%0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e66473 %T Older Adults’ Perspectives on Participating in a Synchronous Online Exercise Program: Qualitative Study %A Coletta,Giulia %A Noguchi,Kenneth S %A Beaudoin,Kayla %A McQuarrie,Angelica %A Tang,Ada %A Ganann,Rebecca %A Phillips,Stuart M %A Griffin,Meridith %K exercise %K older adults %K qualitative study %K qualitative %K experience %K attitude %K opinion %K perception %K perspective %K interview %K internet %K kinesiology %K physiotherapy %K synchronous %K online %K home-based %K gerontology %K geriatric %K older %K aging %K physical activity %D 2025 %7 3.4.2025 %9 %J JMIR Aging %G English %X Background: Older adults face several barriers to exercise participation, including transportation, lack of access, and poor weather conditions. Such barriers may influence whether older adults meet the Canadian 24-Hour Movement Guidelines. Recently, older adults have adopted technology for health care and are increasingly using digital health technologies to improve their access to care. Therefore, technology may be a valuable tool to reduce barriers to exercise and increase exercise participation rates within this population. Objective: This study aimed to explore older adults’ perceptions and experiences of exercise, in general, and specifically related to our synchronous online exercise program for community-dwelling older adults. Methods: A total of 3 registered kinesiologists and 1 physiotherapist with experience working with older adults delivered an 8-week, thrice-weekly synchronous online group-based exercise program for older adults in 3 cohorts. The program focused on strength, balance, and aerobic activity. Following the program, a qualitative study with interpretive descriptive design was conducted to explore participants’ perceptions and experiences. Participants were invited to take part in a 30-minute, one-on-one semistructured interview via Zoom with a research team member. Interview data were thematically analyzed to identify common themes. Results: A total of 22 older adults (16 women, 6 men; mean age 70, SD 4 years) participated in interviews. Three themes were identified as follows: (1) health, exercise, and aging beliefs; (2) the pandemic interruption and impacts; and (3) synchronous online exercise programs attenuate barriers to exercise. Participants discussed their exercise beliefs and behaviors and their desire to safely and correctly participate in exercise. Older adults found that their physical activity was curtailed, routines disrupted, and access to in-person exercise programs revoked due to the pandemic. However, many suggested that our synchronous online exercise program was motivational and attenuated commonly reported environmental barriers to participation, such as transportation concerns (eg, time spent traveling, driving, and parking), accessibility and convenience by participating at a location of their choice, and removing travel-related concerns during poor weather conditions. Conclusions: Given these reported experiences, we posit that synchronous online exercise programs may help motivate and maintain adherence to exercise programs for older adults. These findings may be leveraged to improve health outcomes in community-dwelling older adults. Trial Registration: ClinicalTrials.gov NCT04627493; https://clinicaltrials.gov/study/NCT04627493 %R 10.2196/66473 %U https://aging.jmir.org/2025/1/e66473 %U https://doi.org/10.2196/66473 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e66772 %T Sexual Response Problems and Their Correlates Among Older Adults From the Sexual Well-Being (SWELL) Study in China: Multicenter Cross-Sectional Study %A Liang,Bingyu %A Xu,Chen %A Wang,Bingyi %A Li,Xinyi %A Peng,Xin %A Wang,Ying %A Li,Hui %A Lu,Yong %A Shen,Xiaopei %A Ouyang,Lin %A Wu,Guohui %A Yu,Maohe %A Liu,Jiewei %A Meng,Xiaojun %A Cai,Yong %A Zou,Huachun %K dysfunction %K sexual health %K sexual well-being %K sexually active %K sexual activity %K well-being %K correlate %K sex partner relationship %K gerontology %K geriatrics %K older adults %K elder %K elderly %K older person %K aging %K China %K cross-sectional study %D 2025 %7 1.5.2025 %9 %J JMIR Aging %G English %X Background: Sexual response problems among older adults are not an inevitable consequence of aging but rather a response to sexual health. However, there is a lack of recent and multicenter data on this issue in China. Objective: This study aims to assess the prevalence of sexual response problems and their correlates among older adults. Methods: A multicenter cross-sectional study on sexual well-being was conducted among individuals aged more than 50 years in China between June 2020 and December 2022. Data on sociodemographics, physical health, psychological health, and sexual response problems were collected through face-to-face interviews. We included sexually active older adults who reported either vaginal, oral, or anal sex in the past 12 months for this study. Sexual response problems included a lack of interest or enjoyment in sex; feeling anxious, having pain, or no excitement during sex; no desire or orgasms; and the lack of lubrication in sex. The stepwise logistic regression models were used to examine the correlates of sexual response problems. Results: A total of 1317 sexually active older adults (842 men, 475 women) were included. Older women reported a higher prevalence of sexual response problems than older men (52.0% [247/475] vs 43.1% [363/842]). Common factors associated with at least one of the sexual response problems included living in rural areas (men: adjusted odds ratio [aOR]=0.31, 95% CI 0.22‐0.43; women: aOR=0.29, 95% CI 0.19‐0.43) and abnormal BMI (aOR=men: 1.52, 95% CI1.11‐2.07; women: aOR=2.19, 95% CI 1.47‐3.28). Among older men, sleep quality (aOR=1.87, 95% CI 1.30‐2.68), emotional connection with sex partners during sexual intercourse (aOR=0.69, 95% CI 0.50‐0.96), frequently experienced fatigue (aOR=2.47, 95% CI 1.59‐3.90), anxiety (aOR=4.26, 95% CI 1.12‐21.27), and seeking professional help for sex life (aOR=1.58, 95% CI 1.14‐2.21) were associated with sexual response problems. Among older women, sexual response problems were associated with a lack of physical exercise (aOR=1.69, 95% CI 1.13‐2.54), poor sex-partner relationships (aOR=1.70, 95% CI 1.12‐2.60), and depressive symptoms (aOR=3.18, 95% CI 1.18‐10.24). Conclusions: Sexual response problems are common among older adults. These problems were associated with adverse physical health, mental health, and poor sex-partner relationships. These findings highlight the importance for health care providers to take into account the physical and psychological health of older adults, as well as the quality of their relationships with sexual partners when diagnosing and addressing sexual response problems. %R 10.2196/66772 %U https://aging.jmir.org/2025/1/e66772 %U https://doi.org/10.2196/66772 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e65636 %T Frailty, Fitness, and Quality of Life Outcomes of a Healthy and Productive Aging Program (GrandMove) for Older Adults With Frailty or Prefrailty: Cluster Randomized Controlled Trial %A Tang,Jennifer Yee Man %A Luo,Hao %A Tse,Michael %A Kwan,Joseph %A Leung,Angela Yee Man %A Tsien Wong,Teresa Bik-kwan %A Lum,Terry Yat Sang %A Wong,Gloria Hoi Yan %K physical activity %K physical exercise %K exercising %K gerontology %K geriatric %K older adult %K older person %K older people %K active aging %K postretirement work %K cluster randomized controlled trial %K RCT %D 2025 %7 14.5.2025 %9 %J JMIR Aging %G English %X Background: Exercise interventions can reverse frailty. However, their scalability and sustainability are limited by manpower, which is reducing due to population aging. GrandMove is a program that combines healthy and productive aging strategies to (1) train and employ robust older adults as exercise coaches and (2) improve fitness and motivate the adoption of an exercise habit in older adults with frailty and prefrailty. Objective: The aim of this study is to examine the effectiveness of GrandMove in improving frailty, fitness, and quality of life in older adults with frailty and prefrailty. Methods: This cluster randomized controlled trial recruited older adults with frailty and prefrailty (N=390) living in the community. The 18-month exercise program consisted of three 6-month phases of lifestyle education (E), resistance exercise (R), and aerobic exercise (A). Each group of participants was randomized into 3 intervention sequence arms: the E-R-A group, the A-R-E group, and the R-A-E group. Results: At 6, 12, and 18 months, 346, 305, and 264 participants completed the frailty assessment, respectively. At 6 months, 100 of 346 participants (28.9%) were robust. A-R-E and R-A-E were no better than E-R-A as the active control in addressing frailty over the first 6 months (A-R-E: interaction coefficient 0.07, 95% CI −0.35 to 0.49, P=.68; R-A-E: interaction coefficient −0.02, 95% CI −0.42 to 0.38, P=.90). Compared to lifestyle education, resistance training and aerobic training over the first 6 months were associated with greater improvement in fitness measures of grip strength for the left hand (A-R-E: interaction coefficient 2.99, 95% CI 0.76 to 5.23, P=.009; R-A-E: interaction coefficient 2.21, 95% CI 0.63 to 4.36, P=.04) and right hand (A-R-E: interaction coefficient 3.75, 95% CI 1.54 to 5.97, P=.001; R-A-E: interaction coefficient 2.29, 95% CI 0.16 to 4.42, P=.04) and arm curl test (A-R-E: interaction coefficient 1.42, 95% CI 0.39 to 2.46, P=.007; R-A-E: interaction coefficient 1.11, 95% CI 0.12 to 2.11, P=.03). The sequence of exercise interventions (R-A-E vs A-R-E) did not make a difference in primary outcomes at 12 months, but the R-A-E group showed better quality of life (interaction coefficient 4.50, 95% CI 0.12 to 8.88, P=.008). Improved frailty outcomes were maintained by the end of the study, but the change in overall physical activity level was limited. Conclusions: Combining healthy and productive aging strategies is a scalable and sustainable way to improve frailty, fitness, and quality of life in older adults with frailty and prefrailty. Different combinations of lifestyle education and physical interventions improved frailty. Trial Registration: HKU Clinical Trials Registry HKUCTR-1964; https://www.hkuctr.com/Study/Show/75c5d2e6825c4b5498f0c65c82714c4b %R 10.2196/65636 %U https://aging.jmir.org/2025/1/e65636 %U https://doi.org/10.2196/65636 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64539 %T Effect of Physical Exercise on Telomere Length: Umbrella Review and Meta-Analysis %A Sánchez-González,Juan Luis %A Sánchez-Rodríguez,Juan Luis %A González-Sarmiento,Rogelio %A Navarro-López,Víctor %A Juárez-Vela,Raúl %A Pérez,Jesús %A Martín-Vallejo,Javier %K aging %K chromosome %K exercise %K meta-analysis %K telomere %K telomerase %K genes %K genome %K DNA %D 2025 %7 10.1.2025 %9 %J JMIR Aging %G English %X Background: Telomere length (TL) is a marker of cellular health and aging. Physical exercise has been associated with longer telomeres and, therefore, healthier aging. However, results supporting such effects vary across studies. Our aim was to synthesize existing evidence on the effect of different modalities and durations of physical exercise on TL. Objective: The aim of this study was to explore the needs and expectations of individuals with physical disabilities and their interventionists for the use of a virtual reality physical activity platform in a community organization. Methods: We performed an umbrella review and meta-analysis. Data sources included PubMed, Embase, Web of Science, Cochrane Library, and Scopus. We selected systematic reviews and meta-analyses of randomized and nonrandomized controlled clinical trials evaluating the effect of physical exercise on TL. Results: Our literature search retrieved 12 eligible systematic reviews, 5 of which included meta-analyses. We identified 22 distinct primary studies to estimate the overall effect size of physical exercise on TL. The overall effect size was 0.28 (95% CI 0.118-0.439), with a heterogeneity test value Q of 43.08 (P=.003) and I² coefficient of 51%. The number of weeks of intervention explained part of this heterogeneity (Q_B=8.25; P=.004), with higher effect sizes found in studies with an intervention of less than 30 weeks. Exercise modality explained additional heterogeneity within this subgroup (Q_B=10.28, P=.02). The effect sizes were small for aerobic exercise and endurance training, and moderate for high-intensity interval training. Conclusions: Our umbrella review and meta-analysis detected a small-moderate positive effect of physical exercise on TL, which seems to be influenced by the duration and type of physical exercise. High quality studies looking into the impact of standardized, evidence-based physical exercise programs on TL are still warranted. Trial Registration: PROSPERO CRD42024500736; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=500736 %R 10.2196/64539 %U https://aging.jmir.org/2025/1/e64539 %U https://doi.org/10.2196/64539 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e63348 %T Exploring the Feasibility of a 5-Week mHealth Intervention to Enhance Physical Activity and an Active, Healthy Lifestyle in Community-Dwelling Older Adults: Mixed Methods Study %A Daniels,Kim %A Vonck,Sharona %A Robijns,Jolien %A Quadflieg,Kirsten %A Bergs,Jochen %A Spooren,Annemie %A Hansen,Dominique %A Bonnechère,Bruno %+ , Centre of Expertise in Care Innovation, Department of PXL – Healthcare, PXL University of Applied Sciences and Arts, Guffenslaan 39, Hasselt, 3500, Belgium, 32 485763451, kim.daniels@pxl.be %K mobile health %K mHealth %K feasibility %K physical activity %K older adults %K health promotion %K usability %K mobile phone %D 2025 %7 27.1.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Advancements in mobile technology have paved the way for innovative interventions aimed at promoting physical activity (PA). Objective: The main objective of this feasibility study was to assess the feasibility, usability, and acceptability of the More In Action (MIA) app, designed to promote PA among older adults. MIA offers 7 features: personalized tips, PA literacy, guided peer workouts, a community calendar, a personal activity diary, a progression monitor, and a chatbot. Methods: Our study used a mixed methods approach to evaluate the MIA app’s acceptability, feasibility, and usability. First, a think-aloud method was used to provide immediate feedback during initial app use. Participants then integrated the app into their daily activities for 5 weeks. Behavioral patterns such as user session duration, feature use frequency, and navigation paths were analyzed, focusing on engagement metrics and user interactions. User satisfaction was assessed using the System Usability Scale, Net Promoter Score, and Customer Satisfaction Score. Qualitative data from focus groups conducted after the 5-week intervention helped gather insights into user experiences. Participants were recruited using a combination of web-based and offline strategies, including social media outreach, newspaper advertisements, and presentations at older adult organizations and local community services. Our target group consisted of native Dutch-speaking older adults aged >65 years who were not affected by severe illnesses. Initial assessments and focus groups were conducted in person, whereas the intervention itself was web based. Results: The study involved 30 participants with an average age of 70.3 (SD 4.8) years, of whom 57% (17/30) were female. The app received positive ratings, with a System Usability Scale score of 77.4 and a Customer Satisfaction Score of 86.6%. Analysis showed general satisfaction with the app’s workout videos, which were used in 585 sessions with a median duration of 14 (IQR 0-34) minutes per day. The Net Promoter Score was 33.34, indicating a good level of customer loyalty. Qualitative feedback highlighted the need for improvements in navigation, content relevance, and social engagement features, with suggestions for better calendar visibility, workout customization, and enhanced social features. Overall, the app demonstrated high usability and satisfaction, with near-daily engagement from participants. Conclusions: The MIA app shows significant potential for promoting PA among older adults, evidenced by its high usability and satisfaction scores. Participants engaged with the app nearly daily, particularly appreciating the workout videos and educational content. Future enhancements should focus on better calendar visibility, workout customization, and integrating social networking features to foster community and support. In addition, incorporating wearable device integration and predictive analytics could provide real-time health data, optimizing activity recommendations and health monitoring. These enhancements will ensure that the app remains user-friendly, relevant, and sustainable, promoting sustained PA and healthy behaviors among older adults. Trial Registration: ClinicalTrials.gov NCT05650515; https://clinicaltrials.gov/study/NCT05650515 %M 39869906 %R 10.2196/63348 %U https://aging.jmir.org/2025/1/e63348 %U https://doi.org/10.2196/63348 %U http://www.ncbi.nlm.nih.gov/pubmed/39869906 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e63856 %T Adapting the Technology Acceptance Model to Examine the Use of Information Communication Technologies and Loneliness Among Low-Income, Older Asian Americans: Cross-Sectional Survey Analysis %A DeLange Martinez,Pauline %A Tancredi,Daniel %A Pavel,Misha %A Garcia,Lorena %A Young,Heather M %+ Betty Irene Moore School of Nursing, University of California, Davis, 2750 48th St, Sacramento, CA, 95817, United States, 1 9164262862, pdmartinez@ucdavis.edu %K social isolation %K loneliness %K aged %K older adults %K Asian American %K immigrant %K vulnerable populations %K internet %K information and communication technologies %K ICTs %K digital divide %K technology acceptance model %K mobile phone %D 2025 %7 8.1.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Loneliness is a significant issue among older Asian Americans, exacerbated by the COVID-19 pandemic. Older age, lower income, limited education, and immigrant status heighten loneliness risk. Information communication technologies (ICTs) have been associated with decreased loneliness among older adults. However, older Asian Americans are less likely to use ICTs, particularly if they are immigrants, have limited English proficiency, or are low income. The Technology Acceptance Model posits that perceived usefulness (PU), and perceived ease of use (PEOU) are key factors in predicting technology use. Objective: This study aimed to examine associations between PU, PEOU, ICT use, and loneliness among low-income, older Asian Americans. Methods: Cross-sectional survey data were gathered from predominately older Asian Americans in affordable senior housing (N=401). Using exploratory factor analysis and Horn parallel analysis, we examined 12 survey items to identify factors accounting for variance in ICT use. We deployed structural equation modeling to explore relationships among the latent factors and loneliness, adjusting for demographic and cognitive factors. Results: Exploratory factor analysis and Horn parallel analysis revealed 3 factors that accounted for 56.48% (6.78/12) total variance. PEOU combined items from validated subscales of tech anxiety and comfort, accounting for a 28.44% (3.41/12) variance. ICT use combined years of technological experience, computer, tablet, and smartphone use frequency, accounting for 15.59% (1.87/12) variance. PU combined 2 items assessing the usefulness of technology for social connection and learning and accounted for a 12.44% (1.49/12) variance. The 3-factor structural equation modeling revealed reasonable fit indexes (χ2133=345.132; P<.001, chi-square minimum (CMIN)/df = 2595, comparative fit index (CFI)=0.93, Tucker-Lewis Index (TLI)=0.88). PEOU was positively associated with PU (β=.15; P=.01); PEOU and PU were positive predictors of ICT use (PEOU β=.26, P<.001; PU β=.18, P=.01); and ICT use was negatively associated with loneliness (β=–.28, P<.001). Demographic and health covariates also significantly influenced PU, PEOU, ICT use, and loneliness. English proficiency and education positively predicted PEOU (r=0.25, P<.001; r=0.26, P<.001) and ICT use (β=1.66, P=.03; β=.21, P<.001), while subjective cognitive decline and Asian ethnicity were positively associated with loneliness (β=.31, P<.001; β=.25, P<.001). Conclusions: This study suggests that targeted interventions enhancing PU or PEOU could increase ICT acceptance and reduce loneliness among low-income Asian Americans. Findings also underscore the importance of considering limited English proficiency and subjective cognitive decline when designing interventions and in future research. %M 39778204 %R 10.2196/63856 %U https://aging.jmir.org/2025/1/e63856 %U https://doi.org/10.2196/63856 %U http://www.ncbi.nlm.nih.gov/pubmed/39778204 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64661 %T Identifying Food Preferences and Malnutrition in Older Adults in Care Homes: Co-Design Study of a Digital Nutrition Assessment Tool %A Connelly,Jenni %A Swingler,Kevin %A Rodriguez-Sanchez,Nidia %A Whittaker,Anna C %+ Faculty of Health Sciences and Sport, University of Stirling, 3a74a Cottrell Building, Stirling, FK9 4LA, United Kingdom, 44 1786 466399, jenni.connelly1@stir.ac.uk %K ageing %K digital technology %K dietary measurement %K care homes %K co-design %K dietary intake %K food diary %D 2025 %7 3.3.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Malnutrition is a challenge among older adults and can result in serious health consequences. However, the dietary intake monitoring needed to identify malnutrition for early intervention is affected by issues such as difficulty remembering or needing a dietitian to interpret the results. Objective: This study aims to co-design a tool using automated food classification to monitor dietary intake and food preferences, as well as food-related symptoms and mood and hunger ratings, for use in care homes. Methods: Participants were 2 separate advisory groups and 2 separate sets of prototype testers. The testers for the first prototype were 10 community-dwelling older adults based in the Stirlingshire area in Scotland who noted their feedback on the tool over 2 weeks in a food diary. The second set of testers consisted of 14 individuals (staff: n=8, 57%; and residents: n=6, 43%) based in 4 care homes in Scotland who provided feedback via interview after testing the tool for a minimum of 3 days. In addition, 130 care home staff across the United Kingdom completed the web-based survey on the tool’s needs and potential routes to pay for it; 2 care home managers took part in follow-up interviews. Data were collected through food diaries, a web-based survey, audio recordings and transcriptions of focus groups and interviews, and research notes. Systematic text condensation was used to describe themes across the different types of data. Results: Key features identified included ratings of hunger, mood, and gastrointestinal symptoms that could be associated with eating specific foods, as well as a traffic light system to indicate risk. Issues included staff time, Wi-Fi connectivity, and the accurate recognition of pureed food and fortified meals. Different models for potential use and commercialization were identified, including peer support among residents to assist those considered less able, staff-only use of the tool, care home–personalized database menus for easy meal photo selection, and targeted monitoring of residents considered to be at the highest risk using the traffic light system. Conclusions: The tool was deemed useful for monitoring dietary habits and associated symptoms, but necessary design improvements were identified. These should be incorporated before formal evaluation of the tool as an intervention in this setting. Co-design was vital to help make the tool fit for the intended setting and users. %M 40053797 %R 10.2196/64661 %U https://aging.jmir.org/2025/1/e64661 %U https://doi.org/10.2196/64661 %U http://www.ncbi.nlm.nih.gov/pubmed/40053797 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64074 %T Values of Stakeholders Involved in Applying Surveillance Technology for People With Dementia in Nursing Homes: Scoping Review %A van Gaans-Riteco,Daniëlle %A Stoop,Annerieke %A Wouters,Eveline %+ Academic Collaborative Center Care for Older Adults, Tranzo, Scientific Center for Care and Wellbeing, Tilburg School of Social and Behavioral Sciences, Tilburg University, Professor Cobbenhagenlaan 125, Tilburg, 5037DB, The Netherlands, 31 134662969, d.p.c.vangaans-riteco@tilburguniversity.edu %K surveillance technology %K nursing home %K stakeholders %K values %K dementia %K safety %D 2025 %7 20.3.2025 %9 Review %J JMIR Aging %G English %X Background: Due to the progressive nature of dementia, concerns about the safety of nursing home residents are frequently raised. Surveillance technology, enabling visual and auditory monitoring, is often seen as a solution for ensuring safe and efficient care. However, tailoring surveillance technology to individual needs is challenging due to the complex and dynamic care environment involving multiple formal and informal stakeholders, each with unique perspectives. Objective: This study aims to explore the scientific literature on the perspectives and values of stakeholders involved in applying surveillance technology for people with dementia in nursing homes. Methods: We conducted a scoping review and systematically searched 5 scientific databases. We identified 31 articles published between 2005 and 2024. Stakeholder characteristics were extracted and synthesized according to the theory of basic human values by Schwartz. Results: In total, 12 stakeholder groups were identified, with nursing staff, residents, and informal caregivers being the most frequently mentioned. Among stakeholder groups close to residents, values related to benevolence, security, conformity, and tradition were most commonly addressed. Furthermore, values such as self-direction, power, and achievement seemed important to most stakeholder groups. Conclusions: Several stakeholder groups emphasized the importance of being and feeling involved in the application of surveillance technologies. In addition, they acknowledged the necessity of paying attention to stakeholders’ perspectives and values. Across these stakeholder groups, values related to benevolence, security, and self-direction were represented, although various stakeholders assigned different meanings to these values. Awareness of stakeholders’ perspectives demands a willingness to acknowledge each other’s values and bridge differences. %M 39899267 %R 10.2196/64074 %U https://aging.jmir.org/2025/1/e64074 %U https://doi.org/10.2196/64074 %U http://www.ncbi.nlm.nih.gov/pubmed/39899267 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e71950 %T Barriers to and Facilitators of Implementing Overnight Nursing Teleconsultation in Small, Rural Long-Term Care Facilities: Qualitative Interview Study %A Nabelsi,Veronique %A Plouffe,Véronique %A Leclerc,Marie Chantal %+ Département des sciences administratives, Université du Québec, 101, rue St-Jean-Bosco, Québec, J8X 3X7, Canada, 1 8195953900 ext 1915, veronique.nabelsi@uqo.ca %K teleconsultation %K long-term facilities %K nursing %K barriers and facilitators %K rural %K telehealth %K qualitative %K pilot study %K Quebec %D 2025 %7 7.5.