The effects of exercise on anxiety and depression in adults with cancer: A meta-review of meta-analyses
Bibliographic record
Abstract
Background This meta-review aimed to synthesize the evidence of the effects of exercise on anxiety and depression symptoms amongst adults with cancer, and cancer-survivors, comparing effects sizes between meta-analysis.Methods Major databases were searched up to February 9, 2024 for meta-analyses evaluating the effects of exercise, using anxiety and/or depression scales. Effect size (ES) values were calculated as standardized differences in the means and expressed as the Standardized Mean Difference (SMD) with the 95% confidence interval (95%CI). A total of eight unique meta-analyses were included.Results Among meta-analyses examining both anxiety and depression symptoms overall, a small beneficial effect of exercise was shown [SMD = 0.41 (0.25–0.57); p < 0.0001]. Subgroup analyses revealed that exercise has a small effect on decreasing anxiety [SMD = 0.42 (0.04–0.79); p = 0.027], and depressive symptoms [SMD = 0.38 (0.21–0.56); p < 0.0001]. In addition, aerobic exercise has a moderate effect on reducing depressive symptoms [SMD = 0.54 (0.16–0.93); p = 0.005], whereas resistance exercise has no effect. Subgroup analyses by type of cancer observed a moderate effect on decreasing depressive symptoms in patients with breast cancer [SMD = 0.51 (0.27–0.74); p < 0.0001].Conclusions Exercise (specifically aerobic) should be considered by healthcare professionals as a strategy to treat/manage symptoms of anxiety and depression amongst adults with cancer and cancer-survivors.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.049 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".