The Effectiveness of Exercise Interventions for Improving Health-Related Quality of Life From Diagnosis Through Active Cancer Treatment
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To evaluate the effectiveness of exercise interventions on overall health-related quality of life (HRQOL) and its domains among adults scheduled to, or actively undergoing, cancer treatment. DATA SOURCES: 11 electronic databases were searched through November 2011. In addition, the authors searched PubMed's related article feature, trial registries, and reference lists of included trials and related reviews. DATA SYNTHESIS: 56 trials with 4,826 participants met the inclusion criteria. At 12 weeks, people exposed to exercise interventions had greater improvement in overall HRQOL, physical functioning, role functioning, social functioning, and fatigue. Improvement in HRQOL was associated with moderate-to-vigorous intensity exercise interventions. CONCLUSIONS: Exercise can be a useful tool for managing HRQOL and HRQOL domains for people scheduled to, or actively undergoing, cancer treatment. More methodologically rigorous trials are needed to examine the attributes of exercise programs most effective for improving HRQOL. IMPLICATIONS FOR NURSING: Evidence from this review supports the incorporation of exercise programs of moderate-to-vigorous intensity for the management of HRQOL among people scheduled to, or actively undergoing, cancer treatment into clinical guidelines through the Oncology Nursing Society's Putting Evidence Into Practice resources.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".