JMIR mHealth and uHealth最新文献

{"title":"Feasibility, Usability, and Preliminary Effectiveness of an mHealth App to Promote Screening Behaviors Among High-Risk Populations for Breast Cancer: Randomized Controlled Pilot Study.","authors":"Linping Zhang, Mengjiao Xu, Xian Zhang, Xiaoxu Li, Shuo Wang, Fang Zhou, Jing Han","doi":"10.2196/86429","DOIUrl":"10.2196/86429","url":null,"abstract":"<p><strong>Background: </strong>Breast cancer is a major public health challenge worldwide. Women at high risk for breast cancer are more likely to develop the disease; yet, screening participation remains. Mobile health interventions may improve breast health awareness and screening behaviors, but evidence in high-risk populations for breast cancer remains limited.</p><p><strong>Objective: </strong>This study aimed to evaluate the feasibility, usability, and preliminary signals of potential effectiveness of the \"Ruaikang\" mobile health app, designed to support breast health awareness and engagement with early screening among high-risk populations for breast cancer.</p><p><strong>Methods: </strong>In this pilot randomized controlled trial, eligible high-risk populations for breast cancer were randomly assigned (1:1) to a 4-week intervention using the \"Ruaikang\" app or a control group receiving standard breast cancer screening guidelines. Feasibility was assessed via recruitment and completion rates. Usability was measured using the System Usability Scale. Use data were collected through the app's back-end system. Preliminary effectiveness was assessed through changes in willingness to participate in breast cancer screening, screening knowledge, health beliefs, and self-efficacy at both the fourth (T1) and eighth (T2) weeks. Semistructured interviews were conducted with a purposive subsample of intervention participants to explore user experience and suggestions for improvement.</p><p><strong>Results: </strong>Of 62 eligible women approached, 50 enrolled (recruitment rate 80.6%) and were randomized to intervention (n=25) and control (n=25) groups. In total, 19 participants in the intervention group completed the study (completion rate 76%), reflecting good feasibility. The mean usability score was 88.82 (SD 10.29), indicating high usability. Back-end use data showed that participants actively engaged during the intervention period. At T1, the intervention group showed preliminary signals of improvement in willingness to participate in breast cancer screening, screening knowledge, health beliefs, and self-efficacy, although these changes were less pronounced at T2. Qualitative interviews with 8 participants identified 2 core themes: \"perceived value and acceptability of the app\" and \"system limitations and user-centered improvement needs.\" Participants highlighted that the app was easy to use and content-rich and provided actionable feedback for optimizing interactive features, navigation, and content depth.</p><p><strong>Conclusions: </strong>The \"Ruaikang\" mobile health app demonstrated good feasibility and usability and provided preliminary signals of potential effectiveness in improving willingness to participate in breast cancer screening, screening knowledge, health beliefs, and self-efficacy among high-risk populations for breast cancer. Participants' feedback indicates that the app aligns with users' core needs and supports engagement with ","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e86429"},"PeriodicalIF":6.3,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13367949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148446965","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 an AI Image Recognition-Based Smartphone App in Personalized Bowel Preparation Before Colonoscopy: Prospective Randomized Controlled Trial.","authors":"Fei Han, Shilin Gao, Hanqiang Zhan, Nan Wang, Zhaoxiang Song, Jinlin Xie, Yuhan Hou, Yimeng Deng, Jianning Yao","doi":"10.2196/77701","DOIUrl":"10.2196/77701","url":null,"abstract":"<p><strong>Background: </strong>Sufficient bowel preparation is critical for increasing the quality of colonoscopy. However, current bowel preparation guidelines have limitations. We used a constructed and validated convolutional neural network model to assist patients in assessing the adequacy of bowel preparation and guide the use of laxatives based on individual variations.</p><p><strong>Objective: </strong>This research intended to explore the influence of AI-guided bowel preparation on patients' use of laxatives and bowel cleansing quality.