JMIR Publications

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  mHealth for Telemedicine and Homecare

  Jun 9, 2026

  Influence of Online Sessions via a Deep Brain Stimulation Device: Prospective, Single-Arm, Longitudinal, Nonrandomized Self-Controlled Cohort Study

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  Authors List:

  • Ryoma Morigaki  
  • Hideo Mure  
  • Kouki Takasuka  
  • Joji Fujikawa  
  • Toshiro Shinmen  
  • Shin Tarui  
  • Masataka Hatanaka  
  • Keisuke Shinohara  
  • Taku Matsuda  
  • Kazuhisa Miyake  
  • Yasushi Takagi  

  Abstract:

  Background: Deep brain stimulation (DBS) is widely performed in patients with advanced Parkinson disease (PD). Recent advances in technology have facilitated remote programming of DBS devices, reflecting an emerging trend in neuromodulation approaches, and offering a potential framework for patient-centered care. These online sessions for patients with PD who underwent de novo implantation of DBS devices have been reported to be safe and effective, similar to in-clinic sessions. Currently, evidence for patients with chronically implanted DBS devices remains limited. Objective: This study aimed to evaluate the safety, feasibility, and preliminary efficacy of online sessions for patients with PD who had chronically implanted DBS devices, and to determine whether this approach could reduce patient burden without compromising clinical management. Methods: A prospective, single-arm, longitudinal, nonrandomized, self-controlled cohort study was conducted at 2 centers in Japan. Eighteen patients with PD who had chronically implanted DBS devices were enrolled. Two in-clinic sessions were substituted with online sessions. The Movement Disorder Society Unified Parkinson’s Disease Rating Scale Part III scores were assessed at baseline, after in-clinic sessions, and online sessions. The Multidimensional Evaluation Scale for Patient Impression Change—Japanese version, Patient’s Global Impression of Change, and Clinical Global Impression of Change were each administered after both in‑clinic and online sessions. The Telehealth Usability Questionnaire and the Online Usability Questionnaire for Programming were evaluated once after online sessions. Results: The Movement Disorder Society Unified Parkinson’s Disease Rating Scale Part III scores did not differ significantly across the 3 assessments. The Multidimensional Evaluation Scale for Patient Impression Change—Japanese version scores revealed that no items but item 3, “overall sleep,” significantly worsened after online sessions. The Patient’s Global Impression of Change and Clinical Global Impression of Change scores were comparable between in-clinic and online sessions. The Telehealth Usability Questionnaire scores indicated that online sessions were generally favorable, although the "Reliability" was rated comparatively lower. The mean total time and cost saved per visit were 280 minutes and 7974 yen (US $1≈155 JPY), respectively. One patient experienced a fall and lumbar compression fracture during the online visit period and dropped out of the study because of hospitalization. Conclusions: Online sessions may be a feasible option for a subset of patients with PD carrying chronically implanted DBS devices. Although online sessions cannot fully replace in-person assessments, they may serve as a practical alternative when adequate caregiver support, sufficient digital literacy, and a reliable connection are ensured. Clinicians should note that sleep-related symptoms may worsen in some patients. While time and cost savings are not direct indicators of care quality, these benefits still provide meaningful support for patients and caregivers. Overall, online sessions may complement routine follow-up in stable patients undergoing chronic DBS therapy. Trial Registration: Japan Registry of Clinical Trials jRCT1062230055; https://jrct.mhlw.go.jp/latest-detail/jRCT1062230055

  • JMIR Mhealth Uhealth 2026;14(1):e80223

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  mHealth for Data Collection and Research

  Jun 9, 2026

  Daily Negative Affect and Reaction Time Inconsistency in Emerging Adults: Ecological Momentary Assessment Study

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  Authors List:

  • Lauren Alexandra Rutter  
  • Pei-Ying Chen  
  • Prabhvir Lakhan  
  • Laura Thi Germine  
  • Zoe Hawks  

  Abstract:

