Understanding Behavioral Dark Patterns of High BMI Individuals

2026-08-29

AI Summary

研究利用智能手机数据和隐藏马尔可夫模型分析453名大学生的行为模式，揭示了高BMI个体的不良饮食习惯及生活方式，为个性化健康干预提供依据。

Abstract

Understanding how everyday behaviors influence body weight is essential for designing effective and personalized health interventions. Existing studies largely rely on self-reported questionnaires or limited sensing modalities, making it difficult to capture the temporal dynamics of daily behavior. In this work, we analyze the DiversityOne dataset, comprising four weeks of passive smartphone sensing and ecological momentary assessments collected from 453 university students across eight countries. We extract behavioral features spanning dietary habits, physical activity, screen time, and smartphone usage, and investigate their associations with self-reported Body Mass Index (BMI). Beyond feature-level analysis, we employ Hidden Markov Models (HMMs) to uncover latent behavioral patterns. Our analysis reveals that higher BMI is associated with more frequent consumption of soda, alcohol, and processed meat. We further reveal that overweight and obese individuals spend longer periods in food delivery apps and are more likely to transition back to unhealthy eating and drinking routines after starting to exercise. In contrast, normal-weight individuals lead a more balanced lifestyle. These findings highlight key behavioral patterns that make weight loss particularly challenging.

Problem

• Behavioral Patterns
• Body Mass Index (BMI)
• Smartphone Sensing

Innovation

• Hidden Markov Models
• passive smartphone sensing
• behavioral patterns
• Body Mass Index (BMI)