A Single Session of Exercise Reduces Blood Pressure Reactivity to Stress: a Systematic Review With Meta-analysis
Bibliographic record
Abstract
Abstract Stressful situations are common in everyday life and disturb homeostasis. So, an exercise session is a strategy to mitigate blood pressure (BP) peaks in response to stress (i.e., BP reactivity), decreasing the cardiovascular risk of these individuals. This is a systematic review with a meta-analysis that aims to verify the effects of a single session of physical exercises on BP reactivity to stress. The searches were realized in digital databases (PUBMED, LILACS, EMBASE and PsycInfo) and 28 studies were included, totaling 846 individuals (meta-analysis stage: k = 24, n = 710). As for exercise characteristics, 23 of the 28 studies focused on aerobic exercises, and 24 studies focused on low to moderate intensities. Favorable metanalytic results (standardized mean differences through random-effects approach) for the exercises were found, with attenuated reactivity in systolic BP (pooled effect size = -0.35 [-0.46; -0.23], representing average reductions of 3.8 ± 3.5 mmHg), diastolic BP (pooled effect size = -0.49 [-0.68; -0.30], representing average reductions of 3.1 ± 3.6 mmHg), and mean BP (pooled effect size = -0.48 [-0.70; -0.26], representing average reductions of 4.1 ± 3.0 mmHg). So, acute physical exercise lowers systolic, diastolic, and mean blood pressure reactivity in response to stressor tasks.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.005 | 0.005 |
| 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".