  Background: Anxiety and mood disorders, characterized by elevated negative affect (NA) and cognitive impairments, are highly prevalent among college students. Within-person (WP) NA variability, which captures moment-to-moment fluctuations in NA, provides unique insights into emotional processes that are not reflected in mean NA levels. Cognitive variability, particularly reaction time (RT) inconsistency, is increasingly recognized as a sensitive marker of cognitive health and functional integrity. Although prior research links NA to cognitive variability, the short-term dynamics of these associations in naturalistic settings remain understudied. College students provide an ideal population for examining these dynamics using ecological momentary assessment (EMA). Objective: This study investigated the association between WP NA and RT inconsistency, hypothesizing that higher WP fluctuations in NA would predict increased RT inconsistency. We also examined the moderating roles of practice effects and covariates, including neuroticism, insomnia, and sex. Methods: Using EMA, 99 university students completed morning and evening assessments over 14 days, including a cognitive task measuring RT inconsistency (standard deviation in trial-level RT) and self-reported NA. Multilevel modeling was used to assess WP fluctuations in NA and their impact on RT inconsistency, accounting for time (session number), between-person differences in NA, and covariates such as sleep problems, neuroticism, age, sex, and use of a touch device. Results: WP fluctuations in NA significantly predicted increased RT inconsistency (exp(β)=1.022, 95% CI 1.008‐1.037; =.007), supporting the hypothesis that NA variability disrupts cognitive performance. Male students exhibited lower RT inconsistency than female students, with a small effect size (exp(β)=0.824, 95% CI 0.694‐0.977; =.049). Finally, EMA sessions were inversely associated with RT inconsistency, with a stronger effect up to session 3 (exp(β)=0.930, 95% CI 0.879‐0.985; =.03) than after session 3 (exp(β)=0.986, 95% CI=0.979‐0.992; <.001), indicating practice effects. Conclusions: Momentary fluctuations in NA influence cognitive variability, particularly in the early stages of repeated cognitive tasks, underscoring the role of emotional processes in cognitive performance. Practice effects and individual differences, such as sex and insomnia, influence these associations. These findings highlight the use of EMA for understanding cognitive-affective processes and suggest potential intervention targets, such as addressing NA, to improve cognitive functioning in emotionally vulnerable populations like college students.

  • JMIR Mhealth Uhealth 2026;14(1):e64397

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  Wearable Devices and Sensors

  Jun 9, 2026

  Evaluation of the Importance of Stopping Elderly Accidents, Deaths, and Injuries (STEADI)–Based Factors in Wearable Fall Risk Assessment: Secondary Data...

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  Authors List:

  • Nozhan Ghoreishi  
  • Stella Ansah  
  • Jiayu Lu  
  • Wei Lu  
  • Sanghee Moon  
  • Ferdinand Delgado  
  • Diliang Chen  

  Abstract:

  Background: Falls among older adults are a growing and costly public health problem that often leads to mobility decline and loss of independence. Although clinical frameworks such as the Centers for Disease Control and Prevention’s (CDC) Stopping Elderly Accidents, Deaths, and Injuries (STEADI) initiative recommend multifactor screening (gait, balance, strength, fear of falling, and fall history), most wearable fall risk assessment systems rely on a small set of risk factors (typically gait), which creates a gap between clinical practice and automated wearable assessment. Objective: This study aims to evaluate the importance of STEADI-based fall risk factors and provide design guidance for clinically compatible wearable fall risk assessment systems. Methods: We created a dataset of 24 older adults (10 low fall risk and 14 high fall risk) from a publicly available plantar pressure dataset of 48 participants by retaining only those with consistent fall risk labels based on both the Berg Balance Scale and the Timed Up and Go test. A total of 18 features were extracted to quantify gait, strength, balance, fear of falling, and fall history. Random forest (RF) models were trained with leave-one-subject-out cross-validation to assess fall risk. Importance of STEADI-based factors was assessed by two methods: (1) estimating Shapley Additive Explanations values based on a single RF model trained on all features; and (2) training 5 separate RF models, each on 1 STEADI factor category, and comparing their fall risk classification accuracies. Results: In this secondary analysis, the RF model trained on all features achieved a subject-level accuracy of 87.53% (95% CI 75%-100%). Shapley Additive Explanations analysis identified the right foot flat phase ratio (fear of falling feature) as the highest-ranked feature, followed by maximum right forefoot ground reaction force (strength feature), whereas traditional gait features did not appear in the top 10. The 5 separate RF models trained on individual STEADI-based factor categories showed a similar trend in mean participant-level accuracy: fear of falling, 87.59% (95% CI 75%-100%); strength, 79.18% (95% CI 62.5%-95.83%); balance, 70.5% (95% CI 50%-87.5%); gait 70.81% (95% CI 54.17%-87.5%); and fall history 62.37% (95% CI 50%-75.1%). However, paired comparisons did not show statistically significant differences in accuracy between the gait model and the models trained on other factors. Conclusions: These preliminary results show that commonly overlooked nongait factors are potentially as informative as gait, although clear superiority was not demonstrated in this dataset. The novel foot flat phase ratio ranked higher than all other evaluated features, which showed the value of domain knowledge–informed feature engineering. These preliminary findings indicate that nongait STEADI factors merit consideration in the design of wearable fall risk assessment systems.

  • JMIR Mhealth Uhealth 2026;14(1):e93877

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  mHealth for Rehabilitation

  Jun 9, 2026

  Digital Physiotherapeutic Elbow-Specific Training System for Patients After Arthroscopic Release of Elbow Contracture: Noninferiority Randomized Controlled...

