{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T08:15:24Z","timestamp":1768896924739,"version":"3.49.0"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T00:00:00Z","timestamp":1724371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3603800"],"award-info":[{"award-number":["2023YFC3603800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3603801"],"award-info":[{"award-number":["2023YFC3603801"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Conventional physical activity (PA) metrics derived from wearable sensors may not capture the cumulative, transitions from sedentary to active, and multidimensional patterns of PA, limiting the ability to predict physical function impairment (PFI) in older adults. This study aims to identify unique temporal patterns and develop novel digital biomarkers from wrist accelerometer data for predicting PFI and its subtypes using explainable artificial intelligence techniques.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>Wrist accelerometer streaming data from 747 participants in the National Health and Aging Trends Study (NHATS) were used to calculate 231 PA features through time-series analysis techniques\u2014Tsfresh. Predictive models for PFI and its subtypes (walking, balance, and extremity strength) were developed using 6 machine learning (ML) algorithms with hyperparameter optimization. The SHapley Additive exPlanations method was employed to interpret the ML models and rank the importance of input features.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Temporal analysis revealed peak PA differences between PFI and healthy controls from 9:00 to 11:00 am. The best-performing model (Gradient boosting Tree) achieved an area under the curve score of 85.93%, accuracy of 81.52%, sensitivity of 77.03%, and specificity of 87.50% when combining wrist accelerometer streaming data (WAPAS) features with demographic data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>The novel digital biomarkers, including change quantiles, Fourier transform (FFT) coefficients, and Aggregated (AGG) Linear Trend, outperformed traditional PA metrics in predicting PFI. These findings highlight the importance of capturing the multidimensional nature of PA patterns for PFI.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>This study investigates the potential of wrist accelerometer digital biomarkers in predicting PFI and its subtypes in older adults. Integrated PFI monitoring systems with digital biomarkers would improve the current state of remote PFI surveillance.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocae224","type":"journal-article","created":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T02:14:04Z","timestamp":1724465644000},"page":"2571-2582","source":"Crossref","is-referenced-by-count":11,"title":["Predicting physical functioning status in older adults: insights from wrist accelerometer sensors and derived digital biomarkers of physical activity"],"prefix":"10.1093","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2297-4434","authenticated-orcid":false,"given":"Lingjie","family":"Fan","sequence":"first","affiliation":[{"name":"College of Computer Science, Sichuan University , Chengdu, Sichuan 610000,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0316-8365","authenticated-orcid":false,"given":"Junhan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Harvard Medical School , Boston, MA 02114,","place":["United States"]},{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, MA 02114,","place":["United States"]},{"name":"Center for Engineering in Medicine and Surgery, Department of Surgery, Massachusetts General Hospital , Boston, MA 02114,","place":["United States"]}]},{"given":"Yao","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications , Chongqing 400000,","place":["China"]}]},{"given":"Junjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University , Chengdu, Sichuan 610000,","place":["China"]}]},{"given":"Xiyue","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Stanford University School of Medicine , Stanford, CA 94305,","place":["United States"]}]},{"given":"Fengyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, West China Hospital, Sichuan 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