A 3-week multimodal intervention involving high-intensity interval training in female cancer survivors: a randomized controlled trial
Bibliographic record
Abstract
To compare the effects of a 3-week multimodal rehabilitation involving supervised high-intensity interval training (HIIT) on female breast cancer survivors with respect to key variables of aerobic fitness, body composition, energy expenditure, cancer-related fatigue, and quality of life to those of a standard multimodal rehabilitation program. A randomized controlled trial design was administered. Twenty-eight women, who had been treated for cancer were randomly assigned to either a group performing exercise of low-to-moderate intensity (LMIE; n = 14) or a group performing high-intensity interval training (HIIT; n = 14) as part of a 3-week multimodal rehabilitation program. No adverse events related to the exercise were reported. Work economy improved following both HIIT and LMIE, with improved peak oxygen uptake following LMIE. HIIT reduced mean total body fat mass with no change in body mass, muscle or fat-free mass (best P < 0.06). LMIE increased muscle and total fat-free body mass. Total energy expenditure (P = 0.45) did not change between the groups, whereas both improved quality of life to a similar high extent and lessened cancer-related fatigue. This randomized controlled study demonstrates that HIIT can be performed by female cancer survivors without adverse health effects. Here, HIIT and LMIE both improved work economy, quality of life and cancer-related fatigue, body composition or energy expenditure. Since the outcomes were similar, but HIIT takes less time, this may be a time-efficient strategy for improving certain aspects of the health of female cancer 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".