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Organizing and Analyzing the Activity Data in NHANES

  • Andrew LerouxEmail author
  • Junrui Di
  • Ekaterina Smirnova
  • Elizabeth J Mcguffey
  • Quy Cao
  • Elham Bayatmokhtari
  • Lucia Tabacu
  • Vadim Zipunnikov
  • Jacek K Urbanek
  • Ciprian Crainiceanu
Article
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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.

Keywords

Accelerometry Physical activity NHANES Prediction 

Notes

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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Copyright information

© International Chinese Statistical Association 2019

Authors and Affiliations

  • Andrew Leroux
    • 1
    Email author
  • Junrui Di
    • 1
  • Ekaterina Smirnova
    • 2
    • 4
  • Elizabeth J Mcguffey
    • 3
  • Quy Cao
    • 4
  • Elham Bayatmokhtari
    • 4
  • Lucia Tabacu
    • 5
  • Vadim Zipunnikov
    • 1
  • Jacek K Urbanek
    • 6
  • Ciprian Crainiceanu
    • 1
  1. 1.Department of BiostatisticsBloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUSA
  2. 2.Department of BiostatisticsVirginia Commonwealth UniversityRichmondUSA
  3. 3.Department of MathematicsUnited States Naval AcademyAnnapolisUSA
  4. 4.Department of Mathematical SciencesUniversity of MontanaMissoulaUSA
  5. 5.Department of Mathematics and StatisticsOld Dominion UniversityNorfolkUSA
  6. 6.Division of Geriatric Medicine and Gerontology, Department of MedicineCenter on Aging and Health, School of Medicine, Johns Hopkins UniversityBaltimoreUSA

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