{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T21:31:41Z","timestamp":1793309501109,"version":"4.5.0"},"reference-count":22,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Aging"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Falls are a major cause of disability among older adults, and early identification of functional decline is essential for prevention. Artificial intelligence (AI) systems may enhance mobility screening by providing objective, real-time feedback.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to evaluate whether AI-assisted dynamic postural control screening combined with adaptive training improves functional mobility outcomes in older adult populations.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n A quasi-experimental study was conducted with 2005 older adults recruited from community centers and health care institutions in Keelung, Taiwan. Participants were assigned to either an experimental group (n=1451), which underwent AI-assisted screening with adaptive exercise prescriptions, or a control group (n=554), which completed follow-ups through regular physical assessments with standard care without AI-tailored training. The AI system integrated skeletal tracking with the Short Physical Performance Battery to assess balance, gait speed (4-m walk), and sit-to-stand performance. Independent-samples 2-tailed\n t<\/jats:italic>\n tests and repeated-measures ANOVA were applied, and effect sizes (Cohen\n d<\/jats:italic>\n and \u03b7\u00b2) with 95% CIs were reported.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The experimental group demonstrated significantly greater improvements compared with the control group in Short Physical Performance Battery scores (\u0394=0.8 vs 0.3;\n t<\/jats:italic>\n 2003<\/jats:sub>\n =3.41;\n P<\/jats:italic>\n =.001; Cohen\n d<\/jats:italic>\n =0.45, 95% CI 0.18\u20100.72), gait speed (\u0394=15 cm\/s vs 5 cm\/s;\n t<\/jats:italic>\n 2003<\/jats:sub>\n =4.85;\n P<\/jats:italic>\n <.001; Cohen\n d<\/jats:italic>\n =0.62, 95% CI 0.35\u20100.88), and sit-to-stand time (\u0394=\u20131.4 s vs \u20130.6 s;\n t<\/jats:italic>\n 2003<\/jats:sub>\n =3.12;\n P<\/jats:italic>\n =.002; Cohen\n d<\/jats:italic>\n =0.39, 95% CI 0.12\u20100.65). Here \u201c\u0394\u201d refers to the change score, calculated as post-intervention minus baseline (ie, the amount of improvement during the study period). Participation rate was strongly associated with outcomes, with 1-way ANOVA showing significant group differences (\n F<\/jats:italic>\n 2,1448<\/jats:sub>\n =8.74\u201012.21;\n P<\/jats:italic>\n <.001; \u03b7\u00b2=0.07\u20100.10).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n AI-assisted dynamic postural control screening combined with adaptive training substantially improved functional performance in mobility, balance, and gait among older adults. While fall incidence was not directly measured, these functional gains may have implications for fall risk reduction. Future longitudinal studies with extended follow-up (12\u201024 mo) and prospective fall incidence tracking across diverse populations are required to validate whether these improvements translate into actual reductions in fall risk.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/73290","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T12:56:56Z","timestamp":1764680216000},"page":"e73290-e73290","source":"Crossref","is-referenced-by-count":5,"title":["AI-Assisted Dynamic Postural Control Screening to Improve Functional Mobility in Older Adult Populations: Quasi-Experimental Study"],"prefix":"10.2196","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9708-4358","authenticated-orcid":false,"given":"Kai-Chih","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5483-7445","authenticated-orcid":false,"given":"Rong-Jong","family":"Wai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1229-2856","authenticated-orcid":false,"given":"Hung-Yu","family":"Chang Chien","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"R1","unstructured":"Step safely: strategies for preventing and managing falls across the life-course. World Health Organization; 2021. URL: https:\/\/www.who.int\/publications\/i\/item\/978924002191-4 [Accessed 19-11-2025]"},{"key":"R2","unstructured":"Falls. World Health Organization. Apr 26, 2021. URL: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/falls [Accessed 26-08-2025]"},{"issue":"2","key":"R3","doi-asserted-by":"publisher","first-page":"149","DOI":"10.3390\/healthcare9020149","article-title":"A deep