Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing
This work addresses the need for better alcohol consumption interventions and reduced reliance on self-reports in young adults, but it is incremental as it applies existing sensing methods to a new domain.
The study tackled the problem of inferring social contexts of alcohol drinking in young adults using smartphone sensing data, achieving accuracies of 75%-86% for seven inference tasks and over 70% for identifying group sex composition.
According to prior work, the type of relationship between the person consuming alcohol and others in the surrounding (friends, family, spouse, etc.), and the number of those people (alone, with one person, with a group, etc.) are related to many aspects of alcohol consumption, such as the drinking amount, location, motives, and mood. Even though the social context is recognized as an important aspect that influences the drinking behavior of young adults in alcohol research, relatively little work has been conducted in smartphone sensing research on this topic. In this study, we analyze the weekend nightlife drinking behavior of 241 young adults in Switzerland, using a dataset consisting of self-reports and passive smartphone sensing data over a period of three months. Using multiple statistical analyses, we show that features from modalities such as accelerometer, location, application usage, bluetooth, and proximity could be informative about different social contexts of drinking. We define and evaluate seven social context inference tasks using smartphone sensing data, obtaining accuracies of the range 75%-86% in four two-class and three three-class inferences. Further, we discuss the possibility of identifying the sex composition of a group of friends using smartphone sensor data with accuracies over 70%. The results are encouraging towards (a) supporting future interventions on alcohol consumption that incorporate users' social context more meaningfully, and (b) reducing the need for user self-reports when creating drink logs.