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

项目摘要/摘要 行为肥胖治疗(BOT)在临床上可显著减轻体重，降低疾病风险/严重程度 对于许多超重/肥胖的人来说。然而，许多患者没有达到预期的结果，这可能是 这在很大程度上归因于推荐饮食的失误。我们的研究表明，饮食失误(特别是 在减肥尝试期间，不遵守规定的卡路里目标(BOT中的S)的情况经常发生， 并与较差的体重减轻和较高的每日能量摄入量有关。尽管有可能出现以下情况 影响BOT结果和健康，鲜为人知的失误行为类型及其变异性 机制会干扰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 与失误相关的行为(例如，吃计划外的食物，吃太多的食物)。横跨多个 在研究中，我们确立了饮食失误类型的概念(即特定的饮食行为(S)和语境 饮食失误的潜在因素)。我们已经证明了行为、心理社会和背景机制 饮食失误的类型可能有所不同，一些饮食失误类型似乎比其他类型的饮食失误更有害 体重控制。因此，阐明明确的饮食失误类型对理解和 在BOT中提高依从性，但我们一直无法做到这一点，因为我们的工作仅限于二级 对来自较大规模试验的数据进行分析，这些试验对失误类型、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)来扩展我们的研究 识别健康行为的潜在行为、心理和背景因素)以建立 了解表型，并了解它们对临床结果的影响。而典型的表型研究聚集在一起 通过独特的特征，我们的目标是理解失误的表型作为一种特定的行为 个人。我们将在多层次因素分析框架内使用多模式实时评估工具 发现表型，同时解释发生在个人内部和几天内的行为。成年人患有 超重/肥胖(n=150)将参加公认的12个月。在线机器人和6-mo。体重减轻 维护期。参与者将在基线上完成为期14天的表型鉴定，4， 8个月、12个月和18个月。EMA和被动传感工具(例如，手腕设备、地理定位)将评估饮食失误 以及从我们之前的工作中确定的相关表型特征。参与者的能量摄入量将被评估 24小时饮食召回和体重将在评估前和评估后测量。结果将产生一组 错失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(以及其他饮食依从性至关重要的治疗)。这 创新的方法将通过支持发展更广泛的坚持科学 复杂的粘着行为理论模型，并产生了新的表型方法，可以 用来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).