ArticleThe international journal of behavioral nutrition and physical activity2022
Rest-activity profiles among U.S. adults in a nationally representative sample: a functional principal component analysis.
Article in The international journal of behavioral nutrition and physical activity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors.The international journal of behavioral nutrition and physical activity · 2024Trial
- Cross-sectional and longitudinal associations between circadian alignment and stroke in US adults.iScience · 2026Article
- From wrist data to lifespan: elucidating inflammation-driven biological aging via activity rhythms captured by wearable devices.npj aging · 2026Article
- Exploring person-centred sleep and rest-activity cycle dynamics over 6 months.Journal of sleep research · 2025Article
- Accelerometry-Assessed Physical Activity and Circadian Rhythm to Detect Clinical Disability Status in Multiple Sclerosis: Cross-Sectional Study.JMIR mHealth and uHealth · 2025Article
- Motif clustering and digital biomarker extraction for free-living physical activity analysis.BioData mining · 2025Article
- Temporal Patterns of Diet and Physical Activity and of Diet Alone Have More Numerous Relationships With Health and Disease Status Indicators Compared to Temporal Patterns of Physical Activity Alone.Journal of the Academy of Nutrition and Dietetics · 2023Article
- Alignment Between 24-h Light-Dark and Activity-Rest Rhythms Is Associated With Diabetes and Glucose Metabolism in a Nationally Representative Sample of American Adults.Diabetes care · 2023Article
- U.S. Adolescent Rest-Activity patterns: insights from functional principal component analysis (NHANES 2011-2014).The international journal of behavioral nutrition and physical activity · 2023Article
- Association between circadian physical activity patterns and mortality in the UK Biobank.The international journal of behavioral nutrition and physical activity · 2023Article
- Analysis of the 24-h activity cycle: An illustration examining the association with cognitive function in the Adult Changes in Thought study.Frontiers in psychology · 2023Review
- The association between sedentary behavior, exercise, and sleep disturbance: A mediation analysis of inflammatory biomarkers.Frontiers in immunology · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundThe 24-h rest and activity behaviors (i.e., physical activity, sedentary behaviors and sleep) are fundamental human behaviors essential to health and well-being. Functional principal component analysis (fPCA) is a flexible approach for characterizing rest-activity rhythms and does not rely on a priori assumptions about the activity shape. The objective of our study is to apply fPCA to a nationally representative sample of American adults to characterize variations in the 24-h rest-activity pattern, determine how the pattern differs according to demographic, socioeconomic and work characteristics, and examine its associations with general health status.
methodsThe current analysis used data from adults 25 or older in the National Health and Nutrition Examination Survey (NHANES, 2011-2014). Using 7-day 24-h actigraphy recordings, we applied fPCA to derive profiles for overall, weekday and weekend rest-activity patterns. We examined the association between each rest-activity profile in relation to age, gender, race/ethnicity, education, income and working status using multiple linear regression. We also used multiple logistic regression to determine the relationship between each rest-activity profile and the likelihood of reporting poor or fair health.
resultsWe identified four distinct profiles (i.e., high amplitude, early rise, prolonged activity window, biphasic pattern) that together accounted for 86.8% of total variation in the study sample. We identified numerous associations between each rest-activity profile and multiple sociodemographic characteristics. We also found evidence suggesting the associations differed between weekdays and weekends. Finally, we reported that the rest-activity profiles were associated with self-rated health.
conclusionsOur study provided evidence suggesting that rest-activity patterns in human populations are shaped by multiple demographic, socioeconomic and work factors, and are correlated with health status.
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