JMIR Aging最新文献

{"title":"The Dual Impact of Time and Content Exposure of Social Media on Diabetes Self-Management in Older Adults: Cross-Sectional Study.","authors":"Qingyuan Ye, Ruiyang Xu, Li Li, Meng Zhao, Shan Wang, Sijing Peng, Si Chen, Fatema Ahmed, Chen Wu, Kefang Wang","doi":"10.2196/67312","DOIUrl":"10.2196/67312","url":null,"abstract":"<p><strong>Background: </strong>Self-management is critical for older adults with type 2 diabetes mellitus (T2DM); however, its practice remains suboptimal. Social media has become an accessible and effective stimulus source for the public, which has the potential to promote health behaviors, but its effect on the self-management of older adults with T2DM remains unknown.</p><p><strong>Objective: </strong>We aimed to investigate the relationship between social media exposure, specifically time exposure and content exposure, and the self-management of older adults with T2DM.</p><p><strong>Methods: </strong>In this cross-sectional study, we enrolled 257 older adults with T2DM who used short-form video apps from community health care centers. We assessed subjective and objective time and content exposure. We transformed text-based content exposure into diabetes-related content exposure encompassing irrelevant, harmful, hypobeneficial, and hyperbeneficial categories using Q-methodology. Self-management was assessed through a validated questionnaire. We used restricted cubic splines and linear regression models to model the relationships between time exposure and content exposure and self-management, respectively.</p><p><strong>Results: </strong>Of 257 older adults with T2DM, the median age was 69 (IQR 65-72) years, 53.3% (n=137) were women, the mean sum score of self-management was 35.7 (SD 10.4), the median subjective time exposure was 120 (IQR 60-120) minutes, and 61.1% (n=157) of them were exposed to hyperbeneficial content. There was an approximate L-shaped dextrorotatory relationship between time exposure and self-management, with a decline in self-management when time exposure surpassed 139.8 minutes daily. Exposure to hyperbeneficial content was positively associated with the overall self-management (B=3.46, 95% CI 0.71-6.21). For participants exposed for more than 139.8 minutes daily, this positive association remained robust (B=7.27, 95% CI 1.54-13.00). In subdimensional analyses, hyperbeneficial content exposure was positively associated with general diet (B=1.51, 95% CI 0.54-2.49) and blood glucose testing (B=1.31, 95% CI 0.25-2.38).</p><p><strong>Conclusions: </strong>Social media exposure presented a double-edged sword for self-management of older adults with T2DM. Self-management declined when the daily time spent on social media exceeded 139.8 minutes. However, exposure to hyperbeneficial content was associated with better self-management of individuals, regardless of excessive time spent on social media. Future longitudinal and experimental studies that validate the multifaceted association between social media exposure and health behaviors are needed. If confirmed, these findings would support the implementation of media prescription programs by health care providers in communities.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e67312"},"PeriodicalIF":4.8,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145087491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Evaluation of a Mobile App for Intergenerational Communication: User-Centered Participatory Design and Experimental Mixed Methods Study.","authors":"Soondool Chung, Hannah Lee, Jeehye Jung","doi":"10.2196/75950","DOIUrl":"10.2196/75950","url":null,"abstract":"<p><strong>Background: </strong>Social isolation and weakened intergenerational ties pose significant threats to the emotional well-being and social support networks of older adults. Although structured intergenerational programs can reduce age-related stereotypes and promote connectedness, their accessibility is often hindered by physical and logistical constraints. The increasing digital literacy among older populations presents new opportunities for technology-based interventions to support meaningful cross-generational engagement.</p><p><strong>Objective: </strong>This study aimed to design and evaluate a mobile app that fosters intergenerational communication and enhances perceived social support in older adults using a user-centered design framework grounded in the double diamond model.</p><p><strong>Methods: </strong>The development process followed the 4 phases of the double diamond model. In the discover phase, surveys with older and younger adults identified distinct usability preferences. The define phase synthesized these insights into key design principles. In the develop phase, a prototype was created and iteratively refined through usability testing. Finally, in the deliver phase, a 2-week experimental study involving 39 participants (20 older adults aged 68-82 years and 19 younger adults aged 22-39 years) assessed changes in intergenerational interaction, perceived social support, and user satisfaction.</p><p><strong>Results: </strong>The app appeared to enhance intergenerational communication and perceived social support, particularly among older participants. Users reported increased comfort and emotional connection in cross-generational conversations. Accessibility features and engaging content were noted as contributing to positive user experiences across age groups.