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Teleconsultation has expanded rapidly in recent years, especially during the COVID-19 pandemic, and has become standard practice among physicians. The benefits of teleconsultation, namely, improving access to care, ensuring continuity and quality of care, increasing patient satisfaction, and reducing costs and wait times, are well documented. However, its use in nursing practice, especially in long-term care settings, remains underresearched despite its significant transformative potential, particularly in resource-limited and rural settings, where it could address major challenges such as nursing shortages and access to care. Objective: This study aimed to identify barriers to and facilitators of implementing overnight nursing teleconsultation in rural residential and long-term care centers in Quebec, Canada (centres d’hébergement et de soins de longue durée [CHSLDs]), with ≤50 beds. Methods: A 6-month pilot project was rolled out sequentially in 3 rural CHSLDs in 2 administrative regions of Quebec between July 2022 and March 2023. A total of 38 semistructured interviews were conducted with managers (n=27, 71%), nursing staff members (n=9, 24%), and resident committee presidents (n=2, 5%) between February 2023 and July 2023. Results: The study identified several barriers to the implementation of teleconsultation. The main barriers reported included union opposition (managers: 23/27, 85%), network instability (resident committee presidents: 2/2, 100%), limited technology skills (nursing staff members: 7/9, 78%), a perceived increase in workload (nursing staff members: 8/9, 89%; resident committee presidents: 2/2, 100%), and a low volume of teleconsultations (nursing staff members: 8/9, 89%). Despite the barriers, participants also identified key facilitators. These included the care setting (nursing staff members: 9/9, 100%; managers: 21/27, 78%), buy-in from senior management and managers (managers: 27/27, 100%; resident committee presidents: 2/2, 100%), collaboration between the departments (nursing staff members: 9/9, 100%), nursing staff motivation (nursing staff members: 9/9, 100%), and improvements in professional practices (nursing staff members: 8/9, 89%). Finally, the relative benefits of teleconsultation, such as enhanced mutual vision, faster assessment of clinical situations, improved resident care management quality, and greater flexibility and safety, were unanimously recognized (38/38, 100%) as contributing to its acceptability and potential for success. Conclusions: This study provides an in-depth understanding of the barriers to and facilitators of implementing overnight nursing teleconsultation in small rural CHSLDs. This constitutes a sound basis for developing tailored strategies aimed at overcoming identified barriers and optimizing facilitators. The results also provide practical guidelines for decision makers, highlighting the need to adapt implementation approaches to the unique context of each facility. Furthermore, this study highlights the importance of further research to broaden our knowledge on the dissemination and scale-up of health care innovations. This includes the development of learning health systems capable of responding in an agile and effective way to the needs of rural and vulnerable populations both in Quebec and elsewhere. %M 40334266 %R 10.2196/71950 %U https://aging.jmir.org/2025/1/e71950 %U https://doi.org/10.2196/71950 %U http://www.ncbi.nlm.nih.gov/pubmed/40334266 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e66723 %T Development of a Longitudinal Model for Disability Prediction in Older Adults in China: Analysis of CHARLS Data (2015-2020) %A Chu,Jingjing %A Li,Ying %A Wang,Xinyi %A Xu,Qun %A Xu,Zherong %K disability %K prediction model %K older adults %K China Health and Retirement Longitudinal Study %K CHARLS %K medical resources allocation %D 2025 %7 17.4.2025 %9 %J JMIR Aging %G English %X Background: Disability profoundly affects older adults’ quality of life and imposes considerable burdens on health care systems in China’s aging society. Timely predictive models are essential for early intervention. Objective: We aimed to build effective predictive models of disability for early intervention and management in older adults in China, integrating physical, cognitive, physiological, and psychological factors. Methods: Data from the China Health and Retirement Longitudinal Study (CHARLS), spanning from 2015 to 2020 and involving 2450 older individuals initially in good health, were analyzed. The dataset was randomly divided into a training set with 70% data and a testing set with 30% data. LASSO regression with 10-fold cross-validation identified key predictors, which were then used to develop an Extreme Gradient Boosting (XGBoost) model. Model performance was evaluated using receiever operating characteristic curves, calibration curves, and clinical decision and impact curves. Variable contributions were interpreted using SHapley Additive exPlanations (SHAP) values. Results: LASSO regression was used to evaluate 36 potential predictors, resulting in a model incorporating 9 key variables: age, hand grip strength, standing balance, the 5-repetition chair stand test (CS-5), pain, depression, cognition, respiratory function, and comorbidities. The XGBoost model demonstrated an area under the curve of 0.846 (95% CI 0.825‐0.866) for the training set and 0.698 (95% CI 0.654‐0.743) for the testing set. Calibration curves demonstrated reliable predictive accuracy, with mean absolute errors of 0.001 and 0.011 for the training and testing sets, respectively. Clinical decision and impact curves demonstrated significant utility across risk thresholds. SHAP analysis identified pain, respiratory function, and age as top predictors, highlighting their substantial roles in disability risk. Hand grip and the CS-5 also significantly influenced the model. A web-based application was developed for personalized risk assessment and decision-making. Conclusion: A reliable predictive model for 5-year disability risk in Chinese older adults was developed and validated. This model enables the identification of high-risk individuals, supports early interventions, and optimizes resource allocation. Future efforts will focus on updating the model with new CHARLS data and validating it with external datasets. %R 10.2196/66723 %U https://aging.jmir.org/2025/1/e66723 %U https://doi.org/10.2196/66723 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e65221 %T Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study %A Paek,Hunki %A Fortinsky,Richard H %A Lee,Kyeryoung %A Huang,Liang-Chin %A Maghaydah,Yazeed S %A Kuchel,George A %A Wang,Xiaoyan %K dementia %K memory loss %K memory %K cognitive %K Alzheimer disease %K natural language processing %K NLP %K deep learning %K machine learning %K real-world insights %K electronic health records %K EHR %K cohort %K diagnosis %K diagnostic %K trajectory %K pattern %K prognosis %K geriatric %K older adults %K aging %D 2025 %7 25.2.2025 %9 %J JMIR Aging %G English %X Background: Understanding the dementia disease trajectory and clinical practice patterns in outpatient settings is vital for effective management. Knowledge about the path from initial memory loss complaints to dementia diagnosis remains limited. Objective: This study aims to (1) determine the time intervals between initial memory loss complaints and dementia diagnosis in outpatient care, (2) assess the proportion of patients receiving cognition-enhancing medication prior to dementia diagnosis, and (3) identify patient and provider characteristics that influence the time between memory complaints and diagnosis and the prescription of cognition-enhancing medication. Methods: This retrospective cohort study used a large outpatient electronic health record (EHR) database from the University of Connecticut Health Center, covering 2010‐2018, with a cohort of 581 outpatients. We used a customized deep learning–based natural language processing (NLP) pipeline to extract clinical information from EHR data, focusing on cognition-related symptoms, primary caregiver relation, and medication usage. We applied descriptive statistics, linear, and logistic regression for analysis. Results: The NLP pipeline showed precision, recall, and F1-scores of 0.97, 0.93, and 0.95, respectively. The median time from the first memory loss complaint to dementia diagnosis was 342 (IQR 200-675) days. Factors such as the location of initial complaints and diagnosis and primary caregiver relationships significantly affected this interval. Around 25.1% (146/581) of patients were prescribed cognition-enhancing medication before diagnosis, with the number of complaints influencing medication usage. Conclusions: Our NLP-guided analysis provided insights into the clinical pathways from memory complaints to dementia diagnosis and medication practices, which can enhance patient care and decision-making in outpatient settings. %R 10.2196/65221 %U https://aging.jmir.org/2025/1/e65221 %U https://doi.org/10.2196/65221 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e68267 %T Predictive Validity of Hospital-Associated Complications of Older People Identified Using Diagnosis Procedure Combination Data From an Acute Care Hospital in Japan: Observational Study %A Mitsutake,Seigo %A Ishizaki,Tatsuro %A Yano,Shohei %A Hirata,Takumi %A Ito,Kae %A Furuta,Ko %A Shimazaki,Yoshitomo %A Ito,Hideki %A Mudge,Alison %A Toba,Kenji %+ Human Care Research Team, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173 0015, Japan, 81 3 3964 3241 ext 4229, mitsu@tmig.or.jp %K delirium %K functional decline %K Japan %K older adult %K routinely collected health data %K elder %K hospital complication %K HAC-OP %K incontinence %K pressure injury %K inpatient care %K diagnosis procedure combination %K predictive validity %K hospital length of stay %K administrative data %K acute care %K index hospitalization %K diagnostic code %K linear regression %K logistic regression %K long-term care %K retrospective cohort %K observational study %K patient care %K gerontology %K hospital care %K patient complication %D 2025 %7 6.2.2025 %9 Original Paper %J JMIR Aging %G English %X Background: A composite outcome of hospital-associated complications of older people (HAC-OP; comprising functional decline, delirium, incontinence, falls, and pressure injuries) has been proposed as an outcome measure reflecting quality of acute hospital care. Estimating HAC-OP from routinely collected administrative data could facilitate the rapid and standardized evaluation of interventions in the clinical setting, thereby supporting the development, improvement, and wider implementation of effective interventions. Objective: This study aimed to create a Diagnosis Procedure Combination (DPC) data version of the HAC-OP measure (HAC-OP-DPC) and demonstrate its predictive validity by assessing its associations with hospital length of stay (LOS) and discharge destination. Methods: This retrospective cohort study acquired DPC data (routinely collected administrative data) from a general acute care hospital in Tokyo, Japan. We included data from index hospitalizations for patients aged ≥65 years hospitalized for ≥3 days and discharged between July 2016 and March 2021. HAC-OP-DPC were identified using diagnostic codes for functional decline, incontinence, delirium, pressure injury, and falls occurring during the index hospitalization. Generalized linear regression models were used to examine the associations between HAC-OP-DPC and LOS, and logistic regression models were used to examine the associations between HAC-OP-DPC and discharge to other hospitals and long-term care facilities (LTCFs). Results: Among 15,278 patients, 3610 (23.6%) patients had coding evidence of one or more HAC-OP-DPC (1: 18.8% and ≥2: 4.8%). Using “no HAC-OP-DPC” as the reference category, the analysis showed a significant and graded association with longer LOS (adjusted risk ratio for patients with one HAC-OP-DPC 1.29, 95% CI 1.25-1.33; adjusted risk ratio for ≥2 HAC-OP-DPC 1.97, 95% CI 1.87-2.08), discharge to another hospital (adjusted odds ratio [AOR] for one HAC-OP-DPC 2.36, 95% CI 2.10-2.65; AOR for ≥2 HAC-OP-DPC 6.96, 95% CI 5.81-8.35), and discharge to LTCFs (AOR for one HAC-OP-DPC 1.35, 95% CI 1.09-1.67; AOR for ≥2 HAC-OP-DPC 1.68, 95% CI 1.18-2.39). Each individual HAC-OP was also significantly associated with longer LOS and discharge to another hospital, but only delirium was associated with discharge to LTCF. Conclusions: This study demonstrated the predictive validity of the HAC-OP-DPC measure for longer LOS and discharge to other hospitals and LTCFs. To attain a more robust understanding of these relationships, additional studies are needed to verify our findings in other hospitals and regions. The clinical implementation of HAC-OP-DPC, which is identified using routinely collected administrative data, could support the evaluation of integrated interventions aimed at optimizing inpatient care for older adults. %M 39913911 %R 10.2196/68267 %U https://aging.jmir.org/2025/1/e68267 %U https://doi.org/10.2196/68267 %U http://www.ncbi.nlm.nih.gov/pubmed/39913911 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e67294 %T Detecting Sleep/Wake Rhythm Disruption Related to Cognition in Older Adults With and Without Mild Cognitive Impairment Using the myRhythmWatch Platform: Feasibility and Correlation Study %A Jones,Caleb D %A Wasilko,Rachel %A Zhang,Gehui %A Stone,Katie L %A Gujral,Swathi %A Rodakowski,Juleen %A Smagula,Stephen F %K sleep %K sleep/wake %K circadian %K activity pattern %K dementia %K cognition %K mobile sensing %K actigraphy %K accelerometer %D 2025 %7 7.4.2025 %9 %J JMIR Aging %G English %X Background: Consumer wearable devices could, in theory, provide sufficient accelerometer data for measuring the 24-hour sleep/wake risk factors for dementia that have been identified in prior research. To our knowledge, no prior study in older adults has demonstrated the feasibility and acceptability of accessing sufficient consumer wearable accelerometer data to compute 24-hour sleep/wake rhythm measures. Objective: We aimed to establish the feasibility of characterizing 24-hour sleep/wake rhythm measures using accelerometer data gathered from the Apple Watch in older adults with and without mild cognitive impairment (MCI), and to examine correlations of these sleep/wake rhythm measures with neuropsychological test performance. Methods: Of the 40 adults enrolled (mean [SD] age 67.2 [8.4] years; 72.5% female), 19 had MCI and 21 had no cognitive disorder (NCD). Participants were provided devices, oriented to the study software (myRhythmWatch or myRW), and asked to use the system for a week. The primary feasibility outcome was whether participants collected enough data to assess 24-hour sleep/wake rhythm measures (ie, ≥3 valid continuous days). We extracted standard nonparametric and extended-cosine based sleep/wake rhythm metrics. Neuropsychological tests gauged immediate and delayed memory (Hopkins Verbal Learning Test) as well as processing speed and set-shifting (Oral Trails Parts A and B). Results: All participants meet the primary feasibility outcome of providing sufficient data (≥3 valid days) for sleep/wake rhythm measures. The mean (SD) recording length was somewhat shorter in the MCI group at 6.6 (1.2) days compared with the NCD group at 7.2 (0.6) days. Later activity onset times were associated with worse delayed memory performance (β=−.28). More fragmented rhythms were associated with worse processing speed (β=.40). Conclusions: Using the Apple Watch-based myRW system to gather raw accelerometer data is feasible in older adults with and without MCI. Sleep/wake rhythms variables generated from this system correlated with cognitive function, suggesting future studies can use this approach to evaluate novel, scalable, risk factor characterization and targeted therapy approaches. %R 10.2196/67294 %U https://aging.jmir.org/2025/1/e67294 %U https://doi.org/10.2196/67294 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e65292 %T Performance of a Digital Cognitive Assessment in Predicting Dementia Stages Delineated by the Dementia Severity Rating Scale: Retrospective Study %A Huynh,Duong %A Sun,Kevin %A Patterson,Mary %A Hosseini Ghomi,Reza %A Huang,Bin %K stage %K severity %K progression %K correlation %K association %K cognitive impairment %K functional activities %K cognitive assessment %K BrainCheck %K dementia %K Alzheimer disease %K gerontology %K geriatric %K old %K elderly %K aging %K retrospective analysis %K digital assessment %K patient assessment %K digital cognitive assessment %K digital health %K neurodegeneration %K memory loss %K memory function %K risk factors %D 2025 %7 26.2.2025 %9 %J JMIR Aging %G English %X Background: Dementia is characterized by impairments in an individual’s cognitive and functional abilities. Digital cognitive assessments have been shown to be effective in detecting mild cognitive impairment and dementia, but whether they can stage the disease remains to be studied. Objective: In this study, we examined (1) the correlation between scores obtained from BrainCheck standard battery of cognitive assessments (BC-Assess), a digital cognitive assessment, and scores obtained from the Dementia Severity Rating Scale (DSRS), and (2) the accuracy of using the BC-Assess score to predict dementia stage delineated by the DSRS score. We also explored whether BC-Assess can be combined with information from the Katz Index of Independence in activities of daily living (ADL) to obtain enhanced accuracy. Methods: Retrospective analysis was performed on a BrainCheck dataset containing 1751 patients with dementia with different cognitive and functional assessments completed for cognitive care planning, including the DSRS, the ADL, and the BC-Assess. The patients were staged according to their DSRS total score (DSRS-TS): 982 mild (DSRS-TS 10‐18), 656 moderate (DSRS-TS 19-26), and 113 severe (DSRS-TS 37-54) patients. Pearson correlation was used to assess the associations between BC-Assess overall score (BC-OS), ADL total score (ADL-TS), and DSRS-TS. Logistic regression was used to evaluate the possibility of using patients’ BC-OS and ADL-TS to predict their stage. Results: We found moderate Pearson correlations between DSRS-TS and BC-OS (r=−0.53), between DSRS-TS and ADL-TS (r=−0.55), and a weak correlation between BC-OS and ADL-TS (r=0.37). Both BC-OS and ADL-TS significantly decreased with increasing severity. BC-OS demonstrated to be a good predictor of dementia stages, with an area under the receiver operating characteristic curve (ROC-AUC) of classification using logistic regression ranging from .733 to .917. When BC-Assess was combined with ADL, higher prediction accuracies were achieved, with an ROC-AUC ranging from 0.786 to 0.961. Conclusions: Our results suggest that BC-Assess could serve as an effective alternative tool to DSRS for grading dementia severity, particularly in cases where DSRS, or other global assessments, may be challenging to obtain due to logistical and time constraints. %R 10.2196/65292 %U https://aging.jmir.org/2025/1/e65292 %U https://doi.org/10.2196/65292 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64372 %T Identifying Optimal Wearable Devices for Monitoring Mobility in Hospitalized Older Adults: Feasibility, Acceptability, and Validity Study %A Nascimento,Paulo %A Kirkwood,Renata %A Griffith,Lauren E %A Duong,Mylinh %A Cooper,Cody %A Hao,Yujiao %A Zheng,Rong %A Raza,Samir %A Beauchamp,Marla %K older adults %K gerontology %K geriatric %K aging %K feasibility %K acceptability %K mobility %K wearable %K inpatient %K hospital-acquired disability %K physical performance %K mHealth %K mobile health %K hospital %K physical activity %K exercise %K Fitbit %K posture %K walk %D 2025 %7 12.5.2025 %9 %J JMIR Aging %G English %X Background: Hospitalized, frail older adults have an increased risk of developing hospital-acquired disability associated with hospital practices of restricted physical activity and immobilization. The use of activity tracking wearable devices may allow identification and prevention of mobility decline, reducing hospital-acquired disability. Objective: This study aimed to identify the optimal wearable device and wear location for monitoring mobility in older hospitalized patients. Specific objectives included (1) comparison of the feasibility and acceptability of ActiGraph wGT3X-BT (ActiGraph LLC), MOX1 (Maastricht Instruments), MetaMotionC (mBientLab), and Fitbit Versa (Google) for continuous mobility monitoring and (2) determination of the concurrent validity of the selected device for detecting body posture and step count. Methods: Participants were recruited for this observational study in the acute medical care unit of an academic hospital in Hamilton, Ontario, Canada. Eligible patients were aged 60 years and older, able to undertake the mobility protocol, and had an anticipated length of stay greater than 4 days. The study was divided into 2 experiments. Experiment 1 evaluated the feasibility of 4 wearable devices and validated the derived data for body posture and step count. Experiment 2 involved a mobility assessment session and a 24-hour monitoring and feasibility period with the selected device from experiment 1. Results: The ActiGraph wGT3X-BT emerged as the most feasible device, demonstrating superior usability, data acquisition, and management. The thigh-worn ActiGraph accurately detected sedentary behavior, while the ankle-worn device provided detailed information on step counts and body postures. Bland-Altman plots and intraclass correlation coefficients indicated that the ankle-worn ActiGraph showed excellent reliability for step counting, with minimal bias and narrow limits of agreement. Patients expressed a high willingness to wear a continuous mobility tracking device at the hospital and at home. Conclusions: Thigh- and ankle-worn ActiGraph are optimal for assessing and monitoring mobility in older hospitalized patients. Challenges such as discomfort and device removal observed during the 24-hour monitoring period highlight areas for future studies. Overall, our findings support the integration of wearable technology in hospital settings to enhance mobility monitoring and early intervention strategies. Further research is warranted to evaluate the long-term use of wearable data for predicting health outcomes post hospitalization and informing clinical decision-making to promote early mobility. %R 10.2196/64372 %U https://aging.jmir.org/2025/1/e64372 %U https://doi.org/10.2196/64372 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e60297 %T Implementation of New Technologies in an Aged Care Social Day Program: Mixed Methods Evaluation %A Post,Dannielle %A Whitson,Kathleen %A Parfitt,Gaynor %K aged care %K older adults %K interactive robots %K social engagement %K evaluation %K geriatric %K robot %K day program %K perception %D 2025 %7 12.5.2025 %9 %J JMIR Aging %G English %X Background: Australia’s aging population is looking to age in place, accessing care alternatives external to the traditional model of residential aged care facilities. This evaluation is situated in a Social Day Program, delivered by an aged care organization. It is designed to cater for people living with dementia, located in an environment equipped with new technologies including age-specific interactive computer gaming, social robots, sensory stimulation, and virtual reality. The technologies are designed to support older adults, enabling them to stay connected and maintain physical and cognitive functioning, independence, and quality of life. Objective: This project aimed to undertake a multifaceted evaluation of the implementation of the new technologies, including an exploration of the barriers and enablers to uptake. The key issue is how to enhance the potential for optimizing the use of these technologies in the Social Day Program environment, to help inform decision-making regarding the implementation of these technologies at the organization’s other sites, and future investment in such technologies by aged care organizations generally. Methods: Observation of technology use within the organization was conducted over a 16-week period. Surveys and semistructured interviews were used to collect information from staff related to their experiences with the technology. Thematic analysis was used to analyze the interviews. Data were triangulated across the sample. Results: Forty-eight observation periods were completed, totaling 126.5 observation hours. Technology use by clients was observed on 24 occasions, for 22 (17.4% of the observation time) hours. Nineteen staff completed surveys. Nearly three-quarters (n=14) of the staff perceived there to be barriers to the clients’ use of technology, and 18 (95%) staff reported that they assisted clients to use the technology. Ten (53%) staff reported receiving training to use the technology and feeling confident in their knowledge of the technology to assist clients in using it. Twelve staff members participated in an interview. Key themes identified from the interview data were: technology has potential but is not for everyone, incorporating the subtheme technology as a placation tool, staff knowledge and confidence, and technology functionality and support. Conclusions: This evaluation identified that technology was not being used for the purposes of enrichment or experience enhancement, nor extensively. Multiple barriers to the implementation and sustained use of the technology items were identified. Recommendations to improve implementation and promote sustained use of technology, based on the findings of this evaluation and evidence from the literature, may apply to other organizations seeking to implement these technologies in similar programs. %R 10.2196/60297 %U https://aging.jmir.org/2025/1/e60297 %U https://doi.org/10.2196/60297 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64254 %T Unveiling the Frailty Spatial Patterns Among Chilean Older Persons by Exploring Sociodemographic and Urbanistic Influences Based on Geographic Information Systems: Cross-Sectional Study %A Ormazábal,Yony %A Arauna,Diego %A Cantillana,Juan Carlos %A Palomo,Iván %A Fuentes,Eduardo %A Mena,Carlos %K aging %K frailty %K geospatial clustering %K urban factors %K neighborhood conditions. %D 2025 %7 17.4.2025 %9 %J JMIR Aging %G English %X Background: Frailty syndrome increases the vulnerability of older adults. The growing proportion of older adults highlights the need to better understand the factors contributing to the prevalence of frailty. Current evidence suggests that geomatic tools integrating geolocation can provide valuable information for implementing preventive measures by enhancing the urban physical environment. Objective: The aim of this study was to analyze the relationship between various elements of the urban physical environment and the level of frailty syndrome in older Chilean people. Methods: A cohort of 251 adults aged 65 years or older from Talca City, Chile, underwent comprehensive medical assessments and were geographically mapped within a Geographic Information Systems database. Frailty was determined using the Fried frailty criteria. The spatial analysis of the frailty was conducted in conjunction with layers depicting urban physical facilities within the city, including vegetables and fruit shops, senior centers or communities, pharmacies, emergency health centers, main squares and parks, family or community health centers, and sports facilities such as stadiums. Results: The studied cohort was composed of 187 women and 64 men, with no significant differences in age and BMI between genders. Frailty prevalence varied significantly across clusters, with Cluster 3 showing the highest prevalence (14/47, P=.01). Frail individuals resided significantly closer to emergency health centers (960 [SE 904] m vs 1352 [SE 936] m, P=.04), main squares/parks (1550 [SE 130] m vs. 2048 [SE 105] m, P=.03), and sports fields (3040 [SE 236] m vs 4457 [SE 322]m, P=.04) compared with nonfrail individuals. There were no significant differences in urban quality index across frailty groups, but frail individuals lived in areas with higher population density (0.013 [SE 0.001] vs 0.01 [SE 0.0007], P=.03). Conclusions: Frail individuals exhibit geospatial patterns suggesting intentional proximity to health facilities, sports venues, and urban facilities, revealing associations with adaptive responses to frailty and socioeconomic factors. This highlights the crucial intersection of urban environments and frailty, which is important for geriatric medicine and public health initiatives. %R 10.2196/64254 %U https://aging.jmir.org/2025/1/e64254 %U https://doi.org/10.2196/64254 