Control Systems Engineering to Address the Problem of Weight Loss Maintenance: A System Identification Experiment to Model Behavioral & Psychosocial Factors Measured by Ecological Momentary Assessment

项目总结/摘要 治疗肥胖和合并症的最主要和最关键的障碍是减肥 上维护一系列已建立的治疗方法可靠地产生临床显著的初始体重减轻3- 体重的30%，这大大降低了疾病的风险和严重程度，即使体重减轻 谦虚了然而，体重减轻的维持一致性较差，大多数患者至少恢复了一些 体重和行为治疗的患者在5年内恢复到基线体重， 与体重有关的疾病虽然恢复是常见的，但很难预测个体何时或为何会开始 恢复失去的体重。因此，我们提出了一个实验，将使未来的即时自适应 干预（JITAI），被动监测触发失效，确定哪些触发最有可能 有助于为每个病人的失误，准确地预测病人何时进入一个高风险的时期 对于失效，确定可能防止失效的干预类型，实施干预 只要需要重新建立健康的行为模式，以维持体重，然后返回 被动监测这种自动干预几乎可以通过移动的 技术，分析和行为干预技术，我们的团队已经建立， 包括：（a）生态瞬时评估平台，用于测量每日体重和相关行为 和心理社会影响;（B）一个分析框架，控制系统工程，能够建模 行为和心理社会对体重影响的复杂模式，并确定如何最好地干预 在这个“系统”，以促进减肥维持;和（c）一个工具箱，经验验证的行为 已知的干预策略对解决体重反弹的常见原因有效。这项建议 旨在支持我们的多学科团队，将我们的专业领域和先前的工作相结合， “系统识别（ID）实验”，N=120名参与者最近失去了≥3%的初始身体 在6个月的行为肥胖治疗中的体重（N=180将经历行为肥胖治疗以产生 该样本），将在12个月的维持期内进行研究。这些数据将用于验证和 完善维持减肥的理论模型。在研究过程中，4个干预措施，从行为 工具箱将随机管理，从而提供以下方面的必要数据：（a）失效触发因素与以下方面的关系 （B）哪些干预措施对解决哪些触发因素有效，对谁有效，以及 在什么情况下这个项目的最终结果将是一个控制系统的算法，可以预测 何时、为谁、如何干预以防止体重反弹，以及一个移动的平台，可以用来 在下一阶段的研究中交付吉泰。这种高度创新的减肥方法可以 最终阻止大量个人以低成本使用智能手机技术， 已经无处不在。

PROJECT SUMMARY/ABSTRACT The most major and critical barrier to the treatment of obesity and comorbid conditions is weight loss maintenance. A range of established treatments reliably produce clinically significant initial weight losses of 3- 30% of body weight, which substantially reduce risk and severity of disease, even when the weight loss is modest. However, weight loss maintenance is uniformly poor, with most patients regaining at least some weight and behaviorally treated patients returning to baseline weight within 5 years, thereby renewing risk for weight-related illness. While regain is common, it is difficult to predict when or why an individual will begin to regain lost weight. We therefore propose an experiment that will enable a future just-in-time adaptive intervention (JITAI) that passively monitors triggers for lapse, identifies which triggers are most likely to contribute to lapse for each patient, accurately predicts when a patient is entering a period of heightened risk for lapse, determines the type(s) of intervention(s) that are likely to prevent the lapse, administers intervention for as long as needed to reestablish healthy behavioral patterns for weight maintenance, and then returns to passive monitoring. This automated intervention is nearly within reach via a combination of mobile technologies, analytics, and behavioral intervention techniques that our team has already established, including: (a) an Ecological Momentary Assessment platform to measure daily weight and related behavioral and psychosocial influences; (b) an analytic framework, Control Systems Engineering, capable of modeling complex patterns of behavioral and psychosocial influences on weight, and determining how best to intervene on this “system” to facilitate weight loss maintenance; and (c) a toolbox of empirically validated behavioral intervention strategies known to be effective for addressing common causes of weight regain. This proposal aims to support our multidisciplinary team in combining our areas of expertise and prior work to enable a “system identification (ID) experiment” with N=120 participants who have recently lost ≥3% of initial body weight in a 6-month behavioral obesity treatment (N=180 will undergo behavioral obesity treatment to produce this sample), who will be studied over a 12-month maintenance period. The data will be used to validate and refine a theoretical model of weight loss maintenance. During the study, 4 interventions from the behavioral toolbox will be administered randomly, thus providing necessary data on: (a) how lapse triggers are related to each other and weight, and (b) which interventions are effective for addressing which triggers, for whom, and under what circumstances. The end result of this project will be a control systems algorithm that can predict when, for whom, and how to intervene to prevent weight regain, and a mobile platform that can be used to deliver JITAI in the next phase of research. This highly innovative approach to weight loss maintenance could ultimately prevent regain in large numbers of individuals at low cost using smartphone technology that is already ubiquitous.