Rest-activity profiles among U.S. adults in a nationally representative sample: a functional principal component...

Area of Science:

• Chronobiology
• Public Health
• Data Science

Background:

• 24-hour rest and activity behaviors (physical activity, sedentary behavior, sleep) are crucial for health.
• Functional principal component analysis (fPCA) offers a flexible method to analyze rest-activity rhythms without prior assumptions.
• Understanding variations in daily rest-activity patterns is essential for public health.

Purpose of the Study:

• To apply fPCA to a national sample of US adults to characterize 24-hour rest-activity patterns.
• To determine how these patterns differ across demographic, socioeconomic, and work characteristics.
• To examine the association between rest-activity patterns and general health status.

Main Methods:

• Utilized data from the National Health and Nutrition Examination Survey (NHANES, 2011-2014) for adults aged 25+.
• Applied fPCA to 7-day, 24-hour actigraphy recordings to derive rest-activity profiles (overall, weekday, weekend).
• Employed multiple linear and logistic regression to analyze associations with sociodemographic factors and self-rated health.

Main Results:

• Identified four distinct rest-activity profiles (high amplitude, early rise, prolonged activity window, biphasic) explaining 86.8% of variation.
• Found significant associations between rest-activity profiles and sociodemographic characteristics, with differences between weekdays and weekends.
• Demonstrated a correlation between identified rest-activity profiles and self-rated health status.

Conclusions:

• Human rest-activity patterns are influenced by demographic, socioeconomic, and work-related factors.
• These daily behavioral patterns are demonstrably associated with an individual's health status.
• fPCA is a valuable tool for characterizing complex rest-activity rhythms in large populations.