{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:28:30Z","timestamp":1772252910797,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:00:00Z","timestamp":1620777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AG042525"],"award-info":[{"award-number":["R01AG042525"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Claude D. Pepper Older Americans Independence Centers at the University of Florida","award":["P30AG028740"],"award-info":[{"award-number":["P30AG028740"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four machine learning models: decision tree, random forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO), to estimate the hallmark measures of physical activities in young (20\u201350 years), middle-aged (50\u201370 years], and older adults (70\u201389 years]. Our models were built to recognize physical activity types, recognize physical activity intensities, estimate energy expenditure (EE) and recognize individual physical activities using wrist-worn tri-axial accelerometer data (33 activities per participant) from a large sample of participants (n = 253, 62% women, aged 20\u201389 years old). Results showed that the machine learning models were quite accurate at recognizing physical activity type and intensity and estimating energy expenditure. However, models performed less optimally when recognizing individual physical activities. F1-Scores derived from XGBoost\u2019s models were high for sedentary (0.955\u20130.973), locomotion (0.942\u20130.964) and lifestyle (0.913\u20130.949) activity types with no apparent difference across age groups. Low (0.919\u20130.947), light (0.813\u20130.828) and moderate (0.846\u20130.875) physical activity intensities were also recognized accurately. The root mean square error range for EE was approximately 1 equivalent of resting EE [0.835\u20131.009 METs]. Generally, random forest and XGBoost models outperformed other models. In conclusion, machine learning models to label physical activity types, activity intensity and energy expenditure are accurate and there are minimal differences in their performance across young, middle-aged and older adults.<\/jats:p>","DOI":"10.3390\/s21103352","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T22:46:14Z","timestamp":1620859574000},"page":"3352","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5345-8811","authenticated-orcid":false,"given":"Mamoun T.","family":"Mardini","sequence":"first","affiliation":[{"name":"Department of Aging and Geriatric Research, College of Medicine, University of Florida, Gainesville, FL 32610, USA"},{"name":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0961-1927","authenticated-orcid":false,"given":"Chen","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5763-5184","authenticated-orcid":false,"given":"Amal A.","family":"Wanigatunga","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA"}]},{"given":"Santiago","family":"Saldana","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Data Science, School of Medicine, Wake Forest University, Winston-Salem, NC 27101, USA"}]},{"given":"Ramon","family":"Casanova","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Data Science, School of Medicine, Wake Forest University, Winston-Salem, NC 27101, USA"}]},{"given":"Todd M.","family":"Manini","sequence":"additional","affiliation":[{"name":"Department of Aging and Geriatric Research, College of Medicine, University of Florida, Gainesville, FL 32610, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e1077","DOI":"10.1016\/S2214-109X(18)30357-7","article-title":"Worldwide trends in insufficient physical activity from 2001 to 2016: A pooled analysis of 358 population-based surveys with 1.9 million participants","volume":"6","author":"Guthold","year":"2018","journal-title":"Lancet Glob. 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