JMIR Formative Research

Pain assessment remains challenging in clinical practice. Automated pain assessment using artificial intelligence (AI) and facial expression analysis has emerged as a potential approach to support more objective and continuous pain monitoring, particularly for patients who are unable to communicate effectively. However, successful implementation depends on clinician acceptance. We conducted a cross-sectional survey of physicians, advanced practice providers (APPs), and nurses at a tertiary academic medical center to evaluate current pain assessment practices and perceptions of a facial-analysis-based pain assessment system. Eighty-seven eligible clinicians participated. For current assessment practices, clinicians commonly relied on observational indicators of pain, including vital signs (88.5%), physical examination findings (85.1%), and facial expressions (79.3%). Attitudes toward automated pain assessment were generally neutral to positive. Half of respondents agreed that such a system would be useful for monitoring patients over time (50.6%), and many perceived value for nonverbal patients (86.5%), patients with dementia or cognitive impairment (78.4%), and patients with language barriers (62.2%). Most clinicians reported requiring high levels of accuracy before routine use, with nearly 60% requiring at least 90% accuracy. Key concerns included alert fatigue and unequal performance across patient populations. These findings suggest clinicians view automated facial-analysis systems as potentially valuable adjuncts for pain monitoring, particularly in communication-impaired populations, while emphasizing the importance of accuracy, fairness, explainability, and workflow integration during system development and implementation.

Healthcare access barriers among Nigerian university students remain under-researched, particularly regarding localized digital health interventions. This study designed, developed, and evaluated a mobile health platform integrating AI-assisted triage, teleconsultation, appointment scheduling, and records management for students at the University of Abuja Teaching Hospital (UATH). A single-group pre-post design was conducted with 50 enrolled student-patients, with each participant observed for 30 days. The platform's triage feature utilized MedGemma, deployed locally via Ollama on UATH infrastructure using a multi-turn conversational architecture. Evaluation metrics included a survey operationalizing the Penchansky and Thomas access framework, the System Usability Scale (SUS), and triage validation against two independent clinicians using weighted Cohen's kappa. Among 61 in-scope triage sessions from 28 participants, mean triage response time was 34.8 seconds with no performance degradation. Among 22 participants completing the SUS, the mean score was 73.2, significantly exceeding the standard 68 benchmark (p = .046). Healthcare access improved directionally across four of five measured dimensions. AI triage classifications demonstrated substantial agreement with independent clinicians (kappa = 0.66-0.72), strengthening in longer conversations. No significant access equity differences were detected between on-campus and off-campus participants in the underpowered pilot subgroup analysis. This study demonstrates the technical feasibility, usability, and clinical concordance of a locally deployed, AI-assisted health platform in a resource-constrained Nigerian setting, providing, to our knowledge, the first evaluation of MedGemma for AI-assisted triage among Nigerian university students and justifying a larger, fully powered follow-up study.

Background: Nurses are disproportionately affected by occupational violence, yet conventional incident reporting and retrospective surveys underestimate its extent and miss its context. Ecological momentary assessment (EMA) offers a way to capture violence in near-real time. Aim: To evaluate the feasibility of a smartphone EMA application for capturing the incidence, characteristics, and reporting of occupational violence among hospital nurses and midwives. Methods: In a prospective, single-site feasibility study at a large public hospital, nurses and midwives used a commercially available EMA application (MetricWire) to report occupational violence in two-hourly blocks across their shifts over a three-week period. Incidence, incident characteristics, reporting, and usability were analysed descriptively. Findings: Of 108 enrolled participants, 69 contributed 607 momentary assessments, recording 111 incidents. Violence was reported in 18.6% of assessments, and 62% of participants reported at least one incident. Most incidents were verbal (69%) and patient-perpetrated (86%); 59% involved witnessing violence against a colleague. Although 57% of incidents were reported to someone, only 6% reached the formal incident management system. Respondents (n = 16) found the application easy to use and acceptable. Conclusion: Smartphone-based EMA feasibly and acceptably captures contextually rich, near-real-time data on occupational violence, including the witnessed exposure and under-reporting that conventional systems miss.

