Using ecological momentary assessments to predict relapse after adult substance use treatment

Abstract

BackgroundA key component of relapse prevention is to self-monitor the internal (feelings or cravings) and external (people, places, activities) factors associated with relapse. Smartphones can deliver ecological momentary assessments (EMA) to help individuals self-monitor. The purpose of this exploratory study was to develop a model for predicting an individual's risk of future substance use after each EMA and validate it using a multi-level model controlling for repeated measures on persons. MethodsData are from 21,897 observations from 43 adults following their initial episode of substance use treatment in Chicago from 2015 to 2016. Participants were provided smartphones for six months and asked to complete two to three minute EMAs at five random times per day (81% completion). In any given EMA, 2.7% reported substance use and 8% reported any use in the next five completed EMA. Chi-square Automatic Interaction Detector (CHAID) was used to classify EMAs into six levels of risk and then validated with a hierarchical linear model (HLM). ResultsThe major predictors of substance use in the next five completed EMAs were substance use pattern over the current and prior five EMAs (no recent/current use, either recent or current use [but not both], continued use [both recent and current]), negative affect (feelings), and craving (rating). Negative affect was important for EMAs with no current or recent use reported; craving was important for EMAs with either recent or current use; and neither mattered for EMAs with continued use. The CHAID gradated EMA risk from 0.7% to 36.6% of the next five completed EMAs with substance use reported. It also gradated risk of “any” use in the next five completed EMAs from 3% to 82%. ConclusionsThis study demonstrated the potential of using smartphone-based EMAs to monitor and provide feedback for relapse prevention in future studies.