Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors.
Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors.
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DOI：
10.1145/3090051
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发表时间：
2017-06-01
影响因子：
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通讯作者：
Dey, Anind K
中科院分区：
文献类型：
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作者：
Bae, Sangwon;Ferreira, Denzil;Dey, Anind K
年轻人饮酒很常见，发病率和死亡率很高，主要是由于周期性的大量饮酒事件（HDE）。通过电子通信方式提供的行为干预（例如，短信）可以减少年轻人发生HDE的频率，但效果很小。扩大这些影响的一种方法是在饮酒场合附近提供支持材料，但这需要了解何时会发生。移动的电话具有内置的传感器，其可以潜在地用于监测与饮酒场合的开始相关联的行为模式。我们的工作的目的是探索使用移动的手机传感器和他们的效用，在识别饮酒场合的日常生活行为标记的检测。我们利用了30名21-28岁年轻人过去危险饮酒的数据，并收集了连续28天的移动的手机传感器数据和每日饮酒经验采样方法（ESM）。我们建立了一个基于机器学习的模型，在识别非饮酒、饮酒和重度饮酒事件方面的准确率为96.6%。我们强调了检测饮酒事件的最重要特征，并确定了准确检测所需的历史数据量。我们的研究结果表明，移动的手机传感器可用于自动化，持续监测高危人群，以检测饮酒事件，并支持及时提供干预措施。
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.