Effects of cardiorespiratory fitness and exercise training on cerebrovascular blood flow and reactivity: a systematic review with meta-analyses
Bibliographic record
Abstract
We address two aims: Aim 1 (Fitness Review) compares the effect of higher cardiorespiratory fitness (CRF) (e.g., endurance athletes) with lower CRF (e.g., sedentary adults) on cerebrovascular outcomes, including middle cerebral artery velocity (MCAv), cerebrovascular reactivity and resistance, and global cerebral blood flow, as assessed by transcranial Doppler (TCD) or magnetic resonance imaging (MRI). Aim 2 (Exercise Training Review) determines the effect of exercise training on cerebrovascular outcomes. Systematic review of studies with meta-analyses where appropriate. Certainty of evidence was assessed by the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Twenty studies (18 using TCD) met the eligibility criteria for Aim 1, and 14 studies (8 by TCD) were included for Aim 2. There was a significant effect of higher CRF compared with lower CRF on cerebrovascular resistance (effect size = −0.54, 95% confidence interval = −0.91 to −0.16) and cerebrovascular reactivity (0.98, 0.41–1.55). Studies including males only demonstrated a greater effect of higher CRF on cerebrovascular resistance than mixed or female studies (male only: −0.69, −1.06 to −0.32; mixed and female studies: 0.10, −0.28 to 0.49). Exercise training did not increase MCAv (0.05, −0.21 to 0.31) but showed a small nonsignificant improvement in cerebrovascular reactivity (0.60, −0.08 to 1.28; P = 0.09). Exercise training showed heterogeneous effects on regional but little effect on global cerebral blood flow as measured by MRI. High CRF positively effects cerebrovascular function, including decreased cerebrovascular resistance and increased cerebrovascular reactivity; however, global cerebral blood flow and MCAv are primarily unchanged following an exercise intervention in healthy and clinical populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".