JMIR Aging最新文献

{"title":"The 4 Ss Framework for Designing Age-Friendly Communities of the Future: Focus Group Study.","authors":"Inga Hunter, Caroline Lockhart","doi":"10.2196/90923","DOIUrl":"10.2196/90923","url":null,"abstract":"<p><strong>Background: </strong>Digital technology is increasingly being used to deliver interventions and initiatives to support the well-being of older adults. However, few studies have conducted needs assessments to identify the future well-being service requirements of an older adult population and their preferred modes of delivery, whether via digital technology or, in- person, or a combination of both (ie, a hybrid model).</p><p><strong>Objective: </strong>This study aims to investigate the requirements of a rural region in New Zealand to inform planning to meet the future well-being needs of its older adult population over the next 30 years.</p><p><strong>Methods: </strong>In total, 2 focus group discussions and 13 interviews were held with participants using a combination of phone and video calls. A total of 31 adults aged 61 years or older participated. The participants were asked how they saw the future well-being needs of the older adult population evolving, the role of digital technology and/or in-person interactions in delivering well-being services, and perceived barriers to, and enablers of, digital technology for providing services. Focus group and interview transcripts were thematically analyzed.</p><p><strong>Results: </strong>A total of 4 key well-being themes were identified across both focus group discussions and interviews with participants: -\"services,\" \"support,\" \"spaces,\" and \"social connection,\" -with an overarching finding that future age-friendly planning must prioritize social and community well-being outcomes, ensuring that investments in services, digital inclusion, and public spaces strengthen older adults' ability to connect with others and participate fully in community life.</p><p><strong>Conclusions: </strong>Digital technology is developing at a rapid pace, and as a result, we need to consider how to plan for the transition and bridge the gap identified between the current use of digital technology and its potential future use if technology is to support the older adults of the future. The findings indicate that older adults prefer to engage in- person, while trust is a barrier to digital technology use for some participants. The future offers many opportunities to support the well-being of older adults and communities through the application of the proposed 4 Ss Framework.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"9 ","pages":"e90923"},"PeriodicalIF":4.6,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13621887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076302","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":"Rationale, Design, and Baseline Characteristics of the TARGET (Targeted Assessment and Recruitment of Geriatrics for Effective Fall Prevention Treatments) Cohort of Older Adults for Assessing Fall and Fracture Risk: Prospective Cohort Study.","authors":"Angelique Chan, Abhijit Visaria, Rahul Malhotra, Navrag B Singh, Benedikt Helgason, Victor R Schinazi, Kok Yang Tan, David B Matchar, Rita Sim, Vanessa Jean Wen Koh, Wei Xuan Lai, Lakkhina Troeung, Catherine Wen Huey Lim, Sai G S Pai, Kai Zhe Tan, Anitha D Praveen, Dheeraj Jha, Stephen J Ferguson, Giorgio Colombo, Karolina Minta, Ecosse L Lamoureux, William R Taylor","doi":"10.2196/89285","DOIUrl":"10.2196/89285","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Falls and fractures are a major clinical concern for older adults. International clinical guidelines recommend annual fall risk screening for all adults aged 60 years and older. However, existing falls risk-screening algorithms have shown limited sensitivity and specificity in detecting future falls. Emerging health technologies, including wearable sensors, in-silico finite element models (FEMs), and virtual reality (VR) technology, show promise in enhancing fall and fracture risk assessments by providing more personalized objective data to support targeted primary prevention. However, large-scale longitudinal studies are required to evaluate their predictive accuracy and cost-effectiveness for systematic community-based screening.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The TARGET (Targeted Assessment and Recruitment of Geriatrics for Effective Fall Prevention Treatments) study is an ongoing national prospective cohort study in Singapore designed to develop cost-effective and scalable frameworks for the early detection and prevention of falls and fractures using novel health technologies. This paper describes the rationale, design, and baseline characteristics of TARGET, and provides preliminary insights into how these novel technologies perform in discriminating between older adults with and with no history of falls at study baseline.