Effects of Exercise on Depression and Anxiety in Lung Cancer Survivors: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
This study aims to investigate the effects of exercise on depression and anxiety in lung cancer survivors and identify the optimal exercise prescription for this population. Searches were conducted in PubMed, Web of Science, Cochrane Library, Embase, Scopus, CNKI, and Wanfang Data up to 7 January 2024. A meta-analysis was performed to calculate the standardized mean difference (SMD) and 95% confidence interval. Thirteen studies were included in this meta-analysis. Exercise significantly alleviated depression (SMD, −0.54; p = 0.002) and anxiety (SMD, −0.66; p = 0.0002) in lung cancer survivors. Subgroup analyses showed that aerobic exercise, exercise conducted >3 times per week, <60 min per session, and ≥180 min per week, were more effective in alleviating depression and anxiety, particularly in middle-aged individuals. In conclusion, exercise alleviates depression and anxiety in lung cancer survivors, particularly those who are middle-aged, and aerobic exercise may be the most effective intervention. This meta-analysis provides clinicians with evidence to recommend that lung cancer survivors engage in exercise more than three times per week, with each session lasting less than 60 min, aiming to achieve a total of 180 min per week by increasing the frequency of exercise.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".