JMIR mHealth and uHealth

Insights on Postimplementation and Delivery of Telemedicine in Remote Areas of Rajasthan: Qualitative Study

2026-10-16T17:30:24-04:00

Background: Telemedicine has emerged as a critical strategy to address health care access gaps in hard-to-reach areas, particularly in resource-constrained settings. While telemedicine services have expanded across India, evidence on post-implementation experiences, specifically regarding the integration of portable diagnostic tools, and the assisting role of frontline health care workers, remains limited. Understanding these sociotechnical dynamics is essential for optimizing digital health interventions in remote areas. Objective: This study aimed to explore perceptions, experiences, and implementation processes of an assisted telemedicine model where frontline health care workers facilitate real-time, doorstep patient-assisted-to-doctor consultations using portable diagnostics and a customized portal among health care workers and patients in hard-to-reach villages of Rajasthan, India. Methods: A qualitative descriptive study was conducted in 5 remote villages of Balotra district, Rajasthan, between December 2025 and January 2026, following a 10-month telemedicine intervention. In-depth, semistructured interviews were conducted with 28 participants, including medical officers (n=3, 10.71%), nurses, and accredited social health activists or auxiliary nurse midwives (n=10, 35.71%), and patients (n=15, 53.57%). Cadre-specific semistructured interview guides were developed using the Consolidated Framework for Implementation Research and Technology Acceptance Model. Interviews were analyzed using a reflexive thematic analysis approach, incorporating both inductive and deductive coding approaches in NVivo 15 (Lumivero). Triangulation across participant groups and analytic perspectives was used to enhance credibility and trustworthiness. Results: The analysis revealed that the assisted telemedicine model fundamentally reconfigured treatment-seeking behavior by providing doorstep care, significantly reducing travel burdens, and out-of-pocket expenditures. Frontline health care workers acted as crucial sociotechnical mediators, bridging the digital divide and fostering patient trust, particularly among women and older adults. Health care providers reported increased clinical confidence and workflow efficiency, supported by real-time vital monitoring and cross-verification via digital chatbots. However, implementation was challenged by intermittent network connectivity, diagnostic limitations for conditions requiring physical palpation, and occasional stock-outs of essential medicines. Despite these barriers, the gradual acceptance of the system was heavily facilitated by the continuous engagement and cultural mediation of local health workers. Conclusions: Telemedicine was perceived as a feasible and acceptable model for extending primary health care services in hard-to-reach areas, provided it is embedded within an assisted model supported by trained frontline workers and responsive technical systems. The findings emphasize the need to shift health care policy toward mediated-digital models, formally recognizing community health workers as digital health navigators. Future scale-up must prioritize strengthening rural digital infrastructure and ensuring a reliable supply chain for essential medicines to sustain long-term engagement and health equity. Trial Registration: Clinical Trials Registry—India (CTRI) CTRI/2024/10/076070; https://tinyurl.com/yr952847 International Registered Report Identifier (IRRID): RR2-10.2196/92996

Tablet-Based Neurocognitive Tests for Ugandan Adolescents Living With HIV: Cross-Sectional Validation Study

