Interactions between cognitive and sensory load while planning and controlling complex gait adaptations in Parkinson’s disease
Bibliographic record
Abstract
BACKGROUND: Recent research has argued that removal of relevant sensory information during the planning and control of simple, self-paced walking can result in increased demand on central processing resources in Parkinson's disease (PD). However, little is known about more complex gait tasks that require planning of gait adaptations to cross over an obstacle in PD. METHODS: In order to understand the interaction between availability of visual information relevant for self-motion and cognitive load, the current study evaluated PD participants and healthy controls while walking toward and stepping over an obstacle in three visual feedback conditions: (i) no visual restrictions; (ii) vision of the obstacle and their lower limbs while in complete darkness; (iii) vision of the obstacle only while in complete darkness; as well as two conditions including a cognitive load (with a dual task versus without a dual task). Each walk trial was divided into an early and late phase to examine changes associated with planning of step adjustments when approaching the obstacle. RESULTS: Interactions between visual feedback and dual task conditions during the obstacle approach were not significant. Patients with PD had greater deceleration and step time variability in the late phase of the obstacle approach phase while walking in both dark conditions compared to control participants. Additionally, participants with PD had a greater number of obstacle contacts when vision of their lower limbs was not available specifically during the dual task condition. Dual task performance was worse in PD compared to healthy control participants, but notably only while walking in the dark regardless of visual feedback. CONCLUSIONS: These results suggest that reducing visual feedback while approaching an obstacle shifts processing to somatosensory feedback to guide movement which imposes a greater demand on planning resources. These results are key to fully understanding why trips and falls occur in those with 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 | 0.000 | 0.002 |
| 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 | 0.001 | 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".