  Understanding food consumption patterns and contexts using mobile sensing is
fundamental to build mobile health applications that require minimal user
interaction to generate mobile food diaries. Many available mobile food
diaries, both commercial and in research, heavily rely on self-reports, and
this dependency limits the long term adoption of these apps by people. The
social context of eating (alone, with friends, with family, with a partner,
etc.) is an important self-reported feature that influences aspects such as
food type, psychological state while eating, and the amount of food, according
to prior research in nutrition and behavioral sciences. In this work, we use
two datasets regarding the everyday eating behavior of college students in two
countries, namely Switzerland (N_ch=122) and Mexico (N_mx=84), to examine the
relation between the social context of eating and passive sensing data from
wearables and smartphones. Moreover, we design a classification task, namely
inferring eating-alone vs. eating-with-others episodes using passive sensing
data and time of eating, obtaining accuracies between 77% and 81%. We believe
that this is a first step towards understanding more complex social contexts
related to food consumption using mobile sensing.
