Acute physiological responses of blood flow restriction between high‐intensity interval repetitions in trained cyclists
Bibliographic record
Abstract
Abstract Blood flow restriction (BFR) is increasingly being used to enhance aerobic performance in endurance athletes. This study examined physiological responses to BFR applied in recovery phases within a high‐intensity interval training (HIIT) session in trained cyclists. Eleven competitive road cyclists (mean ± SD, age: 28 ± 7 years, body mass: 69 ± 6 kg, peak oxygen uptake: 65 ± 9 mL · kg−1 · min−1) completed two randomised crossover conditions: HIIT with (BFR) and without (CON) BFR applied during recovery phases. HIIT consisted of six 30‐s cycling bouts at an intensity equivalent to 85% of maximal 30‐s power (523 ± 93 W), interspersed with 4.5‐min recovery. BFR (200 mmHg, 12 cm cuff width) was applied for 2‐min in the early recovery phase between each interval. Pulmonary gas exchange (V̇O2, V̇CO2, and V̇E), tissue oxygen saturation index (TSI), heart rate (HR), and serum vascular endothelial growth factor concentration (VEGF) were measured. Compared to CON, BFR increased V̇CO2 and V̇E during work bouts (both p < 0.05, dz < 0.5), but there was no effect on V̇O2, TSI, or HR (p > 0.05). In early recovery, BFR decreased TSI, V̇O2, V̇CO2, and V̇E (all p < 0.05, dz > 0.8) versus CON, with no change in HR (p > 0.05). In late recovery, when BFR was released, V̇O2, V̇CO2, V̇E, and HR increased, but TSI decreased versus CON (all p < 0.05, dz > 0.8). There was a greater increase in VEGF at 3‐h post‐exercise in BFR compared to CON (p < 0.05, dz > 0.8). Incorporating BFR into HIIT recovery phases altered physiological responses compared to exercise alone.
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 | 0.000 | 0.001 |
| 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".