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A total of 2291 community-dwelling Singapore residents aged ≥60 years were enrolled between 2022 and 2024. Participants underwent a comprehensive baseline fall risk assessment comprising (1) home-based interview to assess sociodemographic, anthropometric, cognitive, physical, functional, and psychosocial status; (2) gait assessment using wearable inertial measurement units (IMUs) motion sensors; (3) dual-energy X-ray absorptiometry and whole-body 3D scans to construct subject-specific FEMs; and (4) VR-based cognitive assessment to probe spatial navigation deficits. Prospective follow-up for 2 years is ongoing, with linkage to national electronic medical records (EMRs) to obtain detailed clinical, medication, and pathological data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of the total cohort, 58% (1327/2291) were female, with a mean age of 74.6 years at baseline. TARGET represents a relatively healthy older adult population, with 83% (1898/2291) remaining fully independent, 44% (1016/2290) with mild cognitive impairment, 50% (440/879) with osteopenia, and 15% (358/2291) reporting falls in the past year. Significant baseline functional, psychosocial, cognitive, and biomechanical differences were identified between fallers and nonfallers. Fallers had poorer physical and psychosocial health, greater gait variability, and a higher risk of osteoporotic fractures. Novel risk prediction models integrating epidemiological, IMUs, FEM, VR, and EMR data are in development and being validated against gold standard clinical assessments, with cost-effectiveness evaluations of the ","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"9 ","pages":"e89285"},"PeriodicalIF":4.6,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13614221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149065021","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":"Prediction of 2-Year Incident Clinical Knee Osteoarthritis From Extensor Mechanism Mechanical Properties in Asymptomatic Community-Dwelling Older Adults: Prospective Cohort Study.","authors":"Ui-Jae Hwang, Zongpan Li, Peng Xia, Tianxiang Fan, Arnold Wong, Oh-Yun Kwon, Siu-Ngor Fu","doi":"10.2196/84494","DOIUrl":"10.2196/84494","url":null,"abstract":"<p><strong>Background: </strong>Knee osteoarthritis (KOA) lacks disease-modifying treatment, making early identification of modifiable risk factors in asymptomatic older adults a priority. Patellar tendon stiffness (PTS) and knee extensor strength (KESt) may independently influence KOA development, yet their prognostic value in individuals free of knee symptoms is unestablished.</p><p><strong>Objective: </strong>This study aimed to determine whether baseline extensor mechanism mechanical properties predict 2-year incident clinical KOA in community-dwelling older adults who were asymptomatic at enrollment, and to quantify the relative contribution of each parameter.</p><p><strong>Methods: </strong>This 2-year prospective cohort study enrolled 116 community-dwelling older adults (220 knees) free of knee pain at baseline. Incident clinical KOA was diagnosed according to the American College of Rheumatology criteria. Prognostic performance was evaluated across 7 survival algorithms using 5-fold nested cross-validation with the Harrell C-index. Predictor importance was quantified using Shapley Additive Explanations (SHAP) values from the best-performing model, and absolute KOA risk was estimated across tertiles of the linear predictor score.</p><p><strong>Results: </strong>Over 2 years, 55 (25%) knees developed incident clinical KOA. PTS and KESt were consistently selected across all cross-validation folds, whereas quadriceps muscle passive stiffness was not selected in any fold. The gradient boosted survival model achieved the highest discrimination (C-index=0.720, 95% CI 0.686-0.754), significantly exceeding chance (<i>P</i><.05). SHAP analysis identified KESt as the dominant predictor (mean |SHAP| 0.314, 95% CI 0.251-0.388), with PTS as the second most important predictor (0.166, 95% CI 0.128-0.200). Knees in the highest-risk tertile showed a 24-month KOA incidence of 38.4% (28/73), which was more than twice that in the lowest-risk tertile (13/74, 17.6%).</p><p><strong>Conclusions: </strong>PTS and KESt independently and consistently predicted 2-year incident clinical KOA in asymptomatic community-dwelling older adults. Extensor mechanism mechanical profiling may identify individuals at elevated KOA risk prior to symptom onset, although these exploratory findings require confirmation in larger cohorts before KESt and PTS can be considered for screening or preventive intervention.