Introduction: Auditory verbal hallucinations (AVH) affect 60-80% of people with schizophrenia, yet existing assessment tools inadequately capture their phenomenological complexity. Aim: To determine whether individuals with schizophrenia spectrum disorders could accurately match auditory verbal hallucination loudness to an external audio track, and to explore participant perspectives on this approach. Methods: Eight participants with schizophrenia spectrum disorders and active AVHs rated loudness via a Likert scale and by adjusting a headphone audio track to match their experience. Structured interviews and thematic analysis captured participant viewpoints. Results: No significant correlation was found between audio tool and Likert scale scores. Seven of eight participants reported the audio tool provided greater precision in quantifying AVH loudness and better enabled them to convey their internal experience to others. Discussion: Audio-matching tools may offer meaningful advantages over traditional scales for quantifying AVH loudness, even where statistical convergence with existing measures is absent. Limitations: Small sample size; loudness alone cannot fully capture the qualitative experience of hearing voices. Implications: This tool shows promise for longitudinal tracking of AVH loudness. Recommendations: Further investigation of digital approaches to AVH assessment is warranted.

Background: Digital mental health applications offer scalable pathways to expand mental healthcare access in low and middle-income countries, yet limited evidence exists on factors predicting willingness to use such applications in South Asia. This study examined associations between digital mental health literacy (DMHL), Technology Acceptance Model (TAM) constructs, mental health symptoms, and willingness to use smartphone-based mental health applications among adults in Bangladesh. Methods: Cross-sectional study conducted from February to June 2026 across urban (Dhaka), semi-urban (Chattogram), and rural (Sylhet) divisions of Bangladesh. Adults aged 18-60 years attending primary care facilities with smartphone access (n = 400) completed a face-to-face interview in Bangla assessing sociodemographic, depression (PHQ-9), anxiety (GAD-7), DMHL (6-item scale, = .762), TAM perceived usefulness (5-item, = .937) and ease of use (5-item, = .924), and willingness to use mental health applications (5-item scale, = .946). Hierarchical binary logistic regression examined associations with willingness (dichotomized at mean [≥]3.5). Results: Greater DMHL was associated with higher willingness (adjusted odds ratio [aOR] = 1.24, 95% CI: 1.11-1.38). Both perceived usefulness (aOR = 1.29, 95% CI: 1.18-1.41) and perceived ease of use (aOR = 1.17, 95% CI: 1.07-1.28) independently predicted willingness. Anxiety symptoms were positively associated (aOR = 1.08 per 1-point increase, 95% CI: 1.01-1.15), whereas depression symptoms, age, and sex were not independently significant after adjustment for other variables. Conclusions: Digital literacy, perceived utility of technology, and symptom-specific phenomenology but not demographics alone are associated with willingness to use digital mental health applications. Building DMHL, optimizing the usefulness and usability of the app, and overcoming implementation barriers should all be given priority.

Social media has an estimated global user base of 3.5 billion. Adolescents, constituting over 90% of this group, and thereby university students. are extensively involved in social media use, emphasizing its significance in their daily lives. Excessive use of social media poses risks such as depression, affecting academic performance. The objective of this study was to determine the association between social media use and depression among university students in Urban Northern Tanzania. This was a cross-sectional study that involved university students from three universities. Data was collected through an online close-ended questionnaire. Statistical analysis included the use of frequencies, percentages, chi-square and bivariate logistic regression at 95% confidence intervals (CIs) and significance at p value <0.05. A total of 384 participants were enrolled in the study. 36.7% reported that they used social media for 1 to 3 hours daily. 58.1% of all participants reported high frequency of social media use for social purposes. 29.2% of university students who participated in the study were screened to have depression. The prevalence of depression increased with longer daily duration of social media use. Those using social media for more than 5 hours daily, had 3.049 times higher odds of being depressed. Moderate frequency of social media use for social purposes was linked to reduced depression. Daily duration of social media was significantly associated with depression levels among university students. Significant associations were also found between moderate use of social media for socializing, with lower risk of depression. We recommend that universities develop targeted mental health interventions and promote balanced, purposeful social media use among students.

