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

Fu, Rui; O’Connor, Shawn; Pelletier, Hayley; Eissenberg, Thomas; Cohen, Joanna E.; Schwartz, Robert

doi:10.1016/j.addbeh.2020.106814

Cited by 3 publications

(9 citation statements)

References 39 publications

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“…Consistent with early studies done by our group [11,46], we identified the VES, an index for measuring the experiences of smokers during a vaping-assisted quit attempt, as the most important predictor of self-reported smoking cessation. Notably, when holding other characteristics constant, smokers with excellent vaping experiences were associated with 16-times averaged predicted probability of quitting smoking compared to those who scored in the lowest quartile (77.1% vs. 4.8%).…”

Section: Plos Onesupporting

confidence: 84%

“…A binary outcome variable was created to represent the status of self-reported success in vaping-assisted smoking cessation in the past 12 months [11]. This variable was measured using the survey question, "Over the past 12 months, would you say that vaping has helped you .…”

Section: Outcomementioning

confidence: 99%

“…For each item, participants rated how true the statement was for them on a 7-point scale with 1 being "not true at all" and 7 being "extremely true". The factor analysis revealed six vaping experiences factors, including Relationships, Flexibility of Vaping, Side Effects, Vaping Devices, Public Reactions and Sensory Functions (see S1 Appendix and the factor analysis done by our group [11]). The VES was estimated as the weighted sum of factor scores over the six factors by the proportion of variance explained.…”

Section: Candidate Predictorsmentioning

confidence: 99%

“…Thus, our primary goal was to maximize the predictive power of the final model, rather than to convey any underlying relational or causal relationships as seen in an explanatory modeling study [33]. Interested readers are referred to a previous work done by our group where multivariable logistic modeling was applied to quantify the association between the odds of successful vaping-assisted smoking cessation and various individuallevel characteristics [11]. In the present study, we used the R package "gbm" [34] to develop a gradient boosting machine (GBM) model that classified successful vaping-assisted quitters and their unsuccessful counterparts using variables selected from the previous procedure.…”

Section: Developing and Validating A Gradient Boosting Machinementioning

confidence: 99%

“…Real-world studies have revealed successful vaping-assisted quitting attempters to share a set of unique characteristics, including increased vaping intensity [3], daily use of e-cigarettes [4,5], longer vaping history [6], the use of a specific type of vaping device [7], and being male [8]. Both qualitative and quantitative studies also point to the use of certain non-tobacco flavors [9,10] and more positive overall experiences during a vaping-assisted quit attempt [11][12][13][14][15][16][17] to be associated with potential cessation benefits.…”

Section: Introductionmentioning

confidence: 99%

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

Schwartz

Mitsakakis

et al. 2022

PLoS ONE

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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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Section: Plos Onesupporting

confidence: 84%

Section: Outcomementioning

confidence: 99%

Section: Candidate Predictorsmentioning

confidence: 99%

Section: Developing and Validating A Gradient Boosting Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Schwartz

Mitsakakis

et al. 2022

PLoS ONE

Self Cite

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

Shi

Chaiton

et al. 2021

Nicotine &Amp; Tobacco Research

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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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Vaping among youth: reasons, realization and intention to quit

Thapa

Pandey

Panda

et al. 2022

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Purpose Vaping has become a prominent public health problem that has impacted young adults. The purpose of this study is to empirically examine the effects of different intrinsic and extrinsic motivations on young adults’ realization of excessive vaping and their intention to quit vaping. Design/methodology/approach A survey was used to collect data from 232 young vapers (primarily Generation Z and Millennials) to test the hypothesized relationships using a covariance-based structural equation model. Findings The findings of this study suggest that “realization of excessive vaping” is negatively associated with “sensation seeking” and positively associated with “deal proneness,” “environmental cues” and “negative repercussion.” The “intention to quit vaping” is negatively associated with “marketing cues” and positively associated with “alternative to smoking” and “environmental cues.” Finally, the “realization of excessive vaping” is positively associated with “intention to quit vaping.” Originality/value This study takes a two-dimensional approach to understand the complex motivations behind a relatively new addictive behavior – vaping. It contributes to the literature of addictive behavior, social cognitive theory and theory of planned behavior. Further, it has important implications for public policy and the marketing of addictive products to youths.

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

Cited by 3 publications

References 39 publications

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

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

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

Vaping among youth: reasons, realization and intention to quit

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