4.4 Article

Detecting Impending Symptom Transitions Using Early-Warning Signals in Individuals Receiving Treatment for Depression

Journal

CLINICAL PSYCHOLOGICAL SCIENCE
Volume -, Issue -, Pages -

Publisher

SAGE PUBLICATIONS INC
DOI: 10.1177/21677026221137006

Keywords

complexity; destabilization; symptom remission; sudden improvement; personalized models; idiographic change; replicated single-subject design; experience sampling method

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Drawing on dynamical systems theory, this study investigated whether early-warning signals in momentary affect could predict transitions to lower levels of depressive symptoms in individuals undergoing therapy for depression. The results showed that rising autocorrelation was found in 89% of individuals with transitions, and in a consistently higher proportion of the separate variables, compared to those without transitions. Rising variance was found for a few individuals, both preceding transitions and in individuals without transitions. In addition, some participants showed critical slowing down.
Drawing on dynamical systems theory, we investigated whether within-persons-detected early-warning signals in momentary affect preceded critical transitions toward lower levels of depressive symptoms during therapy. Participants were 41 depressed individuals who were starting psychological treatment. Positive and negative affect (high and low arousal) were measured 5 times a day using ecological momentary assessments over 4 months (M = 522 observations per individual). Depressive symptoms were assessed weekly over 6 months. Within-persons rising autocorrelation was found for 89% of individuals with transitions in at least one variable (vs. 62.5% for individuals without transitions) and in a consistently higher proportion of the separate variables (similar to 44% across affect measures) than for individuals without transitions (similar to 27%). Rising variance was found for few individuals, both preceding transitions (similar to 11%) and for individuals without transitions (similar to 12%). Part of our sample showed critical slowing down, but early-warning signals may have limited value as a personalized prediction method.

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