Inferring Mood-While-Eating with Smartphone Sensing and Community-Based Model Personalization

The interplay between mood and eating episodes has been extensively researched within the fields of nutrition, psychology, and behavioral science, revealing a connection between the two. Previous studies have relied on questionnaires and mobile phone self-reports to investigate the relationship between mood and eating. In more recent work, phone sensor data has been utilized to characterize both eating behavior and mood independently, particularly in the context of mobile food diaries and mobile health applications. However, current literature exhibits several limitations: a lack of investigation into the generalization of mood inference models trained with data from various everyday life situations to specific contexts like eating; an absence of studies using sensor data to explore the intersection of mood and eating; and inadequate examination of model personalization techniques within limited label settings, a common challenge in mood inference (i.e., far fewer negative mood reports compared to positive or neutral reports). In this study, we examined the everyday eating and mood using two separate datasets from two different studies: (i) Mexico (N \({}_{MEX}\) = 84, 1,843 mood-while-eating reports with a label distribution of positive: 51.7%, neutral: 38.6%, and negative: 9.8%) in 2019, and (ii) eight countries (N \({}_{MUL}\) = 678, 329K mood reports, including 24K mood-while-eating reports with a label distribution of positive: 83%, neutral: 14.9%, and negative: 2.2%) in 2020, which contain both passive smartphone sensing and self-report data. Our results indicate that generic mood inference models experience a decline in performance in specific contexts, such as during eating, highlighting the issue of sub-context shifts in mobile sensing. Moreover, we discovered that population-level (non-personalized) and hybrid (partially personalized) modeling techniques fall short in the commonly used three-class mood inference task (positive, neutral, negative). Additionally, we found that user-level modeling posed challenges for the majority of participants due to insufficient labels and data in the negative class. To overcome these limitations, we implemented a novel community-based personalization approach, building models with data from a set of users similar to the target user. Our findings demonstrate that mood-while-eating can be inferred with accuracies 63.8% (with F1 score of 62.5) for the MEX dataset and 88.3% (with F1 score of 85.7) with the MUL dataset using community-based models, surpassing those achieved with traditional methods.