Lecture – Examining the Active Ingredients of Depression Treatment for Adolescents

Available with English captions.

Presented by Christian A. Webb, PhD, McLean Hospital – McLean Forum lecture

Psychotherapies, including cognitive behavior therapy (CBT), have been shown to be effective at improving depressive symptoms in adolescents.

However, the mechanisms through which depressed teens improve—and why many do not—remain largely unknown. A clearer understanding of the “active ingredients” of treatment and underlying mechanisms of symptom change may inform the development of more effective and efficient treatments for depressed youth.

In this presentation, Christian A. Webb, PhD, reviews research on the processes of symptom change in CBT for depressed teens. Webb is the director of McLean’s Treatment and Etiology of Depression in Youth Laboratory.

Watch the lecture to see Webb discuss:

  • Approaches used in psychotherapy research to study the processes that account for depressive symptom change
  • The neural impact of CBT treatment
  • How pre-treatment patient characteristics (e.g., clinical and demographic details) can be used to predict treatment outcomes and inform treatment recommendations
  • How smartphone-delivered surveys can be used to collect clinically useful information on the use of therapeutic skills and improvement in mood and symptoms

Specifically, Webb discusses research that examines the impact of increased cognitive skills use vs. increased behavioral skills use on symptom change. The findings address the perspective of the patient and the therapist.

How much does a patient’s relationship with their therapist matter? Webb addresses research on this topic. Does a positive alliance lead to symptom improvement? Does symptom improvement cause improvement in the alliance?

The lecture touches on the pursuit of personalized medicine in psychiatry. Webb discusses research aimed at identifying pre-treatment predictors of symptom improvement in depression treatments. Greater knowledge of variables predicting treatment response before the start of treatment may have important clinical implications regarding which interventions are best suited for whom.

In current clinical practice, treatment selection for depressed youth (and adults) is largely based on trial-and-error and clinician/patient preference. Recent research is aimed at developing actionable, algorithm-guided treatment recommendations to improve outcomes for depressed individuals.