Exercise effects on maternal vascular health and blood pressure during pregnancy and postpartum: a systematic review and meta-analysis
Bibliographic record
Abstract
AIMS: This systematic review aimed to assess the effects of exercise training during pregnancy and the postpartum period on maternal vascular health and blood pressure (BP). METHODS AND RESULTS: The outcome of interest was pulse wave velocity (PWV), flow-mediated dilation (FMD), and BP from pregnancy to 1-year postpartum. Five databases, including Ovid MEDLINE, EMBASE, CINAHL, Web of Science, and Cochrane Library, were systematically searched from inception to August 2023. Studies of randomized controlled trials (RCTs) comparing the effects of prenatal or postpartum exercise to a non-exercise control group were included. The risk of bias and the certainty of evidence were assessed. Random-effects meta-analyses and sensitivity analyses were conducted. In total, 20 RCTs involving 1221 women were included. Exercise training, initiated from Week 8 during gestation or between 6 and 14 weeks after delivery, with the programme lasting for a minimum of 4 weeks up to 6 months, showed no significant impact on PWV and FMD. However, it resulted in a significant reduction in systolic BP (SBP) [mean difference (MD): -4.37 mmHg; 95% confidence interval (CI): -7.48 to -1.26; P = 0.006] and diastolic BP (DBP) (MD: -2.94 mmHg; 95% CI: -5.17 to -0.71; P = 0.01) with very low certainty. Subgroup analyses revealed consistent trends across different gestational stages, types of exercise, weekly exercise times, and training periods. CONCLUSION: Exercise training during pregnancy and the postpartum period demonstrates a favourable effect on reducing maternal BP. However, further investigations with rigorous methodologies and larger sample sizes are needed to strengthen these conclusions.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".