2026-10-13T15:15:19-04:00

Background: Adolescents living with perinatally acquired HIV (PHIV) remain at risk of neurocognitive difficulties, yet access to valid neurocognitive tests is limited in sub-Saharan Africa (SSA), where most of this population resides. Tests that can be administered by nonspecialist personnel are culturally and linguistically appropriate for SSA’s diverse populations and are brief enough for routine use are needed to expand access, but few such tools exist. Objective: We adapted , a brief tablet-based neurocognitive test battery, into Luganda and evaluated its validity and acceptability for use among Ugandan adolescents with PHIV. Methods: In this cross-sectional validation study, 100 Ugandan adolescents aged 11 to 20 years (50 with PHIV; 50 HIV-negative controls) completed the Luganda- or English-language battery and a commonly used traditional test battery. Participants were recruited from an existing longitudinal cardiovascular cohort at the Joint Clinical Research Centre in Kampala, Uganda. We examined convergent validity with correlations between the 2 batteries’ individual and global -scores, known-groups validity with unadjusted and age-adjusted group comparisons, criterion validity by examining classification agreement between the 2 batteries quantified with Cohen κ, and acceptability through self-reported technology use and ease of use. Results: and the traditional battery global -scores were strongly correlated (=0.75; <.001). Correlations between tests assessing similar domains were generally small to moderate, and were strongest for processing speed, attention, and working memory. Adolescents with PHIV scored significantly lower than controls on global scores for both (=.008) and the traditional battery (=.003), even after adjusting for age. Agreement between batteries in classifying participants ≤1 SD (κ=0.56) and ≤2 SD (κ=0.66) below the global -score mean was moderate, with both batteries classifying more adolescents with PHIV than controls below these thresholds. Although fewer than half of participants (42/97, 43%) had previously used a tablet, most rated as easy to use, and prior tablet experience was not independently associated with performance after age adjustment. Conclusions: The Luganda-adapted demonstrated convergent, known-groups, and criterion validity in Ugandan adolescents with PHIV. By delivering automated administration, automated scoring, and standardized multimedia instructions in the local language on a portable tablet, addresses several persistent barriers to neurocognitive assessment in low-resource settings, including shortages of specialist personnel, the time burden of traditional batteries, and the scarcity of culturally and linguistically appropriate tools. The battery was found to be highly acceptable among adolescents with little or no prior tablet experience. These findings add to a growing body of evidence supporting tablet-based assessment as a practical and scalable mobile health approach to expanding access to neurocognitive testing. Future research should evaluate test-retest reliability, sensitivity to change over time, normative benchmarks, and implementation within real-world clinical workflows.

Exploring College Students’ Compliance During a 60-Day Alcohol-Related Ecological Momentary Assessment Using Location- and Time-Based Questions: Prospective Observational Study

2026-10-09T18:00:03-04:00

Background: Ecological momentary assessment (EMA) methods can provide assessment of alcohol-related beliefs and behaviors when people are in their natural environments. An increasingly common EMA approach involves the use of passive sensors (eg, continuous sharing of Bluetooth and GPS cell phone data) to collect rich data and trigger alcohol assessment in specific contexts. While collecting real-time assessments allows researchers to explore contextual factors impacting alcohol use, there is a lack of research examining compliance rates to alcohol-related EMAs that use daily questions for study periods over 2 weeks. EMAs over longer periods allow researchers to assess meaningful changes over time and sample low-base-rate events. Further, there is limited research exploring how EMA compliance is influenced by individual-level factors (eg, privacy concerns and alcohol use). Identifying factors associated with compliance can help inform EMA protocol design. Objective: This study had 2 primary goals. First, we add to the literature by examining the feasibility and acceptability of a 60-day college student alcohol-EMA that uses phone-based, passive sensors. Second, we examined whether compliance was associated with event-level alcohol use, privacy concerns, and types of questions asked (ie, questions triggered by time or GPS location). Methods: College students (N=68) who reported regular alcohol use completed the 60-day EMA study. At baseline, participants completed an online survey that included an assessment of privacy concerns. During the study period, participants completed up to 15 questions a day, including evening and morning self-reports of alcohol use. Feasibility was assessed based on enrollment, retention, and compliance rates. Acceptability was examined at the end of the study period using open- and close-answered questions on perceptions of the EMA experience. Results: The findings supported the feasibility of the 60-day EMA with enrollment exceeding benchmarks and high compliance across the study period (average compliance=90%). Overall, students found the protocol acceptable with 88% (58/66) indicating that they would be willing to complete another EMA study. Participants’ compliance was not associated with alcohol use at the event-level (odds ratio [OR] 0.94, 95% CI 0.80-1.10) or at the trait level (OR 0.92, 95% CI 0.73-1.17). Participants’ compliance was greater for time-trigger assessment than GPS-triggered questions (P<.001). Privacy concerns were not associated with reduced responding. Conclusions: This study has implications for those designing alcohol EMAs for college populations that involve the use of passive sensors, including recommendations for balancing predictable timing of questions and variability in question content.

