High-intensity interval training combined with blood-flow restriction enhances anaerobic and aerobic power in endurance athletes
Bibliographic record
Abstract
High-intensity interval training (HIIT) can improve endurance performance.We investigated the concurrent impact of HIIT and blood-flow restriction (BFR) as a novel approach to further enhance maximal aerobic and anaerobic physiology and performances in trained athletes.In a randomized controlled trial, eighteen endurance-trained males (V ̇O2peak 65.6±5.1 ml.min -1 .kg- 1 ) included three sessions of HIIT per week (sets of 15-s efforts at 100% maximal aerobic power, interspersed by 15-s recovery) into their usual training for three weeks, either with restriction imposed on both lower limbs at 50-70% of arterial occlusion pressure (BFR group, n=10) or without (CTL group, n=8), and were tested for aerobic and anaerobic exercise performance.The total mechanical work developed during a 30-sec Wingate test increased only in BFR (3.6%, P=0.02).During the Wingate, changes in near-infrared spectroscopyderived vastus lateralis muscle oxygenation (Δ(deoxy[Hb+Mb]), % arterial occlusion) were attenuated after BFR training (-8.8%,P=0.04).The maximal aerobic power measured during an incremental cycling test also increased only in BFR (4.8%, P=0.0004), but there was no change in V ̇O2peak among groups.The improvement in time to complete a 5-km cycling time trial and associated changes in key blood variables (e.g., pH, lactate, bicarbonate and potassium ion concentration, hemoglobin) were not different between groups.Combining short-duration HIIT at 100% MAP with BFR elicited greater improvements in anaerobic performance and maximal aerobic power in endurance-trained athletes, associated with locomotor muscle metabolic adaptations but no meaningful effect on cardiorespiratory fitness.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".