Pulse wave reflection responses to bench press with and without practical blood flow restriction
Bibliographic record
Abstract
Resistance exercise is recommended to increase muscular strength but may also increase pulse wave reflection. The effect of resistance exercise combined with practical blood flow restriction (pBFR) on pulse wave reflection is unknown. The purpose of this study was to evaluate the differences in pulse wave reflection characteristics between bench press with pBFR and traditional high-load bench press in resistance-trained men. Sixteen resistance-trained men participated in the study. Pulse wave reflection characteristics were assessed before and after low-load bench press with pBFR (LL-pBFR), traditional high-load bench press (HL), and a control (CON). A repeated-measures ANOVA was used to evaluate differences in pulse wave reflection characteristics among the conditions across time. There were significant (p ≤ 0.05) interactions for heart rate, augmentation index, augmentation index normalized at 75 bpm, augmentation pressure, time-tension index, and wasted left ventricular energy such that they were increased after LL-pBFR and HL compared with rest and CON, with no differences between LL-pBFR and HL. Aortic pulse pressure (p < 0.001) was elevated only after LL-pBFR compared with rest. In addition, there was a significant (p ≤ 0.05) interaction for aortic diastolic blood pressure (BP) such that it was decreased after LL-pBFR compared with rest and CON but not HL. The subendocardial viability ratio and diastolic pressure-time index were significantly different between LL-pBFR and HL compared with rest and CON. There were no significant interactions for brachial systolic or diastolic BP, aortic systolic BP, or time of the reflected wave. In conclusion, acute bench press resistance exercise significantly altered pulse wave reflection characteristics without differences between LL-pBFR and HL.
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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 | 0.001 |
| 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) | 0.002 | 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".