Background: Radial artery puncture is a common clinical procedure essential for assessing gas exchange but is frequently perceived as stressful by inexperienced operators, who fear causing pain to their patients. Despite its practical relevance, formal training in this procedure is inconsistently integrated into medical curricula. This study evaluated whether a structured training program—combining theoretical instruction, simulation-based practice, and debriefing—could influence students’ procedural confidence and decision-making and patient experience during their first clinical arterial puncture. Objective: This study aimed to determine whether structured simulation-based training influences medical students’ anxiety, confidence, and technical performance and patient experience during their first arterial puncture. Methods: Third-year medical students who had never performed an arterial puncture were assigned to 1 of 2 groups: a structured training group (group 1) or a control group receiving informal or no specific training (group 2). After performing their first arterial puncture under supervision, students completed a questionnaire assessing apprehension, satisfaction, and confidence. The decision to use local anesthesia, puncture success, and patient-rated pain and apprehension were also recorded. A total of 67 students participated (group 1: n=24, 35.8%; group 2: n=43, 64.2%), with 61 patients included. Statistical comparisons were performed using the Fisher exact and nonparametric Mann-Whitney tests (α=.05). Results: Self-reported apprehension and confidence were similar between groups. However, group 1 students were significantly less likely to use local anesthesia compared to group 2 students (7/20, 35% vs 28/36, 77.8%, respectively; =.003), suggesting greater procedural confidence. First-attempt success rates were comparable (group 1: 3/13, 23.1%; group 2: 14/29, 48.3%; =.18). Median patient-reported pain scores were numerically but not statistically significantly lower when anesthesia was used (2.1, IQR 1.2‐4.0 vs 4.8, IQR 2.1‐6.4; =.08). Conclusions: Structured training influenced students’ behavior during their first arterial puncture, reducing reliance on anesthesia despite similar levels of self-reported apprehension. Although confidence ratings did not differ, behavioral indicators suggested improved self-efficacy and readiness for clinical performance. These findings support the behavioral impact of structured procedural education and call for future research using validated assessment tools and long-term follow-up.

Date Submitted: Feb 6, 2026. Open Peer Review Period: Feb 18, 2026 - Apr 15, 2026.

Background: Multiple-choice examinations (MCQs) are widely used in medical education to ensure standardized and objective assessment. Developing high-quality items requires both subject expertise and methodological rigor. Large language models (LLMs) offer new opportunities for automated item generation. However, most evaluations rely on general-purpose prompting, and psychometric comparisons with faculty-written items remain scarce. Objective: This study aimed to evaluate whether a fine-tuned LLM can generate MCQs (Type A) in anesthesiology with psychometric properties comparable to those written by expert faculty. Methods: The study was embedded in the regular written anesthesiology examination of the eighth-semester medical curriculum with 157 students. The examination comprised 30 single best-answer MCQs, of which 15 were generated by senior faculty and 15 by a fine-tuned GPT-based model. A custom GPT-based (GPT-4) model was adapted with anesthesiology lecture slides, the National Competence-Based Learning Objectives Catalogue (NKLM 2.0), past examination questions, and faculty publications using supervised instruction-tuning with standardized prompt–response pairs. Item analysis followed established psychometric standards. Results: In total, 29 items (14 expert, 15 LLM-generated) were analyzed. Expert-generated questions had a mean difficulty of 0.81 (SD 0.19), point-biserial correlation of 0.19 (SD 0.07), and discrimination index of 0.09 (SD 0.08). LLM-generated items had a mean difficulty of 0.79 (SD 0.18), point-biserial correlation of 0.17 (SD 0.04), and discrimination index of 0.08 (SD 0.11). Mann-Whitney tests revealed no significant differences between expert- and LLM-generated items for difficulty (=.38), point-biserial correlation coefficient (=.96), or discrimination index (=.59). Categorical analyses confirmed no significant group differences. Both sets, however, showed only modest psychometric quality. Conclusions: Supervised fine-tuned LLMs are capable of generating MCQs with psychometric properties comparable to those written by experienced faculty. Given the limitations and cohort-dependency of psychometric indices, automated item generation should be considered a complement rather than a replacement for manual item writing. Further research with larger item sets and multi-institutional validation is needed to confirm generalizability and optimize integration of LLM-based tools into assessment development.

