ArticleMedicine and science in sports and exercise2016
New Insights into Activity Patterns in Children, Found Using Functional Data Analyses.
Article in Medicine and science in sports and exercise, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Trial
- Smartphone-Based Passive Sensing of Activity Levels and Behavioral Activation During Psychosocial Interventions for Older Adults With Depression: Longitudinal Observational Study.Journal of medical Internet research · 2026Observational
- Variable Selection for Fixed and Random Effects in Multilevel Functional Mixed Effects Models.Statistics in medicine · 2026Article
- Functional quantile principal component analysis.Biostatistics (Oxford, England) · 2025Article
- Upper Limb Capacity, Performance, and Leisure Participation in Children with Unilateral Cerebral Palsy.Sensors (Basel, Switzerland) · 2025Article
- Negative binomial mixed effects location-scale models for intensive longitudinal count-type physical activity data provided by wearable devices.Biometrics · 2025Article
- Differences in the activity intensity distribution over the day between boys and girls aged 3 to 17 years.Scientific reports · 2025Article
- Improvements in Walking During Subacute Stroke Rehabilitation Translate to Physical Activity at the Chronic Stage: A Sub-Analysis From the Phys Stroke Trial.Brain & NeuroRehabilitation · 2024Article
- Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review.Journal of medical Internet research · 2024Article
- Direct Bayesian linear regression for distribution-valued covariates.Electronic journal of statistics · 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
- Assessing adult physical activity and compliance with 2008 CDC guidelines using a Bayesian two-part measurement error model.Journal of applied statistics · 2023Article
- 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
- Bayesian analysis of longitudinal and multidimensional functional data.Biostatistics (Oxford, England) · 2022Article
- From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas.Journal of multivariate analysis · 2022Article
- Multivariate Functional Kernel Machine Regression and Sparse Functional Feature Selection.Entropy (Basel, Switzerland) · 2022Article
- Sociodemographic, Health and Lifestyle, Sampling, and Mental Health Determinants of 24-Hour Motor Activity Patterns: Observational Study.Journal of medical Internet research · 2021Observational
- Brain amyloid burden, sleep, and 24-hour rest/activity rhythms: screening findings from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration Studies.Sleep advances : a journal of the Sleep Research Society · 2021Article
- The association between accelerometer-assessed physical activity and respiratory function in older adults differs between smokers and non-smokers.Scientific reports · 2019Article
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4 authors.
Funding
Abstract
introduction/purposeContinuous monitoring of activity using accelerometers and other wearable devices provides objective, unbiased measurement of physical activity in minute-by-minute or finer resolutions. Accelerometers have already been widely deployed in studies of healthy aging, recovery of function after heart surgery, and other outcomes. Although common analyses of accelerometer data focus on single summary variables, such as the total or average activity count, there is growing interest in the determinants of diurnal profiles of activity.
methodsWe use tools from functional data analysis (FDA), an area with an established statistical literature, to treat complete 24-h diurnal profiles as outcomes in a regression model. We illustrate the use of such models by analyzing data collected in New York City from 420 children participating in a Head Start program. Covariates of interest include season, sex, body mass index z-score, presence of an asthma diagnosis, and mother's birthplace.
resultsThe FDA model finds several meaningful associations between several covariates and diurnal profiles of activity. In some cases, including shifted activity patterns for children of foreign-born mothers and time-specific effects of asthma on activity, these associations exist for covariates that are not associated with average activity count.
conclusionFDA provides a useful statistical framework for settings in which the effect of covariates on the timing of activity is of interest. The use of similar models in other applications should be considered, and we make code public to facilitate this process.
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