Treatment of the sensory and motor components of urges to eat (eating addiction?): a mobile-health pilot study for obesity in young people

PURPOSE: Compelling evidence indicates that an addictive process might contribute to overeating/obesity. We hypothesize that this process consists of two components: (a) a sensory addiction to the taste, texture, and temperature of food, and (b) a motor addiction to the actions of eating (e.g., biting, chewing, crunching, sucking, swallowing). Previously, we reported a mobile health application (mHealth app) obesity intervention addressing the sensory addiction component, based on staged food withdrawal. We propose that the motor addiction component can be treated using cognitive behavioral therapy (CBT)-based strategies for body-focused repetitive behaviors (BRFB), e.g., nail biting, skin picking, and hair pulling. METHODS: The present study tested the effectiveness of CBT-based, BFRB therapies added to the staged withdrawal app. Thirty-five participants, ages 8–20, 51.4% females, mean zBMI 2.17, participated in a 4-month study using the app, followed by a 5-month extension without the app. Using staged withdrawal, participants withdrew from specific, self-identified, “problem” foods until cravings resolved; then from non-specific snacking; and lastly from excessive mealtime amounts. BFRB therapies utilized concurrently included: distractions, competing behaviors, triggers avoidance, relaxation methods, aversion techniques, and distress tolerance. RESULTS: Latent growth curve analysis determined that mean body weight and zBMI decreased significantly more than in a previous study that used only staged withdrawal (p < 0.01). In the 5-month follow-up, participants maintained overall weight loss. CONCLUSIONS: This study provides further preliminary evidence for the acceptability of an addiction model treatment of obesity in youth, and that the addition of CBT-based, BFRB therapies increased the effectiveness of staged food withdrawal. LEVEL OF EVIDENCE: Level IV, Evidence obtained from multiple time series analysis with the intervention.