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Background Sleep disturbances are pervasive among adolescent patients with major depressive disorder (MDD), yet the heterogeneity of sleep phenotypes within this population remains underexplored. This study aimed to identify latent profiles of sleep quality in Chinese adolescent patients with MDD and to examine their associations with sociodemographic, clinical, and psychosocial factors. Methods A total of 1,467 adolescent patients with MDD were assessed using the Pittsburgh Sleep Quality Index (PSQI), Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), the Borderline Personality Features Scale for Children (BPFS-C), and a self-designed sociodemographic and clinical inventory. Latent profile analysis (LPA) was conducted using the seven PSQI component scores as profile indicators. Multinomial logistic regression was subsequently performed to determine factors associated with profile membership. Results A five-profile solution was identified: Normal Sleep Group (13%), Mild Disturbance-Low Hypnotic Use (36%), Mild Disturbance-High Hypnotic Use (22%), Severe Disturbance-Low Hypnotic Use (16%), and Severe Disturbance-High Hypnotic Use (13%). Logistic regression revealed that the presence of non-suicidal self-injury (NSSI) and exposure to negative life events significantly increased the odds of belonging to more severe sleep disturbance profiles ( OR = 2.92-8.37 and 2.33-2.60, respectively; all p < 0.05). Notably, patients without left-behind experiences exhibited higher odds of being classified into the Severe Disturbance-High Hypnotic Use profile compared to those with such histories ( OR = 0.46, p < 0.001). Furthermore, profiles characterized by poorer sleep quality and profiles characterized by more frequent self-reported sleep medication use were cross-sectionally associated with greater anxiety and borderline features in this study. Conclusion Sleep quality among adolescent patients with MDD exhibits distinct typological heterogeneity, characterized primarily by severity and hypnotic use patterns. These findings provide preliminary exploratory evidence for the development of personalized, profile-informed clinical interventions for this vulnerable population.
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Hu et al. (2026) studied this question.
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