Using machine learning to identify predictors of imminent drinking and create tailored messages for at-risk drinkers experiencing homelessness




Walters, Scott T.
Businelle, Michael S.
Suchting, Robert
Li, Xiaoyin
Hebert, Emily T.
Mun, Eun-Young


0000-0003-4074-6141 (Walters, Scott T.)
0000-0002-1820-615X (Mun, Eun-Young)
0000-0001-8028-8910 (Li, Xiaoyin)

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Elsevier Inc.


Adults experiencing homelessness are more likely to have an alcohol use disorder compared to adults in the general population. Although shelter-based treatments are common, completion rates tend to be poor, suggesting a need for more effective approaches that are tailored to this understudied and underserved population. One barrier to developing more effective treatments is the limited knowledge of the triggers of alcohol use among homeless adults. This paper describes the use of ecological momentary assessment (EMA) to identify predictors of "imminent drinking" (i.e., drinking within the next 4 h), among a sample of adults experiencing homelessness and receiving health services at a homeless shelter. A total of 78 mostly male (84.6%) adults experiencing homelessness (mean age = 46.6) who reported hazardous drinking completed up to five EMAs per day over 4 weeks (a total of 4557 completed EMAs). The study used machine learning techniques to create a drinking risk algorithm that predicted 82% of imminent drinking episodes within 4 h of the first drink of the day, and correctly identified 76% of nondrinking episodes. The algorithm included the following 7 predictors of imminent drinking: urge to drink, having alcohol easily available, feeling confident that alcohol would improve mood, feeling depressed, lower commitment to being alcohol free, not interacting with someone drinking alcohol, and being indoors. The research team used the results to develop intervention content (e.g., brief tailored messages) that will be delivered when imminent drinking is detected in an upcoming intervention phase. Specifically, we created three theoretically grounded message tracks focused on urge/craving, social/availability, and negative affect/mood, which are further tailored to a participant's current drinking goal (i.e., stay sober, drink less, no goal) to support positive change. To our knowledge, this is the first study to develop tailored intervention messages based on likelihood of imminent drinking, current drinking triggers, and drinking goals among adults experiencing homelessness.



Walters, S. T., Businelle, M. S., Suchting, R., Li, X., Hébert, E. T., & Mun, E. Y. (2021). Using machine learning to identify predictors of imminent drinking and create tailored messages for at-risk drinkers experiencing homelessness. Journal of substance abuse treatment, 127, 108417.


© 2021 The Authors.


Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)