Effectiveness of e-cigarettes as a stop smoking intervention in adults: a systematic review
Bibliographic record
Abstract
BACKGROUND: This systematic review aims to identify the benefits and harms of electronic cigarettes (e-cigarettes) as a smoking cessation aid in adults (aged ≥ 18 years) and to inform the development of the Canadian Task Force on Preventive Health Care's (CTFPHC) clinical practice guidelines on e-cigarettes. METHODS: We searched Ovid MEDLINE®, Ovid MEDLINE® Epub Ahead of Print, In-Process & Other Non-Indexed Citations, PsycINFO, Embase Classic + Embase, and the Cochrane Library on Wiley. Searches were conducted from January 2016 to July 2019 and updated on 24 September 2020 and 25 January 2024. Two reviewers independently performed title-abstract and full-text screening according to the pre-determined inclusion criteria. Data extraction, quality assessments, and the application of Grading of Recommendations Assessment, Development and Evaluation (GRADE) were performed by one independent reviewer and verified by another. RESULTS: We identified 18 studies on 17 randomized controlled trials that compared e-cigarettes with nicotine to e-cigarettes without nicotine and e-cigarettes (with or without nicotine) to other interventions (i.e., no intervention, waitlist, standard/usual care, quit advice, or behavioral support). Considering the benefits of e-cigarettes in terms of smoking abstinence and smoking frequency reduction, 14 studies showed small or moderate benefits of e-cigarettes with or without nicotine compared to other interventions; although, with low, very low or moderate evidence certainty. With a focus on e-cigarettes with nicotine specifically, 12 studies showed benefits in terms of smoking abstinence when compared with usual care or non-nicotine e-cigarettes. In terms of harms following nicotine or non-nicotine e-cigarette use, 15 studies reported mild adverse events with little to no difference between groups and low to very low evidence certainty. CONCLUSION: The evidence synthesis on the e-cigarette's effectiveness shows data surrounding benefits having low to moderate evidence certainty for some comparisons and very low certainty for others, indicating that e-cigarettes may or probably increase smoking cessation, whereas, for harms, there is low to very low evidence certainty. Since the duration for outcome measurement varied among different studies, it may not be long-term enough for Adverse Events (AEs) to emerge, and there is a need for more research to understand the long-term benefits and potential harms of e-cigarettes. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018099692.
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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 | 0.018 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".