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"9 ","pages":"e84494"},"PeriodicalIF":4.6,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13608216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041132","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":"Multimodal Dementia Prediction With Large Language Models: Cross-Attention Over Text, Audio, and Image.","authors":"Felix Agbavor, Hualou Liang","doi":"10.2196/93279","DOIUrl":"10.2196/93279","url":null,"abstract":"<p><strong>Background: </strong>Alzheimer disease (AD) is a leading cause of dementia, and there is growing interest in scalable approaches for early screening using speech-based tasks. While prior work has demonstrated promising results using either transcript-based language features or acoustic cues, most approaches remain unimodal or rely on simple fusion strategies that do not explicitly consider interactions across modalities.</p><p><strong>Objective: </strong>In this study, we propose an attention-based trimodal fusion framework that integrates text, audio, and image representations of the Cookie Theft picture, which serves as the shared visual stimulus in the picture-description task.</p><p><strong>Methods: </strong>Our method uses a new bidirectional cross-attention mechanism to achieve a unified multimodal embedding for downstream tasks. We evaluate the approach on 2 tasks: AD detection by classifying whether the participant has AD or not, and AD severity assessment by predicting Mini-Mental Status Examination cognitive scores.</p><p><strong>Results: </strong>On the AD detection task, trimodal fusion achieves the best overall performance (<i>F</i>₁-score=0.8667, area under the receiver operating characteristic curve=0.9032), outperforming unimodal baselines, bimodal fusion, and conventional early or late fusion methods. For AD severity assessment, the proposed multimodal representation reduces prediction error of root mean squared error to about 4.20, improving over both unimodal and bimodal fusion settings. We further perform the ablation analysis to show that bidirectional cross-attention consistently outperforms conventional unidirectional cross-attention.</p><p><strong>Conclusions: </strong>These results demonstrate that attention-based multimodal fusion can enhance dementia prediction from picture-description responses and provide a strong foundation for developing multimodal cognitive screening pipelines.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"9 ","pages":"e93279"},"PeriodicalIF":4.6,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13592564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018029","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 Temporal External Validation of a Parsimonious, Interpretable Machine Learning Model for Predicting 6-Month Mortality in Long-Term Care Facilities: A Retrospective Cohort Study.","authors":"Yun-Cheng Tsai, Yi-Lin Wu, Yung-Chen Yu, Yi-Wen Chen, Yi-Wen Chen, Yi-Ching Yang, Yu-Tai Lo","doi":"10.2196/94567","DOIUrl":"10.2196/94567","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Early mortality after long-term care facility (LTCF) admission is common; yet, prognostic tools are often derived from Western minimum dataset-based cohorts or require hospital electronic health record linkages that are unavailable at intake in many LTCFs. There is also limited evidence on explainable, admission-feasible machine learning-based prognostication in Asian LTCF settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of the study is to develop and temporally externally validate an interpretable machine learning model for predicting 6-month all-cause mortality among older adults newly admitted to LTCFs in Taiwan using routinely collected LTCF assessment data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a retrospective cohort study using the JUBO Long-Term Care Database, a nationwide private administrative registry covering 636 LTCFs (37.4% of the national LTCFs) in Taiwan. We included residents with first-time LTCF admission and prespecified nonoverlapping cohorts for temporal validation: development (January 1, 2020, to December 31, 2023; n=23,901) and external validation (January 1 to December 31, 2024; n=6216). The outcome measure was death within 180 days of admission. We compared a nonlinear ensemble model (hybrid of extreme gradient boosting and random forest [HybridXGBRF]) with 7 other algorithms, including tree-based and linear benchmarks. Discrimination (area under the receiver operating characteristic curve [AUROC]), classification metrics (accuracy, precision, recall, and &lt;i&gt;F&lt;/i&gt;&lt;sub&gt;1&lt;/sub&gt;), and calibration (Brier score and calibration plots) were assessed. Model interpretability was examined using Shapley Additive Explanations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In the development cohort, 5272 of 23,901 (22.1%) residents died within 180 days. In the 2024 temporal validation cohort, 1781 of 6216 (28.7%) residents died. In internal cross-validation, HybridXGBRF had the highest AUROC among the evaluated models (0.89, 95% CI 0.88-0.89). In temporal validation, HybridXGBRF maintained strong discrimination (AUROC 0.90, 95% CI 0.89-0.91), with an accuracy of 0.85 and an &lt;i&gt;F&lt;/i&gt;&lt;sub&gt;1&lt;/sub&gt;-score of 0.68. Calibration plots indicated close agreement between predicted and observed risks across most probability ranges, with mild divergences at higher predicted risks. Shapley Additive Explanations analysis identified frequent hospitalizations within 6 months, activities of daily living impairment, and weight loss as influential predictors. The model showed stable AUROC across sex and age strata (0.89-0.90) and maintained high discrimination among residents with improving activities of daily living scores (AUROC 0.91, 95% CI 0.90-0.92).