</p><p><strong>Methods: </strong>We conducted a prospective randomized controlled trial that included patients who had a colonoscopy at the First Affiliated Hospital of Zhengzhou University and used sodium phosphate solution for bowel cleansing. Included individuals were randomly categorized into 2 groups: the AI-guided group, which used an AI program to assess the adequacy of bowel cleansing and stopped the use of laxatives once the assessment was passed, and the control group, which evaluated bowel preparation adequacy based on standard practices and used the recommended doses of laxatives. The primary outcomes were the amount of laxatives used and the rate of adequate bowel cleansing based on the Boston Bowel Preparation Scale (BBPS). Other outcomes included overall BBPS scores, BBPS scores by colon region, adenoma detection rate (ADR), polyp detection rate, and advanced ADR, as well as adverse reactions and compliance.</p><p><strong>Results: </strong>This study included 513 patients (AI-guided group: n=255, 49.7%; control group: n=258, 50.3%). The volume of bowel-cleansing solution consumed by patients in the AI-guided group was 1245.5 (SD 377.2) mL, significantly lower than the 1586.2 (SD 50.4) mL in the control group (P<.001). Reduced use of laxatives was independently predicted by younger age, lower BMI, and higher defecation frequency. The rate of adequate bowel preparation in the AI group was not inferior to that in the control group (238/255, 93.3% vs 229/258, 88.8%, respectively; 1-sided P<.001 for noninferiority). No significant differences were observed in segmental or total BBPS scores, ADR, polyp detection rate, and advanced ADR between the 2 groups. Additionally, the medication adherence rate in the AI-guided group was significantly higher than in the control group (247/255, 96.9% vs 236/258, 91.5%, respectively; P=.009), whereas the incidence of adverse reactions was notably lower (5/255, 2% vs 15/258, 5.8%, respectively; P=.02).</p><p><strong>Conclusions: </strong>Using the AI-driven bowel preparation program to assist patients in personalized bowel preparation can reduce unnecessary use of laxatives while achieving results comparable to those of standard practices.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT06610630; https://clinicaltrials.gov/study/NCT06610630.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e77701"},"PeriodicalIF":6.3,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13416303/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148446346","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":"Support Strategies to Enhance Adherence to a Prescription Digital Therapeutic for Erectile Dysfunction: Retrospective Quasi-Experimental Cohort Study.","authors":"Leo Dieter, Mara Haschke, Kurt Miller, Laura Elisa Wiemer","doi":"10.2196/76724","DOIUrl":"10.2196/76724","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Sustained engagement is a central challenge for digital therapeutics. In routine operations, inactivity-triggered supports (eg, SMS reminders and telephone follow-up) are used to nudge patients back after lapses, but evidence in digital erectile dysfunction (ED) therapy is limited.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to evaluate whether low-threshold support in the form of SMS reminders and structured telephone outreach, both triggered after 7 or more days of inactivity, improves engagement and patient-centered outcomes in a certified German digital health application (Digitale Gesundheitsanwendung [DiGA]) for ED.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In a pragmatic, calendar-time (rotating-week) quasi-experimental allocation, 470 men with physician-diagnosed ED entered 1 of 3 groups: control (no additional support), SMS (automated reminders), or call (structured telephone contact). Analyses were conducted as assigned. The primary end point was active weeks (number of weeks with ≥1 completed session) during the 12-week study period, derived from app logs with built-in completion rules. A prespecified mechanistic end point captured any reactivation after an inactive week (yes or no). Secondary end points included Clinical Global Impression-Improvement (CGI-I), week-12 5-item International Index of Erectile Function (IIEF-5), intention to continue therapy, and documented continuation (\"conversion\") within 3 months. Covariate-adjusted models included age, BMI, smoking, and baseline pharmacotherapy; calendar-week sensitivity was planned.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Participants completed a mean of 6.34 (SD 4.44) active weeks. Unadjusted means favored both support groups (control: 5.79; call: 6.57; SMS: 6.80); the prespecified SMS-vs-control contrast was nominally significant (P=.049, small effect). In covariate-adjusted models, planned pairwise contrasts were not significant. In a calendar-adjusted sensitivity model, the omnibus group term reached significance, but adjusted pairwise contrasts remained nonsignificant; stricter adherence definitions led to the same overall inference. By contrast, any reactivation after inactivity was more common in the intervention groups than in the control group (omnibus chi-square test P=.008), consistent with the intended mechanism of breaking inactivity spells. The call group showed a higher intention to continue therapy (49% vs 38% in the control arm; P=.04); conversion did not differ. CGI-I