Background: Sensor-based footwear is increasingly discussed as a promising tool for mobility monitoring and fall-risk assessment, yet its applicability in long-term care remains largely unexamined. In particular, little is known about whether such systems and the study procedures needed to evaluate them can be feasibly integrated into dementia care settings. This #feasibility study provides practice-based evidence of using a sensor-equipped insole system with cognitively impaired residents. Objective: The study examines recruitment #feasibility, integration of the device into daily care routines, and the operationalization of the study design under everyday institutional conditions. Furthermore, it explores whether movement patterns, including agitation-related behaviors, can be detected in situ, acknowledging the exploratory nature of this aim due to the rarity and unpredictability of natural agitation episodes. Methods: An exploratory #feasibility study was conducted in 2 long-term care facilities in Eastern Switzerland. Six residents with mild-to-moderate cognitive impairment and increased fall risk were recruited through a multistep, staff-supported process, offering rare documentation of real-world recruitment constraints. The insole system recorded continuous gait and movement data during daily activities and weekly walking tests. Controlled simulations of agitation-related patterns were conducted to generate reference data, as natural agitation events were infrequent and difficult to capture systematically. #feasibility outcomes were informed focusing on recruitment capability, acceptability and adherence, #feasibility of continuous data acquisition, and the practicality of integrating the system into daily care routines. Results: The study identified substantial #feasibility constraints, including contextual limitations posed by sedation practices, fluctuating health status, and consent-related barriers. Practical challenges such as inconsistent battery charging, communication gaps across rotating staff, and difficulties achieving adequate shoe fit directly affected protocol adherence and data yield. Continuous sensor-based data collection was technically feasible when the shoes were worn, and machine-learning models consistently classified predefined agitation-related patterns under controlled conditions. Natural agitation episodes were rare, making simulated reference data essential for establishing fundamental detectability. Resident acceptance varied, with some individuals reporting increased perceived stability due to the shoe’s firm construction rather than its vibration feature. Conclusions: This #feasibility study demonstrates that sensor-based footwear is technically feasible and shows situational acceptability in long-term care, while also highlighting key contextual constraints related to recruitment, workflow integration, device handling, and adherence. The findings provide early empirical evidence on what is practically achievable in this population and setting, clarifying the exploratory nature and methodological necessity of using simulated agitation patterns, and underscoring the need for context-sensitive, participatory strategies when introducing sensor-based technologies into dementia care.

Background: The Michigan Oncology Quality Consortium developed a rapid patient-reported outcome measure (RapidPRO) focused on oral anti#Cancer agents (OAAs). We piloted this measure in 6 oncology practices to determine its usefulness in representing the symptom experience and medication adherence among individuals taking OAAs. It is common in oncology for #Cancer-specific approaches to be used. We sought to use 1 instrument for all OAAs as a means to simplify future implementation in practice. Objective: This study aimed to describe the use of RapidPRO in practice and quantify clinical metrics in RapidPRO for symptom burden, confidence to manage symptoms, confidence to know when to seek care, and OAA medication adherence. Methods: This observational study was conducted across 6 practices from July 2016 to December 2018. RapidPRO assesses symptoms, patient confidence, and medication adherence with respect to OAAs. Results: There were 2252 RapidPROs completed by 695 patients. Among individuals completing at least 2 RapidPROs, the median number of days between them was 28 (IQR 14-42). Of the 2252 completed RapidPROs, 1213 (53.9%) reported at least one moderate or severe symptom, and 28% (485/1705) reported medication nonadherence. Most bothersome symptoms (MBSs; n=1045) were reported in 35.1% (790/2252) of the RapidPROs, and 46.5% (323/695) of all patients reported an MBS. In exploratory analyses, RapidPROs that reported a moderate or severe symptom or lower confidence to manage symptoms were more likely to be nonadherent to OAA therapy. The most common reason for medication nonadherence was “experienced side effects.” Conclusions: These results show that most RapidPROs reported at least one moderate or severe symptom and 28% (485/1705) reported medication nonadherence. As well, RapidPRO was able to capture most patients’ MBSs. By implementing RapidPRO, practices can identify patients who experience symptoms, as well as those who report medication nonadherence.

