JMIR mHealth and uHealth最新文献

{"title":"Leveraging Smartphone Mobility Data to Understand HIV Risk Among Rural South African Young Adults: Feasibility Study.","authors":"Thulile Mathenjwa, Elphas Luchemo Okango, Khai Hoan Tram, Maxime Inghels, Diego Cuadros, Hae-Young Kim, Fiona Walsh, Till Barnighausen, Adrian Dobra, Frank Tanser","doi":"10.2196/67519","DOIUrl":"https://doi.org/10.2196/67519","url":null,"abstract":"<p><strong>Background: </strong>Smartphones provide a precise method to study human mobility at an unprecedented scale, allowing researchers to explore the links between mobility, HIV risk, and treatment outcomes. However, leveraging smartphone technology to study HIV risk in rural settings presents unique challenges and opportunities.</p><p><strong>Objective: </strong>This study assessed the feasibility of using smartphone GPS technology to collect mobility data from young adults in rural KwaZulu Natal, South Africa. We also present key lessons learned during the study.</p><p><strong>Methods: </strong>The study was conducted in 2 phases (June 2021-May 2023) with males and females aged 20-30 years old. In phase I, participants received smartphones with a customized study app (Avicenna research software). In phase II, they used their personal smartphones and installed the study app. The app used Android location services to record the smartphone location every 30 minutes and send it to a secure study server hourly. Participants were followed up for 6 months (26 wk). If location data were missing for 48-72 hours, participants were contacted for troubleshooting. Engagement strategies, including reverse billing and gamification (Wheel of Fortune), were implemented to address internet connection barriers and aid data collection.</p><p><strong>Results: </strong>A total of 207 participants were enrolled (phase I: 163; phase II: 44) with 204 providing mobility data. Participants recorded 27.6 million location points with a median number of 74,865 (IQR 28,471-186,578) per participant. The mean weekly location points recorded was 95.3 out of 336 possible half-hour intervals (28.4%). Phase II saw more stable data collection in the latter half of the study, due to increased user engagement with the app. Challenges included phone-related issues (screen malfunctions, lost and broken phone), app terminations, and limited internet connectivity. Reverse billing and gamification strategies improved location data collection through increased user engagement.</p><p><strong>Conclusions: </strong>This study demonstrates that the use of smartphone-based GPS technology is feasible among young adults in a rural South African setting. Although only 28.4% (95.3/336) of expected weekly location data were collected, the study offers insights into engagement strategies that can be used to enhance location data collection in similar contexts. Continuous troubleshooting identified challenges and informed solutions to data collection gaps. Reverse billing system and gamification resulted in significant increases in location data received. These findings underscore the potential of integrating mobile health tools into health systems to better support high-risk mobile populations.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e67519"},"PeriodicalIF":6.2,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12377519/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144954726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Should Digital Interventions for HIV Self-Testing Be Regulated with World Health Organization Prequalification?","authors":"Alex Emilio Fischer, Samanta Tresha Lalla-Edward, Vinodh Edward, Musaed Abrahams, Luke Shankland, John de Wit","doi":"10.2196/60276","DOIUrl":"https://doi.org/10.2196/60276","url":null,"abstract":"<p><strong>Unlabelled: </strong>HIV self-testing (HIVST) allows people to test for HIV outside traditional health facilities, but this presents challenges around pre- and posttest counseling, reporting results, and linking to care. Digital interventions for HIVST, a type of Software as a Medical Device (SaMD), have been shown to address these challenges, but there is currently no standardized system for regulating or approving these interventions. The World Health Organization Prequalification Program (WHOPQ) is an international regulatory body that approves vaccines, medications, and in vitro diagnostics (IVDs) for low- and middle-income countries that do not have the capacity to do their own approvals. This paper explores whether WHOPQ could be used to prequalify digital interventions for HIVST. Over half the World Health Organization (WHO) member states have national regulatory bodies for medical devices, but low- and middle-income countries, especially in Africa, do not have the capacity to regulate medical devices, let alone SaMD. This gap parallels the gap in vaccine regulation that initially led to the development of WHOPQ, and while sophisticated artificial intelligence (AI)-enabled SaMD are being developed, digital interventions for HIVST could be used as a low-risk test case for prequalification of SaMD. The WHOPQ already has a strong history with HIV; over half the WHOPQ funding is from HIV-related funders and half of all prequalified medicines and IVDs are for treatment and diagnosis of HIV; however, only 2% are manufactured in Africa. If digital interventions for HIVST become prequalified, this could improve interoperability and ensure quality, accelerating their adoption at scale. However, care must be taken to remove barriers for African developers and ensure that prequalification does not delay access to people testing for HIV.