Fluctuations in real-life drinking in alcohol use disorder: A one-year longitudinal study using gamified tasks of decision making and cognitive control.

BACKGROUND

Alcohol use disorder (AUD) is a major contributor to global disability and mortality. Cross-sectional studies have linked AUD to reduced cognitive control and heightened risky decision-making. However, the temporal direction of these effects remains unknown: are cognitive-behavioral alterations a consequence or a precursor of changes in drinking?

METHODS

We deployed a battery of smartphone-based, gamified tasks in a one-year longitudinal ecological momentary assessment study of N=603 participants diagnosed with mostly mild to moderate AUD. Tasks measured cognitive control (working memory, response inhibition) and decision-making (risk-taking, information sampling). Participants completed tasks monthly and reported alcohol consumption every two days.

RESULTS

We found that monthly fluctuations in aspects of decision-making predicted subsequent consumption. Specifically, participants shifted to higher monthly alcohol consumption when their risk-taking was higher in a mixed gambling context and lower in a loss context in the preceding month. Further, when information sampling biases decreased, participants consumed more alcohol in the subsequent month. This temporal direction-that within-subject fluctuations in task outcomes preceded changes in monthly drinking-was specific, was not observed vice versa and survived correction for autocorrelation in drinking, indicating risky decision-making as a precursor of subsequent drinking. Fluctuations in cognitive control and risk-taking in a win context were not associated with fluctuations in drinking.

DISCUSSION: Our findings offer novel insights into the cognitive-behavioral forces driving changes in alcohol consumption in AUD: specific decision-making alterations precede changes in consumption. These findings suggest that smartphone-based gamified tasks show promise to identify periods of heightened risk paving the way for mechanism-based, real-time interventions in AUD.