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Organizing and Analyzing the Activity Data in NHANES

Author

Listed:
  • Andrew Leroux

    (Bloomberg School of Public Health, Johns Hopkins University)

  • Junrui Di

    (Bloomberg School of Public Health, Johns Hopkins University)

  • Ekaterina Smirnova

    (Virginia Commonwealth University
    University of Montana)

  • Elizabeth J Mcguffey

    (United States Naval Academy)

  • Quy Cao

    (University of Montana)

  • Elham Bayatmokhtari

    (University of Montana)

  • Lucia Tabacu

    (Old Dominion University)

  • Vadim Zipunnikov

    (Bloomberg School of Public Health, Johns Hopkins University)

  • Jacek K Urbanek

    (Center on Aging and Health, School of Medicine, Johns Hopkins University)

  • Ciprian Crainiceanu

    (Bloomberg School of Public Health, Johns Hopkins University)

Abstract

The NHANES study contains objectively measured physical activity data collected using hip-worn accelerometers from multiple cohorts. However, using the accelerometry data has proven daunting because (1) currently, there are no agreed-upon standard protocols for data storage and analysis; (2) data exhibit heterogeneous patterns of missingness due to varying degrees of adherence to wear-time protocols; (3) sampling weights need to be carefully adjusted and accounted for in individual analyses; (4) there is a lack of reproducible software that transforms the data from its published format into analytic form; and (5) the high dimensional nature of accelerometry data complicates analyses. Here, we provide a framework for processing, storing, and analyzing the NHANES accelerometry data for the 2003–2004 and 2005–2006 surveys. We also provide an NHANES data package in R, to help disseminate high-quality, processed activity data combined with mortality and demographic information. Thus, we provide the tools to transition from “available data online” to “easily accessible and usable data”, which substantially reduces the large upfront costs of initiating studies of association between physical activity and human health outcomes using NHANES. We apply these tools in an analysis showing that accelerometry features have the potential to predict 5-year all-cause mortality better than known risk factors such as age, cigarette smoking, and various comorbidities.

Suggested Citation

  • Andrew Leroux & Junrui Di & Ekaterina Smirnova & Elizabeth J Mcguffey & Quy Cao & Elham Bayatmokhtari & Lucia Tabacu & Vadim Zipunnikov & Jacek K Urbanek & Ciprian Crainiceanu, 2019. "Organizing and Analyzing the Activity Data in NHANES," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 262-287, July.
    • Handle: RePEc:spr:stabio:v:11:y:2019:i:2:d:10.1007_s12561-018-09229-9
    DOI: 10.1007/s12561-018-09229-9
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    References listed on IDEAS

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    1. Karoline Krane-Gartiser & Tone Elise Gjotterud Henriksen & Gunnar Morken & Arne Vaaler & Ole Bernt Fasmer, 2014. "Actigraphic Assessment of Motor Activity in Acutely Admitted Inpatients with Bipolar Disorder," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-9, February.
    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. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
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    Cited by:

    1. Junrui Di & Adam Spira & Jiawei Bai & Jacek Urbanek & Andrew Leroux & Mark Wu & Susan Resnick & Eleanor Simonsick & Luigi Ferrucci & Jennifer Schrack & Vadim Zipunnikov, 2019. "Joint and Individual Representation of Domains of Physical Activity, Sleep, and Circadian Rhythmicity," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 371-402, July.
    2. Paul A. Parker & Scott H. Holan, 2023. "A Bayesian functional data model for surveys collected under informative sampling with application to mortality estimation using NHANES," Biometrics, The International Biometric Society, vol. 79(2), pages 1397-1408, June.
    3. Maximilian Andreas Storz & Gianluca Rizzo & Mauro Lombardo, 2022. "Shiftwork Is Associated with Higher Food Insecurity in U.S. Workers: Findings from a Cross-Sectional Study (NHANES)," IJERPH, MDPI, vol. 19(5), pages 1-14, March.
    4. Maximilian Andreas Storz & Mauro Lombardo & Gianluca Rizzo & Alexander Müller & Ann-Kathrin Lederer, 2022. "Bowel Health in U.S. Shift Workers: Insights from a Cross-Sectional Study (NHANES)," IJERPH, MDPI, vol. 19(6), pages 1-17, March.

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