Background: Antenatal care has been crucial in reducing maternal mortality. Currently, screening programs of pregnant women include blood pressure (BP) measurements, urine protein tests, and the identification of risk factors. Home monitoring can enhance the early detection and management of pregnancy-related hypertension, while also empowering women to take an active role in their own health care. Objective: This study aimed to evaluate the reliability and accuracy of contactless BP monitoring using the Anura smartphone app and to compare it to conventional manual cuff measurements. This was done in normotensive and high-risk pregnancies, as well as in women diagnosed with preeclampsia. A secondary objective was to assess women’s experience using the Anura app. Methods: Pregnant women with normotensive or high-risk pregnancies were enrolled from pregnancy weeks 8‐14, and women with preeclampsia were enrolled at the time of diagnosis. The 3 study groups consisted of 132 women with normotensive pregnancies, 40 women with high-risk pregnancies, and 87 women with preeclampsia. They were instructed to use the Anura smartphone app and perform a 30-second facial scan, alongside manual BP measurements, throughout pregnancy. Differences between the 2 methods were analyzed with linear mixed models accounting for repeated measures, reporting beta coefficients with 95% CIs, stratified by patient group and trimester. Outliers were detected visually in the Bland-Altman plots. A digital survey was answered in the Anura app at gestational weeks 37‐39, about their experiences using the Anura app. Results: A total of 4932 BP measurements were recorded with Anura, of which 539 had corresponding manual measurements. In normotensive pregnancies, Anura consistently showed slightly higher diastolic values (approximately 5‐7 mm Hg) and lower systolic values, with significant differences in the second and third trimesters. In high-risk pregnancies, both the systolic and diastolic BP were generally lower with Anura, especially in the second and third trimesters, while women with preeclampsia showed the largest differences, with Anura clearly showing lower systolic and diastolic values. Bland-Altman analyses confirmed these patterns and showed increasing variability and wider limits of agreement in the high-risk pregnancies with preeclampsia. Of 172 women with normotensive and high-risk pregnancies, 56 (32.5%) evaluated their experiences that were predominantly positive, with high perceived safety, better control, and a feeling of increased responsibility for their own health. Some experienced the measurement as somewhat uncomfortable. Conclusions: The Anura app is well accepted by pregnant women and supported them to take an active role in their own health care. Agreement with manual BP measurements was acceptable in normotensive pregnancies but lower in high-risk and preeclamptic pregnancies. These findings indicate potential for Anura as a complementary self-monitoring tool. Further development is needed to improve the app’s accuracy in high-risk groups before broader implementation can be recommended.

 

Background: Finger tapping tasks assess fine motor control and have been proposed as potential markers of cognitive function. With smartphones widely available, these tasks can be easily administered at home or in other nonclinical settings. However, the relationship between smartphone-based finger tapping measures and cognitive performance is still not well understood. Objective: This study aimed to examine the association between smartphone digital finger tapping features and cognitive performance in an aging population. Methods: Participants were enrolled in the study as part of the electronic Framingham Heart Study. They were instructed to perform a 2-finger tapping task every 2 months over a 1-year period. In each session, they tapped with 2 fingers of the left hand for 10 seconds and then with 2 fingers of the right hand for 10 seconds, for a total of 20 seconds. Global cognition performance and 4 cognitive domains, including memory, executive function, language, and visuospatial function, were assessed using a standardized neuropsychological battery. Fifteen tapping features were extracted to capture aspects of motor performance, including the mean, SD, skewness, and slope of the intertap interval (ITI). Associations between tapping-derived features and cognitive performance were assessed using linear regression models, adjusting for age, sex, handedness, education, cohort, and the time interval between cognitive and tapping assessments. Results: A total of 302 participants (mean age 74.7, SD 6.3 years; n=169, 56% female participants, and n=40, 13.2% non-White participants) completed the digital finger tapping tasks. Eleven tapping features such as basic temporal properties (eg, number of taps and mean ITI), temporal variability (eg, SD and coefficient of variation of ITI, ITI range, and Microfluctuation Index), and fatigue/temporal drift (ITI slope) were significantly associated with global cognitive function (all

