The interplay between mood and eating has been the subject of extensive research within the fields of nutrition and behavioral science, indicating a strong connection between the two. Further, phone sensor data have been used to characterize both eating behavior and mood, independently, in the context of mobile food diaries and mobile health applications. However, limitations within the current body of literature include: i) the lack of investigation around the generalization of mood inference models trained with passive sensor data from a range of everyday life situations, to specific contexts such as eating, ii) no prior studies that use sensor data to study the intersection of mood and eating, and iii) the inadequate examination of model personalization techniques within limited label settings, as we commonly experience in mood inference. In this study, we sought to examine everyday eating behavior and mood using two datasets of college students in Mexico (N_mex = 84, 1843 mood-while-eating reports) and eight countries (N_mul = 678, 329K mood reports incl. 24K mood-while-eating reports), containing both passive smartphone sensing and self-report data. Our results indicate that generic mood inference models decline in performance in certain contexts, such as when eating. Additionally, we found that population-level (non-personalized) and hybrid (partially personalized) modeling techniques were inadequate for the commonly used three-class mood inference task (positive, neutral, negative). Furthermore, we found that user-level modeling was challenging for the majority of participants due to a lack of sufficient labels and data from the negative class. To address these limitations, we employed a novel community-based approach for personalization by building models with data from a set of similar users to a target user.

翻译：情绪与饮食之间的相互作用已在营养学和行为科学领域得到广泛研究，表明二者存在密切关联。此外，在移动饮食记录和移动健康应用中，手机传感器数据已被分别用于表征饮食行为和情绪状态。然而，现有文献存在以下局限性：(i) 缺乏对基于日常场景下被动传感器数据训练的情绪推断模型向特定情境（如用餐）泛化能力的探究，(ii) 尚无利用传感器数据研究情绪与饮食交叉领域的前期工作，以及(iii) 在情绪推断常见的有限标签设置下，对模型个性化技术的评估不足。本研究使用两组数据集分析日常饮食行为与情绪，分别来自墨西哥的大学生群体（N_墨=84，1843份用餐情绪报告）和八个国家的大学生群体（N_多=678，32.9万份情绪报告，含2.4万份用餐情绪报告），数据包含被动智能手机感知数据与自我报告数据。结果表明，通用情绪推断模型在特定情境（如用餐时）下的性能会下降。此外，我们发现群体级（非个性化）和混合（部分个性化）建模技术无法适用于常用的三类情绪推断任务（积极、中性、消极）。同时，由于多数参与者缺乏足够的负类标签和数据，用户级建模存在困难。为解决这些局限，我们提出一种新型基于社区的个性化方法，通过为目标用户构建来自相似用户群的数据模型实现个性化。