Children's unique symptom trajectories during school-based preventive interventions: an individual-level meta-analytic trajectory modelling approach

Recent research has used data-driven approaches, such as growth mixture modelling (GMM), to explore differences in how children and adolescents respond to school-based mental health programs or to acute behavioural and pharmacological treatments for anxiety in clinical settings. However, to our knowledge, this approach has not yet been applied to preventative school-based interventions or examined in relation to the onset of clinical depression. Understanding distinct trajectories of depressive symptoms during an intervention—and how these trajectories predict the later onset of major depressive disorder—is a crucial step toward identifying for whom and how such interventions are most effective. In the present study, we aim to identify latent trajectories of depressive symptoms among children using harmonised, individual-level data from multiple universal, school-based prevention trials. We will then examine how these trajectories relate to baseline characteristics such as gender and age, and the subsequent onset of clinical depression. Ultimately, this work seeks to clarify whether universal preventative interventions can be tailored to meet the specific needs of different children and reduce the risk of developing clinical depression.