Determining the Effect of Dual-task Training and Virtual Reality on Cognitive-motor Interference in Patients with Parkinson’s Disease: A Three-arm Single-blinded Multicentered Study Protocol
Bibliographic record
Abstract
Introduction: In Parkinson’s Disease (PD), rehabilitation is a highpotential strategy for enhancing mental and physical abilities. Numerous studies have examined the impact of dual-task training on enhancing gait, balance, motor symptoms, and cognitive function in individuals with PD. Research have shown that virtual reality significantly enhances gait and balance in patients with PD compared to traditional therapy. However, there is a scarcity of literature that explores the combined effects of Dual-task Training (DTT) and Virtual Reality (VR) on Cognitive-Motor Interference (CMI) in individuals with PD. Need for this study: DTT effectively improves cognitive deficits, while VR enhances motor abilities in individuals with PD. Hence, it would be expected that the combined treatment can greatly benefit the patients with PD. Aim: To determine the effect of DTT and VR on CMI in patients with PD. Materials and Methods: The participants recruited in this study protocol will be between 50 and 70 years old and randomly allocated into three groups. For five times a day for four weeks, experimental group 1 will receive treatment with VR, group 2 will receive DTT and group 3 will receive combined treatment of group 1 and group 2. Outcome measures, such as a modified version of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), Montreal Cognitive Assessment (MoCA), and the Timed Up-andGo test (TUG), will be used to assess the subject pre-intervention and post-intervention.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".