EFFECT OF EXERCISE OR MENTAL AND SOCIAL ACTIVITIES ON INTRAINDIVIDUAL VARIABILITY OF COGNITIVE PERFORMANCE IN STROKE
Bibliographic record
Abstract
Abstract Stroke is associated with increased risk of cognitive impairment. Evidence has shown exercise to be a useful strategy for improving cognitive function in this population. Intraindividual variability (IIV), a sensitive measure of cognitive performance, is the within-person trial-to-trial variation in reaction time during cognitive tasks. Whether exercise training also improves IIV of cognitive performance in stroke is unknown. The study was a six-month single-blinded, 3-group parallel randomised controlled trial. Participants included community dwelling adults (N= 119, 38.7% female) with a history of stroke, aged above 55 years (mean= 70.71, SD= 8.59), able to walk 6 meters, and without dementia. Participants were randomly allocated to twice-weekly supervised classes of 1) exercise training (EX); (2) cognitive and social activities (ENRICH); or (3) a balance and tone (BAT; control). Residualised intraindividual standard deviation (rISD) was used as measure of IIV and computed using trial latencies of a computerised Stroop Task. IIV was assessed at baseline, 6-and-12 months. At 6-and-12 months, there was no significant reduction in IIV for all three groups. Furthermore, there were no significant differences in the IIV scores of the EX and BAT (estimated mean difference= 0.30, SE= 0.53, p-value = 0.89) or ENRICH and BAT (estimated mean difference= 0.64, SE= 0.55, p-value = 0.40) groups. The findings suggest that a 6-month program of exercise or mental and social activities does not significantly impact IIV of cognitive performance in stroke. Further studies are required to determine if IIV can be improved using other types of exercise or cognitive training programs.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".