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  Authors List:

  • Yanmao Wang  
  • Lihua Huang  
  • Ruixin Wang  
  • Zhihao Xu  
  • Yanhong Ma  
  • Shiyang Yu  
  • Jian Ding  
  • Yun Shen  
  • Shengdi Lu  

  Abstract:

  Background: The effectiveness of a digital training (DT) system in which patients receive individually tailored physiotherapeutic elbow-specific training (PEST) delivered via a digital platform remains unclear. Objective: This study determines the effectiveness of a DT system in which patients receive individually tailored PEST supervision and guidance via the Joymotion Intelligent Rehabilitation System and educational videos, compared with conventional training (CT) conducted by qualified physiotherapists at outpatient clinics and unsupervised home-based PEST in patients following arthroscopic release for posttraumatic elbow stiffness. Methods: This single-center, noninferiority randomized controlled trial was conducted at the Rehabilitation Department of Shanghai Sixth People’s Hospital between September 2020 and June 2024. Patients aged 16-65 years undergoing arthroscopic release for posttraumatic elbow stiffness were randomized to receive either a 12-week DT program or conventional outpatient clinic-based training. Outcome measures included elbow flexion-extension range of motion (primary outcome); forearm rotation; isometric and dynamic muscle strength; American Shoulder and Elbow Surgeons (ASES) and Disabilities of the Arm, Shoulder, and Hand (DASH) scores; EQ-5D-5L; cost-effectiveness; adherence; and adverse events, assessed at 4, 12, and 24 weeks postoperatively. Results: At 12 weeks, the mean elbow flexion-extension range of motion improved similarly in the DT and CT groups (between-group difference –1.6°, 95% CI –8.2° to 4.9°; P=.53), confirming noninferiority. Forearm rotation gains were slightly greater with DT (difference 14.2°, 95% CI 2.9°-25.6°). Patient-reported outcomes were equivalent between groups: ASES function (difference 0.6, 95% CI –0.3 to 1.5; P=.39) and pain (difference 0, 95% CI –8.3 to 8.5; P=.68) subscores, DASH (difference 0.23, 95% CI –1.54 to 1.99; P=.68), and EQ-5D-5L index (difference 0.001, 95% CI –0.012 to 0.015; P=.56) showed no significant between-group differences. Nearly all patients completed the 12-week program in both arms (104/106, 98.1%, vs 101/102, 99%, adherence; odds ratio 0.52, 95% CI 0.05-5.77). Adverse events occurred in 29 out of 106 (27.4%) participants in the DT group and 32 out of 102 (31.4%) participants in the CT group (odds ratio 0.82, 95% CI 0.45-1.50). Total rehabilitation costs per patient were lower in the DT group by an average of CNY –7418.58, and incremental cost-effectiveness analysis indicated that DT provided comparable outcomes at lower cost. Conclusions: Individually tailored PEST delivered via a DT system is a viable, cost-effective, and safe alternative to conventional outpatient clinic-based training following arthroscopic release for posttraumatic elbow stiffness. These findings support its integration into routine postsurgical care, particularly for patients facing barriers to traditional therapy. Trial Registration: Chinese Clinical Trial Registry Chictr2400093415; https://www.chictr.org.cn/showprojEN.html?proj=240693

  • JMIR Mhealth Uhealth 2026;14(1):e87459

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  Usability of Apps and User...

  Jun 9, 2026

  Mobile Health App Acceptance in Japan’s Aging Society: Multigroup Structural Equation Modeling Based on the Extended Unified Theory of Acceptance and Use...

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  Authors List:

  • Yasue Fukuda  
  • Koji Fukuda  

  Abstract:

  Background: Mobile health (mHealth) technologies are increasingly promoted as tools for chronic disease management and healthy aging, yet adoption remains persistently uneven across demographic groups. Japan, where 29.1% of the population is 65 years or older—the highest proportion globally—exemplifies the challenges of mHealth promotion in super-aging societies. Despite high smartphone penetration (90.1%) and active national digital transformation initiatives, only 21.6% of Japanese adults report regular mHealth app use, with marked disparities by age and sex. Objective: This study examined determinants of mHealth acceptance by extending the unified theory of acceptance and use of technology to incorporate eHealth literacy, self-efficacy, perceived risk, distrust, and health-related factors (health status and health interest). Age- and sex-specific differences in acceptance mechanisms were also investigated using multigroup structural equation modeling (SEM). Methods: We conducted a cross-sectional online survey in November 2023 with 960 Japanese adults sampled across 7 age strata (aged 18-27 years to aged ≥78 years). SEM tested hypothesized relationships among 9 constructs. Health status and health interest were included as observed covariates. Multigroup SEM with configural, metric, and structural invariance testing examined age- and sex-specific differences, and binary logistic regression identified predictors of current mHealth app use. Results: The structural model demonstrated good fit (χ²/df=2.06; comparative fit index 0.953; Tucker-Lewis index 0.945; root mean square error of approximation 0.047) and explained 71.6% of the variance in behavioral intention. Effort expectancy (β=0.404), facilitating conditions (β=0.349), and performance expectancy (β=0.188) were the primary proximal predictors of behavioral intention. Social influence exerted strong upstream effects on effort expectancy (β=0.811), eHealth literacy (β=0.507), and self-efficacy (β=0.422). Health interest positively influenced performance expectancy (β=0.133), whereas neither health interest nor health status showed a significant direct effect on distrust. Distrust did not directly predict behavioral intention in the overall sample. Multigroup analyses identified 5 significant age differences and 5 sex differences. eHealth literacy increased distrust among young adults but reduced perceived risk among middle-aged and older adults. Self-efficacy negatively predicted performance expectancy among young adults yet positively predicted it among middle-aged and older adults. Distrust significantly reduced behavioral intention only among middle-aged adults. Conclusions: mHealth acceptance in Japan’s aging society is characterized by stable proximal determinants of behavioral intention alongside heterogeneous upstream belief formation processes that vary systematically by age and sex. Health interest, rather than health status, emerged as the key contextual driver of perceived usefulness. At the theoretical level, this study clarifies how eHealth literacy, self-efficacy, and distrust function as age- and sex-contingent antecedents within an extended unified theory of acceptance and use of technology framework. At the practical level, these findings highlight the need for trust-centered, demographically tailored, and literacy-sensitive strategies to promote equitable mHealth adoption in rapidly aging societies.

  • JMIR Mhealth Uhealth 2026;14(1):e87832

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  Use and User Demographics of mHealth

  Jun 8, 2026

  Real-World Meditation App Engagement: Longitudinal Study of the Medito Meditation App

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  Authors List:

  • Julia Adams  
  • Jonathan Davies  
  • Prai Wattanatakulchat  
  • Julieta Galante  
  • Simon D'Alfonso  
  • Nicholas T Van Dam  

  Abstract:

  Background: Meditation apps are increasingly popular but face significant engagement challenges. Most research does not meaningfully capture real-world engagement or associated user characteristics. Engagement patterns and reasons for engaging or disengaging remain relatively unexplored. Objective: This study aimed to examine Medito app user engagement over the first 30 days after download and how intended use, demographics, user traits, and mental health factors predict engagement. Methods: A prospective online survey was conducted among 668 Medito app users from 30 countries. Factors assessed included demographic factors (eg, age, sex, education, employment, and country of residence); user factors (eg, number of apps tried, hours of experience, meditation-related adverse events, expectations, readiness to change, and personality); and mental health factors (eg, quality of life, perceived stress, psychological distress, well-being, and satisfaction with life). Detailed engagement data included days of use, meditations completed, app opens, and minutes of use obtained via a data-sharing agreement with Medito. Minutes of use in the first 30 days after download served as the main outcome variable. Results: App use was relatively low, with 50% (328/655) of users engaging for a total of 16 minutes or less in the first month after download (median 16.11, IQR 0‐74.51 min). Fewer than 20% (124/655, 18.86%) of users continued using the app after 14 days. Intended use (mean 418.56, SD 472.5) significantly exceeded actual use (mean 70.02 SD 176.81; =0.710; <.001). In terms of user factors, expectation match (ie, extent to which outcomes from the app matched initial expectations; ρ0.214; =.002), expectations for anxiety (ρ=0.102; =.01), expectations for attention or focus (ρ=0.091; =.02), and conscientiousness (ρ=0.124; =.003) were associated with higher engagement. Neuroticism was negatively associated with engagement (ρ=−0.103; =.010). For mental health factors, satisfaction with life (ρ=0.123; =.002) and well-being (ρ=0.135, <.001) were associated with higher engagement, while perceived stress (ρ=−0.107; =.007), psychological distress (ρ=−0.138, <.001) and quality of life (ρ=−0.100; =.011) were associated with lower engagement. Only readiness to change showed unique associations with higher engagement (semipartial =0.156; <.001). Regression analysis showed that only perceived stress predicted higher engagement (β=.020; =.04). However, when mental health was included as a single component, expectations for anxiety (β=.015; =.049) and readiness to change (β=.011; =.048) predicted greater engagement, and mental ill health predicted lower engagement (β=−0.008; =.049). Conclusions: Overall, app engagement is generally quite low. Acute stress motivated meditation app use, while chronic stress disrupted it. Engagement is optimal when experiences match expectations and users are prepared to make a change. More transparency is necessary in the promotion of meditation apps so that users have a realistic understanding of the time and effort required to achieve benefits. Trial Registration: OSF Registries osf.io/3v897; https://osf.io/3v897/

  • JMIR Mhealth Uhealth 2026;14(1):e79366

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  mHealth for Wellness, Behavior Change...

