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Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors.

Sangwon Bae1, Denzil Ferreira2, Brian Suffoletto3

  • 1Carnegie Mellon University.

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PubMed
Summary

Young adults

Keywords:
Alcohol consumptionBehavioral modelMachine learningSmartphone sensorsYoung adults

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Area of Science:

  • Digital health
  • Behavioral science
  • Mobile sensing

Background:

  • Young adults frequently engage in heavy drinking episodes (HDEs), leading to significant health risks.
  • Current electronic interventions show limited effectiveness in reducing HDEs.
  • Timely interventions require precise identification of drinking occasions, which is challenging.

Purpose of the Study:

  • To explore the use of mobile phone sensors for detecting behavioral markers associated with drinking occasions.
  • To assess the utility of these sensors in identifying non-drinking, drinking, and heavy drinking episodes.
  • To inform the development of sensor-based, just-in-time adaptive interventions for alcohol use.

Main Methods:

  • Collected mobile phone sensor data and daily drinking logs (via Experience Sampling Method) from 30 young adults (aged 21-28) with hazardous drinking histories over 28 days.
  • Developed and validated a machine learning model to classify drinking states based on sensor-derived behavioral patterns.
  • Analyzed feature importance and data requirements for accurate drinking episode detection.

Main Results:

  • A machine learning model achieved 96.6% accuracy in distinguishing between non-drinking, drinking, and heavy drinking episodes.
  • Identified key behavioral features from mobile phone sensors indicative of drinking occasions.
  • Determined the amount of historical data necessary for reliable detection.

Conclusions:

  • Mobile phone sensors can accurately detect daily-life behavioral patterns associated with alcohol consumption.
  • Automated monitoring using mobile sensors offers a promising avenue for real-time detection of drinking episodes in at-risk young adults.
  • This technology can facilitate the delivery of timely, personalized interventions to reduce alcohol-related harm.