</p><p><strong>Conclusions: </strong>This study suggests the potential of user-centered digital platforms to promote social well-being among older adults. By addressing the unique needs of multiple generations, such interventions may help foster inclusive digital environments and contribute to age-friendly, connected societies. Despite limitations related to sample size, duration, and cultural context, the study provides preliminary evidence for the potential of co-designed digital tools in supporting intergenerational communication and aging-in-place.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e75950"},"PeriodicalIF":4.8,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12489411/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145081770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Smart Displays to Implement an eHealth System for Older Adults with Multiple Chronic Conditions: A Randomized Controlled Trial.","authors":"Gina Landucci, David H Gustafson, Marie-Louise Mares, Klaren Pe-Romashko, John J Curtin, Yaxin Hu, Adam Maus, Kasey Thompson, Sydney Saunders, Kaitlyn Brown, Judith Woodburn, Bilge Mutlu","doi":"10.2196/75991","DOIUrl":"https://doi.org/10.2196/75991","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Smart displays and smart speakers offer ease of voice interaction, which may be more accessible and appealing for older adults experiencing chronic pain with other multimorbid chronic conditions. Previous ElderTree trials found that the socio-emotional benefits of ElderTree (vs control) were stronger for older adults with high rates of primary care use and for older adults with multiple chronic conditions and that the socio-emotional benefits were stronger for women than for men.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study tested whether older adults suffering from chronic pain, in the context of multiple other chronic conditions, would show more use and benefits of ElderTree, a digital/eHealth intervention targeting pain and quality of life, if they were given the intervention on a smart display rather than a laptop, and whether both the smart display and laptop groups would show benefits relative to a control group given no device or access to the intervention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In a non-blinded, randomized controlled trial, 269 participants with chronic pain and at least 3 high-risk chronic conditions were recruited from the University of Wisconsin-Madison Department of Family Medicine and General Internal Medicine system (UW Health) as well as community organizations in the Madison, Milwaukee, and Beloit, Wisconsin, areas and were assigned 1:1:1 to (1) a smart display with Internet access and ElderTree, along with their usual care; (2) a touchscreen laptop with Internet access and ElderTree, along with usual care; or (3) usual care alone. All participants were age 60 or older, had a chronic pain diagnosis and/or reported chronic pain, and had at least three common chronic conditions. Primary outcomes were pain interference and psychosocial quality of life. Proposed mediators of effects of study arm (ElderTree vs active control) on changes in primary outcomes over time were ElderTree use at 4 months (for ElderTree arms only), competence, relatedness, motivation, exercise, and pain intensity. Moderators were gender, number of chronic conditions, and barriers to technology use. Data sources were surveys at baseline, 4, and 8 months and continuously collected ElderTree usage.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;There was not a significant difference between the laptop vs smart display groups for changes in pain interference over time (arm x time interaction b = -0.11, 95% Cl -1.07 to 0.85; p = .82) or psychosocial quality of life over time (arm x time interaction b = -0.21, 95% CI -0.96 to 0.55, p = .56). There was also a non-significant difference between the combined laptop + smart display group vs control group for change in pain interference over time (arm x time interaction b = -0.41, 95% Cl -1.23 to 0.41; p = .33) and psychosocial quality of life over time (arm x time interaction: b = 0.04, 95% Cl -0.61 to 0.69; p = .90). No mediation was tested on the primary outcomes since the effects of study ar","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145081764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Interactions Among Sleep Duration, Cognitive Function, and Depressive Symptoms in Middle-Aged and Older Chinese Adults: Temporal Network Analysis From CHARLS.","authors":"Furong Chen, Jiaying Li, Junchen Guo, Ying Xiong, Zengjie Ye","doi":"10.2196/76210","DOIUrl":"10.2196/76210","url":null,"abstract":"<p><strong>Background: </strong>While bidirectional associations among sleep duration, cognitive function, and depression are established, the symptom-level temporal interactions among these factors in China's aging population, which is experiencing unprecedented growth, remain poorly characterized.</p><p><strong>Objective: </strong>We aim to use a novel temporal network analysis to clarify these dynamics and guide targeted interventions, with a focus on sex-specific dynamic pathways.