JMIR Aging最新文献

{"title":"Association Between Sleep Duration and Cognitive Frailty in Older Chinese Adults: Prospective Cohort Study.","authors":"Ruixue Cai, Jianqian Chao, Chenlu Gao, Lei Gao, Kun Hu, Peng Li","doi":"10.2196/65183","DOIUrl":"https://doi.org/10.2196/65183","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>The objective of our study was to examine the associations of sleep duration and its changes with cognitive frailty.</p><p><strong>Methods: </strong>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 future incidence of cognitive frailty using Cox proportional hazards models. Restricted cubic splines were employed to explore potential non-linear associations.</p><p><strong>Results: </strong>Among 11,303 participants, 1,298 (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 [OR] =1.71, 95% confidence interval [CI] =1.48-1.97, p<0.001). A J-shaped association between sleep duration and cognitive frailty was also observed (p<0.001). Additionally, during a median follow-up of 6.7 years among 5,201 participants who were not cognitively frail at baseline, 521 (10.0%) developed cognitive frailty. A higher risk of cognitive frailty was observed in participants with long sleep duration (hazard ratio [HR] =1.32, 95% CI =1.07-1.62, p=0.008).</p><p><strong>Conclusions: </strong>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.</p><p><strong>Clinicaltrial: </strong></p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143671137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network Analysis of Key Instrumental Activities of Daily Living and Cognitive Domains for Targeted Intervention in US Older Adults Without Dementia: Cross-Sectional Study.","authors":"Jiaying Li, Rendong He, Erh-Chi Hsu, Junxin Li","doi":"10.2196/67632","DOIUrl":"https://doi.org/10.2196/67632","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e67632"},"PeriodicalIF":5.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Passive Remote Monitoring Technologies' Influence on Home Care Clients' Ability to Stay Home: Multiprovincial Randomized Controlled Trial.","authors":"Lorie Donelle, Bradley Hiebert, Grace Warner, Michael Reid, Jennifer Reid, Salimah Shariff, Emily Richard, Sandra Regan, Lori Weeks, Kathleen Ledoux","doi":"10.2196/69107","DOIUrl":"https://doi.org/10.2196/69107","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Trial registration: </strong>ISRCTN ISRCTN79884651; https://www.isrctn.com/ISRCTN79884651.</p><p><strong>International registered report identifier (irrid): </strong>RR2-10.2196/15027.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e69107"},"PeriodicalIF":5.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Smart Health Wearable Adoption Among Singaporean Older Adults Based on Self-Determination Theory: Web-Based Survey Study.","authors":"Hyunjin Kang, Tingting Yang, Nazira Banu, Sheryl Wei Ting Ng, Jeong Kyu Lee","doi":"10.2196/69008","DOIUrl":"https://doi.org/10.2196/69008","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;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).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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 m","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e69008"},"PeriodicalIF":5.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Deep Learning to Perform Automatic Quantitative Measurement of Masseter and Tongue Muscles in Persons With Dementia: Cross-Sectional Study.","authors":"Mahdi Imani, Miguel G Borda, Sara Vogrin, Erik Meijering, Dag Aarsland, Gustavo Duque","doi":"10.2196/63686","DOIUrl":"https://doi.org/10.2196/63686","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e63686"},"PeriodicalIF":5.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcultural Adaptation, Validation, Psychometric Analysis, and Interpretation of the 22-Item Thai Senior Technology Acceptance Model for Mobile Health Apps: Cross-Sectional Study.","authors":"Nida Buawangpong, Penprapa Siviroj, Kanokporn Pinyopornpanish, Wachiranun Sirikul","doi":"10.2196/60156","DOIUrl":"https://doi.org/10.2196/60156","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to adapt and validate the senior technology acceptance model (STAM) questionnaire for assessing mHealth acceptance in the Thai context.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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 &gt;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 R&lt;sup&gt;2&lt;/sup&gt; for each item&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The 22-item Thai mHealth STAM demonstrated satisfactory psychometric properties in both validity and reliability. The questionnaire has the potential t","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e60156"},"PeriodicalIF":5.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143606688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building Consensus on the Relevant Criteria to Screen for Depressive Symptoms Among Near-Centenarians and Centenarians: Modified e-Delphi Study.","authors":"Carla Gomes da Rocha, Armin von Gunten, Pierre Vandel, Daniela S Jopp, Olga Ribeiro, Henk Verloo","doi":"10.2196/64352","DOIUrl":"https://doi.org/10.2196/64352","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The number of centenarians worldwide is expected to increase dramatically, reaching 3.4 million by 2050 and &gt;25 million by 2100. Despite these projections, depression remains a prevalent yet underdiagnosed and undertreated condition among this population that carries significant health risks.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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 im","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e64352"},"PeriodicalIF":5.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"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.","authors":"Doris van der Smissen, Maud A Schreijer, Lisette J E W C van Gemert-Pijnen, Rudolf M Verdaasdonk, Agnes van der Heide, Ida J Korfage, Judith A C Rietjens","doi":"10.2196/49507","DOIUrl":"10.2196/49507","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e49507"},"PeriodicalIF":5.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"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.","authors":"Gauthier Everard, Louise Declerck, Thierry Lejeune, Martin Gareth Edwards, Justine Bogacki, Cléo Reiprich, Kelly Delvigne, Nicolas Legrain, Charles Sebiyo Batcho","doi":"10.2196/64004","DOIUrl":"10.2196/64004","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT04694833; https://clinicaltrials.gov/study/NCT04694833.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e64004"},"PeriodicalIF":5.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study.","authors":"Shayan Nejadshamsi, Vania Karami, Negar Ghourchian, Narges Armanfard, Howard Bergman, Roland Grad, Machelle Wilchesky, Vladimir Khanassov, Isabelle Vedel, Samira Abbasgholizadeh Rahimi","doi":"10.2196/67715","DOIUrl":"10.2196/67715","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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 F&lt;sub&gt;1&lt;/sub&gt;-score. Shapely addictive explanations and local interpretable model-agnostic explanations were used to explain the model's predictions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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.\"&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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 furt","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e67715"},"PeriodicalIF":5.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}