Feasibility Of Supervised Aerobic Interval Exercise Training Following Treatment For Breast Cancer
Bibliographic record
Abstract
Aerobic interval training (AIT) can be more effective in improving cardiorespiratory fitness, and muscle oxidative capacity than moderate continuous training (MCT) in a variety of healthy and clinical populations. Due to physical deconditioning associated with breast cancer treatment, AIT is of interest in this population. However, the feasibility and safety of AIT among breast cancer patients is unknown. PURPOSE: To assess the feasibility and occurrence of major adverse events (MAE) with AIT among breast cancer patients immediately post completion of adjuvant chemotherapy and radiation. METHODS: Women with early stage breast cancer were enrolled in the Nutrition and Exercise During Adjuvant Treatment trial within the first half of chemotherapy. MCT aerobic exercise was prescribed 3x/week during chemotherapy and radiation (20-30 min at 50-75% Heart Rate Reserve (HRR)). Upon treatment completion, eligible participants were prescribed AIT (4 sets of 4 min at 75-85% VO2R/HRR and 4 min at 40-65% VO2R/HRR) at least 1x/week, with the choice of either MCT or AIT for remaining sessions. AIT eligibility included an absence of angina, dyspnea, uncontrolled hypertension, asthma or current prescription for heart medications. The ACSM’s metabolic equation for treadmill walking was used to prescribe interval speed/grade, while HRR was used for intervals performed on a cycle ergometer or elliptical trainer. RESULTS: 57 women (age 51±11) entered the post-treatment phase of the study, of which 44 (75%) were eligible for AIT. 36 (82%) participants performed at least one AIT session. 66% of the total sessions performed were AIT workouts, indicating a potential preference for AIT vs. MCT. Those performing AIT attended significantly more sessions overall relative to those who were not performing AIT(18±6 vs 13±8, p=0.01). Adherence to AIT intensity was achieved in 68% of all sessions, with no difference between those performed on the treadmill, bike/elliptical, nor relative to MCT sessions. The most common barrier to AIT intensity adherence was the prescription being too difficult (75%). No MAE occurred. CONCLUSIONS: AIT after treatment completion for breast cancer appears to be feasible, potentially preferable to MCT, and may result in greater attendance than MCT alone.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".