Background: Type 2 #Diabetes mellitus (T2DM) is a prevalent chronic metabolic disorder that poses substantial challenges to global #Health care systems and patient management. #Telemedicine, defined as the use of information and communication technologies to enhance #Health care delivery, has emerged as a potential tool to improve access to care and facilitate the management of T2DM. Objective: This systematic review and meta-analysis aimed to evaluate the clinical effectiveness of various #Telemedicine interventions compared with usual care in glycemic control, and cardiovascular #Health in adults with T2DM. Methods: A comprehensive literature search was conducted across databases such as PubMed, Cochrane Library, and Web of Science for randomized controlled trials (RCTs) published up to August 23, 2024. Eligible RCTs compared #Telemedicine interventions with usual care in adults with T2DM. The primary outcome assessed was hemoglobin A1c (HbA1c) levels, while the secondary outcomes included mean glucose, fasting blood glucose, BMI, weight, systolic blood pressure, diastolic blood pressure, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The quality of the included studies was examined via the Cochrane risk-of-bias tool. Data were extracted and analyzed using a random-effects model, and meta-regression was performed to explore potential moderators. The quality of the evidence was assessed via the Grading of Recommendations, Assessment, Development, and Evaluation approach. Results: A total of 58 RCTs, encompassing 13,942 participants, were included in the analysis. Our findings showed that #Telemedicine interventions significantly improved HbA1c levels compared with usual care (mean difference [MD] –0.38, 95% CI –0.49 to –0.27; Z=6.94; P

Background: HIV/AIDS is a disease associated with stigma and discrimination. This can hinder the adoption of preventive and treatment methods, especially in vulnerable populations, such as the #Transgender community. Objective: The primary objectives of this #Study are to explore awareness barriers related to HIV/AIDS, develop and pilot a mobile-based HIV awareness #App, and evaluate its acceptability and usability within the #Transgender community. Methods: The #Research will employ a quasi-experimental design, utilizing a pre- and posttest comparison between an intervention group that will use the mobile #App and a comparison group that will not. Phase 1 involves a situational analysis, including key informant interviews, focus group discussions, and a cross-sectional survey. An #App will be designed and developed in Phase 2. Phase 3 will comprise a preintervention assessment recruiting 150 #Transgender people, implementation of the #App on the cell phones of 75 #Transgender people, and a postapp assessment. Statistical techniques will be employed to analyze the captured data and assess the effectiveness of the #App. Results: The recruitment began on August 25, 2025, for the first phase, with the subsequent phases to follow. The data collection and analysis will be completed and finalized by August 31, 2026, following the intervention deployment. No funding was received from any external source for this #Study. Conclusions: The results of this #Study will reveal the effectiveness of a mobile #App for the #Transgender community. These results will determine the continuation and further scale-up of this intervention. The findings will create evidence to inform favorable strategies for vulnerable populations.

