{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T03:31:02Z","timestamp":1786505462894,"version":"build-2736575974"},"reference-count":46,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T00:00:00Z","timestamp":1778976000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T00:00:00Z","timestamp":1778976000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2022YFC3602600"],"award-info":[{"award-number":["2022YFC3602600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82401664"],"award-info":[{"award-number":["82401664"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Care Science"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Disability in older adults, characterized by progressive limitations in performing activities of daily living, poses a significant public health challenge in aging societies. Early identification of individuals at risk is critical for implementing targeted interventions to mitigate functional decline. However, existing prediction models often prioritize disease\u2010related physiological indicators and overlook psychosocial dimensions, limiting their practicality and scalability in community\u2010based settings. This study aimed to develop and validate a practicable prediction model for 1\u2010year disability risk by integrating multidimensional indicators.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A prospective community\u2010based cohort in Beijing, functionally independent at baseline, was followed for 1 year. Disability was defined as a decline in the Barthel Index score. Potential predictors included demographic, lifestyle, clinical, physical, and psychosocial measures. The least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by logistic regression to construct the final model. Model performance was evaluated through fivefold cross\u2010validation repeated 100 times, with assessment of discrimination and calibration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 1003 community\u2010dwelling older adults (mean age 68.4\u2009\u00b1\u20095.2 years; 58.3% female), 163 (16.3%) developed disability at follow\u2010up. The LASSO regression identified seven predictors: age, dyslipidemia, gait speed, waist circumference, difficulty in lifting weights, appetite, and emotion regulation ability. The model demonstrated moderate discrimination, with an area under the receiver operating characteristic curve of 0.716 (95% CI: 0.712\u20130.720). Calibration curves indicated good overall agreement between predicted and observed risks. A nomogram was developed to facilitate individualized risk prediction in clinical practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study presents a practical disability risk prediction model incorporating physical, metabolic, and psychosocial factors. The model exhibits acceptable discrimination and calibration, supporting its potential for early screening and stratified management of community\u2010dwelling older adults. Future multicenter validations are warranted to enhance generalizability and explore dynamic interventions targeting modifiable factors like emotion regulation and gait speed.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hcs2.70080","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T02:29:13Z","timestamp":1779071353000},"page":"320-329","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Prediction Model for One\u2010Year Disability Risk Among Community\u2010Dwelling Older Adults in China: Integrating Physical, Metabolic, and Psychosocial Factors"],"prefix":"10.1002","volume":"5","author":[{"given":"Yanjun","family":"Ma","sequence":"first","affiliation":[{"name":"National Center for Neurological Disorders, Xuanwu Hospital Capital Medical University Beijing 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