Blood Flow Restriction during Rest Periods of High-Intensity Interval Training Enhances Endurance Performance: A Randomized Placebo-Controlled Study
Bibliographic record
Abstract
PURPOSE: This study investigated the effects of incorporating blood flow restriction (BFR) during rest periods between sets of high-intensity interval training (HIIT) on aerobic, sprint, and muscular performance in recreationally active participants. METHODS: Forty healthy males (28.7 ± 6.0 yr) were pair matched and randomized into either blood flow restriction (BFR + HIIT) or sham (SHAM + HIIT) groups and completed nine sessions of HIIT (three sets, 8 × 30 s cycling at 90%-105% maximal aerobic power (W max ), 30 s active recovery, with 4 min rest between sets) over 3 wk. The BFR + HIIT group had 80% limb occlusion pressure applied for the first 2 min of rest between sets, whereas the SHAM + HIIT group cycled under sham hypoxic conditions. RESULTS: Training variables (power output, heart rate, perceived effort, and pain) were similar between groups. Posttraining, endurance performance significantly improved in BFR + HIIT compared with SHAM + HIIT, indicated by greater increases in W max (+25.6 vs +17.2 W, P = 0.014) and time to exhaustion (+61.7 vs +38.4 s, P = 0.008) during an incremental graded cycling test, and increased mean power output (TT MPO : +20.3 vs +9.3 W, P = 0.017) and reduced time to completion (-79.1 vs -39.3 s, P = 0.014) during a 20 km time trial. Conversely, no differences were found between groups in power outputs assessed by the 30 s cycling sprint test, or in muscular power and strength, as measured by countermovement jump and isometric mid-thigh pull tests. CONCLUSIONS: Using BFR during rest periods of HIIT enhances aerobic performance without altering training variables, although it may not provide additional advantages for sprint power or strength development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".