Sleep health is multidimensional (Buysse, 2014), as multiple aspects (i.e., duration, timing, efficiency, regularity, rhythmicity, daytime alertness) together describe an individual’s sleep behavior (Ji et al., 2023). Most studies only examine one sleep aspect at a time when studying the relationship between sleep and other health outcomes (e.g., cardiometabolic health, cognition), despite multiple sleep aspects are intercorrelated (e.g., duration and timing). A recent statement emphasized the importance of evaluating multidimensional sleep health aspects jointly while studying its relationship with health outcomes (St-Onge et al., 2025), calling for “firm and reproducible findings” with the comprehensive multidimensional sleep approach.
Among various approaches, cluster analysis allows the examination of multiple domains jointly while preserving the multidimensional structure, compared to a more traditional composite score approach where individuals are assigned to ordinal categories based on a priori cut-offs. However, most sleep clustering studies to date took a data-driven approach, making the identified clusters difficult to interpret, thus bringing more challenges when translating the findings to clinical or public health settings. For the few studies taking a multidimensional sleep health theory-driven approach, most of them used subjective survey data, where some sleep dimensions (e.g., regularity, rhythmicity) were left out due to insufficient information.
To address these limitations, the proposed study will take a theory-driven approach to construct multidimensional sleep health profiles using actigraphy sleep data. Specifically, we will study the cross-sectional and longitudinal associations between sleep profiles, cardiometabolic health, and cognition.