Ischemic preconditioning increases muscle perfusion, oxygen uptake, and force in strength-trained athletes
Bibliographic record
Abstract
Muscle ischemia and reperfusion induced by ischemic preconditioning (IPC) can improve performance in various activities. However, the underlying mechanisms are still poorly understood. The purpose of this study was to examine the effects of IPC on muscle hemodynamics and oxygen (O2) uptake during repeated maximal contractions. In a cross-over, randomized, single-blind study, 10 strength-trained men performed 5 sets of 5 maximal voluntary knee extensions of the right leg on an isokinetic dynamometer, preceded by either IPC of the right lower limb (3×5-min compression/5-min reperfusion cycles at 200 mm Hg) or sham (20 mm Hg). Changes in deoxyhemoglobin, expressed as a percentage of arterial occlusion, and total hemoglobin ([THb]) concentrations of the vastus lateralis muscle were monitored continuously by near-infrared spectroscopy. Differences between IPC and sham were analyzed using Cohen's effect size (ES) ± 90% confidence limits, and magnitude-based inferences. Compared with sham, IPC likely increased muscle blood volume at rest (↑[THb], 46.5%; ES, 0.56; 90% confidence limits for ES, -0.21, 1.32). During exercise, peak force was almost certainly higher (11.8%; ES, 0.37; 0.27, 0.47), average force was very likely higher (12.6%; ES, 0.47; 0.29, 0.66), and average muscle O2 uptake was possibly increased (15.8%; ES, 0.36; -0.07, 0.79) after IPC. In the recovery periods between contractions, IPC also increased blood volume after sets 1 (23.6%; ES, 0.30; -0.05, 0.65) and 5 (25.1%; ES, 0.32; 0.09, 0.55). Three cycles of IPC immediately increased muscle perfusion and O2 uptake, conducive to higher repeated force capacity in strength-trained athletes. This maneuver therefore appears relevant to enhancing exercise training stimulus.
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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 | 0.001 |
| 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) | 0.001 | 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".