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Related Concept Videos

Obesity01:24

Obesity

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The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
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Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq
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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.

JMIR Human Factors
|February 26, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a smartphone app using reinforcement learning to aid obesity treatment. The app demonstrated feasibility and potential effectiveness, with participants showing improved BMI and reduced energy intake.

Keywords:
cognitive behavioral therapyecological momentary interventionmachine learningmultiarmed banditobesityreinforcement learningsmartphone app

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Area of Science:

  • Obesity research
  • Behavioral science
  • Digital health interventions

Background:

  • Traditional behavioral interventions for obesity are often time-intensive.
  • Smartphone apps offer a scalable solution for delivering interventions.
  • Personalized optimization using reinforcement learning can enhance behavior change support.

Purpose of the Study:

  • To develop and assess the feasibility of a smartphone application for individuals with obesity.
  • To investigate the potential of reinforcement learning in optimizing daily behavioral support for weight management.

Main Methods:

  • A smartphone app was created to help users set and review daily weight loss behaviors.
  • Thompson sampling, a multiarmed bandit algorithm, optimized the presentation order of behaviors.
  • Twenty individuals with obesity used the app for 4 weeks, with daily monitoring of weight, mood, and app usage.

Main Results:

  • All 20 participants completed the 4-week study with a high median app usage rate of 98.3%.
  • Significant improvements were noted in Body Mass Index (BMI), daily energy intake, and weekend sitting time.
  • A significant association was found between higher preceding depressive mood levels and fewer daily behaviors performed.

Conclusions:

  • The smartphone app utilizing reinforcement learning is feasible for obesity management and shows potential effectiveness.
  • Preceding depressive mood may negatively impact daily behaviors crucial for weight loss.
  • Digital health tools integrated with AI present a promising avenue for obesity interventions.