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;An interpretable machine learning model using routinely collected Taiwanese LTCF assessment data achieved strong discrimination, acceptable calibration, and stable temporal validation performance without requiring hospital-based electronic ","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"9 ","pages":"e94567"},"PeriodicalIF":4.6,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13581260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148983928","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":"Correction: Feasibility and Preliminary Effects of a Pressure Sensor-Based Exergame on Cognitive and Sensorimotor Frailty in Low-Income Older Adults Receiving Public Community Care Services: Pilot Randomized Controlled Trial.","authors":"Sunmi Song, Suk-Chan Hahm, Seyun Chang, Jinseung Lee, Heewon Kang, Sanghun Lee, Junesun Kim","doi":"10.2196/109510","DOIUrl":"10.2196/109510","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/93467.].</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"9 ","pages":"e109510"},"PeriodicalIF":4.6,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13601869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921173","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":"Community Exergaming and Maintenance-Related Outcomes in Older Adults: Multi-Wave Observational Evaluation.","authors":"Yuezhong Liu, Bing Xun Chia, Herng Yeow Koh, Quoc Nam Tran Nguyen, Jiahui Jin, Kimberly Miracle Wei Yan Tan, Samuel Qing An Long, Yin-Leng Theng","doi":"10.2196/100007","DOIUrl":"10.2196/100007","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Community exergaming may help older adults remain physically active by combining movement, enjoyment, and social interaction, but less is known about whether observed changes persist after supervised delivery ends in real-world community settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study evaluated immediate and maintenance-related outcomes of the International Singapore Intergenerational National Games, a community-delivered exergaming program for older adults in Singapore. We examined positive mental health, intrinsic motivation, habit strength, physical activity, and exploratory implementation determinants aligned with the capability, opportunity, and motivation-behavior (COM-B) framework.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a multi-wave observational evaluation from October 2025 to January 2026 among 280 older adults recruited from 14 senior activity centers or active aging centers in Singapore. Assessments were conducted at baseline (T0), immediately after an 8-week supervised training phase (T1), after a 4-week unsupervised free-play phase (T2), and at a later follow-up for selected measures in 6 centers (T3). Immediate within-person changes were examined using 2-tailed paired &lt;i&gt;t&lt;/i&gt; tests. Maintenance-related trajectories were examined using adjusted linear mixed-effects models. Exploratory analyses examined whether COM-B-aligned capability, opportunity, and motivation composites were associated with physical activity trajectories.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Participants had a mean age of 72.54 (SD 6.15) years, and 77.1% (216/280) were female. From T0 to T1, mean positive mental health increased from 55.19 (SD 9.79) to 57.59 (SD 8.70; Cohen &lt;i&gt;dz&lt;/i&gt;=0.28; &lt;i&gt;P&lt;/i&gt;&lt;.001), mean intrinsic motivation increased from 5.94 (SD 1.00) to 6.29 (SD 0.81; Cohen &lt;i&gt;dz&lt;/i&gt;=0.40; &lt;i&gt;P&lt;/i&gt;&lt;.001), and mean habit strength increased from 3.34 (SD 1.11) to 3.81 (SD 0.84; Cohen &lt;i&gt;dz&lt;/i&gt;=0.42; &lt;i&gt;P&lt;/i&gt;&lt;.001). In adjusted mixed models, habit strength remained higher at T2 than at baseline (β=0.362, 95% CI .113-.611; &lt;i&gt;P&lt;/i&gt;=.005), and intrinsic motivation remained higher at T2 (β=0.397, 95% CI .156-.638; &lt;i&gt;P&lt;/i&gt;=.001) and T3 (β=0.305, 95% CI .009-.601; &lt;i&gt;P&lt;/i&gt;=.04). Positive mental health was higher immediately after supervised training (β=4.140, 95% CI 2.169-6.111; &lt;i&gt;P&lt;/i&gt;&lt;.001) and at T2 (β=7.390, 95% CI 5.370-9.410; &lt;i&gt;P&lt;/i&gt;&lt;.001) than at baseline. Among the COM-B domains, only opportunity was associated with greater immediate posttraining physical activity improvement (time × opportunity interaction β=26.464, 95% CI 3.191-49.737; &lt;i&gt;P&lt;/i&gt;=.03), with no evidence of a sustained interaction at T2.