Effects of Galvanic Vestibular Stimulation on Upper and Lower Extremities Motor Symptoms in Parkinson’s Disease
Bibliographic record
Abstract
As a neurodegenerative movement disorder, Parkinson’s disease (PD) is commonly characterized by motor symptoms such as resting tremor, rigidity, bradykinesia, and balance and postural impairments. While the main cause of PD is still not clear, it is shown that the basal ganglia loop, which has a role in adjusting a planned movement execution through fine motor control, is altered during this disease and contributes towards the manifested motor symptoms. Galvanic vestibular stimulation (GVS) is a non-invasive technique to influence the vestibular system and stimulate the motor system. This study explores how the motor symptoms of upper and lower extremities in PD are instantly affected by vestibular stimulation. In this regard, direct current GVS was applied to 11 individuals with PD on medication while they were performing two sets of experiments: 1) Instrumented Timed Up and Go (iTUG) test and 2) finger tapping task. The performance of participants was recorded with accelerometers and cameras for offline processing of data. Several outcome measures including coefficient of variation of the step duration, gait phase, phase coordination index, tapping score, and the number and duration of manual motor blocks were considered for objective quantifying of performance. Results showed that almost all of considered outcome measures were improved with the application of GVS and that the improvement in the coefficient of variation of the step duration, the tapping score, and the number of manual motor blocks was statistically significant (p-value<0.05). The results of this study suggest that GVS can be used to alleviate some of the common motor symptoms of PD. Further research is required to fully characterize the effects of GVS and determine its efficacy in the long term.
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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 | 0.000 |
| 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".