Growing evidence supports a role for rest-activity rhythms (RARs) in metabolic health. Epidemiological studies in adolescents and young adults showed that RAR characteristics consistent with weakened rhythmicity were associated with obesity. However, studies in older adults are lacking. The objective of this study was to examine the cross-sectional and prospective associations between RAR and obesity in older men using the Harmonic Hidden Markov Model (HHMM), a novel analytical approach with several advantages over conventional methods for characterizing RAR. The analysis included nearly 3,000 participants in the Osteoporotic Fractures in Men study with 5-day 24-h actigraphy data. The strength of RAR was measured by rhythmic index (RI), a scaled value between 0 and 1 with higher values indicating better RAR. Multiple linear and logistic regression adjusting for multiple confounders were performed to examine the RI in relation to body mass index (BMI) and obesity status at baseline and after ~3.5 years of follow-up. We showed that the HHMM can derive both meaningful visual profile and quantifier of RAR. A lower RI was associated with higher BMI and obesity at baseline, and an elevated likelihood for developing obesity over follow-up. Specifically, when compared with men in the highest quartile of RI, those in the lowest quartile on average had a higher BMI (β [95% confidence interval (CI)], 1.76 [1.39, 2.13]) and were more likely to be obese at baseline (odds ratio (OR) [95% CI], 2.63 [2.03, 3.43]). Moreover, among nonobese men at baseline, those in the lowest quartile of RI were 2.06 times (OR [95% CI], 2.06 [1.02, 4.27]) more likely to develop obesity over follow-up when compared with those in the highest quartile. In conclusion, our study demonstrated the utility of HHMM in characterizing RAR and showed that rhythmicity strength was associated with BMI and risk of obesity in older men.
Keywords: Hidden Markov Models; obesity; older adults; prospective cohort; rest-activity rhythms.