The Effectiveness of Exercise on Cognitive Performance in Individuals with Known Vascular Disease: A Systematic Review
Bibliographic record
Abstract
Patients with known vascular disease are at increased risk for cognitive impairments. Exercise has been shown to improve cognition in healthy elderly populations and those with mild cognitive impairments. We explored the literature to understand exercise as a modality to improve cognition in those with vascular disease, focusing on dose-responses. A systematic review was conducted through 2017 using Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Ovid Embase, and Ovid MEDLINE databases. Eligible studies examined effects of exercise on memory and cognition in cardiovascular (CVD) or cerebrovascular disease (CBVD). Data extracted included group characteristics, exercise dosage and outcomes measures employed. Twenty-two studies (12 CVD, 10 CBVD) met the inclusion criteria. Interventions included aerobic, resistance, or mixed training, with neuropsychological test batteries assessing cognition. In CVD populations, five studies demonstrated improved cardiovascular fitness and cognition with aerobic training, and another seven studies suggested a dose-response. In CBVD trials, four studies reported improved cognition, with no effects observed in the fifth study. Another study found enhanced cognition with resistance training and four demonstrated a positive association between functional capacity and cognition following combined aerobic and resistance training. Exercise is able to positively affect cognitive performance in those with known vascular disease. There is evidence to suggest a dose⁻response relationship. Further research is required to optimize prescription.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".