JMIR mHealth and uHealth最新文献

{"title":"Noninvasive Interstitial Glucose Estimation Using Wearables and Machine Learning in Healthy Individuals and Individuals With Obesity: Observational Cohort Study.","authors":"Franziska Schmelter, Xinyu Huang, Annika Heidmann, Paul Beier, Friederike Miriam Rundfeldt, Christina Plöger, Artur Piet, Md Abid Hasan, Yuanheng Zhang, Lennart Jablonski, Oliver Witt, Torsten Schröder, Stefanie Derer, Chris Easthope Awai, Marcin Grzegorzek, Christian Sina","doi":"10.2196/91724","DOIUrl":"https://doi.org/10.2196/91724","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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<sub>1c</sub> 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.</p><p><strong>Conclusions: </strong>This study demonstrates that IG levels can be predicted from multimodal, noninvasive wearable sensor data using a mach","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e91724"},"PeriodicalIF":6.3,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13626402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149109762","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":"A Cardiovascular Care Pathway Supported by Remote Patient Management in Dutch Primary Care: Cost-Effectiveness, Budget Impact, and Workload Analysis.","authors":"Jan Heijdra Suasnabar, Margot Rakers, Nicoline van Hattem, Eric Hiddink, Marcel Haas, Xuerui Zhang, Douwe Atsma, Tobias N Bonten, M Elske van den Akker-van Marle, Hendrikus J A van Os","doi":"10.2196/79512","DOIUrl":"10.2196/79512","url":null,"abstract":"<p><strong>Background: </strong>Remote patient management (RPM) that supports patient self-monitoring of vital parameters and lifestyle factors may improve cardiovascular risk management (CVRM) in primary care. However, large-scale implementation remains limited, partly due to insufficient evidence on long-term value for money, budget impact, and implications for health care professionals' workload.</p><p><strong>Objective: </strong>This study aimed to estimate the long-term cost-effectiveness, 5-year health care budget impact, and expected changes in general practitioner (GP) and practice nurse (PN) workload associated with the CVRM-Box intervention in the Netherlands.</p><p><strong>Methods: </strong>We conducted a model-based economic evaluation comparing CVRM-Box with care as usual in Dutch primary care integrated CVRM programs. CVRM-Box is a multicomponent RPM intervention comprising a digital blood pressure (BP) monitor, digital weight scale, step counter/activity tracker, and a mobile app, with measurements transferred to the GP practice for periodic review. A time-inhomogeneous cohort Markov model simulated lifetime transitions among health states, including at-risk, post-myocardial infarction (MI), poststroke, recurrent events, cardiovascular death, and noncardiovascular death. Cardiovascular risks were modeled using prediction equations (SCORE2, SCORE2-OP, and SMART2) populated with subgroup-specific risk factor profiles. Intervention effects were modeled as changes in systolic BP derived from a matched cohort study of CVRM-Box. Other key parameters (costs and utilities) were similarly obtained from the matched cohort study, or routine primary care data, and published sources. Outcomes included incremental cost-effectiveness ratios (ICERs, indicating cost per quality-adjusted life year [QALY] gained), cost-effectiveness probabilities, 5-year health care budget impact, and an exploratory workload analysis estimating annual changes in visit time and remote consultation frequency for GPs and PNs. Probabilistic and scenario uncertainty analyses were performed.</p><p><strong>Results: </strong>In the overall population, CVRM-Box increased costs and QALYs versus care as usual, yielding an ICER of €17,340/QALY gained (EUR €1=US $1.11 as of 29 December 2023) and a 60% probability of cost-effectiveness at a willingness-to-pay threshold of €20,000/QALY. Cost-effectiveness was more favorable in higher-risk subgroups (uncontrolled BP and/or prior MI/stroke), with ≥70% probability of cost-effectiveness at €20,000/QALY, whereas in the lower-risk subgroup with controlled BP and no prior MI/stroke, the intervention is unlikely to be cost-effective (ICER: €42,384/QALY). The 5-year health care budget impact was €2.9 million and €662.2 million for regional and national rollout, respectively. In a typical Dutch primary care practice, the CVRM-Box reduced PN workload by 25.9 h and 44.7 remote consultations annually, while the change in GP workload was