The Effect of Yoga Interventions on Cancer-Related Fatigue and Quality of Life for Women with Breast Cancer: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Women with breast cancer (BC) are living longer with debilitating side effects such as cancer-related fatigue (CRF) that affect overall well-being. Yoga promotes health, well-being and may be beneficial in reducing CRF. Although there have been previous systematic reviews and meta-analyses, the effects of yoga on CRF and quality of life (QOL) remain unclear, particularly in comparison with other types of physical activity (PA). Our objective is to carry out a systematic review and meta-analysis of the effects of yoga on CRF and QOL in women with BC. METHODS: Electronic databases were searched (MEDLINE, Embase Classic+Embase and EMB Reviews, Cochrane Central CT) from inception to May 2018. Randomized controlled trials were included if they were full text, in English, included a yoga intervention, a comparator (including non-PA usual care or alternate PA intervention), and reported on CRF or QOL. Effects of yoga were pooled using standardized mean difference (SMD) via a random effects model. RESULTS: Of the 2468 records retrieved, 24 trials were included; 18 studies compared yoga to a non-PA comparator and 6 to a PA comparator. Yoga demonstrated statistically significant improvements in CRF over non-PA (SMD -0.30 [-0.51; -0.08]) but not PA (SMD -0.17 [-0.50; 0.17]) comparators. Additionally, yoga demonstrated statistically significant improvements in QOL over non-PA (SMD -0.27 [-0.46; -0.07]) but not PA (SMD 0.04 [-0.22; +0.31]) comparators. DISCUSSION: This meta-analysis found that yoga provides small to medium improvements in CRF and QOL compared to non-PA, but not in comparison to other PA interventions.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".