Effect and moderators of exercise on fatigue in patients with cancer: Meta-analysis of individual patient data.
Bibliographic record
Abstract
104 Background: Fatigue is a common and disabling complaint in patients with cancer and can be reduced by exercise. To further personalize exercise prescriptions, moderators of exercise effects on fatigue should be investigated. However, most randomized controlled trials (RCTs) are not adequately powered to identify heterogeneity in responses to exercise. Therefore, we conducted a meta-analysis using individual patient data (IPD) of exercise RCTs to investigate the effect and moderators of exercise on cancer-related fatigue. Methods: Within the Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) consortium, principal investigators of 34 exercise RCTs worldwide have shared their IPD, including in total 4366 cancer patients. A 1-step IPD meta-analysis, using a linear mixed-effect model with a random intercept on study was undertaken to investigate effect on fatigue. The result, a between-group difference in standardized z-scores, corresponds to a Cohen’s d effect size. An interaction term was included in the model to assess potential moderators including demographic (sex, age, marital status, education), clinical (body mass index, distant metastasis), intervention-related (timing, delivery mode, duration) and exercise-related (type, frequency, intensity, duration) characteristics. Results: Exercise significantly reduced fatigue (β = -0.17, 95% CI -0.22;-0.12). The effect was not moderated by demographic, clinical or exercise-related characteristics. Supervised exercise had significantly larger effects on fatigue than unsupervised exercise (βdifference= -0.18, 95%CI -0.28;-0.08). Compared to the control group, supervised exercise significantly improved fatigue (β = -0.23, 95%CI = -0.29;-0.17), while unsupervised exercise did not (β = -0.04, 95%CI = -0.13;0.04). Conclusions: Exercise significantly reduces cancer-related fatigue across subgroups formed on the basis of demographic and clinical characteristics. The effect of exercise is significantly larger when performed under supervision. Hence, exercise, and preferably supervised exercise, represents a viable intervention for the prevention and treatment of fatigue among patients with cancer.
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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 | 0.034 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.092 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".