THE LINK BETWEEN INTRAINDIVIDUAL VARIABILITY IN COGNITIVE PERFORMANCE AND MOBILITY IN CHRONIC STROKE
Bibliographic record
Abstract
Abstract Intraindividual variability (IIV) is the within-person trial-to-trial variation in reaction time during cognitive tasks. Higher IIV represents reduced consistency in responses, which may be due to lapses in attention, and is associated with reduced mobility in older adults. IIV may also be a more sensitive measure of cognitive performance versus traditional summary scores. A stroke can significantly impact one’s cognitive function and mobility. Whether IIV in cognitive performance is associated with mobility among individuals who have experienced a stroke is unknown. We aimed to examine this relationship by using baseline data from 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 55 years and older (mean= 70.71 years, SD= 8.59), able to walk 6 meters, and without dementia. Mobility was assessed based on timed up and go (TUG) performance. Residualised intra-individual standard deviation (rISD) was used as measure of IIV and computed using trial latencies of a computerised Stroop Task. TUG scores significantly predicted rISD of congruent (beta= 0.03, SE= 0.01, p-value = 0.001) and neutral trials (beta= 0.02, SE= 0.01, p-value = 0.04), but not incongruent trials (beta= 0.01, SE= 0.01, p-value = 0.18). However, these effects were not found when age was included as a covariate. Overall, these findings suggest that in adults with stroke, higher IIV measured from a simple reaction time task is associated with worse mobility. Further longitudinal studies are needed to determine if higher IIV is predictive of mobility decline post-stroke.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".