Predicting the first smoking lapse during a quit attempt: A machine learning approach. (1st January 2021)

• Record Type: Journal Article
• Authors: Hébert, Emily T.; Suchting, Robert; Ra, Chaelin K.; Alexander, Adam C.; Kendzor, Darla E.; Vidrine, Damon J.; Businelle, Michael S.
• Is Part Of: Drug and alcohol dependence. Volume 218(2021)
• Publication Date: 2021-01-01
• Subjects: Smartphones -- Smoking cessation -- Just-in-time adaptive intervention -- mHealth -- Machine learning; Drug abuse -- Periodicals; Alcoholism -- Periodicals; 616.86
• DOI: 10.1016/j.drugalcdep.2020.108340
• Languages: English
• ISSNs: 0376-8716

Abstract

Highlights: Mobile interventions can provide tailored, timely support for smoking cessation. Accurate identification of high-risk moments for lapse is critical. We can identify the most important predictors of lapse using machine learning. The elastic net algorithm identified five predictors related to time to lapse. Abstract: Background: Just-in-time adaptive interventions (JITAI) aim to prevent smoking lapse using tailored support delivered via mobile technology in the moments when it is most needed. Effective smoking cessation JITAI rely on the development of accurate decision rules that determine when someone is most likely to lapse. The primary goal of the present study was to identify the strongest predictors of first lapse among smokers undergoing a quit attempt. Methods: Smokers attending a clinic-based smoking cessation program (n = 74) were asked to complete ecological momentary assessments five times daily on study-provided smartphones for 4 weeks post-quit. A three-stage modeling process utilized Cox proportional hazards regression to examine time to lapse a function of 31 predictors. First, univariate models evaluated the relationship between each predictor and time to lapse. Second, the elastic net machine learning algorithm was used to select the best predictors. Third, backwards elimination further reduced the set of predictors to optimize parsimony. Results: Univariate models identified seven predictors significantly related to time to lapse. The elastic net algorithm retained five: perceived odds of smoking today, confidence in ability to avoid smoking, motivation to avoid smoking, urge to smoke, and cigarette availability. The reduced model demonstrated inadequate approximation to the non-penalized baseline model. Conclusions: Accurate estimation of moments of high risk for smoking lapse remains an important goal in the development of JITAI. These results demonstrate the utility of exploratory data-driven approaches to variable selection. The results of this study can inform future JITAI by highlighting targets for intervention.