Interference-based methods to mitigate gambling craving: a proof-of-principle pilot study

Abstract

ABSTRACT Craving is central in the prognosis of gambling disorder. The elaborated intrusion theory (EIT) provides a sound framework to account for craving in addictive disorders, and interference methods inspired from the EIT have substantiated their effectiveness in mitigating substance and food-related cravings. The principle of these methods is to recruit the cognitive resources underlying craving (e.g., visuospatial skills, mental imagery) for another competitive and cognitively demanding task, thus reducing the vividness and overwhelming nature of craving. Here we conducted two experiments employing a between-subjects design to test the efficacy of interference methods for reducing laboratory-induced craving. In these experiments, gamblers (n = 38 for both experiments) first followed a craving induction procedure. They then performed either a visuospatial interference task (making a mental and vivid image of a bunch of keys [experiment 1] or playing the video game Tetris [experiment 2]; experimental conditions) or another task supposed not to recruit visuospatial skills and mental imagery (exploding bubble pack [experiment 1] or counting backwards [experiment 2]; control conditions). Results show that all methods successively mitigated induced craving. Although previous research evidenced the superiority of visuospatial tasks to reduce substance-related craving, our findings question their superiority in the context of gambling craving. Abbreviations EIT: Elaborated intrusion theory of desire; GD: Gambling disorder; CEQ: Craving Experience Questionnaire; g-CEQ: gambling Craving Experience Questionnaire; g-CEQ-F: Gambling Craving Experience Questionnaire – Frequency form; g-CEQ-S: Gambling Craving Experience Questionnaire – Strength form; Psi-Q: Plymouth Sensory Imagery Questionnaire; PGSI: Problem Gambling Severity Index; S-UPPS-P: Short UPPS-P Impulsive Behavior Scale; DASS-21: Depression, Anxiety and Stress Scales; ANCOVA: Analysis of covariance.