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e60276"},"PeriodicalIF":6.2,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12373297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144954937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolving Digital Health Technologies: Aligning With and Enhancing the National Institute for Health and Care Excellence Evidence Standards Framework.","authors":"Shayan Bahadori, Peter Buckle, Tayana Soukup Ascensao, Saira Ghafur, Patrick Kierkegaard","doi":"10.2196/67435","DOIUrl":"https://doi.org/10.2196/67435","url":null,"abstract":"<p><strong>Unlabelled: </strong>The rapid advancement of artificial intelligence (AI)-driven diagnostics and wearable health technologies is transforming health care delivery by enabling real-time health monitoring and early disease detection. These innovations are catalyzing a shift toward personalized medicine, with interventions tailored to individual patient profiles with unprecedented precision. This paper examines the current National Institute for Health and Care Excellence (NICE) evidence standards framework (ESF) for digital health technologies (DHTs) and evaluates the challenges associated with integrating DHTs into existing health and care systems. A comprehensive review of the NICE ESF guidelines was conducted, alongside an evaluation of their applicability to emerging AI and wearable technologies. Key limitations and barriers were identified, with particular focus on the framework's responsiveness to technologies that evolve through machine learning and real-world data integration. Our findings indicate that while the NICE ESF provides a structured approach for evaluating DHTs, it lacks the adaptability required for rapidly evolving innovations. The framework does not sufficiently incorporate real-world evidence or support continuous learning models, which are critical for the safe and effective deployment of AI-based diagnostics and wearables. To remain effective and relevant, the NICE ESF should transition to a dynamic, adaptive model co-designed with industry stakeholders. By embedding real-world evidence-based strategies and promoting transparency, efficiency, and collaborative innovation, the updated framework would better facilitate the integration of AI-driven diagnostics and wearables into health care systems, ultimately enhancing patient outcomes and optimizing health care delivery.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e67435"},"PeriodicalIF":6.2,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12373408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144954549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Completion and Compliance Rates for an Intensive mHealth Study Design to Promote Self-Awareness and Self-Care Among Care Partners of Individuals With Traumatic Brain Injury: Secondary Analysis of a Randomized Controlled Trial.","authors":"Noelle E Carlozzi, Jonathan Troost, Wendy L Lombard, Jennifer A Miner, Christopher M Graves, Sung Won Choi, Zhenke Wu, Srijan Sen, Angelle M Sander","doi":"10.2196/73772","DOIUrl":"https://doi.org/10.2196/73772","url":null,"abstract":"<p><strong>Background: </strong>Compliance rates for mobile health (mHealth) studies that involve intensive study designs are highly variable. Both person- and study-specific factors likely contribute to this variability. We were interested in understanding the impact that care partner characteristics and demographics have on study engagement, given that engagement is critical to the success of mHealth interventions.</p><p><strong>Objective: </strong>The primary objective of this report was to analyze the overall and component-specific completion and compliance rates for an intensive 6-month mHealth intervention (CareQOL app) designed to promote self-awareness and self-care among care partners of individuals with traumatic brain injury.</p><p><strong>Methods: </strong>This randomized controlled trial was designed to test the CareQOL app, an mHealth app designed to promote care partner self-awareness (through self-monitoring) and self-care (through personalized self-care push notifications). The study design consisted of a baseline assessment, a 6-month home-monitoring period that included 3 daily ecological momentary assessment (EMA) questions, monthly patient-reported outcome (PRO) surveys, continuous activity and sleep monitoring using a Fitbit, and 2 follow-up PRO surveys at 3 and 6 months posthome monitoring. Three participants withdrew prior to the initiation of the home-monitoring period, resulting in a final analytical sample size of 254. All participants had access to a self-monitoring dashboard (CareQOL app) that included graphical displays of the daily survey scores, as well as daily steps and sleep data from the Fitbit.