Internet use has increased rapidly around the world, along with reports of overuse among internet users. This can affect not just the psychosocial well-being but also the physical health of the young population, who are its major victims. However, very limited studies have been conducted on internet addiction among the young population in Nepal. This study aims to find the prevalence of internet addiction and its associated factors among undergraduate students of the Institute of Medicine (IOM), Nepal. We conducted a cross-sectional study among 211 undergraduate students of IOM. A structured questionnaire was used to collect socio-demographic, academic, and internet-use-related information. Internet addiction was assessed using Youngs Internet Addiction Test. Descriptive and bivariate analyses were performed. Frequencies and percentages were calculated for univariate analysis, while bivariate analysis was performed to assess the association between the dependent and independent variables. One in two students had internet addiction, with 36.5% having mild addiction, 12.3% having moderate addiction, and 1.4% having severe addiction. The mean age ({+/-}SD) of the students was 21.6 {+/-} 2.0 years. The variable found to be associated with internet addiction was the duration of internet use per day. Students who used the internet for more than five hours per day were about 2.3 times more likely to have internet addiction than those who used the internet for five hours or less (OR = 2.399, 95% CI: 1.334-4.314). Internet addiction is a significant concern among undergraduate students and may increase if not appropriately addressed. Excessive and problematic internet use may contribute to mental health problems such as difficulty concentrating, anxiety, and compulsive behaviors, particularly among young people who are frequent users of the internet.

Documentation burden remains a persistent challenge in nursing practice, and Ambient artificial intelligence (Ambient AI) may offer new opportunities to mitigate the burden and improve nursing practice. This brief scientific communication reports survey findings on the current status of Ambient AI implementation in U.S. inpatient nursing settings and nurses' perceptions of its anticipated benefits and concerns. We conducted a cross-sectional online survey of practicing inpatient nurses recruited from nursing-focused social media communities. Descriptive analyses were used to summarize Ambient adoption, user experience, and perceived benefits and concerns. Among 61 eligible respondents, nearly 60% (n = 37) reported that Ambient AI had been piloted or implemented in nursing at their healthcare institutions, and approximately half (49.2 %, n = 30) reported having direct experience using Ambient AI in clinical practice. Most nurses with direct experience (73.3%, n = 22) described positive experience using the technology in practice. The frequently identified perceived benefits of Ambient AI include reduced documentation time and improved patient care workflow, and improved data quality. However, nurses also raised concerns about patients' acceptance of the new technology in their care, potential nursing job displacement, and loss of nursing skills due to overreliance on AI technology. Findings suggest that Ambient AI may help reduce nursing documentation burden and workload, yet implementation strategies should address nurses' concerns about its influence on nursing professions, foster trust among clinicians and patients, and support integration into everyday nursing practice.