</p><p><strong>Methods: </strong>We conducted a longitudinal temporal network analysis on 3136 Chinese adults aged ≥45 years from the China Health and Retirement Longitudinal Study (CHARLS) across 5 waves (2011, 2013, 2015, 2018, and 2020). A graphical vector autoregressive (GVAR) model delineated the interdependencies among sleep duration, cognitive performance (assessed via the Mini-Mental State Examination [MMSE]), and depressive symptoms (evaluated with the 10-item Center for Epidemiologic Studies Depression Scale [CESD-10]). We also examined sex-specific differences in network structures.</p><p><strong>Results: </strong>The symptom \"bothered\" was found to predict all other CESD-10 symptoms. There were significant predictive links between sleep and the CESD-10 node (ie, bothered, drained, and depressed), along with sleep and the MMSE functions (ie, numerical ability). Furthermore, sleep duration served as a bridge between depression symptoms and cognitive functions. There were significant differences in longitudinal network structure between sexes. Sex-specific analyses revealed distinct network patterns. Among female participants, the \"bothered\" node significantly predicted several outcomes over time. In contrast, the temporal network for male participants was sparser, with the \"stuck\" node in the depression domain being predominantly influenced by other nodes.</p><p><strong>Conclusions: </strong>Our study revealed that emotional distress, especially the \"bothered\" symptom, plays a central role in depressive symptoms and cognitive decline. The bridging effect of short sleep duration underscores the potential of interventions targeting both sleep and emotional distress for alleviating depressive symptoms and delaying cognitive deterioration in older adults.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e76210"},"PeriodicalIF":4.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12440260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technology-Assisted Physical Activity Interventions for Older People in Their Home-Based Environment: Scoping Review.","authors":"Rosemary Dubbeldam, Rafal Stemplewski, Iuliia Pavlova, Magdalena Cyma-Wejchenig, Sunwoo Lee, Patrick Esser, Ellen Bentlage, Veysel Alcan, Özge Selin Çevik, Eleni Epiphaniou, Francesca Gallè, Antoine Langeard, Simone Gafner, Mona Ahmed, Niharika Bandaru, Arzu Erden Güner, Evrim Göz, Ilke Kara, Ayşe Kabuk, Ilayda Türkoglu, Zada Pajalic, Jan Vindiš, Damjan Jaksic, Uǧur Verep, Ioanna Chouvarda, Vera Simovska, Yael Netz, Jana Pelclova","doi":"10.2196/65746","DOIUrl":"10.2196/65746","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Technology-assisted physical activity interventions for older adults in their home-based environment have been used to promote physical activity. Previous research has reported that such interventions benefit body composition, aerobic fitness, cognitive abilities, and postural control, reducing the risk of falls and maintaining regular physical activity among the older population.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;While previous reviews on technology-assisted physical activity interventions focused on health-related outcomes, this scoping review explores the characteristics of the technology in relation to the characteristics of populations, purpose of the activity, and usability in terms of adverse events, drop-outs, adherence, and user experience.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A full search was performed in Medline, Embase, CINAHL, SportDiscus, and Web of Science. Sources were considered for inclusion if the participants aged on average 60 years and older, if the physical activity intervention was assisted by technology, and if performed within home-based environments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We identified 8496 sources. After title and abstract screening, 455 full texts were assessed, and 148 were included, representing 12,717 participants aged 74 (SD 6) years. In total, 63% (93/148) of the sources reported on the population's health status. The main purpose of the interventions was balance (75/148, 51%), and strength and power (64/148, 43%) and the intervention purposes were not related to the embedded technology. In studies where the participant's health status was reported as healthy, 53% (78/148) implemented exergames compared to only 27% (40/148) in studies with participants with a clinical condition. Mobile apps (30/148, 20%) and trackers (16/148, 11%) were implemented likewise in both groups. The technology was embedded to provide continuous exercise information (40/148, 27%) and exercise feedback (40/148, 27%) or to record real-time movement data (38/148, 26%). Adverse events were reported in 46% (68/148) of the sources with three quarters (49/68) reporting no adverse events. Only two mild events were related to technology. Dropout rates were reported in 68% (100/148) of the sources, with no differences between intervention (16 SD 16%) and control (14 SD 12%) groups. Dropout reasons related to technology are only 3% (3/100). Adherence was reported in 53% (79/148) sources and was slightly higher in the intervention group (80 SD 18%) compared to the control group (71 SD 25%). A significantly higher adherence was found between interventions that were tailored (83 SD 15%) versus those that