DrinkSense: Characterizing Youth Drinking Behavior Using Smartphones

Abstract

Abstract-Alcohol consumption is the number one risk factor for morbidity and mortality among young people. In late adolescence and early adulthood, excessive drinking and intoxication are more common than in any other life period, increasing the risk of adverse physical and psychological health consequences. In this paper, we examine the feasibility of using smartphone sensor data and machine learning to automatically characterize and classify drinking behavior of young adults in an urban, ecologically valid nightlife setting. Our work has two contributions. First, we use previously unexplored data from a large-scale mobile crowdsensing study involving 241 young participants in two urban areas in a European country, which includes phone data (location. accelerometer, Wifi, Bluetooth, battery, screen, and app usage) along with self-reported, fine-grain data on individual alcoholic drinks consumed on Friday and Saturday nights over a three-month period. Second, we build a machine learning methodology to infer whether an individual consumed alcohol on a given weekend night, based on her/his smartphone data contributed between 8PM and 4AM. We found that accelerometer data is the most informative single cue, and that a combination of features results in an overall accuracy of 76.6%.

The contexts of heavy drinking: A systematic review of the combinations of context-related factors associated with heavy drinking occasions

“…luminosity, noise level and density restrictions, location-specific text message interventions) [100]. Technologies such as smartphone-based environmental measurement tools may be useful for measuring physical environments and investigating associations with heavy drinking behaviours [101–103].…”

“…Continuous objective monitoring of blood alcohol concentration in real time via objective measures such as transdermal sensors may reduce the risk of self-reporting biasing measurements of alcohol consumption and intoxication levels [107]. Smartphones may provide useful tools for objectively measuring other contextual factors and behaviours [100, 101, 103, 108]. Although, data gathered via built-in sensors, camera, microphone and other features are not without limitation and subjective self-report questionnaires may temporarily remain the most practical method for measuring many contextual factors and behaviours.…”

#Drink Or #Drunk

“…In addition, participants are asked to answer questionnaires or drinking habits, which are used as validation data. For example, [57] uses sensor and log data to classify drinking nights with 76.6% accuracy.…”

“…limited recall [24,43]. The advent of ubiquitous sensors and smartphones aided researchers to collect larger amounts of data including insitu responses via SMS on features phone [44], wearable sensor data [9,11,38], and hybrid data (including sensor data, and human-generated data like photos, captions, and location) [16,56,57,62]. However, these methods of data collection have the disadvantage of being intrusive because participants are asked to intentionally report their alcohol intake.…”

Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety

“…Some of the only phenotyping literature that focusses explicitly on a youth population has explored whether alcohol-related exposures can be predicted using passive monitoring of location data 42–47 and, separately, if alcohol consumption behaviors can be predicted using smartphone sensing and activity data. 48,49 These uses highlight how digital phenotyping can also be used for public health purposes by generating information not only about individuals but concerning aggregate patterns of behavior that can then be used to inform structural interventions, for example, ensuring that retailers are complying with the law in relation to the supply of alcohol to minors in locations where problem drinking emerges as a pattern from digital data.…”