Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing
Lakmal Meegahapola, Florian Labhart, Thanh-Trung Phan, Daniel Gatica-Perez
摘要
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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引用它的顶会 Paper6
- 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 等UbiComp 2023 · 被引用 55 次
- M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial TrainingLakmal Meegahapola, Hamza Hassoune, Daniel Gatica-PerezUbiComp 2024 · 被引用 30 次
- Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk drivingKevin Koch, Martin Maritsch, Eva van Weenen, Stefan Feuerriegel 等CHI 2023 · 被引用 28 次
- 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 等CHI 2023 · 被引用 26 次
- Learning About Social Context From Smartphone Data: Generalization Across Countries and Daily Life MomentsAurel Ruben Mäder, Lakmal Meegahapola, Daniel Gatica-PerezCHI 2024 · 被引用 11 次
它引用的顶会 Paper3
- 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 等UbiComp 2021 · 被引用 37 次
- 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 等UbiComp 2020 · 被引用 24 次
- 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 · 被引用 15 次
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