BackgroundPatient education is critical to safe and effective postoperative bariatric surgery care. Large language models (LLMs) may improve access to personalized patient education but evidence regarding direct patient use in clinical settings remains limited. We evaluated BariBot, an LLM-powered educational application for patients undergoing bariatric surgery. MethodsWe conducted a prospective, single-arm mixed-methods feasibility study at a tertiary academic medical center from May 2024 to May 2025. Adult patients undergoing bariatric surgery used BariBot independently on postoperative day 1. The application provided conversational postoperative education and adapted language complexity to participants educational attainment. Outcomes included feasibility, usability, acceptability, perceived trustworthiness, and anticipated future use. Usability was assessed using the System Usability Scale (SUS). Immediately after BariBot use, participants completed a semi-structured qualitative interview assessing acceptability, perceived trustworthiness, and anticipated future use. Interview transcripts underwent thematic analysis. ResultsSixteen participants completed the BariBot use session, SUS assessment, and post-intervention interview. No technical failures prevented application use, and no participant requested early discontinuation. The median SUS score was 91.3 (IQR, 81.3-98.1), consistent with excellent perceived usability. Qualitative analysis showed that participants valued the applications immediacy, structured responses, conversational context, ability to address follow-up questions, judgement-free nature, and future availability between clinical encounters. Trust was strengthened by alignment with prior clinical guidance and perceived institutional association, although participants emphasized the need for transparency, clinician oversight, and use as an adjunct rather than a replacement of clinician guidance. ConclusionsBariBot was feasible to implement and was associated with high perceived usability and acceptability postoperatively after bariatric surgery. Larger, longitudinal, and comparative studies are needed to evaluate safety during unsupervised use and determine whether LLM-based educational applications improve patient understanding, engagement, and clinical outcomes.

Background: Digital health technologies, spanning mobile applications, telemedicine, and AI-driven platforms, are rapidly reshaping healthcare delivery globally. Although Generation Z university students are classified as digital natives, empirical data evaluating their eHealth literacy, technology acceptance, and specific trust barriers in developing South Asian nations like Sri Lanka remain scarce. Objective: This study evaluated eHealth literacy, technology acceptance, online health information-seeking behaviors, and adoption barriers among Gen Z undergraduates in Sri Lanka, focusing on the interplay between eHealth literacy, AI trust, and digital care preferences. Methods: A cross-sectional survey (N = 172) was conducted among Sri Lankan university undergraduates utilizing adapted, validated instruments: the eHealth Literacy Scale (eHEALS) and the Technology Acceptance Model (TAM). Statistical analysis included scale reliability validation (Cronbach's alpha), descriptive profiling, Chi-Square ({chi}{superscript 2}) contingency tests, Pearson correlations, and Multiple Linear OLS Regression models. Results: Participants demonstrated high overall eHealth literacy (Mean = 3.84 {+/-} 0.58) and strong endorsement of digital health utility (Mean = 3.99 {+/-} 0.59). Online health searches were reported by 86.6% of respondents. AI tools (e.g., ChatGPT, Gemini) emerged as the second most frequent source for health queries (57.6%), surpassing YouTube (44.2%) and social media (26.2%), with medical students showing significantly higher AI utilization ({chi}{superscript 2} = 8.70, p = .003). In multiple regression analysis, digital platform preference over physical clinic visits (R{superscript 2} = .352, p < .001) was significantly predicted by Perceived Ease of Use ({beta} = 0.371, p = .001) and Trust in AI Recommendations ({beta} = 0.370, p < .001), whereas face-to-face consultation preference (76.7%) and personal data privacy risks (50.0%) remained predominant adoption barriers. Conclusion: Gen Z students in Sri Lanka exhibit high digital health readiness and substantial reliance on AI-driven information seeking. However, institutional deployment must address privacy concerns and integrate hybrid clinical workflows to bridge the gap between high perceived utility and physical consultation preferences.