  Jun 5, 2026

  Automated Physical Activity Support for Adults and Youth From Low-Income Communities: Single-Arm Pilot Study

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  Authors List:

  • Jordan A Carlson  
  • Frank Materia  
  • Mallory Moon  
  • Suryeon Ryu  
  • Cory Yeager  
  • Chelsea Steel  
  • Kacee Shields  
  • Harpreet Singh Gill  
  • Jannette Berkley-Patton  
  • Delwyn Catley  

  Abstract:

  Background: Mobile health (mHealth) interventions are growing in popularity, but less research has focused on low-income families, particularly interventions integrating wearable devices with automated personalized messages. Objective: We tested a preliminary wearable-integrated mHealth intervention with initial personalization elements among adults and youth from low-income urban communities, focusing on feasibility, acceptability, and preliminary evidence of physical activity behavior. Methods: Participants were 83 adults and 31 youth recruited through community health events held in low-income urban communities. Using a single-arm pre-post design, participants were enrolled into a 7-week beta-version mHealth intervention that integrated a Garmin activity monitor with automated text messages. Messages were sent 4 days/week and focused on increasing step counts using theory-based behavior change techniques related to goal setting, self-monitoring, reinforcement, contextual factors, and self-efficacy. Most messages were personalized by including calculations based on the step-count and step-goal data, using branching logic, and using 2-way question-and-response messages. Feasibility measures included enrollment, retention, fidelity of message delivery, and adherence to wearing the Garmin device. Acceptability measures included survey items and engagement with responding to 2-way messages. Changes in daily steps were explored using mixed-effects linear regression. Results: Enrollment and eligibility rates were 64% (84/132, adults) and 63% (31/49, youth), retention for physical activity measures was 84% (70/83) and 77% (24/31), and 99% (3910/3955) of the intended messages were delivered. Adults and youth adhered to wearing the Garmin on 82% (45/56) and 79% (44/56) of the study days, respectively. Overall acceptability ratings were 83% to 100%, with 97% (75/77) of adults and 100% (27/27) of youth indicating they would recommend the program. Adults and youth replied to a mean of 2.6 (SD 2.2) and 3.2 (SD 2.7) of the 7 text messages that asked for a reply, with higher engagement among adults who participated with their child. Pre-post changes in daily steps were β=240 (95% CI –387 to 866) for adults and β=413 (95% CI –877 to 1703) among youth, with larger changes observed among those in the highest tertile of engagement (adults: β=584, 95% CI –784 to 1952; n=19; youth: β=941, 95% CI –827 to 2709; n=11) and those who were meeting less than two-thirds of the physical activity guideline at baseline (adults: β=609, 95% CI –30 to 1247; n=47; youth: β=1406, 95% CI –94 to 2907; n=22). Conclusions: Personalized mHealth physical activity interventions integrating wearable step trackers with automated text messaging appear to be feasible and acceptable among adults and youth from low-income communities. Step-count findings show promise for the intervention’s ability to support individuals who are further from meeting physical activity guidelines and warrant more research among parent–child dyads. Overall, findings support additional research to optimize and evaluate similar interventions within this population group using fully powered randomized controlled trials. Trial Registration: ClinicalTrials.gov NCT05110508; https://clinicaltrials.gov/ct2/show/NCT05110508

  • JMIR Mhealth Uhealth 2026;14(1):e76991

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  mHealth for Data Collection and Research

  Jun 5, 2026

  The Impact of Sunlight and Artificial Light at Night on Sleep Stages: Evidence From a 7-Day Sensor-Based Observational Study

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  Authors List:

  • Andrea Montanari  
  • Li Min Wang  
  • Amit Birenboim  
  • Basile Chaix  

  Abstract:

