Prenatal exercise and cardiovascular health (PEACH) study: the remote effect of aerobic exercise training on conduit artery and resistance vessel function
Bibliographic record
Abstract
We assessed the impact of a structured lower-limb aerobic exercise training intervention during pregnancy on brachial artery endothelial function, shear rate and patterns, and forearm blood flow and reactive hyperemia. Twenty-seven pregnant women were recruited and randomized into either a control group (n = 11; 31.0 ± 0.7 years), or an exercise intervention group (n = 16; 32.6 ± 0.9 years). The exercise group completed 40 minutes of aerobic exercise (50–70% heart rate reserve) 3–4 times per week, between the second and third trimester of pregnancy. Endothelial function was assessed using flow-mediated dilation (FMD, normalized for shear stress) at pre- (16–20 weeks) and post-intervention (34–36 weeks). The exercise training group experienced an attenuated increase in mean arterial pressure (MAP) relative to the control group (ΔMAP exercise: +2 ± 2 mm Hg vs. control: +7 ± 3 mm Hg; p = 0.044) from pre- to post-intervention. % FMD change corrected for shear stress was not different between groups (p = 0.460); however, the post-occlusion mean flow rate (exercise: 437 ± 32 mL/min vs. control: 364 ± 35 mL/min; p = 0.001) and post-occlusion anterograde flow rate (exercise: 438 ± 32 mL/min vs. control: 364 ± 46 mL/min; p = 0.001) were larger for the exercise training group compared with controls, post-intervention. Although endothelial function was not different between groups, we observed an increase in microcirculatory dilatory capacity, as suggested by the augmented reactive hyperemia in the exercise training group. Registered at ClinicalTrials.gov: NCT02948439. Novelty: Endothelial function was not altered with exercise training during pregnancy. Exercise training did contribute to improved cardiovascular outcomes, which may have been associated with augmented reactive hyperemia, indicative of increased microcirculatory dilatory capacity.
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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 | 0.001 |
| 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 | 0.001 |
| 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".