Sleep Quality Predicts Body Shape Index While Adjusting for Physical Activity

Peter D. Hart^{1, 2,}

¹Health Promotion Research, Havre, Montana, USA

²Kinesmetrics Lab, Tallahassee, Florida, USA

Cite this paper:
Peter D. Hart. Sleep Quality Predicts Body Shape Index While Adjusting for Physical Activity. American Journal of Public Health Research. 2024; 12(3):40-47. doi: 10.12691/ajphr-12-3-1

Abstract

Background: Obesity is a major health problem in the U.S. with prevalence estimated at over 40% in adults. The consequences of obesity are well known and include such health conditions as hypertension, dyslipidemia, hyperglycemia, asthma, arthritis, depression, and poor health-related quality of life. The most common measure used to assess obesity is body mass index (BMI). However, a more recent metric, body shape index (BSI), may provide greater predictive value for the clinical detection of obesity and its connection to chronic disease. Additionally, adequate sleep is currently a popular concern with poor sleep quality (PSQ) seen as a risk factor for many diseases including overweight and obesity. Thus, the aim of this study was to examine the predictive ability of PSQ in estimating BSI while holding physical activity (PA) constant. Methods: A cross-sectional convenience sample of 5,945 adults 18+ years of age were used in this study. BSI (m^11/6/kg^2/3) was computed from objectively measured height, weight, and waist circumference (WC). Sleep quality was assessed by a self-report questionnaire with two items asking about sleep time, two items asking about sleep interruptions, one item asking about patient-reported sleep issues, and a final item asking about overall sleepiness. Each sleep quality item was dichotomized to indicate PSQ with six item (PSQ6) (quality and quantity) and four item (PSQ4) (quality only) scores created. Control variables included moderate-to-vigorous PA (MVPA, min/week), sedentary time (ST, min/day), age, sex, race, and income. SAS GLMSELECT was used to predict BSI with PSQ while adjusting for PA and all covariates. Results: Approximately 38% of adults had high risk BSI (BSI > .083, p < .001) and 42% PSQ (PSQ6 ≥ 2, p < .001). BSI was normally distributed (K-S p > .15) with mean of .0814 and SD of .0050. Notable bivariate correlations with BSI included PSQ4 (r = .151, p < .001), PSQ6 (r = .113, p < .001), MVPA (r = -.206, p < .001), and age (r = .569, p < .001). PSQ6 scores predicted BSI (ϐ = 0.0265, p = .011, AIC = -59,790) after adjusting for PA and demographic variables. PSQ4 scores were a stronger predictor of BSI (ϐ = 0.0490, p < .001, AIC = -59,805) after adjusting for covariates. Finally, PSQ status (good vs poor) variables were able to predict BSI where adults with poor PSQ6 and PSQ4 saw significantly (p < .001) greater BSI than those with good PSQ6 and PSQ4. Conclusion: Results from this study indicate that PSQ is an important predictor of BSI after considering PA. Moreover, quality may be a more important characteristic than quantity when considering poor sleep a risk factor for obesity in adults.

Keywords:
Sleep quality Body Shape Index (BSI) Physical activity Population health

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