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

Journal: Journal of behavioral medicine
PMID: 30145623

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.

Authors

  • Evan M Forman
    1 Center for Weight, Eating, and Lifestyle Science and Department of Psychology, Drexel University, Philadelphia, PA, USA.
  • Stephanie G Kerrigan
    Department of Psychology, WELL Center, Drexel University, Stratton Hall, 3141 Chestnut Street, Philadelphia, PA, 19104, USA.
  • Meghan L Butryn
    1 Center for Weight, Eating, and Lifestyle Science and Department of Psychology, Drexel University, Philadelphia, PA, USA.
  • Adrienne S Juarascio
    Department of Psychology, WELL Center, Drexel University, Stratton Hall, 3141 Chestnut Street, Philadelphia, PA, 19104, USA.
  • Stephanie M Manasse
    Center for Weight, Eating, and Lifestyle Sciences (WELL Center), Drexel University, 3201 Chestnut Street, Philadelphia, PA, 19104, USA.
  • Santiago Ontañón
    Department of Computer Science, Drexel University, 3401 Market Street, Philadelphia, PA, 19104, USA.
  • Diane H Dallal
    Department of Psychology, WELL Center, Drexel University, Stratton Hall, 3141 Chestnut Street, Philadelphia, PA, 19104, USA.
  • Rebecca J Crochiere
    Department of Psychology, WELL Center, Drexel University, Stratton Hall, 3141 Chestnut Street, Philadelphia, PA, 19104, USA.
  • Danielle Moskow
    Department of Psychology, WELL Center, Drexel University, Stratton Hall, 3141 Chestnut Street, Philadelphia, PA, 19104, USA.

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