negligi","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e79512"},"PeriodicalIF":6.3,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13618991/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149074673","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":"Incremental Value of Smartphone Sensing for Monitoring Momentary Affect Intensity in Adults Using Transformer-Based Models: Observational Study.","authors":"Yiqin Zhu, Yuyi Yang, Renee J Thompson","doi":"10.2196/90970","DOIUrl":"https://doi.org/10.2196/90970","url":null,"abstract":"<p><strong>Background: </strong>Ubiquitous smartphone access and statistical advances offer opportunities to continuously track affect intensity, which is central to various psychological processes and behaviors. Research demonstrated the potential of personalized predictions of momentary negative affect (NA) and positive affect (PA) using passive sensing. However, studies typically incorporated all available data sources without differentiating their added value, nor did they investigate whether refining location features with self-reported semantic location (eg, workplaces) improved personalized predictions.</p><p><strong>Objective: </strong>We evaluated three specific aims: (1) how different combinations of data sources improved performance compared to personalized baseline models, (2) whether model predictions differed across passive data aggregation timescales, and (3) whether incorporating self-reported semantic locations improved model predictions.</p><p><strong>Methods: </strong>Adults (final n=133) completed a 14-day ecological momentary assessment (EMA) protocol reporting emotional experiences 5 times daily alongside smartphone sensing. Testing data (n=532 EMAs) used the last 4 surveys for each individual, with the remaining used for training and validation (n=6805 [NA]/6800 [PA] EMAs). We evaluated whether combinations of personalization, passive sensing, and affect history improved baseline prediction, and how full-information temporal fusion transformers (TFTs) performed across 6 timescales (1, 3, 6, 12, 24, and 48 hours), with or without self-reported semantic location features.</p><p><strong>Results: </strong>The baseline model, using each individual's mean affect in the training set, demonstrated moderate predictive performance for NA (mean absolute error [MAE]=0.66, 95% CI 0.60-0.73; <i>R</i>²=40.2%) and PA (MAE=0.71, 95% CI 0.65-0.78; <i>R</i>²=36.1%). Full-information TFTs improved NA prediction (MAE=0.62, 95% CI 0.56-0.69; <i>R²</i>=45.2%; ΔMAE=-0.04, 95% CI -0.06 to -0.01; <i>P</i> values ≤.004; Cohen <i>d</i>=-0.27) but not PA prediction (MAE=0.70, 95% CI 0.64-0.78; <i>R</i>²=32.5%; ΔMAE=-0.01, 95% CI -0.03 to 0.02; <i>P</i> values >.10; Cohen <i>d</i>=-0.04). No pairwise timescale comparison survived false discovery rate (FDR) correction (NA: <i>P</i><sub>FDR</sub>=.05-.98; PA: <i>P</i><sub>FDR</sub>=.08-.99). Adding self-reported locations did not improve NA prediction (ΔMAE=0.02, 95% CI -0.01 to 0.04; <i>P</i> values >.20; Cohen <i>d</i>=0.11) or PA prediction (ΔMAE=0.01, 95% CI -0.01 to 0.03; <i>P</i> values>.33; Cohen <i>d</i>=0.07). However, incorporating self-reported semantic locations changed the composition and relative ranking of important inputs, with these changes varying across NA and PA and between past and future inputs.</p><p><strong>Conclusions: </strong>Incorporating smartphone features provided a modest and significant improvement in momentary NA prediction, but not PA prediction. Model performance did no","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e90970"},"PeriodicalIF":6.3,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13626072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149109797","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":"Mobile Heat Early Warning System for Pregnant and Postpartum Women in South Africa: Prospective Observational Evaluation Study.","authors":"Clara Sofia Heil, Shobna Sawry, Susanne Frennert, Jean Le Roux, Pascalia Ozida Munyewende, Sibusiso Mkwananzi, Ijeoma Solarin, Perkins Watambwa, Koen van der Sanden, Isabelle Lange, Günter Alce, Veronique Filippi, Stanley Luchters, Chuansi Gao","doi":"10.2196/102863","DOIUrl":"10.2196/102863","url":null,"abstract":"<p><strong>Background: </strong>Early warning systems (EWS) have the potential to reduce heat-related health risks, yet few are designed specifically for vulnerable populations. Pregnant women and infants, often cared for by postpartum women, are particularly susceptible to heat-related illnesses. The MotherHeat Alert mobile app is a heat EWS specifically developed for pregnant and postpartum women.