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;In this observational evaluation, positive mental health, intrinsic motivation, and habit strength were higher at T2 than at baseline among respondents with available follow-up data, and intrinsic motivation was also higher at the selected-center T3 follow-up. Because this stu","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"9 ","pages":"e100007"},"PeriodicalIF":4.6,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13557100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921191","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":"Assessing a Digital Platform (KOKU-Nut) to Improve Nutrition in Older Adults: Mixed Methods Feasibility Randomized Controlled Trial.","authors":"Chloe French, Sorrel Burden, Jaheeda Gangannagaripalli, Emma Stanmore","doi":"10.2196/87351","DOIUrl":"10.2196/87351","url":null,"abstract":"<p><strong>Background: </strong>Keep-on-Keep-up Nutrition (KOKU-Nut) is a free, tablet-based digital platform that focuses on increasing physical activity and improving the dietary intake of older adults.</p><p><strong>Objective: </strong>The aim of this study was to assess the feasibility of using KOKU-Nut among community-dwelling older adults. Feasibility was assessed by considering recruitment and retention rates, acceptability of the intervention, and study design.</p><p><strong>Methods: </strong>Participants (community-dwelling adults aged ≥65 years) were randomized 1:1 to either the intervention or control group. The intervention group was asked to engage with KOKU-Nut 3 times a week for 12 weeks. Participants in the control group received a leaflet promoting a healthy lifestyle. All participants completed questionnaires at baseline and 12 weeks. A sample of participants was asked to complete an optional interview. The study collected data on dietary intake (24-hour food diary), physical function (grip strength and 5-time sit-to-stand exercise), usability of the intervention (System Usability Scale), and safety (adverse events).</p><p><strong>Results: </strong>Of 51 participants assessed for eligibility, 31 were randomized, and 28 completed the 12-week follow-up. Of the 14 participants in the intervention group, 10 (71.4%) reported engagement with KOKU-Nut 3 or more times a week. There was no difference in physical function or dietary intake at 12 weeks between participants in the intervention and control groups after adjusting for age and baseline values.</p><p><strong>Conclusions: </strong>This feasibility randomized controlled trial indicates that the study design was appropriate and acceptable to older adults, as demonstrated by the recruitment and retention rates. This is promising and demonstrates the potential for conducting a future powered randomized controlled trial to assess the effectiveness of KOKU-Nut.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"9 ","pages":"e87351"},"PeriodicalIF":4.6,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13524216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148881857","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":"A Smart Wheeled Walker With Integrated Physiological Monitoring to Improve Mobility in Older Adults: Randomized Crossover Trial.","authors":"Phurichaya Werasirirat, Pornpimol Muanjai, Oranat Sukkho, Nongnuch Luangpon, Audrius Snieckus, Juntip Namsawang","doi":"10.2196/99522","DOIUrl":"10.2196/99522","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mobility limitations in older adults are associated with an increased physiological cost of walking, reduced functional independence, and elevated fall risk. Conventional wheeled walkers improve stability but lack real-time physiological monitoring and safety feedback. Digital health-enabled assistive devices integrating physiological monitoring and fall detection may enhance mobility performance and safety.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the short-term effects of a smart wheeled walker equipped with real-time physiological monitoring and fall detection compared with a standard wheeled walker in improving walking efficiency, dynamic balance, and fear of falling among community-dwelling older adults.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A single-blind randomized crossover trial was conducted in 30 community-dwelling older adults aged 65 to 80 years with mild balance impairment (Timed Up and Go &gt;13.5 seconds). Participants completed walking trials using both a standard wheeled walker and a smart wheeled walker in a randomized order, with a 30-minute washout period. The smart walker integrated photoplethysmography-based heart rate (HR) monitoring, pulse oximetry (oxygen saturation [SpO&lt;sub&gt;₂&lt;/sub&gt;]), and a tilt-based fall detection system with mobile alert functionality. The primary outcome was walking efficiency assessed using the Physiological Cost Index (PCI). Secondary outcomes included dynamic balance (Expanded Timed Up and Go [ETUG]) and fear of falling (Falls Efficacy Scale-International). Statistical analysis was performed using paired comparisons with a significance level of &lt;i&gt;P&lt;/i&gt;&lt;.05.