learning approach for TUG and SPPB score prediction of (pre-) frail older adults on real-life IMU data","volume":"9","author":"Friedrich","journal-title":"Healthcare (Basel)"},{"issue":"8","key":"R4","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.1016\/j.jamda.2020.09.038","article-title":"The Short Physical Performance Battery (SPPB): a quick and useful tool for fall risk stratification among older primary care patients","volume":"22","author":"Welch","journal-title":"J Am Med Dir Assoc"},{"key":"R5","doi-asserted-by":"publisher","DOI":"10.2196\/54934","article-title":"The applications of artificial intelligence for assessing fall risk: systematic review","volume":"26","author":"Gonz\u00e1lez-Castro","journal-title":"J Med Internet Res"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.1016\/j.exger.2022.112044","article-title":"GDLAM and SPPB batteries for screening sarcopenia in community-dwelling Spanish older adults: healthy-age network study","volume":"172","author":"Marcos-Pardo","journal-title":"Exp Gerontol"},{"issue":"11","key":"R7","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.3390\/bioengineering11111154","article-title":"Introduction of AI technology for objective physical function assessment","volume":"11","author":"Kouno","journal-title":"Bioengineering (Basel)"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3123297","article-title":"A feasible fall evaluation system via artificial intelligence gesture detection of gait and balance for sub-healthy community- dwelling older adults in Taiwan","volume":"9","author":"Lin","journal-title":"IEEE Access"},{"issue":"1","key":"R9","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1109\/JBHI.2024.3430226","article-title":"Frailty modeling using machine learning methodologies: a systematic review with discussions on outstanding questions","volume":"29","author":"Yang","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"R10","doi-asserted-by":"publisher","DOI":"10.1186\/s12877-025-05878-w","article-title":"A systematic review on the efficacy of artificial intelligence in geriatric healthcare: a critical analysis of current literature","volume":"25","author":"Imran","journal-title":"BMC Geriatr"},{"issue":"2","key":"R11","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.gerinurse.2021.02.016","article-title":"Short Physical Performance Battery (SPPB) score as a discriminator of dynapenic abdominal obesity among community-dwelling older adults","volume":"42","author":"Santos","journal-title":"Geriatr Nurs"},{"issue":"12","key":"R12","doi-asserted-by":"publisher","first-page":"1861","DOI":"10.1016\/j.jamda.2023.07.020","article-title":"In-hospital fall risk prediction by objective measurement of lower extremity function in a high-risk population","volume":"24","author":"Tanaka","journal-title":"J Am Med Dir Assoc"},{"issue":"1","key":"R13","doi-asserted-by":"publisher","DOI":"10.1186\/s12984-024-01310-3","article-title":"Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach","volume":"21","author":"Liang","journal-title":"J NeuroEngineering Rehabil"},{"key":"R14","doi-asserted-by":"publisher","DOI":"10.2196\/54395","article-title":"Telemedicine-based risk program to prevent falls among older adults: protocol for a randomized quality improvement trial","volume":"13","author":"Rein","journal-title":"JMIR Res Protoc"},{"issue":"12","key":"R15","doi-asserted-by":"publisher","first-page":"e30558","DOI":"10.2196\/30558","article-title":"A digital health fall prevention program for older adults: feasibility study","volume":"5","author":"Jacobson","journal-title":"JMIR Form Res"},{"issue":"4","key":"R16","doi-asserted-by":"publisher","first-page":"e16217","DOI":"10.2196\/16217","article-title":"Alcohol avoidance training as a mobile app for problem drinkers: longitudinal feasibility study","volume":"8","author":"Laurens","journal-title":"JMIR Mhealth Uhealth"},{"key":"R17","doi-asserted-by":"publisher","DOI":"10.1016\/j.archger.2022.104762","article-title":"Association of food insecurity with physical frailty among older adults: study based on LASI, 2017-18","volume":"103","author":"Muhammad","journal-title":"Arch Gerontol Geriatr"},{"key":"R18","doi-asserted-by":"crossref","unstructured":"Duncan L Zhu S Pergolotti M et al. Automated camera-based assessment of Short Physical Performance Battery (SPPB) for older adults with cancer. In: Dom\u00ednguez-Morales M Luna-Perej\u00f3n F , editors. Human Gait - Recent Findings and Research. IntechOpen; 2024. 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