Comparison of Aerobic Exercise Intensity Prescription Methods in Breast Cancer
Bibliographic record
Abstract
INTRODUCTION: Exercise plays an important role in cancer rehabilitation, but a precise prescription of exercise intensity is required to maximize the benefits of this intervention. It is unknown whether different methods of prescribing aerobic exercise intensity achieve the same intensity. Breast cancer treatments may alter exercise response and thereby may affect the accuracy of these methods. PURPOSE: The purpose of this study was to compare the accuracy and achieved intensity of four common methods of prescribing exercise intensity within and between breast cancer patients recently finished chemotherapy (n = 10), survivors finished treatment (n = 10), and healthy controls (n = 10). METHODS: The methods compared were as follows: the American College of Sports Medicine's metabolic equation for treadmill walking (METW), heart rate reserve (HRR), direct heart rate (DIRECT HR), and RPE. The methods were used to prescribe 60% oxygen consumption reserve (VO2R) in four randomly assigned 10-min periods of treadmill walking with expired gas collection to evaluate 1) achieved intensity (measured % VO2R) and 2) accuracy (defined as: [60% VO2R-achieved intensity]). RESULTS: The accuracy of the methods was not equivalent across groups (P = 0.04). HRR and METW did not differ across groups and were most accurate in patients. HRR, METW, and DIRECT HR were all more accurate than RPE in survivors (P ≤ 0.01). RPE was the least accurate in all groups. The accuracy of DIRECT HR was much lower in patients than that in survivors and controls (P ≤ 0.01). CONCLUSIONS: The four methods of exercise intensity prescription varied in accuracy in prescribing 60% VO2R and did not achieve equivalent exercise intensities within breast cancer patients, survivors, and healthy controls. HRR and METW were the most accurate methods for exercise intensity prescription in breast cancer patients and survivors.
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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 | 0.003 | 0.015 |
| 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) | 0.001 | 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".