Background
Sleep and depression are comorbid problems that contribute to the development of chronic medical conditions (CMC) over time. Although racial and gender differences in the bidirectional associations between sleep, depression, and CMC are known, very limited information exists on heterogeneity of the residual effects of sleep problems over depressive symptoms on CMC across race by gender groups.
Aim
Using a life-course perspective, the present study compared race by gender groups for residual effects of restless sleep over depressive symptoms on CMC.
Methods
We used data from waves 1 (year 1986), 4 (year 2001), and 5 (year 2011) of the Americans’ Changing Lives Study (ACL). The study followed 294 White men, 108 Black men, 490 White women, and 237 Black women for 25 years. Restless sleep, depressive symptoms (Center for Epidemiological Studies-Depression Scale [CES-D]) and number of chronic medical conditions (hypertension, diabetes, chronic lung disease, heart disease, stroke, cancer, and arthritis) were measured in 1986, 2001, and 2011. We employed multi-group cross-lagged modeling, with chronic medical conditions as the outcome, and race by gender as the groups.
Results
Major group differences were found in the residual effect of restless sleep on CMC over depressive symptoms across race by gender groups. Restless sleep in 2001 predicted CMC 10 years later in 2011 among Black women (Standardized Adjusted B=.135, P<.05) and White men (Standardized Adjusted =0.145, P<.01) and White women (Standardized Adjusted B=.171, P<.001) but not Black men (Standardized Adjusted B = .001, P > 0.05).
Conclusion
Race by gender heterogeneity in the residual effect of restless sleep over depressive symptoms on CMC over 25 years suggests that comorbid poor sleep and depressive symptoms differently contribute to development of multi-morbidity among subpopulations based on the intersection of race and gender. Thus, interventions that try to prevent comorbid sleep problems and depression as a strategy to prevent medical conditions may benefit from tailoring based on the intersection of race and gender.