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.
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Acknowledgements
Zhang and Li were supported by the Natural Sciences and Engineering Research Council of Canada (RGPIN-2015-04409). Keadle was supported by a National Cancer Institute grant (R01-CA121005). Carroll was supported by a grant from the National Cancer Institute (U01-CA057030).
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Zhang, Y., Li, H., Keadle, S.K. et al. A Review of Statistical Analyses on Physical Activity Data Collected from Accelerometers. Stat Biosci 11, 465–476 (2019). https://doi.org/10.1007/s12561-019-09250-6
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DOI: https://doi.org/10.1007/s12561-019-09250-6