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Organizing and Analyzing the Activity Data in NHANES

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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.

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Acknowledgements

We would like to thank the CDC, specifically the National Center for Health Statistics for collecting, organizing, and making public this unique data resource. We would also like to thank them for the permission to repost the publicly available NHANES and NDI data in analytic format. Also, we would like to thank the thousands of anonymous participants in the NHANES, whose data led to the exciting findings in this paper.

Funding

This research was supported by National Heart, Lung, and Blood Institute (R 01 HL123407), National Institute of Neurological Disorders and Stroke (R 01 NS060910), and National Institute on Aging Training Grant (T 32 AG000247).

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Authors and Affiliations

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

    Andrew Leroux, Junrui Di, Vadim Zipunnikov & Ciprian Crainiceanu

  2. Department of Biostatistics, Virginia Commonwealth University, Richmond, USA

    Ekaterina Smirnova

  3. Department of Mathematics, United States Naval Academy, Annapolis, USA

    Elizabeth J Mcguffey

  4. Department of Mathematical Sciences, University of Montana, Missoula, USA

    Ekaterina Smirnova, Quy Cao & Elham Bayatmokhtari

  5. Department of Mathematics and Statistics, Old Dominion University, Norfolk, USA

    Lucia Tabacu

  6. 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. Andrew Leroux
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  2. Junrui Di
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  3. Ekaterina Smirnova
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  4. Elizabeth J Mcguffey
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  7. Lucia Tabacu
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  9. Jacek K Urbanek
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  10. Ciprian Crainiceanu
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Correspondence to Andrew Leroux.

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Leroux, A., Di, J., Smirnova, E. et al. Organizing and Analyzing the Activity Data in NHANES. Stat Biosci 11, 262–287 (2019). https://doi.org/10.1007/s12561-018-09229-9

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  • DOI: https://doi.org/10.1007/s12561-018-09229-9

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