- Main
Actigraphy-Derived Daily Rest-Activity Patterns and Body Mass Index in Community-Dwelling Adults.
Published Web Location
https://doi.org/10.1093/sleep/zsx168Abstract
STUDY OBJECTIVES: To examine associations between 24-hour rest-activity patterns and body mass index (BMI) among community-dwelling US adults. Rest-activity patterns provide a field method to study exposures related to circadian rhythms. METHODS: Adults (N = 578) wore an actigraph on their nondominant wrist for 7 days. Intradaily variability and interdaily stability (IS), M10 (most active 10-hours), L5 (least active 5-hours), and relative amplitude (RA) were derived using nonparametric rhythm analysis. Mesor, acrophase, and amplitude were calculated from log-transformed count data using the parametric cosinor approach. RESULTS: Participants were 80% female and mean (standard deviation) age was 52 (15) years. Participants with higher BMI had lower values for magnitude, RA, IS, total sleep time (TST), and sleep efficiency. In multivariable analyses, less robust 24-hour rest-activity patterns as represented by lower RA were consistently associated with higher BMI: comparing the bottom quintile (least robust) to the top quintile (most robust 24-hour rest-activity pattern) of RA, BMI was 3-kg/m2 higher (p = .02). Associations were similar in magnitude to an hour less of TST (1-kg/m2 higher BMI) or a 10% decrease in sleep efficiency (2-kg/m2 higher BMI), and independent of age, sex, race, education, and the duration of rest and/or activity. CONCLUSIONS: Lower RA, reflecting both higher night activity and lower daytime activity, was associated with higher BMI. Independent of the duration of rest or activity during the day or night, 24-hour rest, and activity patterns from actigraphy provide aggregated measures of activity that associate with BMI in community-dwelling adults.
Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.
Main Content
Enter the password to open this PDF file:
-
-
-
-
-
-
-
-
-
-
-
-
-
-