Feasibility of a Supervised Virtual Exercise Program for Women on Hormone Therapy for Breast Cancer
Bibliographic record
Abstract
ABSTRACT Introduction/Purpose Adjuvant endocrine therapy significantly improves survival in women with hormone receptor–positive breast cancer and is typically administered for 5 yr or longer. Adverse treatment side effects, including arthralgias, reduce treatment adherence and physical activity levels. Aerobic and resistance exercise is one strategy to decrease treatment side effects and improve treatment adherence. This study aimed to explore the feasibility of a virtually delivered exercise program for women receiving adjuvant endocrine therapy as part of breast cancer treatment. Methods This is a single-arm pilot study with recruitment by self-referral or oncologist referral of female breast cancer survivors. To adapt to coronavirus disease 2019 (COVID-19) restrictions, a supervised strength and aerobic group exercise program was delivered virtually twice weekly via Zoom over 6 wk. Feasibility was evaluated based on a priori targets specific to program recruitment (>30% recruitment ratio), transition to virtual delivery (>75%), attendance (>70% virtual session attendance), attrition (<30% dropout), and fidelity of group belongingness (average score ≥15 on belongingness questionnaire) at the end of the program. Physical function (30-s chair stand test), quality of life RAND Short-Form 36-item test, and medication adherence (Voils Domains of Subjective Extent of Nonadherence) were assessed at baseline and 6 wk. Results A total of 24 participants completed the program. All feasibility measures were met. Statistically significant changes were found in physical function ( P < 0.001), self-reported energy/fatigue ( P < 0.001), emotional well-being ( P < 0.001), and pain ( P = 0.01). There was also a positive trend toward improvement in patient-reported medication adherence (17%). Conclusion A 6-wk supervised strength and aerobic group exercise intervention delivered virtually was feasible and improved physical function, energy/fatigue, emotional well-being, and pain. The trend toward improvement in adherence to adjuvant endocrine therapy should be explored further. These findings provide preliminary data to inform a future appropriately powered trial on exercise and physical function using a virtual platform that has the potential for greater reach.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".