Concurrent E-cigarette Use While Enrolled in a Smoking Cessation Program: Associations Between Frequency of Use, Motives for Use, and Smoking Cessation
Bibliographic record
Abstract
INTRODUCTION: Trial evidence suggests that e-cigarettes may aid in quitting smoking, while observational studies have found conflicting results. However, many observational studies have not adjusted for important differences between e-cigarette users and non-users. AIMS AND METHODS: We aimed to determine the association between e-cigarette use frequency and motivation to use e-cigarettes to quit smoking, and smoking cessation using data from Canada's largest smoking cessation program. Participants who completed a baseline assessment and 6-month follow-up questionnaire were divided post hoc into four groups based on their self-reported e-cigarette use during the 30 days before baseline: (1) non-users; (2) users of e-cigarettes not containing nicotine; (3) occasional users; and (4) frequent users. Occasional and frequent users were further divided into two groups based on whether they reported using e-cigarettes to quit smoking. Abstinence at 6-month follow-up (7-day point prevalence abstinence) was compared among groups. RESULTS: Adjusted quit probabilities were significantly higher (both p < .001) for frequent baseline e-cigarette users (31.6%; 95% CI = 29.3%, 33.8%) than for non-users (25.8%; 25.3% and 26.3%) or occasional users (24.2%; 22.5% and 26.0%). Unadjusted proportions favored non-users over occasional users (p < .001), but this was not significant after adjustment (p = .06). People using e-cigarettes to quit smoking were not likelier than other users to be successful, but were likelier to report frequent e-cigarette use during follow-up. CONCLUSIONS: Frequent baseline e-cigarette use predicted successful smoking cessation, compared to occasional and non-users. Use of e-cigarettes to quit did not predict smoking cessation but was associated with continued use during follow-up, perhaps due in part to planned transitions to e-cigarettes. IMPLICATIONS: Prior observational studies investigating e-cigarette use for smoking cessation have found that occasional users have poorer outcomes than either frequent or non-users. Consistent with these studies, occasional users in our data also had poorer outcomes. However, after adjustment for variables associated with cessation success, we found that cessation probabilities did not differ between occasional and non-users. These findings are consistent with trial data showing the benefit of e-cigarette use among people trying to quit smoking. Results of this study suggest that differences between trials and previous observational studies may be because of unaddressed confounding in the latter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".