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

项目总结/摘要 行为肥胖治疗（BOT）产生临床显著的体重减轻和降低疾病风险/严重程度 对于许多超重/肥胖的人来说。然而，许多患者达不到预期的结果，这可能是 这主要归因于没有遵循推荐的饮食习惯。我们的研究表明，饮食失误（具体 在减肥尝试期间经常出现不遵守规定的卡路里目标的情况， 并且与较差的体重减轻和较高的每日能量摄入有关。尽管有可能出现失误， 影响BOT结果和健康，对失误行为类型及其 机制干扰了我们干预它们的能力。在我们的研究中，参与者发现了不同的 与失效相关联的行为（例如，吃计划外的食物，吃太多的食物）。跨若干 研究中，我们已经建立了“饮食失误类型”的概念（即，具体的饮食行为和背景 饮食失误的潜在因素）。我们已经证明，行为，心理和背景机制， 可能不同的饮食失误类型，有些失误类型似乎比其他更有害， 体重控制因此，阐明明确的饮食失误类型对理解和 提高BOT的依从性，但我们无法做到这一点，因为我们的工作仅限于次要 分析来自大型试验的数据，这些试验对失效类型、潜在机制和 临床结果。我们建议通过使用行为表型（即，数据驱动 确定健康行为的潜在行为、心理和环境因素）， 失效表型，并了解其对临床结果的影响。虽然典型的表型研究 通过独特的特征，我们的目标是了解失误的表型作为一种特定的行为， 个体我们将在多层次因素分析框架内使用多模态实时评估工具， 揭示表型，同时解释个体内和几天内发生的行为。成人 超重/肥胖者（n=150）将参加一项完善的12个月研究。在线机器人和6-mo。减肥 维护期。参与者将在基线时完成14天的失效表型评估组合， 8、12和18个月。EMA和无源传感工具（即，手腕设备，地理定位）将评估饮食失误 以及从我们先前的工作中鉴定的相关表型特征。将评估参与者的能量摄入 24小时的饮食回顾和体重将在评估前后进行测量。结果将产生一组 失效表型及其潜在机制的知识，这将为新的干预措施提供信息， 改善BOT中的饮食依从性（以及饮食依从性至关重要的其他治疗）。这 创新的方法将通过支持发展 粘附行为的复杂理论模型，并产生新的表型分析方法， 用于更好地理解和治疗不遵守其他健康行为（例如，药物，活动）。

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).