performance constraints emerged, particularly in hospitals with limited computing resources, threatening timely access to critical information. ObjectiveThis study aimed to describe optimization strategies applied to the NEST-IT dashboard and evaluate their impact before and after implementation. MethodsA structured optimization process was implemented following established R Shiny performance principles. Dashboard profiling was first conducted to identify key bottlenecks, after which targeted improvements were applied to improve efficiency and responsiveness. A quasi-experimental pre-post evaluation (December 2023-August 2024) assessed performance using three indicators: server processing time, visualization rendering time (VRT), and Time to First Byte (TTFB). Metrics were measured repeatedly during one-month baseline and post-optimization periods and summarized using mean values. ResultsFour primary bottlenecks were identified: delayed server responses, slow visualization rendering, inefficient data handling, and inconsistent device performance. Following optimization, interactive plot load time decreased from 10.1 to 2.7 {+/-} 0.6 seconds (73.3% improvement). Visualization rendering improved from 3.61 to 1.62 seconds, while server processing time fell from 2.3 {+/-} 0.7 to 0.8 {+/-} 0.3 seconds. TTFB improved from 1.9 {+/-} 0.4 to 0.6 {+/-} 0.2 seconds, and system uptime increased from 92.5% to 99.2%. ConclusionPerformance optimization substantially improved dashboard responsiveness, enabling timely access to critical neonatal information in resource-constrained hospital settings. The findings provide practical, evidence-based framework for improving the performance of R Shiny dashboards and demonstrate scalable strategies for delivering reliable digital decision-support tools in low-resource health systems.

Objectives: Heart failure is a leading cause of mortality, necessitating identification of patients at increased risk needing intervention. In this study, we investigated if Fitbit data can reveal physiological trends associated with hospital visit risk. Materials and methods: Individuals with heart failure (n=249) were randomized into three arms for prospective 180-day monitoring. All arms received a Fitbit and wireless weight scale. Arm 1 received devices only; Arm 2 received a mobile app with surveys; Arm 3 received the app plus financial incentives. Results: 51 participants had hospital visits during the study period. These individuals took fewer steps (p=.002) and reported increased symptom severity (p=.044). Resting heart rate increased three days prior to a visit (p=.022). Baseline steps revealed a higher visit probability for less active participants (p=.003). Discussion and conclusion: Passive physiological monitoring can effectively identify individuals at risk of health exacerbation, demonstrating the potential of wearable devices for timely clinical intervention.

Withdrawal StatementThis article has been withdrawn by medRxiv because it was submitted with false information.