SMART Weight Loss Management

 描述（由申请人提供）：肥胖的高患病率和成本使其成为公共卫生危机，但目前的护理治疗标准通过采取一刀切的方法阻碍了吸收和耗尽资源。指南建议提供昂贵、繁琐的治疗成分（例如，咨询、代餐）持续地提供给所有消费者，而不管减肥反应如何。首先尝试成本较低的循证治疗的阶梯式护理，为次优反应者保留更多资源密集型治疗，是一种合理、公平的人口健康管理策略。然而，肥胖治疗的阶梯式护理方法尚未纳入廉价，广泛使用的mHealth工具。目前尚不清楚是否通过提供低成本，低强度，自主控制的mHealth治疗作为无反应风险的初始治疗，或通过提供更昂贵的传统肥胖治疗，可能会产生破坏自主动机的依赖性，从而更好地优化联合临床和成本结果。从mHealth治疗开始的潜在陷阱是，如果对最初不充分的治疗没有反应，长期结果可能很差，从而导致士气低落。为了降低这种风险，我们将通过应用从我们先前的mHealth肥胖研究中得出的预测模型，比以前更早地识别无应答者，并将快速重新分配无应答者以进行增强治疗。我们建议使用一种新的实验方法，SMART（序贯多重分配随机试验），将400名超重/肥胖成年人随机分配到两种一线治疗之一，（1）单独的应用程序（APP），或（2）应用程序加指导（APP + C）。那些对一线治疗没有反应的人（即，通过减肥失败证明）将被随机分配到两个后续增强策略之一：（1）适度增加：添加另一个mHealth组件（例如，文本消息），或者（2）大力提升：添加mHealth组件（例如，文本）和更传统的组件（例如，指导、代餐）。缓解者将继续接受相同的一线治疗12周。评估将在3、6和12个月时进行，以确定（1）移动健康或传统肥胖治疗（指导）是否是超重/肥胖成人的最佳一线治疗;以及（2）对减肥失败的最佳反应是否是适度或大力加强一线治疗。作为整合移动健康工具并实施我们的减肥失败预测模型的第一步护理试验，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.