Ischemic Preconditioning with High and Low Pressure Enhances Maximum Strength and Modulates Heart Rate Variability
Bibliographic record
Abstract
Background: The application of ischemic preconditioning (IPC) to resistance exercise has attracted some attention, owing to increases in muscle performance. However, there is still no consensus on the optimal occlusion pressure for this procedure. This study compared the acute effects of IPC with high and low pressure of occlusion on upper and lower limb maximal strength and heart rate variability in recreationally trained individuals. Methods: Sixteen recreationally trained men (25.3 ± 1.7 years; 78.4 ± 6.2 kg; 176.9 ± 5.4 cm; 25.1 ± 1.5 m2 kg−1) were thoroughly familiarized with one repetition maximum (1 RM) testing in the following exercises: bench press (BP), front latissimus pull-down (FLPD), and shoulder press (SP) for upper limbs, and leg press 45º (LP45), hack machine (HM), and Smith Squat (SS) for lower limbs. The 1 RM exercises were then randomly performed on three separate days: after a high pressure (220 mmHg, IPChigh) and a low pressure (20 mmHg, IPClow) IPC protocol and after no intervention (control, CON). Heart rate variability was also measured at rest, during and after the entire IPC protocol, and after the exercises. Results: Maximal strength was significantly (p < 0.05) higher in both IPChigh and IPClow compared with CON in all upper- and lower-limb exercises. There was no difference between the two experimental conditions. No significant differences were found in the comparison across the different experimental conditions for LFnu, HFnu, LF/HF ratio, and RMSSDms. Conclusions: IPC performed with both high and low pressures influenced heart rate variability, which may partly explain the maximal strength enhancement.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".