Using Multimodal Real-Time Assessment to Phenotype Dietary Non-Adherence Behaviors that Contribute to Poor Outcomes in Behavioral Obesity Treatment

项目总结/文摘

PROJECT SUMMARY/ABSTRACT Behavioral obesity treatment (BOT) produces clinically significant weight loss and reduced disease risk/severity for many individuals with overweight/obesity. Yet, many patients fall short of expected outcomes, which can be largely attributed to lapses from the recommended diet. Our work has shown that dietary lapses (specific instances of nonadherence to the prescribed calorie target(s) in BOT) are frequent during weight loss attempts, and are associated with poorer weight losses and higher daily energy intake. Despite the potential for lapses to influence BOT outcomes and health, poorly understood variability in types of lapse behaviors and their mechanisms interferes with our ability to intervene on them. In our research, participants have identified distinct behaviors associated with lapse (e.g., eating an off-plan food, eating too large a portion of food). Across several studies, we have established the concept of “dietary lapse types” (i.e., specific eating behavior(s) and contextual factors underlying a dietary lapse). We have shown that behavioral, psychosocial, and contextual mechanisms may differ across dietary lapse types, and that some lapse types appear to be more detrimental than others for weight control. Elucidating clear dietary lapse types therefore has major potential for understanding and improving adherence in BOT, but we have been unable to do so because our work is limited to secondary analyses of data from larger trials that have incomplete measures of lapse types, potential mechanisms, and clinical outcomes. We propose to extend our research by using behavioral phenotyping (i.e., data-driven identification of underlying behavioral, psychological, and contextual factors of a health behavior) to establish lapse phenotypes, and understand their impact on clinical outcomes. While typical phenotyping studies cluster individuals via unique characteristics, we aim to understand phenotypes of lapses as a specific behavior within individuals. We will use multimodal real-time assessment tools within a multi-level factor analysis framework to uncover phenotypes while accounting for behaviors occurring within individuals and within days. Adults with overweight/obesity (n=150) will participate in a well-established 12-mo. online BOT and 6-mo. weight loss maintenance period. Participants will complete a 14-day lapse phenotyping assessment battery at baseline, 4, 8, 12 and 18 months. EMA and passive sensing tools (i.e., wrist devices, geolocation) will assess dietary lapses and relevant phenotyping characteristics identified from our prior work. Participant energy intake will be assessed with 24-hour dietary recalls and weight will be measured pre- and post- assessment. Results will yield a set of lapse phenotypes and knowledge of their underlying mechanisms, which will can inform novel interventions to improve dietary adherence in BOT (and in other treatments for which dietary adherence is critical). This innovative approach will advance the science of adherence more broadly by supporting the development of sophisticated theoretical models of adherence behavior and give rise to novel phenotyping methods that can be leveraged to better understand and treat non-adherence to other health behaviors (e.g., medications, activity).