发表机构
Indian Institute of Technology Jodhpur; Indian Institute of Technology Kharagpur（印度焦特普尔理工学院; 印度卡拉格普尔理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究分析DiversityOne数据集，用隐马尔可夫模型挖掘高BMI个体的行为暗模式，发现超重肥胖者更易频繁摄入不健康食物、外卖耗时久，锻炼后更易回归不良饮食，明确了减肥难的关键行为因素。
AI 中文摘要
理解日常行为如何影响体重对于设计有效且个性化的健康干预措施至关重要。现有研究在很大程度上依赖自我报告问卷或有限的感知模态，难以捕捉日常行为的时间动态。在本研究中，我们分析了DiversityOne数据集，该数据集包含来自8个国家的453名大学生为期4周的被动智能手机感知数据和生态瞬时评估数据。我们提取涵盖饮食习惯、身体活动、屏幕时间和智能手机使用的行为特征，并研究这些特征与自我报告的体重指数（BMI）之间的关联。除特征层面分析外，我们采用隐马尔可夫模型（HMM）来揭示潜在的行为模式。我们的分析表明，较高的BMI与更频繁地摄入苏打、酒精和加工肉类相关。我们进一步发现，超重和肥胖个体在外卖应用上花费更长时间，且在开始锻炼后更有可能回到不健康的饮食和饮水习惯中。相比之下，正常体重个体的生活方式更均衡。这些发现凸显了使减肥尤为困难的关键行为模式。
英文摘要
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.
Comments5 pages, 5 figures, accepted to DiversityOne Open Challenge at UbiComp/ISWC 2026
