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Accelerometry Data in Health Research: Challenges and Opportunities

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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.

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Acknowledgements

The authors would like to acknowledge Annemarie Koster, Ph.D. and Paolo Caserotti, Ph.D. for designing the DECOS experiments.

Funding

This research was supported by Pittsburgh Claude D. Pepper Older Americans Independence Center, Research Registry, and Developmental Pilot Grant (PI: Glynn)—NIH P30 AG024826 and NIH P30 AG024827; National Institute on Aging Professional Services Contract HHSN271201100605P; NIA Aging Training Grant (PI: AB Newman) T32-AG-000181. The project was supported, in part, by the Intramural Research Program of the National Institute on Aging.

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Authors and Affiliations

  1. Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA

    Marta Karas, Jiawei Bai, Vadim Zipunnikov & Ciprian Crainiceanu

  2. Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, USA

    Marcin Strączkiewicz & Jaroslaw Harezlak

  3. Department of Epidemiology, Center for Aging and Population Health, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, USA

    Nancy W. Glynn

  4. Laboratory of Epidemiology Demography, and Biometry, National Institute on Aging, Bethesda, USA

    Tamara Harris

  5. Division of Geriatric Medicine and Gerontology, Department of Medicine, Center on Aging and Health, School of Medicine, Johns Hopkins University, Baltimore, USA

    Jacek K. Urbanek

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  1. Marta Karas
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Corresponding author

Correspondence to Marta Karas.

Appendix A

Appendix A

See Table 3.

Table 3 Summary of the four statistics: ENMO, VMC, \(\hbox {AI}_0\), and AI for five selected subjects and all subjects: median, 25-th percentile and 75-th percentile (percentiles are reported in brackets), obtained from accelerometry data collected at the hip during five activities: writing, washing dishes, vacuuming, getting dressed, and walking
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Karas, M., Bai, J., Strączkiewicz, M. et al. Accelerometry Data in Health Research: Challenges and Opportunities. Stat Biosci 11, 210–237 (2019). https://doi.org/10.1007/s12561-018-9227-2

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