Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing
Lakmal Meegahapola, Florian Labhart, Thanh-Trung Phan, Daniel Gatica-Perez
Abstract
According to prior work, the type of relationship between a 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) 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 a European country, 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 supporting future interventions on alcohol consumption that incorporate users' social context more meaningfully and reducing the need for user self-reports when creating drink logs for self-tracking tools and public health studies.
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Install the CLIlune papers fulltext 97d9586f-9fec-4d97-86f3-2dd411c8b016Cited by top-tier papers6
- Generalization and Personalization of Mobile Sensing-Based Mood Inference Models: An Analysis of College Students in Eight CountriesLakmal Meegahapola, William Droz, Peter Kun, Amalia de Götzen et al.UbiComp 2023 · 55 citations
- M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial TrainingLakmal Meegahapola, Hamza Hassoune, Daniel Gatica-PerezUbiComp 2024 · 30 citations
- Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk drivingKevin Koch, Martin Maritsch, Eva van Weenen, Stefan Feuerriegel et al.CHI 2023 · 28 citations
- Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UKKarim Assi, Lakmal Meegahapola, William Droz, Peter Kun et al.CHI 2023 · 26 citations
- Learning About Social Context From Smartphone Data: Generalization Across Countries and Daily Life MomentsAurel Ruben Mäder, Lakmal Meegahapola, Daniel Gatica-PerezCHI 2024 · 11 citations
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- One More Bite?: Inferring Food Consumption Level of College Students Using Smartphone Sensing and Self-ReportsLakmal Meegahapola, Salvador Ruiz-Correa, Viridiana del Carmen Robledo-Valero, Emilio Ernesto Hernandez-Huerfano et al.UbiComp 2021 · 37 citations
- Foundations for Systematic Evaluation and Benchmarking of a Mobile Food Logger in a Large-scale Nutrition StudyJisu Jung, Lyndal Wellard-Cole, Colin Cai, Irena Koprinska et al.UbiComp 2020 · 24 citations
- Quantifying the Relationships between Everyday Objects and Emotional States through Deep Learning Based Image Analysis Using SmartphonesVictor-Alexandru Darvariu, Laura Convertino, Abhinav Mehrotra, Mirco MusolesiUbiComp 2020 · 15 citations
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