and IIEF-5 showed no arm-wise differences over 12 weeks; follow-up availability was limited in routine care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Inactivity-triggered supports in a real-world ED DiGA reactivated use after lapses, and telephone outreach increased motivation to continue, while effects on weekly dose were small and not significant after adjustment, and clinical outcomes did not differ over 12 weeks. Programs may consider a stepped approach (SMS first-line nudge and call","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e76724"},"PeriodicalIF":6.3,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13416304/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148446378","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":"Safer Sleep Guidance in Standalone UK Smartphone Apps for Parents of Newborns and Infants: Systematic mHealth App Review.","authors":"Senna Yousef, Niamh Meaney, Dawn Dowding, Norina Gasteiger","doi":"10.2196/95642","DOIUrl":"10.2196/95642","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Parents are increasingly using smartphone apps to help guide decision-making around aspects of caregiving, including safer sleep. However, it remains unclear whether the guidance provided aligns with best practice guidelines.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This review aimed to identify smartphone apps that provide safer sleep guidance and to assess their content, technical features, functionality, and quality. A secondary aim was to make recommendations on the improvements that existing and future apps can make to provide safer sleep guidance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In January 2026, we searched the UK Google Play Store and Apple App Store for safer sleep guidance apps for parents and caregivers of newborns and infants (babies aged ≤12 months). Information regarding content, technical features, and functional features was extracted and summarized descriptively. Each app was evaluated by 2 raters using the Mobile App Rating Scale (MARS) and the IMS Institute for Healthcare Informatics functionality score. Adherence to best practice sleep guidance was assessed using bespoke criteria. Interrater reliability was calculated.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 1345 apps were identified, with 12 meeting the inclusion criteria. All were free to download, with 42% (5/12) containing in-app purchases. Only 17% (2/12) were affiliated with a professional health, medical, clinical, or government body. All 12 apps contained some information regarding safer sleep practices, although this was addressed and named differently (eg, calling it cot death, sudden infant death syndrome, or safe sleep). Of the 9 criteria relating to safer sleep best practice guidance, the apps mentioned an average of 6.75 (SD 1.71; range 3-9) criteria. Some promoted unsafe sleep practices (ie, around swaddling and cosleeping). The apps included an average of 5.4 of the 11 IMS functionality criteria (SD 2.5; range 2-9). The mean MARS score was 3.7 out of 5 (SD 0.43; range 2.92-4.13), and most (11/12, 92%) had a minimum acceptability score of 3 or above. Two had been formally trialed or tested. Although the language used was considered accessible (easy to understand), none of the apps allowed the language to be changed (beyond English), and very few (3/12, 25%) enabled users to change to night/dark mode, which would be considered essential when using these apps at night.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Several apps about safer baby sleep exist. Few discuss safer sleep guidance comprehensively or raise awareness about sudden infant death syndrome and its links with unsafe sleep. All the reviewed apps met the minimum acceptability criteria for quality; however, not many were formally trialed or tested. More long-term research should be conducted on the effectiveness of safer sleep apps on adherence to safer sleep guidance. We recommend that apps relating to infant and baby safety in general include information on realistic safer sleep pr","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e95642"},"PeriodicalIF":6.3,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13401075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148421063","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":"Week-Ahead Prediction of High-Risk Drinking Episodes Among Young Adults Using Wearable Biosignals and Psychological Vulnerabilities: Prospective Observational Machine Learning Study.","authors":"Jae Seok Kwak, Hae Kook Lee, Sun-Jin Jo, Jun Hyuk Kwon, Sun Jung Kwon, Yena Kim, Haejung Lee","doi":"10.2196/88223","DOIUrl":"10.2196/88223","url":null,"abstract":"<p><strong>Background: </strong>Although machine learning has increasingly been used to predict mental health symptoms and maladaptive behaviors, real-world prediction of addiction-related risk remains limited. Emotional and temperamental vulnerabilities are established correlates of alcohol-related problems, yet few studies have integrated these factors with wearable-derived biosignals in alcohol-risk prediction models.