Are Exercise Programs Effective for Improving Health-Related Quality of Life Among Cancer Survivors? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To evaluate the effectiveness of exercise interventions on overall health-related quality of life (HRQOL) and its domains among cancer survivors who have completed primary treatment. DATA SOURCES: 11 electronic databases were searched from inception (dates varied) to October 2011. The authors also identified eligible trials through a search of additional sources. DATA SYNTHESIS: 40 trials with 3,694 participants met the inclusion criteria. At 12 weeks, cancer survivors exposed to exercise interventions had greater positive improvement in overall HRQOL (standardized mean difference [SMD] 0.48; 95% confidence interval [CI] [0.16, 0.81]), emotional well-being (SMD 0.33; 95% CI [0.05, 0.61]), and social functioning (SMD 0.45; 95% CI [0.02, 0.87]); and had a significant reduction in anxiety (SMD -0.26; 95% CI [-0.44, -0.07]) and fatigue (SMD -0.82; 95% CI [-1.5, -0.14]). CONCLUSIONS: Exercise programs have a beneficial effect on HRQOL and most of its domains and can be integrated into the management plans for cancer survivors who have completed treatment. Future research is needed to help understand specific attributes of exercise programs that are beneficial for improving HRQOL within and across cancer types. IMPLICATIONS FOR NURSING: Evidence presented in this review supports the inclusion of exercise programs in clinical guidelines for the management of cancer survivors who have completed treatment, such as the Oncology Nursing Society's Putting Evidence Into Practice resource.
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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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.032 |
| 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 | 0.003 | 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".