Can the artificial intelligence technique of reinforcement learning use continuously-monitored digital data to optimize treatment for weight loss?

Journal of Behavioral Medicine
Evan M FormanDanielle Moskow

Abstract

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 tim...Continue Reading

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Citations

Nov 9, 2019·Current Obesity Reports·Lauren E BradleyMegan M Hood
Feb 17, 2021·Public Health Nutrition·Han Shi Jocelyn ChewYing Lau
Jul 3, 2021·International Journal of Environmental Research and Public Health·Shihan WangBen Kröse
Sep 17, 2020··Robert C. GraySantiago Ontañón

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