Examining the relationship between emotion regulation, sleep quality, and anxiety disorder diagnosis

Abstract

Anxiety disorders are highly comorbid with sleep disturbance and have also been associated with deficits in emotion regulation, the ability to control and express emotions. However, the extent to which specific dimensions of sleep disturbance and emotion regulation are associated with anxiety diagnosis is not well-explored. This study examined dimensions of emotion regulation and sleep disturbance that may predict greater likelihood of anxiety diagnosis using novel machine learning techniques. Participants (Mean(SD) age= 28.6(11.3) years, 62.7% female) with primary anxiety disorders (n = 257), including generalized anxiety disorder (n = 122) and social anxiety disorder (n = 135), and healthy controls (n = 89) completed the Difficulties in Emotion Regulation Scale and Pittsburgh Sleep Quality Index. A conditional inference tree was fit to classify likelihood of current anxiety diagnosis based on predictors. The best model fit included 4 split nodes and 5 terminal nodes. Worse scores on two emotion regulation subscales, strategies directed to manage negative emotions and nonacceptance of negative emotions, were the best predictors of current anxiety diagnosis (99.3% probability of diagnosis). For those with better emotion regulation, poor sleep quality and worse daytime functioning due to sleep were important predictors of anxiety diagnosis. Good emotion regulation and non-disturbed sleep predicted high likelihood of being a non-psychiatric control (88.2%). Limitations include cross-sectional design precluding designating directionality of effects of sleep and emotion regulation on anxiety onset; limited sample size; and self-reported sleep. Facets of emotion regulation and sleep disturbance may be important early targets for brief intervention for anxiety disorders.

Keywords

• Emotion regulation
• Sleep
• Anxiety disorders
• Machine learning