Improved cognition after high-intensity exercise paired with motor practice in individuals with stroke and older adults: A randomized controlled trial
Bibliographic record
Abstract
Abstract Introduction Stroke is a leading cause of long-term disability resulting in cognitive and motor impairments. Exercise may improve cognition and motor function. We paired multiple bouts of high-intensity interval training (HIIT) exercise with motor practice to positively affect cognitive and motor function after stroke and age-matched controls. Methods Using a randomized controlled parallel group design, 31 individuals with chronic stroke and 41 older adult controls were randomized to either 23 minutes of HIIT exercise or rest prior to completing motor task practice using their paretic/non-dominant arm across five days. Primary outcomes were reaction time and motor function. Assessors were blinded to the intervention group. Trail Making Test-A and B (TMT-A, TMT-B), and object hit and avoid (OHA) were used to assess processing speed and inhibitory control. Results All participants showed evidence of motor learning; HIIT exercise did not confer an additional benefit. For stroke participants, motor function ( p = .047), but not motor impairment, improved. The stroke exercise group displayed significant reductions in TMT-A completion time ( p = .026). Exercise with motor practice also led to a reduced number of distractors hit ( p = .035) in the OHA task for both participant groups. There were no adverse events. Conclusions Five days of HIIT exercise paired with motor practice led to improved processing speed for individuals with stroke. Both participant, exercise groups showed improved visuospatial skills and inhibitory control. Together, HIIT exercise paired with motor practice appears to be a safe and effective means of enhancing cognitive-motor skills after stroke and in older adults. Trial Registry ClinicalTrials.gov , ID: NCT02980796 Trial registry name The Influence of Exercise on Neuroplasticity and Motor Learning After Stroke (EX-ML3)
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".