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DrinkSense: Characterizing youth drinking behavior using smartphones


Santani, Darshan; Do, Trinh Minh Tri; Labhart, Florian; Landolt, Sara; Kuntsche, Emmanuel; Gatica-Perez, Daniel (2018). DrinkSense: Characterizing youth drinking behavior using smartphones. IEEE Transactions on Mobile Computing, 17(10):2279-2292.

Abstract

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 crowd sensing 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 8PM and 4AM. 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%.

Abstract

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 crowd sensing 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 8PM and 4AM. 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%.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2018
Deposited On:08 Mar 2018 19:36
Last Modified:31 Aug 2018 01:01
Publisher:Institute of Electrical and Electronics Engineers
ISSN:1536-1233
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/TMC.2018.2797901

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