(PS3-A22) Implicit Self-depressed Associations Are a Prognostic Indicator of Depressive Recurrence After Recovery: A Canadian Biomarker Integration Network for Depression (CAN-BIND) Study

Cognitive theories of depression posit that dysfunctional thoughts about self are central to the onset and recurrence of major depressive episodes. Moreover, dual process models suggest that automatic, implicit self-relevant thoughts are distinct from explicit thoughts and have a unique influence on mood and behavior. When individuals hold implicit associations between elements of depression and self (e.g., hopelessness, worthlessness), activation of these networks may elicit negative thoughts, maladaptive behaviors, and low mood, thereby promoting symptoms of depression. Thus, self-depressed associations may represent a key risk factor for depressive recurrence. However, a paucity of research has examined self-depressed associations as a predictor of recurrence, and no research to date has examined changes in self-depressed associations over time. Thus, the current multi-wave study investigated, for the first time, whether self-depressed associations and their change over a period of remission are prognostic indicators of depressive episode recurrence, above and beyond the influence of depressive symptoms. A sample of 100 adults (mean age = 38.04, SD = 11.73, 62% female), who had recovered from major depressive disorder participated in this Canadian Biomarker Integration Network for Depression (CAN-BIND) study. Participants completed the Depression Implicit Association Test (DIAT), an assessment of self-depressed associations, and the Montgomery-Asberg Depression Rating Scale (MADRS) at baseline and at every 8 weeks for up to two years follow up. MADRS scores were used to monitor participants for episode recurrences, which were verified at an additional clinic visit. Twenty-five participants experienced a recurrence. We conducted latent growth models (LGMs) to assess the prospective associations of baseline self-depressed association and the slope of change in self-depressed associations with recurrence. First, we conducted an unconditional LGM, which indicated that self-depressed associations significantly increased over time, B = 0.01, SE = .005, p = .042. We then conducted an LGM that included the baseline and slope of self-depressed associations over follow up as predictors of episode recurrence. Baseline depression symptoms were included as a time-invariant covariate. The model demonstrated good fit to the data, χ²(80) = 89.14, p = .227, RMSEA = .034, CFI = .98, TLI = .98. Findings indicated that, even after accounting for baseline depressive symptoms, baseline self-depressed associations predicted recurrence, B = 0.38, SE = 0.14, p =.005, such that individuals with greater self-depressed associations were more likely to experience a recurrence. However, change in self-depressed associations was not a significant predictor of recurrence, B = -1.82, SE = 3.36, p =.588. Results suggest that self-depressed associations represent a static risk factor for depressive episode recurrence during periods of remission. Moreover, findings have implications for the integration of the DIAT into clinical practice and suggest that a single assessment of self-depressed associations may be a promising prognostic indicator for identifying patients who are at higher risk of experiencing a recurrence.