were not (75 SD 21%). General enjoyment of the technology was captured in 37% (55/148) of the sources. Within those sources, 91% rated positive (91/100), 7% neutral (7/100), and 2% negative (2/100). Occasionally reported wishes were related to goal setting, feedback, technical support, exercise variation, and soci","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e65746"},"PeriodicalIF":4.8,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145065772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Physical Activity and Physical Function in Predicting Physical Frailty Transitions in Chinese Older Adults: Longitudinal Observational Study From CHARLS.","authors":"Ziwei Zeng, Chun Liang Hsu, Cindy Hui-Ping Sit, Stephen Heung-Sang Wong, Yijian Yang","doi":"10.2196/75887","DOIUrl":"10.2196/75887","url":null,"abstract":"<p><strong>Background: </strong>Frailty is a dynamic geriatric syndrome associated with adverse health outcomes, yet its progression can be mitigated through targeted interventions.</p><p><strong>Objective: </strong>This study aimed to investigate predictors of frailty transitions in Chinese older adults, focusing on physical activity (PA) and physical function.</p><p><strong>Methods: </strong>Using data from the China Health and Retirement Longitudinal Study (CHARLS), we examined transitions between frailty states (robust, prefrail, and frail) from 2011 (baseline) to 2013 (follow-up) among 1014 participants aged 65 years and older. The following outcomes were assessed, including frailty using the physical frailty phenotype, PA using a modified International Physical Activity Questionnaire, and physical function using the Short Physical Performance Battery (SPPB) and handgrip strength. Ordinal logistic regression models were used to examine the relationship between PA, physical function, and frailty transitions.</p><p><strong>Results: </strong>Results showed that higher PA levels and better physical function reduced the likelihood of worsening frailty or increased the probability of transitioning to robustness. Key findings from the subgroup include: among robust individuals, greater handgrip strength predicted maintained robustness (average marginal effects [AME]=1.12%; P=.02); in prefrail individuals, higher vigorous PA (AME=21.76%; P=.04) and handgrip strength (AME=0.64%; P=.003) increased transitions to robustness; for frail individuals, increased low-intensity PA (AME =22.48%; P=.04) and higher SPPB walking subscores (AME=27.73%; P=.02) promoted improvement to nonfrailty.</p><p><strong>Conclusions: </strong>These findings highlight the importance of tailored interventions based on baseline frailty status. Promoting PA and improving physical function, particularly muscle strength and mobility function, may help delay or reverse frailty progression.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e75887"},"PeriodicalIF":4.8,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443357/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145070799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study.","authors":"Xiaoping Zheng, Ziwei Zeng, Kimberley S van Schooten, Yijian Yang","doi":"10.2196/77140","DOIUrl":"10.2196/77140","url":null,"abstract":"<p><strong>Background: </strong>Frailty affects over 50% of older adults in long-term care (LTC), and early detection is critical due to its potential reversibility. Wearable sensors enable continuous monitoring of gait and physical activity, and machine learning has shown promise in detecting frailty among community-dwelling older adults. However, its applicability in LTC remains underexplored. Furthermore, dynamic gait outcomes (eg, gait stability and symmetry) may offer more sensitive frailty indicators than traditional measures like gait speed, yet their potential remains largely untapped.</p><p><strong>Objective: </strong>This study aimed to evaluate whether frailty in LTC facilities could be effectively identified using machine learning models trained on gait and daily physical activity data derived from a single accelerometer.</p><p><strong>Methods: </strong>This study is a cross-sectional secondary analysis of baseline data from a 2-arm cluster randomized controlled trial. Of the 164 individuals initially enrolled, 51 participants (age: mean 85.0, SD 9.0 years; female: n=24, 47.1%) met the inclusion criteria of completing all assessments required for this study and were included in the final analysis. Frailty status was assessed using the fatigue, resistance, ambulation, incontinence, loss of weight, nutritional approach, and help with dressing (FRAIL-NH) scale. Participants completed a 5-meter walking task while wearing a 3D accelerometer. Following this task, the accelerometer was used to record daily physical activity over approximately 1 week. A total of 34 dynamic and spatial-temporal gait outcomes, 3 physical activity variables, and 6 demographic characteristics were extracted. Five conventional machine learning models were trained to classify frailty status using a leave-one-out cross-validation approach. Model performance was evaluated based on accuracy and the area under the receiver operating characteristic curve. To enhance model interpretability, explainable artificial intelligence techniques were used to identify the most influential predictive outcomes.