Emily T Hébert1, Robert Suchting2, Chaelin K Ra3, Adam C Alexander3, Darla E Kendzor4, Damon J Vidrine5, Michael S Businelle4. 1. University of Texas Health Science Center (UTHealth) School of Public Health, Austin, TX, United States. Electronic address: emily.t.hebert@uth.tmc.edu. 2. UTHealth McGovern Medical School, Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX, United States. 3. TSET Health Promotion Research Center, Oklahoma City, OK, United States. 4. TSET Health Promotion Research Center, Oklahoma City, OK, United States; Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States. 5. Moffitt Cancer Center, Tampa, FL, United States.
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.
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.
Authors: Robert Suchting; Joshua L Gowin; Charles E Green; Consuelo Walss-Bass; Scott D Lane Journal: Front Behav Neurosci Date: 2018-05-07 Impact factor: 3.558
Authors: Chaelin K Ra; Emily T Hébert; Adam C Alexander; Angela Helt; Rachel Moisiuc; Darla E Kendzor; Damon J Vidrine; Rachel K Funk-Lawler; Michael S Businelle Journal: J Med Internet Res Date: 2020-03-09 Impact factor: 5.428
Authors: Mingrui Liang; Matthew D Koslovsky; Emily T Hébert; Darla E Kendzor; Michael S Businelle; Marina Vannucci Journal: Psychol Methods Date: 2021-12-20