Effects of exercise on cancer related fatigue in adults: A literature review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Cancer related fatigue (CRF) is one among the common distressing symptoms experienced by cancer patients. Evidence showed that exercise interventions are effective in decreasing CRF. This review is to evaluate the evidence of the effectiveness of exercise interventions on CRF among adults with varied types of cancer in all phases of the cancer trajectory. A literature review with meta-analysis of randomized controlled trials (RCTs) was conducted. The results of RCTs (n = 20) that examined the effects of exercise on CRF were combined using two approaches: meta-analysis (n = 18) and summative analysis (n = 2). A summary effects size of the standardized mean difference (SMD) with 95% confidence intervals was calculated using random effects model and heterogeneity was assessed with the I2 statistic. The results showed overall, a small but significant decrease in the level of CRF (SMD, -0.32; 95% CI, -0.51 to -0.12; p = .002) was observed following exercise intervention. Subgroup analyses showed that both mixed modes (combination of resistance and aerobic exercises) and aerobic exercises were effective in significantly reducing CRF (p = .033; p = .046 respectively). The results indicated substantial heterogeneity between studies (I2 = 79%; p ≤ .0001). Summative analysis also suggested that exercise may be effective in reducing CRF. In conclusion, both resistance and aerobic exercises may be effective in decreasing CRF in adult patients. The result needs to be interpreted with caution due to considerable between-study heterogeneity.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.007 | 0.007 |
| 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".