Correlations of gait kinematics and cognitive skills in Parkinson disease
Bibliographic record
Abstract
Cognitive impairments and gait disturbances are often concurrent in Parkinson disease (PD), but the extent to which specific cognitive deficits relate to gait abnormalities remains unclear, especially in early-stage PD. To address this gap, we conducted an observational correlational study to determine if cognitive performance in distinct domains is associated with gait kinematics in PD. This study included a cohort of 19 individuals with early-stage PD who underwent gait analysis with a three-dimensional marker-less motion capture system (Theia Markerless Inc., Kingston, ON, CA) during three conditions: single-task walking, cognitive dual-task walking, and the Timed Up and Go (TUG) test. Cognitive abilities were assessed using computerized tests (Creyos, Toronto, ON, CA) of working memory (WM), response inhibition (RI), and mental rotation (MR). We found that higher WM scores significantly correlated with better gait performance under dual-task conditions, including faster gait velocity (r = 0.6524, p = 0.0025), longer stride length (r = 0.6758, p = 0.0015), higher toe clearance (r = 0.5234, p = 0.0215), and greater hip range of motion (ROM) (r = 0.6803, p = 0.0014). Similarly, better MR ability was associated with longer stride length (r = 0.5178, p = 0.0231) and greater hip ROM (r = 0.4886, p = 0.0338) during dual-task walking. Furthermore, superior WM and MR performance were linked to faster TUG completion times, particularly during the second walking phase of the TUG (WM: r = -0.682, p = 0.0013; MR: r = -0.6755, p = 0.002). These results indicate that WM and MR may be linked to gait performance in PD, especially during cognitively challenging walking tasks. These findings may have clinical, rehabilitative, and neuroscientific utility for those involved in the study and management of PD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".