Data-driven, generalizable prediction of adolescent sleep disturbances in the multisite Adolescent Brain Cognitive Development Study

Abstract

Study Objectives: Sleep disturbances are common in adolescence and associated with a host of negative outcomes. Here, we assess associations between multifaceted sleep disturbances and a broad set of psychological, cognitive, and demographic variables using a data-driven approach, canonical correlation analysis (CCA). Methods: Baseline data from 9093 participants from the Adolescent Brain Cognitive Development (ABCD) Study were examined using CCA, a multivariate statistical approach that identifies many-to-many associations between two sets of variables by finding combinations for each set of variables that maximize their correlation. We combined CCA with leave-one-site-out cross-validation across ABCD sites to examine the robustness of results and generalizability to new participants. The statistical significance of canonical correlations was determined by non-parametric permutation tests that accounted for twin, family, and site structure. To assess the stability of the associations identified at baseline, CCA was repeated using 2-year follow-up data from 4247 ABCD Study participants. Results: Two significant sets of associations were identified: (1) difficulty initiating and maintaining sleep and excessive daytime somnolence were strongly linked to nearly all domains of psychopathology (r2 = 0.36, p < .0001). These associations generalized to unseen participants at all 22 ABCD sites and were replicated using 2-year follow-up data. Conclusions: These findings underscore interwoven links between sleep disturbances in early adolescence and psychological, social, and demographic factors.