Understanding addiction in e-cigarette users – the EVAPE project
Bibliographic record
Abstract
Abstract Background Electronic cigarettes (e-cigarettes) are often advertised as a healthier option to combustible cigarettes and as smoking cessation aid. However, e-cigarettes are a growing health concern and their addictive potential remains to be fully understood. Within the EValuation of the Addictive Potential of E-cigarettes (EVAPE) project, we studied subjective and objective measures of addiction in relation to e-cigarette use. Methods This cross-sectional analysis was based on 832 participants of the first wave (2016) of England from the ITC Four Country Smoking and Vaping (4CV) Survey, who were using e-cigarettes daily or weekly for at least four months. Perceived addiction to e-cigarettes was categorised as very vs. not/somewhat addicted, and perceived addictiveness of e-cigarettes relative to combustible cigarettes as equally/more addictive vs. less addictive. Objective measures of addiction included urge to vape, time to first vape after waking, frequency of use, and used nicotine strength. We examined associations between these objective and subjective measures of addiction using multivariate logistic regression, adjusted for age, gender, education, and cigarette smoking. Results 17.8% of participants reported feeling very addicted to e-cigarettes and 42.3% considered e-cigarettes equally/more addictive than combustible cigarettes. Those who felt very addicted had higher odds of regarding e-cigarettes as more addictive (OR 3.43 (95%-CI 2.29-5.19)). All objective measures of addiction were associated with higher perceived addiction, whereas only a shorter time to first vape was associated with perceived product addictiveness. Conclusions Subjective measures of addiction to e-cigarettes, in particular perceived addiction, correspond with objective measures. Understanding the addictive potential of e-cigarettes is the cornerstone for developing new strategies for prevention and treatment, and ultimately understanding their role from a public health perspective. Key messages
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".