Detecting drinking episodes in young adults using smartphone-based sensors

被引：50

作者：

Sangwon, B.A.E. ^{[1
]}

Ferreira, Denzil ^{[2
]}

Suffoletto, Brian ^{[3
]}

Puyana, Juan C. ^{[3
]}

Kurtz, Ryan ^{[3
]}

Chung, Tammy ^{[4
]}

Dey, Anind K. ^{[1
]}

机构：

[1] Human-Computer Interaction Institute, Carnegie Mellon University, United Kingdom

[2] Center for Ubiquitous Computing, University of Oulu, Finland

[3] Emergency Medicine, University of Pittsburgh Medical Center, United States

[4] Department of Psychiatry, University of Pittsburgh Medical Center, United States

来源：

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies | 2017年 / 1卷 / 02期

基金：

芬兰科学院; 欧盟地平线“2020”;

关键词：

Learning systems - Smartphones;

D O I：

10.1145/3090051

中图分类号：

学科分类号：

摘要：

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. Copyright © ACM 2017.

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