Impact of exercise training on pulse wave velocity in healthy and clinical populations: a systematic review of systematic reviews
Bibliographic record
Abstract
Elevated pulse wave velocity (PWV) determined arterial stiffness is a prominent marker of cardiovascular disease (CVD) risk. Exercise training delays the progression of CVD, but existing reviews on the impact of training on PWV are conflicting. We synthesized the evidence on the effects of exercise training interventions on PWV. We searched Scopus, EMBASE, PubMed, CINAHL, and Academic Search Premier databases for systematic reviews including PWV, and examined the effects of exercise training on PWV. We screened 842 citations that resulted in 44 systematic reviews, including 22 meta-analyses [unique participants, n = 6,719 (3,390 females)]. Studies were conducted in general adults with/without disease(s) ( n = 19, 8 meta-analyses), kidney disease ( n = 9, 6 meta-analyses), increased CVD risk or CVD ( n = 7, 5 meta-analyses), type 2 diabetes mellitus ( n = 3, 2 meta-analyses), and other conditions ( n = 6, 2 meta-analyses). In general adults, PWV was reduced by aerobic exercise (β, −0.75 to −0.52 m/s) and low-to-moderate intensity resistance exercise training (β, −0.34 m/s). Exercise training was beneficial for patients with kidney disease (β, −1.13 to −0.56 m/s). Aerobic exercise improved PWV in adults with CVD or high CVD risk (β, −0.70 to −0.42 m/s). Combined aerobic and resistance exercise training decreased carotid-femoral (CF) PWV in patients with CVD (β, −1.15 m/s) and decreased brachial-ankle (BA) PWV in postmenopausal females (β, −1.18 m/s). Neither aerobic nor combined training improved PWV in individuals with type 2 diabetes. The potential physiological mechanisms involved are discussed. Overall, the included systematic reviews and meta-analyses documented that exercise training was an effective strategy to improve PWV, but the optimal type of training varied between populations.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".