Using artificial intelligence to optimize delivery of weight loss treatment: Protocol for an efficacy and cost-effectiveness trial.
Using artificial intelligence to optimize delivery of weight loss treatment: Protocol for an efficacy and cost-effectiveness trial.
复制标题
使用人工智能优化减肥治疗的实施：功效和成本效益试验方案。
DOI：
10.1016/j.cct.2022.107029
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发表时间：
2023
影响因子：
2.2
通讯作者：
Zhang,Fengqing
中科院分区：
文献类型：
--
作者：
Forman,EvanM;Berry,MichaelP;Butryn,MeghanL;Hagerman,CharlotteJ;Huang,Zhuoran;Juarascio,AdrienneS;LaFata,EricaM;Ontañón,Santiago;Tilford,JMick;Zhang,Fengqing
黄金标准的行为减肥（BWL）受到专家临床医生和高成本的限制。强化学习（RL）的人工智能（AI）技术是一种优化解决方案，它跟踪与特定动作相关的结果，并随着时间的推移，学习哪些动作会产生期望的结果。RL越来越多地用于优化医学治疗（例如，化疗剂量），并且最近开始被行为治疗所利用。例如，我们之前证明了RL通过基于数字数据的自动监测（例如，重量变化）。在这项初步工作中，随机分配到AI条件的参与者需要的教练联系量是随机分配到黄金标准条件的参与者的三分之一，但体重减轻几乎相同。目前的协议扩展了我们的试点工作，将是第一个全面的随机对照试验的RL系统的体重控制。主要目的是评估基于RL的12个月BWL计划将产生非劣效于标准BWL治疗的体重减轻，但成本较低的假设。次要目标包括测试机械目标（卡路里摄入量，体力活动）和预测因子（抑郁，暴食）。因此，超重/肥胖成年人（N=336）将被随机分配到金标准条件（12个月的每周BWL组）或AI优化的每周干预措施（代表专家领导的小组，专家领导的电话，辅助专业人员领导的电话和自动消息的组合）。将在0、1、6和12个月时对受试者进行评估。
Gold standard behavioral weight loss (BWL) is limited by the availability of expert clinicians and high cost of delivery. The artificial intelligence (AI) technique of reinforcement learning (RL) is an optimization solution that tracks outcomes associated with specific actions and, over time, learns which actions yield a desired outcome. RL is increasingly utilized to optimize medical treatments (e.g., chemotherapy dosages), and has very recently started to be utilized by behavioral treatments. For example, we previously demonstrated that RL successfully optimized BWL by dynamically choosing between treatments of varying cost/intensity each week for each participant based on automatic monitoring of digital data (e.g., weight change). In that preliminary work, participants randomized to the AI condition required one-third the amount of coaching contact as those randomized to the gold standard condition but had nearly identical weight losses. The current protocol extends our pilot work and will be the first full-scale randomized controlled trial of a RL system for weight control. The primary aim is to evaluate the hypothesis that a RL-based 12-month BWL program will produce non-inferior weight losses to standard BWL treatment, but at lower costs. Secondary aims include testing mechanistic targets (calorie intake, physical activity) and predictors (depression, binge eating). As such, adults with overweight/obesity (N=336) will be randomized to either a gold standard condition (12 months of weekly BWL groups) or AI-optimized weekly interventions that represent a combination of expert-led group, expert-led call, paraprofessional-led call, and automated message). Participants will be assessed at 0, 1, 6 and 12 months.