</p><p><strong>Objective: </strong>This study aimed to assess the usability and effectiveness of the MotherHeat Alert in the Tshwane District, South Africa.</p><p><strong>Methods: </strong>This prospective observational evaluation study included a cohort of 201 participants (100 pregnant and 101 postpartum) who were followed over a 6-month period. In addition, a questionnaire was administered at 3 time points (baseline, midpoint, and end point) to collect self-reported information on health, living situation, and heat exposure, among other parameters. Usability was evaluated in the end point questionnaire through a validated Post-Study System Usability Questionnaire (PSSUQ), using descriptive statistics. Effectiveness was assessed through changes in self-reported heat-protective behaviors between baseline, midpoint, and end point, as well as through end point assessments of heat-health knowledge increase and self-reported behavior change attributable to the intervention. Generalized estimating equations were used to analyze repeated measures of heat-protective behaviors, while descriptive statistics were used to summarize end point-reported knowledge gains and behavior change.</p><p><strong>Results: </strong>Usability scores indicated high overall usability, with mean PSSUQ scores of 1.76 (SD 0.60) among pregnant women and 1.37 (SD 0.45) among postpartum women. Similar patterns were observed across most PSSUQ subscales, including system usefulness, interface quality, and information quality. Most self-reported behavior change due to alerts received through the app was rated as large to very large by the pregnant women (47/70, 67%) and postpartum women (68/82, 83%). Nevertheless, comparing behavior change at baseline and end point mainly highlighted a decrease in protective behavior in both pregnant and postpartum cohorts. Self-reported knowledge increase among pregnant and postpartum women was reported as large to very large following the use of the EWS (55/70, 79% and 72/82, 88%, respectively). Importantly, however, attrition was substantial, particularly among pregnant participants, reaching 30% (30/100) in the pregnant cohort and 19% (19/101) in the postpartum cohort by end point.</p><p><strong>Conclusions: </strong>The MotherHeat Alert app demonstrated positive outcomes in usability. Effectiveness showed conflicting results, with positive self-reports on behavior change and knowledge increase, yet a decrease in protective heat behaviors when comparing baseline and midpoint with end point results, likely due to seasonality effects. Nevertheless, the","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e102863"},"PeriodicalIF":6.3,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13653204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149061458","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":"Caregiver App Use and Child Behavioral Adherence in Relation to Obesity-Related Outcomes in an mHealth App-Assisted Childhood Obesity Intervention: Secondary Analysis of a Cluster Randomized Trial.","authors":"Shiyu Yan, Jinlang Lyu, Chunjie Yin, Wenhao Li, Yan Li, Hai-Jun Wang","doi":"10.2196/89548","DOIUrl":"10.2196/89548","url":null,"abstract":"<p><strong>Background: </strong>Childhood obesity remains a major public health challenge. Mobile health (mHealth) interventions offer a scalable approach to support behavior change, but their effectiveness may depend on participant adherence. While both caregiver and child adherence are important, few studies have jointly examined caregiver app-use adherence and child behavioral adherence within digital obesity interventions.</p><p><strong>Objective: </strong>This study aimed to evaluate the independent and joint associations of caregiver app-use adherence and child behavioral adherence with obesity-related outcomes in a school-based mHealth intervention.</p><p><strong>Methods: </strong>We conducted a secondary analysis of 684 child-caregiver dyads in the intervention arm of the diet, exercise, and cardiovascular health-children cluster-randomized trial, implemented across 3 socioeconomically diverse regions in China. Caregiver app-use adherence was measured using app-recorded app-use frequency and total usage duration. Child behavioral adherence was assessed through weekly behavior monitoring scores covering diet, screen time, and physical activity. Linear mixed models estimated associations between adherence levels and changes in anthropometric and physical fitness outcomes over 9 months.