Yoga for Health-Related Quality of Life in Adult Cancer: A Randomized Controlled Feasibility Study
Bibliographic record
Abstract
An increase in patient-led uptake of complementary therapies in adult cancer has led to a need for more rigorous study of such interventions and their outcomes. This study therefore aimed to evaluate the feasibility and acceptability of a yoga intervention in men and women receiving conventional treatment for a cancer diagnosis. Prospective, mixed methods feasibility trial allocated participants to receive one of three yoga interventions over a four-week study period. Data collection was completed through online survey of QOL-CA/CS and customized surveys. Fifteen participants were included (11 female) undergoing treatment for breast, prostate, colorectal, brain, and blood and lung cancer. Two participants dropped out and complete qualitative and quantitative data sets were collected from 12 participants and four yoga instructors. Other outcome measures included implementation costs patient-reported preferences for yoga intervention and changes in QOL-CA/CS. Three types of yoga intervention were safely administered in adult cancer. Mixed methods, cost-efficiency, QOL-CA/CS, and evidence-based design of yoga intervention have been used to establish feasibility and patient-preferences for yoga delivery in adult caner. Results suggest that, with some methodological improvements, a large-scale randomized controlled trial is warranted to test the efficacy of yoga for male and female cancer patients. This trial is registered with Clinicaltrials.gov NCT02309112.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".