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

Author

Listed:
  • 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
    University of Technology Sydney)

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.

Suggested Citation

  • Yukun Zhang & Haocheng Li & Sarah Kozey Keadle & Charles E. Matthews & Raymond J. Carroll, 2019. "A Review of Statistical Analyses on Physical Activity Data Collected from Accelerometers," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 465-476, July.
    • Handle: RePEc:spr:stabio:v:11:y:2019:i:2:d:10.1007_s12561-019-09250-6
    DOI: 10.1007/s12561-019-09250-6
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    References listed on IDEAS

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    Cited by:

    1. Hsin‐wen Chang & Ian W. McKeague, 2022. "Empirical likelihood‐based inference for functional means with application to wearable device data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1947-1968, November.

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