Association between actigraphic sleep metrics and body composition

Michael D. Wirth, James R. Hébert, Gregory A. Hand, Shawn Youngstedt, Thomas G. Hurley, Robin P. Shook, Amanda E. Paluch, Xuemei Sui, Shelli L. James, Steven N. Blair

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Purpose: Determine if individuals with poor sleep characteristics (i.e., late sleep onset or wake times, short sleep duration, long sleep latency, low sleep efficiency, high wake after sleep onset) have greater body mass index (BMI=kg/m2) or body fat. Methods: Data for these cross-sectional analyses were from the Energy Balance Study (University of South Carolina). Participants were between 21 and 35years of age and had a BMI of 20 to 35kg/m2. Body fat percent was measured by dual X-ray absorptiometry. Sleep and physical activity were measured by actigraphy (BodyMedia's SenseWear physical activity armband). General linear models were used to estimate mean BMI and body fat percent by sleep metric categories. Results: Greater BMI and body fat percent were associated with low sleep efficiency (BMI=25.5 vs. 24.8kg/m2, P<.01; body fat=27.7% vs. 26.5%, P=.04) and high wake after sleep onset (BMI=25.6 vs. 25.0kg/m2, P=.02; body fat=28.0% vs. 26.7%, P=.03). Elevated BMI or body fat percent also were observed for later wake times, shorter sleep duration, and longer sleep latency. Sex modified the association between wake times and body composition. Conclusions: Understanding the complex relationships between sleep and health outcomes could help reduce chronic disease burden by incorporating sleep components, measured through novel noninvasive techniques (SenseWear armband), into weight loss interventions.

Original languageEnglish (US)
Pages (from-to)773-778
Number of pages6
JournalAnnals of Epidemiology
Volume25
Issue number10
DOIs
StatePublished - Oct 1 2015

Keywords

  • Abdominal fat
  • Absorptiometry
  • Actigraphy
  • Body mass index
  • Sleep

ASJC Scopus subject areas

  • Epidemiology

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