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e60506 %T A Comparison of Patient and Provider Perspectives on an Electronic Health Record–Based Discharge Communication Tool: Survey Study %A Wang,Dorothy Yingxuan %A Wong,Eliza Lai-Yi %A Cheung,Annie Wai-Ling %A Tang,Kam-Shing %A Yeoh,Eng-Kiong %K older adult %K gerontology %K geriatric %K old %K older %K elderly %K aging %K aged %K post-acute care %K communication %K satisfaction %K medication information %K patient-provider comparison %K technology-based intervention %K technology acceptance model %K discharge %K EHR %K record %K portal %K cross-sectional %K survey %K questionnaire %K experience %K attitude %K opinion %K perception %K perspective %K acceptance %K adoption %K design %K user experience %D 2025 %7 29.1.2025 %9 %J JMIR Aging %G English %X Background: Hospital discharge for older adult patients carries risks. Effective patient-provider communication is crucial for postacute care. Technology-based communication tools are promising in improving patient experience and outcomes. However, there is limited evidence comparing patient and provider user experiences on a large-scale basis, hindering the exploration of true patient-provider shared understanding. Objective: This study aimed to evaluate an electronic health record–based discharge communication tool by examining and comparing patient and provider perspectives. Methods: This study comprised a cross-sectional self-administered staff survey and a pre-post cross-sectional patient survey. Physicians, nurses, and older adult patients aged 65 years and older discharged from 4 public hospitals were included. Patient-provider comparison items focused on 3 aspects of the design quality of the tool (information clarity, adequacy, and usefulness) and overall satisfaction with the tool. In addition, patients’ experience of discharge information and their medication-taking behaviors before and after the program implementation were compared based on a validated local patient experience survey instrument. Providers’ perceived usefulness of this tool to their work and implementation intentions were measured based on the technology acceptance model to enhance understanding of their experiences by conducting structural equation modeling analysis. Results: A total of 1375 and 2353 valid responses were received from providers and patients, respectively. Patients’ overall satisfaction with this communication tool is significantly higher than providers’, and patients rated the information clarity and usefulness presented by this tool higher as well (P<.001). However, patients rated information adequacy significantly lower than providers (P<.001). Meanwhile, patients reported a significant improvement in their experience of discharge medication information, and fewer patients reported side effects encounters after the program implementation (126/1083, 11.6% vs 111/1235, 9%; P=.04). However, providers showed inconsistent implementation fidelity. Providers’ perceived quality of the tool design (β coefficient=0.24, 95% CI 0.08-0.40) and perceived usefulness to their work (β coefficient=0.57, 95% CI 0.43-0.71) significantly impacted their satisfaction. Satisfaction can significantly impact implementation intentions (β coefficient=0.40, 95% CI 0.17-0.64), which further impacts implementation behaviors (β coefficient=0.16, 95% CI 0.10-0.23). Conclusions: A notable disparity exists between patients and health care providers. This may hinder the achievement of the tool’s benefits. Future research should aim for a comprehensive overview of implementation barriers and corresponding strategies to enhance staff performance and facilitate patient-provider shared understanding. %R 10.2196/60506 %U https://aging.jmir.org/2025/1/e60506 %U https://doi.org/10.2196/60506 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64033 %T Association of Subjective Cognitive Concerns With Performance on Mobile App–Based Cognitive Assessment in Cognitively Normal Older Adults: Observational Study %A Nester,Caroline O %A De Vito,Alyssa N %A Prieto,Sarah %A Kunicki,Zachary J %A Strenger,Jennifer %A Harrington,Karra D %A Roque,Nelson %A Sliwinski,Martin J %A Rabin,Laura A %A Thompson,Louisa I %K subjective cognitive concerns %K subjective cognitive decline %K digital cognitive assessment %K mobile app %K app-based %K preclinical Alzheimer disease %K mild cognitive impairment %K MCI %K preclinical dementia %K mobile monitoring of cognitive change %K Cognitive Function Instrument %K mHealth %K mobile health %K applications %K cognition %K assessment %K remote %K geriatrics %K gerontology %K aging %K memory %K older adult %K elderly %K digital health %K mobile phone %D 2025 %7 4.2.2025 %9 %J JMIR Aging %G English %X Background: Subjective cognitive concerns (SCCs) may be among the earliest clinical symptoms of dementia. There is growing interest in applying a mobile app–based cognitive assessment to remotely screen for cognitive status in preclinical dementia, but the relationship between SCC and relevant mobile assessment metrics is uncertain. Objective: This study aimed to characterize the relationship between SCC and adherence, satisfaction, and performance on mobile app assessments in cognitively unimpaired older adults. Methods: Participants (N=122; Meanage=68.85 [SD 4.93] years; Meaneducation=16.85 [SD 2.39] years; female: n=82, 66.7%; White:n=106, 86.2%) completed 8 assessment days using Mobile Monitoring of Cognitive Change (M2C2), an app-based testing platform, with brief daily sessions within morning, afternoon, and evening time windows (24 total testing sessions). M2C2 includes digital working memory, processing speed, and episodic memory tasks. Participants provided feedback about their satisfaction and motivation related to M2C2 upon study completion. SCC was assessed using the Cognitive Function Instrument. Regression analyses evaluated the association between SCC and adherence, satisfaction, and performance on M2C2, controlling for age, sex, depression, and loneliness. Linear-mixed effects models evaluated whether SCC predicted M2C2 subtest performance over the 8-day testing period, controlling for covariates. Results: SCC was not associated with app satisfaction or protocol motivation, but it was significantly associated with lower rates of protocol adherence (ß=−.20, P=.37, 95% CI −.65 to −.02). Higher SCC endorsement significantly predicted worse overall episodic memory performance (ß=−.20, P=.02, 95% CI −.02 to −.01), but not working memory or processing speed. There was a main effect of SCC on working memory performance at day 1 (estimate=−1.05, SE=0.47, P=.03) and a significant interaction between SCC and working memory over the 8-day period (estimate=0.05, SE=0.02, P=.03), such that SCC was associated with initially worse, then progressively better working memory performance. Conclusions: SCCs are associated with worse overall memory performance on mobile app assessments, patterns of cognitive inefficiency (variable working memory), and mildly diminished adherence across an 8-day assessment period. Findings suggest that mobile app assessments may be sensitive to subtle cognitive changes, with important implications for early detection and treatment for individuals at risk for dementia. %R 10.2196/64033 %U https://aging.jmir.org/2025/1/e64033 %U https://doi.org/10.2196/64033 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e66017 %T Effectiveness of a Dyadic Technology–Enhanced Home-Based Horticultural Therapy on Psychosocial Well-Being Among People With Dementia and Their Family Caregivers: Multimethods Pilot Study %A Kor,Patrick Pui Kin %A Liu,Justina Yat Wa %A Wong,Arkers Kwan Ching %A Tsang,Alex Pak Lik %A Tan,Han Zhi %A Cheung,Daphne Sze Ki %A Leung,Humphrey Kwong Wai %A Wong,Frances Kam Yuet %+ School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Road, Hung Hom, Kowloon, Hong Kong, China (Hong Kong), 852 27665622, patrick.kor@polyu.edu.hk %K horticultural activity %K dementia %K caregivers %K dyadic intervention %K technology–enhanced intervention %D 2025 %7 5.2.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Horticultural therapy (HT) has been proposed to be an effective intervention for improving the psychosocial well-being of people with dementia and their caregivers. However, constraints such as limited land space in high-density cities, unstable weather, and lack of gardening experience may hamper the delivery of HT to people with dementia and their caregivers. Objective: This pilot study aimed to examine the feasibility and preliminary effects of a technology-enhanced home-based HT for people with dementia and their caregivers using a hydroponic indoor growing system. Methods: A single-group pre-post design was adopted. A total of 37 dyads of people with dementia and their caregivers participated in 3 weekly face-to-face sessions, followed by 8 weeks of home-based horticultural activities. Outcomes were measured at baseline and postintervention (at week 11), including feasibility outcomes, cognitive function, neuropsychiatric symptoms, and happiness levels of people with dementia. Caregivers’ outcomes included positive aspects of caregiving, perceived stress levels, depressive symptoms, caregiver distress, and happiness levels. Semistructured focus group interviews were conducted with the caregivers to further explore their horticultural experience. Results: Intervention feasibility was established with a completion rate of 83.78% and an attrition rate of 2.63% (n=1). Significant improvements were detected in caregiver distress (P<.05) and the happiness level of people with dementia (P<.01). The qualitative findings indicated that HT improved the psychological well-being of both people with dementia and caregivers, enhanced the relationships between caregivers and people with dementia, expanded the caregivers’ social networks, and enhanced the autobiographical memory of people with dementia. Conclusions: This pilot study provides evidence on the feasibility of using a hydroponic indoor grower to conduct home-based HT for people with dementia and their caregivers. The findings suggest positive effects on the psychological well-being of both people with dementia and their caregivers. Caregivers reported potential positive effects of HT on the autobiographical memory retrieval of people with dementia. Due to the pilot nature of this study, a control group was not employed. Therefore, large-scale randomized controlled trials are encouraged to further confirm the effectiveness of the intervention. Trial Registration: ClinicalTrials.gov NCT05577975; https://clinicaltrials.gov/study/NCT05577975 %M 39908077 %R 10.2196/66017 %U https://aging.jmir.org/2025/1/e66017 %U https://doi.org/10.2196/66017 %U http://www.ncbi.nlm.nih.gov/pubmed/39908077 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e59665 %T Enhancing Older Adults’ Lives Through Positive Aging Perception, Quality-of-Life Enhancement, and Social Support to Drive Acceptance and Readiness Toward Indoor Assistive Technology: Cross-Sectional Study %A Wong,Ka Po %A Teh,Pei-Lee %A Lim,Weng Marc %A Lee,Shaun Wen Huey %+ , Gerontechnology Laboratory and School of Business, Monash University Malaysia, Jalan Lagoon Selatan, Sunway City, 47500, Malaysia, 60 355144971, teh.pei.lee@monash.edu %K indoor assistive technology %K positive aging perceptions %K quality of life %K social support %K technology acceptance %K technology readiness %D 2025 %7 5.2.2025 %9 Original Paper %J JMIR Aging %G English %X Background: The growing aging population faces increasing mobility limitations, highlighting the need for assistive technologies as potential solutions. These technologies support the independence and well-being of older adults and individuals with mobility challenges. Indoor mobility is essential for daily activities and significantly impacts their lives. Limited indoor mobility can reduce quality of life and heighten the risk of falls. Objective: This study explores how positive aging perceptions, quality-of-life enhancements, and social support influence the acceptance and readiness of indoor assistive technologies among older adults. Methods: A cross-sectional study was conducted at a gerontechnology laboratory, requiring participants to visit the facility in person. Each 60-minute session included demonstrations of various indoor assistive technologies and the completion of a questionnaire. The assistive technologies showcased encompassed a wide range of devices. Participants’ positive aging perceptions, quality-of-life enhancements, social support, technology acceptance, and readiness were measured using validated scales. Data were analyzed with AMOS (version 28; IBM Corp) and SPSS (version 28; IBM Corp), using structural equation modeling and multivariate analysis of covariance to assess the effects of predictors while controlling for demographic factors. Results: A total of 104 older adults aged 60 years and older participated, with a mean age of 67.92 (SD 5.68) years. Structural equation modeling indicated that positive aging perception has a significant influence on older adults’ control beliefs (P=.095), comfort (P=.047), and confidence (P<.001) in gerontechnology. Multivariate analysis revealed significant combined effects of quality-of-life enhancement (P=.01) and social support (P=.03) on technology acceptance and readiness, wherein quality-of-life enhancement (P=.001) and social support (P=.008) negatively impacted security perception. Among demographic variables, educational level significantly impacted gerontechnology confidence (P=.004) while ethnicity influenced optimism (P=.003). Conclusions: This study sheds light on key factors affecting older adults’ acceptance and readiness to adopt indoor assistive technologies. Findings highlight the importance of fostering positive aging perceptions through these technologies. Addressing issues related to control beliefs, comfort, and confidence in gerontechnology is essential to enhance technology acceptance and readiness among older adults. Future research should investigate the underlying mechanisms and create targeted interventions to support successful technology adoption in this population. %M 39908542 %R 10.2196/59665 %U https://aging.jmir.org/2025/1/e59665 %U https://doi.org/10.2196/59665 %U http://www.ncbi.nlm.nih.gov/pubmed/39908542 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64004 %T A Self-Adaptive Serious Game to Improve Motor Learning Among Older Adults in Immersive Virtual Reality: Short-Term Longitudinal Pre-Post Study on Retention and Transfer %A Everard,Gauthier %A Declerck,Louise %A Lejeune,Thierry %A Edwards,Martin Gareth %A Bogacki,Justine %A Reiprich,Cléo %A Delvigne,Kelly %A Legrain,Nicolas %A Batcho,Charles Sebiyo %+ School of Rehabilitation Sciences, Faculty of Medicine, Laval University, 2325 Rue de l'Université, Québec, QC, G1V0A6, Canada, 1 4185299141 ext 46914, gauthier.everard@uclouvain.be %K virtual reality %K aged %K learning %K upper extremity %K video games %K kinematics %D 2025 %7 3.3.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Despite their potential, the use of serious games within immersive virtual reality (iVR) for enhancing motor skills in older adults remains relatively unexplored. In this study, we developed a self-adaptive serious game in iVR called REAsmash-iVR. This game involves swiftly locating and striking a digital mole presented with various distractors. Objective: This short-term longitudinal pre-post study aims to evaluate REAsmash-iVR’s efficacy in promoting motor learning in older adults. Specifically, we seek to determine the transfer and retention of motor learning achieved through REAsmash-iVR to other iVR tasks. Methods: A total of 20 older adults participated in the study, engaging with REAsmash-iVR over 7 consecutive days. The evaluation included iVR tests such as KinematicsVR and a VR adaptation of the Box and Block Test (BBT-VR). KinematicsVR tasks included drawing straight lines and circles as fast and as accurately as possible, while BBT-VR required participants to move digital cubes as quickly as possible within 60 seconds. Assessments were conducted before and after the intervention, with a follow-up at 1 week post intervention. The primary outcome focused on evaluating the impact of REAsmash-iVR on speed-accuracy trade-off during KinematicsVR tasks. Secondary outcomes included analyzing movement smoothness, measured by spectral arc length, and BBT-VR scores. Results: Results revealed significant improvements in speed-accuracy trade-off post intervention compared to that before the intervention, with notable retention of skills for straight lines (t19=5.46; P<.001; Cohen d=1.13) and circle drawing (t19=3.84; P=.001; Cohen d=0.787). Likewise, there was a significant enhancement in spectral arc length, particularly for circle drawing (χ²2=11.2; P=.004; ε2=0.23), but not for straight-line drawing (χ²2=2.1; P=.35; ε2=0.003). Additionally, participants demonstrated transfer with significant improvement (q=5.26; P<.001; Cohen r=0.678) and retention (q=6.82; P<.001; Cohen r=0.880) in BBT-VR skills. Conclusions: These findings provide perspectives for the use of iVR to improve motor learning in older adults through delivering self-adaptive serious games targeting motor and cognitive functions. Trial Registration: ClinicalTrials.gov NCT04694833; https://clinicaltrials.gov/study/NCT04694833 %M 40053708 %R 10.2196/64004 %U https://aging.jmir.org/2025/1/e64004 %U https://doi.org/10.2196/64004 %U http://www.ncbi.nlm.nih.gov/pubmed/40053708 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e59942 %T Identifying Unmet Needs of Informal Dementia Caregivers in Clinical Practice: User-Centered Development of a Digital Assessment Tool %A Biernetzky,Olga A %A Thyrian,Jochen René %A Boekholt,Melanie %A Berndt,Matthias %A Hoffmann,Wolfgang %A Teipel,Stefan J %A Kilimann,Ingo %K unmet needs %K assessment development %K family caregivers of people with dementia %K dementia %K need %K Alzheimer %K self-guided %K self-reported %K caregiver %K informal care %K spousal care %K interview %K qualitative %K thematic %K usability %K mHealth %K tablet %K self-completed %K aging %K patient care %K health interventions %K care giver %K digital health %K ehealth %K digital assessment %K memory %D 2025 %7 7.4.2025 %9 %J JMIR Aging %G English %X Background: Despite the increasing interventions to support family caregivers of people with dementia, service planning and delivery is still not effective. Objective: Our study aimed to develop a digitally-supported needs assessment tool for family caregivers of people with dementia that is feasible, time-efficient, understood by users, and can be self-completed in the primary care setting. Methods: The development of the unmet needs assessment tool was part of a cluster-randomized controlled trial examining the effectiveness of a digitally supported care management programme to reduce unmet needs of family caregivers of people with dementia (GAIN [Gesund Angehörige Pflegen]) and was conducted in 3 phases. Using an iterative participatory approach with informal caregivers, health care professionals including general practitioners, neurologists, psychologists, psychiatrists, nurses, and Alzheimer Society representatives, we developed a digital self-completion unmet needs assessment tool focusing on informal caregivers’ biopsychosocial health und quality of life in connection to their caregiver responsibilities. Data were collected through group discussions, written feedback, protocols, think-aloud protocols, and interviews, and analyzed thematically. Results: Data from 27 caregivers, including caregivers of people with dementia (n=18), health care professionals (n=7), and Alzheimer Society representatives (n=2) were collected. Thematic analysis identified 2 main themes: content of the assessment tool and usability and handling of the digital tablet-based assessment tool. The feedback provided by the stakeholders led to new aspects and changes to make the tool comprehensive, easy to read, and easy to handle. The overall mean completion time was reduced from the initial 37 minutes to 18 minutes, which renders the assessment tool fit to be self-completed in waiting rooms of primary care practices or other settings. Conclusions: The input of the 3 stakeholder groups has supported the development of the assessment tool ensuring that all aspects considered important were covered and understood and the completion of the assessment procedure was time-efficient and practically feasible. Further validation of the assessment tool will be performed with the data generated as part of the GAIN trial. Trial Registration: ClinicalTrials.gov NCT04037501; https://clinicaltrials.gov/study/NCT04037501 %R 10.2196/59942 %U https://aging.jmir.org/2025/1/e59942 %U https://doi.org/10.2196/59942 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e60652 %T Uncovering Specific Navigation Patterns by Assessing User Engagement of People With Dementia and Family Caregivers With an Advance Care Planning Website: Quantitative Analysis of Web Log Data %A Dupont,Charlèss %A Smets,Tinne %A Potts,Courtney %A Monnet,Fanny %A Pivodic,Lara %A De Vleminck,Aline %A Van Audenhove,Chantal %A Mulvenna,Maurice %A Van den Block,Lieve %K dementia %K advance care planning %K user engagement %K web-based tool %K care %K website %K caregiver %K communication %K tool %K online %D 2025 %7 11.2.2025 %9 %J JMIR Aging %G English %X Background: Web-based tools have gained popularity to inform and empower individuals in advance care planning. We have developed an interactive website tailored to the unique needs of people with dementia and their families to support advance care planning. This website aims to break away from the rigid pathways shown in other tools that support advance care planning, in which advance care planning is shown as a linear process from information to reflection, communication, and documentation. Objective: This study aimed to assess the website’s usage by people with dementia and their family caregivers, identify distinct user engagement patterns, and visualize how users navigated the website. Methods: We analyzed the website’s log data obtained from an 8-week evaluation study of the site. Interactions with the website were collected in log data files and included visited web pages or clicked-on hyperlinks. Distinct user engagement patterns were identified using K-means clustering process mining, a technique that extracts insights from log data to model and visualize workflows, was applied to visualize user pathways through the website. Results: A total of 52 participants, 21 individuals with dementia and their family caregivers as dyads and 10 family caregivers were included in the study. Throughout the 8-week study, users spent an average of 35.3 (SD 82.9) minutes over 5.5 (SD 3.4) unique days on the website. Family caregivers mostly used the website (alone or with a person with dementia) throughout the 8-week study. Only 3 people with dementia used it on their own. In total, 3 distinct engagement patterns emerged: low, moderate, and high. Low-engagement participants spent less time on the website during the 8 weeks, following a linear path from information to communication to documentation. Moderate- and high-engagement users showed more dynamic patterns, frequently navigating between information pages and communication tools to facilitate exploration of aspects related to advance care planning. Conclusions: The diverse engagement patterns underscore the need for personalized support in advance care planning and challenge the conventional linear advance care planning representations found in other web-based tools. %R 10.2196/60652 %U https://aging.jmir.org/2025/1/e60652 %U https://doi.org/10.2196/60652 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e63715 %T The PDC30 Chatbot—Development of a Psychoeducational Resource on Dementia Caregiving Among Family Caregivers: Mixed Methods Acceptability Study %A Cheng,Sheung-Tak %A Ng,Peter H F %+ Department of Health and Physical Education, The Education University of Hong Kong, 10 Lo Ping Road, Tai Po, China (Hong Kong), 852 29486563, takcheng@eduhk.hk %K Alzheimer %K caregiving %K chatbot %K conversational artificial intelligence %K dementia %K digital health %K health care technology %K psychoeducational %K medical innovations %K language models %K mobile phone %D 2025 %7 6.1.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Providing ongoing support to the increasing number of caregivers as their needs change in the long-term course of dementia is a severe challenge to any health care system. Conversational artificial intelligence (AI) operating 24/7 may help to tackle this problem. Objective: This study describes the development of a generative AI chatbot—the PDC30 Chatbot—and evaluates its acceptability in a mixed methods study. Methods: The PDC30 Chatbot was developed using the GPT-4o large language model, with a personality agent to constrain its behavior to provide advice on dementia caregiving based on the Positive Dementia Caregiving in 30 Days Guidebook—a laypeople’s resource based on a validated training manual for dementia caregivers. The PDC30 Chatbot’s responses to 21 common questions were compared with those of ChatGPT and another chatbot (called Chatbot-B) as standards of reference. Chatbot-B was constructed using PDC30 Chatbot’s architecture but replaced the latter’s knowledge base with a collection of authoritative sources, including the World Health Organization’s iSupport, By Us For Us Guides, and 185 web pages or manuals by Alzheimer’s Association, National Institute on Aging, and UK Alzheimer’s Society. In the next phase, to assess the acceptability of the PDC30 Chatbot, 21 family caregivers used the PDC30 Chatbot for two weeks and provided ratings and comments on its acceptability. Results: Among the three chatbots, ChatGPT’s responses tended to be repetitive and not specific enough. PDC30 Chatbot and Chatbot-B, by virtue of their design, produced highly context-sensitive advice, with the former performing slightly better when the questions conveyed significant psychological distress on the part of the caregiver. In the acceptability study, caregivers found the PDC30 Chatbot highly user-friendly, and its responses quite helpful and easy to understand. They were rather satisfied with it and would strongly recommend it to other caregivers. During the 2-week trial period, the majority used the chatbot more than once per day. Thematic analysis of their written feedback revealed three major themes: helpfulness, accessibility, and improved attitude toward AI. Conclusions: The PDC30 Chatbot provides quality responses to caregiver questions, which are well-received by caregivers. Conversational AI is a viable approach to improve the support of caregivers. %R 10.2196/63715 %U https://aging.jmir.org/2025/1/e63715 %U https://doi.org/10.2196/63715 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e60566 %T Designing a Multimodal and Culturally Relevant Alzheimer Disease and Related Dementia Generative Artificial Intelligence Tool for Black American Informal Caregivers: Cognitive Walk-Through Usability Study %A Bosco,Cristina %A Otenen,Ege %A Osorio Torres,John %A Nguyen,Vivian %A Chheda,Darshil %A Peng,Xinran %A Jessup,Nenette M %A Himes,Anna K %A Cureton,Bianca %A Lu,Yvonne %A Hill,Carl V %A Hendrie,Hugh C %A Barnes,Priscilla A %A Shih,Patrick C %+ , Luddy School of Informatics, Computing, and Engineering, Indiana University, 700 N Woodlawn Ave, Bloomington, IN, 4740, United States, 1 812 856 5754, cribosco@iu.edu %K multimodality %K artificial intelligence %K AI %K generative AI %K usability %K black %K African American %K cultural %K Alzheimer's %K dementia %K caregivers %K mobile app %K interaction %K cognition %K user opinion %K geriatrics %K smartphone %K mHealth %K digital health %K aging %D 2025 %7 8.1.