</p><p><strong>Results: </strong>Higher caregiver app-use frequency and duration were both significantly associated with reductions in children's obesity-related indicators, such as BMI (β=-0.19; <i>P</i>=.005), BMI <i>z</i>-score (β=-0.08; <i>P</i>=.006), and body fat percentage (BFP) (β=-0.70; <i>P</i>=.005) for frequency. Greater child behavioral adherence was associated with lower BFP (β=-0.63; <i>P</i>=.01) and better physical fitness, including shorter shuttle run time (β=-3.63; <i>P</i><.001) and longer standing long jump distance (β=2.88; <i>P</i>=.005). App-use frequency and duration showed a significant interaction for rope-skipping performance (<i>P</i>=.05). In joint adherence analyses, children in the high caregiver-high child adherence group showed the most favorable changes, including lower BMI (β=-0.22; <i>P</i>=.02), lower BFP (β=-1.18 percentage points; <i>P</i>=.001), and 7.45 additional rope-skipping repetitions (<i>P</i>=.003), compared with the low caregiver-low child adherence group.</p><p><strong>Conclusions: </strong>In this secondary analysis of an mHealth app-assisted childhood obesity intervention, higher caregiver app-use adherence and child behavioral adherence were associated with more favorable changes in weight status and physical fitness. These findings underscore the importance of coengagement in digital health programs and highlight the potential of app-supported, family-engaged strategies for childhood obesity prevention.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e89548"},"PeriodicalIF":6.3,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13608226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149037799","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":"Dynamics of Digital Support Platform Use Among Family Caregivers of Children With Disabilities: Longitudinal Study.","authors":"Xiaojiao Yuan, Xi Zhao, Meng Zhang, Yurou Duan, Yizhong Shui","doi":"10.2196/92481","DOIUrl":"10.2196/92481","url":null,"abstract":"<p><strong>Background: </strong>Family caregivers of children with disabilities frequently experience high levels of stress and psychological burden. Digital support platforms offer accessible channels for information and emotional support, but their long-term use patterns remain underexplored.</p><p><strong>Objective: </strong>This study explored the longitudinal patterns of digital support platform use among family caregivers in China, with a focus on factors potentially associated with initial adoption and continued engagement.</p><p><strong>Methods: </strong>A 3-wave survey was conducted over 6 months with 240 primary caregivers of children with disabilities in China. A 2-part latent growth curve model was used to jointly model adoption (use vs nonuse) and engagement (use frequency among users). Key demographic, psychosocial, and child-related factors were examined as predictors.</p><p><strong>Results: </strong>Adoption increased significantly over time (probit slope=0.54, SE 0.16; <i>P</i>=.001). Higher baseline adoption was associated with male sex (β=.19; <i>P</i>=.02), higher education (β=.42; <i>P</i><.001), better socioeconomic status (β=.26; <i>P</i>=.001), stronger social support (β=.17; <i>P</i>=.03), and more depressive symptoms (β=.18; <i>P</i>=.02). Among users, engagement frequency showed no significant average change over time (mean slope=-0.07, SE 0.07; <i>P</i>=.31). Higher initial engagement was associated with higher education (β=.36; <i>P</i><.001) and social support (β=.20; <i>P</i>=.04). Better child rehabilitation status was associated with steeper declines in engagement over time (β=-.28; <i>P</i>=.03).</p><p><strong>Conclusions: </strong>Adoption and engagement showed distinct longitudinal patterns and were associated with different factors. The findings suggest that reducing digital barriers and aligning support with caregivers' evolving needs may be relevant to facilitating platform use and sustained engagement. This study offers a theory-informed lens for describing digital support platform use among vulnerable caregiving populations.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e92481"},"PeriodicalIF":6.3,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149030576","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":"Timed Up-and-Go Test Using Instrumented Insoles to Monitor Tibial or Malleolar Fracture Healing in Patients With Healing Fractures or Nonunion: Longitudinal and Cross-Sectional Exploratory Study.","authors":"Elke Warmerdam, Lea-Marie Burger, Bergita Ganse","doi":"10.2196/92493","DOIUrl":"10.2196/92493","url":null,"abstract":"<p><strong>Background: </strong>The timed up-and-go (TUG) test is a mobility-related functional task commonly performed in clinical practice for a variety of clinical applications. By instrumenting the TUG test with sensors, creating an instrumented TUG (iTUG), additional parameters can be extracted, which may add clinical value. Instrumented insoles are among the most promising wearable devices that are currently being explored for clinical purposes.