Sleep health profiles across six population-based cohorts and recommendations for validating clustering models.

Abstract

Objectives: Model-based clustering is increasingly used to identify multidimensional sleep health profiles. However, generalizability is rarely assessed because of complexities of data sharing and harmonization. Our goal was to evaluate the generalizability of multidimensional sleep health profiles across older adult populations in Western countries and assess whether they predict depressive symptoms over time.

Methods: We harmonized five self-reported sleep health indicators (satisfaction, alertness, timing, efficiency, and duration) across six population-based cohorts from the United States and Netherlands (N=614 - 3209 each) and performed identical latent class analysis in each cohort. Novel multivariable similarity metrics, patterns of sleep health and cluster sizes were used to match clusters and assess generalizability across cohorts. We compared cluster characteristics cross-sectionally and used linear mixed-effects modeling to relate sleep health clusters to depressive symptoms over time.

Results: "Average sleep health" (moderate duration; high quality/efficiency; 42.7%-76.7% of sample) and "poor sleep health" (short duration; low quality/efficiency; high daytime sleepiness; 9.4%-20.4% of sample) clusters were generalizable across cohorts. In four cohorts "inefficient sleep" clusters were identified and in two cohorts "long, efficient sleep" clusters were identified. At 3years, those in the poor sleep cluster had depression symptoms that were 1.40-2.79 times greater than in the average sleep cluster, across all cohorts.

Conclusions: We identified two profiles - average sleep health and poor sleep health - that were generalizable across six samples of older adults and predicted depressive symptoms, underscoring the importance of the sleep health paradigm.