The explosive growth of ambient AI scribes in healthcare has focused almost exclusively on relieving clinician documentation burdens and enhancing clinician efficiency and reducing burnout. While most research has focused on the clinician experience, these same types of tools, such as a patient held AI Personal Health Assistant, can be put in the hands of patients to facilitate communication with healthcare providers, to provide greater autonomy and activation for patients in managing their own healthcare, and to facilitate the task of receiving and retaining complex new information. However, to ensure these tools serve as more than just administrative aids, we partnered with a large technology company to conceptually codesign how such a tool might be studied in real world settings in a way that reflected outcomes of greatest importance to patients and families. We used the Nominal Group Technique and engaged thirty three patients and caregivers and ten multidisciplinary clinicians in structured virtual sessions to identify the outcomes on which subsequent studies should hinge. Our findings reveal a significant misalignment between current industry trends and patient priorities. While the market emphasizes the scribe function for billing, stakeholders overwhelmingly prioritized the AIPHAs role in longitudinal care management and post visit coordination. The most critical outcome identified was the impact on communication and respect, suggesting that patients view AI as a mechanism to improve the human to human dynamic and ensure a shared understanding of the care plan. Our findings further highlighted that the primary hurdle for the use of patient held AI is not technical utility but clinician trust and the need for formal consent processes. Importantly, as this technology is new and integration into the clinical experience is still being explored, patients cannot assume they will have access to the transcript of their own encounter when that record is created and held by the health system. This gap highlights the potential value of a patient held tool, preserving discussions from their visit and follow up steps in clearer detail than they may receive in their clinical note or traditional visit summary. From a system perspective, this transition requires a strategic shift from measuring workflow focused efficiency to measuring patient side advocacy and engagement. By centering the evaluation of AI on these patient defined metrics and fundamentally designing subsequent AI investigations using a partnership model, health systems can move beyond simple automation toward a model of care delivery that empowers patients to navigate complex clinical journeys with greater autonomy and clarity.

Background: Adverse events (AEs) after hospitalization are common and disproportionately affect adults with multiple chronic conditions (MCC). Capturing patient-reported symptoms and self-assessed health may enable earlier detection of post-discharge AEs. Objective: To identify and test user requirements for an automated remote monitoring system to enhance AE surveillance during the transition home following discharge. Methods: We conducted a mixed-methods study using an iterative, user-centered design approach. Semi-structured interviews with patients and clinicians informed system requirements, followed by real-world field testing in 20 patients who used the system for up to 7 days after discharge. The prototype leveraged interoperable electronic health record data services, delivered automated post-discharge check-ins using a combined questionnaire assessing new or worsening symptoms and patient-reported outcomes (PROs), provided risk-stratified health advice (when and with whom to initiate contact), and escalated high-risk symptoms to clinicians in real-time. Descriptive statistics assessed feasibility and utilization; conventional content analysis identified user needs and implementation considerations. Results: Thirty-seven patients with MCC and 23 clinicians participated. Key requirements for patients included clear communication of personalized risk based on red-flag symptoms, and actionable guidance aligned with discharge instructions. Key requirements for clinicians included explicit delineation of responsibility across inpatient and outpatient setting, and selective escalation to minimize burden. Field testing patients completed 60% of the combined questionnaires. Seven patients received Level 2 or Level 3 health advice after reporting new or worsening symptoms. Three patients triggered Level 3 alerts, resulting in one-time, secure escalation emails to clinicians. Four of the 7 patients who received Level 2 or 3 health advice had chart-confirmed emergency department visits within 1 week of discharge. Patients found the system understandable and helpful, while clinicians noted challenges interpreting PRO trends. Conclusions: These observations support the feasibility and acceptability among patients and clinicians of collecting patient-reported symptoms and PROs during the early post-discharge period. Future iterations should prioritize clear risk communication, role clarity, and interpretable patient-reported data. Formal validation is required to assess predictive performance and clinical utility of symptom-based escalation for post-discharge AE surveillance.

With the growing use of artificial intelligence (AI) tools in clinical care, it is essential to assess ethical issues related to using these tools, both before and after deployment. To address this need, a multidisciplinary team of clinical and AI experts developed and tested a scoring rubric to evaluate AI tools in clinical settings from an ethical perspective, considering potential issues that may arise during their use. The rubric provides a replicable scoring system comprising five content areas and four scoring categories. The content areas include evaluation population, accessibility, clinical bias, user control, and monitoring and updates. To test the applicability of the rubric, we applied it to three AI tools designed for clinical decision-making, clinical documentation, and clinical image analysis. The evaluation relied solely on publicly available information to simulate typical clinical settings. The rubric addresses a critical gap in the evaluation and governance of clinical AI tools by providing a structured framework to assess their ethical aspects. This approach enables healthcare systems to address and monitor the ethical impact of AI tools, fostering greater trust among clinicians and patients and guiding improvements in clinical practice.

