JMIR mHealth and uHealth最新文献

{"title":"Natural Language Processing Chatbot-Based Interventions for Improvement of Diet, Physical Activity, and Tobacco Smoking Behaviors: Systematic Review.","authors":"Jing Chen, Run-Ze Hu, Yu-Xuan Zhuang, Jia-Qi Zhang, Rui Shan, Yang Yang, Zheng Liu","doi":"10.2196/66403","DOIUrl":"10.2196/66403","url":null,"abstract":"<p><strong>Background: </strong>The rapid development of artificial intelligence technology has enabled chatbots to increasingly promote health-related behaviors, addressing the high demand for human resources in traditional interventions. Several systematic reviews have been conducted in this area. However, the existing reviews have not focused on the rigorously designed randomized trials of the state-of-the-art chatbots (interacting with users through unconstrained natural language), thus calling for an updated review.</p><p><strong>Objective: </strong>We aimed to explore the effects of natural language processing (NLP) chatbot-based interventions on improving diet, physical activity, and tobacco smoking behaviors in the general population and to evaluate the chatbot use behaviors during the implementation process.</p><p><strong>Methods: </strong>We comprehensively searched 12 databases or registers for eligible studies published from January 1, 2010, until July 16, 2024, and obtained a total of 6301 studies. We included randomized controlled trials (RCTs) that used NLP-chatbots to promote diet, physical activity, or tobacco smoking behaviors among adults or children. Due to considerable heterogeneity across the included studies, we adopted the synthesis without meta-analysis guidelines and summarized the effectiveness of NLP chatbot-based interventions. We used the new evidence-mapping method (bubble plot) to visualize the results. We also described the results related to the changes in diet, physical activity, or tobacco smoking behaviors (eg, change of BMI and stage of change). To evaluate the implementation process of the intervention, we summarized users' interaction with NLP-chatbots and their feelings (eg, satisfaction) about NLP-chatbot use. Additionally, we assessed the risk of bias of studies using the RoB 2.0 (Risk of Bias; The Cochrane Collaboration) tools.</p><p><strong>Results: </strong>We finally included 7 RCTs. Concerning dietary and physical activity behaviors, the effectiveness of NLP chatbot-based interventions was inconsistent among adults, while no evidence of effect was observed among children. Concerning tobacco smoking behaviors, the included studies showed consistent evidence of improving this behavior among adults. Regarding the risk of bias of the changes in diet, physical activity, and tobacco smoking behaviors, 2 of 3, 2 of 4, and 1 of 2 studies had a high risk of bias, respectively, while the remaining had a low risk of bias. Concerning the interactions with NLP-chatbots, studies showed an overall high percentage of general interaction between users and NLP-chatbots, but not a satisfactorily high percentage of interactions specific to health behaviors. Concerning feelings about NLP-chatbot use, users showed a positive impression of NLP-chatbot use, feeling it was useful, credible, and financially feasible.</p><p><strong>Conclusions: </strong>NLP chatbot-based interventions were beneficial for adults' tobacco smoki","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e66403"},"PeriodicalIF":5.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12175970/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144274934","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 Telemedicine on Wound-Related and Patient-Reported Outcomes in Patients With Chronic Wounds: Systematic Review and Meta-Analysis.","authors":"Xiaoyan Zhang, Zhanghui Guo, Yu Duan, Chao Sun, Jiayin Luo","doi":"10.2196/58553","DOIUrl":"10.2196/58553","url":null,"abstract":"<p><strong>Background: </strong>Telemedicine may provide new vitality and opportunities to the field of wound care and has been advocated as being a potential and feasible strategy for chronic wound management.</p><p><strong>Objective: </strong>This systematic review and meta-analysis aimed to assess the effectiveness of telemedicine on wound-related outcomes and patient-reported outcomes in patients with chronic wounds.</p><p><strong>Methods: </strong>A comprehensive search of 9 databases, including PubMed, Embase, PsycINFO, the Cochrane Library, CINAHL, Web of Science, the China National Knowledge Infrastructure database, the Wanfang database, and the VIP database, was performed to identify eligible randomized controlled trials that investigated the effectiveness of telemedicine for patients with chronic wounds. The primary outcome was wound healing, including healing score, healing time, and healing rate. The quality of the included studies was examined via the Cochrane risk-of-bias tool. Data synthesis was conducted via Review Manager (version 5.4; the Cochrane Collaboration). Due to anticipated heterogeneity, a random-effects meta-analysis was used. Effect estimates are presented as risk ratio (RR) or standard mean differences (SMDs) with 95% CI. The quality of the evidence was assessed via the Grading of Recommendations, Assessment, Development, and Evaluation approach.