Citation

Bae S, Ferreira D, Suffoletto B, Puyana JC, Kurtz R, Chung T, Dey AK. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2017; 1(2): e3090051.

Copyright

(Copyright © 2017, Association for Computing Machinery)

DOI

10.1145/3090051

PMID

35146236

PMCID

PMC8827207

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.

Language: en

Keywords

Young adults; Machine learning; Alcohol consumption; Behavioral model; Smartphone sensors