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Smartphone App Using Reinforcement Learning for Obesity: Single-Arm Feasibility Study
Ken Kurisu1, Yoshiharu Yamamoto2, Tomohisa Aoyama3
1Department of Stress Sciences and Psychosomatic Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan, 81 3-5800-9764.
Background:
While behavioral interventions remain an evidence-based treatment for obesity, they often require long durations and frequent sessions. To address this, we hypothesized that interventions delivered in daily life via a smartphone app combined with personalized optimization using reinforcement learning may effectively support behavior changes.
Objective:
This study aimed to develop and evaluate the feasibility of such an app for individuals with obesity.
Methods:
We developed a smartphone app to assist in setting and reviewing daily behaviors related to weight loss. On the screen on which daily behaviors were shown, the order of presentation was optimized using Thompson sampling, a multiarmed bandit algorithm. Twenty individuals with obesity used the app for 4 weeks, and the daily app use rates were quantified. Body weight and mood status were measured daily during the study, and a brief-type self-administered diet history questionnaire and the International Physical Activity Questionnaire were administered at the beginning and end of the study. Changes in these measures were evaluated using the Wilcoxon signed rank test. Furthermore, the longitudinal data collected during this study were analyzed using a linear mixed-effects model to examine factors related to the number of behaviors performed daily.
Results:
All 20 recruited individuals with obesity completed the 4-week study schedule. The median app use rate was 98.3% (range 76.9%-100%). Significant improvements were observed in BMI (median at start 34.9 kg/m2, range 27.4-52.9; median at end 34.1 kg/m2, range 26.7-51.0; P=.01), as well as daily energy intake and weekend sitting time. The linear mixed-effects model showed a significant association between higher preceding depressive mood levels and fewer behaviors (P<.001).
Conclusions:
The feasibility of the smartphone app using reinforcement learning for obesity was sufficient, and the potential effectiveness of the treatment was suggested. Preceding depressive mood may influence daily behaviors related to weight loss.
