Smoking Cessation, Depression, and Exercise: Empirical Evidence, Clinical Needs, and Mechanisms
Bibliographic record
Abstract
INTRODUCTION: Smoking is significantly more common among persons with major depressive disorders (MDDs). Furthermore, smokers with MDD report more difficulties when they quit smoking (greater withdrawal symptoms, higher probability of relapse). The aim of this narrative review is to describe research on exercise and depression and exercise and smoking cessation. METHODS: We have critically reviewed various smoking cessation intervention programs for depressive smokers examining (a) the protective effect of exercise against relapse for smokers with MDD and (b) the benefits of exercise for treating withdrawal symptoms. We have also reviewed the current literature investigating the mechanisms between exercise-depression and exercise-smoking. RESULTS: This review suggests that exercise may reduce depressive symptoms following cessation and provide a useful strategy for managing withdrawal symptoms in smokers with MDD. Various psychological, biological, and genetic hypotheses have been tested (e.g., distraction hypothesis, expectations hypothesis, cortisol hypothesis) and few have obtained significant results. CONCLUSIONS: It might be beneficial for health professionals to recommend physical activity and promote supervised exercise sessions for smokers with MDD during smoking cessation. Future research needs to examine relationships between exercise, smoking, and depression with transdisciplinary and ecological momentary assessment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".