Organizing and Analyzing the Activity Data in NHANES

Andrew Leroux (), Junrui Di, Ekaterina Smirnova, Elizabeth J Mcguffey, Quy Cao, Elham Bayatmokhtari, Lucia Tabacu, Vadim Zipunnikov, Jacek K Urbanek and Ciprian Crainiceanu
Additional contact information
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
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

Statistics in Biosciences, 2019, vol. 11, issue 2, No 4, 262-287

Abstract: 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 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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DOI: 10.1007/s12561-018-09229-9

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