Prospective Single-Arm Feasibility of Supervised Virtual Exercise in Women Living with Ovarian Cancer: The BE-BALANCED Study
Bibliographic record
Abstract
Background: Access to exercise programming that addresses the unique needs of women living with and beyond ovarian cancer is limited. Feasible and accessible supportive care programs to enhance physical function and quality of life are needed. We aimed to assess the feasibility of a 12-week virtually delivered exercise program for women living with and beyond ovarian cancer. Methods: BE-BALANCED was a prospective single-arm feasibility pilot study. Women who had completed primary chemotherapy treatment of ovarian cancer within the past year were recruited through oncologist referrals or self-referral. The 12-week group exercise program targeting aerobic capacity, functional strength, balance, and range of motion was conducted virtually twice weekly using Zoom. Feasibility measures were accrual, attendance, adherence, and attrition. Physical function was evaluated using the Short Physical Performance Battery and selected components of the Senior Fitness Test. Results: Fourteen participants enrolled in the study (47% of the accrual target). Feasibility goals for the exercise sessions were met for attendance (84% ± 19%), adherence to virtual sessions (78% ± 19%), and fidelity of group belonging (18% ± 4%), and met for overall attrition (21%). Improvements were observed in gait speed, 30-second bicep curls, 6-minute walk, chair stand, and emotional well-being (P < .05). Participant satisfaction with the program was high (4.4/5). Conclusion: Our findings demonstrated the feasibility of a virtually delivered exercise program for women living with and beyond ovarian cancer, with favorable attendance, adherence, and safety data. The program showed potential in improving physical outcomes and quality of life for participants. However, recruitment was a challenge. Future interventions could consider different approaches to increase recruitment.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".