</p><p><strong>Results: </strong>The extreme gradient boosting model demonstrated the optimal performance with an accuracy of 86.3% and an area under the curve of 0.92. Explainable artificial intelligence analysis revealed that older adults with frailty exhibited more variable, complex, and asymmetric gait patterns, which were characterized by higher stride length variability, increased sample entropy, and a higher gait symmetry score.</p><p><strong>Conclusions: </strong>Our findings suggest that dynamic gait outcomes may serve as more sensitive indicators of frailty than spatial-temporal gait outcomes (eg, gait speed) in LTC settings, offering valuable insights for enhancing frailty detection and management.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e77140"},"PeriodicalIF":4.8,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145070860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Data-Informed Care in Long-Term Care: Qualitative Analysis.","authors":"Suleyman Bouchmal, Katya Yj Sion, Jan Ph Hamers, Sil Aarts","doi":"10.2196/69423","DOIUrl":"10.2196/69423","url":null,"abstract":"<p><strong>Background: </strong>In long-term care (LTC) for older adults, data on client, employee, and organization levels are collected in various ways, covering quality of care, life, and work. There is, however, a lack of understanding of how to introduce data-informed care in LTC and thus create value from data.</p><p><strong>Objective: </strong>This study aims to investigate the experiences and perceptions of various stakeholders in LTC regarding data and data-informed care.</p><p><strong>Methods: </strong>A qualitative study using the World Café cocreation technique was conducted with a diverse group of LTC stakeholders. Four questions were addressed: (1) What thoughts do you have when you hear the term \"data\" in relation to LTC? (2) What purposes do data have (in the future) in LTC? (3) What knowledge and skills are needed to enable data-informed care? (4) How can data contribute to and improve multidisciplinary learning? Stakeholders' notes and the plenary summary were analyzed using conventional content analysis.</p><p><strong>Results: </strong>Stakeholders included nurses, members of client councils, data specialists, researchers, and managers (N=20; mean age 50, SD 13 years). Five themes were identified: (1) despite uncertainty, the benefits of using data outweigh the associated risks; (2) the lack of accessibility and uniformity hinders integrating data-informed care; (3) human resources and finance departments pioneer data usage; however, potential lies in clinical decision-making; (4) data-informed care demands individual, collective, and organizational prerequisites; and (5) multidisciplinary collaboration enriches collective knowledge regarding data.</p><p><strong>Conclusions: </strong>Introducing data-informed care requires enhancing data literacy of health care professionals, establishing clear communication about the role of data within the organization, and introducing new job positions, such as data scientists. Data-informed care was considered a multidisciplinary approach in which data have a supportive role to enhance collective understanding and are considered crucial for improving quality of care. .</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e69423"},"PeriodicalIF":4.8,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12431161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145055938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Barriers to and Facilitators of Digital Health Technology Adoption Among Older Adults With Chronic Diseases: Updated Systematic Review.","authors":"Jennifer Hepburn, Lynn Williams, Lisa McCann","doi":"10.2196/80000","DOIUrl":"10.2196/80000","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Older adults with chronic diseases are key beneficiaries of digital health technologies, yet adoption remains inconsistent, particularly in rural areas and among certain demographic groups, such as older women.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This systematic review aimed to identify barriers to and facilitators of digital health adoption among older adults with chronic diseases, with particular attention to rural-urban differences, co-design, and equity-relevant factors.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This updated review built on a previously published review by extending the search to include PsycArticles, Scopus, Web of Science, and PubMed databases for studies published between April 2022 and September 2024. Gray literature from August 2021 onward was also included. Studies were eligible if they reported barriers to or facilitators of digital health adoption among adults aged ≥60 years with chronic diseases. Findings were mapped to the capability, opportunity, and motivation-behavior model and analyzed using the PROGRESS-Plus (place of residence; race, ethnicity, culture, and language; occupation; gender and sex; religion; education; socioeconomic status; and social capital-plus) equity framework. Quality was assessed using the Mixed Methods Appraisal Tool, and all results are reported in line with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In total, 12 studies from the original review were retained, with 17 new peer-reviewed studies