Literature DB >> 28992486

Emotion differentiation predicts likelihood of initial lapse following substance use treatment.

Deepika Anand1, Yun Chen2, Kristen A Lindquist3, Stacey B Daughters4.   

Abstract

BACKGROUND: An estimated 40% to 70% of individuals treated for a substance use disorder relapse within one year following treatment (Walitzer and Dearing, 2006). Relapse is often driven by the need to cope with intense negative affect (Koob, 2013). Emotion differentiation, defined as the ability to distinguish among various emotion states, has been linked to better behavioral control in the face of negative affect (Kashdan et al., 2015). The aim of the current study was to determine if higher levels of emotion differentiation are associated with the risk of experiencing an initial lapse following entry into residential substance use treatment.
METHODS: A total of 213 substance users (69.5% male, 94.4% African American, M age=43.01±11.35years) entering residential treatment were assessed on study variables at pre- and post-treatment, and at 1-, 3-, 6- and 12-month post-treatment. Emotion differentiation was calculated using ratings on five negative affect items derived from the Positive and Negative Affect Schedule (PANAS; Watson et al., 1988) at five assessment points.
RESULTS: A Cox proportional-hazards regression model adjusting for age and negative affect intensity demonstrates that for every unit increase in emotion differentiation, there is a 27% reduction in the likelihood of initial lapse on any given day (OR=0.73; 95% CIs [0.56, 0.95]).
CONCLUSIONS: The ability to differentiate among negative emotion states protects against initial lapse following treatment.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Abstinence; Emotion differentiation; Negative affect; Relapse; Substance use

Mesh:

Year:  2017        PMID: 28992486      PMCID: PMC5802350          DOI: 10.1016/j.drugalcdep.2017.09.007

Source DB:  PubMed          Journal:  Drug Alcohol Depend        ISSN: 0376-8716            Impact factor:   4.492


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