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

ABSTRACT: 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

SUBMITTER: Walters ST 

PROVIDER: S-EPMC8217726 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature