SMART Weight Loss Management

 描述(由申请人提供)：肥胖症的高流行率和高成本使其成为公共健康危机，但目前的护理治疗标准阻碍了吸收，并因采取一刀切的方法而耗尽资源。指南建议继续向所有消费者提供昂贵、繁重的治疗成分(例如，咨询、膳食替代)，而不考虑减肥反应。阶梯式护理尝试成本较低的循证治疗首先，将更多的资源密集型治疗保留给不太理想的应答者是一种合理、公平的人口健康管理战略。然而，肥胖症治疗的分步护理方法尚未纳入廉价、广泛可用的mHealth工具。目前尚不清楚，是通过提供低成本、低强度、自主控制的mHealth治疗作为有无反应风险的初始治疗，还是通过提供成本更高、可能造成依赖、破坏自主动机的传统肥胖治疗，来更好地优化联合临床和成本结果。开始mHealth治疗的潜在陷阱是，如果对最初不足的治疗没有反应，导致士气低落，长期结果可能很差。为了降低这种风险，我们将通过应用从我们之前的mHealth肥胖症研究得出的预测模型，比以前更早地识别无应答者，并将迅速将无应答者重新分配到强化治疗。我们建议使用一种新的实验方法，SMART(序贯多任务随机试验)，将400名超重/肥胖成年人随机分成两种一线治疗方法之一，(1)单独使用APP(APP)，或(2)APP加教练(APP+C)。那些对一线治疗无效(即，减肥失败的证据)的人将被电子随机分成两种后续增强策略之一：(1)适度增加：增加另一个mHealth成分(例如，短信)，或(2)大力增加：增加mHealth成分(例如，文本)和更传统的成分(例如，教练、换餐)。应答者将继续进行相同的一线治疗12周。将在3个月、6个月和12个月进行评估，以确定(1)mHealth或传统肥胖治疗(教练)是超重/肥胖成年人的最佳一线治疗；以及(2)对减肥失败的最佳反应是适度或大力增加一线治疗。作为第一个整合mHealth工具并实施我们的减肥失败预测模型的阶段性护理试验，SMART将是迄今为止评估的时间和资源效率最高的策略。

 DESCRIPTION (provided by applicant): Obesity's high prevalence and costs make it a public health crisis, but current standard of care treatment impedes uptake and depletes resources by taking a one-size-fits-all approach. Guidelines recommend provision of expensive, burdensome treatment components (e.g., counseling, meal replacement) continuously to all consumers regardless of weight loss response. Stepped care that tries less costly evidence-based treatments first, reserving more resource-intensive treatments for suboptimal responders is a logical, equitable population health management strategy. However, stepped care approaches to obesity treatment have not yet incorporated inexpensive, widely available mHealth tools. It is unclear whether conjoint clinical and cost outcomes are better optimized by providing a low cost, low intensity, autonomously controlled mHealth treatment as the initial treatment with risk of nonresponse, or by providing a more costly, traditional obesity treatment with the potential to create a dependency that undermines autonomous motivation. The potential pitfall of beginning with mHealth treatment is that long-term outcome may be poor if nonresponse to initially insufficient treatment allows demoralization to set in. To reduce that risk, we will identify nonresponders earlier than previously has been possible by applying a predictive model derived from our prior mHealth obesity research and will quickly reallocate nonresponders to augmented treatment. We propose to use a novel experimental approach, the SMART (Sequential Multiple Assignment Randomized Trial), to randomize 400 overweight/obese adults to one of two first line treatments, either (1) an app alone (APP), or (2) the app plus coaching (APP +C). Those who do not respond to the first line treatment (i.e., evidenced by failure to lose weight) will be e-randomized to one of two subsequent augmentation tactics, either: (1) Modestly Step-Up: add another mHealth component (e.g., text messages), or (2) Vigorously Step-Up: add both a mHealth component (e.g., texts) and a more traditional component (e.g., coaching, meal replacement). Responders will continue with the same first line treatment for 12 weeks. Assessments will occur at 3, 6, and 12 months to determine (1) whether mHealth or traditional obesity treatment (coaching) is the optimal first line treatment for overweight/obese adults; and (2) whether the optimal response to weight loss failure is to modestly or vigorously augment the first line treatment. As the first stepped care trial to integrate mHealth tools and implement our predictive model of weight loss failure, SMART will be the most temporally and resource efficient strategy evaluated to date.