</p><p><strong>Results: </strong>Overall compliance for the different aspects of the study was high. On average, the full-sample daily EMA PRO completion rate was 84% (SD 19%), Fitbit-based step count compliance was 90% (SD 21%), and Fitbit-based sleep duration compliance was 75% (SD 32%); there was no difference between the study arms for daily EMA PROs and Fitbit compliance rates. Completion rates for monthly and follow-up PRO surveys were even higher, with average end-of-month completion rates ranging from 97% to 100%, and follow-up completion rates of 95% for both time points. Again, these rates did not differ by study arm. The data were represented by 3 engagement groups: high-compliance-all data; high-compliance-PROs and steps only; and moderate PRO compliance-low Fitbit compliance. Group membership was predicted by both race (P<.001) and relationship to the care recipient (P=.001), but not by the other person-specific variables.</p><p><strong>Conclusions: </strong>The compliance rates for this intensive study design are consistent, but at the high end, with what has been reported previously in the literature for studies with shorter time durations. Except for race and relationship to the care recipient, person-specific factors did not appear to be significantly associated with the engagement group. As such, we ant","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e73772"},"PeriodicalIF":6.2,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12370270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144954900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boosting Physical Activity Among Individuals With Low Engagement Through Double-Point Incentives in a Community-Based mHealth Intervention: Retrospective Observational Study.","authors":"Jungin Joo, Mangyeong Lee, Junghee Yoon, Hyeonjin Cho, Govind Warrier, Johannes Thrul, Juhee Cho","doi":"10.2196/66227","DOIUrl":"https://doi.org/10.2196/66227","url":null,"abstract":"<p><strong>Background: </strong>The administration of incentives to promote physical activity, such as the amount or timing, can vary depending on target health behaviors, research settings, intervention delivery channels, and participants' preferences. Interventions implemented at scale necessitate the consideration of potential fiscal constraints for public health promotion. Since limited funding is a barrier to implementing community-based interventions, examining both immediate and sustained effects of temporary incentive increases on physical activity is important.</p><p><strong>Objective: </strong>This study aimed to evaluate the effect of a 1-week double-point event on increasing physical activity among low-engaged individuals in the context of a community-based mobile intervention.</p><p><strong>Methods: </strong>Using retrospective data from a Seoul Metropolitan Government mobile health (mHealth) intervention, we evaluated the effects of a 1-week double-point incentive on participants' physical activity. During 3 registration phases from November to December 2021, a total of 50,145 individuals enrolled. Our analysis focused on the low-engaged group (n=27,833, 55.5%), who averaged fewer than 3 days per week of meeting the daily step challenge (at least 7000 steps) before the intervention. We performed a segmented regression analysis to assess changes in physical activity before and after the event. Multivariable logistic regression and Cox proportional hazards models were used to identify factors associated with improving and maintaining physical activity after starting the intervention.</p><p><strong>Results: </strong>Of 27,833 low-engaged participants, only 13.7% (n=3835) improved their physical activity. Daily challenge engagements per week increased by 2.53 times, and average daily steps increased by 1924.97 (standardized mean difference 0.55, 95% CI 0.51-0.58). In multivariable logistic regression, older age was significantly associated with improved physical activity immediately after starting the intervention. However, 50% (1918/3835) of the improved group was likely to return to low engagement 3 weeks after the intervention ended. Older age and use of certain wearable devices were associated with maintaining physical activity after the intervention.</p><p><strong>Conclusions: </strong>Double-point incentives in the short term may serve as a cue-to-action to motivate low-engagement targets; however, they do not seem to guarantee long-term maintenance in the context of community-based mHealth interventions. Further research is needed to identify additional strategies beyond monetary incentives to sustain long-term healthy behavior.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e66227"},"PeriodicalIF":6.2,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12370261/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144954920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Behavior Change Resources Used in Mobile App-Based Interventions Addressing Weight, Behavioral, and Metabolic Outcomes in Adults With Overweight and Obesity: Systematic Review and Meta-Analysis of Randomized Controlled Trials.","authors":"Sijia Li, You Zhou, Ying Tang, Haoming Ma, Yuying Zhang, Aoqi Wang, Xingyi Tang, Runyuan Pei, Meihua Piao","doi":"10.2196/63313","DOIUrl":"10.2196/63313","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Overweight and obesity have become a public health issue. Lifestyle modifications delivered through mobile devices, especially mobile phones, present an opportunity to support weight loss efforts. However, evidence regarding the effects of mobile apps on other outcomes, such as blood pressure and physical activity (PA), remains limited. Recent studies on this topic require a systematic review and updating, and the active elements that promote behavior change remain unclear.