Exercise Programming Modelling a Standard of Care Approach Improves Physical Health and Patient-Reported Outcomes in Individuals Living with Breast Cancer: A Pilot Study
Bibliographic record
Abstract
Controlled study designs widely report that exercise improves the health of individuals living with breast cancer. Still, many individuals living with breast cancer are not active enough to experience the benefits of exercise. The Activating Cancer Communities through an Exercise Strategy for Survivors study was developed to reach more individuals living with cancer. This report describes the effects of a 12-week individualized exercise program that models a standard-of-care approach on body composition, physical fitness, and patient-reported outcomes in individuals living with breast cancer. Individuals living with breast cancer were recruited for the study and completed an exercise program twice weekly overseen by a Clinical Exercise Physiologist. A total of 43 participants completed the exercise intervention, and 36 withdrew from the study. All participants had significantly improved aerobic fitness, waist circumference, hip circumference, lower body endurance, physical activity behaviour, health-related quality of life, emotional status, and fatigue levels after completing the program. Flexibility, balance, and sleep scores did not change. The results from the 12-week individualized exercise program largely align with the results from more controlled study designs. These results support future initiatives integrating exercise therapy into the standard of care for individuals living with breast cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".