</p><p><strong>Objective: </strong>The study aims to assess additional parameters obtained by instrumenting the TUG test with pressure-sensing insoles for monitoring fracture healing and to analyze whether these parameters reveal differences between patients with and without union of tibial or malleolar fractures.</p><p><strong>Methods: </strong>Patients with tibial or malleolar fractures and matched controls were enrolled. Patients' TUG test data were collected as follows: (1) longitudinally-at 6 weeks, 3 months, and 6 months after surgery-and (2) cross-sectionally-at 6 months after surgery when patients were referred for nonunion treatment. All participants performed the TUG test using instrumented insoles. TUG tests were video-recorded and analyzed in terms of the time taken to complete each part of the TUG test (conventional TUG). The insole data-derived outcome measures were maximal force during sit-to-stand and stand-to-sit movements, stance time and stride time during walking, and asymmetry of the previously mentioned parameters (iTUG).</p><p><strong>Results: </strong>Twenty-seven patients with fracture, a similar number of healthy adults, and 9 patients with fracture nonunion were assessed. All TUG parameters improved throughout the healing process (all <i>P</i><.001). The greatest improvements were detected between 6 weeks and 3 months (all <i>P</i><.001). Among the classical TUG parameters, between 3 and 6 months, only the total TUG time, walking time, stand-to-sit time, and total number of steps significantly improved (<i>P</i>=.004, <i>P</i>=.01, <i>P</i>=.03, and <i>P</i>=.002). Based on the additional iTUG measures, 6 months after surgery, the loading of the injured side was reduced in patients compared with healthy controls during sit-to-stand and stand-to-sit movements (<i>P</i><.001). Six months after surgery, patients with fracture nonunion had significantly poorer performance in terms of all of the TUG test parameters, except for stance time, than patients with fracture union.</p><p><strong>Conclusions: </strong>This study indicates the potential for using functional outcomes to identify patients at risk of nonunion. Additional tasks and functional outcomes require further investigation.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e92493"},"PeriodicalIF":6.3,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13592240/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149016882","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":"The Usage Effects, Effect Modifiers, and Experiences of a Web-Based App for Healthy Habit Formation in Adults: Exploratory Analysis of a Quasi-Experimental Real-World Intervention.","authors":"Eeva Rantala, Mikko Valtanen, Adil Umer, Suvi Parikka, Jussi Pihlajamäki, Ilona Ruotsalainen, Jaana Lindström","doi":"10.2196/84076","DOIUrl":"10.2196/84076","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Digital interventions provide a scalable approach to promote healthy lifestyles, but at-scale evidence of their effects and effect modifiers is limited-even scarcer for habit-based digital interventions, although habits are promising lifestyle intervention targets.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We conducted a 1-group pre-post effect and effect modification assessment of a web-based app designed to support the formation of health-promoting lifestyle habits by exploring the relationship between objective app use and subjective changes in behavioral outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Three-month app access was offered to a subsample of the population-based Healthy Finland survey participants aged 20-74 years via SMS text messaging or mail. The app provided personalized behavioral suggestions that translated evidence-based lifestyle guidelines into simple, repeatable actions (\"habits\"). Users could browse and select these habits, and report and monitor their performances. The assessment used app log data, questionnaires collected through the app at baseline and at 45 and 90 days, and background information obtained from the national population register and the Healthy Finland Survey. Linear mixed effects models explored the (1) associations between app use and changes in self-reported diet quality (Healthy Diet Index), physical activity (metabolic equivalent of task hours per week), and BMI, and (2) modification of these associations by baseline characteristics related to sociodemographics, health, lifestyle, and e-service use. App use measures comprised the percentage of app use days (ie, days with logins/days of follow-up×100) and the number of reported habit performances (total, diet-related, physical activity-related, and BMI-related).