Can the artificial intelligence technique of reinforcement learning use continuously-monitored digital data to optimize treatment for weight loss?
Can the artificial intelligence technique of reinforcement learning use continuously-monitored digital data to optimize treatment for weight loss?
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DOI：
10.1007/s10865-018-9964-1
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发表时间：
2019-04-01
影响因子：
3.1
通讯作者：
Moskow, Danielle
中科院分区：
文献类型：
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作者：
Forman, Evan M.;Kerrigan, Stephanie G.;Moskow, Danielle
行为减肥（WL）试验表明，平均而言，除非提供长期的、密集的、因而昂贵的干预，否则参与者的体重会恢复。优化解决方案取得了不同程度的成功。“强化学习”（RL）的人工智能原理提供了一种新的、更复杂的优化形式，其中每个个体的干预强度根据反应模式不断调整。在该试验中，我们评估了基于rl的WL修井的可行性和可接受性，以及与非优化的强化修井相比，优化是否能以更低的成本获得同等的效益。参与者（n = 52）完成了为期1个月的以小组为基础的面对面行为WL干预，然后（在第二阶段）被随机分配接受3个月的每周两次的远程干预，这些干预是非优化的（没有；10分钟的电话）或优化的（电话，短信交换和由算法选择的自动消息的组合）。个体优化（IO）和群体优化（GO）算法分别根据每个参与者和每个小组成员在固定时间（例如1小时）内的每个干预措施的过去表现来选择干预措施。结果表明，该系统部署可行，学员和教练员均可接受。正如假设的那样，我们能够以大约三分之一的成本（IO和GO分别为1.73和1.77小时/参与者，而NO为4.38小时/参与者）实现等效的第二阶段体重减轻（NO = 4.42%, IO = 4.56%, GO = 4.39%），这表明RL系统方法在减肥和维持体重方面有很大的前景。
Behavioral weight loss (WL) trials show that, on average, participants regain lost weight unless provided long-term, intensive-and thus costly-intervention. Optimization solutions have shown mixed success. The artificial intelligence principle of "reinforcement learning" (RL) offers a new and more sophisticated form of optimization in which the intensity of each individual's intervention is continuously adjusted depending on patterns of response. In this pilot, we evaluated the feasibility and acceptability of a RL-based WL intervention, and whether optimization would achieve equivalent benefit at a reduced cost compared to a non-optimized intensive intervention. Participants (n = 52) completed a 1-month, group-based in-person behavioral WL intervention and then (in Phase II) were randomly assigned to receive 3 months of twice-weekly remote interventions that were non-optimized (NO; 10-min phone calls) or optimized (a combination of phone calls, text exchanges, and automated messages selected by an algorithm). The Individually-Optimized (IO) and Group-Optimized (GO) algorithms selected interventions based on past performance of each intervention for each participant, and for each group member that fit into a fixed amount of time (e.g., 1 h), respectively. Results indicated that the system was feasible to deploy and acceptable to participants and coaches. As hypothesized, we were able to achieve equivalent Phase II weight losses (NO = 4.42%, IO = 4.56%, GO = 4.39%) at roughly one-third the cost (1.73 and 1.77 coaching hours/participant for IO and GO, versus 4.38 for NO), indicating strong promise for a RL system approach to weight loss and maintenance.