Dancing Through Time: Cognitive Changes Over Six-Years of Community Dance in Parkinson’s Disease
Bibliographic record
Abstract
Abstract Background Parkinson’s Disease (PD) is the most common neurodegenerative disorder after Alzheimer’s, and is characterized by motor and non-motor symptoms, including gait dysfunction and cognitive decline. Dance has emerged as a promising intervention for improving motor and non-motor symptoms in persons with PD (PwPD), yet long-term effects remain underexplored. Objective To assess changes in cognitive function and gait performance over six years among PwPD who participated in a weekly dance program, compared to a Reference group who remained physically inactive. Methods This six-year longitudinal observational study included 43 PwPD who attended weekly dance classes and were evaluated using the Mini-Mental State Examination (MMSE) and Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). A Reference group of 28 PwPD, matched on age, gender, and Hoehn & Yahr scores, were selected from the Parkinson’s Progression Marker Initiative, and assessed using the MDS-UPDRS and Montreal Cognitive Assessment (MoCA). Cognitive scores were standardized. Generalized estimating equations were used to compare cognitive and gait outcomes across time. Results The Dance group was significantly different from the Reference group ( p < 0.001), with improved cognitive scores in 2016, 2017, and 2018. The Dance group had worse gait at baseline, however, the Reference group showed significantly poorer gait performance by 2018. No significant association was found between gait and cognitive scores. Conclusion After two years of weekly dance, the Dance group showed improvements in cognition and maintained stability in gait performance. The findings highlight the potential neuroprotective benefits of continued dance engagement over six years.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".