</p><p><strong>Results: </strong>A total of 22 randomized controlled trials involving 2397 participants met the inclusion criteria. This review demonstrated that telemedicine significantly improved the healing score (SMD -1.46, 95% CI -2.27 to -0.66; P<.001; I2=95%; P<.001), healing time (SMD -0.47, 95% CI -0.92 to 0.02; P=.04; I2=85%; P<.001), amputation rate (RR 0.52, 95% CI 0.31-0.88; P=.02; I2=23%; P=.28), pain (SMD-0.62, 95% CI -0.90 to -0.34; P<.001; I2=0%; P=.32), and quality of life (SMD 1.90, 95% CI 0.32-3.48; P=.02; I2=98%; P<.001). Although the meta-analysis results indicated that telemedicine enhanced the healing rate (RR 1.16, 95% CI 1.02-1.33; P=.03; I2=50%; P=.03), potential publication bias was detected (Egger test, bias=1.801; SE 0.367; P<.001). Upon imputing the missing studies using the trim-and-fill method, the recalculated pooled RR was adjusted, resulting in a new estimate of RR 1.06 (95% CI 0.98-1.15; P=.16). In addition, no significant differences were found in mortality, depression, anxiety, or patient satisfaction.</p><p><strong>Conclusions: </strong>There is some evidence that telemedicine contributes to improvements in the healing score, healing time, amputation rate, pain, and quality of life of patients with chronic wounds. Nevertheless, further high-quality studies are essential to examine the impact of telemedicine on healing rate and patient-reported outcomes in patients with chronic wounds.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e58553"},"PeriodicalIF":5.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12173094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144266269","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":"Combined Use of Digital and Analog Physical Therapy in Patients With Musculoskeletal Disorders and Indicators of Chronicity: German Claims Data Analysis.","authors":"Silke Frey, Annika Schmitz, Udo Schneider, Linda Kerkemeyer, Birgitta Weltermann","doi":"10.2196/63935","DOIUrl":"10.2196/63935","url":null,"abstract":"<p><strong>Background: </strong>Musculoskeletal disorders are highly prevalent worldwide and contribute significantly to the overall burden of disease. Regular physical therapy with trained physiotherapists is recommended in the guidelines. Recently, digital physical therapy offered by digital health interventions was shown to be effective. However, the evidence on its real-world usage in health care systems is limited.</p><p><strong>Objective: </strong>Based on claims data, this study examined the current usage of digital health applications (DiGAs) for musculoskeletal disorders in the German health care system. Patients with standalone digital physical therapy were compared to those with a combination of analog and digital physical therapy. In addition, predictors for concomitant use were identified.</p><p><strong>Methods: </strong>This retrospective cohort study analyzed claims data from Germany's largest statutory health insurance. Patients who used DiGA for musculoskeletal disorders at least once were included. Sociodemographic and medical characteristics of patients receiving standalone and concomitant physical therapy were compared. Statistical analyses comprised univariate analyses and binomial logistic regression.</p><p><strong>Results: </strong>Of the 6090 individuals, 58.2% (3543/6090) were prescribed physical therapy within 6 months before or after DiGA prescription. In this population, 36.3% (2210/6090) used DiGA and analog physical therapy at the same time. Concomitant physical therapy was significantly more likely in patients with chronicity risk (odds ratio [OR] 1.49, 95% CI 1.31-1.69; P<.001) or established chronicity (OR 2.76, 95% CI 2.22-3.47; P<.001), female gender (OR 1.48, 95% CI 1.33-1.66; P<.001), and higher age (OR 1.02, 95% CI 1.02-1.02; P<.001).</p><p><strong>Conclusions: </strong>The findings highlight the diverse utilization patterns of DiGAs among patients with musculoskeletal disorders. Chronicity emerged as an important predictor for combined digital and analog physical therapy. These findings support considerations on integrating digital health interventions into current guidelines.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e63935"},"PeriodicalIF":5.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12168610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144247948","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":"Artificial Intelligence-Based Mobile Phone Apps for Child Mental Health: Comprehensive Review and Content Analysis.","authors":"Fan Yang, Jianan Wei, Xuejun Zhao, Ruopeng An","doi":"10.2196/58597","DOIUrl":"10.2196/58597","url":null,"abstract":"<p><strong>Background: </strong>Mobile phone apps powered by artificial intelligence (AI) have emerged as powerful tools to address mental health challenges faced by children.</p><p><strong>Objective: </strong>This study aimed to comprehensively review AI-driven apps for child mental health, focusing on their availability, quality, readability, characteristics, and functions.</p><p><strong>Methods: </strong>This study systematically analyzed AI-based mobile apps for child mental health. Quality was evaluated using the Mobile Application Rating Scale, which assessed various dimensions of app quality, including subjective quality, engagement, functionality, aesthetics, and information. An automatic readability index calculator was implemented to assess readability by using the count of words, syllables, and sentences to generate a score indicative of the reading difficulty level. Content analysis was conducted to examine the apps' availability, characteristics, and functionality.