Background: Although fitness apps could promote healthier lifestyles, evidence on the effectiveness of app-based interventions remains inconsistent. Previous studies have used affordance theory to identify the factors that generate exercise-related value for users. However, many fitness app affordance studies have examined multiple fitness apps collectively, assuming similar design intentions across platforms. Moreover, most have relied on predefined affordances rather than investigating emergent or novel ones that may reveal unique user–fitness app interactions. Objective: This study aimed to identify the common affordances actualized by Fitbit users and uncover novel affordances that emerge from their interactions with this specific app, thereby extending the understanding of how affordances contribute to user engagement and health outcomes. Methods: We used a 2-stage mixed methods design. First, a cross-sectional web-based survey was conducted with 442 US-based Fitbit users engaging in regular exercise. The participants selected affordances from a list identified in prior literature and could report additional affordances in open-text responses. To corroborate and extend the survey findings, 15,000 user reviews were collected from the Google Play Store, of which 2674 (17.8%) comments were automatically categorized into affordance themes and 1182 (7.9%) were manually validated as relevant. Reviews were thematically classified into affordance categories via a generative pretrained transformer–based approach guided by survey-identified affordances. Results: The survey revealed that the most frequently actualized affordances were updating (351 participants and 749 review mentions; total=1100) and reminding (319 participants and 143 mentions; total=462), underscoring Fitbit’s role in tracking progress and sustaining routines. Competing (99 participants and 88 mentions; total=187) and rewards (133 participants and 32 mentions; total=165) highlighted gamification, whereas comparing (151 participants and 8 mentions; total=159) and guidance (118 participants and 25 mentions; total=143) reflected benchmarking and instructional support. Other affordances such as searching (135 participants and 2 mentions; total=137), encouraging (75 participants and 19 mentions; total=94), and watching others (68 participants and 3 mentions; total=71) were less common, whereas recognizing (58 participants and 0 mentions; total=58) and self-presentation (47 participants and 1 mention; total=50) were the least common. The novel affordances included encouraging others (14 participants and 1 mention; total=15), accountability (3 participants and 9 mentions; total=12), and self-comparison (3 participants and 5 mentions; total=8). Conclusions: Most Fitbit users actualized updating and reminding affordances, whereas a limited number of users actualized the other affordances. Moreover, few Fitbit users actualized novel affordances that reflect self-regulation, an extension of social connection, and personal meaning. This study emphasizes that Fitbit should focus on core tracking and reminding for most users while providing optional features that foster guidance, community, accountability, and personal relevance. Designing features that facilitate the actualization of common and novel affordances may improve app effectiveness and, ultimately, the health benefits of fitness technologies.

Background: The growing use of artificial intelligence (AI) chatbots for seeking health-related information is concerning, as they were not originally developed for delivering medical guidance. The quality of AI chatbots’ responses relies heavily on their training data and is often limited in medical contexts due to their lack of specific training data in medical literature. Findings on the quality of AI chatbot responses related to health are mixed. Some studies showed the quality surpassed physicians’ responses, while others revealed occasional major errors and low readability. This study addresses a critical gap by examining the performance of various AI chatbots in a complex, misinformation-rich environment. Objective: This study examined AI chatbots' responses to human papillomavirus (HPV)–related questions by analyzing structure, linguistic features, information accuracy and currency, and #Vaccination stance. Methods: We conducted a qualitative content analysis following the approach outlined by Schreier to examine 4 selected AI chatbots’ (ChatGPT 4, Claude 3.7 Sonnet, DeepSeek V3, and Docus [General AI Doctor]) responses to HPV #Vaccine questions. These questions, simulated by young adults, were adapted from items on the #Vaccine Conspiracy Beliefs Scale and Google Trends. The selection criteria for AI chatbots included popularity, accessibility, countries of origin, response update methods, and intended use. Two #Researchers, simulating a 22-year-old man or woman, collected 8 conversations between February 22 and 28, 2025. We used a deductive approach to develop initial code groups, then an inductive approach to generate codes. The responses were analyzed based on a comprehensive codebook, with codes examining response structure, linguistic features, information accuracy and currency, and #Vaccination stance. We also assessed readability using the Flesch-Kincaid Grade Level and Reading Ease Score. Results: All AI chatbots cited evidence-based sources from reputable health organizations. We found no fabricated information or inaccuracies in numerical data. For complex questions, all AI chatbots appropriately deferred to health care professionals’ suggestions. All AI chatbots maintained a neutral or pro#Vaccine stance, corresponding with scientific consensus. The mean and range of response lengths varied [word count; ChatGPT: 436.4 (218-954); Claude: 188.0 (138-255); DeepSeek: 510.0 (325-735); and Docus: 159.4 (61-200)], as did readability [Flesch-Kincaid Grade Level; ChatGPT: 10.7 (6.0-14.9); Claude: 13.2 (7.7-17.8); DeepSeek: 11.3 (7.0-14.7); and Docus: 12.2 (8.9-15.5); and Flesch-Kincaid Reading Ease Score; ChatGPT: 46.8 (25.4-72.2); Claude: 32.5 (6.3-67.3); DeepSeek: 43.7 (22.8-67.4); and Docus: 40.5 (19.6-58.2)]. ChatGPT and Claude offered personalized responses, while DeepSeek and Docus lacked this. Occasionally, some responses included broken or irrelevant links and medical jargon. Conclusions: Amidst an online environment saturated with misinformation, AI chatbots have the potential to serve as an alternative source of accurate HPV-related information to conventional online platforms (websites and #SocialMedia). Improvements in readability, personalization, and link accuracy are still needed. Furthermore, we recommend that users treat AI chatbots as complements, not replacements, to health care professionals’ guidance on clinical settings. Trial Registration:

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Background: Cyberbullying victimization is prevalent and closely linked to mental health problems. However, existing research, often limited by cross-sectional designs and a focus on direct relationships, has yielded inconsistent results. Furthermore, the biological mechanisms underlying the relationship between cyberbullying victimization and psychopathological outcomes remain largely unclear at present. Objective: This retrospective cohort study aimed to explore the longitudinal associations among cyberbullying victimization, inhibitory control, brain activation during error processing, and mental health problems among adolescents. Methods: We curated the clinical, behavioral, and neuroimaging data (551/1186, 46.5% girls; 9-10 years at baseline) from the Adolescent Brain Cognitive Development study, a nationally representative cohort established through school-based probability sampling (selected factors included gender, race/ethnicity, socioeconomic status, and urbanicity). Participants were assessed by the cyberbullying question, the functional magnetic resonance imaging stop signal task for inhibitory control and error processing, and the Child Behavioral Checklist for externalizing and internalizing problems at 2-year (T1) and 4-year follow-up (T2). Linear mixed models were used to examine the retrospective longitudinal associations between these clinical, behavioral, and neuroimaging factors. Results: Linear mixed models showed that victims of cyberbullying at T1 exhibited significantly greater externalizing problems at T2 (β=0.25, 95% CI 0.06-0.45, PFDR=.02), but not for internalizing problems (β=–0.01, 95% CI –0.20 to 0.19, PFDR=.99) or deficits in inhibitory control (Correct Stop Rate: β=–0.02, 95% CI –0.26 to 0.21, PFDR=.85; Stop Signal Reaction Time: β=–0.07, 95% CI –0.27 to 0.13, PFDR=.85). Furthermore, cyberbullying victimization at T1 contributed to higher activation in the bilateral superior parietal gyri (left: β=0.36, 95% CI 0.10-0.61, PFDR=.04; right: β=0.34, 95% CI 0.08-0.59, PFDR=.04), right inferior parietal gyrus (β=0.32, 95% CI 0.07-0.57, PFDR=.04), and right posterior cingulate cortex (β=0.34, 95% CI 0.09-0.60, PFDR=.04) during error processing at T2. However, these neural alterations did not significantly mediate between cyberbullying victimization at T1 and externalizing problems at T2. Conclusions: This longitudinal functional magnetic resonance imaging study investigates neural correlates of cyberbullying victimization in adolescents. By extending prior research that has relied primarily on cross-sectional or behavioral data, this research demonstrates that this form of victimization is associated with altered neural activation during error processing in later development. The pattern of nonsignificant impairment in inhibitory control and mediation to externalizing problems suggests that these neural impacts may be better characterized by a state of heightened sensitivity and compensatory engagement than by direct damage. Overall, this study points to the error-processing network as a potential target for cognitive interventions and establishes a foundation for further exploration of other neural mechanisms between cyberbullying victimization and mental health outcomes. Trial Registration:

Date Submitted: Feb 15, 2026. Open Peer Review Period: Feb 17, 2026 - Apr 14, 2026.

