Exercise effects on consolidation of speech and language training in post-stroke aphasia: a case report
Bibliographic record
Abstract
Post-stroke aphasia severely impacts communication and quality of life. Aerobic exercise enhances learning and memory in healthy adults, with evidence suggesting benefits for verbal tasks. Research exploring its effects in stroke patients with aphasia is minimal. This case study investigated the effects of combining speech and language therapy (SLT) with high-intensity aerobic exercise on speech performance in post-stroke aphasia. Over 4 weeks, two participants with post-stroke anomic aphasia engaged in daily 20-min SLT sessions focused on naming activities. Speech training was followed by 20-min of high-intensity interval exercise on alternate days (Tuesday, Thursday). Speech performance was assessed daily, and the Western Aphasia Battery was used to assess expressive and receptive language skills before and after the intervention. Participants demonstrated greater day-to-day speech performance gains the following days when exercise was performed immediately after speech training (Cohen’s d range: 2.40–2.59), suggesting that exercise enhanced consolidation of learned speech skills. Participants also demonstrated improved aphasia quotient scores via the Western Aphasia Battery following completion of the intervention. Results suggest potential benefits of combining SLT with aerobic exercise for rehabilitation of anomic aphasia. Findings may contribute to the development of novel approaches to facilitate response to post-stroke language rehabilitation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".