Evaluation of an Adaptive Intervention for Weight Loss Maintenance

项目摘要 肥胖仍然是美国的一个重大公共卫生挑战。行为体重管理 项目已经证明了减肥的有效性，但长期维持这些减肥 治疗结束后往往是穷人。有证据表明，那些能够保持 饮食和活动的变化可以成功地维持他们的体重减轻;因此，试图改善体重 损失维持通常涉及通过每月“延长护理”提供持续支助 干预会议。虽然这些干预措施已经证明了减肥的显着改善 维护，效果不大。一个关键的挑战是持续的参与者参与（通常评估 参加干预会议）。出勤率与体重结果密切相关，但出勤率往往 贫穷并随着时间的推移而衰落。现有项目每月一次的静态治疗时间表可能 导致这些次优结果;参与者在与体重相关的行为中经历了一个小失误 可能几个星期都得不到支持，到那时他们可能会经历更大的失误或体重增加。 恢复。这可能会导致沮丧，羞愧或尴尬的感觉，并脱离 干预与此相反，定制干预措施的提供，使会议提供时，个人在 体重反弹的“高风险”提供了破坏这一周期并显着提高计划参与度的可能性， 坚持计划目标和长期体重维持结果。我们建议评估一个 提供适合参与者需求基于电话的延长护理的创新方法。我们已经建立了一个 参与者可以使用智能手机应用程序来跟踪体重、饮食摄入量和身体活动 (key在传统的行为体重管理程序中的自我监测行为），并可以进一步查询 参与者在整个星期关于自我报告因素（例如，饥饿的重要性 保持在体重管理目标的轨道上），这表明体重反弹的风险很高。我们还 开发了一种预测算法，使用这些数据来识别个体何时处于体重“高风险”状态 恢复。我们建议进行一项随机对照试验，评估ADAPTIVE（已交付） 仅当我们的算法指示或当参与者通过应用程序内支持请求发起时）与 静态（现有扩展护理计划中使用的每月预定格式）治疗提供 258名在金标准治疗期间成功减掉≥ 5%初始体重的成年人在24个月时的体重恢复 16-每周行为体重管理计划。这项研究的结果有明确的治疗意义， 延长护理体重维持计划中的会议时间/频率，本研究将导致 在一个创新的，低成本的，易于扩展的干预减肥维护。此外，拟议的 研究将通过建立以下基本证据库来填补体重管理文献中的关键空白： 未来适应性干预发展的体重相关行为的近端预测因子。

PROJECT SUMMARY Obesity remains a substantial public health challenge in the United States. Behavioral weight management programs have demonstrated effectiveness for weight loss, but long-term maintenance of these weight losses after the end of treatment tends to be poor. Evidence has demonstrated that individuals who can maintain their changes in eating and activity can successfully maintain their weight loss; thus, attempts to improve weight loss maintenance have often involved provision of continued support through monthly “extended-care” intervention sessions. While these interventions have demonstrated significant improvements in weight loss maintenance, effects have been modest. A key challenge is continued participant engagement (often assessed as attendance at intervention sessions). Attendance has been closely tied to weight outcomes, but rates tend to be poor and decline over time. The once-per-month, static treatment schedules of existing programs may contribute to these suboptimal outcomes; a participant experiencing a small lapse in weight-related behaviors may not receive support for several weeks, by which point they may be experiencing a larger lapse or weight regain. This can lead to feelings of frustration, shame, or embarrassment and disengagement from intervention. In contrast, tailoring intervention delivery such that sessions are provided when individuals are at “high risk” for weight regain offers potential to disrupt this cycle and significantly improve program engagement, adherence to program goals, and long-term weight maintenance outcomes. We propose to evaluate an innovative method of providing phone-based extended-care adaptive to participant needs. We have built a smartphone application that can be used by participants to track weight, dietary intake, and physical activity (key self-monitoring behaviors in traditional behavioral weight management programs) and can further query participants throughout the week regarding self-report factors (e.g., ratings of hunger and the importance of staying on track with weight management goals) that indicate high risk for weight regain. We have also developed a predictive algorithm that uses this data to identify when individuals are at “high risk” of weight regain. We propose to conduct a randomized controlled trial evaluating the impact of ADAPTIVE (delivered only when indicated by our algorithm or when initiated by participants via an in-app support request) versus STATIC (the monthly, pre-scheduled format used in existing extended-care programs) treatment provision on weight regain at 24 Months in 258 adults who successfully lose ≥ 5% of initial weight during a gold-standard 16-week behavioral weight management program. Results of this study have clear treatment implications for the timing/frequency of sessions within extended-care weight maintenance programs, and this study will result in an innovative, low-cost, and easily scalable intervention for weight loss maintenance. Further, the proposed research will fill a critical gap in the weight management literature by building a foundational evidence base of proximal predictors of weight-related behaviors for future adaptive intervention development.