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Many members of Black American communities, faced with the high prevalence of Alzheimer disease and related dementias (ADRD) within their demographic, find themselves taking on the role of informal caregivers. Despite being the primary individuals responsible for the care of individuals with ADRD, these caregivers often lack sufficient knowledge about ADRD-related health literacy and feel ill-prepared for their caregiving responsibilities. Generative AI has become a new promising technological innovation in the health care domain, particularly for improving health literacy; however, some generative AI developments might lead to increased bias and potential harm toward Black American communities. Therefore, rigorous development of generative AI tools to support the Black American community is needed. Objective: The goal of this study is to test Lola, a multimodal mobile app, which, by relying on generative AI, facilitates access to ADRD-related health information by enabling speech and text as inputs and providing auditory, textual, and visual outputs. Methods: To test our mobile app, we used the cognitive walk-through methodology, and we recruited 15 informal ADRD caregivers who were older than 50 years and part of the Black American community living within the region. We asked them to perform 3 tasks on the mobile app (ie, searching for an article on brain health, searching for local events, and finally, searching for opportunities to participate in scientific research in their area), then we recorded their opinions and impressions. The main aspects to be evaluated were the mobile app’s usability, accessibility, cultural relevance, and adoption. Results: Our findings highlight the users’ need for a system that enables interaction with different modalities, the need for a system that can provide personalized and culturally and contextually relevant information, and the role of community and physical spaces in increasing the use of Lola. Conclusions: Our study shows that, when designing for Black American older adults, a multimodal interaction with the generative AI system can allow individuals to choose their own interaction way and style based upon their interaction preferences and external constraints. This flexibility of interaction modes can guarantee an inclusive and engaging generative AI experience. %M 39778201 %R 10.2196/60566 %U https://aging.jmir.org/2025/1/e60566 %U https://doi.org/10.2196/60566 %U http://www.ncbi.nlm.nih.gov/pubmed/39778201 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e62936 %T Assessment of Technology Readiness in Norwegian Older Adults With Long-Term Health Conditions Receiving Home Care Services: Cross-Sectional Questionnaire Study %A Bergh,Sverre %A Benth,Jūratė Šaltytė %A Høgset,Lisbeth Dyrendal %A Rydjord,Britt %A Kayser,Lars %+ , Research Centre for Age-related Functional Decline and Disease, Innlandet Hospital Trust, Pb 68, Ottestad, 2312, Norway, 47 45679393, sverre.bergh@sykehuset-innlandet.no %K eHealth literacy %K digital health services %K technology readiness %K Readiness and Enablement Index for Health Technology %K READHY %K chronic conditions %D 2025 %7 7.2.2025 %9 Original Paper %J JMIR Aging %G English %X Background: With the increasing number of older adults globally, there is a constant search for new ways to organize health care services. Digital health services are promising and may reduce workload and at the same time improve patient well-being. A certain level of eHealth literacy is needed to be able to use digital health services. However, knowledge of technology readiness in this target group of older adults is unclear. Objective: The aim of this study was to understand the technology readiness level of a group of older adults who were provided home care services in order to address the present and future needs of this group in relation to the implementation of digital health care services. Methods: This quantitative cross-sectional study included 149 older adults from Norway receiving home care services. The participants completed the Readiness and Enablement Index for Health Technology (READHY) instrument, assessments of well-being (World Health Organization-Five Well-Being Index [WHO-5]), and assessments of demographic and clinical variables (sex, age, education, living situation, comorbidity, use of digital devices, and use of IT). Cluster analyses were used to group the users according to their technology readiness. Results: The mean participant age was 78.6 (SD 8.0) years, and 55.7% (83/149) were women. There was good consistency within the assumed READHY scales (Cronbach α=.61-.91). The participants were grouped into 4 clusters, which differed in terms of READHY scores, demographic variables, and the use of IT in daily life. Participants in cluster 1 (n=40) had the highest scores on the READHY scales, were younger, had a larger proportion of men, had higher education, and had better access to digital devices and IT. Participants in cluster 4 (n=16) scored the lowest on eHealth literacy knowledge. Participants in cluster 1 had relatively high levels of eHealth literacy knowledge and were expected to benefit from digital health services, while participants in cluster 4 had the lowest level of eHealth literacy and would not easily be able to start using digital health services. Conclusions: The technology readiness level varied in our cohort of Norwegian participants receiving home care. Not all elderly people have the eHealth literacy to fully benefit from digital health services. Participants in cluster 4 (n=16) had the lowest scores in the eHealth Literacy Questionnaire scales in the READHY instrument and should be offered nondigital services or would need extensive management support. The demographic differences between the 4 clusters may inform stakeholders about which older people need the most training and support to take advantage of digital health care services. %M 39918862 %R 10.2196/62936 %U https://aging.jmir.org/2025/1/e62936 %U https://doi.org/10.2196/62936 %U http://www.ncbi.nlm.nih.gov/pubmed/39918862 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e66690 %T Experiences of Older Mental Health Patients and Their Care Partners Using a Proxy Account to Access Open Notes: Qualitative Interview Study %A Meier-Diedrich,Eva %A Esch,Tobias %A Hägglund,Maria %A Heinze,Martin %A Hochwarter,Stefan %A Speck,Justin %A Wagener,Marie %A Dahling,Volker %A Schwarz,Julian %+ Department of Psychiatry and Psychotherapy, Center for Mental Health, Immanuel Hospital Rüdersdorf, Brandenburg Medical School Theodor Fontane, Seebad 82/83, Rüdersdorf, 15562, Germany, 49 33638 83 501, Eva.Meier-Diedrich@mhb-fontane.de %K psychiatry %K eHealth %K mental health %K digital literacy %K older patients %K older adult %K care partner %K proxy access %K open record access %K Open Notes %K patient portal %K artificial intelligence %K AI %D 2025 %7 24.2.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Older patients with serious mental illnesses such as cognitive disorders often rely on family members or spouses (care partners [CPs]) to meet their health care needs. CPs frequently lack essential information to fully understand the patients’ illnesses and effectively support their treatment. Open Notes provide patients with digital access to their health care professionals’ clinical notes and are associated with many positive outcomes, such as increased adherence and empowerment. However, older patients who use Open Notes may encounter use barriers such as limited digital literacy. Recent developments allow CPs to access Open Notes (proxy access) and receive valuable information, which holds significant potential for improving the care of older patients. Objective: This study explored the experiences, barriers, and opportunities of older mental health patients and their CPs related to using Open Notes. Furthermore, influencing factors and interdependencies were identified. Methods: Older patients (n=10) and their CPs (n=10) were provided with web-based proxy access to clinical documentation through a web-based patient portal. In-depth qualitative interviews (N=20) were conducted to explore experiences with this access. Data analysis was conducted in accordance with the constructivist grounded theory approach. Results: The prerequisites for using Open Notes with proxy access were sufficient digital literacy on the part of the patient or CP, as well as the establishment of a trusting relationship between patients and CPs. Access to Open Notes enabled patients and CPs to gain a deeper understanding of the illness and its treatment while also facilitating enhanced contact with health care professionals. This resulted in greater involvement in the treatment process but may also prompt changes in relationship dynamics—CPs are better equipped to support patients in their health care but may also tend to monitor or control them through Open Notes. As a result, the introduction of Open Notes was accompanied by mixed feelings. Conclusions: It is of utmost importance to provide older patients with comprehensive access to Open Notes to preserve their health autonomy. However, the involvement of CPs through proxy access is of great value in improving the care of older patients, especially those with cognitive impairments. %M 39993284 %R 10.2196/66690 %U https://aging.jmir.org/2025/1/e66690 %U https://doi.org/10.2196/66690 %U http://www.ncbi.nlm.nih.gov/pubmed/39993284 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e68061 %T Employers’ Perspectives of Caregiver-Friendly Workplace Policies for Caregiver-Employees Caring for Older Adults in Hong Kong: Thematic Analysis %A Lee,Maggie Man-Sin %A Yeoh,Eng-Kiong %A Wong,Eliza Lai-Yi %+ JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, School of Public Health Building, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, China (Hong Kong), 852 2252 8772, lywong@cuhk.edu.hk %K caregiver %K aging %K burnout %K stress %K mental health %K employees %D 2025 %7 31.3.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Caregiver-friendly workplace policies (CFWPs) are rare in Hong Kong. With Hong Kong facing a “silver tsunami” in the near future, it is important to understand the need for such policies and the views of employers for future facilitation. Objective: This study aimed to identify the support that is currently provided or that could be provided to caregiver-employees (CEs) caring for older adults in Hong Kong and assess the challenge and facilitative support for employers to adopt CFWPs in the specific context of Hong Kong. Methods: A qualitative research design with semistructured individual in-depth interviews with employers from Hong Kong was adopted for this study. A purposive snowball sampling method was used to recruit participants from the 7 primary industries mentioned in the Hong Kong census and from all 3 employer types (private, public, and nongovernmental organizations), which allowed the inclusion of participants sensitized to the idea and potential of CFWPs. Thematic framework analysis was used to evaluate the data collected during the interviews. Results: We interviewed 17 employers and managers from 7 major industries in Hong Kong (2.5 to 120,000 employees). There were 4 (24%) male and 13 (76%) female participants, and the participant age ranged from 30 to 50 years. All participants held managerial positions at the time of the interview. Of the 17 participants, 13 were from private companies, 2 were from public institutions, and 2 were from nongovernmental organizations. Four of the companies had a global presence. Four main themes were identified: (1) current support and potential support for CEs (which was limited to discretionary annual leave and unpaid leave when annual leave was exhausted), (2) challenges in adopting CFWPs, (3) facilitating support for adopting CFWPs, and (4) incentives for adopting CFWPs. The participants rated information and resources for CEs (mean 8.56, SD 0.37), bereavement leave (mean 8.47, SD 0.63), flexible working hours (mean 8.32, SD 0.48), and caregiver-inclusive corporate culture (mean 8.32, SD 0.48) as essential CFWPs for CEs in Hong Kong. Conclusions: While several studies have reported the types of CFWPs and their impacts on CEs, stakeholders’ perspectives on CFWPs have been rarely investigated. This study found that although employers consider CFWPs as necessary and see them as a catalyst for a long-term win-win situation, the current support for CEs is discretionary and industry-specific. Government leadership is critical for formulating, piloting, and implementing CFWPs to create a friendly environment that encourages disclosure with trust and respect across industrial sectors in Hong Kong. This study identified the current unmet needs and demands of CEs from the employer’s perspective, the barriers to large-scale adoption of CFWPs, and the path forward to inform further discourse and policy formulation in Hong Kong. %M 40163861 %R 10.2196/68061 %U https://aging.jmir.org/2025/1/e68061 %U https://doi.org/10.2196/68061 %U http://www.ncbi.nlm.nih.gov/pubmed/40163861 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e68957 %T Building Strong Foundations: Nonrandomized Interventional Study of a Novel, Digitally Delivered Fall Prevention Program for Older Adults %A Wing,David %A Nichols,Jeanne F %A Barkai,Hava Shoshana %A Culbert,Olivia %A Moreno,Daniel %A Higgins,Michael %A O'Brien,Anna %A Perez,Mariana %A Davey,Hope %A Moran,Ryan %+ , Exercise and Physical Activity Resource Center, University of California, San Diego, 9500 Gilman Drive #0811, CALIT2/Atkinson Hall Room 3504, San Diego, CA, 92093, United States, 1 858 534 9315, dwing@eng.ucsd.edu %K exercise %K older adults %K digital intervention %K Zoom %K balance %K posture %K strength %K fall prevention %D 2025 %7 26.2.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Injuries from falls are a major concern among older adults. Targeted exercise has been shown to improve fall risk, and recommendations for identifying and referring older adults for exercise-based interventions exist. However, even when very inexpensive or free, many do not use available fall prevention programs, citing barriers related to convenience and safety. These issues are even greater among older adults residing in rural areas where facilities are less abundant. These realities highlight the need for different approaches to reducing falls in novel ways that increase reach and are safe and effective. Web-based delivery of exercise interventions offers some exciting and enticing prospects. Objective: Our objective was to assess the efficacy of the Strong Foundations exercise program to change markers of physical function, posture, balance, strength, and fall risk. Methods: Strong Foundations is a once weekly (60 minutes), 12-week iterative program with 3 core components: postural alignment and control, balance and mobility, and muscular strength and power. We used a quasi-experimental design to determine changes in physical function specific to balance, postural control, and muscular strength among older adults at low or moderate risk of falling. Results: A total of 55 low-risk and 37 moderate-risk participants were recruited. Participants significantly improved on the 30-second Chair Stand (mean change of 1, SD 3.3 repetitions; P=.006) and Timed Up and Go (mean change of 0.2, SD 0.7 seconds; P=.004), with the moderate-risk group generally improving to a greater degree than the low-risk group. Additionally, Short Physical Performance Battery performance improved significantly in the moderate-risk category (P=.02). The majority of postural measures showed statistically significant improvement for both groups (P<.05). Measures of “relaxed” posture showed improvements between 6% and 27%. When an “as tall as possible” posture was adopted, improvements were ~36%. Conclusions: In this 12-week, iterative, web-based program, we found older adults experienced improvement not only in measures used in clinical contexts, such as the 30-second Chair Stand and Timed Up and Go, but also contextualized gains by providing deeper phenotypical measurement related to posture, strength, and balance. Further, many of the physical improvements were attenuated by baseline fall risk level, with those with the highest level of risk having the greater gains, and, thus, the most benefit from such interventions. %M 40009847 %R 10.2196/68957 %U https://aging.jmir.org/2025/1/e68957 %U https://doi.org/10.2196/68957 %U http://www.ncbi.nlm.nih.gov/pubmed/40009847 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e63609 %T Development of a Predictive Dashboard With Prescriptive Decision Support for Falls Prevention in Residential Aged Care: User-Centered Design Approach %A Silva,S Sandun Malpriya %A Wabe,Nasir %A Nguyen,Amy D %A Seaman,Karla %A Huang,Guogui %A Dodds,Laura %A Meulenbroeks,Isabelle %A Mercado,Crisostomo Ibarra %A Westbrook,Johanna I %+ Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde, NSW, 2113, Australia, 61 29850 4045, sandunmalpriya.silva@mq.edu.au %K falls prevention %K dashboard architecture %K predictive %K sustainability %K challenges %K decision support %K falls %K aged care %K geriatric %K older adults %K economic burden %K prevention %K electronic health record %K EHR %K intervention %K decision-making %K patient safety %K risks %K older people %K monitoring %D 2025 %7 7.4.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Falls are a prevalent and serious health condition among older people in residential aged care facilities, causing significant health and economic burdens. However, the likelihood of future falls can be predicted, and thus, falls can be prevented if appropriate prevention programs are implemented. Current fall prevention programs in residential aged care facilities rely on risk screening tools with suboptimal predictive performance, leading to significant concerns regarding resident safety. Objective: This study aimed to develop a predictive, dynamic dashboard to identify residents at risk of falls with associated decision support. This paper provides an overview of the technical process, including the challenges faced and the strategies used to overcome them during the development of the dashboard. Methods: A predictive dashboard was co-designed with a major residential aged care partner in New South Wales, Australia. Data from resident profiles, daily medications, fall incidents, and fall risk assessments were used. A dynamic fall risk prediction model and personalized rule-based fall prevention recommendations were embedded in the dashboard. The data ingestion process into the dashboard was designed to mitigate the impact of underlying data system changes. This approach aims to ensure resilience against alterations in the data systems. Results: The dashboard was developed using Microsoft Power BI and advanced R programming by linking data silos. It includes dashboard views for those managing facilities and for those caring for residents. Data drill-through functionality was used to navigate through different dashboard views. Resident-level change in daily risk of falling and risk factors and timely evidence-based recommendations were output to prevent falls and enhance prescriptive decision support. Conclusions: This study emphasizes the significance of a sustainable dashboard architecture and how to overcome the challenges faced when developing a dashboard amid underlying data system changes. The development process used an iterative dashboard co-design process, ensuring the successful implementation of knowledge into practice. Future research will focus on the implementation and evaluation of the dashboard’s impact on health processes and economic outcomes. International Registered Report Identifier (IRRID): RR2-https://doi.org/10.1136/bmjopen-2021-048657 %M 40193194 %R 10.2196/63609 %U https://aging.jmir.org/2025/1/e63609 %U https://doi.org/10.2196/63609 %U http://www.ncbi.nlm.nih.gov/pubmed/40193194 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e68444 %T Edentulousness and the Likelihood of Becoming a Centenarian: Longitudinal Observational Study %A Wei,Xindi %A Zhuang,Longfei %A Li,Yuan %A Shi,Junyu %A Yang,Yijie %A Lai,Hongchang %A Liu,Beilei %K public health %K edentulous %K oral-systemic disease %K epidemiology %K cohort studies %D 2025 %7 21.3.2025 %9 %J JMIR Aging %G English %X Background: In recent decades, the global life expectancy has risen notably to approximately 73.5 years worldwide, coinciding with a rapid growth in the older adult population, which presents a significant public health challenge in promoting healthy aging and longevity. Objective: This study aimed to prospectively investigate the link between edentulousness and the likelihood of reaching centenarian status among individuals aged 80 years and older. Methods: Data from the Chinese Longitudinal Healthy Longevity Survey were analyzed. Logistic regression models were used to assess the relationship between edentulousness and the likelihood of becoming a centenarian. Demographic characteristics, lifestyle habits, and disease histories were adjusted as confounding factors. Several sensitivity analyses, including propensity score matching and 2-year lag analyses, were conducted to further assess the association between edentulousness and the likelihood of becoming a centenarian. The correlation between the number of natural teeth as a continuous variable and the likelihood of becoming a centenarian was evaluated as well. Results: The study included 4239 participants aged 80-100 years. After adjusting for all covariates, the likelihood for becoming a centenarian increased in the nonedentulous group compared to the edentulous group (odds ratio [OR] 1.384, 95% CI 1.093‐1.751). The relationship persisted after propensity score matching analysis (OR 1.272, 95% CI 1.037‐1.561). The association remained statistically significant after excluding participants with a follow-up duration of less than 2 years (OR 1.522, 95% CI 1.083‐2.140; P=.02). Furthermore, a significant positive association between the number of natural teeth and the likelihood of becoming a centenarian was found after adjusting for all covariates (OR 1.022, 95% CI 1.002‐1.042; P=.03), which aligned with the main results of the study. Conclusions: The findings revealed that the presence of natural teeth was linked to an increased probability of becoming a centenarian, underscoring the importance of maintaining oral health even in advanced age. %R 10.2196/68444 %U https://aging.jmir.org/2025/1/e68444 %U https://doi.org/10.2196/68444 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64473 %T Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study %A Jeong,Chang-Uk %A Leiby,Jacob S %A Kim,Dokyoon %A Choe,Eun Kyung %K biological age %K aging clock %K mortality %K artificial intelligence %K machine learning %K record %K history %K health checkup %K clinical relevance %K gerontology %K geriatric %K older %K elderly %K aging %K prediction %K predictive %K life expectancy %K AI %D 2025 %7 11.4.2025 %9 %J JMIR Aging %G English %X Background: The global increase in life expectancy has not shown a similar rise in healthy life expectancy. Accurate assessment of biological aging is crucial for mitigating diseases and socioeconomic burdens associated with aging. Current biological age prediction models are limited by their reliance on conventional statistical methods and constrained clinical information. Objective: This study aimed to develop and validate an aging clock model using artificial intelligence, based on comprehensive health check-up data, to predict biological age and assess its clinical relevance. Methods: We used data from Koreans who underwent health checkups at the Seoul National University Hospital Gangnam Center as well as from the Korean Genome and Epidemiology Study. Our model incorporated 27 clinical factors and employed machine learning algorithms, including linear regression, least absolute shrinkage and selection operator, ridge regression, elastic net, random forest, support vector machine, gradient boosting, and K-nearest neighbors. Model performance was evaluated using adjusted R2 and the mean squared error (MSE) values. Shapley Additive exPlanation (SHAP) analysis was conducted to interpret the model’s predictions. Results: The Gradient Boosting model achieved the best performance with a mean (SE) MSE of 4.219 (0.14) and a mean (SE) R2 of 0.967 (0.001). SHAP analysis identified significant predictors of biological age, including kidney function markers, gender, glycated hemoglobin level, liver function markers, and anthropometric measurements. After adjusting for the chronological age, the predicted biological age showed strong associations with multiple clinical factors, such as metabolic status, body compositions, fatty liver, smoking status, and pulmonary function. Conclusions: Our aging clock model demonstrates a high predictive accuracy and clinical relevance, offering a valuable tool for personalized health monitoring and intervention. The model’s applicability in routine health checkups could enhance health management and promote regular health evaluations. %R 10.2196/64473 %U https://aging.jmir.org/2025/1/e64473 %U https://doi.org/10.2196/64473 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64324 %T Patient-Related Barriers to Digital Technology Adoption in Alzheimer Disease: Systematic Review %A Panzavolta,Andrea %A Arighi,Andrea %A Guido,Emanuele %A Lavorgna,Luigi %A Di Lorenzo,Francesco %A Dodich,Alessandra %A Cerami,Chiara %K digital technology %K digital e-health %K accessibility %K user-friendliness %K neurocognitive disorders %K Alzheimer disease %K dementia %D 2025 %7 10.4.2025 %9 %J JMIR Aging %G English %X Background: Digital technology in dementia is an area of great development with varying experiences across countries. However, novel digital solutions often lack a patient-oriented perspective, and several relevant barriers prevent their use in clinics. Objective: In this study, we reviewed the existing literature on knowledge, familiarity, and competence in using digital technology and on attitude and experiences with digital tools in Alzheimer disease. The main research question is whether digital competence and attitudes of patients and caregivers may affect the adoption of digital technology. Methods: Following the PRISMA guidelines, a literature search was conducted by two researchers in the group. Inter-rater reliability was calculated with Cohen κ statistics. The risk of bias assessment was also recorded. Results: Of 597 initial records, only 18 papers were considered eligible. Analyses of inter-rater reliability showed good agreement levels. Significant heterogeneity in study design, sample features, and measurement tools emerged across studies. Quality assessment showed a middle-high overall quality of evidence. The main factors affecting the adoption of digital technology in patients and caregivers are severity of cognitive deficits, timing of adoption, and the availability of training and support. Additional factors are age, type of digital device, and ease of use of the digital solution. Conclusions: Adoption of digital technology in dementia is hampered by many patient-related barriers. Improving digital competence in patient-caregiver dyads and implementing systematic, patient-oriented strategies for the development and use of digital tools are needed for a successful incorporation of digital technology in memory clinics. %R 10.2196/64324 %U https://aging.jmir.org/2025/1/e64324 %U https://doi.org/10.2196/64324 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e60107 %T Best Practices for Implementing Electronic Care Records in Adult Social Care: Rapid Scoping Review %A Snow,Martha %A Silva-Ribeiro,Wagner %A Baginsky,Mary %A Di Giorgio,Sonya %A Farrelly,Nicola %A Larkins,Cath %A Poole,Karen %A Steils,Nicole %A Westwood,Joanne %A Malley,Juliette %+ Care Policy and Evaluation Centre, London School of Economics and Political Science (LSE), PAN 8.01J, Houghton Street, London, WC2A 2AE, United Kingdom, 44 (0)20 7405 7686, m.snow@lse.ac.uk %K digital care records %K adult social care %K digitization %K domiciliary care %K care homes %K electronic care records %K PRISMA %D 2025 %7 14.2.2025 %9 Review %J JMIR Aging %G English %X Background: In the past decade, the use of digital or electronic records in social care has risen worldwide, capturing key information for service delivery. The COVID-19 pandemic accelerated