How People Integrate Mental Health Apps Into Everyday Self-Care: Qualitative Study

2026-10-09T16:45:24-04:00

Background: Mobile mental health (MH) apps are increasingly promoted as tools for self-care. However, less is known about how users integrate them into everyday life and how they combine with other forms of support. Objective: This qualitative study examined what motivates individuals to adopt MH apps and how these tools are used alongside other care practices. Methods: We conducted semistructured interviews with 31 Czech MH app users aged 18‐56 (23 women), recruited through social media, researchers’ networks, and snowball sampling. Eligible participants had used an MH app for at least 2 weeks; individuals currently experiencing a psychological crisis were excluded. Interviews were conducted online or face-to-face and analyzed using reflexive thematic analysis by Braun and Clarke. The analysis was inductive and informed by a critical realist epistemological stance. Initial analytic engagement was conducted independently, followed by collaborative theme development and refinement. Results: We identified the following three motivational patterns for MH app use: (1) regular use to maintain well-being and support preventive self-care; (2) reactive use during periods of acute distress; and (3) temporary, problem-focused use aimed at understanding or addressing a specific difficulty. Beyond motivations for adoption, our findings revealed the following four distinct trajectories of app engagement and disengagement: (1) emancipation, with apps acting as ongoing support for self-care when therapy is reduced or ended; (2) independence, as techniques learned through apps are transferred into everyday life and used without the app; (3) reliance, where users persist with app-based self-care despite insufficient benefit, occasionally delaying or limiting other forms of help; and (4) episodic use, characterized by intermittent engagement during specific life periods. These findings extend existing research by showing that disengagement is not necessarily a marker of failure but can reflect successful self-care and the transfer of learned skills. We also identified tensions and unintended consequences, including the burden of sustaining engagement, risks of overreliance, emotional costs associated with sustained self-monitoring, and tensions between digital self-care and efforts to reduce screen use. Participants generally viewed self-monitoring as beneficial when guided by clear personal goals and embedded within a broader system of self-care. Conclusions: Our findings suggest that appropriate disengagement can signal a successful step in self-care. App design and policy should acknowledge episodic use and strengthen connections to human support with clear guidance and accessible pathways to additional care when needed.

Stakeholder Perspectives on Digital Health Technologies in Poststroke Self-Management and Rehabilitation: Systematic Review Based on the NASSS Framework

2026-10-09T16:30:06-04:00

Background: Digital health technologies (DHTs) offer opportunities to support poststroke self-management and rehabilitation, but their implementation may be hindered by mismatches between technological design, stakeholder needs, and care contexts. Synthesizing stakeholder perspectives can clarify implementation priorities and gaps in the evidence. Objective: This study aimed to synthesize stakeholders’ perceptions, experiences, and recommendations regarding DHT implementation in poststroke self-management and rehabilitation and to map the findings to the NASSS (Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability) framework. Methods: Six databases (PubMed, MEDLINE, Embase, Scopus, CINAHL, and Web of Science) were searched from inception through October 2025. Eligible studies were English-language, peer-reviewed primary studies using qualitative, quantitative, or mixed methods designs to report stakeholder perspectives on DHTs in poststroke care. Two reviewers independently screened studies, extracted data, and assessed methodological quality using the Mixed Methods Appraisal Tool (MMAT). A convergent integrated synthesis was conducted: quantitative findings were transformed into textual descriptors, combined with qualitative findings, coded inductively, and subsequently mapped deductively to NASSS domains and subdomains. Disagreements were resolved through consensus or by third-reviewer adjudication. Results: Of 15,262 records identified, 42 studies published between 2019 and 2025 were included: 24 qualitative, 8 quantitative, and 10 mixed methods studies. Patients, health care professionals, caregivers, and members of the public were represented in 32, 19, 8, and 3 studies, respectively, with some studies including multiple groups. Evidence concentrated on the condition, technology, value proposition, and adopter-system domains. Stakeholders valued personalized functions, intuitive interfaces, timely feedback, and digital support that complemented human care. Barriers included stroke-related motor, cognitive, communication, and sensory impairments; unequal digital access and literacy; caregiver demands; staff workload and training needs; and poor integration with clinical routines. Data privacy and unclear payment arrangements also raised concerns. Evidence on organizational readiness and wider-system determinants was limited, and no eligible study directly examined interaction and adaptation over time. Methodological concerns included nonresponse bias and insufficient interpretation of integrated findings in mixed methods studies. Conclusions: Stakeholder perspectives indicate that DHT implementation in poststroke care should combine accessible, personalized design with support for patients, caregivers, and health care professionals, alongside integration into clinical workflows and clear data governance and financing arrangements. The NASSS mapping identified priorities beyond usability, but current evidence does not establish sustained adoption, scale-up, or spread. Longitudinal studies in routine care are needed to evaluate adaptation and sustainability.

