Interlimb coordination in Parkinson’s Disease is minimally affected by a visuospatial dual task
Bibliographic record
Abstract
Parkinson’s disease (PD) leads to reduced spatial and temporal interlimb coordination during gait as well as reduced coordination in the upper or lower limbs. Multi-tasking when walking is common during real-world activities, and affects some gait characteristics, like gait speed and variability. However, the impact of a dual task (DT) on intra and interlimb coordination of both lower and upper limbs when walking in people with PD remains unknown. Seventeen volunteers with mild to moderate PD (11 males, 65 ± 8 years, 173 ± 8 cm, 74 ± 20 kg, Unified Parkinson’s Disease Rating Scale motor section 10 ± 5) participated in gait trials in an Extended-CAREN system, which includes a treadmill, 12-camera Vicon motion capture system, and a 180° field-of-view virtual reality projection screen. Participants completed a 3 min walking trial and a 2 min visuospatial word recognition DT trial at their preferred walking pace. Single and DT were compared with a paired t-test, and the less and more affected (LA, MA) sides were tested for equivalence in sensitivity to the DT. During the DT, we found the LA shoulder ROM decreased by 1.5°, and the LA shoulder peak flexion decreased by 1.1° (p<.028, g av >.12). The LA and MA hip ROM were differently affected by the dual task (p=.023), and intralimb coordination was affected by dual tasking equivalently between sides (p=.004). These results suggest that during normal single-task gait, people with PD use attentional resources to compensate for reduced arm swing. Furthermore, our results indicate that any effect of DT on lower intralimb coordination is not meaningfully different between the LA and MA sides.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".