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

项目摘要/摘要 治疗肥胖症和合并症的最主要和最关键的障碍是减肥。 维修。一系列公认的治疗方法可靠地产生了临床上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。然而，减肥的维持性普遍很差，大多数患者至少恢复了一些 体重和接受行为治疗的患者在5年内恢复到基线体重，从而增加了 体重相关疾病。虽然恢复是很常见的，但很难预测一个人何时或为什么会开始 恢复减掉的体重。因此，我们提出了一种实验，它将使未来的即时自适应 干预(JITAI)被动监控失效的触发因素，确定哪些触发因素最有可能 有助于每个患者的失误，准确预测患者何时进入风险高发期 对于失效，确定可能防止失效的干预类型(S)(S)，管理干预 只要需要重新建立保持体重的健康行为模式，然后回到 被动监控。这种自动化干预几乎触手可及，通过组合移动 我们团队已经建立的技术、分析和行为干预技术， 包括：(A)测量每日体重和相关行为的生态瞬时评估平台 和心理社会影响；(B)能够建模的分析框架--控制系统工程 行为和心理社会影响体重的复杂模式，并确定如何最好地进行干预 以促进减肥维持；以及(C)经验验证的行为学工具箱 众所周知，干预策略对于解决体重回升的常见原因是有效的。这项建议 旨在支持我们的多学科团队将我们的专业领域与先前的工作相结合，以实现 “系统识别(ID)实验”，参与者为N=120人，他们最近丢失了3%的初始≥ 体重在为期6个月的行为肥胖治疗中(N=180将接受行为肥胖治疗)产生 该样本)，将在12个月的维护期内进行研究。数据将用于验证和 提炼出减肥维持的理论模型。在研究过程中，来自行为的4项干预 工具箱将随机管理，从而提供有关以下方面的必要数据：(A)失效触发器与 彼此和重量，以及(B)哪些干预措施有效地解决了哪些触发因素，对谁，以及 在什么情况下。这个项目的最终结果将是一种控制系统算法，它可以预测 何时、为谁、如何干预以防止体重回升，以及可用于 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能会 最终防止大量个人以低成本使用智能手机技术重新获得 已经无处不在了。

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.