Gender-Affirming Voice Training Delivered by a Novel Mobile App: Mixed Methods Study

2026-10-08T17:00:32-04:00

Background: Gender-affirming voice training (GAVT) has reduced gender incongruence for transgender and gender-nonconforming (TGNC) individuals, but remains limited by cost and scarcity of specialized providers. Mobile health (mHealth) is a bridge to these access gaps; yet, few high-quality, clinically informed mHealth apps exist within voice clinical sciences. Objective: We previously created a free mobile app (Attuned; Weill Cornell Medicine and Ithaca College) to deliver clinically informed GAVT through structured instructional videos narrated by a speech-language pathologist and a laryngologist, practice exercises, and pitch-tracking tools, developed with a community advisory board of TGNC individuals. This mixed methods study aimed to assess the app’s usability and users’ impressions. Methods: Adult TGNC individuals were recruited nationwide via word of mouth and advertising through lesbian, gay, bisexual, transgender, queer, or questioning (LGBTQ+) community organizations. Participants completed a baseline semistructured interview and demographics survey, used Attuned for one week, and completed a second interview regarding their impressions. Interviews were transcribed verbatim and coded inductively in MaxQDA (VERBI Software GmbH). Three authors independently generated codebooks that were reconciled via consensus, then 2 authors applied the finalized codebooks to the remaining transcripts (preconsensus agreement 95.7%; Cohen κ=0.86). Attuned was also evaluated by 2 reviewers experienced in mHealth evaluation and not involved in the app development, using the Mobile App Rating Scale (MARS), the Intercontinental Medical Statistics (IMS) Institute for Health Care Informatics Functionality Scores, the Centers for Disease Control (CDC) Modified Clear Communication Index, the Institute of Medicine (IOM) strategies for creating health-literate mHealth apps, the Patient Education Materials Assessment Tool (PEMAT), and the System Usability Scale (SUS). Readability of the didactic scripts was also assessed with 6 validated formulas. Results: Eighteen TGNC individuals completed the study (median age 27.5 years, age range 19‐50 years). Ten identified as women and/or transgender women, 6 as nonbinary or gender-nonconforming, and 2 as men or transgender men. Eight themes were identified. Participants identified voice as a crucial determinant of others’ perceptions of their gender, with voice-related dysphoria common. Cost and difficulty finding providers were primary barriers to GAVT, and existing free resources were viewed as unstructured and untrustworthy. Postuse impressions were largely positive, particularly regarding the structured curriculum, pitch tracker, convenience, and free access, though lack of individualized feedback was a key limitation. Mean scores were 4.21/5 (SD 0.53) for MARS; IMS functionality, 6.5/11 (SD 0.7); CDC index, 82.5% (SD 24.7%); IOM strategies, 28.5/33 (SD 2.1); and PEMAT understandability and actionability 100%, and SUS 92.5/100 (SD 10.6), with most readability formulas at or below an eighth-grade level. Conclusions: Voice is central to gender identity for TGNC individuals; yet, structured and affordable GAVT remains largely inaccessible. Attuned was positively received by this initial cohort and scored highly on usability, health literacy, and readability, though findings are limited by potential acquiescence bias and the raters’ authorship roles.