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The meta-analysis aimed to explore the effects of mobile phone apps on weight-related outcomes (weight, BMI, waist circumference [WC], fat mass, fat mass percentage), behavioral outcomes (moderate-to-vigorous physical activity [MVPA], energy intake), and metabolic outcomes (systolic blood pressure [SBP], diastolic blood pressure [DBP], triglycerides, hemoglobin A1c [HbA1c]) among adults with overweight and obesity. Behavior change techniques (BCTs), the smallest replicable intervention elements, were also identified to clarify the components used in current studies, along with associated resources, including facilitating, boosting, and nudging. In addition, factors influencing the effectiveness of these interventions were explored.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Six databases (PubMed, Embase, CENTRAL, Web of Science, PsycINFO, and CINAHL) were searched for relevant randomized controlled trials (RCTs) published in English from inception to May 20, 2024. Two independent authors conducted study selection, data extraction, and quality assessment. The effect size of interventions was calculated using the mean difference (MD), and a random-effects model was applied for data analysis. Subgroup and sensitivity analyses were conducted to explore potential influencing factors and identify possible sources of heterogeneity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 29 studies were included. The results indicated that mobile phone app interventions significantly reduced weight (MD=-1.45 kg, 95% CI -2.01 to -0.89; P&lt;.001), BMI (MD=-0.35 kg/m2, 95% CI -0.57 to -0.13; P=.002), WC (MD=-1.98 cm, 95% CI -3.42 to -0.55; P=.007), fat mass (MD=-1.32 kg, 95% CI -1.94 to -0.69; P&lt;.001), DBP (MD=-1.76 mm Hg, 95% CI -3.47 to -0.04; P=.04), and HbA1c (MD=-0.13%, 95% CI -0.22 to -0.04; P=.005). However, nonsignificant effects were observed for other outcomes. The most frequently used BCTs included 2.3 \"self-monitoring of behavior\" (n=25), 4.1 \"instruction on how to perform the behavior\" (n=24), 2.2 \"feedback on behavior\" (n=20), 1.1 \"goal setting (behavior)\" (n=19), and 1.4 \"action planning\" (n=15). Fifty-nine percent of included studies used 3 resource types (ie, facilitating, boosting, and nudging). Subgroup analyses identified combined diet and PA interventions, medium-term intervention duration, and the use of ≥8 BCTs as potential reference interventions for improving outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This meta-analysis ","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e63313"},"PeriodicalIF":6.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12392691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144882906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of a Digital Peer-Supported App Intervention in Promoting Smoking Cessations: Nonrandomized Controlled Trial.","authors":"Shota Yoshihara, Kayoko Takahashi, Chiaki Uemura, Shin Murakami, Daichi Harada, Hiroshi Yamato","doi":"10.2196/68638","DOIUrl":"10.2196/68638","url":null,"abstract":"<p><strong>Background: </strong>Smoking cessation has become a global priority, with peer support interventions shown to improve abstinence rates. However, no studies have examined the effectiveness of a group-based digital peer-supported app combined with nicotine gum for smoking cessation among working populations.</p><p><strong>Objective: </strong>This study aimed to assess whether adding a digital peer-supported app to standard nicotine gums improves 12-week smoking abstinence rates among current working smokers in employment-based settings.</p><p><strong>Methods: </strong>A nonrandomized comparison trial was conducted with current working smokers in Japan. Eligible participants smoked at least 1 cigarette per day, owned a smartphone (iOS or Android), and were enrolled in their company's health insurance program. Participants were self-selected into one of the two intervention groups (digital peer-supported app + nicotine gums) or a control group (nicotine gums only). The digital peer-supported app creates a group chat for up to 5 people aimed at smoking cessation, where participants can anonymously post counts, photos, and comments daily. Logistic regression analyses adjusted for demographic and smoking-related variables were used to estimate the odds ratios for smoking cessation. Engagement with the app (usage days and posting frequency) was analyzed within the intervention groups.</p><p><strong>Results: </strong>A total of 451 participants were included in the per-protocol analysis (260 in the intervention groups and 191 in the control group). The 12-week abstinence rate was significantly higher in the digital peer-supported app + nicotine gum group compared to the gum-only group (59.2% [154/260] vs 38.7% [74/191]). The adjusted odds ratio of smoking cessation was 2.41 (95% CI 2.07-2.81), indicating a significant impact of digital peer support. Both higher duration of digital peer-supported app usage and increased posting frequency were positively associated with cessation success (P for trend <.001).