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of 6975 invitees, 1282 (18.4%) accepted the invitation and 382 (5.5%; mean age 51, SD 15 y; women: n=263, 69%) completed the data collection required for the assessment. Over 90 days, the median percentage of app use days was 5.9% (IQR 3.3%-11%), and the total number of reported habit performances was 22 (IQR 3-68.5). A higher percentage of use days (by 10 percentage points) was associated with a 3.09 (95% CI 0.79-5.39) metabolic equivalent of task hours per week greater increase in physical activity and a 1-unit higher logarithmic number of reported performances with a 0.51 (95% CI 0.07-0.95) Healthy Diet Index point greater improvement in diet quality. Other associations between app use and outcomes were nonsignificant. Greater physical activity and a more positive attitude to e-services at baseline appeared to enhance the effect of app use on physical activity (&lt;i&gt;P&lt;/i&gt; values &lt;.05). The app received a median acceptability rating of 2.9 (IQR 2.4-3.3, scale: 1-4) and a median overall score of 7 (IQR 5-8, scale: 0-10).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;An inexpensive, low-intensity digital support for healthy habits could foster small ben","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e84076"},"PeriodicalIF":6.3,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13592743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149016895","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":"Engagement With a Relaxation-Based Mobile Health Intervention for Perioperative Anxiety: Prospective Longitudinal Cohort Mixed Methods Study.","authors":"Xinghui Yan, Afton L Hassett, Jennifer F Waljee, Mark W Newman, Sun Young Park, Rongqi Bei, Noelle E Carlozzi","doi":"10.2196/64278","DOIUrl":"10.2196/64278","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Relaxation-based mobile health (mHealth) interventions hold strong potential to address perioperative anxiety and pain management in a scalable manner. However, there is limited research on how surgical patients engage with these interventions and what aspects may be most helpful.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study had 3 goals: (1) explore how users interact with a relaxation-based mHealth intervention during the perioperative period; (2) understand how user performance and perceptions relate to perceived benefits; and (3) identify design considerations to enhance user engagement and behavior change outcomes in mHealth interventions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a prospective longitudinal cohort, mixed methods study to evaluate user engagement with MiCarePath, a relaxation-based mHealth intervention. A total of 19 perioperative patients (mean age 41.3 years; 14/19, 74%, female) undergoing elective surgery and reporting sometimes-to-always anxiety were enrolled using 3 recruitment methods. Participants used the app from 10 days before surgery to 4 weeks after surgery. We collected quantitative data on video engagement metrics (eg, duration of watching, response rate to video prompts) and ecological momentary assessments of anxiety and pain. Concurrently, we conducted semistructured, data-prompted interviews at 3 time points to assess user perceptions. These data were integrated using descriptive statistics, repeated-measures ANOVA, and qualitative thematic analysis to examine the relationship between user performance and perceived benefits.