  Background: Exposure to circadian entrainers, such as sunlight, positively impacts sleep architecture, while exposure before bedtime to circadian disruptors, such as artificial light and smartphone use, can negatively affect sleep. However, real-world evidence from longitudinal observational studies that simultaneously capture these factors alongside electroencephalography-derived sleep stages remains limited. Objective: This study aimed to investigate the effects of specific environmental and behavioral factors on sleep metrics and architecture by using sensor-based measurements over 7 consecutive days. Specifically, it examined day-to-day associations between (1) daytime sunlight exposure and (2) prebedtime artificial light exposure and smartphone use with selected sleep outcomes on the following night. Methods: A total of 21 participants from the Jerusalem metropolitan area were monitored continuously using the Dreem wearable electroencephalography for sleep staging, HOBO data loggers for light exposure, the wGT3X+ triaxial accelerometer for physical activity, and a dedicated mobile app to record smartphone usage. Sleep outcomes included total sleep time (TST), sleep onset latency (SOL), and the proportions of light sleep (N1) and deep sleep (N3). Sunlight exposure was defined as the number of hours above 1000 lux during daytime, and artificial light and smartphone use before bedtime were quantified as the duration of exposure accumulated in the 2 hours preceding sleep onset. Linear mixed-effects models with a random intercept at the individual level estimated the associations between these exposures and next-night sleep outcomes, adjusting for step count and other individual covariates. Results: The average TST was 420 (SD 85) minutes, and SOL averaged 17.6 (SD 18) minutes. Light sleep (N1) represented 6.6% (SD 2.1%) of sleep, and deep sleep (N3) accounted for 20.1% (SD 7.6%). Each additional hour of daytime sunlight exposure was associated with an increase of 10.67 (95% CI 0.6-20.7) minutes in TST the following night and with a 0.3 (95% CI –0.6 to –0.0) percentage-point decrease in light sleep (N1) percentage. No associations were found between evening artificial light exposure and sleep outcomes, while each minute of smartphone use before bedtime was linked to an increase in SOL of 0.2 (95% CI 0.0-0.4) minutes. Conclusions: These findings emphasize the importance of daylight exposure for circadian alignment and the potential sleep-disruptive effects of evening digital engagement. This study demonstrates the feasibility and value of integrating wearable electroencephalography and environmental and behavioral sensors in naturalistic settings to uncover behavioral and environmental correlates of sleep architecture.

  • JMIR Mhealth Uhealth 2026;14(1):e75898

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  mHealth for Telemedicine and Homecare

  Jun 4, 2026

  The Role of Videoconferencing Teleconsultation in Improving Transfer Efficiency and Functional Outcomes in Rural Stroke Care: Retrospective Cohort Study

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  Authors List:

  • Chi Sheng Wang  
  • Yi-Ju Chen  
  • Tzu-Chieh Lin  
  • Hui-Mei Huang  
  • Pei-Ru Tu  
  • Po-Lin Chen  
  • Jin-An Huang  

  Abstract:

  Background: Interhospital transfer delays remain a major barrier to timely reperfusion therapy and are associated with worse functional outcomes in acute ischemic stroke (AIS), particularly in rural regions. Objective: This study evaluated whether videoconferencing teleconsultation, compared with the standard referral process, was associated with improved transfer efficiency, treatment delivery, and functional outcomes for patients with AIS requiring interhospital transfer in a hub-and-spoke model. Methods: We conducted a retrospective cohort study of patients with AIS identified as potential candidates for endovascular thrombectomy (EVT) who were transferred from a primary stroke center (PSC) to a comprehensive stroke center (CSC) between January 2022 and December 2024. Patients were managed using either videoconferencing teleconsultation or the standard referral process, defined as telephone-based consultation between emergency physicians at the PSC and CSC, in which clinical evaluation and thrombolysis decisions were made primarily by the PSC emergency physicians. Group allocation was determined via institutional workflow. The primary outcome was door-in-door-out time, with additional analyses on its components. Secondary outcomes included intravenous thrombolysis rate at the PSC, EVT rates at the CSC, door-to-puncture time, reperfusion rates, and 90-day functional outcomes assessed via modified Rankin Scale shift analysis. Safety outcomes included all-cause mortality within 90 days and symptomatic intracranial hemorrhage after intravenous thrombolysis and/or EVT. Results: A total of 83 patients were included, with 41 (49.4%) in the teleconsultation group and 42 (50.6%) in the standard referral process group (mean age 73.3, SD 12.9 years), and baseline characteristics were comparable. Teleconsultation was associated with a significant reduction in door-in-door-out time (mean 95.2, SD 22.9 vs 132.3, SD 41.5 minutes; <.001) by shortening computed tomography angiography–to-ambulance notification time (mean 44.6, SD 17.4 vs 79.5, SD 37.6 minutes; <.001). The teleconsultation group had higher intravenous thrombolysis rates at the PSC (26/41, 63.4% in the teleconsultation group vs 17/42, 40.5% in the standard referral process group; =.04), higher EVT rates (14/41, 34.1% in the teleconsultation group vs 6/42, 14.3% in the standard referral process group; =.03), and shorter door-to-puncture time (mean 83.0, SD 35.5 vs 118.5, SD 25.9 minutes; =.04) at the CSC. Patients who received teleconsultation demonstrated a greater shift toward better functional outcomes at the 90th day (27/41, 65.9%; odds ratio 4.55, 95% CI 1.96-11.11; <.001) than patients who did not (13/42, 31.0%; odds ratio 1.35, 95% CI 0.63-2.94; =.07). Safety outcomes were comparable between groups. Conclusions: Videoconferencing teleconsultation was associated with improved transfer efficiency and higher use of reperfusion therapies and was potentially associated with better functional outcomes. This model may represent a feasible strategy for optimizing stroke care pathways in rural settings. Future studies are warranted to assess its applicability in broader stroke populations beyond conventional EVT eligibility criteria across multicenter networks.

