Cognitive and Linguistic Benefits of Aerobic Exercise: A State-of-the-Art Systematic Review of the Stroke Literature
Bibliographic record
Abstract
This systematic review aimed to determine how aerobic exercise affects cognition after stroke, with particular focus on aphasia and language improvement. Methodological quality was assessed with the PEDro+ scale with half of the 27 included studies rated as high quality. Data extraction focused on cognitive effects of aerobic exercise post-stroke, intervention characteristics, outcome measures, and participant characteristics. Whereas attention, memory, and executive functioning measures were common across the included studies, no study included a language-specific, performance-based measure. Seventeen studies reported positive cognitive effects, most frequently in the domains of attention, memory and executive functioning. Variability in outcome measures, intervention characteristics, and participant characteristics made it difficult to identify similarities among studies reporting positive cognitive effects of exercise or among those studies reporting null outcomes. Only three studies provided specific information about the number of individuals with aphasia included or excluded, who comprise approximately one-third of the stroke population. The review identified patent gaps in our understanding of how aerobic exercise may affect not only the cognitive domain of language post-stroke but also the broader cognitive functioning of individuals with post-stroke aphasia. Methodological limitations of the reviewed studies also warrant further examination of the direct impact of aerobic exercise on cognition post-stroke with careful attention to the selection and reporting of population, intervention, and outcomes.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| 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) | 0.006 | 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".