Relationship of Sleep Health Behavior Score with Body Mass Index and Obesity Among Working-Age Adults at the Lifestyle Medicine Clinic, Health Promotion Center Region 5, Ratchaburi
Keywords:
sleep health behavior score, body mass index, obesity, working-age adults, lifestyle medicineAbstract
Objective: To examine the relationship between sleep health behavior scores and body mass index (BMI) and obesity among working-age adults at the Lifestyle Medicine Clinic, Health Promotion Center Region 5, Ratchaburi
Methods: A retrospective analytic study was conducted among 170 working-age clients aged 18–59 years. Data were obtained from electronic medical records (sex, age, weight, height, and body mass index) and the Lifestyle Medicine Clinic’s health behavior assessment database (sleep, physical activity, dietary behavior, smoking, alcohol consumption, and comorbidities). The sample size was determined using the criterion of 10 observations per independent variable. Data were analyzed using descriptive statistics, the Pearson correlation coefficient, univariable binary logistic regression, and multivariable binary logistic regression (enter method). Statistical significance was set at p < .05.
Results: Most participants were female (64.7%); a mean age was 45.97 ± 10.33 years with a mean BMI of 27.73 ± 6.49 kg/m²; 60.6% were classified as obese (BMI ≥25.0 kg/m², Asian-Pacific criteria). There were 46.5% of participants who slept fewer than 7 hours per night. The mean sleep duration score was 2.91 ± 2.74 and the mean sleep quality score was 2.29 ± 1.65. Sleep behavior scores were significantly and inversely correlated with BMI: sleep duration score (r = −0.237; p = .002) and sleep quality score (r = −0.269; p < .001). In the multivariable binary logistic regression model (enter method), after adjusting for age, sleep duration score, resistance exercise, aerobic exercise, sedentary time, diabetes mellitus, and dyslipidemia, a one-point increase in the sleep quality score was independently associated with lower odds of obesity (adjusted OR = 0.66; 95% CI = 0.50–0.86; p = .002), as were age (adjusted OR = 0.96; 95% CI = 0.91–1.00; p = .036) and aerobic exercise (adjusted OR = 0.61; 95% CI = 0.43–0.87; p = .006), whereas diabetes mellitus was associated with higher odds of obesity (adjusted OR = 6.10; 95% CI = 1.47–25.35; p = .013).
Conclusion: Sleep quality score was inversely and independently associated with both BMI and obesity after adjustment for age, physical activity, and comorbidities. Because of the cross-sectional design, causal inference cannot be drawn. These findings support incorporating sleep quality assessment into routine evaluation at lifestyle medicine clinics, alongside physical activity and comorbidity assessment.
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