</p><p><strong>Results: </strong>Out of 369 apps initially identified, 27 met the eligibility criteria for inclusion. The quality of the apps was assessed using Mobile Application Rating Scale, with an average score of 3.45 out of 5 (SD 0.5), indicating a need for quality improvement. The readability analysis revealed suboptimal scores, with an average grade level of 6.62 (SD 2.2) for in-app content and 9.93 (SD 2.6) for app store descriptions. These results, combined with a monotonous user interface, suggest that many apps lack a child-friendly design, potentially hindering their usability and engagement for young users. Content analysis categorized the apps into 3 functional groups-chatbot-based apps (15 apps), journal logging apps (9 apps), and psychotherapeutic treatment apps (3 apps). While 20 out of 27 apps (74%) used clinically validated technologies, rigorous clinical tests of the apps were often missing, with only 2 apps undergoing clinical trials. Of the 27 apps analyzed, only 7 (26%) were free to use, while the majority, 20 apps, required a subscription or one-time payment. Among the paid apps, the average cost was US $20.16 per month, which may pose a financial barrier and limit accessibility for some users, particularly those from lower-income households.</p><p><strong>Conclusions: </strong>AI-based mental health apps hold significant potential to address the unique challenges of child mental health but face critical limitations in design, accessibility, and validation. To fully realize their benefits, future research and development should focus on integrating child-centric design principles, ensuring affordability, and prioritizing rigorous clinical testing. These efforts are essential to harness the power of AI technologies in creating equitable, effective, and engaging solutions for improving child mental health outcomes.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e58597"},"PeriodicalIF":5.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12165445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144247947","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":"Correction: Clinical Efficacy of Multimodal Exercise Telerehabilitation Based on AI for Chronic Nonspecific Low Back Pain: Randomized Controlled Trial.","authors":"Chongwu Xiao, Yijin Zhao, Gege Li, Zhuodong Zhang, Siyu Liu, Weichao Fan, Jinjing Hu, Qiuru Yao, Chengduan Yang, Jihua Zou, Qing Zeng, Guozhi Huang","doi":"10.2196/78188","DOIUrl":"10.2196/78188","url":null,"abstract":"","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e78188"},"PeriodicalIF":5.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12165623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144247949","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":"Outdoor Exercise Facility-Based Integrative Mobile Health Intervention to Support Physical Activity, Mental Well-Being, and Exercise Self-Efficacy Among Older Adults With Prefrailty and Frailty in Hong Kong: Pilot Feasibility Randomized Controlled Trial Study.","authors":"Janet Lok Chun Lee, Arnold Y L Wong, Peter H F Ng, S N Fu, Kenneth N K Fong, Andy S K Cheng, Karen Nga Kwan Lee, Rui Sun, Hao Yi Zhang, Rong Xiao","doi":"10.2196/69259","DOIUrl":"10.2196/69259","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Engaging in an adequate amount of physical activity (PA) serves as a protective factor against frailty. While previous PA interventions have been effective in improving physical functioning outcomes, they have not consistently succeeded in sustaining PA behavioral changes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The primary aim of this pilot randomized controlled trial (RCT) is to explore the feasibility and acceptability of an integrative mobile health (mHealth) intervention among community-dwelling older adults with prefrailty and frailty. The secondary aim was to investigate the potential effects of the intervention on sustaining PA levels and improving mental well-being and exercise self-efficacy in this population.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A 2-armed pilot feasibility randomized controlled trial was conducted. A total of 38 inactive, community-dwelling older adults (aged&gt;55 years) with prefrailty and frailty were randomized to either the intervention group (n=19), which received 4 weekly educational workshops at a university and a mobile app to support their use of outdoor exercise facilities in their neighborhood, or the control group (n=19), which received 4 weekly health education workshops with exercise experiential sessions tailored for older adults with frailty. To assess the acceptability of the intervention, individual semistructured interviews were conducted with, and a self-developed questionnaire was administered to, 14 participants from the intervention group.