Issue No. 10 - Oct. (2018 vol. 17)
Darshan Santani , Social Computing Group, Idiap Research Institute, Martigny, Valais, Switzerland
Trinh-Minh-Tri Do , Social Computing Group, Idiap Research Institute, Martigny, Valais, Switzerland
Florian Labhart , Addiction Switzerland, Research Institute, Lausanne, Vaud, Switzerland
Sara Landolt , Department of Geography, University of Zurich, Zurich, Switzerland
Emmanuel Kuntsche , Addiction Switzerland, Research Institute, Lausanne, Vaud, Switzerland
Daniel Gatica-Perez , Social Computing Group, Idiap Research Institute, Martigny, Valais, Switzerland
Alcohol consumption is the number one risk factor for morbidity and mortality among young people. In late adolescence and early adulthood, excessive drinking and intoxication are more common than in any other life period, increasing the risk of adverse physical and psychological health consequences. In this paper, we examine the feasibility of using smartphone sensor data and machine learning to automatically characterize and classify drinking behavior of young adults in an urban, ecologically valid nightlife setting. Our work has two contributions. First, we use previously unexplored data from a large-scale mobile crowdsensing study involving 241 young participants in two urban areas in a European country, which includes phone data (location. accelerometer, Wifi, Bluetooth, battery, screen, and app usage) along with self-reported, fine-grain data on individual alcoholic drinks consumed on Friday and Saturday nights over a three-month period. Second,we build a machine learning methodology to infer whether an individual consumed alcohol on a given weekend night, based on her/his smartphone data contributed between 8 PM and 4 AM. We found that accelerometer data is the most informative single cue, and that a combination of features results in an overall accuracy of 76.6 percent.
Alcoholic beverages, Smart phones, Mobile communication, Stress, Accelerometers, Feature extraction, Urban areas
D. Santani, T. Do, F. Labhart, S. Landolt, E. Kuntsche and D. Gatica-Perez, "DrinkSense: Characterizing Youth Drinking Behavior Using Smartphones," in IEEE Transactions on Mobile Computing, vol. 17, no. 10, pp. 2279-2292, 2018.