Exercise is an effective treatment modality for reducing cancer-related fatigue and improving physical capacity in cancer patients and survivors: a meta-analysis
Bibliographic record
Abstract
The use of exercise interventions to manage cancer-related fatigue (CRF) is a rapidly developing field of study. However, results are inconsistent and difficult to interpret across the literature, making it difficult to draw accurate conclusions regarding the true effectiveness of exercise interventions for CRF management. The aims of this study were to apply a meta-analysis to quantitatively assess the effects of exercise intervention strategies on CRF, and to elucidate appropriate exercise prescription guidelines. A systematic search of electronic databases and relevant journals and articles was conducted. Studies were eligible if subjects were over the age of 18 years, if they had been given a diagnosis of or had been treated for cancer, if exercise was used to treat CRF as a primary or secondary endpoint, and if the effects of the intervention were evaluated quantitatively and presented adequate statistical data for analysis. A total of 16 studies, representing 1426 participants (exercise, 759; control, 667) were included in a meta-analysis using a fixed-effects model. The standardized mean difference effect size (SMD) was used to test the effect of exercise on CRF between experimental and control groups. The results indicate a small but significant effect size in favour of the use of exercise interventions for reducing CRF (SMD 0.26, p < 0.001). Furthermore, aerobic exercise programs caused a significant reduction in CRF (SMD 0.21, p < 0.001) and overall, exercise was able to significantly improve aerobic and musculoskeletal fitness compared with control groups (p < 0.01). Further investigation is still required to determine the effects of exercise on potential underlying mechanisms related to the pathophysiology of CRF.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.069 |
| Bibliometrics | 0.006 | 0.005 |
| 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) | 0.003 | 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".