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Eleven participants reported developing or improving relaxation-based self-management strategies (high benefit), whereas 8 reported little benefit from the intervention. The high-benefit group showed significantly greater total watching time (difference 223.8 minutes, 95% CI 95.54-366.46 minutes, P&lt;.001), higher adherence to video prompts (73.45% vs 34.60%, P&lt;.001), and greater perceived helpfulness of the content (79.80% vs 25.50%, P&lt;.001) compared with the low-benefit group. A significant group-by-time interaction effect was observed for engagement (F&lt;sub&gt;1,17&lt;/sub&gt;=17.02, P&lt;.001). No difference was observed in delay in response to video prompts. However, within each group, participant perceptions were not fully reflected in performance. Some participants in the high-benefit group showed decreased video watching after surgery but still reported feeling engaged with the intervention. Interview data revealed that \"intentional use\" behavior-where users actively seek content to manage symptoms independent of prompts-distinguished these participants. Those who developed intentional use were able to practice relaxation techniques without the app and resume app engagement following contextual disruptions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study explored user engagement with a relaxation-based mHealth intervention for per","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e64278"},"PeriodicalIF":6.3,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149001873","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":"Personalized Mobile App-Based Messaging and Step Tracking for Improving Walking Behavior in Adults: Randomized Controlled Trial.","authors":"Sarang Jang, Aeree Sohn","doi":"10.2196/84430","DOIUrl":"10.2196/84430","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Walking is one of the most effective forms of physical activity; however, maintaining long-term engagement in digital step-counting interventions remains challenging.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the effects of personalized message interventions integrating the Information-Motivation-Behavioral Skills (IMB) model and regulatory focus theory (RFT) on walking behavior.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Participants were recruited offline through community advertisements containing a QR code that linked to an online sign-up form and were screened using an online baseline questionnaire by research staff, while all intervention delivery and outcome assessments were conducted via the WalkOn mobile app. A 12-week randomized controlled trial targeting 174 adults in Mapo-gu, Seoul, Korea, was conducted from April 1 to June 22, 2024. Of the 174 randomized participants, 141 (67 in the intervention group and 74 in the control group) engaged in the intervention. Both groups received incentives based on goal achievement and general health information, whereas the intervention group received weekly personalized messages provided by research staff according to midweek step-count performance. Primary outcomes included weekly goal achievement rates for the 25,000-step goal, step-count patterns, and consecutive 2-week goal achievement failure events. Analyses were conducted using generalized estimating equations (GEEs), linear mixed models (LMMs), and Cox proportional hazards models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of the 174 participants randomized, 141 (81%) engaged in the intervention and were included in the analyses (67 intervention; 74 control), while 33 participants did not participate after randomization. The overall 12-week mean goal achievement rate was 87% in the intervention group and 87% in the control group, with no significant difference between the groups (odds ratio [OR] 1.12, 95% CI 0.29-4.37; P=.87). Weekly step-count patterns also showed no significant differences (group × time interaction: estimate=-417; SE 09; P=.31). However, the risk of consecutive 2-week goal achievement failure events was significantly lower in the intervention group (hazard ratio [HR] 0.67, 95% CI 0.52-0.86; P=.002). The intervention group showed an approximately 33% lower likelihood of consecutive 2-week goal achievement failure events than the control group.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Personalized message interventions did not show significant effects on overall step-count increase but were associated with a reduced risk of consecutive 2-week goal achievement failure events. These findings suggest that personalized messages may play an important role in improving behavior change sustainability and failure resilience. Incorporating personalized messaging strategies may enhance the effectiveness of digital health intervention design.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Trial registration: &lt;/strong&gt;Clinical Research Inform","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e84430"},"PeriodicalIF":6.3,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13631492/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148990038","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}