Incentivizing behavior change skills to promote weight loss

 描述（由申请人提供）：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，但许多人并没有坚持下去，甚至那些体重显著减轻的人在干预后的一年内又恢复了体重。需要新的方法来提高有效减肥干预措施的依从性，并促进长期减肥维持。一个有希望的战略是通过财政激励来加强。正强化与可变比率的时间表已收到理论和经验的支持，在各种行为领域。然而，为数不多的测试这种减肥策略的研究在很大程度上是无效的。一个关键问题是是否应该为过程提供激励措施（例如，饮食自我监测）或结果（体重减轻）。鼓励饮食自我监测和减肥可能比单独鼓励更有效。在以前的研究中，患者必须亲自参加会议，提交自我监测记录并称重，因此自我监测和中期体重减轻与出席混淆。为了实时激励饮食自我监测和临时减肥，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个有效的，24周，低碳水化合物减肥计划，通过每两周一次的小组课程。我们将开发一种创新的信息技术（IT）解决方案，将整理饮食自我监测数据（由患者通过移动的手机饮食应用程序输入）和减肥数据（由患者通过远程秤输入）。一种算法将参与者分类为实现充分或不充分的饮食自我监测和体重减轻，以实时获得不同价值的间歇性奖励。 2（自我监测激励：是与否）× 2（体重减轻激励：是与否）受试者间设计将使我们能够确定单独和联合激励自我监测和中期体重减轻的可行性和可接受性。具体目标是：（1）确定使用自动化算法的可行性和可接受性，该算法分析饮食自我监测和中期体重减轻数据，以使用可变比率激励提供真实的强化;（2）评估各种招募方法的有效性，并描述招募、干预依从性和结果评估依从率;（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，一旦取消。如果有效，这种方法可以减少数百万美国人的肥胖患病率，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.