Heterogeneous developmental trajectories and predictors of self-harm in early adolescence: A machine learning study

Although prior studies have examined developmental trajectories of adolescent self-harm in terms of frequency and severity, a fundamental gap remains: we lack a dynamic, state-specific understanding of how self-harm ideation and behavior develop during early adolescence. Moreover, few studies have prospectively examined how baseline predictors relate to subsequent self-harm pathways.

Using four annual waves of data from a large-scale Chinese adolescent cohort (N = 11,366; 48.6% female; T1: M_age = 10.72 ± 0.29 years), this study used a person-centered approach to delineate distinct self-harm trajectories and interpretable machine learning methods to identify their baseline predictors.

Five heterogeneous trajectories were identified: persistently low-risk, persistent ideation, ideation remission, behavior-to-ideation, and ideation-to-behavior. Although depressive symptoms, self-blame, family stress, and gender emerged as the most influential predictors overall, the predictors varied substantially across trajectories, indicating meaningful differences in their developmental drivers.

These findings demonstrate the heterogeneous developmental trajectories of self-harm states in early adolescents and reveal trajectory-specific risk predictors, underscoring the importance of prevention efforts that should consider both shared and distinct factors across pathways.