Validity of the Addiction-like Eating Behavior Scale among Patients with Compulsive Eating.
Camille BourqueMaxime LegendreSylvain IcetaCatherine BéginPublished in: Nutrients (2024)
Food addiction (FA) and binge eating disorder (BED) co-occur and share compulsive eating symptoms. When using an FA measure, it is important to evaluate its performance in a population presenting compulsive eating. The study aims to validate the Addiction-like Eating Behavior Scale (AEBS) among a clinical sample characterized by compulsive eating and overweight/obesity and to evaluate its incremental validity over the Yale Food Addiction Scale 2.0 (YFAS). Patients seeking help for compulsive eating (n = 220), between January 2020 and July 2023, completed online questionnaires, including FA, compulsive eating, and BMI evaluations. The factor structure, internal consistency, and convergent, divergent, and incremental validity were tested. The sample had a mean age of 44.4 years old (SD = 12.7) and a mean BMI of 38.2 (SD = 8.0). The two-factor structure provided a good fit for the data, with factor loadings from 0.55 to 0.82 (except for item 15) and the internal consistency was high (ω = 0.84-0.89). The AEBS was positively correlated with the YFAS (r = 0.66), binge eating (r = 0.67), grazing (r = 0.47), craving (r = 0.74), and BMI (r = 0.26), and negatively correlated with dietary restraint (r = -0.37), supporting good convergent and divergent validity. For each measure of compulsive eating, linear regression showed that the AEBS "appetite drive" subscale had a unique contribution over the YFAS. This study provided evidence that the AEBS is a valid measure among a clinical sample of patients with compulsive eating and overweight/obesity. However, questions remain as to whether the AEBS is a measure of FA or compulsive eating.
Keyphrases
- weight loss
- physical activity
- obsessive compulsive disorder
- weight gain
- metabolic syndrome
- insulin resistance
- end stage renal disease
- healthcare
- ejection fraction
- machine learning
- chronic kidney disease
- mental health
- peritoneal dialysis
- deep brain stimulation
- newly diagnosed
- sleep quality
- climate change
- deep learning
- case report
- prognostic factors
- body weight
- data analysis