What's in a mood?
Looking for dynamic predictors of individual improvement in depression
The process by which depression arises and evolves over time is complex. It is not yet well understood who will experience improvement or recurrence of symptoms, and what predicts such symptom change. Understanding how individuals recover from depression is an important clinical goal. In the search for predictors of clinical change, the studies in the current thesis have used intensive longitudinal data to examine the role of moment-to-moment affect dynamics and test hypotheses based on dynamical systems theory.
We studied idiographic change over the course of therapy and test the role of affect dynamics, indicators of instability like early warning signals, and overall change patterns as predictors of symptom improvement and treatment response. The overarching aim of this body of work was to examine depression as a dynamic process. Taken together, the studies in this thesis provide important insights that will allow us to reflect on the theoretical and clinical promise of complex dynamical systems approaches to the study of change in depression.