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

使用多模式实时评估对导致行为性肥胖治疗效果不佳的饮食不依从行为进行表型分析

• 批准号: 10418847
• 负责人: Stephanie Paige Goldstein
• 金额: $ 67.47万
• 依托单位: MIRIAM HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10418847
• 关键词: Accounting; Adherence; Adult; Assessment tool; Behavior; Behavior Therapy; Behavior assessment; Behavioral; Body Weight Changes; Body Weight decreased; Calories; Cellular Phone; Characteristics; Chronic; Clinic Visits; Clinical; Complex; Data; Data Analyses; Development; Devices; Diabetes Mellitus; Diet; Eating; Eating Behavior; Ecological momentary assessment; Energy Intake; Environmental Risk Factor; Factor Analysis; Food; Future; Goals; Health; Health behavior; Hour; Hyperphagia; Individual; Intervention; Kidney Diseases; Knowledge; Maintenance; Measurement; Measures; Methods; Obesity; Outcome; Overweight; Participant; Patient Self-Report; Patients; Pattern; Persons; Pharmaceutical Preparations; Phenotype; Predisposition; Psychological Factors; Psychosocial Factor; Research; Science; Secondary to; Severities; Smoking; Surveys; Techniques; Testing; Theoretical model; Time; Treatment Failure; Treatment outcome; Weight; Weight maintenance regimen; Work; Wrist; adaptive intervention; behavior influence; behavioral adherence; behavioral phenotyping; clinically significant; contextual factors; dietary; dietary adherence; disorder risk; falls; improved; individual variation; innovation; multilevel analysis; multimodality; novel; obesity treatment; personalized intervention; precision medicine; prevent; psychosocial; telephone-based; tool; weight loss intervention

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).

项目摘要/摘要 行为肥胖治疗（BOT）会产生临床上显着的体重减轻，疾病风险/严重程度降低 对于许多超重/肥胖的人。然而，许多患者没有预期的结果，这可能是 在很大程度上归因于推荐饮食中的失误。我们的工作表明饮食失误（具体 在减肥尝试期间，bot中规定的卡路里目标不遵守的实例经常是 并与减肥和较高的每日能量摄入有关。尽管有潜力 影响机器人的结果和健康，不了解失误行为类型的变异性及其 机制干扰了我们干预它们的能力。在我们的研究中，参与者确定了独特的 与失误相关的行为（例如，吃非平衡的食物，吃太大的食物）。遍布几个 研究，我们已经建立了“饮食流失类型”的概念（即特定的饮食行为和上下文 饮食过渡的因素）。我们已经证明了行为，社会心理和上下文机制 在饮食中的类型中可能有所不同，并且某些失误类型似乎比其他类型更有害 体重控制。因此，阐明清晰的饮食失误类型具有重要的理解潜力和 提高机器人的依从性，但我们无法这样做，因为我们的工作仅限于次要 分析来自较大试验的数据，这些试验措施不完整 临床结果。我们建议通过使用行为表型来扩展我们的研究（即数据驱动 确定健康行为的潜在行为，心理和背景因素）以建立 衰退表型，并了解它们对临床结果的影响。而典型的表型研究集群 通过独特特征的个人，我们旨在理解失落的表型作为一种特定行为 个人。我们将在多层次因素分析框架内使用多模式的实时评估工具 在考虑个人内部和几天之内发生的行为时，发现表型。成年人 超重/肥胖症（n = 150）将参加公认的12-MO。在线机器人和6-MO。减肥 维护期。参与者将在基线4，4天完成14天的失效表型评估电池 8、12和18个月。 EMA和被动传感工具（即手腕设备，地理位置）将评估饮食失误 以及从我们先前的工作中确定的相关表型特征。将评估参与者能量摄入 在24小时的饮食召回下，将在评估前和评估后进行衡量。结果将产生一组 失误表型和对其基本机制的了解，这将为新颖的干预措施提供信息 改善饮食依从性（以及饮食依从性至关重要的其他治疗方法）。这 创新的方法将通过支持发展的发展，从而更广泛地推进依从性科学 复杂的依从性行为的理论模型，并引起可以成为的新表型方法 利用以更好地理解和治疗其他健康行为（例如药物，活动）。