Avoidant Restrictive Food Intake Disorder

Children with Avoidant/Restrictive Food Intake Disorder (ARFID) often lack access to specialty dietitians, and scalable nutritional guidance/food chaining tools are currently not available. The investigators will evaluate a web-based, clinician-supervised, generative-AI assistant that produces individualized food-chaining plans. Develop an AI assistant that generates ≥15 allergy-safe, evidence-based chaining steps per participant and meets ≥90 % expert agreement for safety/appropriateness. Validate the assistant against gold-standard clinician recommendations (Cohen's κ ≥ 0.80). Test clinical impact in a three-month pilot RCT (n = 96) by comparing change in Nine-Item ARFID Screen (NIAS) scores between intervention and usual-care groups. Hypothesis: AI-generated plans will reduce NIAS scores by ≥3 points relative to controls.

The overarching intention of the Eating Disorder Genetics Initiative 2 (EDGI2) is to increase sample size, diversity, and eating disorder phenotypes. The investigators are enrolling 20,000 new participants with anorexia nervosa (AN), bulimia nervosa (BN), binge-eating disorder (BED), avoidant/restrictive food intake disorder (ARFID), and controls in the US, Mexico, Australia, New Zealand, Sweden, and Denmark. A primary study goal is to enroll at least 30% of participants from underrepresented groups. Participants are asked to complete a series of questionnaires and submit a saliva sample for genotyping. The goal is to better understand eating disorders and how they relate to each other so that better treatments can be developed.