A Pilot Study Comparing Speech Characteristics in People With Parkinson’s Disease and Controls Dancing Weekly Over 5-Years
Bibliographic record
Abstract
INTRODUCTION: Parkinson's Disease (PD) is a neurodegenerative disorder that affects motor and non-motor functions. Speech impairments, such as reduced variability in pitch (F0SD) and intensity (IntSD), are commonly observed. Early identification of these changes through voice biomarkers offers a noninvasive approach for detecting PD, tracking disease progression, and investigating the effects of interventions on this population. This study investigates the impact of dance on people with PD voice features over a five-year period and explores whether dance interventions can mitigate these impairments. METHODS: A longitudinal dance program involved 29 individuals with PD and 29 healthy controls. Voice recordings were collected before and after dance sessions over 5 years (2014-2019) and analyzed using machine learning models to extract F0SD and IntSD. Statistical analyses, including ANOVA and mixed-effect models, were performed using R studio to evaluate group differences, longitudinal changes, and the effects of dance on voice parameters. RESULTS: The analysis revealed a significant main effect of time on F0SD, indicating measurable changes over the study period. However, the interaction between group and time was not statistically significant, suggesting similar trends in both groups. While the PD group did not exhibit the expected decline in F0SD seen in previous studies, IntSD remained largely unchanged, suggesting it may be less responsive to intervention. CONCLUSION: These findings demonstrate the potential of F0SD and IntSD as biomarkers for tracking PD progression. Dance interventions provide measurable benefits for F0SD, though further research is needed to determine optimal intervention duration and explore additional speech features such as jitter, shimmer, HNR, and CPP. Early and targeted interventions, such as combining dance with speech therapy, may enhance communication abilities and improve the 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".