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Gepubliceerd in: Journal of Behavioral Medicine 2/2019

25-08-2018

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

Auteurs: Evan M. Forman, Stephanie G. Kerrigan, Meghan L. Butryn, Adrienne S. Juarascio, Stephanie M. Manasse, Santiago Ontañón, Diane H. Dallal, Rebecca J. Crochiere, Danielle Moskow

Gepubliceerd in: Journal of Behavioral Medicine | Uitgave 2/2019

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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 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.
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Metagegevens
Titel
Can the artificial intelligence technique of reinforcement learning use continuously-monitored digital data to optimize treatment for weight loss?
Auteurs
Evan M. Forman
Stephanie G. Kerrigan
Meghan L. Butryn
Adrienne S. Juarascio
Stephanie M. Manasse
Santiago Ontañón
Diane H. Dallal
Rebecca J. Crochiere
Danielle Moskow
Publicatiedatum
25-08-2018
Uitgeverij
Springer US
Gepubliceerd in
Journal of Behavioral Medicine / Uitgave 2/2019
Print ISSN: 0160-7715
Elektronisch ISSN: 1573-3521
DOI
https://doi.org/10.1007/s10865-018-9964-1

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