Obesity and disordered eating (e.g., binge eating, unhealthy weight control behaviors) are complex, inter- related public health problems. The role of dieting in the etiology of obesity and disordered eating is controversial. Although dieting in the context of behavioral weight loss interventions can lead to clinically significant weight and binge eating reductions, dieting is also a robust risk factor for obesity and disordered eating. This ?dieting paradox? presents a challenge for developing and disseminating public health messages and interventions that prevent and treat obesity and disordered eating. Given potential adverse consequences of dieting and the fact that a failure to implement strategies to address obesity could lead to increased prevalence of adverse obesity-associated health consequences (e.g., diabetes), it is critical to move beyond a ?one size fits all? approach and identify which strategies should be promoted, for whom, and under what circumstances. Untangling the ?dieting paradox? and developing effective tailored and/or targeted interventions requires a better understanding of the heterogeneity of dieting. For example, many people who endorse dieting do not reduce their caloric intake. Even in the absence of energy intake reductions, however, dieting can be associated with adverse affective consequences and binge eating. To date, research addressing this heterogeneity has not fully leveraged mathematical and computational tools that account for the complexity inherent in the interplay between these weight-related behaviors, cognitions, affective experiences, and health. Moreover, few longitudinal studies have sufficient depth and breadth to comprehensively examine how different dieting patterns are associated with weight and disordered eating trajectory variations. To address these paradoxical issues around dieting, disordered eating, and obesity and inform interventions in school, healthcare, and community settings, this project will use data from the Project EAT studies and the advantages offered by combining two powerful computational approaches, topological data analysis (TDA) and agent based modeling (ABM). Project EAT is uniquely suited for this study given its long-term follow-up over critical life course periods and comprehensive assessment of variables relevant to obesity and disordered eating. TDA will be utilized to characterize different ?dieting paradox? sub-groups using self-reported dieting, as well as weight-related behavioral and psychological variables, and to examine their association with disordered eating and weight status during the adolescent-to-adult transition. The TDA results will be utilized to develop, test, and validate ABMs that will serve as virtual laboratories to test etiologic hypotheses and potential intervention solutions. Utilizing these cutting edge approaches, the overarching goal of this work is to better characterize the complexity and clinically meaningful heterogeneity across the spectrum of weight and disordered eating behaviors and generate real world prevention and treatment approaches that move beyond ?one size fits all? intervention strategies that could inadvertently lead to adverse consequences for vulnerable sub-populations.
The proposed study aims to use existing longitudinal data and cutting-edge computational methods to better understand the complexity, heterogeneity, and dynamic interplay over time among dieting, obesity, and disordered eating during the adolescence to young adulthood transition to inform intervention development. We plan to use 1) topological data analysis (TDA) to better characterize the heterogeneity of dieting and identify ?dieting paradox? subgroups with varied weight and disordered eating trajectories; and 2) agent-based modeling (ABM) to simulate strategies for improving sub-group and population-wide outcomes. Better characterizing the complexity and clinically meaningful heterogeneity across the spectra of dieting, weight and disordered eating will help the obesity and eating disorder fields move beyond ?one size fits all? intervention strategies that could inadvertently lead to adverse consequences for vulnerable sub-populations.
