Alone or With Others? Understanding Eating Episodes of College Students with Mobile Sensing

TL;DR

This study leverages passive sensing data from wearables and smartphones to classify whether college students eat alone or with others, aiming to improve mobile health applications with minimal user input.

Contribution

It introduces a novel classification approach using passive sensing to infer social eating contexts, advancing mobile health monitoring capabilities.

Findings

Achieved 77-81% accuracy in classifying social eating contexts.

Used datasets from college students in Switzerland and Mexico.

Demonstrated potential for passive sensing in understanding eating behaviors.

Abstract

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.