Integration of Ecological Momentary Assessment and Intervention With Wearable-Based Digital Phenotypes to Treat Anxiety in Young Adults: Mixed Methods Experimental Study

2026-10-08T17:00:20-04:00

Background: Young adults report high levels of anxiety, yet their access to timely treatment remains limited. Mobile-based digital therapeutics, particularly ecological momentary assessment and intervention (EMA/EMI), offer a promising approach to deliver context-responsive care. The integration of wearable-derived digital phenotypes with digital therapeutics may enhance the personalization of feedback in such interventions, but empirical evidence from real-world settings remains scarce. Objective: This study examined the real-world applicability and effectiveness of an EMA/EMI-based digital therapeutic, ANZEILAX-Green (HAII Corp), for use in anxiety reduction in young adults and investigated how personalized feedback generated by combining EMA/EMI data with wearable-based digital phenotypes ameliorates symptoms and promotes self-management and user engagement. Methods: An 8-week exploratory experimental study was conducted with young adults aged 19-39 years. Participants used a smartphone app in which EMA/EMI modules had been integrated with a smartwatch that continuously collected digital phenotyping data, including heart rate, sleep, and activity. Psychological outcomes were assessed using the Generalized Anxiety Disorder 7-item scale (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) at baseline and weeks 2, 4, 6, and 8, and the Rosenberg Self-Esteem Scale (RSES) at baseline and week 8. Quantitative changes were analyzed using paired-samples t tests and repeated-measures ANOVA, and qualitative interviews were thematically analyzed to identify user experience mechanisms and design implications. Results: In all, 29 participants were included in the final analysis. Significant improvements were observed in their GAD-7 (mean difference –4.79, 95% CI –6.56 to –3.02; Cohen d=1.03; P<.001), PHQ-9 (mean difference –3.86, 95% CI –5.79 to –1.93; Cohen d=0.76; P<.001), and RSES scores (mean difference +5.21, 95% CI 3.47 to 6.95; Cohen d=1.14; P<.001). Consistent improvements in GAD-7 scores were observed across the assessment points, with statistically significant reductions from week 4 onward. Participants demonstrated high adherence, completing 80% of the EMA/EMI prompts. The digital phenotype showed trends of increased deep sleep and reduced mobility, and sleep indicators were significantly correlated with depressive symptoms. Analysis of the interview findings revealed 5 mechanisms: enhanced self-awareness, data-driven reflection, behavioral change, intrinsic motivation, and extension of treatment into daily life. Conclusions: EMA/EMI-based digital therapeutics integrated with wearable-derived digital phenotypes are feasible and acceptable for use by young adults and were associated with within-group reductions in anxiety, suggesting acceptable efficacy, though this warrants confirmation in a controlled trial. The combination of real-time self-reported assessments of emotional states and passive physiological monitoring enables personalized feedback suited to users’ emotional and physiological states. These findings offer empirical and design insights for automated, personalized mental health intervention systems. Trial Registration:

Correction: Perceptions of Adults Aged 50 Years and Older Regarding the Use of Wearable mHealth Technologies to Promote Physical Activity: Systematic Review and Meta-Ethnography

2026-10-08T15:45:21-04:00

This is a correction of "Perceptions of Adults Aged 50 Years and Older Regarding the Use of Wearable mHealth Technologies to Promote Physical Activity: Systematic Review and Meta-Ethnography."

Reporting Gaps in mHealth Intervention Studies for Adults With Diabetes: Systematic Review

