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Association between actigraphic sleep metrics and body composition.
Wirth, Michael D; Hébert, James R; Hand, Gregory A; Youngstedt, Shawn D; Hurley, Thomas G; Shook, Robin P; Paluch, Amanda E; Sui, Xuemei; James, Shelli L; Blair, Steven N.
Afiliación
  • Wirth MD; South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, Columbia; Department of Epidemiology and Biostatistics, University of South Carolina, Columbia. Electronic address: wirthm@mailbox.sc.edu.
  • Hébert JR; South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, Columbia; Department of Epidemiology and Biostatistics, University of South Carolina, Columbia.
  • Hand GA; Department of Epidemiology, West Virginia University, Morgantown.
  • Youngstedt SD; College of Nursing and Health Innovation, Arizona State University, Phoenix; College of Health Solutions, Arizona State University, Phoenix.
  • Hurley TG; South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, Columbia.
  • Shook RP; Department of Kinesiology, Iowa State University, Ames.
  • Paluch AE; Department of Exercise Science, University of South Carolina, Columbia.
  • Sui X; Department of Exercise Science, University of South Carolina, Columbia.
  • James SL; Department of Epidemiology, West Virginia University, Morgantown.
  • Blair SN; Department of Epidemiology and Biostatistics, University of South Carolina, Columbia; Department of Exercise Science, University of South Carolina, Columbia.
Ann Epidemiol ; 25(10): 773-8, 2015 Oct.
Article en En | MEDLINE | ID: mdl-26071309
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/m(2)) 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 35 years of age and had a BMI of 20 to 35 kg/m(2). 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. 24.8 kg/m(2), P < .01; body fat = 27.7% vs. 26.5%, P = .04) and high wake after sleep onset (BMI = 25.6 vs. 25.0 kg/m(2), 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sueño / Trastornos del Sueño-Vigilia / Composición Corporal / Ejercicio Físico / Índice de Masa Corporal Tipo de estudio: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Female / Humans / Male Idioma: En Revista: Ann Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2015 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sueño / Trastornos del Sueño-Vigilia / Composición Corporal / Ejercicio Físico / Índice de Masa Corporal Tipo de estudio: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Female / Humans / Male Idioma: En Revista: Ann Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2015 Tipo del documento: Article