Emotion regulation dynamics predict substance use in high-risk adolescents. (July 2020)

• Record Type: Journal Article
• Authors: McKee, Kevin; Russell, Michael; Mennis, Jeremy; Mason, Michael; Neale, Michael
• Is Part Of: Addictive behaviors. Volume 106(2020)
• Publication Date: 2020-07
• Subjects: Substance abuse -- Periodicals; Alcoholism -- Periodicals; Drug addiction -- Periodicals; Nicotine addiction -- Periodicals; Smoking -- Periodicals; Gambling -- Psychological aspects -- Periodicals; Electronic journals; 362.29
• DOI: 10.1016/j.addbeh.2020.106374
• Languages: English
• ISSNs: 0306-4603

Abstract

Highlights: Repeated self-reported assessments of affect taken in Experience Sampling designs may show patterns of variation that reveal underlying properties of feedback control. Substance use may result from, supplant, or disrupt normal patterns of emotion regulation. A feedback-control state-space model of emotional homeostasis was used to produce an index of emotion regulation from affect time series. The model-based measure of emotion regulation was associated with substance use independently from simple descriptive statistics, adding complementary predictive value. Abstract: Emotional factors such as stress and anxiety contribute to risk of substance use in adolescents. Descriptive measures of within-person affect variability are often used to predict morbidity, but indices derived from theoretical models may provide more interpretable alternatives. A continuous-time state-space model of emotion regulation as closed-loop feedback control was used to estimate the homeostatic tendency of affect in each of 94 adolescent participants. The resulting indices of emotion regulation were then compared to within-person affect sum score means and standard deviations in predicting total counts of nicotine, alcohol, and cannabis use. Model-based emotion regulation was significantly associated with lower frequencies of nicotine, alcohol, and cannabis use, while mean negative affect sum score was associated with higher frequencies. Model comparisons revealed that while model-based predictors and descriptive statistics explained similar amounts of variance in substance use, the explained variance proportions were independent between the approaches. The greatest predictive value was achieved by a combined model with both sets of affect indices. We conclude that theoretically defined and model-estimated individual characteristics may serve an important role in conceptualizing and predicting substance use behavior.