Does Health At Every Size® fit all? A group-based trajectory modeling of a non-diet intervention

Objective: Health At Every Size (R) (HAES (R)) interventions have been increasingly recognized as a sustainable strategy in obesity management. Nevertheless, heterogeneity among obese individuals leads to challenges as it translates in mixed responses to treatment. In this context, our objective was to identify trajectories of responses to a non-diet intervention for adult overweight/obese women to highlight profiles of responders. Method: Based on data from a multicentric quasi-experimental study, a latent class growth modeling (LCGM) was performed. Two hundred and ten women with high body mass index (BMI >= 25, M-BMI = 36.53) who followed a non-diet intervention offered in Health and Social Services Centres completed questionnaires at T = 0, 4 and 16 months. Outcomes used in the LCGM were intuitive eating and body esteem, two central components in HAES (R) interventions. Types of responders were then profiled on sociodemographic, weight, lifestyle, psychological and eating variables. Results: The LCGM revealed a 4-trajectory model (p < .001), comprising non-responders (14.67%), moderate improvement with low maintenance responders (49.89%), moderate improvement with high maintenance responders (29.28%) and high functioning partial responders (6.56%). Analysis of variances showed significant differences between all types of responders with medium to large effect sizes on depressive symptoms, self-esteem and disinhibited eating (p < .001; eta(2) = .23, 0.30 and 0.16 respectively). Fewer differences were found on sociodemographic, lifestyle, health and weight variables. Overall, non-responders (14.67%) had a distinctive profile compared to the other groups by consistently expressing poorer psychological functioning, less adapted eating behaviors and reaching more frequently the clinical cutoff for severe depression (p = .001). Conclusions: Findings strongly support the relevance of considering psychological characteristics to move towards personalized healthcare in obesity management.