Using Artificial Intelligence to Optimize Delivery of Weight Loss Treatment

使用人工智能优化减肥治疗的实施

• 批准号: 10400867
• 负责人: Evan M Forman
• 金额: $ 61.79万
• 依托单位: DREXEL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-04 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10400867
• 关键词: Accelerometer; Accountability; Address; Adult; Affect; Algorithms; American; Area; Artificial Intelligence; Automobile Driving; Behavior Therapy; Behavioral; Behavioral Research; Binge Eating; Body Weight decreased; Calories; Caring; Cellular Phone; Characteristics; Cost Savings; Cost-Benefit Analysis; Data; Effectiveness; Expert Systems; Frequencies; Gender; Goals; Gold; Government; Health system; Hour; Individual; Informal Social Control; Insurance; Intake; Internet; Intervention; Learning; Mental Depression; Minority; Mobile Health Application; Modality; Modeling; Monitor; Motivation; Nature; Obesity; Obesity Epidemic; Outcome; Overweight; Pain management; Participant; Persons; Phase; Physical activity; Policies; Population; Professional counselor; Psychological reinforcement; Race; Randomized; Resources; Robotics; Sample Size; Selection for Treatments; System; Telephone; Testing; Text; Text Messaging; Time; Training; Treatment Efficacy; Weight; acceptability and feasibility; artificial intelligence algorithm; base; care systems; clinically significant; cost; cost effective treatment; cost-effectiveness evaluation; design; digital; effective therapy; improved; improved outcome; innovation; learning progression; medication compliance; overweight adults; programs; psychologic; recruit; remote delivery; responders and non-responders; response; skills; success; treatment responders; video chat; weight loss intervention; weight loss program; wireless

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.

抽象的 70％的美国成年人超重或肥胖，对 国家的卫生系统。存在有效的行为计划，但是这些计划是密集，长期的，并且 需要训练有素的临床医生，使其被禁止昂贵，从而限制了分散性。 降低成本的方法包括用专业人士替换训练有素的临床医生 接触频率和/或自动干预。但是，尽管这些替代干预措施导致 平均体重减轻较低，体重减轻的变异性很高。具体而言，与 支持责任模型，高强度干预措施的少数参与者没有 受益，而接受低强度干预措施的部分则可以实现临床上显着的体重减轻。 理想的减肥治疗系统将通过匹配每个 参与他/她需要的干预措施，从而适应参与者的需求并保存资源 不需要的地方。阶梯护理代表一个这样的系统，但取得了不同的成功和苦难 来自许多缺点。增强学习的创新人工智能（AI）策略 （RL）提供干预的快速而反复变化的特征，继续“学习”哪些功能 提供参与者的最佳响应。我们的团队最近完成了AI减肥的飞行员 超重成年人接受了简短的面对面减肥干预措施，然后随机进行的系统 分配接受3个月的未优化干预措施（即12分钟的电话）或优化 根据每个参与者选择的电话，短信交换和自动化消息的组合 对每个干预措施的响应，按重量和行为数据确定。如假设的那样，我们取得了成就 等效减肥以时间成本的一小部分。拟议的研究将招募320名超重成年人， 提供1个月的基于小组的行为减肥治疗，然后随机将参与者随机进行 继续以遥控格式接收基于组的行为减肥，持续11个月（BWL-S）或 基于增强学习的治疗（BWL-AI）。与我们的支持责任模型BWL-AI一致 基于不断监控参与的数字数据的方式，会改变方式，强度和辅导员技能。 拟议的研究 - 第一个此类研究 - 将以几种方式扩展我们的飞行员，包括样本量，持续时间， AI系统选择的干预特征。该项目的目的是检验 BWL-AI中的减肥结果将等效于或更好，而不是BWL-S，并且每次参与的成本 在BWL-ai中，每公斤体重的体重减轻将比BWL-S少。其他包括表征AI系统（在 所选干预措施的条款），评估精制AI系统的可行性和可接受性，评估 AI干预选择和研究差异的心理和人口统计学预测指标 响应者和非响应者如何分配资源。