Patient registries play a key role in research on chronic conditions like multiple sclerosis (MS), but their success typically depends on patients' willingness to share data. Information provided about ethically relevant registry practices may strongly influence their willingness. This study explores what information about ethics practices people with MS consider important when deciding whether to contribute data to a patient registry, and how they prefer this information to be communicated. We conducted semi-structured online interviews with 21 people with MS and patient representatives from nine European countries. The interview guide first addressed general information needs and then seven predefined ethically relevant topics. The topics registry governance, registry funding, data handling, and use-and-access procedures were adapted from the literature, while communication, access, and rewards for contributing data provided contextual and practical insight. They also served as the initial coding categories for directed qualitative content analysis, after which categories were refined and reorganized during analysis, and subcategories were inductively developed. We found that trust emerged as a cross-cutting factor. Participants required less information about registry practices when they trusted the registry or associated parties and preferred registry communication through trusted intermediaries. When deciding whether to contribute data to a patient registry, participants emphasized the importance of clarity about how the data will be used, who would have access to it, and what privacy protections are in place, while information preferences for technical details regarding data use and access varied widely. Feedback on personal or aggregate results was highly valued and seen as types of personal and communal benefits for participating. In conclusion, patient registries should adopt flexible communication strategies that balance transparency and accessibility. Building and maintaining trust, potentially through trusted affiliates like universities or patient organizations, appears central to properly inform (potential) registry participants about ethically relevant registry practices.

Background. General purpose generative AI (GenAI) chatbots are increasingly used by students for mental health support. Research on prevalence estimates vary widely, rarely link use to validated clinical measures, and have not been reported in a Canadian student population. We estimated the prevalence trends, patterns, perceived impact, and correlates of GenAI use for mental health support among Canadian university students. Methods. We analysed one year (May 2025 to April 2026) repeated cross-sectional data from the Canadian arm of the WHO World Mental Health International College Student survey (WMH-ICS) The primary outcome was past-year prevalence of GenAI use for mental health support. Specific use purposes, perceived impact, reasons for non-use, and future use intent were also analysed. Factors associated with GenAI use were assessed using modified Poisson regression. As a sensitivity analysis, an elastic-net penalised regression model was fitted to assess the robustness of findings to an alternative modelling approach. Results. The past-year prevalence of GenAI chatbot use for mental health support was 25.2% (95% CI: 22.7 - 27.9), with a lifetime prevalence of 30.2%. Use was mostly occasional and predominately for seeking mental health information, stress management, and emotional support/companionship. Students of Asian ethnicity, those with higher clinical burden, recent adverse life experiences, weaker social support, and prior digital help-seeking behaviours were more likely to use GenAI for mental health purposes. Conversely, 2SLGBTQ+ students and those with romantic partners were less likely. Nearly three-quarters (74.2%) of users perceived such use to have a positive impact on their mental health and emotional wellbeing. Non-users reported preference for human interaction, distrust of GenAI in mental health (67.4% each), and privacy/security concerns (50.3%). Non-use also reflected principled objections to AI, including ethical and environmental concerns, with most non-users indicating no future use intention. Conclusions. GenAI chatbot use for mental health support has become commonplace among Canadian university students and is concentrated among those with greater mental health needs and fewer social support resources. Although most users perceived these tools as beneficial, their clinical effectiveness and safety remain uncertain. Rigorous prospective studies are needed to determine whether perceived benefits translate into improved mental health outcomes and whether purpose-built GenAI mental health interventions offer greater clinical benefit and safety than general-purpose chatbots.