</p><p><strong>Conclusions: </strong>The addition of a digital peer-supported app to nicotine gum use significantly improved smoking cessation outcomes among working smokers. These findings provide preliminary evidence for the feasibility and effectiveness of integrating group-based digital peer support into smoking cessation interventions.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e68638"},"PeriodicalIF":6.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12364421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144882907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of Wearable Sensors to Assess Fall Risk in Neurological Disorders: Systematic Review.","authors":"Mirjam Bonanno, Augusto Ielo, Paolo De Pasquale, Antonio Celesti, Alessandro Marco De Nunzio, Angelo Quartarone, Rocco Salvatore Calabrò","doi":"10.2196/67265","DOIUrl":"10.2196/67265","url":null,"abstract":"<p><strong>Background: </strong>Assessing fall risk, especially in individuals with neurological disorders, is essential to prevent hospitalization, hypomobility, and reduced functional independence. Wearable sensors are increasingly used in neurorehabilitation, as they enable unsupervised fall risk assessment by providing continuous monitoring during daily functional tasks, thereby offering a reflection of the individual's real-world fall risk.</p><p><strong>Objective: </strong>We systematically reviewed the literature on reliable biomechanical gait parameters detected with wearable sensors to assess fall risk in neurological disorders, focusing on patients with Parkinson disease, multiple sclerosis, stroke, or Alzheimer disease. In addition, we examined the latest advancements in wearable sensor technology, including best practices for device placement as well as data processing and analysis.</p><p><strong>Methods: </strong>We conducted a comprehensive systematic search for relevant peer-reviewed articles published up to April 18, 2025, using PubMed, Web of Science, Embase, and IEEE Xplore, which are the most used databases in the fields of health and bioengineering.</p><p><strong>Results: </strong>The 19 included studies involved 2630 patients with neurological disorders, including 226 (8.59%) with multiple sclerosis (n=7, 37% studies), 2305 (87.64%) with Parkinson disease (n=8, 53% studies), 51 (1.94%) with stroke (n=3, 16% studies), and 48 (1.83%) with Alzheimer disease or cognitive impairment (n=1, 5% study).</p><p><strong>Conclusions: </strong>This review highlights the role of wearable technologies in assessing fall risk in patients with neurological disorders. Although the included studies showed variation in methods and a focus on technology over clinical context, the lack of standardization reflects ongoing advancements, which may be seen as a strength.</p><p><strong>Trial registration: </strong>PROSPERO CRD42023463944; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023463944.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e67265"},"PeriodicalIF":6.2,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12402735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144873253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User Archetypes of a Well-Being-Promoting Mobile App Among Adults: Cross-Sectional Study and Cluster Analysis of Usage Patterns.","authors":"Hanna Rekola, Tommi Tolmunen, Elina Mattila, Juho Strömmer, Timo A Lakka, Helena Länsimies, Tomi Mäki-Opas","doi":"10.2196/68982","DOIUrl":"10.2196/68982","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;A healthy lifestyle is associated with mental well-being, and digital lifestyle interventions can be effective in promoting a healthy lifestyle. However, they do not appear to work for all, and we have limited knowledge of how users' background characteristics affect their tendency to adopt well-being-promoting digital apps and actively use them.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to explore the association of the study participants' characteristics and current well-being with their likelihood of using a well-being-promoting mobile app.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The BitHabit web app (Wellpro Impact Solutions Ltd) was available for a 2-month trial in spring 2023 after completing a short cross-sectional digital questionnaire with questions about well-being, life satisfaction, and lifestyle. Individuals aged 15 years or younger were excluded from the analysis. We used logistic regression to assess how individual characteristics were associated with the initiation of BitHabit app use. To assess user archetypes among those who initiated app use, and k-means clustering analysis and multinomial logistic regression to assess user archetypes among those who initiated app use.