Impact of electronic cigarette use and sleep duration, sleep issues and insomnia: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The increasing popularity of electronic cigarettes (e-cigarettes) has introduced new public health challenges and concerns. While promoted as safer alternatives to conventional tobacco and as tools for quitting smoking, e-cigarettes have raised alarm about possible long-term health consequences. This systematic review and meta-analysis sought to evaluate the association between electronic cigarette consumption and sleep disturbances. Methods: We performed comprehensive searches in EMBASE, Web of Science, and PubMed up to September 18, 2024, to locate studies examining the link between e-cigarette use and sleep duration, sleep disorders, and insomnia. A meta-analysis was conducted to calculate pooled odds ratios (ORs). The quality of the studies was evaluated using the Newcastle-Ottawa Scale. Meta-analysis was performed using R software (Version 4.3). Results: A total of 14 cross-sectional studies were included from 554 unique records screened. E-cigarette users exhibited a notably elevated risk of having shorter sleep duration compared to non-users, with a pooled odds ratio of 1.38 (95% CI: 1.24-1.55). Several studies reported that e-cigarette users also had higher odds of sleep disturbances, such as insomnia and reliance on sleep medications. Among adolescents, e-cigarette use was associated with a 33 to 61% increased risk of inadequate sleep. Conclusion: E-cigarette use may be associated with sleep disturbances, including shorter sleep duration and increased sleep difficulties. Future long term longitudinal studies are warranted for better evidence.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".