Profiles and Transitions of Sleep Disturbance and Depression Among Chinese Adolescents: The Predictive Roles of Life Stress and Resilience.

Sleep disturbance and depression co-occur frequently, yet their co-occurring and transitional nature among adolescents remains underexplored. Meanwhile, few studies have examined the potential predictive effect of environmental factors (e.g., life stress) and individual factors (e.g., resilience) on their interactive profiles and transitions. This study investigated the profiles and transitions of sleep disturbance and depression for Chinese adolescents, along with the predictive role of life stress and resilience in profiles and transitions. A total of 17,404 adolescents (  = 12.1 ± 1.2 years, ranging from 10 to 17 years; 48.4% of females) were assessed at baseline from April 21 to May 12, 2021 (Time 1, T1), 6 months later from December 17-26, 2021 (Time 2, T2), and 1 year later from May 17 to June 6, 2022 (Time 3, T3). We used latent profile and latent transition analysis (LTA) to explore sleep disturbance and depression profiles and their transitions over time. Multivariate logistic regression was conducted to prove the predictive roles of stress and resilience in these profiles and transitions. Across all three time points, three profiles were consistently identified: low profile, co-occurring moderate profile, and co-occurring high profile. Three profiles presented distinct transition patterns, with adolescents in co-occurring high profiles displaying the highest level of transitions. The logistic regression suggested that adolescents with more interpersonal and academic stress or less resilience were more likely to belong to or transition into at-risk profiles. Recognizing subgroup differences is crucial to understanding the co-occurrence and transitions of sleep disturbance and depression. Stress and resilience, particularly interpersonal stress, are significant predictors. This underscores the need importance for dynamically monitoring changes in sleep disturbance and depression, as well as identifying resilience and stress factors, which are essential for developing intervention programs.