A comparison of accelerometry analysis methods for physical activity in older adult women and associations with health outcomes over time |
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| Authors: | Katie J Thralls Suneeta Godbole Todd M Manini Eileen Johnson Loki Natarajan Jacqueline Kerr |
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| Institution: | 1. Department of Family Medicine and Public Health, San Diego State University, San Diego, CA, USA;2. Department of Family Medicine and Public Health, University of California, San Diego, USAkthralls@ucsd.edu;4. Department of Family Medicine and Public Health, University of California, San Diego, USA https://orcid.org/0000-0002-7358-2078;5. Department of Family Medicine and Public Health, University of Florida, Gianseville, FL, USA;6. Department of Family Medicine and Public Health, University of California, Berkeley, USA;7. Department of Family Medicine and Public Health, University of California, San Diego, USA |
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| Abstract: | ABSTRACTThis study compared five different methods for analyzing accelerometer-measured physical activity (PA) in older adults and assessed the relationship between changes in PA and changes in physical function and depressive symptoms for each method. Older adult females (N = 144, Mage = 83.3 ± 6.4yrs) wore hip accelerometers for six days and completed measures of physical function and depressive symptoms at baseline and six months. Accelerometry data were processed by five methods to estimate PA: 1041 vertical axis cut-point, 15-second vector magnitude (VM) cut-point, 1-second VM algorithm (Activity Index (AI)), machine learned walking algorithm, and individualized cut-point derived from a 400-meter walk. Generalized estimating equations compared PA minutes across methods and showed significant differences between some methods but not others; methods estimated 6-month changes in PA ranging from 4 minutes to over 20 minutes. Linear mixed models for each method tested associations between changes in PA and health. All methods, except the individualized cut-point, had a significant relationship between change in PA and improved physical function and depressive symptoms. This study is among the first to compare accelerometry processing methods and their relationship to health. It is important to recognize the differences in PA estimates and relationship to health outcomes based on data processing method.Abbreviation: Machine Learning (ML); Short Physical Performance Battery (SPPB); Center of Epidemiologic Studies Depression Scale (CES-D); Physical Activity (PA); Activity Index (AI); Activities of Daily Living (ADL) |
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| Keywords: | Physical function SPPB CESD machine learning |
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https://orcid.org/0000-0002-7358-2078;5. Department of Family Medicine and Public Health, University of Florida, Gianseville, FL, USA;6. Department of Family Medicine and Public Health, University of California, Berkeley, USA;7. Department of Family Medicine and Public Health, University of California, San Diego, USA