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Fu R, Schwartz R, Mitsakakis N, Diemert LM, O’Connor S, Cohen JE. Predictors of perceived success in quitting smoking by vaping: A machine learning approach. PLoS One 2022; 17:e0262407. [PMID: 35030208 PMCID: PMC8759658 DOI: 10.1371/journal.pone.0262407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 12/25/2021] [Indexed: 11/18/2022] Open
Abstract
Prior research has suggested that a set of unique characteristics may be associated with adult cigarette smokers who are able to quit smoking using e-cigarettes (vaping). In this cross-sectional study, we aimed to identify and rank the importance of these characteristics using machine learning. During July and August 2019, an online survey was administered to a convenience sample of 889 adult smokers (age ≥ 20) in Ontario, Canada who tried vaping to quit smoking in the past 12 months. Fifty-one person-level characteristics, including a Vaping Experiences Score, were assessed in a gradient boosting machine model to classify the status of perceived success in vaping-assisted smoking cessation. This model was trained using cross-validation and tested using the receiver operating characteristic (ROC) curve. The top five most important predictors were identified using a score between 0% and 100% that represented the relative importance of each variable in model training. About 20% of participants (N = 174, 19.6%) reported success in vaping-assisted smoking cessation. The model achieved relatively high performance with an area under the ROC curve of 0.865 and classification accuracy of 0.831 (95% CI [confidence interval] 0.780 to 0.874). The top five most important predictors of perceived success in vaping-assisted smoking cessation were more positive experiences measured by the Vaping Experiences Score (100%), less previously failed quit attempts by vaping (39.0%), younger age (21.9%), having vaped 100 times (16.8%), and vaping shortly after waking up (15.8%). Our findings provide strong statistical evidence that shows better vaping experiences are associated with greater perceived success in smoking cessation by vaping. Furthermore, our study confirmed the strength of machine learning techniques in vaping-related outcomes research based on observational data.
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Affiliation(s)
- Rui Fu
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Robert Schwartz
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
| | - Nicholas Mitsakakis
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Lori M. Diemert
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Shawn O’Connor
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Joanna E. Cohen
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
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Fu R, Shi J, Chaiton M, Leventhal AM, Unger JB, Barrington-Trimis JL. A Machine Learning Approach to Identify Predictors of Frequent Vaping and Vulnerable Californian Youth Subgroups. Nicotine Tob Res 2021; 24:1028-1036. [PMID: 34888698 DOI: 10.1093/ntr/ntab257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/29/2021] [Accepted: 11/08/2021] [Indexed: 11/13/2022]
Abstract
INTRODUCTION Machine learning presents a unique opportunity to improve electronic cigarette (vaping) monitoring in youth. Here we built a random forest model to predict frequent vaping status among Californian youth and to identify contributing factors and vulnerable populations. METHODS In this prospective cohort study, 1,281 ever-vaping twelfth-grade students from metropolitan Los Angeles were surveyed in Fall and in 6-month in Spring. Frequent vaping was measured at the 6-month follow-up as nicotine-containing vaping on 20 or more days in past 30 days. Predictors (n=131) encompassed sociodemographic characteristics, substance use and perceptions, health status, and characteristics of the household, school and neighborhood. A random forest was developed to identify the top ten predictors of frequent vaping and interactions by sociodemographic variables. RESULTS Forty participants (3.1%) reported frequent vaping at the follow-up. The random forest outperformed a logistic regression model in prediction (C-Index=0.87 vs. 0.77). Higher past-month nicotine concentration in vape, more daily vaping sessions, and greater nicotine dependence were the top three of the ten most important predictors of frequent vaping. Interactions were found between age and perceived discrimination, and between age and race/ethnicity, as those who were younger than their classmates and either reported experiencing discrimination frequently or identified as Asian or Native American/Pacific Islander were at increased risk of becoming frequent vapers. CONCLUSIONS Machine learning can produce models that accurately predict progression of vaping behaviours among youth. The potential association between frequent vaping and perceived discrimination warrants more in-depth analyses to confirm if discrimination constitutes a cause of increased vaping.
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Affiliation(s)
- Rui Fu
- Department of Otolaryngology-Head and Neck Surgery, Sunnybrook Research Institute, University of Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, ON, Canada.,Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Jiamin Shi
- Dalla Lana School of Public Health, University of Toronto, ON, Canada.,Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Michael Chaiton
- Dalla Lana School of Public Health, University of Toronto, ON, Canada.,Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Adam M Leventhal
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.,Institute for Addiction Science, University of Southern California, Los Angeles, CA, USA
| | - Jennifer B Unger
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.,Institute for Addiction Science, University of Southern California, Los Angeles, CA, USA
| | - Jessica L Barrington-Trimis
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.,Institute for Addiction Science, University of Southern California, Los Angeles, CA, USA
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