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The mean age of the participants was 71.8 (SD 9.34) years, and 24 out of 34 (71%) were female. As many as 34 participants out of 38 (89%) completed the study (18/19 in the control group and 16/19 in the intervention group). Workshop attendance rates were very high in both groups (intervention group, 63/68, 93%, and control group, 72/76, 95%). Self-reported adherence to the unsupervised outdoor practical sessions and engagement with the app was over 65% (36/51, 71%, and 35/51, 69%, in the intervention group. Two adverse events were reported in the intervention group, and none in the control group. As hypothesized, secondary outcome analyses showed that both groups increased their PA levels immediately after the intervention; however, only the intervention group maintained this increase at the 3-month follow-up. Additionally, favorable changes in mental well-being and exercise self-efficacy were observed in the intervention group. Feasibility and acceptability data also highlighted areas for improvement that should be addressed before a larger trial.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study provides initial proof-of-concept evidence for the integrative mHealth intervention. However, modifications are needed to enhance user adherence to both the mobile app and the outdoor practice component before proceeding to a larger trial.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Trial registration: &lt;/strong&gt;ClinicalTrials.gov NCT06326710; https:/","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e69259"},"PeriodicalIF":5.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12179572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144225539","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 Effect of a Mobile App (eMOM) on Self-Discovery and Psychological Factors in Persons With Gestational Diabetes: Mixed Methods Study.","authors":"Sini Määttänen, Saila Koivusalo, Hanna Ylinen, Seppo Heinonen, Mikko Kytö","doi":"10.2196/60855","DOIUrl":"10.2196/60855","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Gestational diabetes is a type of diabetes that develops during pregnancy and increases the risk of developing type 2 diabetes later in life. The rising prevalence of gestational diabetes mellitus (GDM) highlights the need for more comprehensive treatment strategies, with a particular emphasis on supporting maternal self-management. We showed recently that a mobile app, eMOM, where glucose, nutrition, and physical activity are combined within a single app, significantly improves multiple clinical outcomes among persons with gestational diabetes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to explore the effects of the eMOM on maternal self-discovery and learning, autonomous motivation to manage GDM, and psychological well-being. Additionally, we examine the correlation between improved maternal clinical outcomes and change in autonomous motivation. We also assess the acceptance and usability of the eMOM app.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Building upon the original randomized controlled trial (RCT), in which the intervention arm used a mobile app (eMOM), we conducted a mixed methods study that included an investigation of eMOM log files, semistructured interviews on self-discovery, and an examination of questionnaires assessing motivation (Treatment Self-Regulation Questionnaire and Perceived Competence Scale), depression (Edinburgh Postnatal Depression Scale), technology use and acceptance (Unified Theory of Acceptance of Use of Technology questionnaire), and usability (modified Software Usability Measurement Inventory). Additionally, we monitored participants' stress levels using wearable electrocardiographic devices (FirstBeat Bodyguard 2). A total of 148 individuals participated in the original RCTs, with 76 in the intervention arm and 72 in the control arm. From the intervention arm, 18 participants were randomly selected for interviews in this study.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Results show that the use rate of eMOM was high, and novel visualization supported self-discovery in persons with GDM. Most participants (17/18, 94%) indicated that the eMOM app helped to find the associations between their daily activities and glucose levels. Especially having nutrition visualized together with glucose was highly appreciated. Participants also reported learning about the associations between physical activity and glucose levels. No differences were observed between the intervention and control arms in autonomous motivation, depression, or stress. Furthermore, there were no correlations between improved clinical outcomes and changes in motivation. Accessibility and usability ratings were consistently high throughout the intervention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The eMOM mobile app combining data from continuous glucose monitor, food diary, and physical activity tracker supports maternal self-discovery related to GDM without contributing to depression or adding extra stress. This encourages the use of","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e60855"},"PeriodicalIF":5.