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Unveiling overeating patterns within digital longitudinal data on eating behaviors and contexts.

Farzad Shahabi1,2, Boyang Wei3,4, Christopher Romano3

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Overeating is a major health concern. The SenseWhy study identified five distinct overeating patterns using wearable sensors and psychological data, paving the way for personalized obesity interventions.

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Area of Science:

  • Behavioral Science
  • Obesity Research
  • Digital Health

Background:

  • Overeating is a significant contributor to obesity, a pressing public health issue.
  • Understanding the nuances of overeating behaviors is crucial for developing effective interventions.

Purpose of the Study:

  • To investigate the behavioral, psychological, and contextual factors contributing to overeating in individuals with obesity.
  • To identify distinct overeating phenotypes using passive sensing and Ecological Momentary Assessments (EMAs).

Main Methods:

  • The SenseWhy study monitored 65 individuals with obesity in free-living settings.
  • Data collection involved wearable cameras, mobile apps, dietary recalls, and EMAs over 657 days.
  • Semi-supervised learning was applied to EMA-derived features to identify overeating phenotypes.

Main Results:

  • Overeating episodes were predicted with high accuracy (mean AUROC=0.86, mean AUPRC=0.84) using EMA and passive sensing data.
  • Five distinct overeating phenotypes were identified: "Take-out Feasting," "Evening Restaurant Reveling," "Evening Craving," "Uncontrolled Pleasure Eating," and "Stress-driven Evening Nibbling."
  • These phenotypes highlight the complex interplay of factors influencing overeating.

Conclusions:

  • Overeating is multifaceted, influenced by a combination of behavioral, psychological, and environmental factors.
  • The identified overeating phenotypes provide a basis for developing personalized interventions to combat obesity.
  • Passive sensing and EMA data offer valuable insights into eating behaviors in real-world settings.