Interactions between different eating patterns on recurrent binge-eating behavior: a machine learning approach

Objective: Previous research has shown that certain eating patterns (rigid restraint, flexible restraint, intuitive eating) are differentially related to binge eating. However, despite the distinctiveness of these eating patterns, evidence suggests that they are not mutually exclusive. Using a machine learning-based decision tree classification analysis, we examined the interactions between different eating patterns in distinguishing recurrent (defined as ≥4 episodes the past month) from nonrecurrent binge eating. Method: Data were analyzed from 1,341 participants. Participants were classified as either with (n = 512) or without (n = 829) recurrent binge eating. Results: Approximately 70% of participants could be accurately classified as with or without recurrent binge eating. Intuitive eating emerged as the most important classifier of recurrent binge eating, with 75% of those with above-average intuitive eating scores being classified without recurrent binge eating. Those with concurrently low intuitive eating and high dichotomous thinking scores were the group most likely to be classified with recurrent binge eating (84% incidence). Low intuitive eating scores were associated with low binge-eating classification rates only if both dichotomous thinking and rigid restraint scores were low (33% incidence). Low flexible restraint scores amplified the relationship between high rigid restraint and recurrent binge eating (81% incidence), and both a higher and lower BMI further interacted with these variables to increase recurrent binge-eating rates. Conclusion: Findings suggest that the presence versus absence of recurrent binge eating may be distinguished by the interaction among multiple eating patterns. Confirmatory studies are needed to test the interactive hypotheses generated by these exploratory analyses.