  • JMIR Mhealth Uhealth 2026;14(1):e86436

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  Use and User Demographics of mHealth

  Jun 3, 2026

  Managing BMI and Emotional Distress Using mHealth: Nationally Representative Survey Study

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  Authors List:

  • Ranran Z Mi  
  • Fei Shen  
  • Julia Strugala  

  Abstract:

  Background: Mobile health (mHealth) technologies, including smartphone health apps and wearable trackers, are increasingly used to promote health behaviors. However, their impact on physical and mental well-being remains complex, with both benefits and potential unintended negative consequences. Objective: This study aimed to examine the relationship between mHealth use (ie, health app and wearable tracker) and 2 health outcomes (BMI and emotional distress), as well as the mediating roles of healthy eating, sleep, and physical activity based on a representative sample. Methods: We analyzed data from a nationally representative sample of US adults aged 33 to 43 years (N=1931). Chi-square tests and 1-way ANOVA were used to compare demographic differences between mHealth users and nonusers. A path model examined the relationship between mHealth use (ie, smartphone health apps and wearable trackers) and health outcomes (ie, BMI and emotional distress), with lifestyle factors (ie, healthy eating, physical activity, and sleep) as mediators. Mediation analyses tested indirect effects through these lifestyle factors. Results: mHealth users were more likely to be female, married, have higher levels of education and income, and have health insurance. The primary use of mHealth was the management of physical activity. Smartphone health app use positively correlated with wearable tracker use (β=.394; <.001). Smartphone health app use predicted greater BMI (β=.068; =.006), whereas wearable tracker use did not significantly predict BMI. Smartphone health app use was unrelated to emotional distress, while wearable tracker use was associated with lower emotional distress (β=–.074; =.003). Mediation analyses showed that physical activity negatively mediated the relationships between both types of mHealth use and health outcomes, indicating that mHealth users were more physically active, which was linked to lower BMI and emotional distress. Sleep hours mediated only the association between wearable tracker use and health outcomes, such that greater tracker use was related to fewer sleep hours, predicting higher BMI and emotional distress. Finally, healthy eating mediated only the associations between mHealth use and emotional distress, suggesting that healthier dietary behaviors among mHealth users contributed to lower emotional distress. Conclusions: mHealth technologies can potentially promote healthier behaviors, but their effectiveness depends on users taking the initiative to sustain lifestyle changes. While wearable trackers may aid in mental well-being, their association with reduced sleep warrants further investigation.

  • JMIR Mhealth Uhealth 2026;14(1):e75499

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  mHealth in the Developing...

  Jun 3, 2026

  Terminal Digit Preference and Threshold Avoidance in Digital Blood Pressure Measurements During Pregnancy: Secondary Analysis of Data From the CLIP and...

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  Authors List:

  • Peter von Dadelszen  
  • Akshdeep Sandhu  
  • Jeffrey N Bone  
  • Rahat N Qureshi  
  • Olukayode A Dada  
  • Ashalata A Mallapur  
  • Marleen Temmerman  
  • Marie-Laure Volvert  
  • Joseph Waiswa  
  • Moses Mukhanya  
  • Hiten D Mistry  
  • Shivaprasad S Goudar  
  • Hawanatu Jah  
  • Angela Koech  
  • Charfudin Sacoor  
  • Anifa Vala  
  • Khatia Munguambe  
  • Anna Roca  
  • Olelekan O Adetoro  
  • Esperanca Sevene  
  • Marianne Vidler  
  • Mrutyunjaya B Bellad  
  • John Sotunsa  
  • Hannah J Blencowe  
  • Zulfiqar A Bhutta  
  • Umberto d'Alessandro  
  • John Mark Ansermino  
  • Guy A Dumont  
  • Laura A Magee  
  • CLIP Trials Study Group
  • PRECISE Network

  Abstract:

