Evaluating the Impact of Adding Lee Silverman Voice Treatment BIG into Routine Physiotherapy on Both Motor and Nonmotor Functions in Individuals with Parkinson’s Disease
Bibliographic record
Abstract
Introduction: Parkinson’s Disease (PD) is a disorder that causes both motor and nonmotor symptoms. While PD typically appears in older adults, it can also affect individuals in the later stages of middle age. Traditional drug therapies have side effects and diminishing returns. Exercise, particularly high-intensity programs like Lee Silverman Voice Treatment BIG (LSVT BIG), can enhance motor skills and overall quality of life by recalibrating the sensory system. Method: This research included 40 individuals with PD, who were separated into a control group (standard physiotherapy) and an intervention group (physiotherapy + LSVT-BIG). Participants were from a local hospital, aged between 35 and 70, with stable medication and Montreal Cognitive Assessment (MoCA) scores > 20. Exclusions included active exercise programs and severe mental disorders. Participants were evaluated by an LSVT BIG-certified physical therapist and completed surveys on medical history and current issues. Tests included Timed Up & Go (TUG), TUG manual, and TUG cognitive. Both groups received 16 one-hour exercise sessions over 4 weeks. Statistical analyses included Kolmogorov-Smirnov for normality, independent t-test for baseline values, paired-sample t-test for within-group comparisons, and ANCOVA for post-test differences. Results: Demographic and clinical attributes were consistently and normally distributed across groups (p > 0.05). Both groups demonstrated notable improvements across all outcomes (p < 0.05); however, the experimental group had a notably greater improvement in TUG cognitive scores in comparison to the control group (p < 0.05). No side effects occurred. Discussion: TUG cognitive and manual tests highlighted LSVT-BIG’s effectiveness in enhancing dual-task performance. Conclusion: Improvements in various TUG scores for individuals with PD indicate enhanced mobility and dual-task performance, which are crucial for daily activities and overall quality of life for individuals 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".