Using Artificial Intelligence to Optimize Delivery of Weight Loss Treatment

摘要 70%的美国成年人超重或肥胖，这对 国家的卫生系统。有效的行为计划是存在的，但这些计划是密集的、长期的和 需要训练有素的临床医生，这使得他们的费用高得令人望而却步，从而限制了传播。 降低成本的方法包括用准专业人员取代训练有素的临床医生，减少 联系频率和/或自动干预。然而，尽管这些替代干预措施导致 平均减重幅度较低，减重变异性高。具体地说，并与 支持性问责模型，在高强度干预中，相当一部分参与者没有得到 受益，而接受低强度干预的一部分人在临床上实现了显著的体重减轻。 理想的减肥治疗系统将通过匹配每个治疗系统来提高结果并降低成本 参与者需要的干预，从而适应参与者的需求并节省资源 在不需要它们的地方。阶梯式护理就是这样一种制度，但它既有成功也有失败 从一系列的缺点中。强化学习的创新人工智能(AI)策略 (RL)提供快速且重复变化的干预功能，持续地“学习”哪些功能 为哪些参与者提供最佳回答。我们的团队最近完成了一个人工智能减肥的试点 超重成年人接受短暂的面对面减肥干预，然后随机 被分配接受3个月的非优化干预(即12分钟电话呼叫)或优化的 电话通话、文本交流和自动消息的组合，根据每个参与者的 根据体重和行为数据确定对每次干预的反应。正如假设的那样，我们实现了 同等重量的损失，时间成本的一小部分。这项拟议的研究将招募320名超重成年人， 提供为期1个月的基于团体的行为减肥治疗，然后将参与者随机分配到 继续以远程形式接受基于群体的行为减肥11个月(BWL-S)或 基于强化学习的治疗(BWL-AI)。符合我们的支持性问责模式，BWL-AI 根据持续监测的参与者数字数据，会有不同的模式、强度和辅导员技能。这个 拟议的研究--这是此类研究的第一次--将在几个方面扩大我们的试点，包括样本量、持续时间、 以及人工智能系统选择的干预特征。这个项目的目的是检验假设 BWL-AI的减肥效果将与BWL-S相当或更好，并且每个参与者的成本 每公斤体重减轻，BWL-AI将比BWL-S少。其他包括描述人工智能系统的特征(在 选定的干预措施术语)，评估改进的人工智能系统的可行性和可接受性， 人工智能干预选择的心理和人口学预测因素研究 应答者和非应答者在人工智能系统如何分配资源方面。

Abstract Seventy percent of American adults are overweight or obese, presenting an unprecedented challenge to the nation’s health systems. Effective behavioral programs exist, but these programs are intensive, long-term and require highly-trained clinicians, making them prohibitively expensive and thus limiting disseminability. Approaches to decreasing costs include replacing highly-trained clinicians with paraprofessionals, reducing contact frequency, and/or automating intervention. However, although these alternative interventions result in considerably lower average weight losses, variability of weight loss is high. Specifically, and consistent with a Supportive Accountability Model, a substantial minority of participants in high-intensity interventions receive no benefit, while a subset of those receiving low-intensity interventions achieve clinically significant weight loss. An ideal weight loss treatment system would enhance outcomes and reduce costs by matching each participant to the intervention he/she needs, thus adapting to participants’ needs and conserving resources where they are not needed. Stepped care represents one such system, but has had mixed success and suffers from a number of shortcomings. The innovative artificial intelligence (AI) strategy of reinforcement learning (RL) provides rapidly and repeatedly-varying features of intervention, continuously "learning" which features provide optimal responses for which participants. Our team recently completed a pilot of an AI weight loss system in which overweight adults received a brief in-person weight loss intervention and then were randomly assigned to receive 3 months of non-optimized interventions (i.e., 12-minute phone calls) or an optimized combination of phone calls, text exchanges, and automated messages, selected based on each participants’ response to each intervention as determined by weight and behavioral data. As hypothesized, we achieved equivalent weight losses at a fraction of the time cost. The proposed study would recruit 320 overweight adults, provide 1 month of group-based behavioral weight loss treatment and then randomize participants to either continue to receive group-based behavioral weight loss in a remote format for 11 months (BWL-S) or to reinforcement learning-based treatment (BWL-AI). In line with our Supportive Accountability model, BWL-AI would vary modality, intensity and counselor skill based on continuously-monitored participant digital data. The proposed study--the first of its kind--would expand on our pilot in several ways including sample size, duration, and features of intervention selected by the AI system. Aims of this project are to test the hypotheses that weight loss outcomes in BWL-AI will be equivalent to or better than BWL-S, and that the cost per participant and per kg of lost weight will be less in BWL-AI than in BWL-S. Other include characterizing the AI system (in terms of interventions selected), assessing feasibility and acceptability of the refined AI system, evaluating psychological and demographic predictors of AI intervention selection and investigating differences between responders and non-responders in how the AI system allocates resources.