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12177430/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144225540","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":"Integrating CEN ISO/TS 82304-2 in the Catalan Health App Assessment Framework: Comparative Case Study.","authors":"Berta Llebot Casajuana, Petra Hoogendoorn, Maria Villalobos-Quesada, Carme Pratdepàdua Bufill","doi":"10.2196/67858","DOIUrl":"10.2196/67858","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Health apps are increasingly being used to promote health, manage diseases, and deliver health care services. Still, there is scarce objective information regarding their quality beyond the required Conformité Européenne mark for medical apps, leading to potential risks for users. To address these challenges, several authorities have developed health app assessment frameworks. In 2017, the TIC Salut Social Foundation (FTSS) in Catalonia developed its own health app assessment framework, which has been in use since that year. The publication of CEN ISO/TS 82304-2 (abbreviated as 82304-2)-a Technical Specification for assessing health apps-and the cocreation of the Label2Enable 82304-2 handbook for certified assessment organizations provide a unique opportunity to harmonize app assessments across the European Union.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to perform a comparative analysis of the FTSS assessment framework with 82304-2 to explore the integration of 82304-2 in Catalonia. Our broader aim was to provide this methodology for health authorities elsewhere to consider integrating 82304-2 or other evaluation frameworks.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;For the comparative analysis, a mixed methods approach was used, combining a qualitative case study with a quantitative analysis of the 2 frameworks. The qualitative evaluation covered rationale for assessment, framework characteristics, governance, workflows, quality aspects, and quality requirements. For the quantitative analysis, all FTSS and 82304-2 requirements were translated into concepts and subconcepts. A scoring system identified matches of the frameworks with these subconcepts, with scores ranging from 0 (no match) to 0.5 (partial match) and 1 (full match). Integration was evaluated considering several scenarios, including adopting the Label2Enable 82304-2 handbook, adopting the 82304-2 requirements, adapting the 82304-2 requirements to local needs, and maintaining the current FTSS framework.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The main difference between the frameworks was the app usage-based assessment (FTSS) versus evidence- and app usage-based assessment (82304-2). All 120 FTSS requirements and 74 quality aspect-related 82304-2 requirements were translated into 78 concepts and 97 subconcepts. Overall, 48% (47/97) of the subconcepts were found in both frameworks, 39% (37.5/97) were specific to 82304-2, and 13% (12.5/97) were specific to FTSS. All 82304-2-specific subconcepts and thus all 82304-2 requirements were found to be relevant to FTSS. FTSS decided to integrate (adopt and adapt) all 74 82304-2 requirements. In total, 5 FTSS-specific requirements were included in the Label2Enable 82304-2 handbook, while another 4 rigor-enhancing requirements, 1 scope-expanding requirement, and 1 context-specific requirement would be assessed on top.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The comprehensive comparative analysis of the FTSS framework and 8230","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e67858"},"PeriodicalIF":5.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12154936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144225526","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":"Trade-Offs Between Simplifying Inertial Measurement Unit-Based Movement Recordings and the Attainability of Different Levels of Analyses: Systematic Assessment of Method Variations.","authors":"Manu Airaksinen, Okko Räsänen, Sampsa Vanhatalo","doi":"10.2196/58078","DOIUrl":"10.2196/58078","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Human movement activity is commonly recorded with inertial measurement unit (IMU) sensors in many science disciplines. The IMU data can be used for an algorithmic detection of different postures and movements, which may support more detailed assessments of complex behaviors, such as daily activities. Studies on human behavior in real-life environments need to strike a balance between simplifying the recording settings and preserving sufficient analytic gains. It is poorly understood, however, what the trade-offs are between alternative recording configurations and the attainable analyses of naturalistic behavior at different levels of inspection, or with respect to achievable scientific questions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study assessed systematically the effects of IMU recording configurations (placement and number of IMU sensors, sampling frequency, and sensor modality) on the high temporal resolution detections of postures and movements, and on their lower temporal resolution derivative statistics when the data represents naturalistic daily activity without excessively repetitive movements.