Do sleep health and cognitive performance add incremental predictive value for depression beyond demographic factors in older Black adults?
Sleep quality and cognitive control uniquely contribute to explaining depressive symptoms in older Black adults, highlighting distinct vulnerability pathways.
Abstract Introduction Depression is associated with sleep disturbances and impairments in cognitive control, which disproportionately affect older Black adults. This study assessed whether sleep health and cognitive performance add incremental predictive value for depression beyond demographic factors using a hierarchical model. Methods Data were collected from 710 participants enrolled in the NIH-funded PRAISE and MOSAIC studies. Participants were Black older adults ages 55-85 years. This sample was comprised of 55.9% females and 29.5% males. History of depression was assessed via self-reported data on the medical history questionnaire and coded as a binary variable (0 = no history, 1 = history present). Predictors were entered hierarchically. Block 1 included demographic covariates (age and sex). Block 2 included sleep health measures (Sleep Quality Index and sleep efficiency). Block 3 included cognitive performance measures (Dimensional Change Card Sort, Flanker task, and backwards digit span). Multiple imputation was used to address missing data. We used hierarchical logistic regression models to assess proposed associations; model fit was evaluated using chi-square tests and Nagelkerke R². Results The demographic-only model (Block 1) significantly predicted depression (χ²(1)= 61.894, p 0.001), with age as a significant negative predictor (OR = 0.91). Adding sleep variables (Block 2) significantly improved model fit (χ²(2)=48.256, p 0.001), increasing explained variance from 7.2% to 12.6%. Poorer subjective sleep quality (OR = 1.03) and lower sleep efficiency (OR = 0.96) were associated with greater odds of depression. Adding cognitive variables (Block 3) further improved model fit (χ²(3)= 28.207, p 0.001), increasing explained variance to 15.6%. Better performance on the DCCS (OR = 1.86) and backward digit span (OR = 0.90) independently predicted lower odds of depression. Gender was significant only after cognitive variables were added, suggesting a potential suppression effect. Conclusion Findings support a multidimensional model of depression in which age, sleep quality, and cognitive control uniquely contribute to explaining observed depressive symptoms. Of note, sex also emerged as a significant predictor in the full model. Results indicate that sleep and cognitive functioning represent distinct vulnerability pathways, highlighting the importance of integrated assessment and intervention strategies targeting both domains. Support (if any) R01AG075007; R01AG067523; T32HL166609
Siebert et al. (2026) studied this question.