Garba Dance Is Effective in Parkinson’s Disease Patients: A Pilot Study
Bibliographic record
Abstract
Background. Dance as therapy is gaining attention as an adjuvant option for Parkinson’s disease (PD). Assessing culturally popular dance forms is crucial for promoting the acceptance of dance therapy in a culturally sensitive context. Objectives. The pilot study evaluated the efficacy of Garba dance for alleviating motor symptoms, nonmotor symptoms, cognitive functions, and mood. Furthermore, this pilot study also assessed the tolerability and safety of this dance form using fatigue severity scale and assessing falls, respectively. Methods. Eligible participants with mild‐to‐moderate PD (H&Y stage 1 to 2.5) were sequentially randomized into the dance therapy, physical therapy, or control groups. Motor symptoms, nonmotor symptoms, cognitive functions, and mood were assessed using standard scales at baseline, week 6, and week 12. Tolerability was measured using the Fatigue Severity Scale, and safety was assessed by monitoring falls. Results. In the Garba dance group, significant improvements in UPDRS scores were observed at week 6 (p = 0.002) and week 12 (p < 0.001) compared to baseline. At week 12, UPDRS scores were better in the Garba dance group as compared to the control and physical therapy groups. Freezing of gait significantly improved at week 6 (p < 0.001) and week 12 (p < 0.001) in the Garba dance group. Garba dance also led to significant improvements in mood and sleep. Tolerability was favorable, with significantly better fatigue severity scores in the Garba dance group at week 12 compared to controls and physical therapy. About 6 patients in Garba dance group experienced near falls. Conclusion. Underscoring a cautious optimism, results of the current study indicate that Garba dance may be an effective, safe, and well‐tolerated intervention for Indian patients with mild‐to‐moderate PD (H&Y stage 1 to 2.5).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".