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We used a dataset from spontaneously moving infants (N=41; age range 4-18 months) recorded with a multisensor wearable suit. The analysis benchmark was obtained using human annotations of postures and movements from a synchronously recorded video, and the reference IMU recording configuration included 4 IMU sensors collecting triaxial accelerometer and gyroscope modalities at 52 Hz. Then, we systematically tested how the algorithmic classification of postures (N=7), and movements (N=9), as well as their distributions and a derivative motor performance score, are affected by reducing IMU data sampling frequency, sensor modality, and sensor placement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our results show that reducing the number of sensors has a significant effect on classifier performance, and the single sensor configurations were nonfeasible (posture classification Cohen kappa&lt;0.75; movement&lt;0.45). Reducing sensor modalities to accelerometer only, that is, dropping gyroscope data, leads to a modest reduction in movement classification performance (kappa=0.50-0.53). However, the sampling frequency could be reduced from 52 to 6 Hz with negligible effects on the classifications (posture kappa=0.90-0.92; movement=0.56-0.58).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The present findings highlight the significant trade-offs between IMU recording configurations and the attainability of sufficiently reliable analyses at different levels. Notably, the single-sensor recordings employed in most of the literature and wearable solutions are of very limited use when assessing the key aspects of real-world movement behavior at relevant temporal resolutions. The minimal configuration with an acceptable classifier performance includes at least a combination of one upper and one lower extremity sensor, at le","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e58078"},"PeriodicalIF":5.4,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12151449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144215882","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":"Behavioral Markers in Older Adults During COVID-19 Confinement: Secondary Analysis of In-Home Sensor Data.","authors":"Knoo Lee, Noah Marchal, Erin L Robinson, Kimberly R Powell","doi":"10.2196/56678","DOIUrl":"10.2196/56678","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Older adults were disproportionately affected by the COVID-19 pandemic, with a high number of deaths occurring in this age group. The impact of social isolation and home confinement continues to impact the mental and emotional health of older adults, despite the end of the COVID-19 pandemic. Unhealthy lifestyle behaviors, including physical and social inactivity, and poor sleep quality, have been reported. Recommendations for healthy lifestyle changes have primarily targeted the general population, highlighting the need for personalized recommendations for vulnerable older adults. Remote sensing technologies may offer an opportunity to understand behavior changes among older adults and provide personalized recommendations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to describe the effects of home confinement and social isolation on community-dwelling older adults during the COVID-19 outbreak and investigate how integrated computing technologies, such as remote sensors installed in homes, can help inform recommendations for safe and healthy lifestyles.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;As part of a larger study and ongoing research with community-dwelling older adults, remote sensors including bed transducers, 3D depth cameras, and passive infrared (PIR) motion sensors were installed in the homes of the study sample. We compared features derived from sensors for approximately one month before the COVID-19 outbreak (January 14, 2020-February 13, 2020) and one month after the onset of the pandemic (March 14, 2020-April 13, 2020). We used descriptive statistics and paired-sample t tests to compare the 2 time periods, pre-COVID-19 and early-COVID-19.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Sensor data from 64 older adults were analyzed, the majority identifying as female (n=51, 80%), aged &gt;76 years (n=58, 92%), and living alone (n=50, 78%). Results from paired-sample t tests demonstrated significant differences in sensor features between the pre-COVID-19 and early-COVID-19 time periods. We found statistically significant differences in bed restlessness (pre-COVID: mean 14.98, SD 5.10; early-COVID: mean 15.56, SD 5.25; t554=-4.10; P&lt;.001), time spent in bed (pre-COVID: mean 32,547.41, SD 9269.96; early-COVID: mean 33,494.73, SD 10,887.33; t554=-2.81; P=.005), pulse (pre-COVID: mean 68.45, SD 3.30; early-COVID: mean 68.10, SD 3.36; t554=3.66; P&lt;.001), respiration (pre-COVID: mean 14.54, SD 1.32; early-COVID: mean 14.41, SD 1.31; t553=3.72; P&lt;.001), and stride length (pre-COVID: mean 29.10, SD 4.813; early-COVID: mean 28.76, SD 5.016; t595=2.17; P=.03). Among the study sample, bed restlessness and time spent in bed increased between the 2 time periods, while pulse, respiration, and stride length decreased.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study highlights that home confinement during the pandemic significantly impacted the behavior and health of older adults, leading to more sedentary lifestyles and poorer sleep quali","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e56678"},"PeriodicalIF":5.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12148247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208604","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}