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Randomized Controlled Trial
. 2014 Jan;46(1):29-35.
doi: 10.1016/j.jsat.2013.08.004. Epub 2013 Sep 10.

Predictive modeling of addiction lapses in a mobile health application

Ming-Yuan Chih  1 , Timothy Patton, Fiona M McTavish, Andrew J Isham, Chris L Judkins-Fisher, Amy K Atwood, David H Gustafson
Affiliations
Randomized Controlled Trial

Predictive modeling of addiction lapses in a mobile health application

Ming-Yuan Chih et al. J Subst Abuse Treat. 2014 Jan.

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.

Keywords: Alcoholism; Lapse prediction; Machine learning; Relapse; mHealth.

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Figures

Fig. 1
Fig. 1
Screen shots of the Weekly Check-in survey
Fig. 2
Fig. 2
Conceptual model of weekly lapse prediction
Fig. 3
Fig. 3
Participants and Weekly Check-in reports flow chart Note: n: the number of patients; m: the number of Weekly Check-in reports
See this image and copyright information in PMC

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