Does exercise modality and posture influence cerebrovascular and cardiovascular systems similarly?
Bibliographic record
Abstract
Cerebral hemodynamics have been quantified during exercise via transcranial Doppler ultrasound, as it has high-sensitivity to movement artifacts and displays temporal superiority. Currently, limited research exists regarding how different exercise modalities and postural changes impact the cerebrovasculature across the cardiac cycle. Ten participants (4 females and 6 males) ages 20–29 completed three exercise tests (treadmill, supine, and upright cycling) to volitional fatigue. Physiological data collected included middle cerebral artery velocity (MCAv), blood pressure (BP), heart rate, and respiratory parameters. Normalized data were analyzed for variance and effect sizes were calculated to examine differences between physiological measures across the three exercise modalities. Systolic MCAv was greater during treadmill compared to supine and upright cycling ( p < 0.001, (large) effect size), and greater during upright versus supine cycling ( p < 0.017, (large)). Diastolic MCAv was lower during treadmill versus cycling exercise only at 60% maximal effort ( p < 0.005, (moderate)) and no differences were observed between upright and supine cycling. No main effect was found for mean and diastolic BP ( p > 0.05, (negligible)). Systolic BP was lower during treadmill versus supine cycling at 40% and 60% intensity ( p < 0.05, (moderate–large)) and greater during supine versus upright at only 60% intensity ( p < 0.003, (moderate)). The above differences were not explained by partial pressure of end-tidal carbon dioxide levels (main effect: p = 0.432) . The current study demonstrates the cerebrovascular and cardiovascular systems respond heterogeneously to different exercise modalities and aspects of the cardiac cycle. As physiological data were largely similar between tests, differences associated with posture and modality are likely contributors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".