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Abstract
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BACKGROUND: Identifying factors that protect against alcohol-related negative consequences associated with emerging adult drinking is a critical public health issue. It has been proposed that high levels of self-regulation moderate risks associated with drinking, decreasing alcohol-related negative consequences. Past research testing this possibility is limited by a lack of advanced methodology for testing moderation and failure to consider facets of self-regulation. This study addressed these limitations.
METHODS: Three hundred fifty-four community emerging adults (56% female; predominantly non-Hispanic Caucasian (83%) or African American (9%)) were assessed annually for 3 years. Moderational hypotheses were tested using multilevel models and the Johnson-Neyman technique was used to examine simple slopes. Data were organized such that repeated measures (level 1) were nested within participants (level 2) to test cross-sectional associations. Self-regulation was operationalized as effortful control and its facets (attentional, inhibitory, and activation control).
RESULTS: We found evidence of moderation. The association between alcohol use during a heavy drinking week and consequences weakened as effortful control increased. This pattern was supported for two facets (attentional and activation control), but not for inhibitory control. Regions of significance results revealed that this protective effect was only evident at very high levels of self-regulation.
CONCLUSIONS: The results provide some evidence that very high levels of attentional and activation control protect against alcohol-related negative consequences associated with drinking. Emerging adults who are very high in attentional and activation control are likely better able to control their attention and engage in goal-directed behavior, like leaving a party at a reasonable hour, or attending school and/or work when experiencing the punishing effects of a hangover.
RESULTS emphasize the importance of distinguishing facets of self-regulation when testing self-regulation models.
Language: en |