Can Breast Cancer Survivors Achieve Exercise Intensity Goals In A Virtual Supervised Exercise Program?
Bibliographic record
Abstract
Exercise dose is important to capture in randomized control trials (RCT) in order to determine if the exercise prescription influenced outcomes of interest. The Borg Rate of Perceived Exertion (RPE) scale (6-20) is a valid tool to self-report exercise intensity and has been used successfully for in-person exercise interventions for cancer survivors. Limited evidence exists on the feasibility of adhering to an exercise prescription in a virtual setting. PURPOSE: To test if participants can achieve exercise intensity goals in a supervised exercise program delivered virtually. METHODS: Women taking endocrine therapy for breast cancer treatment were recruited to a partial cross-over RCT by self-referral or oncologist referral. Participants were eligible if taking endocrine therapy for at least 3-months and up to 3-years of use. The intervention is an 8-week supervised strength and aerobic group exercise program, with education curriculum on key topics in physical activity behaviour change, delivered virtually twice weekly via Zoom by an exercise physiologist. Education on how to meet the exercise intensity goal for combined aerobic and strength training. The intensity goal ranged from RPE 12-15. After each class, participants self-reported RPE via direct message to the instructor. Meeting RPE targets (yes/no) is reported as mean and standard deviation, and difference between the first and second half of the program was assessed using a paired t-test. RESULTS: A total of 24 participants have completed the program as of October 31, 2021, and 132 are projected to finish by December 31, 2022. Overall, participants met RPE targets 93% of classes (n = 288 total sessions). Meeting the target was significantly greater during the second half of the program (weeks 5-8), than the first half of the program (weeks 1-4) (mean difference = -8% (7), p = 0.013). The main reasons for missed RPE targets in weeks 1-4 (n = 17) were: 1) non-treatment related injury (i.e., back pain or ankle sprain (n = 4), 2) the prescribed exercise was not challenging enough (n = 7), 3) unclear about how to use RPE scale (n = 6). CONCLUSION: Preliminary findings support that instructing breast cancer survivors on how to meet exercise intensity goals using rate of perceived exertion is possible and women are more likely to meet RPE targets in the second half of the program.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".