With the rapid advancement of artificial intelligence (AI) technologies, growing interest has emerged in its potential application in pediatric healthcare. Understanding the factors influencing AI acceptability among parents and clinical staff is an important prerequisite for facilitating its integration in clinical routine. To date, these factors have not been systematically assessed across different stakeholder groups. Here we investigated AI acceptability among parents (first cohort n = 198; second cohort: n = 79) and pediatric health care professionals (n = 33) across different disease, diagnosis or treatment scenarios. The effects of demographic variables and contextual predictors, such as data privacy, AI knowledge and perceived disease severity, on willingness to use AI were evaluated. More liberal data privacy was associated with reduced willingness to use AI (p = < .001). A higher perceived disease severity was linked to higher willingness to use AI ({beta} = .10, p = .013) in the second parent group. When the recommendations of AI and clinicians conflicted, parents were more likely to choose AI over the judgment of clinicians in treatment compared to diagnosis scenarios. Healthcare professionals showed similar patterns but additionally weighted perceived disease severity when resolving disagreements. These findings highlight the importance of addressing stakeholder concerns regarding disease specific customization, accuracy, data privacy and accountability when introducing AI into pediatric healthcare settings.

Depression is one of the most prevalent mental health disorders globally. Social media content can reflect emotional states, offering real-time signals of depressive symptoms. This study aimed to develop a deep learning model to predict depression using Twitter/X data, and to characterize potential causes of depression using a large language model (LLM). Twitter/X posts from April 20, 2023, to July 24, 2024, were obtained using the key phrase ("I" or "me") and "diagnosed with depression". After data cleaning and GPT-4o labelling, 2,275 depressive Twitter users, and an additional 1,661 non-depressive users from tweets using the keyword "today", were identified. A deep learning RoBERTa model for predicting depression was built on tweets from both user groups, with an 80/20 training-testing split. GPT-4o models were subsequently employed to analyze depressive users posts to understand potential causes for depression. The RoBERTa model achieved strong performance in predicting depression among Twitter users from their tweets, with an accuracy of 0.822, an F1 score of 0.855, and an AUC of 0.809. Common reasons for depression identified by GPT-4o models included societal pressure, low self-esteem, cultural influences, and identity-related challenges. These findings highlight the potential of deep learning models in early screening of depression using social media data. Insights into potential reasons for depression may inform targeted prevention strategies, public health interventions, and improved mental health support for at-risk populations.

Objectives. To assess how digital inclusion factors and physical access barriers are associated with user trust in smartphone-based remote photoplethysmography (rPPG) hypertension screening, and to identify implications for digital health pol- icy, procurement and implementation in low-resource settings. Methods. Cross-sectional mixed-methods survey in five outpatient clinics in Kebbi State, northern Nigeria (N =287). Trust was measured using comfort, confidence and perceived usefulness Likert scales. Primary analyses used binary logistic models with HC3 robust standard errors; sensitivity analyses are reported in supplementary material. Free-text responses were thematically analysed. Results. Smartphone ownership was 51.2%; Transsion-brand devices comprised 56.5% of owners. Greater distance to a blood pressure facility was independently associated with lower perceived usefulness (OR 0.51, 95% CI 0.30-0.87; p=0.013) and lower comfort (OR 0.61, 0.37-0.98; p=0.042). Among owners, Transsion versus Samsung showed higher confidence odds (OR 3.82, 1.02-14.27; p=0.046). Qualitative themes supported the implementation interpretation: platform-fit and device speed requests among Transsion owners; connectivity and offline-first concerns among those with greater travel distance. No brand contrast achieved FDR-adjusted significance; brand findings are exploratory. Conclusions. Digital health policy and health technology assessment for smartphone-based screening should incorporate local device ecology, connectivity constraints, physical access burden and trust-calibration safeguards. Pre-implementation assessment of these factors is necessary for equitable and safe rPPG adoption in low-resource health systems.

