ArticleBiostatistics (Oxford, England)2015
Quantifying the lifetime circadian rhythm of physical activity: a covariate-dependent functional approach.
Article in Biostatistics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
45 citing papers in PubMed.
- Trial
- The Use of Functional Data Analysis to Evaluate Activity in a Spontaneous Model of Degenerative Joint Disease Associated Pain in Cats.PloS one · 2017Trial
- An Evaluation of Accelerometer-derived Metrics to Assess Daily Behavioral Patterns.Medicine and science in sports and exercise · 2017Trial
- Variable Selection for Fixed and Random Effects in Multilevel Functional Mixed Effects Models.Statistics in medicine · 2026Article
- Circadian physiology changes with aging: age is just a number, but circadian physiology keeps time.Current opinion in physiology · 2026Article
- Functional quantile principal component analysis.Biostatistics (Oxford, England) · 2025Article
- Bayesian covariance regression in functional data analysis with applications to functional brain imaging.The international journal of biostatistics · 2025Article
- Morning physical activity may be more beneficial for blood lipids than afternoon physical activity in older adults: a cross-sectional study.European journal of applied physiology · 2024Article
- A RIEMANN MANIFOLD MODEL FRAMEWORK FOR LONGITUDINAL CHANGES IN PHYSICAL ACTIVITY PATTERNS.The annals of applied statistics · 2023Article
- Distributional data analysis via quantile functions and its application to modeling digital biomarkers of gait in Alzheimer's Disease.Biostatistics (Oxford, England) · 2023Article
- Motivation states to move, be physically active and sedentary vary like circadian rhythms and are associated with affect and arousal.Frontiers in sports and active living · 2023Article
- Assessing adult physical activity and compliance with 2008 CDC guidelines using a Bayesian two-part measurement error model.Journal of applied statistics · 2023Article
- Bouts of rest and physical activity in C57BL/6J mice.PloS one · 2023Article
- Fixed-effects inference and tests of correlation for longitudinal functional data.Statistics in medicine · 2022Article
- Scalar on time-by-distribution regression and its application for modelling associations between daily-living physical activity and cognitive functions in Alzheimer's Disease.Scientific reports · 2022Article
- From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas.Journal of multivariate analysis · 2022Article
- Adaptive empirical pattern transformation (ADEPT) with application to walking stride segmentation.Biostatistics (Oxford, England) · 2021Article
- Analyzing wearable device data using marked point processes.Biometrics · 2021Article
- Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study.Sensors (Basel, Switzerland) · 2021Article
- Patterns of Daily Physical Activity across the Spectrum of Visual Field Damage in Glaucoma Patients.Ophthalmology · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.