The effects of smoking cessation on the progression of depressive disorders: A systematic review and meta analysis
Bibliographic record
Abstract
INTRODUCTION Smoking and depression frequently co-occur, posing a major public health challenge.While the physical benefits of smoking cessation are well established, its impact on depressive disorders remains debated.Clarifying this relationship is essential for optimizing mental health interventions.METHODS We conducted a systematic review and metaanalysis of randomized controlled trials and longitudinal cohort studies assessing changes in depressive symptoms following smoking cessation among adults (18 years) diagnosed with depressive disorders.In addition, the reference lists of three relevant meta-analyses were screened to identify additional eligible primary studies, but these meta-analyses were not counted as included studies.Searches were performed in PubMed, Scopus, Web of Science, and PsycINFO up to 30 April 2025.Risk of bias was assessed using the Cochrane RoB 2.0 tool for RCTs and the Newcastle-Ottawa Scale for cohort studies.Effect sizes were pooled using a random-effects (DerSimonian-Laird) model, and heterogeneity (I) was evaluated.RESULTS A total of 22 primary studies (10 randomized controlled trials and 12 cohort studies; >30000 participants) met the inclusion criteria, and 18 contributed to the quantitative synthesis.The primary outcome -change in depressive symptom severity measured using validated scales (PHQ-9, BDI, CES-D, HAM-D) -showed a pooled standardized mean difference of -0.25 (95% CI: -0.37 --0.12; p<0.001), indicating a modest but significant reduction in depressive symptoms among abstainers.Findings were consistent across study designs and populations, with moderate heterogeneity (I about 60%).CONCLUSIONS This review provides consistent evidence that smoking cessation is safe and beneficial for individuals with depressive disorders, improving depressive symptoms and psychological well-being.Although some individuals experience transient increases in symptoms post-cessation, structured support effectively mitigates these effects.Integrating cessation treatment within mental healthcare and developing scalable, tailored interventions should be prioritized in future research.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".