added, yielding a total of 29 studies in addition to 30 documents identified in the gray literature search. Barriers included limited digital literacy and physical and cognitive challenges (capability); infrastructural deficits and usability challenges (opportunity); and privacy concerns, mistrust, and high satisfaction with existing care (motivation). Facilitators included tailored training and accessible design (capability), health care provider endorsement and hybrid care models (opportunity), and recognition of digital health benefits (motivation). Health care providers emerged as both facilitators and barriers, positively influencing adoption when engaged and trained but hindering it when lacking confidence or involvement. Comparative analysis of rural and urban contexts was limited by inconsistent reporting of equity-relevant variables. However, gray literature suggested that rural users face additional infrastructural challenges but express higher satisfaction with local care, potentially reducing motivation for digital uptake. Gender differences were observed in 5% (3/59) of the peer-reviewed studies and gray literature sources, with older women showing lower adoption and differing outcome priorities. Co-design enhanced adoption, especially when involving not just older adults but also health care providers and community stakeholders.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Digital health adoptio","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e80000"},"PeriodicalIF":4.8,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12464506/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145041713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Wearable Sensors to Measure and Predict Personal Circadian Lighting Exposure in Nursing Home Residents: Model Development and Validation.","authors":"Shevvaa Beiglary, Yanxiao Feng, Nan Wang, Neda Ghaeili, Ying-Ling Jao, Yo-Jen Liao, Yuxin Li, Julian Wang","doi":"10.2196/72338","DOIUrl":"10.2196/72338","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Lighting, especially circadian lighting, significantly affects people with dementia, influencing sleep patterns, daytime alertness, and behavioral symptoms such as agitation. Since individuals experience and respond to light differently, measuring personal lighting exposure is essential for understanding its impact on health. Without individual data, the connection between lighting and health outcomes remains unclear. Wearable sensors provide a practical way to track personal light exposure, helping researchers better assess its effects on circadian rhythms and overall well-being.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to develop and validate both calibration and predictive models using wearable lighting sensors to assess individual circadian lighting exposure accurately. By leveraging machine learning techniques and empirical data, we seek to establish a reliable method for health care researchers and practitioners to investigate and optimize lighting conditions for improved circadian health in nursing homes, especially for residents with dementia.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A combination of controlled laboratory experiments and on-site data collection was conducted using professional spectrophotometer measurements as ground truth. Calibration models were developed for photopic lux and correlated color temperature, while predictive models estimated circadian metrics such as circadian stimulus. The sensors and the developed models were implemented in a real-world health care research project about bright light therapy intervention at 2 assisted-living facilities.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The calibration models for photopic lux and correlated color temperature demonstrated strong accuracy, with an adjusted R² of 0.858 and 0.982, respectively, ensuring reliable sensor measurements. Predictive models for circadian stimulus were developed using both simple regression and machine learning techniques. The random forest model outperformed linear regression, achieving an adjusted R² of 0.915 and a cross-validation R² of 0.857, demonstrating high generalization capability. Upon the implementation of these models, significant individual variations in circadian light exposure were found in the study, highlighting the significance of customized lighting evaluations. These results confirm the effectiveness of wearable sensors, combined with the developed calibration and predictive modeling, in accurately assessing personal circadian light exposure and supporting lighting-related health care research.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study introduces an effective and scalable approach to circadian light assessment using wearable sensors and predictive modeling. By replacing labor-intensive and costly spectrometer measurements, the proposed methodology enables continuous, cost-effective monitoring in health care environments. However, challenges related to sensor wearability, durability, and user co","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e72338"},"PeriodicalIF":4.8,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12501904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145239899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}