Predicting heavy episodic drinking using an extended temporal self-regulation theory

Abstract

© 2017 Elsevier LtdIntroduction: Alcohol consumption contributes significantly to the global burden from disease and injury, and specific patterns of heavy episodic drinking contribute uniquely to this burden. Temporal self-regulation theory and the dual-process model describe similar theoretical constructs that might predict heavy episodic drinking. The aims of this study were to test the utility of temporal self-regulation theory in predicting heavy episodic drinking, and examine whether the theoretical relationships suggested by the dual-process model significantly extend temporal self-regulation theory. Methods: This was a predictive study with 149 Australian adults. Measures were questionnaires (self-report habit index, cues to action scale, purpose-made intention questionnaire, timeline follow-back questionnaire) and executive function tasks (Stroop, Tower of London, operation span). Participants completed measures of theoretical constructs at baseline and reported their alcohol consumption two weeks later. Data were analysed using hierarchical multiple linear regression. Results: Temporal self-regulation theory significantly predicted heavy episodic drinking (R2 = 48.0–54.8%, p < 0.001) and the hypothesised extension significantly improved the prediction of heavy episodic drinking frequency (?R2 = 4.5%, p = 0.001) but not peak consumption (?R2 = 1.4%, p = 0.181). Intention and behavioural prepotency directly predicted heavy episodic drinking (p < 0.01). Planning ability moderated the intention-behaviour relationship and inhibitory control moderated the behavioural prepotency-behaviour relationship (p < 0.05). Conclusions: Both temporal self-regulation theory and the extended temporal self-regulation theory provide good prediction of heavy episodic drinking. Intention, behavioural prepotency, planning ability and inhibitory control may be good targets for interventions designed to decrease heavy episodic drinking.