Background: Hospital-Based Violence Intervention Programs (HVIPs), based in Emergency Departments (EDs), have been proposed as a public health response to violence. These programs address the underlying reasons why patients are exposed to violence. In addressing any underlying modifiable risks and vulnerabilities HVIPs can reduce patients’ exposure to violence and therefore subsequent unplanned attendance into ED. Objective: The objectives of this #Study are to (1) assess whether patient involvement with a HVIP reduces the likelihood of unscheduled ED reattendance, (2) determine whether the presence of the HVIP improves ascertainment of violence in ED attendances, and (3) derive the costs of the HVIP and compare those to the benefits of the intervention and understand whether the HVIP represents value for money from a health service perspective. If an effect is observed, then models will estimate the health impacts, costs and potential savings over a longer time (eg, 10 years) period and for a national roll-out. Methods: ED patients are eligible for inclusion in the evaluation if they are normally resident in Wales, United Kingdom, aged 11 years and older. A controlled longitudinal natural experiment will be undertaken. The primary outcome is derived from the Emergency Department Dataset, routinely collected for all EDs in Wales, and is subsequent unplanned ED attendance. Case patients will be matched to control patients attending EDs without an HVIP. Analysis will derive the hazard rate for subsequent unplanned ED attendances using recurrent event analysis. The total monthly count of patients identified as attending because of violence in intervention EDs will be compared to the total count of Welsh control EDs in an interrupted time-series analysis to determine whether HVIPS increase violence ascertainment. To determine whether referral, versus no referral, to the HVIP represents value for money, we will undertake a cost-effectiveness analysis from the perspective of the National Health Service. The approval to access and analyze data housed in the Secure Anonymized Information Linkage (SAIL) databank, an ISO (International Organization for Standardization) 27001 certified and UK Statistics Authority accredited secure data environment, was granted by the SAIL independent Information Governance Review Panel (Ref: 1421). Findings will be presented at local, national, and international conferences and disseminated by peer-reviewed publication. Results: Design inputs arising from public patient involvement and engagement (PPIE) are reported. As a #Protocol, no further results are available. Conclusions: Novel methods are developed to provide the first robust evaluation of Emergency Department Violence Intervention Programs (EDVIPs). Trial Registration: ISRCTN Registry ISRCTN68945844; https://www.isrctn.com/ISRCTN68945844?q=68945844&filters=&sort=&offset=1&totalResults=1&page=1&pageSize=10

Background: Patients frequently search for health information online and value physician support in evaluating and interpreting their findings, yet many hesitate to share their online searches with their physicians. This hesitation hinders shared decision-making and compromises patient care. While extensive research has examined patients’ online health information–seeking behaviors, little has focused on patients’ disclosure of this information to their physicians during consultations. Objective: Guided by the Health Empowerment Model and the Linguistic Model of Patient Participation in Care, this study aims to (1) identify distinct patient profiles based on eHealth literacy and psychological health empowerment levels, (2) examine how these patient profiles differ in online health information seeking and disclosure to physicians, and (3) investigate whether patient-centered communication (PCC) promotes information disclosure and whether this effect varies by patient profile. Methods: This cross-sectional study surveyed 2001 Chinese participants recruited through convenience sampling. Patient profiles were identified using k-means cluster analysis with standardized z scores of eHealth literacy and psychological health empowerment. Differences between profiles in information behaviors were examined using 1-way Welch ANOVA, chi-square tests, and pairwise comparisons. Regression analyses examined the association between PCC and disclosure of online health information. Moderation analyses using the Hayes PROCESS macro assessed whether this association varied across patient profiles. Results: Four distinct patient profiles were identified: effective self-managers (996/2001, 49.8%), moderate-needs dependent patients (408/2001, 20.4%), high-needs patients (68/2001, 3.4%), and dangerous self-managers (529/2001, 26.4%). Profiles differed significantly in information-seeking intentions (F3,289=62.09; P