</p><p><strong>Objective: </strong>This study evaluated whether machine learning models could predict weekly high-risk drinking episodes among young adults with elevated alcohol-use risk by integrating wearable-derived health data with baseline emotional and personality vulnerability indicators.</p><p><strong>Methods: </strong>In this prospective observational study, adults in their 20s completed weekly self-report surveys and wore Fitbit devices for 4 weeks. Features from week t were used to predict the Alcohol Use Disorders Identification Test-Korean version (AUDIT-K)-based high-risk drinking label at week t+1. Positive labels were defined using AUDIT-K high-risk drinking cutoffs, with scores of ≥20 for men and ≥10 for women. Extreme gradient boosting (XGBoost) and random forest models were evaluated across self-report-only, wearable-only, and integrated feature sets using 5-fold participant-level grouped cross-validation.</p><p><strong>Results: </strong>A total of 206 participants contributed 620 week-level observations, of which 85 (13.7%) were labeled as positive high-risk drinking episodes. In participant-level grouped cross-validation, the integrated random forest model showed the most favorable sensitivity-oriented performance, with a mean accuracy of 0.617 (SD 0.078), recall/sensitivity of 0.653 (SD 0.144), area under the receiver operating characteristic curve (ROC AUC) of 0.681 (SD 0.079), and area under the precision-recall curve (PR AUC) of 0.255 (SD 0.090). The integrated XGBoost model achieved an accuracy of 0.670 (SD 0.089), recall/sensitivity of 0.399 (SD 0.174), ROC AUC of 0.651 (SD 0.089), and PR AUC of 0.228 (SD 0.074). Shapley additive explanations analyses indicated that both baseline vulnerability indicators and wearable-derived weekly summaries contributed to model predictions.</p><p><strong>Conclusions: </strong>Integrating baseline emotional and personality vulnerability indicators with wearable-derived weekly health signals may provide useful information for week-ahead prediction of high-risk drinking episodes. These findings provide preliminary support for wearable-assisted alcohol-risk stratification, although the modest positive predictive performance indicates that external validation and more proximal within-person measures are needed before real-world early-warning or just-in-time adaptive intervention applications.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e88223"},"PeriodicalIF":6.3,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13401073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148421089","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":"Perceived Sensitivity of Sensor-Based Digital Health Data: Qualitative Interview Study.","authors":"Christine Deeney, Anika Sonig, Meghan E Hurley, Birkan Tunç, Eric A Storch, John D Herrington, Jennifer Blumenthal-Barby, Kristin Kostick-Quenet","doi":"10.2196/78788","DOIUrl":"10.2196/78788","url":null,"abstract":"<p><strong>Background: </strong>Digital health tools are increasingly used in mental health care to passively collect patient data and analyze health status outside of clinical settings. While technologies such as digital phenotyping, affective computing, and computational behavioral analysis offer new insights into symptom manifestation in daily life, they generate large volumes of potentially sensitive data that raise significant data privacy concerns, requiring high levels of patient awareness and consent. Empirical research is lacking on stakeholder understandings toward the sensitivity of these data and expectations for data stewardship, perspectives that are critical for developing robust informed consent and data protection policies for digital health data use.</p><p><strong>Objective: </strong>This study aimed to explore key stakeholder perspectives on the sensitivity of computer perception (CP) data, trust in existing data protections, willingness to share CP data externally, and desire for transparency of CP data transactions outside of the clinical space.</p><p><strong>Methods: </strong>As part of a larger, multisite study, we conducted qualitative interviews (n=40) via Zoom (Zoom Communications, Inc) with 20 adolescents (aged 12-17 years) familiar with CP tools and their caregivers (n=20). Interviews consisted of a series of open-ended questions regarding stakeholders' perspectives on privacy, data security, and the use and exchange of CP data. We developed a qualitative codebook to identify and label thematic patterns in responses to questions addressing the topics above, using thematic content analysis to identify themes inductively. Each interview was coded by merging work from at least two separate coders, and several team members contributed to qualitative analysis.