An Individualized Exercise Intervention for People with Multiple Myeloma—Study Protocol of a Randomized Waitlist-Controlled Trial
Bibliographic record
Abstract
People with multiple myeloma (MM) are second only to people with lung cancer for the poorest reported health-related quality of life (HRQoL) of all cancer types. Whether exercise can improve HRQoL in MM, where bone pain and lesions are common, requires investigation. This trial aims to evaluate the efficacy of an exercise intervention compared with control on HRQoL in people with MM. Following baseline testing, people with MM (n = 60) will be randomized to an exercise (EX) or waitlist control (WT) group. EX will complete 12-weeks of supervised (24 sessions) and unsupervised (12 sessions) individualized, modular multimodal exercise training. From weeks 12–52, EX continue unsupervised training thrice weekly, with one optional supervised group-based session weekly from weeks 12–24. The WT will be asked to maintain their current activity levels for the first 12-weeks, before completing the same protocol as EX for the following 52 weeks. Primary (patient-reported HRQoL) and secondary (bone health and pain, fatigue, cardiorespiratory fitness, muscle strength, body composition, disease response, and blood biomarkers) outcomes will be assessed at baseline, 12-, 24- and 52-weeks. Adverse events, attendance, and adherence will be recorded and cost-effectiveness analysis performed. The findings will inform whether exercise should be included as part of standard myeloma care to improve the health of this unique population.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".