Exploring vascular mechanical properties regulating blood flow during rhythmic handgrip exercise in humans
Bibliographic record
Abstract
This study tested the hypothesis that vascular compliance and viscoelasticity in the skeletal muscle vascular bed regulate blood flow to the active forearm muscle during rhythmic handgrip (RHG) exercise. Beat-by-beat heart rate (HR; ECG), brachial artery blood flow velocity (Doppler Ultrasound), mean arterial pressure (MAP; Finometer), and cardiac output (CO) were measured in eleven healthy young participants (6 females, 20-27 years) during 5-minute baseline (BSL) condition followed by 5-minutes of RHG exercise (work/rest schedule: 1 second at 40% of maximal voluntary contraction /2 seconds rest). A four-element modified Windkessel model quantified beat-by-beat vascular resistance, compliance, and viscoelasticity from blood pressure and flow velocity waveforms. Brachial artery blood flow was calculated as the product of mean blood velocity and brachial artery cross-sectional area. Repeated-measures one-way analysis of variance (ANOVA, α = 0.05) provided statistical comparisons between BSL and minutes 1 (RHG-Min1), 3 (RHG-Min3), and 5 (RHG-Min5) of RHG. Data are reported as mean ± SD. Post hoc analysis was used to compare minutes 1, 3 and 5 of RHG to BSL. RHG exercise increased blood flow (BSL: 73 ± 35; RHG-Min1: 306 ± 155; RHG-Min3: 392 ± 200; RHG-Min5: 400 ± 219 mL/min; P main effect < 0.0001; all P post hoc < 0.0001 ) and reduced vascular resistance (BSL: 15 ± 8; RHG-Min1: 4 ± 1; RHG-Min3: 3 ± 0.8; RHG-Min5: 3 ± 0.9 mmHg/cm/s; P main effect < 0.0001; all P post hoc < 0.0001 ). RHG increased viscoelasticity (BSL: 0.37 ± 0.15; RHG-Min1: 0.86 ± 0.34; RHG-Min3: 0.98 ± 0.50; RHG-Min5: 0.99 ± 0.48 mmHg/cm/s; P main effect < 0.0001; all P post hoc < 0.0001 ) but did not affect compliance (BSL: 0.0017 ± 0.0005; RHG-Min1: 0.0021 ± 0.0007; RHG-Min3: 0.0023 ± 0.0010; RHG-Min5: 0.0020 ± 0.0008 cm/s/mmHg; P main effect = 0.14; all P post hoc > 0.05 ). Also, RHG increased HR (BSL: 69 ± 13; RHG-Min1: 73 ± 13; RHG-Min3: 75 ± 14; RHG-Min5: 76 ± 14 bpm; P main effect < 0.0001; all P post hoc < 0.05 ), CO (BSL: 6.3 ± 1.7; RHG-Min1: 6.9 ± 1.9; RHG-Min3: 7.0 ± 1.9; RHG-Min5: 7.1 ± 1.8 L/min; P main effect < 0.0001; all P post hoc < 0.01 ), and MAP (BSL: 93± 9; RHG-Min1: 98 ± 9; RHG-Min3: 100 ± 8; RHG-Min5: 101 ± 8 mmHg; P main effect < 0.0001; all P post hoc < 0.001 ). Therefore, these data highlight a role for vascular mechanical properties such as viscoelasticity in the regulation of human skeletal muscle blood flow during dynamic exercise. This work was funded by Brock University Horizon Scholarship, Ontario Graduate Scholarship, and Natural Sciences & Engineering Research Council of Canada Discovery Grant. This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".