</p><p><strong>Results: </strong>Most adolescents and caregivers viewed CP data as highly sensitive and expressed a reluctance to share these data beyond their clinical teams. While many participants expressed trust in existing data protections to protect CP data, they often misunderstood or overestimated the extent of protections to safeguard CP data.</p><p><strong>Conclusions: </strong>Our findings underscore the critical need for clear and effective patient communication and education about the risks, benefits, and protections associated with CP data through informed consent protocols. To promote greater transparency, understanding, and trust, we recommend 5 strategies: educating patients about data protection; studying secondary data exchange and reidentification risks; strengthening transparency regulations; improving data traceability mechanisms, such as distributed ledger technologies, to enhance data traceability and auditability; and adopting dynamic consent models.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e78788"},"PeriodicalIF":6.3,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13345348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148411729","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":"Tobacco Use Trajectories and Associated Changes in Biometrics and Sleep During the First 72 Weeks of Wearable Membership: Observational Cohort Study.","authors":"Dylan J Curran, Josh Leota, William von Hippel, Finnbarr Fielding, Christopher J Chapman, Jenna G Cohen, David M Presby, Reha Jhunjhunwala, Kristen E Holmes, Gregory J Grosicki","doi":"10.2196/98116","DOIUrl":"10.2196/98116","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Tobacco use remains a leading preventable cause of morbidity and mortality. Digital health tools and wearable technologies offer scalable opportunities for behavioral self-monitoring. However, real-world evidence characterizing long-term tobacco use trajectories and associated physiological changes during wearable adoption is limited.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to characterize longitudinal trajectories of self-reported tobacco use during the first 72 weeks of wearable adoption and to examine associations between tobacco use and wearable-derived cardiopulmonary and sleep measures.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We analyzed data from 12,678 new wearable members (18-79 years) who contributed up to 72 weeks of daily self-reported tobacco use and wearable-derived biometric data. Longitudinal trajectories of tobacco use were examined across prespecified 12-week quarters (Q1-Q6) using generalized linear mixed-effects models. Associations between tobacco use and wearable-derived nocturnal resting heart rate (RHR), heart rate variability (HRV), respiratory rate (RR), and sleep duration were evaluated using linear mixed-effects models that accounted for within- and between-person variation and adjusted for demographic and temporal covariates.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Across 3,765,573 person-days, the estimated daily probability of tobacco use declined from 55.1% (95% CI 53.8-56.4) during Q1 to 27.2% (95% CI 26.1-28.3) during Q6, representing an absolute reduction of 27.9 percentage points (95% CI -28.4 to -27.4). Among tobacco users with end-of-follow-up data, over one-quarter (1404/4975, 28.22%) reported no tobacco use during Q6. Greater logging engagement was associated with larger reductions in tobacco use; each 10-percentage-point increase in engagement corresponded to a 0.92-percentage-point greater decline from Q1 to Q6 (95% CI -1.58 to -0.26). Following tobacco use days, RHR was 1.71 beats/minute higher (95% CI 1.70-1.73), HRV was 3.54 ms lower (95% CI -3.59 to -3.49), RR was 0.19 breaths/minute higher (95% CI 0.19-0.20), and sleep duration was 9.78 minutes shorter (95% CI -10.08 to -9.49) relative to nonuse days. Reductions in tobacco use over time were associated with directionally favorable changes in RHR (P=.001), RR (P=.002), and sleep duration (P=.02).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;In this large-scale, observational, real-world study of wearable users, the probability of tobacco use declined over the first 72 weeks of adoption. Among participants with end-of-follow-up data, more than one-quarter reported no tobacco use during Q6. Tobacco use was consistently associated with less favorable cardiopulmonary and sleep measures, while reductions in tobacco use over time co-occurred with directionally favorable changes in these measures, although such changes may also reflect broader lifestyle or health changes. Overall, this study provides large-scale, longitudinal evidence that s","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":" ","pages":"e98116"},"PeriodicalIF":6.3,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13379686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148308027","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":"Inclusive Contactless Monitoring for Older Adults From Diverse Backgrounds: Mixed Methods Study.","authors":"Titilola Yakubu, Nooshin Jafari, Samya Torres, Michael Lim, David Rivest-Henault, Thomas Vaughan, Catherine Proulx, Linda Pecora, Di Jiang, Kendall Ho","doi":"10.2196/79892","DOIUrl":"10.2196/79892","url":null,"abstract":"<p><strong>Background: </strong>As Canada's population ages, accessible tools for chronic health monitoring are increasingly needed. Traditional and contact-based devices pose barriers for underserved populations due to cost, maintenance, and usability. Contactless sensing technologies offer a promising alternative, but equitable development requires inclusive engagement and diverse data collection.