Detecting impending symptom transitions using early warning signals in individuals receiving treatment for depression

Background: The path to depressive symptom improvement during therapy is often complex, as many individuals experience periods of instability and discontinuous symptom change. If the process of remission follows complex dynamic systems principles, early warning signals (EWS) may precede such depressive symptom transitions. Aims: We aimed to test whether EWS, in the form of rises in lag-1 autocorrelation and variance, occur in momentary affect time series preceding transitions towards lower levels of depressive symptoms during therapy. We also investigated the presence of EWS in patients without symptom transitions. Methods: In a sample of 41 depressed individuals who were starting psychological treatment, positive affect and negative affect (high and low arousal) were measured five times a day using ecological momentary assessments (EMA) for four months (521 observations per individual on average; yielding 25,197 observations in total), and depressive symptoms were assessed weekly over six months. We used a moving window method and time-varying autoregressive generalized additive modeling (TV-AR GAM) to determine whether EWS occurred in these momentary affect measures, within-persons. Results: For the moving-window autocorrelation, 89% of individuals with transitions showed at least one EWS in one of the variables (versus 62.5% in the no-transition group), and the proportion of EWS in the separate variables was consistently higher (~44% across affect measures) than for individuals without transitions (~27%). Rising variance was found for few individuals, both preceding transitions (~11%) and for individuals without a transition (~12%). Conclusions: The process of symptom remission showed critical slowing down in at least part of our sample. Our findings indicate that EWS are not generic across all affect measures and may have limited value as a personalized prediction method