Quantifying the lifetime circadian rhythm of physical activity: a covariate-dependent functional approach
Open accessAbstract
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.
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The marginal association between longer sleep duration and late chronotype with lower physical activity converged with a small number of other studies.
“The association between longer sleep duration and late chronotype with lower levels of physical activity found in this study was marginal, but did converge with a small number of other studies examining this question [40, 67, 68].”
From Smoking, screen-based sedentary behavior, and diet associated with habitual sleep duration and chronotype: data from the UK Biobank (2016) · cites this paper as [67]
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- Authors
- Xiao, L., Huang, L., Schrack, J. A., Ferrucci, L., Zipunnikov, V., & Crainiceanu, C. M.
- Affiliations
- Johns Hopkins University (US)
- National Institutes of Health (US)
- Corresponding author
- Xiao, L.
- Venue
- Biostatistics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-10-30
- Volume
- 16
- Issue
- 2
- Pages
- 352-367
- ISSN
- 1465-4644 (ISSN-L)
- 1468-4357
- PMC
- PMC4804116
- OpenAlex
- W2171991316
- Cited by
- 71
- References
- 39
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 3 · 2023: 7 · 2022: 6 · 2021: 8 · 2020: 8 · 2019: 6 · 2018: 3 · 2017: 12 · 2016: 7 · 2015: 2
- FWCI
- 7.40
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Biostatistics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS060910)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- circadian rhythm, physical activity, accelerometry, wearable devices, functional principal component analysis, leave-one-subject-out cross-validation, smoothing parameter estimation, within-subject correlation, multilevel structure, aging, gender differences, time-of-day patterns
- MeSH terms
- Adult, Aged, Aged, 80 and over, Aging, Circadian Rhythm, Female, Humans, Male, Middle Aged, Motor Activity, Sex Factors, Principal Component Analysis, Accelerometry
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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