The effects of habitual resistance exercise training on cerebrovascular responses to lower body dynamic resistance exercise: A cross‐sectional study
Bibliographic record
Abstract
Abstract Dynamic resistance exercise (RE) produces sinusoidal fluctuations in blood pressure with simultaneous fluctuations in middle cerebral artery blood velocity (MCAv). Some evidence indicates that RE may alter cerebrovascular function. This study aimed to examine the effects of habitual RE training on the within‐RE cerebrovascular responses. RE‐trained (n = 15, Female = 4) and healthy untrained individuals (n = 15, Female = 12) completed four sets of 10 paced repetitions (15 repetitions per minute) of unilateral leg extension exercise at 60% of predicted 1 repetition maximum. Beat‐to‐beat blood pressure, MCAv and end‐tidal carbon dioxide were measured throughout. Zenith, nadir and zenith‐to‐nadir difference in mean arterial blood pressure (MAP) and mean MCAv (MCAvmean) for each repetition were averaged across each set. Two‐way ANOVA was used to analyse dependent variables (training × sets), Bonferroni corrected t‐tests were used for post hoc pairwise comparisons. Group age (26 ± 7 trained vs. 25 ± 6 years untrained, P = 0.683) and weight (78 ± 15 vs. 71 ± 15 kg, P = 0.683) were not different. During exercise average MAP was greater for the RE‐trained group in sets 2, 3 and 4 (e.g., set 4: 101 ± 11 vs. 92 ± 7 mmHg for RE trained and untrained, respectively, post hoc tests all P = < 0.012). Zenith MAP and zenith‐to‐nadir MAP difference demonstrated a training effect (P < 0.039). Average MCAvmean and MCAvmean zenith‐to‐nadir difference was not different between groups (interaction effect P = 0.166 and P = 0.459, respectively). Despite RE‐trained individuals demonstrating greater fluctuations in MAP during RE compared to untrained, there were no differences in MCAvmean. Regular RE may lead to vascular adaptations that stabilise MCAv during RE.
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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) | 0.001 | 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".