digitization in health and social care. For example, the UK government created a fund specifically for adult social care provider organizations to adopt digital social care records. These developments offer valuable learning opportunities for implementing digital care records in adult social care settings. Objective: This rapid scoping review aimed to understand what is known about the implementation of digital care records in adult social care and how implementation varies across use cases, settings, and broader contexts. Methods: A scoping review methodology was used, with amendments made to enable a rapid review. Comprehensive searches based on the concepts of digital care records, social care, and interoperability were conducted across the MEDLINE, EmCare, Web of Science Core Collection, HMIC Health Management Information Consortium, Social Policy and Practice, and Social Services Abstracts databases. Studies published between 2018 and 2023 in English were included. One reviewer screened titles and abstracts, while 2 reviewers extracted data. Thematic analysis mapped findings against the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework. Results: Our search identified 2499 references. After screening titles and abstracts, 71 records were selected for full-text review, resulting in 31 references from 29 studies. Studies originated from 11 countries, including 1 multicountry study, with the United Kingdom being the most represented (10/29, 34%). Studies were most often conducted in nursing homes or facilities (7/29, 24%) with older people as the target population (6/29, 21%). Health records were the most investigated record type (12/29, 41%). We identified 45 facilitators and 102 barriers to digital care record implementation across 28 studies, spanning 6 of the 7 NASSS framework domains and aligning with 5 overarching themes that require greater active management regarding implementation. Intended or actual implementation outcomes were reported in 17 (59%) of the 29 studies. Conclusions: The findings suggest that implementation is complex due to a lack of consensus on what digital care records and expected outcomes and impacts should look like. The literature often lacks clear definitions and robust study designs. To be successful, implementation should consider complexity, while studies should use robust frameworks and mixed methods or quantitative designs where appropriate. Future research should define the target population, gather data on carer or service user experiences, and focus on digital care records specifically used in social care. %M 39951702 %R 10.2196/60107 %U https://aging.jmir.org/2025/1/e60107 %U https://doi.org/10.2196/60107 %U http://www.ncbi.nlm.nih.gov/pubmed/39951702 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e49507 %T Implementation of a Web-Based Program for Advance Care Planning and Evaluation of its Complexity With the Nonadoption, Abandonment, Scale-Up, Spread, And Sustainability (NASSS) Framework: Qualitative Evaluation Study %A van der Smissen,Doris %A Schreijer,Maud A %A van Gemert-Pijnen,Lisette J E W C %A Verdaasdonk,Rudolf M %A van der Heide,Agnes %A Korfage,Ida J %A Rietjens,Judith A C %+ Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands, 31 107038460, i.korfage@erasmusmc.nl %K eHealth %K web-based intervention %K implementation %K sustainability %K advance care planning %K NASSS framework %K nonadoption, abandonment, scale-up, spread, and sustainability framework %K health communication %K patient education %K patient-centered care %D 2025 %7 4.3.2025 %9 Original Paper %J JMIR Aging %G English %X Background: The implementation of eHealth applications often fails. The NASSS (nonadoption, abandonment, scale-up, spread, and sustainability) framework aims to identify complexities in eHealth applications; the more complex, the more risk of implementation failure. Objective: This study aimed to analyze the implementation of the web-based advance care planning (ACP) program “Explore Your Preferences for Treatment and Care” using the NASSS framework. Methods: The NASSS framework enables a systematic approach to improve the implementation of eHealth tools. It is aimed at generating a rich and situated analysis of complexities in multiple domains, based on thematic analysis of existing and newly collected data. It also aims at supporting individuals and organizations to handle these complexities. We used 6 of 7 domains of the NASSS framework (ie, condition, technology, value proposition, adopters, external context, and embedding and adaptation over time) leaving out “organization,” and analyzed the multimodal dataset of a web-based ACP program, its development and evaluation, including peer-reviewed publications, notes of stakeholder group meetings, and interviews with stakeholders. Results: This study showed that the web-based ACP program uses straightforward technology, is embedded in a well-established web-based health platform, and in general appears to generate a positive value for stakeholders. A complexity is the rather broad target population of the program. A potential complexity considers the limited insight into the extent to which health care professionals adopt the program. Awareness of the relevance of the web-based ACP program may still be improved among target populations of ACP and among health care professionals. Furthermore, the program may especially appeal to those who value individual autonomy, self-management, and an explicit and direct communicative approach. Conclusions: Relatively few complexities were identified considering the implementation of the web-based ACP program “Explore Your Preferences for Treatment and Care.” The program is evidence-based, freestanding, and well-maintained, with straightforward, well-understood technology. The program is expected to generate a positive value for different stakeholders. Complexities include the broad target population of the program and sociocultural factors. People with limited digital literacy may need support to use the program. Its uptake might be improved by increasing awareness of ACP and the program among a wider population of potential users and among health care professionals. Addressing these issues may guide future use and sustainability of the program. %M 40053753 %R 10.2196/49507 %U https://aging.jmir.org/2025/1/e49507 %U https://doi.org/10.2196/49507 %U http://www.ncbi.nlm.nih.gov/pubmed/40053753 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e60936 %T Determinants of Telehealth Adoption Among Older Adults: Cross-Sectional Survey Study %A Tan,Siow-Hooi %A Yap,Yee-Yann %A Tan,Siow-Kian %A Wong,Chee-Kuan %+ , Faculty of Management, Multimedia University, Persiaran Multimedia, Cyberjaya, 63100, Malaysia, 60 38312 ext 5658, shtan@mmu.edu.my %K telehealth services adoption %K survey %K questionnaire %K telehealth %K older adult population %K subjective well-being %K transition cost %K technology acceptance model %K importance-performance map analysis %K IPMA %D 2025 %7 24.3.2025 %9 Original Paper %J JMIR Aging %G English %X Background: The aging population and the accompanying rise in chronic diseases have intensified the need to study the adoption of telehealth services. However, the success of telehealth services depends not only on their ease and usefulness but also on addressing broader concerns. Despite being a substantial user group in traditional health services, older adults may encounter barriers to adopting telehealth services. Increasing the adoption of telehealth among the older adult population is crucial for enhancing their access to care and managing the challenges of aging effectively. Objective: We aimed to explore factors influencing the adoption of telehealth services among older adults in Malaysia, going beyond the conventional framework by incorporating transition cost and subjective well-being as additional constructs. Methods: A cross-sectional survey was conducted among 119 adults aged ≥60 years in Malaysia, using 39 survey items adapted from existing studies. Data analysis was performed using partial least squares structural equation modeling, with both the measurement model and structural model being evaluated. To determine the predictive relevance of the model, PLSpredict was applied. In addition, importance-performance map analysis was conducted to further expand on the structural model results by assessing the performance of each variable. Results: Of the 119 participants, 52 (43.7%) were women and 67 (56.3%) were men. The study found that subjective well-being (β=0.448; P<.001) was the most significant factor, followed by attitude (β=0.242; P<.001), transition cost (β=−0.163; P<.001), and perceived usefulness (β=0.100, P=.02) in influencing telehealth service intention. Furthermore, perceived ease of use (β=0.271; P<.001), availability (β=0.323; P<.001), subjective well-being (β=0.261; P<.001), and trust (β=0.156, P=.004) positively influenced perceived usefulness, while inertia (β=0.024, P=.22) did not. In addition, availability (β=0.420; P<.001) and subjective well-being (β=0.260; P<.001) were positively related to perceived ease of use, with inertia (β=−0.246; P<.001) having a negative impact. The importance-performance map analysis results showed that subjective well-being (importance=0.532) was the most crucial factor for older adult users, while availability (importance=70.735) had the highest performance in telehealth services. Conclusions: This research underscores the importance of catering to the subjective well-being of older adults and optimizing the availability of telehealth services to encourage adoption, ultimately advancing health care accessibility and quality for this vulnerable demographic. %M 40126531 %R 10.2196/60936 %U https://aging.jmir.org/2025/1/e60936 %U https://doi.org/10.2196/60936 %U http://www.ncbi.nlm.nih.gov/pubmed/40126531 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e67437 %T Using Machine Learning to Predict Cognitive Decline in Older Adults From the Chinese Longitudinal Healthy Longevity Survey: Model Development and Validation Study %A Ren,Hao %A Zheng,Yiying %A Li,Changjin %A Jing,Fengshi %A Wang,Qiting %A Luo,Zeyu %A Li,Dongxiao %A Liang,Deyi %A Tang,Weiming %A Liu,Li %A Cheng,Weibin %K older adults %K cognitive decline %K Alzheimer disease %K machine learning %K blood biomarkers %K disease history %K Mini-Mental State Examination %K MMSE %K Chinese Longitudinal Healthy Longevity Survey %K CLHLS %D 2025 %7 30.4.2025 %9 %J JMIR Aging %G English %X Background: Cognitive impairment, indicative of Alzheimer disease and other forms of dementia, significantly deteriorates the quality of life of older adult populations and imposes considerable burdens on families and health care systems worldwide. The early identification of individuals at risk for cognitive impairment through a convenient and rapid method is crucial for the timely implementation of interventions. Objective: The objective of this study was to explore the application of machine learning (ML) to integrate blood biomarkers, life behaviors, and disease history to predict the decline in cognitive function. Methods: This approach uses data from the Chinese Longitudinal Healthy Longevity Survey. A total of 2688 participants aged 65 years or older from the 2008‐2009, 2011‐2012, and 2014 Chinese Longitudinal Healthy Longevity Survey waves were included, with cognitive impairment defined as a Mini-Mental State Examination (MMSE) score below 18. The dataset was divided into a training set (n=1331), an internal test set (n=333), and a prospective validation set (n=1024). Participants with a baseline MMSE score of less than 18 were excluded from the cohort to ensure a more accurate assessment of cognitive function. We developed ML models that integrate demographic information, health behaviors, disease history, and blood biomarkers to predict cognitive function at the 3-year follow-up point, specifically identifying individuals who are at risk of experiencing significant declines in cognitive function by that time. Specifically, the models aimed to identify individuals who would experience a significant decline in their MMSE scores (less than 18) by the end of the follow-up period. The performance of these models was evaluated using metrics including accuracy, sensitivity, and the area under the receiver operating characteristic curve. Results: All ML models outperformed the MMSE alone. The balanced random forest achieved the highest accuracy (88.5% in the internal test set and 88.7% in the prospective validation set), albeit with a lower sensitivity, while logistic regression recorded the highest sensitivity. SHAP (Shapley Additive Explanations) analysis identified instrumental activities of daily living, age, and baseline MMSE scores as the most influential predictors for cognitive impairment. Conclusions: The incorporation of blood biomarkers, along with demographic, life behavior, and disease history into ML models offers a convenient, rapid, and accurate approach for the early identification of older adult individuals at risk of cognitive impairment. This method presents a valuable tool for health care professionals to facilitate timely interventions and underscores the importance of integrating diverse data types in predictive health models. %R 10.2196/67437 %U https://aging.jmir.org/2025/1/e67437 %U https://doi.org/10.2196/67437 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64633 %T Feasibility of a Cinematic–Virtual Reality Program Educating Health Professional Students About the Complexity of Geriatric Care: Pilot Pre-Post Study %A Beverly,Elizabeth A %A Miller,Samuel %A Love,Matthew %A Love,Carrie %K virtual reality %K VR %K aging %K geriatric syndromes %K diabetes %K elder abuse and neglect %K gerontology %K geriatrics %K older %K elderly %K education %K student %K cinematic %K video %K head mounted %K feasibility %K experience %K attitude %K opinion %K perception %K elder abuse %K chronic conditions %K older adult care %K health intervention %K randomized controlled trial %D 2025 %7 12.2.2025 %9 %J JMIR Aging %G English %X Background: The US population is aging. With this demographic shift, more older adults will be living with chronic conditions and geriatric syndromes. To prepare the next generation of health care professionals for this aging population, we need to provide training that captures the complexity of geriatric care. Objective: This pilot study aimed to assess the feasibility of the cinematic–virtual reality (cine-VR) training in the complexity of geriatric care. We measured changes in attitudes to disability, self-efficacy to identify and manage elder abuse and neglect, and empathy before and after participating in the training program. Methods: We conducted a single-arm, pretest-posttest pilot study to assess the feasibility of a cine-VR training and measure changes in attitudes to disability, self-efficacy to identify and manage elder abuse and neglect, and empathy. Health professional students from a large university in the Midwest were invited to participate in 1 of 4 cine-VR trainings. Participants completed 3 surveys before and after the cine-VR training. We performed paired t tests to examine changes in these constructs before and after the training. Results: A total of 65 health professional students participated in and completed the full cine-VR training for 100% retention. Participants did not report any technological difficulties or adverse effects from wearing the head-mounted displays or viewing the 360-degree video. Out of the 65 participants, 48 completed the pre- and postassessments. We observed an increase in awareness of discrimination towards people with disability (t47=−3.97; P<.001). In addition, we observed significant improvements in self-efficacy to identify and manage elder abuse and neglect (t47=−3.36; P=.002). Finally, we observed an increase in participants’ empathy (t47=−2.33; P=.02). Conclusions: We demonstrated that our cine-VR training program was feasible and acceptable to health professional students at our Midwestern university. Findings suggest that the cine-VR training increased awareness of discrimination towards people with disabilities, improved self-efficacy to identify and manage elder abuse and neglect, and increased empathy. Future research using a randomized controlled trial design with a larger, more diverse sample and a proper control condition is needed to confirm the effectiveness of our cine-VR training. %R 10.2196/64633 %U https://aging.jmir.org/2025/1/e64633 %U https://doi.org/10.2196/64633 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e62930 %T Exploring Dance as a Therapeutic Approach for Parkinson Disease Through the Social Robotics for Active and Healthy Ageing (SI-Robotics): Results From a Technical Feasibility Study %A Bevilacqua,Roberta %A Maranesi,Elvira %A Benadduci,Marco %A Cortellessa,Gabriella %A Umbrico,Alessandro %A Fracasso,Francesca %A Melone,Giovanni %A Margaritini,Arianna %A La Forgia,Angela %A Di Bitonto,Pierpaolo %A Potenza,Ada %A Fiorini,Laura %A La Viola,Carlo %A Cavallo,Filippo %A Leone,Alessandro %A Caroppo,Andrea %A Rescio,Gabriele %A Marzorati,Mauro %A Cesta,Amedeo %A Pelliccioni,Giuseppe %A Riccardi,Giovanni Renato %A Rossi,Lorena %K Parkinson disease %K rehabilitation %K Irish dancing %K balance %K gait %K socially interacting robot %D 2025 %7 14.1.2025 %9 %J JMIR Aging %G English %X Background: Parkinson disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms. Recently, dance has started to be considered an effective intervention for people with PD. Several findings in the literature emphasize the necessity for deeper exploration into the synergistic impacts of dance therapy and exergaming for PD management. Moreover, socially engaging robotic platforms equipped with advanced interaction and perception features offer potential for monitoring patients’ posture and enhancing workout routines with tailored cues. Objective: This paper presents the results of the Social Robotics for Active and Healthy Ageing (SI-Robotics) project, aimed at designing an innovative rehabilitation program targeted at seniors affected by (early-stage) PD. This study therefore aims to assess the usefulness of a dance-based rehabilitation program enriched by artificial intelligence–based exergames and contextual robotic assistance in improving motor function, balance, gait, and quality of life in patients with PD. The acceptability of the system is also investigated. Methods: The study is designed as a technical feasibility pilot to test the SI-Robotics system. For this study, 20 patients with PD were recruited. A total of 16 Irish dance–based rehabilitation sessions of 50 minutes were conducted (2 sessions per week, for 8 wks), involving 2 patients at a time. The designed rehabilitation session involves three main actors: (1) a therapist, (2) a patient, and (3) a socially interacting robot. To stimulate engagement, sessions were organized in the shape of exergames where an avatar shows patients the movements they should perform to correctly carry out a dance-based rehabilitation exercise. Results: Statistical analysis reveals a significant difference on the Performance-Oriented Mobility Assessment scale, both on balance and gait aspects, together with improvements in Short Physical Performance Battery, Unified Parkinson Disease Rating Scale–III, and Timed Up and Go test, underlying the usefulness of the rehabilitation intervention on the motor symptoms of PD. The analysis of the Unified Theory of Acceptance and Use of Technology subscales provided valuable insights into users’ perceptions and interactions with the system. Conclusions: This research underscores the promise of merging dance therapy with interactive exergaming on a robotic platform as an innovative strategy to enhance motor function, balance, gait, and overall quality of life for patients grappling with PD. Trial Registration: ClinicalTrials.gov NCT05005208; https://clinicaltrials.gov/study/NCT05005208 %R 10.2196/62930 %U https://aging.jmir.org/2025/1/e62930 %U https://doi.org/10.2196/62930 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64374 %T Development and Validation of a Predictive Model Based on Serum Silent Information Regulator 6 Levels in Chinese Older Adult Patients: Cross-Sectional Descriptive Study %A You,Yuzi %A Liang,Wei %A Zhao,Yajie %K aging %K coronary artery disease %K nomogram %K SIRT6 %K TyG index %K silent information regulator 6 %K triglyceride glucose index %D 2025 %7 15.1.2025 %9 %J JMIR Aging %G English %X Background: Serum levels of silent information regulator 6 (SIRT6), a key biomarker of aging, were identified as a predictor of coronary artery disease (CAD), but whether SIRT6 can distinguish severity of coronary artery lesions in older adult patients is unknown. Objectives: This study developed a nomogram to demonstrate the functionality of SIRT6 in assessing severity of coronary artery atherosclerosis. Methods: Patients aged 60 years and older with angina pectoris were screened for this single-center clinical study between October 1, 2022, and March 31, 2023. Serum specimens of eligible patients were collected for SIRT6 detection by enzyme-linked immunosorbent assay. Clinical data and putative predictors, including 29 physiological characteristics, biochemical parameters, carotid artery ultrasonographic results, and complete coronary angiography findings, were evaluated, with CAD diagnosis as the primary outcome. The nomogram was derived from the Extreme Gradient Boosting (XGBoost) model, with logistic regression for variable selection. Model performance was assessed by examining discrimination, calibration, and clinical use separately. A 10-fold cross-validation technique was used to compare all models. The models’ performance was further evaluated on the internal validation set to ensure that the obtained results were not due to overoptimization. Results: Eligible patients (n=222) were divided into 2 cohorts: the development cohort (n=178) and the validation cohort (n=44). Serum SIRT6 levels were identified as both an independent risk factor and a predictor for CAD in older adults. The area under the receiver operating characteristic curve (AUROC) was 0.725 (95% CI 0.653‐0.797). The optimal cutoff value of SIRT6 for predicting CAD was 546.384 pg/mL. Predictors included in this nomogram were serum SIRT6 levels, triglyceride glucose (TyG) index, and apolipoprotein B. The model achieved an AUROC of 0.956 (95% CI 0.928‐0.983) in the development cohort. Similarly, in the internal validation cohort, the AUROC was 0.913 (95% CI 0.828‐0.999). All models demonstrated satisfactory calibration, with predicted outcomes closely aligning with actual results. Conclusions: SIRT6 shows promise in predicting CAD, with enhanced predictive abilities when combined with the TyG index. In clinical settings, monitoring fluctuations in SIRT6 and TyG may offer valuable insights for early CAD detection. The nomogram for CAD outcome prediction in older adult patients with angina pectoris may aid in clinical trial design and personalized clinical decision-making, particularly in institutions where SIRT6 is being explored as a biomarker for aging or cardiovascular health. %R 10.2196/64374 %U https://aging.jmir.org/2025/1/e64374 %U https://doi.org/10.2196/64374 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e55455 %T Wearable Smartphone-Based Multisensory Feedback System for Torso Posture Correction: Iterative Design and Within-Subjects Study %A Pereira,Amanda Polin %A Machado Neto,Olibario Jose %A Elui,Valeria Meirelles Carril %A Pimentel,Maria da Graca Campos %+ Institute of Mathematics and Computer Sciences, University of São Paulo, Avenida Trabalhador São-carlense, 400, 1203, São Carlos SP, 13566-590, Brazil, 55 1633739671, mgp@icmc.usp.br %K stroke rehabilitation %K posture %K postural balance %K wearable technology %K multisensory feedback %K smartphone %K stroke %K mHealth %K mobile health %K digital health %K digital technology %K digital intervention %K wearable technology %K gerontology %D 2025 %7 22.1.2025 %9 Original Paper %J JMIR Aging %G English %X Background: The prevalence of stroke is high in both males and females, and it rises with age. Stroke often leads to sensor and motor issues, such as hemiparesis affecting one side of the body. Poststroke patients require torso stabilization exercises, but maintaining proper posture can be challenging due to their condition. Objective: Our goal was to develop the Postural SmartVest, an affordable wearable technology that leverages a smartphone's built-in accelerometer to monitor sagittal and frontal plane changes while providing visual, tactile, and auditory feedback to guide patients in achieving their best-at-the-time posture during rehabilitation. Methods: To design the Postural SmartVest, we conducted brainstorming sessions, therapist interviews, gathered requirements, and developed the first prototype. We used this initial prototype in a feasibility study with individuals without hemiparesis (n=40, average age 28.4). They used the prototype during 1-hour seated sessions. Their feedback led to a second prototype, which we used in a pilot study with a poststroke patient. After adjustments and a kinematic assessment using the Vicon Gait Plug-in system, the third version became the Postural SmartVest. We assessed the Postural SmartVest in a within-subject experiment with poststroke patients (n=40, average age 57.1) and therapists (n=20, average age 31.3) during rehabilitation sessions. Participants engaged in daily activities, including walking and upper limb exercises, without and with app feedback. Results: The Postural SmartVest comprises a modified off-the-shelf athletic lightweight compression tank top with a transparent pocket designed to hold a smartphone running a customizable Android app securely. This app continuously monitors sagittal and frontal plane changes using the built-in accelerometer sensor, providing multisensory feedback through audio, vibration, and color changes. Patients reported high ratings for weight, comfort, dimensions, effectiveness, ease of use, stability, durability, and ease of adjustment. Therapists noted a positive impact on rehabilitation sessions and expressed their willingness to recommend it. A 2-tailed t-test showed a significant difference (P<.001) between the number of the best-at-the-time posture positions patients could maintain in 2 stages, without feedback (mean 13.1, SD 7.12) and with feedback (mean 4.2, SD 3.97), demonstrating the effectiveness of the solution in improving posture awareness. Conclusions: The Postural SmartVest aids therapists during poststroke rehabilitation sessions and assists patients in improving their posture during these sessions. %R 10.2196/55455 %U https://aging.jmir.org/2025/1/e55455 %U https://doi.org/10.2196/55455 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64148 %T Estimation of Machine Learning–Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular Diseases: UK Biobank Study %A Gu,Zhengsheng %A Liu,Shuang %A Ma,Huijuan %A Long,Yifan %A Jiao,Xuehao %A Gao,Xin %A Du,Bingying %A Bi,Xiaoying %A Shi,Xingjie %+ KLATASDS-MOE, Academy of Statistics and Interdisciplinary Sciences, School of Statistics, East China Normal University, No 3663 North Zhongshan Road, Putuo District, Shanghai, 200062, China, 86 21 622 332 23, xjshi@fem.ecnu.edu.cn %K atherosclerotic cardiovascular disease %K dementia %K Alzheimer disease %K vascular dementia %K machine learning %K UK Biobank %D 2025 %7 26.2.2025 %9 Original Paper %J JMIR Aging %G English %X Background: The atherosclerotic cardiovascular disease (ASCVD) is associated with dementia. However, the risk factors of dementia in patients with ASCVD remain unclear, necessitating the development of accurate prediction models. Objective: The aim of the study is to develop a machine learning model for use in patients with ASCVD to predict dementia risk using available clinical and sociodemographic data. Methods: This prognostic study included patients with ASCVD between 2006 and 2010, with registration of follow-up data ending on April 2023 based on the UK Biobank. We implemented a data-driven strategy, identifying predictors from 316 variables and developing a machine learning model to predict the risk of incident dementia, Alzheimer disease, and vascular dementia within 5, 10, and longer-term follow-up in patients with ASCVD. Results: A total of 29,561 patients with ASCVD were included, and 1334 (4.51%) developed dementia during a median follow-up time of 10.3 (IQR 7.6-12.4) years. The best prediction model (UK Biobank ASCVD risk prediction model) was light gradient boosting machine, comprising 10 predictors including age, time to complete pairs matching tasks, mean time to correctly identify matches, mean sphered cell volume, glucose levels, forced expiratory volume in 1 second z score, C-reactive protein, forced vital capacity, time engaging in activities, and age first had sexual intercourse. This model achieved the following performance metrics for all incident dementia: area under the receiver operating characteristic curve: mean 0.866 (SD 0.027), accuracy: mean 0.883 (SD 0.010), sensitivity: mean 0.637 (SD 0.084), specificity: mean 0.914 (SD 0.012), precision: mean 0.479 (SD 0.031), and F1-score: mean 0.546 (SD 0.043). Meanwhile, this model was well-calibrated (Kolmogorov-Smirnov test showed goodness-of-fit P value>.99) and maintained robust performance across different temporal cohorts. Besides, the model had a beneficial potential in clinical practice with a decision curve analysis. Conclusions: The findings of this study suggest that predictive modeling could inform patients and clinicians about ASCVD at risk for dementia. %M 40009844 %R 10.2196/64148 %U https://aging.jmir.org/2025/1/e64148 %U https://doi.org/10.2196/64148 %U http://www.ncbi.nlm.nih.gov/pubmed/40009844 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e68890 %T Online Communities as a Support System for Alzheimer Disease and Dementia Care: Large-Scale Exploratory Study %A Kaliappan,Sidharth %A Liu,Chunyu %A Jain,Yoshee %A Karkar,Ravi %A Saha,Koustuv %+ Siebel School of Computing and Data Science, The Grainger College of Engineering, University of Illinois Urbana-Champaign, 201 N. Goodwin Ave, Siebel Center for Computer Science, 4212, Urbana, IL, 61801, United States, 1 2172443824, ksaha2@illinois.edu %K social media %K natural language %K Alzheimer disease %K social support %K online communities %K machine learning %D 2025 %7 5.5.