Vaping cessation interventions: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Electronic nicotine delivery systems (ENDS), such as e-cigarettes, have surged in popularity. While long-term risks remain unclear, known dangers include nicotine addiction, E-cigarette or Vaping Use-Associated Lung Injury and potential extrapulmonary effects on the heart, immune system and neurodevelopment. Addressing ENDS addiction may require strategies akin to smoking cessation. However, evidence is lacking; there are no currently approved vaping-cessation aids. The objective of this review is to summarise the available literature addressing interventions for vaping cessation. DATA SOURCES AND STUDY SELECTION: A librarian-assisted search was conducted in MEDLINE, Embase and Scopus, as well as preprints in Europe PMC, Open Science Framework, and the grey literature searches until January 2024. Six included studies used self-report methods to determine the abstinence rate, while one study used cotinine-level-verified self-reported abstinence. We included randomised controlled trials (RCTs) that examine 'rates of vaping cessation' outcomes. An intention-to-treat approach was used for data extraction, and random-effects meta-analyses models were applied. DATA SYNTHESIS: Seven RCTs were included, which studied pharmacological therapy, digital interventions and/or educational content. The primary meta-analyses demonstrated a statistically significant increase in the odds of achieving 7-day point prevalence abstinence (PPA) (OR 1.52, 95% CI 1.15 to 2.01, number of participants=3244, moderate-certainty) and continuous abstinence (OR 2.71, 95% CI 1.31 to 5.61, number of participants=164, low-certainty) following intervention. A non-significant increase in odds was noted at 30-day PPA (OR 1.32, 95% CI 0.72 to 2.42, number of participants=1994, very low certainty). CONCLUSIONS: Vaping cessation interventions increase 7-day PPA rates at 1-12 months follow-up and this was consistent across prespecified subgroup analyses of the intervention type, outcome and duration of follow-up. More high-quality studies with reproducible findings are needed to enhance the certainty of the evidence and guide clinical interventions. PROSPERO REGISTRATION NUMBER: CRD42022383670.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".