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Accelerometry Data in Health Research: Challenges and Opportunities

Author

Listed:
  • Marta Karas

    (Johns Hopkins University)

  • Jiawei Bai

    (Johns Hopkins University)

  • Marcin Strączkiewicz

    (Indiana University Bloomington)

  • Jaroslaw Harezlak

    (Indiana University Bloomington)

  • Nancy W. Glynn

    (University of Pittsburgh)

  • Tamara Harris

    (National Institute on Aging)

  • Vadim Zipunnikov

    (Johns Hopkins University)

  • Ciprian Crainiceanu

    (Johns Hopkins University)

  • Jacek K. Urbanek

    (Johns Hopkins University)

Abstract

Wearable accelerometers provide detailed, objective, and continuous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popularity of wearable technology in health research. An ever-increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper, we discuss problems related to the collection and analysis of raw accelerometry data and refer to published solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability, and the effects of sensor location on the body. We also discuss challenges related to sampling frequency, device calibration, data labeling, and multiple PA monitors synchronization. We illustrate these points using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment.

Suggested Citation

  • Marta Karas & Jiawei Bai & Marcin Strączkiewicz & Jaroslaw Harezlak & Nancy W. Glynn & Tamara Harris & Vadim Zipunnikov & Ciprian Crainiceanu & Jacek K. Urbanek, 2019. "Accelerometry Data in Health Research: Challenges and Opportunities," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 210-237, July.
    • Handle: RePEc:spr:stabio:v:11:y:2019:i:2:d:10.1007_s12561-018-9227-2
    DOI: 10.1007/s12561-018-9227-2
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    References listed on IDEAS

    as
    1. Luo Xiao & Bing He & Annemarie Koster & Paolo Caserotti & Brittney Lange-Maia & Nancy W. Glynn & Tamara B. Harris & Ciprian M. Crainiceanu, 2016. "Movement prediction using accelerometers in a human population," Biometrics, The International Biometric Society, vol. 72(2), pages 513-524, June.
    2. Haochang Shou & Vadim Zipunnikov & Ciprian M. Crainiceanu & Sonja Greven, 2015. "Structured functional principal component analysis," Biometrics, The International Biometric Society, vol. 71(1), pages 247-257, March.
    3. Luo Xiao & Yingxing Li & David Ruppert, 2013. "Fast bivariate P-splines: the sandwich smoother," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 577-599, June.
    4. Aiden Doherty & Dan Jackson & Nils Hammerla & Thomas Plötz & Patrick Olivier & Malcolm H Granat & Tom White & Vincent T van Hees & Michael I Trenell & Christoper G Owen & Stephen J Preece & Rob Gillio, 2017. "Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-14, February.
    5. repec:wyi:journl:002174 is not listed on IDEAS
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