Predictors and Mechanisms of Change in the Treatment of Depressed Adolescents

Psychotherapies, including Cognitive Behavioral Therapy (CBT), have been shown to be effective in improving depressive symptoms in adolescents. However, the mechanisms through which depressed teens improve – and why many fail to sufficiently improve – remain largely unknown. A clearer understanding of the “active ingredients” of treatment and underlying mechanisms of symptom change may ultimately inform the development of more targeted – and ideally more effective and efficient – interventions for depressed youth. In this talk, I will present research on the processes of symptom change in CBT for depressed adolescents. In addition, and relevant to the pursuit of precision mental health, I will highlight work on identifying pre-treatment predictors of symptom improvement in psychotherapeutic and smartphone-delivered interventions for depression in youth. Greater knowledge of variables predicting better or worse treatment response prior to the start of treatment may have important clinical implications regarding which interventions are best suited for whom, thus informing treatment selection. In current clinical practice, treatment selection for depressed youth (and adults) is largely based on trial-and-error and clinician/client preference. Consistent with the goals of precision medicine, our recent work aims to develop actionable, algorithm-guided treatment recommendations to improve outcomes for depressed teens. Finally, I’ll conclude by discussing our work using passive sensors from smartphones and wearables (e.g., tracking activity levels, percentage of time spent at home, patterns of phone usage, and estimates of sleep onset/offset) to predict when teens are struggling with negative emotions and may benefit most from a brief smartphone-delivered intervention.