Background Stroke remains one of the leading causes of mortality and long-term disability worldwide, with low- and middle-income countries bearing a disproportionate share of the global disease burden. In India, delays in risk identification, fragmented referral pathways, and limited continuity of preventive care present significant challenges, particularly in rural communities. As a frontline health worker Accredited Social Health Activists (ASHAs) are strategically positioned to support community-based stroke prevention; however, existing workflows are frequently constrained by multi-tasking, predominantly paper-based documentation and fragmented digital systems. Advances in mobile health, artificial intelligence along with digital health ecosystem provided by Ayushman Bharat Digital Mission (ABDM) provide an opportunity to strengthen community healthcare through integrated digital platforms. Objective This protocol describes the design, system architecture, and prospective evaluation framework of ASHA Assist India, an integrated AI-assisted mobile health platform intended to support community-based stroke prevention by connecting citizens, ASHA workers, Primary Health Centres (PHCs), and higher levels of healthcare facilities within a unified digital ecosystem. Methods ASHA Assist India has been designed as a modular, cloud-based digital health platform supporting standardized data collection, longitudinal health monitoring, referral management, and AI-assisted clinical decision support. The proposed system comprises four user-facing applications corresponding to citizens, ASHA workers, PHCs, and referral hospitals, integrated through a centralized backend providing authentication, secure data management, interoperability, analytics, and notification services. The AI framework includes three planned analytical modules: (i) population-level stroke risk stratification, (ii) longitudinal stroke risk prediction, and (iii) acute stroke symptom recognition. A prospective implementation study is planned to evaluate platform usability, feasibility, workflow integration, implementation outcomes, and operational performance within routine community healthcare settings. Future validation of the AI modules will be conducted using prospectively collected longitudinal datasets. Expected Impact The proposed platform aims to strengthen community-based stroke prevention by improving digital workflow integration, facilitating coordinated referral pathways, and supporting longitudinal monitoring through the existing healthcare providers at health and wellness centres like ASHA, Community Health Officers (CHOs), ANM, etc. Beyond stroke prevention, the modular architecture is intended to provide a scalable framework for future digital health programmes addressing multiple non-communicable diseases within primary healthcare systems. Publication of this protocol establishes a transparent implementation and evaluation framework that may guide future research, digital health innovation, and implementation science in resource-constrained settings.

Menstrual health remains a neglected public health challenge in India, where persistent stigma, incomplete education, inadequate sanitation, and limited access to menstrual health resources can adversely affect health and social participation. Institution-based educational programs represent an underexplored opportunity to improve menstrual health literacy while promoting more inclusive discussions surrounding menstruation. Bridging this gap, an exploratory, prospective educational evaluation was conducted at a hospital in Coimbatore, Tamil Nadu and an engineering college in Wayanad, Kerala. Anonymous pre- and post-course surveys assessed menstrual health knowledge, attitudes, practices, perceived barriers, and perspectives on emerging digital health technologies. Quantitative responses were summarized descriptively, and open-ended responses were reviewed to identify recurring perspectives that contextualized the quantitative findings. The standardized educational program integrated evidence-based menstrual health education with discussions of bioengineering and wearable technologies. Eighty-three participants completed the pre-course survey, 68 attended the educational program, and 20 completed the post-course survey. Because responses could not be linked at the individual level, the survey cohorts were compared descriptively. At baseline, 80 of 82 respondents (97.6%) recognized menstruation as a biological process, yet only 38 of 82 (46.3%) indicated that it was not solely a woman's issue. The mean agreement that menstruation remained taboo was 7.44 of 10 (SD 1.99; n=78), and 51 of 77 respondents (66.2%) reported that they or others avoided activities during menstruation. Painful periods were identified as a barrier by 56 of 67 respondents (83.6%), and 18 of 67 (26.9%) considered institutional or community toilets insufficiently clean and private for menstrual management. Open-ended responses illustrated how pain, fear of leakage, stigma, and inadequate facilities constrained participation in school, physical activity, and social life. These findings document a marked gap between basic biological recognition and the social normalization and practical support of menstruation in two South Indian institutions, and support further evaluation of gender-inclusive, context-responsive menstrual health education using paired assessments, stronger follow-up, comparison groups, and long-term outcomes.