Interventions for quitting vaping
Bibliographic record
Abstract
RATIONALE: There is limited guidance on the best ways to stop using nicotine-containing vapes (otherwise known as e-cigarettes) and ensure long-term abstinence, whilst minimising the risk of tobacco smoking and other unintended consequences. Treatments could include pharmacological interventions, behavioural interventions, or both. OBJECTIVES: To conduct a living systematic review assessing the benefits and harms of interventions to help people stop vaping compared to each other or to placebo or no intervention. To also assess how these interventions affect the use of combustible tobacco, and whether the effects vary based on participant characteristics. SEARCH METHODS: We searched the following databases from 1 January 2004 to 24 April 2024: CENTRAL; MEDLINE; Embase; PsycINFO; ClinicalTrials.gov (through CENTRAL); World Health Organization International Clinical Trials Registry Platform (through CENTRAL). We also searched the references of eligible studies and abstracts from the Society for Research on Nicotine and Tobacco 2024 conference, and contacted study authors. ELIGIBILITY CRITERIA: Randomised controlled trials (RCTs) recruiting people of any age using nicotine-containing vapes, regardless of tobacco smoking status. Studies had to test an intervention designed to support people to quit vaping, and plan to measure at least one of our outcomes. OUTCOMES: Critical outcomes: vaping cessation; change in combustible tobacco use at six months or longer; number of participants reporting serious adverse events (SAEs) at one week or longer. RISK OF BIAS: We used the Cochrane RoB 1 tool to assess bias in the included studies. SYNTHESIS METHODS: We followed standard Cochrane methods for screening and data extraction. We grouped studies by comparisons and outcomes reported, and calculated individual study and pooled effects, as appropriate. We used random-effects Mantel-Haenszel methods to calculate risk ratios (RR) with 95% confidence intervals (CI) for dichotomous outcomes. We used random-effects inverse variance methods to calculate mean differences and 95% CI for continuous outcomes. We assessed the certainty of the evidence using the GRADE approach. INCLUDED STUDIES: Nine RCTs, representing 5209 participants motivated to stop using nicotine-containing vapes at baseline, are included. In six studies, participants were abstinent from smoking tobacco cigarettes at baseline, although most studies included some participants who had previously smoked. Eight studies included participants aged 18 or older, three included only young adults (18 to 24 years), and one included 13- to 17-year-olds only. We judged three studies at low risk, three at high risk, and three at unclear risk of bias. SYNTHESIS OF RESULTS: = 0%; 2 studies, 4091 participants). The one study investigating nicotine/vaping behaviour reduction did not report on SAEs. One of the studies investigating text message-based interventions did report on SAEs; however, zero events were reported in both study arms (508 participants; low-certainty evidence due to imprecision). No studies reported change in combustible tobacco smoking at six-month follow-up or longer. AUTHORS' CONCLUSIONS: There is low-certainty evidence that text message-based interventions designed to help people stop nicotine vaping may help more youth and young adults to successfully stop than no/minimal support, and low-certainty evidence that varenicline may also help people quit vaping. Data exploring the effectiveness of combination NRT, cytisine, and nicotine/vaping behaviour reduction are inconclusive due to risk of bias and imprecision. Most studies that measured SAEs reported none; however, more data are needed to draw clear conclusions. Of note, data from studies investigating these interventions for quitting smoking have not demonstrated serious concerns about SAEs. No studies assessed the change in combustible tobacco smoking, including relapse to or uptake of tobacco smoking, at six-month follow-up or longer. It is important that future studies measure this so the complete risk profile of relevant interventions can be considered. We identified 20 ongoing RCTs. Their incorporation into the evidence base and the continued identification of new studies is imperative to inform clinical and policy guidance on the best ways to stop vaping. Therefore, we will continue to update this review as a living systematic review by running searches monthly and updating the review when relevant new evidence that will strengthen or change our conclusions emerges. FUNDING: Cancer Research UK (PRCPJT-Nov22/100012). National Institute of Health Research (NIHR206123) REGISTRATION: Protocol available via DOI: 10.1002/14651858.CD016058.
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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 | 0.022 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.003 |
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".