</p><p><strong>Objective: </strong>This study aimed, first, to gather perspectives from culturally and health-diverse older adults on innovative contactless vital sign monitoring technology and, second, to build on a diverse dataset for developing and testing the contactless sensing software VitalSeer by the National Research Council of Canada.</p><p><strong>Methods: </strong>A mixed methods study was conducted with older adults from diverse backgrounds across 3 sites. Participants were asked to respond to a questionnaire that had closed- and open-ended questions regarding their perspectives on contactless sensing technology. Video and reference vital sign data were collected from each participant using a portable system designed specifically for gathering controlled data.</p><p><strong>Results: </strong>We collected data from 48 participants (mean age 70, SD 8 years), of whom 98% (n=47) expressed a positive perception of the usefulness of a contactless sensing system. We also identified four themes from the qualitative analysis of the open-ended questions: (1) perceived value-system potential and clinical relevance; (2) ease of use-noninvasiveness and comfort; (3) trust and transparency-data security and clarity of design; and (4) inclusion and improvement-accessibility, functionality, and feature expansion. Finally, the collected data, 288 minutes of concurrent video and reference vital sign data, will be used to test and enhance contactless sensing software for diverse older adult populations.</p><p><strong>Conclusions: </strong>This work advances the goal of inclusive medical device research and development. It highlights the potential for contactless sensing to be adopted to support independent living for older, diverse adults. Research is ongoing to adapt the technology for widespread adoption.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e79892"},"PeriodicalIF":6.3,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13330646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148375794","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":"Alleviating Nurse Burnout With an Artificial Intelligence-Selected Mobile Cognitive Behavioral Therapy-Based Intervention: Mixed Methods Randomized Controlled Trial.","authors":"Yeongeun Kim, Chiyoung Cha, Gumhee Baek","doi":"10.2196/85986","DOIUrl":"10.2196/85986","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Nurse burnout is a pervasive global problem. Cognitive behavioral therapy (CBT) has been shown to reduce burnout; however, most digital CBT programs use standardized approaches that overlook individual differences in burnout profiles. With advances in artificial intelligence (AI), algorithm-based recommendation systems now enable personalized intervention delivery by matching specific CBT modules to users.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to test the effects of an AI-selected mobile CBT-based intervention on nurse burnout and to describe participants' experiences with the intervention. Specifically, it evaluated whether an AI-selected CBT-based intervention differentially reduced burnout subdomains compared with an information-only control group and explored how nurses perceived and engaged with the AI-selected program.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study adopted a mixed methods design, integrating a 2-group randomized controlled trial and qualitative content analysis exploring participants' experiences. For this randomized controlled trial, a total of 125 nurses were enrolled and randomly assigned to either the experimental group (n=62) or the control group (n=63) between October 2024 and December 2024. The experimental group received an AI-selected mobile CBT-based intervention, in which an AI algorithm assigned CBT modules based on participants' burnout profiles (client-related, personal, and work-related), job stress, and coping characteristics. The control group received information related to burnout management. Primary outcomes, client-related, personal, and work-related burnout, were assessed at baseline, 2 weeks, and 4 weeks. Secondary outcomes, including coping strategies, job stress, and stress response, were assessed at baseline and 4 weeks. Between-group differences in burnout over time were examined using repeated measures