2026-10-01T16:00:15-04:00

Background: Diabetes self-management education and support requires scalable digital solutions, making mobile health (mHealth) interventions increasingly vital. Despite their proven clinical efficacy, the real-world translation and scalability of these interventions are severely hindered by fragmented and opaque reporting. To address this methodological gap, the World Health Organization developed the mHealth evidence reporting and assessment (mERA) checklist to standardize reporting transparency. Objective: This study aimed to systematically evaluate the reporting completeness of published randomized controlled trials (RCTs) on mHealth interventions for adult diabetes using the mERA checklist; identify critical reporting gaps; analyze how these deficiencies undermine feasibility, scalability, and sustainability; and propose targeted strategies for future optimization. Methods: A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)–compliant systematic review was conducted. Five major databases (PubMed, Embase, Web of Science, MEDLINE, and the Cochrane Library) were systematically searched for relevant RCTs published from inception to July 2026. Data extraction and reporting completeness assessments were conducted using the 16-item mERA core checklist. Nonparametric and categorical analyses were used to evaluate factors associated with reporting completeness. Results: A total of 144 RCTs conducted across 49 countries were included in the final analysis. The mean overall mERA reporting score was 6.74 out of 16 (SD 2.71). While basic characteristics such as intervention delivery (n=128, 88.9%) and technology platform (n=117, 81.3%) were well reported, critical system-level domains were largely omitted. Specifically, interoperability (n=12, 8.3%), data security (n=28, 19.4%), compliance with guidelines (n=41, 28.5%), and intervention fidelity (n=53, 36.8%) were severely underreported. Additionally, reporting completeness was significantly higher in studies published between 2016 and 2026 (=.01) and in multicomponent interventions compared to single-component ones (=.04). Conclusions: Empirical research on mHealth interventions for adult diabetes exhibits systemic structural flaws in reporting, particularly concerning system integration, data security, and regulatory compliance. These omissions significantly undermine the transparency, reproducibility, and real-world scalability of digital diabetes care solutions. Future trials must rigorously adopt standardized reporting frameworks such as the mERA checklist to bridge the gap between clinical efficacy and large-scale public health policy translation.

Noninvasive Interstitial Glucose Estimation Using Wearables and Machine Learning in Healthy Individuals and Individuals With Obesity: Observational Cohort Study

2026-09-30T15:45:11-04:00

Background: Continuous glucose monitoring (CGM) can facilitate weight management and lower the risk of metabolic diseases by providing real-time feedback on glycemic responses, thereby enabling more informed lifestyle decisions. However, current CGM systems remain constrained by invasiveness, cost, and short sensor lifespan, limiting their practicality for guiding individualized postprandial low-glycemic diets. Objective: Extending earlier proof-of-concept findings, this study aimed to validate an interstitial glucose (IG) machine learning algorithm in real-world environments using multimodal, continuous data collected from wearable sensors and smartwatches. In the long term, we aim to embed a noninvasive, sensor-based algorithm for estimating tissue glucose within mobile health apps and postprandial low-glycemic diet frameworks to enable scalable, personalized prevention strategies. Methods: We conducted a 2-week study phase during which participants continuously wore 2 noninvasive sensor devices: a scientific sensor wristband (Empatica EmbracePlus) and a commercially available smartwatch (Fitbit Sense 2). As a reference measurement, an invasive CGM sensor (Abbott FreeStyle Libre 3) measured IG levels. For metabolic characterization, 1-point fasting blood and urine samples were collected, and deep phenotyping using state-of-the-art nuclear magnetic resonance spectroscopy and bioelectrical impedance analysis was performed. For participants with overweight and obesity, clinical standard parameters focusing on glucose metabolism were analyzed. Results: A total of 74 participants, 34 (46%) healthy controls and 40 (54%) metabolically at-risk (MR) individuals, simultaneously used an invasive CGM device together with 2 noninvasive wristbands over 2 weeks. Healthy controls were characterized by a mean age of 24.53 (SD 3.68) years and a mean BMI of 22.36 (SD 2.16) kg/m². In contrast, the MR cohort had a mean age of 55.38 (SD 15.08) years and a mean BMI of 35.38 (SD 4.91) kg/m². Furthermore, the MR cohort showed elevated fasting glucose levels (mean 107.56, SD 19.26 mg/dL), hemoglobin A levels (mean 5.67%, SD 0.63%), and an increased homeostatic model assessment of insulin resistance index (mean 4.34, SD 3.49), indicating a disturbed glucose metabolism. The proposed long short-term memory network based on feature vectors obtained the best IG prediction performance, with an average root-mean-squared error of 21.04 (SD 8.32) mg/dL and 98.3% of predictions in zones A and B of the Clarke error grid analysis, representing a high level of predictive accuracy. In addition, based on data from a smartwatch, we achieved a comparable average root-mean-squared error of 23.49 (SD 11.46) mg/dL for the overall cohort. Conclusions: This study demonstrates that IG levels can be predicted from multimodal, noninvasive wearable sensor data using a machine learning approach under real-world conditions. While further validation in larger and more diverse cohorts is warranted, this approach represents a promising step toward accessible, personalized glycemic monitoring and dietary guidance as a preventive tool in mobile health apps.