Incentivizing behavior change skills to promote weight loss

 描述(申请人提供)：三分之二的美国人口现在被归类为肥胖或超重。尽管存在有效的行为减肥干预措施，但许多人并不坚持，即使体重显著下降的人在干预后的一年内体重也会回升。需要新的方法来提高对有效减肥干预措施的坚持，并促进长期减肥维持。一种有希望的战略是通过财政激励来加强。在不同的行为领域，不同比率的积极强化得到了理论和经验的支持。然而，测试这种减肥策略的为数不多的研究大多是无效的。一个关键问题是，是否应该为过程(例如，饮食自我监测)或结果(减肥)提供激励。鼓励饮食自我监控和减肥可能比单独进行这两种方式都更有效。在以前的研究中，患者必须参加面对面的会议才能上交自我监测记录并称重，因此自我监测和临时减肥与出诊混淆。为了仅为饮食自我监测和临时减肥提供实时激励，数据收集和处理必须自动化。在这项研究中，肥胖社区门诊患者将参加一项有效的、为期24周的低碳水化合物减肥计划，该计划通过两周一次的小组课程进行。我们将开发一种创新的信息技术(IT)解决方案，将饮食自我监测数据(患者通过手机饮食应用程序进行INPT)和减肥数据(由患者通过远程秤输入)进行整理。一种算法将参与者归类为实现足够或不充分的饮食自我监控和减肥，以实时赚取不同价值的间歇性奖励。 2(自我监控激励：是与否)×2(减肥激励：是与否)受试者间设计将允许我们建立单独或联合激励自我监控和中期减肥的可行性和可接受性。具体目标是：(1)确定使用分析饮食自我监测和中期减肥数据的自动算法的可行性和可接受性，以利用可变比率激励提供实时强化；(2)评估各种招募方法的有效性，并描述招募、干预坚持和结果评估遵从率；(3)估计提供干预的成本和患者的成本。该项目将为全面有效性试验提供基础，以测试激励饮食自我监测和临时减肥对短期和长期体重结果的影响，同时提供激励措施，并在激励措施取消后进行。如果有效，这种方法可以降低数百万美国人肥胖的患病率、不良后果和成本。

 DESCRIPTION (provided by applicant): Two-thirds of the United States population is now classified as obese or overweight. Despite the existence of effective behavioral weight loss interventions, many people do not adhere to them, and even people who lose significant weight regain it in the year following intervention. Novel methods are needed to improve adherence to effective weight loss interventions and promote long-term weight loss maintenance. One promising strategy is reinforcement via financial incentives. Positive reinforcement with variable-ratio schedules has received theoretical and empirical support in various behavioral domains. However, the few studies testing this type of strategy for weight loss have been largely ineffective. A key issue is whether incentives should be provided for process (e.g., dietary self-monitoring) or outcome (weight loss). Incenting dietary self-monitoring and weight loss may be more effective than incenting either alone. In previous studies, patients had to attend in-person sessions to turn in self-monitoring records and be weighed, so self- monitoring and interim weight loss were confounded with attendance. To deliver incentives in real-time for dietary self-monitoring and interim weight loss alone, data collection and processing must be automated. In this study, obese community outpatients will participate in an effective, 24-week, low-carbohydrate weight loss program delivered via biweekly group classes. We will develop an innovative information technology (IT) solution that will collate dietary self-monitoring data (inpt by patients via a mobile phone dietary application) and weight loss data (input by patients via remote scale). An algorithm will classify participants as achieving adequate or inadequate dietary self-monitoring and weight loss to earn intermittent rewards of varying value in real-time. The 2 (incentive for self-monitoring: yes vs. no) X 2 (incentive for weight loss: yes vs. no) between-subjects design will allow us to establish feasibility and acceptability of incenting self-monitoring and interim weight loss alone and in conjunction. Specific aims are: (1) Determine feasibility and acceptability of using automated algorithms that analyze dietary self-monitoring and interim weight loss data to provide real- time reinforcement using variable-ratio incentives; (2) Evaluate the effectiveness of various recruitment methods and describe recruitment, intervention adherence, and outcome assessment adherence rates; (3) Estimate cost of delivering the intervention and cost to patients. This project will provide the foundation for a comprehensive effectiveness trial to test the impact of incenting dietary self-monitoring and interim weight loss on short- and longer-term weight outcomes while incentives are delivered and once they are withdrawn. If effective, this approach could reduce the prevalence, adverse outcomes, and costs of obesity for millions of Americans.