analysis of variance, with adjustment for job stress and stress response. Within-group changes and postintervention group differences were analyzed using t tests. Open-ended survey responses and follow-up interviews (n=5 in the experimental group) were analyzed using thematic content analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Follow-up completion rates were 84.6% (137/162) at both 2 and 4 weeks. The experimental group showed a greater reduction in client-related (F1,121=7.548; P=.007), personal (F1,121=6.533; P=.01), and work-related burnout (F1,121=38.194; P&lt;.001) than the control group, reflecting more pronounced within-group improvements over time. No significant between-group differences were observed for coping strategies, job stress, or stress response. Qualitative findings suggested that some participants were receptive to the AI-selected CBT-based intervention and reported increased self-awareness and reflective engagement with coping strategies that they might not have selected independently.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The findings suggest t","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e85986"},"PeriodicalIF":6.3,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13331328/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148381970","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":"Efficacy of a Cognitive Behavioral Therapy-Based Online Self-Help Group for Depression and Suicide Ideation: Randomized Controlled Trial.","authors":"Minkyung Yim, Haeun Kim, Soo-Eun Lee, Eunjin Jo, Ji-Won Hur","doi":"10.2196/76028","DOIUrl":"10.2196/76028","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Despite the high prevalence of depressive disorders, access to effective treatment remains limited due to financial, geographic, and social barriers. Online self-help groups offer a promising and scalable form of peer-based support beyond traditional clinical settings. Integrating cognitive behavioral therapy (CBT) techniques such as cognitive restructuring and behavioral activation into self-help groups may enhance their effectiveness.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study evaluated the efficacy of a cognitive behavioral therapy-based online self-help group (COS) that integrates structured CBT techniques with peer-led group support as a low-intensity intervention for individuals with depressive symptoms. A randomized controlled trial (RCT) comparing COS with a CBT-based mobile application was conducted. Additionally, a separately recruited waitlist control group was included as a supplementary comparison condition.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Participants were recruited online. After eligibility screening via a structured clinical interview, participants were randomly assigned to a COS group (n=79) or a CBT-based mobile application group (n=39). An additional waitlist control group (n=48) was recruited separately during the second phase of the study. The COS intervention involved 7 videoconferencing sessions that incorporated peer-led group discussions, sharing lived experiences, and core CBT techniques such as cognitive restructuring. The primary outcome measure was depressive symptoms, assessed using the Beck Depression Inventory-II, and the secondary outcome was suicidal ideation, estimated using the Beck Scale for Suicide Ideation, measured at baseline, postintervention, and 3-month follow-up. Linear mixed models were used to evaluate group × time interaction effects. Reliable change indices were also calculated to assess clinical significance. All statistical tests were 2-tailed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Among participants assigned to the COS group, 61% (48/79) completed all 7 sessions, and 84% (66/79) attended 5 or more sessions. A significant time × group interaction was observed for depressive symptoms (F4,288.47=7.23, P&lt;.001). The COS group exhibited a substantial reduction in depressive symptoms from baseline to postintervention (t285.76=10.77, two-tailed; P&lt;.001), with a large within-group effect size (d=1.38); this improvement was maintained at the 3-month follow-up. Suicidal ideation also significantly decreased in the COS group (t277.11=4.55, two-tailed; P&lt;.001), with sustained effects at follow-up. Clinically meaningful improvement in depressive symptoms, as defined by the reliable change index, was observed in 75% (56/75) of COS participants. While both the COS and app-based CBT groups achieved comparable reductions in depressive symptoms, only the COS group demonstrated a significant reduction in suicidal ideation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This RCT provides evidence th","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e76028"},"PeriodicalIF":6.3,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13331252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148382019","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}