Predictive modeling of addiction lapses in a mobile health application

Ming Yuan Chih, Timothy Patton, Fiona M. McTavish, Andrew J. Isham, Chris L. Judkins-Fisher, Amy K. Atwood, David H. Gustafson

Research output: Contribution to journalArticlepeer-review

62 Scopus citations

Abstract

The chronically relapsing nature of alcoholism leads to substantial personal, family, and societal costs. Addiction-comprehensive health enhancement support system (A-CHESS) is a smartphone application that aims to reduce relapse. To offer targeted support to patients who are at risk of lapses within the coming week, a Bayesian network model to predict such events was constructed using responses on 2,934 weekly surveys (called the Weekly Check-in) from 152 alcohol-dependent individuals who recently completed residential treatment. The Weekly Check-in is a self-monitoring service, provided in A-CHESS, to track patients' recovery progress. The model showed good predictability, with the area under receiver operating characteristic curve of 0.829 in the 10-fold cross-validation and 0.912 in the external validation. The sensitivity/specificity table assists the tradeoff decisions necessary to apply the model in practice. This study moves us closer to the goal of providing lapse prediction so that patients might receive more targeted and timely support.

Original languageEnglish
Pages (from-to)29-35
Number of pages7
JournalJournal of Substance Abuse Treatment
Volume46
Issue number1
DOIs
StatePublished - Jan 2014

Bibliographical note

Funding Information:
This study was funded by the U.S. National Institute on Alcohol Abuse and Alcoholism (NIAAA) grant R01 AA017192. The sponsors had no further role in the study design; in the collection, analysis, and/or interpretation of the data; in the writing of the report; or in the decision to submit the paper for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIAAA or the National Institutes of Health. Preliminary results of this study have been presented as a poster in the 2011 Annual Symposium of American Medical Informatics Association.

Keywords

  • Alcoholism
  • Lapse prediction
  • MHealth
  • Machine learning
  • Relapse

ASJC Scopus subject areas

  • Psychiatric Mental Health
  • Medicine (miscellaneous)
  • Clinical Psychology
  • Psychiatry and Mental health

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