Investigating Affective and Motor Improvements with Dance in Parkinson’s Disease
Bibliographic record
Abstract
Abstract Background Research has supported the notion that dance alleviates motor symptoms for people with Parkinson’s disease (PD) illustrated by observed improvements in gait, balance, and quality of life. However, what remains unclear is whether engaging in weekly dance classes also positively influences nonmotor symptoms of PD, such as affect regulation (mood). Objectives To examine depressive symptoms of participants in a dance program for people with PD, and to extend previous findings on the topics for motor symptoms. Methods People with PD (n=23) and age-matched healthy controls (n=11) between the ages of 58-75 (M=67.91, SD=5.43) participated in a weekly Dance for PD ® class. Nonmotor symptoms of PD were assessed using the Geriatric Depression Scale (GDS), administered at three time points over the 1 st year of a newly-developed dance program. The Berg Balance Scale (BBS) and the Timed Up and Go (TUG) were also administered at three time points to assess motor function. Results Longitudinal mixed methods analysis showed significant improvements in GDS scores, when examining effects of the dance class over the time, with a significant main effect of time ( p < 0.01) and condition: pre/post dance class ( p < 0.025). Significant improvements were also observed across the motor tests of BBS ( p < 0.001) and TUG ( p < 0.001) measurements. Conclusion Our findings suggest dance can facilitate positive improvements in both motor and mood related symptoms of PD. These findings show important nonmotor effects of dance as an adjunct treatment for mood that may reduce the burden of this disease.
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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 | 0.001 |
| 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".