Individualized management of obesity remains challenging and, to date, most treatment is based on clinical judgment. We developed and validated a novel questionnaire-based tool to identify three pre-defined eating behavior (EB) traits, emotional eating, reduced satiety (constant hunger), and reduced satiation (feasters) that may predict selective medication response given their targeted actions. We recruited 977 individuals from a tertiary academic diabetes clinic to participate in this two-phase validation study. Participants self-reported weight management activities and were asked to self-assess their EB characteristics. The initial questionnaire included both visual analog scale (n=42) and image choice questions (n=4). In Phase I, 729 participants completed the questionnaire, including Māori (11.8%) and Pacific peoples (19.3%). After the random division of the study sample, Exploratory Factor Analysis (EFA) confirmed a three-factor model as the best fit. Stepwise removal of items with inadequate factor loading retained 27 of 42 items, which accounted for 96% of the variance. Confirmatory Factor Analysis (CFA), performed on the second half of the sample, demonstrated a good model fit with the final 27-item questionnaire. Internal consistency was high for factor loading (a = 0.82-0.95) and demographic subgroups, and similar to those obtained in the EFA. Test-retest reliability in a subset of 399 participants who repeated the questionnaire after a four-week interval (Phase II) showed moderate to good reliability. Participants were classified into one of three EB types based on the highest median score among the factors. Test-retest reliability was robust for emotional eaters (71.25%) and constant cravers (68.9%). The correlation between overall EB score and BMI was significant (Spearman rho =0.314, P=0.0005). The author will describe the development of the questionnaire, the three distinct EB traits, and potential future clinical application. We will compare the novel tool with other assessment methods that aim to identify obesity phenotypes (Camilleri & Acosta, 2021).