Similar Recovery of Maximal Cycling Performance after Ischemic Preconditioning, Neuromuscular Electrical Stimulation or Active Recovery in Endurance Athletes
Bibliographic record
Abstract
This study investigated the efficacy of ischemic preconditioning (IPC) on the recovery of maximal aerobic performance and physiological responses compared with commonly used techniques. Nine endurance athletes performed two 5-km cycling time trials (TT) interspersed by 45 minutes of recovery that included either IPC, active recovery (AR) or neuromuscular electrical stimulation (NMES) in a randomized crossover design. Performance, blood markers, arterial O2 saturation (SpO2), heart rate (HR), near-infrared spectroscopy-derived muscle oxygenation parameters and perceptual measures were recorded throughout TTs and recovery. Differences were analyzed using repeated-measures ANOVAs and Cohen’s effect size (ES). The decrement in chronometric performance from TT1 to TT2 was similar between recovery modalities (IPC: -6.1 sec, AR: -7.9 sec, NMES: -5.4 sec, p = 0.84, ES 0.05). The modalities induced similar increases in blood volume before the start of TT2 (IPC: 13.3%, AR: 14.6%, NMES: 15.0%, p = 0.79, ES 0.06) and similar changes in lactate concentration and pH. There were negligible differences between conditions in bicarbonate concentration, base excess of blood and total concentration of carbon dioxide, and no difference in SpO2, HR and muscle O2 extraction during exercise (all p > 0.05). We interpreted these findings to suggest that IPC is as effective as AR and NMES to enhance muscle blood volume, metabolic by-products clearance and maximal endurance performance. IPC could therefore complement the athlete’s toolbox to promote recovery.
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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) | 0.000 | 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".