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 1646 eligible individuals responded to the questionnaire, and 863 initiated app use. Lower odds of initiating app use were detected among males (odds ratio [OR] 0.66, 95% CI 0.51-0.85; P&lt;.001), the unemployed (OR 0.68, 95% CI 0.48-0.97; P=.03), those with higher general life satisfaction (OR 0.94, 95% CI 0.89-1.00; P=.04), and those reporting fewer life challenges (OR 1.13, 95% CI 1.02-1.24; P=.02). We identified (1) thriving non-active users, (2) struggling non-active users, and (3) active users as archetypes based on app use activity, life satisfaction, and reported life challenges. Older participants had lower odds of being thriving nonactive (OR 0.96, 95% CI 0.94-0.99; P=.01) or struggling nonactive users (OR 0.93, 95% CI 0.90-0.96; P&lt;.001) than active users. Retired participants had higher odds of being struggling nonactive than active users (OR 4.06, 95% CI 1.44-11.42; P=.01) and unemployed lower odds of being thriving nonactive than active users (OR 0.2, 95% CI 0.08-0.51; P&lt;.001). Those who were physically more active had higher odds of being thriving nonactive than active users (OR 2.71, 95% CI 1.00-7.32; P=.05). Participants with higher alcohol consumption had higher odds of being struggling nonactive users than active users (OR 3.22, 95% CI 1.16-8.99; P=.03).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;While lower general life satisfaction and less favorable health behavior appeared to increase the likelihood of trying the app, those who eventually actively used the app were more satisfied with their lives at baseline. In addition, among nonactive users, there were recognizable user profiles of thriving and struggling nonactive users, which were associated with various individual charac","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e68982"},"PeriodicalIF":6.2,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12360720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144873254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adolescent Engagement With a Multicomponent mHealth Tool: Identifying Usage Patterns, Determinants, and Health Behavior Change in an Intervention Trial.","authors":"Carmen Peuters, Ann DeSmet, Laura Maenhout, Greet Cardon, Dries Debeer, Geert Crombez","doi":"10.2196/59041","DOIUrl":"10.2196/59041","url":null,"abstract":"<p><strong>Background: </strong>Research about the engagement of adolescents with mobile health (mHealth) interventions is scarce, while it is generally assumed that the engagement affects the intervention efficacy.</p><p><strong>Objective: </strong>Using an mHealth intervention that targets the general population of adolescents to promote healthy behaviors (physical activity, low sedentary time, adequate sleep, and taking breakfast) and mental health, we aimed to investigate (1) how adolescents engage with the intervention, (2) which engagement styles can be identified and how these differ according to personal characteristics, and (3) which style of engagement predicts behavior change. The intervention used, #LIFEGOALS, includes self-regulation techniques, a support chatbot, narrative videos, and gamification, brought together in an app coupled to an activity tracker.</p><p><strong>Methods: </strong>Logged usage data and self-reports of experience with #LIFEGOALS were collected from 159 adolescents (mean age 13.54, SD 0.95 years) over a 12-week intervention period and used to describe behavioral and experiential engagement with the intervention components over time. Baseline data on sociodemographic variables, mental health, and behavioral determinants were explored as determinants of engagement and were used to characterize engagement styles that were identified through exploratory cluster analysis on the frequency of usage of the components. Linear mixed-effects regression was used to analyze the effect of engagement style on health behavior change.</p><p><strong>Results: </strong>Average time in the app was 26 minutes (SD 26) over the 12-week period, with usage decreasing substantially after the first week. The use of self-regulation techniques and gamification was strongly interrelated (0.65 <r <0.70), whereas use of Fitbit showed weaker correlations with other component usage (0.15 <r <0.31). Exploratory analyses suggest that engagement was influenced by immigration background and by adolescents' attitudes, self-efficacy, and intentions toward healthy living. Younger participants tended to use the Fitbit more frequently. Cluster analysis identified 4 engagement styles: narrative usage (n=19), app usage (n=36), Fitbit usage (n=32), and no usage (n=72), which were associated with differences in age, peer support, and mental health. Engagement style did not affect change in health behavior outcomes from preintervention to postintervention.</p><p><strong>Conclusions: </strong>Different engagement styles were identified based on the frequency and type of components used. Findings support the relevance of tailoring mHealth to individual, interpersonal, and contextual characteristics. The overall low engagement with the intervention may have limited the detection of differences in health effects between engagement styles.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e59041"},"PeriodicalIF":6.2,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12360726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144873252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}