A Review of Statistical Analyses on Physical Activity Data Collected from Accelerometers

Yukun Zhang (), Haocheng Li (), Sarah Kozey Keadle (), Charles E. Matthews () and Raymond J. Carroll ()
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Yukun Zhang: University of Calgary
Haocheng Li: University of Calgary
Sarah Kozey Keadle: California Polytechnic State University
Charles E. Matthews: National Cancer Institute
Raymond J. Carroll: Texas A&M University

Statistics in Biosciences, 2019, vol. 11, issue 2, No 13, 465-476

Abstract: Abstract Studies for the associations between physical activity and disease risk have been supported by newly developed wearable accelerometer-based devices. These devices record raw activity/movement information in real time on a second-by-second basis and the data can be converted to a variety of summary metrics, such as energy expenditure, sedentary time and moderate-vigorous intensity physical activity. Here we review some of the methods used to analyze the accelerometer data and the R packages that can generate activity related variables from raw data. We also discuss longitudinal data and functional data approaches to perform analyses for various research purposes.

Keywords: Accelerometer; Functional data analysis; Longitudinal data analysis; Physical activity; R package (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s12561-019-09250-6

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