Bae Sangwon, Ferreira Denzil, Suffoletto Brian, Puyana Juan C, Kurtz Ryan, Chung Tammy, Dey Anind K
Carnegie Mellon University.
University of Oulu.
Proc ACM Interact Mob Wearable Ubiquitous Technol. 2017 Jun;1(2). doi: 10.1145/3090051. Epub 2017 Jun 30.
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 (, 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.
年轻人饮酒现象普遍，发病率和死亡率较高，这主要归因于周期性的大量饮酒事件（HDEs）。通过电子通信方式（如短信）实施的行为干预可以减少年轻人HDEs的发生频率，但效果较小。增强这些效果的一种方法是在饮酒场合临近时提供支持材料，但这需要了解饮酒场合何时会发生。手机内置传感器可能有助于监测与饮酒场合开始相关的行为模式。我们这项工作的目的是探索利用手机传感器检测日常生活行为标记及其在识别饮酒场合方面的效用。我们使用了30名年龄在21 - 28岁、有过危险饮酒经历的年轻人的数据，连续28天收集手机传感器数据以及每日饮酒的经验抽样法（ESM）数据。我们构建了一个基于机器学习的模型，在识别不饮酒、饮酒和大量饮酒事件方面的准确率为96.6%。我们突出了检测饮酒事件最重要的特征，并确定了准确检测所需的历史数据量。我们的结果表明，手机传感器可用于对高危人群进行自动、持续监测，以检测饮酒事件并支持及时干预的实施。
