University of Oulu

Sangwon Bae, Denzil Ferreira, Brian Suffoletto, Juan C. Puyana, Ryan Kurtz, Tammy Chung, and Anind K. Dey. 2017. Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 2, Article 5 (June 2017), 36 pages. DOI: https://doi.org/10.1145/3090051

Detecting drinking episodes in young adults using smartphone-based sensors

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Author: Bae, Sangwon1; Ferreira, Denzil2; Suffoletto, Brian3;
Organizations: 1Carnegie Mellon University
2University of Oulu
3University of Pittsburgh
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe201901212653
Language: English
Published: Association for Computing Machinery, 2017
Publish Date: 2019-01-21
Description:

Abstract

Alcohol use in young adults is common, with high rates of morbidity and mortality largely due to periodic, heavy drinking episodes (HDEs). Behavioral interventions delivered through electronic communication modalities (e.g., text messaging) can reduce the frequency of HDEs in young adults, but effects are small. One way to amplify these effects is to deliver support materials proximal to drinking occasions, but this requires knowledge of when they will occur. Mobile phones have built-in sensors that can potentially be useful in monitoring behavioral patterns associated with the initiation of drinking occasions. The objective of our work is to explore the detection of daily-life behavioral markers using mobile phone sensors and their utility in identifying drinking occasions. We utilized data from 30 young adults aged 21-28 with past hazardous drinking and collected mobile phone sensor data and daily Experience Sampling Method (ESM) of drinking for 28 consecutive days. We built a machine learning-based model that is 96.6% accurate at identifying non-drinking, drinking and heavy drinking episodes. We highlight the most important features for detecting drinking episodes and identify the amount of historical data needed for accurate detection. Our results suggest that mobile phone sensors can be used for automated, continuous monitoring of at-risk populations to detect drinking episodes and support the delivery of timely interventions.

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Series: Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies
ISSN: 2474-9567
ISSN-E: 2474-9567
ISSN-L: 2474-9567
Volume: 1
Issue: 2
Article number: 5
DOI: 10.1145/3090051
OADOI: https://oadoi.org/10.1145/3090051
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Funding: The authors acknowledge support by the National Institute of Alcohol Abuse and Alcoholism (NIAAA) under grants K23 AA023284-01 and R01 AA023650, and partial funding by the Academy of Finland (Grants 276786-AWARE, 286386-CPDSS, 285459-iSCIENCE, 304925-CARE), the European Commission (Grant, 6AIKA-A71143-AKAI), and Marie Skłodowska-Curie Actions (645706-GRAGE).
EU Grant Number: (645706) GRAGE - Grey and green in Europe: elderly living in urban areas
Academy of Finland Grant Number: 276786
286386
285459
304925
Detailed Information: 276786 (Academy of Finland Funding decision)
286386 (Academy of Finland Funding decision)
285459 (Academy of Finland Funding decision)
304925 (Academy of Finland Funding decision)
Copyright information: © Copyright is held by the owner/author(s). Publication rights licensed to ACM. 2017. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., https://doi.org/10.1145/3090051.