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Alzheimer disease (AD) is the leading type of dementia, demanding comprehensive understanding and intervention strategies. In the United States, where over 6 million people are impacted, the prevalence of AD and related dementias (AD/ADRD) presents a growing public health challenge. However, individuals living with AD/ADRD and their caregivers frequently express feelings of marginalization, describing interactions characterized by perceptions of patient infantilization and a lack of respect. Objective: This study aimed to address 2 key research questions (RQs). For RQ1, we investigated the needs and concerns expressed by participants in online social communities focused on AD/ADRD, specifically on 2 platforms–Reddit’s r/Alzheimers and ALZConnected. For RQ2, we examined the prevalence and distribution of social support corresponding to these needs and concerns, and the association between these needs and received support. Methods: We collected 13,429 posts and comments from the r/Alzheimers subreddit spanning July 2014 to November 2023, and 90,113 posts and comments from ALZConnected between December 2020 (the community’s earliest post) and November 2023. We conducted topic modeling using latent Dirichlet allocation (LDA), followed by labeling to identify the major topical themes of discussions. We used transfer learning classifiers to identify the occurrences of emotional support (ES) and informational support (IS) in the comments (or responses) in the discussions. We built regression models to examine how various topical themes are associated with the kinds of support received. Results: Our analysis revealed a diverse range of topics reflecting community members’ varying needs and concerns of individuals affected by AD/ADRD. These themes encapsulate the primary discussions within the online communities: memory care, nursing and caregiving, gratitude and acknowledgment, and legal and financial considerations. Our findings indicated a higher prevalence of IS compared to ES. Regression models revealed that ES primarily occurs in posts relating to nursing and caring, and IS primarily occurs in posts concerning medical conditions and diagnosis, legal and financial, and caregiving at home. Conclusions: This study reveals that online communities dedicated to AD/ADRD support engage in discussions on a wide range of topics, such as memory care, nursing, caregiving, and legal and financial challenges. The findings shed light on the key pain points and concerns faced by individuals managing AD/ADRD in their households, revealing how they leverage online platforms for guidance and support. These insights underscore the need for targeted institutional and social interventions to address the specific needs of AD/ADRD patients, caregivers, and other family members. %M 40324770 %R 10.2196/68890 %U https://aging.jmir.org/2025/1/e68890 %U https://doi.org/10.2196/68890 %U http://www.ncbi.nlm.nih.gov/pubmed/40324770 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e55929 %T Revisits, Readmission, and Mortality From Emergency Department Admissions for Older Adults With Vague Presentations: Longitudinal Observational Study %A Alvarez Avendano,Sebastian Alejandro %A Cochran,Amy %A Odeh Couvertier,Valerie %A Patterson,Brian %A Shah,Manish %A Zayas-Caban,Gabriel %K gerontology %K geriatric %K older adults %K elderly %K older people %K aging %K emergency department %K emergency room %K ED %K disposition decision %K disposition %K discharge %K admission %K revisit %K readmission %K observational study %K health %K hospital %D 2025 %7 6.2.2025 %9 %J JMIR Aging %G English %X Background: Older adults (65 years and older) often present to the emergency department (ED) with an unclear need for hospitalization, leading to potentially harmful and costly care. This underscores the importance of measuring the trade-off between admission and discharge for these patients in terms of patient outcomes. Objective: This study aimed to measure the relationship between disposition decisions and 3-day, 9-day, and 30-day revisits, readmission, and mortality, using causal inference methods that adjust for potential measured and unmeasured confounding. Methods: A longitudinal observational study (n=3591) was conducted using electronic health records from a large tertiary teaching hospital with an ED between January 1, 2014 and September 27, 2018. The sample consisted of older adult patients with 1 of 6 presentations with significant variability in admission: falls, weakness, syncope, urinary tract infection, pneumonia, and cellulitis. The exposure under consideration was the ED disposition decision (admission to the hospital or discharge). Nine outcome variables were considered: ED revisits, hospital readmission, and mortality within 3, 9, and 30 days of being discharged from either the hospital for admitted patients or the ED for discharged patients. Results: Admission was estimated to significantly decrease the risk of an ED revisit after discharge (30-day window: −6.4%, 95% CI −7.8 to −5.0), while significantly increasing the risk of hospital readmission (30-day window: 5.8%, 95% CI 5.0 to 6.5) and mortality (30-day window: 1.0%, 95% CI 0.4 to 1.6). Admission was found to be especially adverse for patients with weakness and pneumonia, and relatively less adverse for older adult patients with falls and syncope. Conclusions: Admission may not be the safe option for older adults with gray area presentations, and while revisits and readmissions are commonly used to evaluate the quality of care in the ED, their divergence suggests that caution should be used when interpreting either in isolation. %R 10.2196/55929 %U https://aging.jmir.org/2025/1/e55929 %U https://doi.org/10.2196/55929 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e69107 %T Passive Remote Monitoring Technologies’ Influence on Home Care Clients’ Ability to Stay Home: Multiprovincial Randomized Controlled Trial %A Donelle,Lorie %A Hiebert,Bradley %A Warner,Grace %A Reid,Michael %A Reid,Jennifer %A Shariff,Salimah %A Richard,Emily %A Regan,Sandra %A Weeks,Lori %A Ledoux,Kathleen %+ College of Nursing, University of South Carolina, 1601 Greene St, Columbia, SC, 29208, United States, 1 803 777 6528, ldonelle@mailbox.sc.edu %K remote monitoring technology %K home care %K health service use %K aging in place %D 2025 %7 19.3.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Researchers in Nova Scotia and Ontario, Canada, implemented a passive remote monitoring (PRM) model of home care unique to their health system contexts. Each PRM model integrated tailored PRM devices (eg, motion sensors, cameras, and door alarms) into home care patients’ residences with the aim of linking patients, family and friend caregivers, and health care providers to support older adults’ aging in place. Objective: The purpose of this study was to examine the use of PRM technologies in the home to support older adults’ safe aging in place and avoidance or delay of higher levels of care. Methods: This multiprovincial pragmatic randomized controlled trial examined how PRM technologies support older adults to safely remain in their home and avoid or delay admission to higher levels of care. Pairs of home care patients and their family and friend caregivers were recruited in Ontario and Nova Scotia. Participant pairs were randomly assigned to one of two conditions: (1) standard home care (ie, control) or (2) standard home care plus study-provided PRM (ie, intervention). Participants provided their provincial health insurance numbers to link with provincial health administrative databases and identify if patients were admitted to higher levels of care after 1 year. Cox proportional hazards models were used to evaluate the primary outcome in each province. Results: In total, 313 patient-caregiver pairs were recruited: 174 pairs in Ontario (intervention: n=60; control: n=114) and 139 pairs in Nova Scotia (intervention: n=45; control: n=94). Results indicate PRM was associated with a nonsignificant 30% reduction in risk of patients being admitted to higher levels of care in Ontario (hazard ratio 0.7, 95% CI 0.3-1.4) and no reduction in risk in Nova Scotia (hazard ratio 1.1, 95% CI 0.3-3.7). Adjusting for patient sex had no impact on model estimates for either province. Conclusions: Limitations related, in part, to the impact of the COVID-19 pandemic may have contributed to the effectiveness of the intervention. While our study did not yield statistically significant results (P=.30 and P=.90) regarding the effectiveness of the PRM model in prolonging home stays, the observed trends suggest that technology-assisted aging in place may be a valuable goal for older adults. Further study is required to understand if longer follow-up time allows more effects of PRM on patients’ avoidance of higher levels of care to be detected. Trial Registration: ISRCTN ISRCTN79884651; https://www.isrctn.com/ISRCTN79884651 International Registered Report Identifier (IRRID): RR2-10.2196/15027 %M 40106817 %R 10.2196/69107 %U https://aging.jmir.org/2025/1/e69107 %U https://doi.org/10.2196/69107 %U http://www.ncbi.nlm.nih.gov/pubmed/40106817 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e69008 %T Exploring Smart Health Wearable Adoption Among Singaporean Older Adults Based on Self-Determination Theory: Web-Based Survey Study %A Kang,Hyunjin %A Yang,Tingting %A Banu,Nazira %A Ng,Sheryl Wei Ting %A Lee,Jeong Kyu %K smart health wearables %K self-determination theory %K AI anxiety %K perceived privacy risk %K health consciousness %D 2025 %7 19.3.2025 %9 %J JMIR Aging %G English %X Background: Smart health wearables offer significant benefits for older adults, enabling seamless health monitoring and personalized suggestions based on real-time data. Promoting adoption and sustained use among older adults is essential to empower autonomous health management, leading to better health outcomes, improved quality of life, and reduced strain on health care systems. Objective: This study investigates how autonomy-related contextual factors, including artificial intelligence (AI) anxiety, perceived privacy risks, and health consciousness, are related to older adults’ psychological needs of competence, autonomy, and relatedness (RQ1). We then examined whether the fulfillment of these needs positively predicts older adults’ intentions to adopt these devices (H1), and how they mediate the relationship between these factors and older adults’ intentions to use smart health wearables (RQ2). Additionally, it compares experienced and nonexperienced older adult users regarding the influence of these psychological needs on use intentions (RQ3). Methods: A web-based survey was conducted with individuals aged 60 years and above in Singapore, using a Qualtrics survey panel. A total of 306 participants (177 male; mean age of 65.47 years, age range 60‐85 years) completed the survey. A structural equation model was used to analyze associations among AI anxiety, perceived privacy risks, and health consciousness, and the mediating factors of competence, autonomy, and relatedness, as well as their relationship to smart health wearable use intention. Results: Health consciousness positively influenced all intrinsic motivation factors—competence, autonomy, and relatedness—while perceived privacy risks negatively affected all three. AI anxiety was negatively associated with competence only. Both privacy risk perceptions and health consciousness were indirectly linked to older adults’ intentions to use smart health wearables through competence and relatedness. No significant differences were found in motivational structures between older adults with prior experience and those without. Conclusions: This study contributes to the application of self-determination theory in promoting the use of smart technology for health management among older adults. The results highlight the critical role of intrinsic motivation—particularly competence—in older adults’ adoption of smart health wearables. While privacy concerns diminish motivation, health consciousness fosters it. The study results offer valuable implications for designing technologies that align with older adults’ motivations, potentially benefiting aging populations in other technologically advanced societies. Developers should focus on intuitive design, transparent privacy practices, and social features to encourage adoption, empowering older adults to use smart wearables for proactive health management. %R 10.2196/69008 %U https://aging.jmir.org/2025/1/e69008 %U https://doi.org/10.2196/69008 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e67298 %T Relationship Between Within-Session Digital Motor Skill Acquisition and Alzheimer Disease Risk Factors Among the MindCrowd Cohort: Cross-Sectional Descriptive Study %A Hooyman,Andrew %A Huentelman,Matt J %A De Both,Matt %A Ryan,Lee %A Duff,Kevin %A Schaefer,Sydney Y %K digital health technology %K web-based assessment %K aging %K APOE %K motor skills %K sensitivity %K risk factors %K adults %K older adults %D 2025 %7 24.4.2025 %9 %J JMIR Aging %G English %X Background: Previous research has shown that in-lab motor skill acquisition (supervised by an experimenter) is sensitive to biomarkers of Alzheimer disease (AD). However, remote unsupervised screening of AD risk through a skill-based task via the web has the potential to sample a wider and more diverse pool of individuals at scale. Objective: The purpose of this study was to examine a web-based motor skill game (“Super G”) and its sensitivity to risk factors of AD (eg, age, sex, APOE ε4 carrier status, and verbal learning deficits). Methods: Emails were sent to 662 previous MindCrowd participants who had agreed to be contacted for future research and have their APOE ε4 carrier status recorded and those who were at least 45 years of age or older. Participants who chose to participate were redirected to the Super G site where they completed the Super G task using their personal computer remotely and unsupervised. Once completed, different Super G variables were derived. Linear and logistic multivariable regression was used to examine the relationship between available AD risk factors (age, sex, APOE ε4 carrier status, and verbal learning) and distinct Super G performance metrics. Results: Fifty-four participants (~8% response rate) from the MindCrowd web-based cohort (mean age of 62.39 years; 39 females; and 23 APOE ε4 carriers) completed 75 trials of Super G. Results show that Super G performance was significantly associated with each of the targeted risk factors. Specifically, slower Super G response time was associated with being an APOE ε4 carrier (odds ratio 0.12, 95% CI 0.02-0.44; P=.006), greater Super G time in target (TinT) was associated with being male (odds ratio 32.03, 95% CI 3.74-1192,61; P=.01), and lower Super G TinT was associated with greater age (β −3.97, 95% CI −6.64 to −1.30; P=.005). Furthermore, a sex-by-TinT interaction demonstrated a differential relationship between Super G TinT and verbal learning depending on sex (βmale:TinT 6.77, 95% CI 0.34-13.19; P=.04). Conclusions: This experiment demonstrated that this web-based game, Super G, has the potential to be a skill-based digital biomarker for screening of AD risk on a large scale with relatively limited resources. %R 10.2196/67298 %U https://aging.jmir.org/2025/1/e67298 %U https://doi.org/10.2196/67298 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e68147 %T Perceptions of the Use of Mobile Apps to Assess Sleep-Dependent Memory in Older Adults With Subjective and Objective Cognitive Impairment: Focus Group Approach %A Lam,Aaron %A Simonetti,Simone %A D'Rozario,Angela %A Ireland,David %A Bradford,DanaKai %A Fripp,Jurgen %A Naismith,Sharon L %K aging %K mild cognitive impairment %K subjective cognitive impairment %K digital health %K cognition %K neuropsychology %K sleep %D 2025 %7 28.4.2025 %9 %J JMIR Aging %G English %X Background: Sleep-dependent memory (SDM) is the phenomenon where newly obtained memory traces are consolidated from short-term memory stores to long-term memory, underpinning memory for daily life. Administering SDM tasks presents considerable challenges, particularly for older adults with memory concerns, due to the need for sleep laboratories and research staff being present to administer the task. In response, we have developed a prototype mobile app aimed at automating the data collection process. Objective: This study investigates the perspectives of older adults, with subjective or objective cognitive impairment, regarding barriers and facilitators to using a new mobile app for at-home assessment of SDM. Methods: In total, 11 participants aged 50 years and older were recruited from the Healthy Brain Ageing memory clinic, a specialized research memory clinic that focuses on the assessment and early intervention of cognitive decline. Two focus groups were conducted and thematically analyzed using NVivo (version 13; Lumivero). Results: On average, participants were aged 68.5 (SD 5.1) years, and 4/11 were male. Eight participants had subjective cognitive impairment, and 3 participants had mild cognitive (objective) impairment. Two main themes emerged from the focus groups, shedding light on participants’ use of mobile phones and the challenges and facilitators associated with transitioning from traditional laboratory-based assessments to home assessments. These challenges include maintaining accurate data, engaging with humans versus robots, and ensuring accessibility and task compliance. Additionally, potential solutions to these challenges were identified. Conclusions: Our findings underscore the importance of app flexibility in accommodating diverse user needs and preferences as well as in overcoming barriers. While some individuals required high-level assistance, others expressed the ability to navigate the app independently or with minimal support. In conclusion, older adults provided valuable insights into the app modifications, user needs, and accessibility requirements enabling home-based SDM assessment. %R 10.2196/68147 %U https://aging.jmir.org/2025/1/e68147 %U https://doi.org/10.2196/68147 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e67322 %T A Smartphone-Based Timed Up and Go Test Self-Assessment for Older Adults: Validity and Reliability Study %A Böttinger,Melissa Johanna %A Mellone,Sabato %A Klenk,Jochen %A Jansen,Carl-Philipp %A Stefanakis,Marios %A Litz,Elena %A Bredenbrock,Anastasia %A Fischer,Jan-Philipp %A Bauer,Jürgen M %A Becker,Clemens %A Gordt-Oesterwind,Katharina %K timed up and go test %K self-assessment %K instrumented assessment %K technology-based assess-ment %K physical capacity %K mobility %K aged %K mobile applications %K smartphone %K diagnostic self evaluation %D 2025 %7 21.3.2025 %9 %J JMIR Aging %G English %X Background: The Timed Up and Go test (TUG) is recommended as an evidence-based tool for measuring physical capacity. Instrumented TUG (iTUG) approaches expand classical supervised clinical applications offering the potential of self-assessment for older adults. Objective: This study aimed to evaluate the concurrent validity and test-retest reliability of a smartphone-based TUG self-assessment “up&go app.” Methods: A total of 52 community-dwelling older adults (>67 years old) were recruited. A validated and medically certified system attached with a belt at the lower back was used as a reference system to validate the “up&go app” algorithm. The participants repeated the TUG 5 times wearing, a smartphone with the “up&go app” in their front trouser pocket and an inertial sensor to test the concurrent validity. A subsample of 37 participants repeated the “up&go app” measurement 2 weeks later to examine the test-retest reliability. Results: The correlation between the “up&go app” and the reference measurement was r=0.99 for the total test duration and r=0.97 for the 5 single repetitions. Agreement between the 5 repetitions was intraclass correlation coefficient (ICC)=0.9 (0.84‐0.94). Leaving out the first repetition, the agreement was ICC=0.95 (0.92‐0.97). Test-retest agreement had an ICC=0.79 (0.53‐0.9). Conclusions: The duration of 5 repetitions of the TUG test, measured with the pocket-worn “up&go app,” was very consistent with the results of a lower-back sensor system, indicating excellent concurrent validity. Participants walked slower in the first round than in the other 4 repetitions within a test run. Test-retest reliability was also excellent. The “up&go app” provides a useful smartphone-based approach to measure 5 repetitions of the TUG. The app could be used by older adults as a self-screening and monitoring tool of physical capacity at home and thereby help to early identify functional limitations and take interventions when necessary. %R 10.2196/67322 %U https://aging.jmir.org/2025/1/e67322 %U https://doi.org/10.2196/67322 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64847 %T Implications of Public Disclosure of Personal Information in a Mobile Alert App for People Living With Dementia Who Go Missing: Qualitative Descriptive Study %A Adekoya,Adebusola %A Daum,Christine %A Neubauer,Noelannah %A Miguel-Cruz,Antonio %A Liu,Lili %+ , School of Public Health Sciences, Faculty of Health, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 519 888 4567, lili.liu@uwaterloo.ca %K alert systems %K technology %K missing persons %K dementia %K autonomy %K privacy %K stigmatization %K consent %D 2025 %7 7.2.2025 %9 Original Paper %J JMIR Aging %G English %X Background: People living with dementia are at risk of getting lost and going missing due to memory loss, confusion, and disorientation. Missing person incidents involving people living with dementia are increasing. Alert systems such as Community ASAP can promote community engagement in locating missing persons with dementia and aid in search and rescue efforts. However, the implications of public disclosure of personal information such as name, age, sex, and physical description within such alert systems have yet to be explored. Objective: This study aimed to identify and discuss the implications of public disclosure of personal information in Community ASAP for people living with dementia at risk of going missing. Methods: This study used a qualitative descriptive research design drawing from naturalistic inquiry. A total of 19 participants including people living with dementia, care partners, first responders, and service providers were recruited from Ontario, Alberta, and British Columbia, Canada. Semistructured interviews were used to explore participants’ perspectives on the perceived implications of the release of personal information when using Community ASAP. NVivo (version 12) was used to manage data, and conventional content analysis was conducted to identify key themes of the implications of public disclosure of personal information in Community ASAP. Results: In total, 10/19 (53%) of the participants were women and 9/19 (47%) were men. Of the 19 participants, 3 (16%) were people living with dementia, 5 (26%) were care partners, 4 (21%) were first responders, and 7 (37%) were service providers. In total, 4 key themes were identified as implications of public disclosure of personal information in Community ASAP: right to autonomy, safety versus privacy, informed and knowledgeable consent, and stigmatization. Participants discussed how the public disclosure of personal information in Community ASAP could undermine a person’s choice not to be found and contribute to stigmatization. Participants emphasized a need to balance safety and privacy concerns. Informed and knowledgeable consent is important when using an alert system to locate missing persons with dementia. Conclusions: Community ASAP can promote community engagement in locating missing persons with dementia. However, the public disclosure of personal information in alert systems has implications. Users’ right to autonomy, a balance between safety and privacy, informed and knowledgeable consent, and risks of stigmatization are perceived impacts of disclosure of personal information in alert systems. %M 39918846 %R 10.2196/64847 %U https://aging.jmir.org/2025/1/e64847 %U https://doi.org/10.2196/64847 %U http://www.ncbi.nlm.nih.gov/pubmed/39918846 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e60156 %T Transcultural Adaptation, Validation, Psychometric Analysis, and Interpretation of the 22-Item Thai Senior Technology Acceptance Model for Mobile Health Apps: Cross-Sectional Study %A Buawangpong,Nida %A Siviroj,Penprapa %A Pinyopornpanish,Kanokporn %A Sirikul,Wachiranun %+ Department of Community Medicine, Faculty of Medicine, Chiang Mai University, 110 Intrawarorot road, Sriphum, Mueng, Chiang Mai, 50200, Thailand, 66 53935472, wachiranun.sir@cmu.ac.th %K STAM %K senior technology acceptance model %K validity %K reliability %K mHealth %K older adult %K technology acceptance %K mobile health %K app %K transcultural adaptation %K psychometric analysis %K geriatrics %K cross-sectional study %K Thai %K theory analysis %K Cronbach α %K McDonald ω %K quality of life %K well-being %K social media %K telehealth %K health informatics %K eHealth %K mobile phone %D 2025 %7 11.3.2025 %9 Original Paper %J JMIR Aging %G English %X Background: The rapid advancement of technology has made mobile health (mHealth) a promising tool to mitigate health problems, particularly among older adults. Despite the numerous benefits of mHealth, assessing individual acceptance is required to address the specific needs of older people and promote their intention to use mHealth. Objective: This study aims to adapt and validate the senior technology acceptance model (STAM) questionnaire for assessing mHealth acceptance in the Thai context. Methods: In this cross-sectional study, we adapted the original, 38-item, English version of the STAM using a 10-point Likert scale for mHealth acceptability