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