Context matters: Exploring the adaptive nature of self-regulation skills in predicting abstinence among Veterans with alcohol use disorder

Abstract: BACKGROUND: Alcohol use disorder (AUD) is highly prevalent among veterans in the United States. Self-regulation skills (e.g., coping and emotion regulation) are important biopsychosocial factors for preventing relapse. However, how variation in self-regulation skills supports abstinence based on contextual demands is understudied in veterans with AUD. METHODS: In a prospective longitudinal design, treatment-seeking veterans (n = 120; 29 females) aged 23-91 with AUD completed the Alcohol Abstinence Self-Efficacy Scale to assess temptation to drink across several high-risk situations (i.e., negative affect, social/positive emotions, physical concerns, and craving/urges) as well as the Brief-COPE and Emotion Regulation Questionnaire to assess self-regulation skills. Abstinence status was assessed at 6 months. T-tests were used to identify self-regulation skills that differed between abstinent and non-abstinent individuals. Multivariate regression with model selection was performed using all possible interactions between each high-risk situation and the self-regulation skills that significantly differed between groups. RESULTS: Overall, 33.3% of participants (n = 40; nine females) were abstinent at 6 months. Abstinent individuals reported significantly higher use of suppression (p = 0.015), acceptance (p = 0.005), and planning (p = 0.045). Multivariate regression identified significant interactions between (1) planning and physical concerns (p = 0.010) and (2) acceptance, suppression, and craving/urges (p = 0.007). Greater planning predicted abstinence in participants with higher temptation to drink due to physical concerns (e.g., pain). For individuals with lower temptation to drink due to cravings/urges, simultaneous higher suppression and acceptance increased the likelihood of abstinence. Conversely, for participants with higher cravings, greater acceptance with lower suppression was linked to a higher probability of abstinence. CONCLUSIONS: Results suggest that the adaptiveness of self-regulation skills in predicting AUD recovery is dependent on contextual demands and highlight the need for culturally sensitive treatments. Collectively, these findings indicate that further research on coping and regulatory flexibility may be an important avenue for tailoring AUD treatment for veterans.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.

    Mental Health Peer reviewed