4.2 Article

Predictors of Heavy Drinking During and Following Treatment

期刊

PSYCHOLOGY OF ADDICTIVE BEHAVIORS
卷 25, 期 3, 页码 426-438

出版社

EDUCATIONAL PUBLISHING FOUNDATION-AMERICAN PSYCHOLOGICAL ASSOC
DOI: 10.1037/a0022889

关键词

relapse; dynamic model; proximal risk; distal risk; latent difference score

资金

  1. NIAAA NIH HHS [R21AA017137, R21 AA017137, R21 AA017137-01] Funding Source: Medline

向作者/读者索取更多资源

Alcohol dependence has been described as a relapsing condition and it has been proposed that alcohol lapses could potentially be explained by dynamic associations between contextual, interpersonal, and intrapersonal risk factors. Yet, few studies have tested the associations between risk factors in the prediction of lapse dynamics. The current study was a secondary analysis of data from the COMBINE study (n = 1,383; COMBINE Study Research Group, 2003). The goal of the current study was to examine static (alcohol dependence severity, treatment history, marital status, psychiatric symptoms) and dynamic (negative affect, craving, stress) predictors of heavy drinking during the course of treatment and up to one year following treatment. Results from dynamic latent difference score models indicated that higher levels of static and dynamic risk and increased dynamic risk over time were significantly associated with greater increases in heavy drinking. Likewise, more frequent heavy drinking and higher static risk predicted higher levels of dynamic risk. In addition, changes in dynamic risk factors significantly mediated the association between changes in heavy drinking and both psychiatric symptoms and treatment history. It is important to note that while the effects of static and dynamic risk factors in the prediction of heavy drinking were statistically significant, the magnitude of the effects were small. The current study provided partial support for a dynamic model of relapse; however future research using intensive longitudinal data collection and more advanced statistical techniques could further elucidate lapse dynamics and potentially improve relapse prevention planning.

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