among the Thai population. We translated the mHealth STAM into Thai using forward and backward translation. A total of 15 older adults and experts completed the pilot questionnaire and were interviewed to assess its validity. The pilot items of the Thai mHealth STAM were then reworded and revised for better comprehension and cross-cultural compatibility. The construct validity of the Thai mHealth STAM was evaluated by a multidimensional approach, including exploratory and confirmatory factor analysis and nonparametric item response theory analysis. Discriminative indices consisting of sensitivity, specificity, and area under the receiver operating characteristic (AUROC) were used to determine appropriate banding and discriminant validity for the intention to use mHealth. Internal consistency was assessed using Cronbach α and McDonald ω coefficients. Results: Out of the 1100 participants with a mean age of 62.3 (SD 8.8) years, 360 (32.7%) were adults aged 45-59 years, and 740 (67.3%) were older adults aged 60 years and older. Of the 40-item pilot questionnaire, exploratory factor analysis identified 22 items with factor loadings >0.4 across 7 principal components, explaining 91.45% of the variance. Confirmatory factor analysis confirmed that 9-dimensional sets of 22 items had satisfactory fit indices (comparative fit index=0.976, Tucker-Lewis index=0.968, root mean square error of approximation=0.043, standardized root mean squared residual=0.044, and R2 for each item>0.30). The score banding D (low≤151, moderate 152-180, and high≥181) was preferred as the optimal 22-item Thai mHealth STAM cutoff score based on the highest sensitivity of 89% (95% CI 86.1%-91.5%) and AUROC of 72.4% (95% CI 70%-74.8%) for predicting the intention to use mHealth. The final Thai mHealth STAM, consisting of 22 items, exhibited remarkable internal consistency, as evidenced by a Cronbach α of 0.88 (95% CI 0.87-0.89) and a McDonald ω of 0.85 (95% CI 0.83-0.87). For all 22 items, the corrected item-total correlations ranged between 0.26 and 0.71. Conclusions: The 22-item Thai mHealth STAM demonstrated satisfactory psychometric properties in both validity and reliability. The questionnaire has the potential to serve as a practical questionnaire in assessing the acceptance and intention to use mHealth among pre-older and older adults. %M 40068149 %R 10.2196/60156 %U https://aging.jmir.org/2025/1/e60156 %U https://doi.org/10.2196/60156 %U http://www.ncbi.nlm.nih.gov/pubmed/40068149 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e67632 %T Network Analysis of Key Instrumental Activities of Daily Living and Cognitive Domains for Targeted Intervention in US Older Adults Without Dementia: Cross-Sectional Study %A Li,Jiaying %A He,Rendong %A Hsu,Erh-Chi %A Li,Junxin %K cognition function %K older adults %K intervention targets %K elder %K elderly %K cognitive impairment %K stimulating activity %K instrumental activities of daily living %K IADL %K daily living activity %K cognitive domain %K non-demented %K cognitive network %K holistic cognition %K holistic cognition function %K network comparison %K central variables %K bridge variables %K network analysis %D 2025 %7 19.3.2025 %9 %J JMIR Aging %G English %X Background: Cognitive impairment in older adults reduces independence and raises health care costs but can be mitigated through stimulating activities. Based on network theory, intricate relationships within and between clusters of instrumental activities of daily living (IADLs) and cognitive domains suggest the existence of central IADLs and cognitive domains, as well as bridge IADLs. Modifying these can significantly enhance daily living activities and cognitive functions holistically. Objective: This study aims to identify central IADLs (key activities within the IADL network), central cognitive domains (key domains within the cognitive network), and bridge IADLs (linking IADL and cognitive networks). These insights will inform targeted interventions to effectively improve IADL and cognitive well-being in older adults. Methods: A cross-sectional analysis of adults aged 65 years and older in the United States focused on 5 IADLs and 6 cognitive domains from the National Health and Aging Trends Study (NHATS). Network analysis identified central and bridge variables. Nonparametric and case-dropping bootstrap methods checked network stability. Network comparison tests assessed sex differences with Benjamini-Hochberg adjustments. Results: Of the 2239 participants, 56.4% were female (n=976). We computed and tested 3 networks: IADL, cognition, and bridge-with correlation stability coefficients of 0.67, 0.75, and 0.44, respectively (all>0.25). Meal preparation was identified as the central IADL, with a centrality index of 3.87, which was significantly higher than that of other IADLs (all P<.05). Visual attention emerged as the central cognition domain, with a centrality index of 0.86, which was significantly higher than that of other cognition domains (all P<.05). Shopping was determined to be the bridge IADL, with a centrality index of 0.41, which was significantly higher than that of other IADLs (all P<.05). Notably, gender differences emerged in the IADL network, with stronger associations between laundry and meal preparation in females (1.69 vs males: 0.74; P=.001) and higher centrality in meal preparation among females (difference=1.99; P=.007). Conclusions: While broad enhancements in all IADL and cognitive domains are beneficial, targeting meal preparation, visual attention, and shopping may leverage their within-network influence to yield a more pronounced improvement in holistic IADL, holistic cognition, and holistic cognition function through IADL interventions among older adults. Notably, meal preparation interventions may be less effective in males, requiring tailored approaches. %R 10.2196/67632 %U https://aging.jmir.org/2025/1/e67632 %U https://doi.org/10.2196/67632 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e66104 %T Comparison of 3 Aging Metrics in Dual Declines to Capture All-Cause Dementia and Mortality Risk: Cohort Study %A Bai,Anying %A He,Shan %A Jiang,Yu %A Xu,Weihao %A Lin,Zhanyi %K gerontology %K geriatrics %K older adults %K older people %K aging %K motoric cognitive risk syndrome %K MCR %K physio-cognitive decline syndrome %K PCDS %K cognitive frailty %K CF %K frailty %K discrimination %K risk factors %K prediction %K dementia risk %K mortality risk %D 2025 %7 30.1.2025 %9 %J JMIR Aging %G English %X Background: The utility of aging metrics that incorporate cognitive and physical function is not fully understood. Objective: We aim to compare the predictive capacities of 3 distinct aging metrics—motoric cognitive risk syndrome (MCR), physio-cognitive decline syndrome (PCDS), and cognitive frailty (CF)—for incident dementia and all-cause mortality among community-dwelling older adults. Methods: We used longitudinal data from waves 10-15 of the Health and Retirement Study. Cox proportional hazards regression analysis was employed to evaluate the effects of MCR, PCDS, and CF on incident all-cause dementia and mortality, controlling for socioeconomic and lifestyle factors, as well as medical comorbidities. Discrimination analysis was conducted to assess and compare the predictive accuracy of the 3 aging metrics. Results: A total of 2367 older individuals aged 65 years and older, with no baseline prevalence of dementia or disability, were ultimately included. The prevalence rates of MCR, PCDS, and CF were 5.4%, 6.3%, and 1.3%, respectively. Over a decade-long follow-up period, 341 cases of dementia and 573 deaths were recorded. All 3 metrics were predictive of incident all-cause dementia and mortality when adjusting for multiple confounders, with variations in the strength of their associations (incident dementia: MCR odds ratio [OR] 1.90, 95% CI 1.30‐2.78; CF 5.06, 95% CI 2.87‐8.92; PCDS 3.35, 95% CI 2.44‐4.58; mortality: MCR 1.60, 95% CI 1.17‐2.19; CF 3.26, 95% CI 1.99‐5.33; and PCDS 1.58, 95% CI 1.17‐2.13). The C-index indicated that PCDS and MCR had the highest discriminatory accuracy for all-cause dementia and mortality, respectively. Conclusions: Despite the inherent differences among the aging metrics that integrate cognitive and physical functions, they consistently identified risks of dementia and mortality. This underscores the importance of implementing targeted preventive strategies and intervention programs based on these metrics to enhance the overall quality of life and reduce premature deaths in aging populations. %R 10.2196/66104 %U https://aging.jmir.org/2025/1/e66104 %U https://doi.org/10.2196/66104 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64352 %T Building Consensus on the Relevant Criteria to Screen for Depressive Symptoms Among Near-Centenarians and Centenarians: Modified e-Delphi Study %A Gomes da Rocha,Carla %A von Gunten,Armin %A Vandel,Pierre %A Jopp,Daniela S %A Ribeiro,Olga %A Verloo,Henk %+ School of Health Sciences, University of Applied Sciences and Arts Western Switzerland (HES-SO), Chemin de l'Agasse 5, Sion, 1950, Switzerland, 41 58 606 84 73, carla.gomesdarocha@hevs.ch %K centenarians %K near-centenarians %K depressive symptoms %K depression diagnosis %K screening %K assessment %K e-Delphi technique %K web-based survey %D 2025 %7 5.3.2025 %9 Original Paper %J JMIR Aging %G English %X Background: The number of centenarians worldwide is expected to increase dramatically, reaching 3.4 million by 2050 and >25 million by 2100. Despite these projections, depression remains a prevalent yet underdiagnosed and undertreated condition among this population that carries significant health risks. Objective: This study aimed to identify and achieve consensus on the most representative signs and symptoms of depression in near-centenarians and centenarians (aged ≥95 years) through an e-Delphi study with an international and interdisciplinary panel of experts. Ultimately, the outcomes of this study might help create a screening instrument that is specifically designed for this unique population. Methods: A modified e-Delphi study was carried out to achieve expert consensus on depressive symptoms in near-centenarians and centenarians. A panel of 28 international experts was recruited. Consensus was defined as 70% agreement on the relevance of each item. Data were collected through a web-based questionnaire over 3 rounds. Experts rated 104 items that were divided into 24 dimensions and 80 criteria to identify the most representative signs and symptoms of depression in this age group. Results: The panel consisted of experts from various countries, including physicians with experience in old age psychiatry or geriatrics as well as nurses and psychologists. The response rate remained consistent over the rounds (20/28, 71% to 21/28, 75%). In total, 4 new dimensions and 8 new criteria were proposed by the experts, and consensus was reached on 86% (24/28) of the dimensions and 80% (70/88) of the criteria. The most consensual potentially relevant dimensions were lack of hope (21/21, 100%), loss of interest (27/28, 96%), lack of reactivity to pleasant events (27/28, 96%), depressed mood (26/28, 93%), and previous episodes of depression or diagnosed depression (19/21, 90%). In addition, the most consensual potentially relevant criteria were despondency, gloom, and despair (25/25, 100%); depressed (27/27, 100%); lack of reactivity to pleasant events or circumstances (28/28, 100%); suicidal ideation (28/28, 100%); suicide attempt(s) (28/28, 100%); ruminations (27/28, 96%); recurrent thoughts of death or suicide (27/28, 96%); feelings of worthlessness (25/26, 96%); critical life events (20/21, 95%); anhedonia (20/21, 95%); loss of interest in activities (26/28, 93%); loss of pleasure in activities (26/28, 93%); and sadness (24/26, 92%). Moreover, when assessing depression in very old age, the duration, number, frequency, and severity of signs and symptoms should also be considered, as evidenced by the high expert agreement. Conclusions: The classification of most elements as relevant highlights the importance of a multidimensional approach for optimal depression screening among individuals of very old age. This study offers a first step toward improving depression assessment in near-centenarians and centenarians. The development of a more adapted screening tool could improve early detection and intervention, enhancing the quality of mental health care for this population. %M 40053803 %R 10.2196/64352 %U https://aging.jmir.org/2025/1/e64352 %U https://doi.org/10.2196/64352 %U http://www.ncbi.nlm.nih.gov/pubmed/40053803 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e70291 %T Nonpharmacological Multimodal Interventions for Cognitive Functions in Older Adults With Mild Cognitive Impairment: Scoping Review %A Chan,Raffy Chi-Fung %A Zhou,Joson Hao-Shen %A Cao,Yuan %A Lo,Kenneth %A Ng,Peter Hiu-Fung %A Shum,David Ho-Keung %A Wong,Arnold Yu-Lok %+ Department of Rehabilitation Sciences, Hong Kong Polytechnic University, QT522, 5/F, Core T, The Hong Kong Polytechnic University, Hong Kong, China (Hong Kong), 852 2766 6741, arnold.wong@polyu.edu.hk %K mild cognitive impairment %K multimodal intervention %K prevention %K randomized controlled trial %K cognitive decline %D 2025 %7 12.5.2025 %9 Review %J JMIR Aging %G English %X Background: As the global population ages, the prevalence of dementia is expected to rise significantly. To alleviate the burden on health care systems and the economy, it is essential to develop effective strategies to enhance cognitive function in older adults. Previous studies have shown that combined nonpharmacological interventions can improve cognition across various domains in older individuals. However, there is no established gold standard for the exact combination and duration of these interventions, which makes it challenging to assess their overall effectiveness. Objective: Given the diversity of nonpharmacological multimodal interventions aimed at preventing cognitive decline in older adults with mild cognitive impairment (MCI), this scoping review sought to identify and summarize the characteristics and outcomes of these interventions. Methods: We adhered to the Arksey and O’Malley methodological framework and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and searched 4 electronic databases (MEDLINE, PsycINFO, CINAHL, and Web of Science) systematically on July 6, 2023, and updated the search on April 17, 2024, using specific terms and keywords. Results: This review included 45 studies from 18 countries with 4705 participants from 2014 to 2024 encompassing different combinations of physical training (PT), cognitive training (CT), nutrition intervention, psychosocial intervention, social activities, and electrical stimulation. There is a growing numbers of studies combining PT and CT for MCI treatment, with additional modalities often added to address various aspects of the condition. Compared to single-modal interventions and usual care, multimodal approaches demonstrated significantly better improvements in cognition domains such as attention, global cognition, executive function, memory, processing speed, and verbal fluency. Technology has been instrumental in delivering these interventions and enhancing the effects of PT and CT. Multimodal interventions also show promise in terms of acceptability and user experience, which can improve treatment adherence. Conclusions: Research is limited regarding the cost-effectiveness and optimal dosage of these interventions, making it difficult to assess the additional benefits of incorporating more modalities. Future research should examine the long-term effects of incorporating multiple modalities, using standardized MCI criteria and outcome measures. %M 40354647 %R 10.2196/70291 %U https://aging.jmir.org/2025/1/e70291 %U https://doi.org/10.2196/70291 %U http://www.ncbi.nlm.nih.gov/pubmed/40354647 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e69175 %T Advancing Remote Monitoring for Patients With Alzheimer Disease and Related Dementias: Systematic Review %A Shaik,Mohmmad Arif %A Anik,Fahim Islam %A Hasan,Md. Mehedi %A Chakravarty,Sumit %A Ramos,Mary Dioise %A Rahman,Mohammad Ashiqur %A Ahamed,Sheikh Iqbal %A Sakib,Nazmus %+ Department of Electrical and Computer Engineering, Kennesaw State University, Kennesaw, GA, United States, 1 4147975981, mshaik14@students.kennesaw.edu %K dementia %K Alzheimer disease %K remote monitoring %K Alzheimer %K caregiver %K fall detection %K artificial intelligence %D 2025 %7 14.5.2025 %9 Review %J JMIR Aging %G English %X Background: Using remote monitoring technology in the context of Alzheimer disease (AD) care presents exciting new opportunities to lessen caregiver stress and improve patient care quality. The application of wearables, environmental sensors, and smart home systems designed specifically for patients with AD represents a promising interdisciplinary approach that integrates advanced technology with health care to enhance patient safety, monitor health parameters in real time, and provide comprehensive support to caregivers. Objective: The objectives of this study included evaluating the effectiveness of various remote sensing technologies in enhancing patient outcomes and identifying strategies to alleviate the burden on health care professionals and caregivers. Critical elements such as regulatory compliance, user-centered design, privacy and security considerations, and the overall efficacy of relevant technologies were comprehensively examined. Ultimately, this study aimed to propose a comprehensive remote monitoring framework tailored to the needs of patients with AD and related dementias. Methods: Guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, we conducted a systematic review on remote monitoring for patients with AD and related dementias. Our search spanned 4 major electronic databases—Google Scholar, PubMed, IEEE Xplore, and DBLP on February 20, 2024, with an updated search on May 18, 2024. Results: A total of 31 publications met the inclusion criteria, highlighting 4 key research areas: existing remote monitoring technologies, balancing practicality and empathy, security and privacy in monitoring, and technology design for AD care. The studies revealed a strong focus on various remote monitoring methods for capturing behavioral, physiological, and environmental data yet showed a gap in evaluating these methods for patient and caregiver needs, privacy, and usability. The findings also indicated that many studies lacked robust reference standards and did not consistently apply critical appraisal criteria, underlining the need for comprehensive frameworks that better integrate these essential considerations. Conclusions: This comprehensive literature review of remote monitoring technologies for patients with AD provides an understanding of remote monitoring technologies, trends, and gaps in the current research and the significance of novel strategies for remote monitoring to enhance patient outcomes and reduce the burden among health professionals and caregivers. The proposed remote monitoring framework aims to inspire the development of new interdisciplinary research models that advance care for patients with AD. %M 40367504 %R 10.2196/69175 %U https://aging.jmir.org/2025/1/e69175 %U https://doi.org/10.2196/69175 %U http://www.ncbi.nlm.nih.gov/pubmed/40367504 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e54143 %T A Digitally Capable Aged Care Workforce: Demands and Directions for Workforce Education and Development %A Gray,Kathleen %A Butler-Henderson,Kerryn %A Day,Karen %+ Centre for Digital Transformation of Health, University of Melbourne, Parkville, Melbourne, 3010, Australia, 61 390355511, kgray@unimelb.edu.au %K aged care %K digital health %K digital literacy %K education %K older adults %K professional development %K digital transformation %K digital resources %K users %K community %K learning %K support %K safe %K ethical %K satisfaction %D 2025 %7 2.4.2025 %9 Viewpoint %J JMIR Aging %G English %X As the aged care sector undergoes digital transformation, greater attention is needed to development of digital health capability in its workforce. There are many gaps in our understanding of the current and future impacts of technology on those who perform paid and unpaid aged care work. Research is needed to understand how to make optimal use of both digital resources and human resources for better aged care. In this Viewpoint, we reflect on a workshop held during an international conference that identified shared concepts and concerns to shape further research into workforce capability. Digital technologies and digital data can increase quality of care in a system that operates through partnerships among service providers, service users, and community members. To realize this potential, digital health learning and development are needed in the aged care workforce. As digital dimensions of aged care services expand, the sector needs clearer direction to implement approaches to workforce learning and development. These must be appropriate to support the safe and ethical performance of care work and to increase the satisfaction of those who care and those for whom they care. %M 40173435 %R 10.2196/54143 %U https://aging.jmir.org/2025/1/e54143 %U https://doi.org/10.2196/54143 %U http://www.ncbi.nlm.nih.gov/pubmed/40173435 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e71777 %T Advancing Emergency Care With Digital Twins %A Li,Haoran %A Zhang,Jingya %A Zhang,Ning %A Zhu,Bin %+ School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Nanshan District, Shenzhen, 518000, China, 86 13530405020, zhub6@sustech.edu.cn %K emergency care %K digital twin %K prehospital emergency care %K in-hospital emergency care %K recovery %D 2025 %7 21.4.2025 %9 Viewpoint %J JMIR Aging %G English %X Digital twins—dynamic and real-time simulations of systems or environments—represent a paradigm shift in emergency medicine. We explore their applications across prehospital care, in-hospital management, and recovery. By integrating real-time data, wearable technology, and predictive analytics, digital twins hold the promise of optimizing resource allocation, advancing precision medicine, and tailoring rehabilitation strategies. Moreover, we discuss the challenges associated with their implementation, including data resolution, biological heterogeneity, and ethical considerations, emphasizing the need for actionable frameworks that balance innovation with data governance and public trust. %M 40258270 %R 10.2196/71777 %U https://aging.jmir.org/2025/1/e71777 %U https://doi.org/10.2196/71777 %U http://www.ncbi.nlm.nih.gov/pubmed/40258270 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e65898 %T Machine Learning for Predicting Postoperative Functional Disability and Mortality Among Older Patients With Cancer: Retrospective Cohort Study %A Hashimoto,Yuki %A Inoue,Norihiko %A Tani,Takuaki %A Imai,Shinobu %K older patients with cancer %K postoperative outcomes %K functional disability %K machine learning %K decision-making %D 2025 %7 14.5.2025 %9 %J JMIR Aging %G English %X Background: The global cancer burden is rapidly increasing, with 20 million new cases estimated in 2022. The world population aged ≥65 years is also increasing, projected to reach 15.9% by 2050, making cancer control for older patients urgent. Surgical resection is important for cancer treatment; however, predicting postoperative disability and mortality in older patients is crucial for surgical decision-making, considering the quality of life and care burden. Currently, no model directly predicts postoperative functional disability in this population. Objective: We aimed to develop and validate machine-learning models to predict postoperative functional disability (≥5-point decrease in the Barthel Index) or in-hospital death in patients with cancer aged ≥ 65 years. Methods: This retrospective cohort study included patients aged ≥65 years who underwent surgery for major cancers (lung, stomach, colorectal, liver, pancreatic, breast, or prostate cancer) between April 2016 and March 2023 in 70 Japanese hospitals across 6 regional groups. One group was randomly selected for external validation, while the remaining 5 groups were randomly divided into training (70%) and internal validation (30%) sets. Predictor variables were selected from 37 routinely available preoperative factors through electronic medical records (age, sex, income, comorbidities, laboratory values, and vital signs) using crude odds ratios (P<.1) and the least absolute shrinkage and selection operator method. We developed 6 machine-learning models, including category boosting (CatBoost), extreme gradient boosting (XGBoost), logistic regression, neural networks, random forest, and support vector machine. Model predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) with 95% CI. We used the Shapley additive explanations (SHAP) method to evaluate contribution to the predictive performance for each predictor variable. Results: This study included 33,355 patients in the training, 14,294 in the internal validation, and 6711 in the external validation sets. In the training set, 1406/33,355 (4.2%) patients experienced worse discharge. A total of 24 predictor variables were selected for the final models. CatBoost and XGBoost achieved the largest AUCs among the 6 models: 0.81 (95% CI 0.80-0.82) and 0.81 (95% CI 0.80-0.82), respectively. In the top 15 influential factors based on the mean absolute SHAP value, both models shared the same 14 factors such as dementia, age ≥85 years, and gastrointestinal cancer. The CatBoost model showed the largest AUCs in both internal (0.77, 95% CI 0.75-0.79) and external validation (0.72, 95% CI 0.68-0.75). Conclusions: The CatBoost model demonstrated good performance in predicting postoperative outcomes for older patients with cancer using routinely available preoperative factors. The robustness of these findings was supported by the identical top influential factors between the CatBoost and XGBoost models. This model could support surgical decision-making while considering postoperative quality of life and care burden, with potential for implementation through electronic health records. %R 10.2196/65898 %U https://aging.jmir.org/2025/1/e65898 %U https://doi.org/10.2196/65898 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e65178 %T Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study %A West,Matthew %A Cheng,You %A He,Yingnan %A Leng,Yu %A Magdamo,Colin %A Hyman,Bradley T %A Dickson,John R %A Serrano-Pozo,Alberto %A Blacker,Deborah %A Das,Sudeshna %+ Massachusetts General Hospital, 65 Landsdowne Street, Cambridge, MA, 02139, United States, 1 617 768 8254, sdas5@mgh.harvard.edu %K Alzheimer disease and related dementias %K electronic health records %K large language models %K clustering %K unsupervised learning %D 2025 %7 31.3.