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Record W7126032588 · doi:10.18332/popmed/215739

The effects of smoking cessation on the progression of depressive disorders: A systematic review and meta analysis

2025· article· W7126032588 on OpenAlexaboutno aff
Ouaamr Ahmed, Boujdid Mohamed, Aziz Mengad, Chikhaoui Mourad, Katim Alaoui

Bibliographic record

VenuePopulation Medicine · 2025
Typearticle
Language
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsMeta-analysisSmoking cessationDepression (economics)MEDLINEDepressive symptoms

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.765
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.353
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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