  Background: Screening for, detecting, and managing pregnancy hypertension is a core function of antenatal care. To reduce both training requirements and the risks of measurement error in blood pressure (BP) values, automated and semiautomated BP devices have been validated in pregnant women with normal BP and pregnant women with hypertension and introduced for serial antenatal measurement of BP. Objectives: The study aimed to (1) determine whether or not repeated BP measurements reduced the presence of terminal digit preference and (2) discern whether or not there was evidence of threshold avoidance in the Community-Level Interventions for Preeclampsia (CLIP) trials compared with the purely observational Pregnancy Care Integrating Translational Science, Everywhere (PRECISE) cohorts. Methods: The BP 3AS1-2 and CRADLE Vital Signs Alert low-cost Microlife BP devices were used by trained research staff in the CLIP trials conducted in India, Mozambique, Nigeria (pilot trial only), and Pakistan and the PRECISE cohorts of unselected pregnant women and nonpregnant women of reproductive age recruited in the Gambia, Kenya, and Mozambique. Both devices algorithmically calculate systolic blood pressure and diastolic blood pressure values displayed on digital read-outs. All BP readings were entered manually into a digital platform, which averaged them as the BP for that visit; the first and second readings were averaged unless they were more than 10 mm Hg different, which triggered a third reading, and the second and third readings were averaged. Results: A total of 51,875 participants had their BP measured 438,404 times. Using raw BP values, there was terminal digit preference (129,539/911,500, 14.21% vs 10%; <.001 values ended in zero). A total of 28,929 out of 437,446 (6.61%) dBP values were 62 mm Hg, compared with 9310 of 195,349 (4.77%) from the averaged values (<.001); errors were obviated by averaging BP values. There was evidence of both threshold preference and avoidance in the CLIP trials and the PRECISE cohort. Conclusions: Given the excess of 62 mm Hg values, there is a shared inherent algorithmic error in the calculation of dBP in the BP 3AS1-2 and CRADLE Vital Signs Alert devices. Averaged BP measurements are important to reduce the impact of user errors in manually recording BP values. We recommend that automated and semiautomated BP devices should be connected wirelessly to automatically transfer readings to digital health records to further optimize care. Trial Registration: ClinicalTrials.gov NCT01911494; https://clinicaltrials.gov/study/NCT01911494

  • JMIR Mhealth Uhealth 2026;14(1):e73307

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  Quality Evaluation and Descriptive...

  Jun 2, 2026

  Digital Health Apps and Web-Based Platforms to Support the Prevention and Management of Snakebite Envenoming: Scoping Review

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  Authors List:

  • Deborah Hosemann  
  • Oliver Gries  
  • Jade Dean Rae  
  • Thao Vi Tran  
  • Philipp Sprengholz  
  • Lars Korn  
  • Thi Thien Thanh Pham  
  • Thi Anh Thu Dang  
  • Benno Kreuels  

  Abstract:

  Background: Neglected tropical diseases disproportionately affect underserved populations, with snakebite envenoming (SBE) remaining one of the most overlooked, despite its significant global burden. Digital health applications (DHAs) offer potential to improve prevention, care, and resource management, especially when integrated into digital health interventions. However, despite growing interest, evidence and structured evaluations are limited, making it difficult to assess their impact without a clear overview of existing tools. Objective: This scoping review aims to provide the first comprehensive mapping of DHAs for SBE, highlighting their potential to strengthen the World Health Organization (WHO) strategy while underscoring the urgent need for structured evaluation, improved quality, and strategic integration to enhance prevention, treatment, and coordination efforts. Methods: This review followed the Joanna Briggs Institute and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, with a protocol registered on the Open Science Framework. We searched the PubMed database, app stores, and Google for DHAs between September 24 and 26, 2024, addressing snakebite prevention or treatment. To be included, the DHA had to be accessible via the recorded link, contain a description with snakebite-related features (eg, identification, first aid, and treatment), and allow user interaction. Descriptions had to appear in abstracts, app store listings, or website text. Results were grouped by type (mobile- or web-based) and by WHO region. Furthermore, we examined the 2 most common features: first aid and snake identification. First aid content was benchmarked against global guidelines, while identification methods were categorized, and selected artificial intelligence (AI)–based identification apps were exploratively tested using images of medically significant snakes. Results: A total of 52 eligible results were included, of which 94.2% (49/52) were mobile apps and 5.8% (3/52) were web-based. Regional focus varied, with most apps targeting South-East Asia (n=11, 21.2%), the Americas (n=9, 17.3%), and the Western Pacific (n=5, 9.6%). However, these numbers largely reflect concentration in just a few countries, namely India (n=10, 19.2%), the United States (n=5, 9.6%), and Australia (n=5, 9.6%). The most frequent feature was snake identification support, for example, through photo upload and algorithm-based recognition. However, AI-driven identification often lacked clarity and performed inconsistently in testing. First aid guidance was also common, but only a handful of apps offered comprehensive, evidence-based advice, while others omitted key steps or recommended unsafe practices. Conclusions: This review provides the first structured evaluation of DHAs for SBE and offers a reproducible framework for assessing digital solutions across neglected tropical diseases. By highlighting key gaps and proposing a foundation for integration into national strategies, it supports the development of equitable, evidence-based digital health innovation in underserved areas. Trial Registration: OSF Registries osf.io/2zsfu; https://osf.io/2zsfu

  • JMIR Mhealth Uhealth 2026;14(1):e83744

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