Literature DB >> 33429257

Real-world vaping experiences and smoking cessation among cigarette smoking adults.

Rui Fu1, Shawn O'Connor2, Lori Diemert3, Hayley Pelletier1, Thomas Eissenberg4, Joanna Cohen5, Robert Schwartz6.   

Abstract

INTRODUCTION: E-cigarettes may have the potential to be an effective cessation aid for some cigarette smokers. However, the extent to which smokers' experiences using e-cigarettes (vaping) to quit smoking impact their cessation outcomes is unclear. In this cross-sectional survey study, we develop a multidimensional measure of vaping experiences in adults who quit smoking by vaping and test its association with perceived success in smoking cessation.
METHODS: In 2019, recruitment invitations were emailed to adult past-year smokers who had accessed cessation services across Ontario, Canada. Respondents who tried vaping to quit smoking in the past year completed a detailed online survey. Factor analysis was performed on ratings of 45 vaping experiences items to identify dimensions of vaping experiences. Factor scores were entered into logistic regressions to test if vaping experiences dimensions had differential impact on perceived success in smoking cessation.
RESULTS: Of the 889 participants, 56.0% were female, 81.1% were Caucasian, and the mean age was 37.7 ± 11.9 years. Twenty percent (19.6%) reported having successfully quit smoking by vaping in the past year. Among the six vaping experiences factors, better experiences in five factors-Relationships, Flexibility of Vaping, Side Effects, Vaping Devices, and Sensory Functions-were each independently and positively associated with improved odds of successful quitting. Notably, Relationships [OR = 2.01, 95% CI: 1.61-2.64] and Side Effects [OR = 1.95; 95% CI 1.54-2.29] were the strongest correlates of perceived success in smoking cessation.
CONCLUSIONS: These findings indicate an opportunity to increase cessation rates by improving the experiences of smokers who vape to quit smoking.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  E-cigarette; Smoking cessation; Vaping

Year:  2020        PMID: 33429257     DOI: 10.1016/j.addbeh.2020.106814

Source DB:  PubMed          Journal:  Addict Behav        ISSN: 0306-4603            Impact factor:   3.913


  2 in total

1.  A Machine Learning Approach to Identify Predictors of Frequent Vaping and Vulnerable Californian Youth Subgroups.

Authors:  Rui Fu; Jiamin Shi; Michael Chaiton; Adam M Leventhal; Jennifer B Unger; Jessica L Barrington-Trimis
Journal:  Nicotine Tob Res       Date:  2022-06-15       Impact factor: 5.825

2.  Predictors of perceived success in quitting smoking by vaping: A machine learning approach.

Authors:  Rui Fu; Robert Schwartz; Nicholas Mitsakakis; Lori M Diemert; Shawn O'Connor; Joanna E Cohen
Journal:  PLoS One       Date:  2022-01-14       Impact factor: 3.240

  2 in total

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