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Alzheimer disease and related dementias (ADRD) exhibit prominent heterogeneity. Identifying clinically meaningful ADRD subtypes is essential for tailoring treatments to specific patient phenotypes. Objective: We aimed to use unsupervised learning techniques on electronic health records (EHRs) from memory clinic patients to identify ADRD subtypes. Methods: We used pretrained embeddings of non-ADRD diagnosis codes (International Classification of Diseases, Ninth Revision) and large language model (LLM)–derived embeddings of clinical notes from patient EHRs. Hierarchical clustering of these embeddings was used to identify ADRD subtypes. Clusters were characterized regarding their demographic and clinical features. Results: We analyzed a cohort of 3454 patients with ADRD from a memory clinic at Massachusetts General Hospital, each with a specialist diagnosis. Clustering pretrained embeddings of the non-ADRD diagnosis codes in patient EHRs revealed the following 3 patient subtypes: one with skin conditions, another with psychiatric disorders and an earlier age of onset, and a third with diabetes complications. Similarly, using LLM-derived embeddings of clinical notes, we identified 3 subtypes of patients as follows: one with psychiatric manifestations and higher prevalence of female participants (prevalence ratio: 1.59), another with cardiovascular and motor problems and higher prevalence of male participants (prevalence ratio: 1.75), and a third one with geriatric health disorders. Notably, we observed significant overlap between clusters from both data modalities (χ24=89.4; P<.001). Conclusions: By integrating International Classification of Diseases, Ninth Revision codes and LLM-derived embeddings, our analysis delineated 2 distinct ADRD subtypes with sex-specific comorbid and clinical presentations, offering insights for potential precision medicine approaches. %R 10.2196/65178 %U https://aging.jmir.org/2025/1/e65178 %U https://doi.org/10.2196/65178 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e75690 %T Correction: Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study %A Isaradech,Natthanaphop %A Sirikul,Wachiranun %A Buawangpong,Nida %A Siviroj,Penprapa %A Kitro,Amornphat %K aged care %K gerontology %K geriatric %K old %K aging %K clinical decision support %K delivering health information and knowledge to the public %K diagnostic systems %K digital health %K epidemiology %K surveillance %K diagnosis %K frailty %K machine learning %K prediction %K predictive %K AI %K artificial intelligence %K Thailand %K community dwelling %K health care intervention %K patient care %D 2025 %7 22.4.2025 %9 %J JMIR Aging %G English %X %R 10.2196/75690 %U https://aging.jmir.org/2025/1/e75690 %U https://doi.org/10.2196/75690 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e69493 %T Correction: Internet-Based Supportive Interventions for Family Caregivers of People With Dementia: Randomized Controlled Trial %A Xie,Yanhong %A Shen,Shanshan %A Liu,Caixia %A Hong,Hong %A Guan,Huilan %A Zhang,Jingmei %A Yu,Wanqi %D 2025 %7 29.1.2025 %9 %J JMIR Aging %G English %X %R 10.2196/69493 %U https://aging.jmir.org/2025/1/e69493 %U https://doi.org/10.2196/69493 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e65183 %T Association Between Sleep Duration and Cognitive Frailty in Older Chinese Adults: Prospective Cohort Study %A Cai,Ruixue %A Chao,Jianqian %A Gao,Chenlu %A Gao,Lei %A Hu,Kun %A Li,Peng %K aging %K frailty %K cognition %K cohort study %K sleep duration %K sleep quality %K longitudinal study %D 2025 %7 23.4.2025 %9 %J JMIR Aging %G English %X Background: Disturbed sleep patterns are common among older adults and may contribute to cognitive and physical declines. However, evidence for the relationship between sleep duration and cognitive frailty, a concept combining physical frailty and cognitive impairment in older adults, is lacking. Objective: This study aimed to examine the associations of sleep duration and its changes with cognitive frailty. Methods: We analyzed data from the 2008‐2018 waves of the Chinese Longitudinal Healthy Longevity Survey. Cognitive frailty was rendered based on the modified Fried frailty phenotype and Mini-Mental State Examination. Sleep duration was categorized as short (<6 h), moderate (6‐9 h), and long (>9 h). We examined the association of sleep duration with cognitive frailty status at baseline using logistic regressions and with the future incidence of cognitive frailty using Cox proportional hazards models. Restricted cubic splines were used to explore potential nonlinear associations. Results: Among 11,303 participants, 1298 (11.5%) had cognitive frailty at baseline. Compared to participants who had moderate sleep duration, the odds of having cognitive frailty were higher in those with long sleep duration (odds ratio 1.71, 95% CI 1.48‐1.97; P<.001). A J-shaped association between sleep duration and cognitive frailty was also observed (P<.001). Additionally, during a mean follow-up of 6.7 (SD 2.6) years among 5201 participants who were not cognitively frail at baseline, 521 (10%) participants developed cognitive frailty. A higher risk of cognitive frailty was observed in participants with long sleep duration (hazard ratio 1.32, 95% CI 1.07‐1.62; P=.008). Conclusions: Long sleep duration was associated with cognitive frailly in older Chinese adults. These findings provide insights into the relationship between sleep duration and cognitive frailty, with potential implications for public health policies and clinical practice. %R 10.2196/65183 %U https://aging.jmir.org/2025/1/e65183 %U https://doi.org/10.2196/65183 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e62942 %T Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study %A Isaradech,Natthanaphop %A Sirikul,Wachiranun %A Buawangpong,Nida %A Siviroj,Penprapa %A Kitro,Amornphat %K aged care %K gerontology %K geriatric %K old %K aging %K clinical decision support %K delivering health information and knowledge to the public %K diagnostic systems %K digital health %K epidemiology %K surveillance %K diagnosis %K frailty %K machine learning %K prediction %K predictive %K AI %K artificial intelligence %K Thailand %K community dwelling %K health care intervention %K patient care %D 2025 %7 2.4.2025 %9 %J JMIR Aging %G English %X Background: Frailty is defined as a clinical state of increased vulnerability due to the age-associated decline of an individual’s physical function resulting in increased morbidity and mortality when exposed to acute stressors. Early identification and management can reverse individuals with frailty to being robust once more. However, we found no integration of machine learning (ML) tools and frailty screening and surveillance studies in Thailand despite the abundance of evidence of frailty assessment using ML globally and in Asia. Objective: We propose an approach for early diagnosis of frailty in community-dwelling older individuals in Thailand using an ML model generated from individual characteristics and anthropometric data. Methods: Datasets including 2692 community-dwelling Thai older adults in Lampang from 2016 and 2017 were used for model development and internal validation. The derived models were externally validated with a dataset of community-dwelling older adults in Chiang Mai from 2021. The ML algorithms implemented in this study include the k-nearest neighbors algorithm, random forest ML algorithms, multilayer perceptron artificial neural network, logistic regression models, gradient boosting classifier, and linear support vector machine classifier. Results: Logistic regression showed the best overall discrimination performance with a mean area under the receiver operating characteristic curve of 0.81 (95% CI 0.75‐0.86) in the internal validation dataset and 0.75 (95% CI 0.71‐0.78) in the external validation dataset. The model was also well-calibrated to the expected probability of the external validation dataset. Conclusions: Our findings showed that our models have the potential to be utilized as a screening tool using simple, accessible demographic and explainable clinical variables in Thai community-dwelling older persons to identify individuals with frailty who require early intervention to become physically robust. %R 10.2196/62942 %U https://aging.jmir.org/2025/1/e62942 %U https://doi.org/10.2196/62942 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e63686 %T Using Deep Learning to Perform Automatic Quantitative Measurement of Masseter and Tongue Muscles in Persons With Dementia: Cross-Sectional Study %A Imani,Mahdi %A Borda,Miguel G %A Vogrin,Sara %A Meijering,Erik %A Aarsland,Dag %A Duque,Gustavo %K artificial intelligence %K machine learning %K sarcopenia %K dementia %K masseter muscle %K tongue muscle %K deep learning %K head %K tongue %K face %K magnetic resonance imaging %K MRI %K image %K imaging %K muscle %K muscles %K neural network %K aging %K gerontology %K older adults %K geriatrics %K older adult health %D 2025 %7 19.3.2025 %9 %J JMIR Aging %G English %X Background: Sarcopenia (loss of muscle mass and strength) increases adverse outcomes risk and contributes to cognitive decline in older adults. Accurate methods to quantify muscle mass and predict adverse outcomes, particularly in older persons with dementia, are still lacking. Objective: This study’s main objective was to assess the feasibility of using deep learning techniques for segmentation and quantification of musculoskeletal tissues in magnetic resonance imaging (MRI) scans of the head in patients with neurocognitive disorders. This study aimed to pave the way for using automated techniques for opportunistic detection of sarcopenia in patients with neurocognitive disorder. Methods: In a cross-sectional analysis of 53 participants, we used 7 U-Net-like deep learning models to segment 5 different tissues in head MRI images and used the Dice similarity coefficient and average symmetric surface distance as main assessment techniques to compare results. We also analyzed the relationship between BMI and muscle and fat volumes. Results: Our framework accurately quantified masseter and subcutaneous fat on the left and right sides of the head and tongue muscle (mean Dice similarity coefficient 92.4%). A significant correlation exists between the area and volume of tongue muscle, left masseter muscle, and BMI. Conclusions: Our study demonstrates the successful application of a deep learning model to quantify muscle volumes in head MRI in patients with neurocognitive disorders. This is a promising first step toward clinically applicable artificial intelligence and deep learning methods for estimating masseter and tongue muscle and predicting adverse outcomes in this population. %R 10.2196/63686 %U https://aging.jmir.org/2025/1/e63686 %U https://doi.org/10.2196/63686 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e66778 %T Exploring Older Adults’ Perspectives and Acceptance of AI-Driven Health Technologies: Qualitative Study %A Wong,Arkers Kwan Ching %A Lee,Jessica Hiu Toon %A Zhao,Yue %A Lu,Qi %A Yang,Shulan %A Hui,Vivian Chi Ching %K artificial intelligence–based health technologies %K health technology %K AI-based health technology %K machine learning %K ML %K artificial intelligence %K AI %K algorithm %K model %K analytics %K perceptions %K acceptability %K gerontology %K geriatrics %K older adult %K elderly %K older person %K older people %K aging %K mobile phone %D 2025 %7 12.2.2025 %9 %J JMIR Aging %G English %X Background: Artificial intelligence (AI) is increasingly being applied in various health care services due to its enhanced efficiency and accuracy. As the population ages, AI-based health technologies could be a potent tool in older adults’ health care to address growing, complex, and challenging health needs. This study aimed to investigate perspectives on and acceptability of the use of AI-led health technologies among older adults and the potential challenges that they face in adopting them. The findings from this inquiry could inform the designing of more acceptable and user-friendly AI-based health technologies. Objective: The objectives of the study were (1) to investigate the attitudes and perceptions of older adults toward the use of AI-based health technologies; (2) to identify potential facilitators, barriers, and challenges influencing older adults’ preferences toward AI-based health technologies; and (3) to inform strategies that can promote and facilitate the use of AI-based health technologies among older adults. Methods: This study adopted a qualitative descriptive design. A total of 27 community-dwelling older adults were recruited from a local community center. Three sessions of semistructured interviews were conducted, each lasting 1 hour. The sessions covered five key areas: (1) general impressions of AI-based health technologies; (2) previous experiences with AI-based health technologies; (3) perceptions and attitudes toward AI-based health technologies; (4) anticipated difficulties in using AI-based health technologies and underlying reasons; and (5) willingness, preferences, and motivations for accepting AI-based health technologies. Thematic analysis was applied for data analysis. The Theoretical Domains Framework and the Capability, Opportunity, Motivation, and Behavior (COM-B) model behavior change wheel were integrated into the analysis. Identified theoretical domains were mapped directly to the COM-B model to determine corresponding strategies for enhancing the acceptability of AI-based health technologies among older adults. Results: The analysis identified 9 of the 14 Theoretical Domains Framework domains—knowledge, skills, social influences, environmental context and resources, beliefs about capabilities, beliefs about consequences, intentions, goals, and emotion. These domains were mapped to 6 components of the COM-B model. While most participants acknowledged the potential benefits of AI-based health technologies, they emphasized the irreplaceable role of human expertise and interaction. Participants expressed concerns about the usability of AI technologies, highlighting the need for user-friendly and tailored AI solutions. Privacy concerns and the importance of robust security measures were also emphasized as critical factors affecting their willingness to adopt AI-based health technologies. Conclusions: Integrating AI as a supportive tool alongside health care providers, rather than regarding it as a replacement, was highlighted as a key strategy for promoting acceptance. Government support and clear guidelines are needed to promote ethical AI implementation in health care. These measures can improve health outcomes in the older adult population by encouraging the adoption of AI-driven health technologies. %R 10.2196/66778 %U https://aging.jmir.org/2025/1/e66778 %U https://doi.org/10.2196/66778 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e67715 %T Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study %A Nejadshamsi,Shayan %A Karami,Vania %A Ghourchian,Negar %A Armanfard,Narges %A Bergman,Howard %A Grad,Roland %A Wilchesky,Machelle %A Khanassov,Vladimir %A Vedel,Isabelle %A Abbasgholizadeh Rahimi,Samira %+ Family Medicine Department, Faculty of Medicine and Health Sciences, McGill University, 5858 Côte des Negies, Montreal, QC, H3S 1Z1, Canada, 1 5143987375, samira.rahimi@mcgill.ca %K depression %K classification %K machine learning %K artificial intelligence %K older adults %D 2025 %7 3.3.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early detection is vital for effective treatment and intervention. While many studies use wearable devices to classify depression based on physical activity, these often rely on intrusive methods. Additionally, most depression classification studies involve large participant groups and use single-stage classifiers without explainability. Objective: This study aims to assess the feasibility of classifying depression using nonintrusive Wi-Fi–based motion sensor data using a novel machine learning model on a limited number of participants. We also conduct an explainability analysis to interpret the model’s predictions and identify key features associated with depression classification. Methods: In this study, we recruited adults aged 65 years and older through web-based and in-person methods, supported by a McGill University health care facility directory. Participants provided consent, and we collected 6 months of activity and sleep data via nonintrusive Wi-Fi–based sensors, along with Edmonton Frailty Scale and Geriatric Depression Scale data. For depression classification, we proposed a HOPE (Home-Based Older Adults’ Depression Prediction) machine learning model with feature selection, dimensionality reduction, and classification stages, evaluating various model combinations using accuracy, sensitivity, precision, and F1-score. Shapely addictive explanations and local interpretable model-agnostic explanations were used to explain the model’s predictions. Results: A total of 6 participants were enrolled in this study; however, 2 participants withdrew later due to internet connectivity issues. Among the 4 remaining participants, 3 participants were classified as not having depression, while 1 participant was identified as having depression. The most accurate classification model, which combined sequential forward selection for feature selection, principal component analysis for dimensionality reduction, and a decision tree for classification, achieved an accuracy of 87.5%, sensitivity of 90%, and precision of 88.3%, effectively distinguishing individuals with and those without depression. The explainability analysis revealed that the most influential features in depression classification, in order of importance, were “average sleep duration,” “total number of sleep interruptions,” “percentage of nights with sleep interruptions,” “average duration of sleep interruptions,” and “Edmonton Frailty Scale.” Conclusions: The findings from this preliminary study demonstrate the feasibility of using Wi-Fi–based motion sensors for depression classification and highlight the effectiveness of our proposed HOPE machine learning model, even with a small sample size. These results suggest the potential for further research with a larger cohort for more comprehensive validation. Additionally, the nonintrusive data collection method and model architecture proposed in this study offer promising applications in remote health monitoring, particularly for older adults who may face challenges in using wearable devices. Furthermore, the importance of sleep patterns identified in our explainability analysis aligns with findings from previous research, emphasizing the need for more in-depth studies on the role of sleep in mental health, as suggested in the explainable machine learning study. %M 40053734 %R 10.2196/67715 %U https://aging.jmir.org/2025/1/e67715 %U https://doi.org/10.2196/67715 %U http://www.ncbi.nlm.nih.gov/pubmed/40053734 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e65629 %T Model-Based Feature Extraction and Classification for Parkinson Disease Screening Using Gait Analysis: Development and Validation Study %A Lim,Ming De %A Connie,Tee %A Goh,Michael Kah Ong %A Saedon,Nor ‘Izzati %+ Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka, 75450, Malaysia, 60 62523592, tee.connie@mmu.edu.my %K model-based features %K gait analysis %K Parkinson disease %K computer vision %K support vector machine %D 2025 %7 8.4.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Parkinson disease (PD) is a progressive neurodegenerative disorder that affects motor coordination, leading to gait abnormalities. Early detection of PD is crucial for effective management and treatment. Traditional diagnostic methods often require invasive procedures or are performed when the disease has significantly progressed. Therefore, there is a need for noninvasive techniques that can identify early motor symptoms, particularly those related to gait. Objective: The study aimed to develop a noninvasive approach for the early detection of PD by analyzing model-based gait features. The primary focus is on identifying subtle gait abnormalities associated with PD using kinematic characteristics. Methods: Data were collected through controlled video recordings of participants performing the timed up and go (TUG) assessment, with particular emphasis on the turning phase. The kinematic features analyzed include shoulder distance, step length, stride length, knee and hip angles, leg and arm symmetry, and trunk angles. These features were processed using advanced filtering techniques and analyzed through machine learning methods to distinguish between normal and PD-affected gait patterns. Results: The analysis of kinematic features during the turning phase of the TUG assessment revealed that individuals with PD exhibited subtle gait abnormalities, such as freezing of gait, reduced step length, and asymmetrical movements. The model-based features proved effective in differentiating between normal and PD-affected gait, demonstrating the potential of this approach in early detection. Conclusions: This study presents a promising noninvasive method for the early detection of PD by analyzing specific gait features during the turning phase of the TUG assessment. The findings suggest that this approach could serve as a sensitive and accurate tool for diagnosing and monitoring PD, potentially leading to earlier intervention and improved patient outcomes. %M 40198116 %R 10.2196/65629 %U https://aging.jmir.org/2025/1/e65629 %U https://doi.org/10.2196/65629 %U http://www.ncbi.nlm.nih.gov/pubmed/40198116 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e69504 %T Identifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study %A Socrates,Vimig %A Wright,Donald S %A Huang,Thomas %A Fereydooni,Soraya %A Dien,Christine %A Chi,Ling %A Albano,Jesse %A Patterson,Brian %A Sasidhar Kanaparthy,Naga %A Wright,Catherine X %A Loza,Andrew %A Chartash,David %A Iscoe,Mark %A Taylor,Richard Andrew %+ Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, 464 Congress Avenue, Suite 260, New Haven, CT, 06510, United States, 1 2037854058, richard.taylor@yale.edu %K deprescribing %K large language models %K geriatrics %K potentially inappropriate medication list %K emergency medicine %K natural language processing %K calibration %D 2025 %7 11.4.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Polypharmacy, the concurrent use of multiple medications, is prevalent among older adults and associated with increased risks for adverse drug events including falls. Deprescribing, the systematic process of discontinuing potentially inappropriate medications, aims to mitigate these risks. However, the practical application of deprescribing criteria in emergency settings remains limited due to time constraints and criteria complexity. Objective: This study aims to evaluate the performance of a large language model (LLM)–based pipeline in identifying deprescribing opportunities for older emergency department (ED) patients with polypharmacy, using 3 different sets of criteria: Beers, Screening Tool of Older People’s Prescriptions, and Geriatric Emergency Medication Safety Recommendations. The study further evaluates LLM confidence calibration and its ability to improve recommendation performance. Methods: We conducted a retrospective cohort study of older adults presenting to an ED in a large academic medical center in the Northeast United States from January 2022 to March 2022. A random sample of 100 patients (712 total oral medications) was selected for detailed analysis. The LLM pipeline consisted of two steps: (1) filtering high-yield deprescribing criteria based on patients’ medication lists, and (2) applying these criteria using both structured and unstructured patient data to recommend deprescribing. Model performance was assessed by comparing model recommendations to those of trained medical students, with discrepancies adjudicated by board-certified ED physicians. Selective prediction, a method that allows a model to abstain from low-confidence predictions to improve overall reliability, was applied to assess the model’s confidence and decision-making thresholds. Results: The LLM was significantly more effective in identifying deprescribing criteria (positive predictive value: 0.83; negative predictive value: 0.93; McNemar test for paired proportions: χ21=5.985; P=.02) relative to medical students, but showed limitations in making specific deprescribing recommendations (positive predictive value=0.47; negative predictive value=0.93). Adjudication revealed that while the model excelled at identifying when there was a deprescribing criterion related to one of the patient’s medications, it often struggled with determining whether that criterion applied to the specific case due to complex inclusion and exclusion criteria (54.5% of errors) and ambiguous clinical contexts (eg, missing information; 39.3% of errors). Selective prediction only marginally improved LLM performance due to poorly calibrated confidence estimates. Conclusions: This study highlights the potential of LLMs to support deprescribing decisions in the ED by effectively filtering relevant criteria. However, challenges remain in applying these criteria to complex clinical scenarios, as the LLM demonstrated poor performance on more intricate decision-making tasks, with its reported confidence often failing to align with its actual success in these cases. The findings underscore the need for clearer deprescribing guidelines, improved LLM calibration for real-world use, and better integration of human–artificial intelligence workflows to balance artificial intelligence recommendations with clinician judgment. %M 40215480 %R 10.2196/69504 %U https://aging.jmir.org/2025/1/e69504 %U https://doi.org/10.2196/69504 %U http://www.ncbi.nlm.nih.gov/pubmed/40215480 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e67242 %T Factors Influencing Older Adults’ Perception of the Age-Friendliness of Their Environment and the Impact of Loneliness, Technology Use, and Mobility: Quantitative Analysis %A Balki,Eric %A Hayes,Niall %A Holland,Carol %+ Department of Health Research, Faculty of Health and Medicine, Lancaster University, Health Innovation One, Sir John Fisher Dr, Bailrigg, Lancaster, LA1 4AT, United Kingdom, 44 1524 65201, e.balki@lancaster.ac.uk %K COVID-19 %K age-friendliness of environments %K physical isolation %K digital communication technologies %K loneliness %K cross-sectional %K WHO %K World Health Organization %K older adults %K reduced mobility %K age friendliness of environments %K adult well-being %K social connections %K aging in place %K life-space mobility %K LSE %K functional mobility %K UCLA loneliness scale %K age-friendly environment assessment tool %K AFEAT %D 2025 %7 6.5.2025 %9 Original Paper %J JMIR Aging %G English %X Background: The World Health Organization’s (WHO) publication on age-friendly environments (AFEs) imagines future cities to become more age-friendly to harness the latent potential of older adults, especially those who have restricted mobility. AFE has important implications for older adults in maintaining social connections, independence, and successful aging-in-place. However, technology is notably absent in the 8 intersecting domains of AFEs that the WHO imagines improve older adult well-being, and we investigated whether technology should form a ninth domain. While mobility was severely restricted, the COVID-19 pandemic provided an opportunity to test how older adults’ perceptions of their AFE changed and what role technology was playing. Objective: This study examined how life-space mobility (LSM), a concept for assessing patterns of functional mobility over time, and loneliness impacted perceived AFEs and the moderating effect of technology. It also explores whether technology should play a greater role as the ninth domain of the WHO’s imagination of the AFE of the future. Methods: In this cross-sectional quantitative observation study, data from 92 older adults aged 65-89 years were collected in England from March 2020 to June 2021 during the COVID-19 pandemic. The Life-space Questionnaire, Technology Experience Questionnaire, UCLA (University of California, Los Angeles) Loneliness Scale, and age-friendly environment assessment tool were used. Correlation and moderation analyses were used to investigate relationships between variables. Results: Most participants (86/92, 93%) had not left their immediate town in the previous 4 weeks before the interview. Restricted LSM was positively correlated to the age-friendly environment assessment tool, that is, rising physical isolation was linked to a better perception of AFEs; however, we discovered this result was due to the moderating impact of increased use of technology, and that restricted LSM actually had a negative effect on AFEs. Loneliness was correlated negatively with the perception of AFEs, but technology use was found to moderate the impact of loneliness. Conclusions: Pandemic-related LSM restrictions impacted perceived AFEs and loneliness negatively, but technology played a moderating role. The findings demonstrate that technology could be considered as a ninth domain in the WHO’s assessment of AFEs for older adults and that there is a need for its explicit acknowledgment. %M 40327856 %R 10.2196/67242 %U https://aging.jmir.org/2025/1/e67242 %U https://doi.org/10.2196/67242 %U http://www.ncbi.nlm.nih.gov/pubmed/40327856