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

This Github repository provides sample data and codes for this published study.

Overview

This study aimed at characterizing the rest-activity patterns in the general population and by demographic socioeconomic and work characteristics, and shed light on individual and environmental factors shaping the rest-activity behaviors. We applied functional Principal Component Analysis (fPCA) using 24-hr actigraphy data on a large sample that representative of the US population from The National Health and Nutrition Examination Survey (NHANES) 2011-2014 cycles.

Research Questions

• How to characterize rest-activity patterns from 24-hr actigraphy data?
• What are the overall rest-activity patterns among the general population?
• How do different features of rest-activity pattern vary by demographics and socioeconomic factors?
• Are there any associations between the rest-activity pattern and health outcomes?

Findings

• Four distinct rest-activity profiles were identified to describe aspects of overall amplitude, early rising time, prolonged daytime activity and biphasic pattern. [insert the profiles figure here. need access to upload figure]
• Rest-activity profiles are highly associated with age, race, education levels and household income levels.
• Rest-activity profiles differ on weekdays and weekends by demographics and socioeconomics.
• Lower overall activity is associated with higher odds of self-reported poor or fair health.

Conclusions

In a nationally representative sample of US adults, we identified four distinct profiles for the 24-h rest-activity cycle. We found considerable variation in these profiles across different subgroups by age, gender, race/ethnicity, SES and work status. We also observed associations between rest-activity profiles and self-rated health status.

Data

A sample data of 50 randomly selected subjects from NHANES 2011-2014 with accelerometer data can be found here.

This dataset includes the following variables:

• SEQN
• day
• fivemin: index of the 5 minutes epoch, integers from 1 to 288 (start from he midnight)
• activity_avg: average activity counts at 5 minutes epoch
• flag_min: number of minutes that have been flagged with invalid recording

Codes

The main code to run fpca analysis on the sample data can be found here

R function to plot a functional object

• code adapted from plot.fd{fda}
• changes are made to present labels of xasis as clock time

R function to generate Figure 1 in the published paper of functional principal components

• code adapted from plot.pca.fd{fda}
• add argument 'flip' to decide which component(s) to have sign(s) flipped ('+' to '-' or vice versa)
• use different colors to represent component's sign
• modified x labels to clock time

Reference:

• J. O. Ramsay, Spencer Graves and Giles Hooker (2020). fda: Functional Data Analysis. R package version 5.1.9. https://CRAN.R-project.org/package=fda