Prescribing Exercise in Early-Stage Breast Cancer During Chemotherapy: A Simple Periodized Approach to Align With the Cyclic Phases of Chemotherapy
Bibliographic record
Abstract
ABSTRACT: Bigaran, A, Howden, EJ, Foulkes, S, Janssens, K, Beaudry, R, Haykowsky, MJ, La Gerche, A, Fraser, SF, and Selig, SE. Prescribing exercise in early-stage breast cancer during chemotherapy: a simple periodized approach to align with the cyclic phases of chemotherapy. J Strength Cond Res 36(10): 2934-2941, 2022-To evaluate whether a periodized aerobic and resistance training plan aligned to the anthracycline chemotherapy (AC) cycles would be well tolerated, feasible, and attenuate the decline in peak oxygen uptake (V̇o2peak) in breast cancer (BC) patients. Twenty-eight women with early-stage BC treated with AC self-selected to undertake exercise training (EX 47 ± 9 years, n = 14) or usual care (53 ± 9 years, n = 14) for 12 weeks as part of a nonrandomized controlled trial. The periodized EX was aligned to the cyclic phases of AC, including AC taper and nontaper weeks. Outcome measures included cardiopulmonary exercise testing. Attendance and adherence variables (relative dose intensity [RDI] and volume load) were calculated to quantify the dose of EX completed relative to the amount of EX prescribed. The mean session attendance was 76% (range 46-88%). The mean ± SD prescribed and completed dose of aerobic training was 332.3 ± 48.7 MET h·wk-1 and 380.6 ± 53.2 MET h·wk-1 (p = 0.02), equating to a mean RDI of 89 ± 17%. For resistance training, the prescribed and completed cumulative dose was 128,264 ± 54,578 and 77,487 ± 26,108 kg (p < 0.001), equating to an RDI of 60 ± 11%. Adherence to the AC taper week RDI (52 ± 14%) for resistance training was significantly lower than the non-AC taper week (72 ± 8%, p = 0.02). The most frequent cause for EX interruption was hospitalization (35%), whereas AC-related illness was the most common cause for missed (57%) or modified EX sessions (64%). This periodized approach was mostly well tolerated for patients with BC. We speculate that a periodized approach may be both more palatable and useful, although this requires further investigation in a randomized controlled trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".