Background: The convergence of digital health and One Health represents an emergent paradigm in global health governance. While widely discussed in high-income settings, there is limited understanding of how this convergence is conceptualized in the Global South, particularly when viewed through a gender- and equity-sensitive foresight lens. Objective: This study aimed to map and classify expert discourse on digital health, One Health, and their convergence in the Global South using latent semantic analysis, with particular attention to structural drivers, emerging issues, weak signals, and gendered patterns of anticipation. Methods: A 3-round online Delphi survey was conducted with 45 experts from 19 countries across the Global South. Open-ended responses were analyzed using latent semantic analysis and stratified by gender. A foresight framework was applied to categorize topics as structural drivers, emerging issues, or weak signals, based on their temporal persistence, salience, and consensus. Results: In digital health, structural drivers included the systemic integration of digital technologies into public health systems, strategic alignment, and infrastructure development. Emerging issues comprised the adoption of artificial intelligence, chronic disease management via mobile health, and concerns about digital inclusion and interoperability. Weak signals included feminist digital ethics, trust in digital systems, and relational accountability—more frequently emphasized by female experts. In One Health, structural drivers were centered on intersectoral coordination, ecological integration, and the institutionalization of health-environment frameworks. Emerging issues encompassed anticipatory risk governance, food system sustainability, and the integration of environmental and population-level data. Weak signals included indigenous knowledge systems, subnational antimicrobial resistance governance, and structural underinvestment in ecological public health, with gendered divergence in framing. In the convergence discourse (digital health and One Health), structural drivers focused on the integration of digital surveillance systems, data infrastructures, and health information platforms to operationalize One Health. Emerging issues included climate-triggered system redesign, artificial intelligence and ecological monitoring, and the governance of cross-sectoral data. Weak signals pointed to algorithmic bias in zoonotic prediction, digital sovereignty in environmental health, and feminist critiques of convergence—all thematically rich but peripheral in consensus. Conclusions: This study revealed a multilayered and gender-influenced foresight architecture shaping the future of digital health and One Health in the Global South. Structural drivers denote maturing domains of implementation, while emerging issues and weak signals highlight latent, often overlooked opportunities and tensions. Incorporating equity-sensitive and gender-aware foresight methods is essential for crafting inclusive and anticipatory health governance strategies.

Background: Artificial intelligence (AI) health care chatbots are gaining widespread adoption worldwide. It is imperative to understand the service quality of AI health care chatbots. However, there is limited guidance on how to comprehensively evaluate their service quality. Objective: This study aimed to develop an index system based on the SERVQUAL framework for evaluating the service quality of AI health care chatbots. Methods: An initial indicator pool was compiled through a comprehensive literature review and consultations with 4 experts. These indicators were mapped and categorized into 5 domains adapted from the SERVQUAL framework. The experts were recruited from hospital, university, and health commission settings by purposive sampling. The service quality index system was identified using a 2-round Delphi process, which included a virtual meeting between the 2 rounds. In the third round, indicator weights within each quality domain and subdomain were determined using the analytic hierarchy process. Results: There were 26 indicators identified in the literature, based on which the 2-round Delphi process was conducted. A total of 20 experts were invited. The response rates in both rounds of Delphi and the analytic hierarchy process were 100%, and the authoritative coefficients were both >0.7. The final service quality index system for AI health care chatbots comprises 5 primary indicators and 17 secondary indicators. There were 3 (18%) indicators on assurance, 4 (24%) on reliability, 3 (18%) on human-likeness, 4 (24%) on tangibility, and 3 (18%) on responsiveness. The primary indicators, ranked from highest to lowest weight, were assurance (0.239), reliability (0.237), human-likeness (0.187), tangibility (0.170), and responsiveness (0.167). Conclusions: This study pioneers the development of a service quality index system for AI health care chatbots adapted from the SERVQUAL framework. The results provide a validated tool for evaluating the performance of chatbots and offer valuable insights for health service managers and developers to enhance AI-driven medical consultation services.