Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions.

Journal: Addictive behaviors
Published Date: Aug 1, 2018

Abstract

BACKGROUND: Real-time detection of drinking could improve timely delivery of interventions aimed at reducing alcohol consumption and alcohol-related injury, but existing detection methods are burdensome or impractical.

Authors

  • Sangwon Bae
    Human Computer Interaction Institute, Carnegie Mellon University, United States.
  • Tammy Chung
    Department of Psychiatry, University of Pittsburgh, United States.
  • Denzil Ferreira
    Center for Ubiquitous Computing, University of Oulu, Finland.
  • Anind K Dey
    Human Computer Interaction Institute, Carnegie Mellon University, United States.
  • Brian Suffoletto
    Department of Emergency Medicine, Stanford University, USA. Electronic address: suffbp@stanford.edu.

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