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Thirty (100%) participants completed both intervention conditions and were included in the final analysis. Walking speed was significantly higher with the smart wheeled walker than the standard wheeled walker (mean 28.77, SD 11.94 vs mean 18.14, SD 14.55 m/min; mean difference -10.63 m/min, 95% CI -17.51 to -3.75 m/min; &lt;i&gt;P&lt;/i&gt;&lt;.001). The PCI was significantly lower with the smart wheeled walker (mean 0.36, SD 0.38 vs mean 0.69, SD 1.23 beats/m; mean difference 0.33 beats/m, 95% CI 0.06-0.60 beats/m; &lt;i&gt;P&lt;/i&gt;=.02), indicating improved walking efficiency. Participants also completed the ETUG test significantly faster with the smart wheeled walker (mean 50.06, SD 24.50 vs mean 64.47, SD 23.04 s; mean difference 14.41 s, 95% CI 2.12-26.70 s; &lt;i&gt;P&lt;/i&gt;=.048), indicating improved dynamic balance. Fear of falling did not differ significantly between walker conditions (mean 31.56, SD 9.58 vs mean 32.91, SD 10.37; mean difference 1.35, 95% CI -3.81 to 6.51; &lt;i&gt;P&lt;/i&gt;=.62).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;A smart wheeled walker integrating real-time physiological monitoring and fall detection significantly improved walking efficiency and dynamic balance during short-term supervised testing in community-dwelling older adults. These findings support the potential clinical va","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"9 ","pages":"e99522"},"PeriodicalIF":4.6,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13524218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148881821","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":"Developing Digital Platforms to Help Older Adults Stay Physically and Socially Active: Co-Design Study to Develop a Toolkit and Evaluate Its Impact.","authors":"Leonie Cooper, Hannah Bradwell, Colin B Shore, Simone A Tomaz, Rory Baxter, Gemma C Ryde, Swen E Gaudl, Richard Haynes, Catherine Hennessy, Ray B Jones, Anna C Whittaker","doi":"10.2196/89785","DOIUrl":"10.2196/89785","url":null,"abstract":"<p><strong>Background: </strong>Digital technologies have the potential to support physical, cognitive, and social activity among older adults, but many small- and medium-sized enterprises (SMEs) lack the resources to conduct meaningful co-design with older adults. Toolkits derived from rigorous co-design processes may offer a scalable mechanism for translating end-user priorities into real-world product development.</p><p><strong>Objective: </strong>The Generating Older Active Lives Digitally study aimed primarily to engage older adults in a co-design process to identify priorities for digital technologies that support physical activity and reminiscence. Secondary objectives were to (1) translate these findings into a practical developer-facing toolkit, and (2) evaluate the toolkit's perceived utility and influence among digital technology SMEs.</p><p><strong>Methods: </strong>A total of 157 participants (121 older, 7 younger, and 29 staff) across 15 community and care settings in England and Scotland engaged in 106 technology interaction sessions, 22 evaluation focus groups, and 10 co-design workshops. These involved more than 20 digital technologies. Thematic analysis and structured card-ranking tasks were used to derive their priorities. Preliminary toolkits were co-designed and used by 10 UK-based SMEs who received small grants to apply the toolkit to active development projects. Their reports and follow-up interviews were analyzed thematically to identify perceived impacts on design decisions, product adaptations, and business outcomes.</p><p><strong>Results: </strong>Co-design activities generated 7 cross-cutting themes: motivation, content, barriers, design and inclusivity, suitability, acceptability, and motivations to use. These were organized into 3 toolkit sections: general design principles, online physical-activity platforms, and virtual reality. SMEs reported that the toolkits enhanced understanding of older adults' needs, validated design decisions, and inspired some new features. Perceived impacts included improved usability, expanded accessibility options, increased content variety, clearer instructional design, enhanced social components, and reduced operational costs. SMEs also reported some business benefits, including strengthened cases for investment and increased product uptake.</p><p><strong>Conclusions: </strong>Co-designed toolkits may offer a useful and cost-effective mechanism for translating older adults' priorities into digital product development. SMEs perceived this toolkit as practical, relevant, and impactful for informing design choices. This approach may complement, but does not replace, direct user involvement. It may help accelerate inclusive digital-health innovation for aging populations.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"9 ","pages":"e89785"},"PeriodicalIF":4.6,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13521207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148881803","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}