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Biostatistics. 2015 Apr;16(2):352-67. doi: 10.1093/biostatistics/kxu045. Epub 2014 Oct 30.

Quantifying the lifetime circadian rhythm of physical activity: a covariate-dependent functional approach.

Author information

1
Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA lxiao16@jhu.edu.
2
Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA.
3
Department of Epidemiology, Johns Hopkins University, Baltimore, MD 21205, USA and Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD 21225, USA.
4
Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD 21225, USA.

Abstract

Objective measurement of physical activity using wearable devices such as accelerometers may provide tantalizing new insights into the association between activity and health outcomes. Accelerometers can record quasi-continuous activity information for many days and for hundreds of individuals. For example, in the Baltimore Longitudinal Study on Aging physical activity was recorded every minute for [Formula: see text] adults for an average of [Formula: see text] days per adult. An important scientific problem is to separate and quantify the systematic and random circadian patterns of physical activity as functions of time of day, age, and gender. To capture the systematic circadian pattern, we introduce a practical bivariate smoother and two crucial innovations: (i) estimating the smoothing parameter using leave-one-subject-out cross validation to account for within-subject correlation and (ii) introducing fast computational techniques that overcome problems both with the size of the data and with the cross-validation approach to smoothing. The age-dependent random patterns are analyzed by a new functional principal component analysis that incorporates both covariate dependence and multilevel structure. For the analysis, we propose a practical and very fast trivariate spline smoother to estimate covariate-dependent covariances and their spectra. Results reveal several interesting, previously unknown, circadian patterns associated with human aging and gender.

KEYWORDS:

Accelerometer; Bivariate smoothing; Covariance; Sandwich smoother; Trivariate smoothing

PMID:
25361695
PMCID:
PMC4804116
DOI:
10.1093/biostatistics/kxu045
[Indexed for MEDLINE]
Free PMC Article

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