Using Behavioral Economic Strategies to Address Obesity in Economically Disadvantaged Adults

肥胖是一个公共健康危机，来自经济弱势背景的成年人中，超过85%的人患有肥胖症。 经历超重或肥胖以及相关的健康疾病。到目前为止，生活方式干预， 这一高风险群体的体重有所减轻。因此，有效的干预措施， 人口迫切需要。来自行为经济学的新证据表明， 目前的强化和偏见可能会改善处于不利地位的成年人的治疗结果， 背景具体来说，贫困的环境已被证明几乎没有健康的来源， 强化，这使得对强化的基本来源（例如，可口的食物）更耐 改变此外，所有的人都被证明对现在有偏见，或者对即时的偏好。 奖励（可口的食物）超过未来的奖励（改善健康），研究表明， 处于不利地位的背景对目前的偏见甚至更大（也许是由于生活需求，压力， 认知负荷）。解决肥胖症的这两个过程（目前缺乏强化和偏见） 治疗可以独特地满足这种高风险，服务不足的人群的需要，并导致体重减轻 成功这项拟议中的研究将测试移动健康行为经济学减肥干预措施的有效性 这解决了目前缺乏加强和偏见的问题。将通过以下方式解决缺乏加强的问题： 在治疗开始时提供的小额货币补贴。加固物将在初始阶段逐渐减少 治疗期，最终结束。随着训练者逐渐减少，参与者将接受未来情节的训练。 思考，这已被证明可以减少目前的偏见，并可能改善长期的体重减轻 成果。这种双管齐下的分阶段方法首先解决缺乏强化，然后解决 现在是必不可少的。在治疗开始时立即提供强化是吸引参与者的必要条件 马上然后，当参与者在减肥方面取得成功时， 提供了自己的强化（改善情绪，健康，外观），减肥者将逐渐减少。在此期间， EFT培训将开始。这种新颖的行为经济学mHealth干预将被比作一个mHealth 只有干预。这两项干预措施将主要通过移动的平台提供，包括治疗 为这一人群量身定制的材料，并与之相匹配。因此，这两种干预措施将 不同的是在BE mHealth中包含行为经济学策略。我们的主要假设是 行为经济学干预将在第12个月（治疗结束）产生显著更好的体重减轻。 中介因素（食物强化，当前偏见），调节因素（压力，弹性，致胖环境）， 还将探讨成本效益。如果有效，这种mHealth行为经济学干预将 是一种新的变革性干预方法，可显著改善肥胖治疗结果， 高风险、得不到充分服务的人群。

Obesity is a public health crisis among adults from economically disadvantaged backgrounds, with over 85% experiencing overweight or obesity and associated health ailments. To date, lifestyle interventions targeting this high-risk group have produced modest weight losses. Thus, effective interventions for this vulnerable population are urgently needed. New evidence from behavioral economics suggests that targeting lack of reinforcement and bias for the present may improve treatment outcomes in adults from disadvantaged backgrounds. Specifically, impoverished environments have been shown to have few sources of healthy reinforcement, which makes responding to basic sources of reinforcement (e.g., palatable food) more resistant to change. Moreover, all humans have been shown to have bias for the present, or a preference for immediate rewards (palatable food) over future rewards (improved health), and studies suggest that individuals from disadvantaged backgrounds have even greater bias for the present (perhaps due to life demands, stress, and cognitive load). Addressing these two processes (lack of reinforcement and bias for the present) in obesity treatment may uniquely meet the needs of this high-risk, underserved population and result in weight loss success. The proposed study will test the efficacy of a mHealth behavioral economics weight loss intervention that addresses lack of reinforcement and bias for the present. Lack of reinforcement will be addressed with small monetary reinforcers delivered at the beginning of treatment. Reinforcers will taper during the initial treatment period and eventually end. As reinforcers taper, participants will be trained in Episodic Future Thinking, which has been shown to reduce bias for the present and may improve longer-term weight loss outcomes. This two-pronged, phased approach that first addresses lack of reinforcement and then bias for the present is essential. Providing reinforcement immediately at treatment start is necessary to engage participants straightaway. Then, as participants are developing success experiences with weight loss, which naturally provides its own reinforcement (improved mood, health, appearance), reinforcers will taper. During this time, EFT training will begin. This novel behavioral economics mHealth intervention will be compared to a mHealth only intervention. The two interventions will be delivered primarily via a mobile platform, include treatment material tailored to this population, and be matched for contact. Thus, the only way the two interventions will differ is in the inclusion of behavioral economics strategies in BE mHealth. Our primary hypothesis is that the behavioral economics intervention will yield significantly better weight losses at month 12 (treatment end). Mediators (food reinforcement, bias for the present), moderators (stress, resilience, obesogenic environment), and cost-effectiveness will also be explored. If effective, this mHealth behavioral economics intervention would be a new and transformative intervention approach that significantly improves obesity treatment outcomes in a high-risk, underserved population.