Emotion rigidity in adolescents prospectively predicts future depressive symptoms assessed via self-report and...

Adolescence is a critical developmental period marked by an elevated risk for Major Depressive Disorder (MDD). Identifying predictive risk factors is crucial for the development and timely delivery of preventative interventions. One potential risk factor is maladaptive and rigid emotion dynamics, which findings suggest is linked with depressive symptoms. Accordingly, to identify individuals at risk, the current study investigated whether emotional rigidity is a prospective predictor of depressive symptoms in adolescence using both self-reported and clinician-rated depression assessments. Adolescents (n = 117, aged 12-18) completed self-reported and clinician-rated measures of depressive symptoms at baseline and every 6 months for two years. Additionally, they received one month of ecological momentary assessment (EMA) surveys at baseline, reporting their emotions four times a day (n = 10,196). Multilevel vector autoregressive (mlVAR) models estimated temporal and contemporaneous density (operationalization of emotion rigidity) of subject-specific networks. Higher temporal and contemporaneous network density, as well as an aggregate density score, predicted greater future depressive symptoms controlling for baseline symptoms, as measured by either participants' self-report or clinician reports. Density emerged as a stronger predictor of future depression than any specific emotion inertia (i.e., predictability of one emotion state). Additionally, and supporting incremental predictive validity, temporal and aggregated density scores predicted future clinician-rated depression, and the aggregated score predicted self-reported depression, over and above mean levels of negative and positive affect. Collectively, these findings may have significant clinical implications for early identification and targeted preventative interventions among youth at risk of future depression by virtue of relatively rigid emotion dynamics.