ArticleSleep health2025
Sleep health profiles across six population-based cohorts and recommendations for validating clustering models.
Article in Sleep health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Associations between multidimensional sleep measures and domain-specific cognitive function across repeated observations in adults aged 55 years and older.GeroScience · 2026Article
- Precision sleep signatures to predict mental health outcomes in youth.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- Pediatric precision sleep network: a study protocol for identifying sleep signatures of mental health risk in peri-adolescents.BMC pediatrics · 2026Article
- Multidimensional self-reported sleep health, cognitive decline, and risk of all-cause dementia: A population-based multi-cohort study.Journal of Alzheimer's disease : JAD · 2026Article
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Authors and funding
13 authors.
Funding
Abstract
objectivesModel-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.
methodsWe 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.
conclusionsWe 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.
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