Dancing through the darkness: a systematic review of dance as a multidimensional therapy for Parkinson’s disease
Bibliographic record
Abstract
INTRODUCTION: Parkinson's disease (PD) is a progressive neurodegenerative disorder with no known cure, affecting both motor and non-motor functions. This review examines the therapeutic potential of dance as a holistic intervention to complement conventional PD treatments. METHODS: This systematic review followed PRISMA guidelines. Studies included were randomized controlled trials, longitudinal studies, and meta-analyses evaluating dance interventions in PD. Databases searched included PubMed, Scopus, and Web of Science. Exclusion criteria were case studies and non-peer-reviewed sources. RESULTS: Sixty-six studies involving over 1,200 participants were identified. Evidence shows that dance improves motor functions such as gait, balance, and coordination, while also enhancing non-motor outcomes including mood, anxiety, depression, and cognition. Dance's rhythmic movements, cognitive demands, and social interaction stimulate neurochemical pathways linked to motor and emotional regulation. Furthermore, group dance fosters social connectedness and reduces isolation. Online and community-based programs demonstrate feasibility and accessibility across diverse populations. CONCLUSIONS: Dance provides multidimensional benefits for individuals with PD, spanning physical, cognitive, emotional, and social domains. Despite these promising findings, limitations such as small sample sizes, methodological heterogeneity, and lack of direct comparisons with other exercise modalities remain. Larger standardized trials are needed to confirm efficacy and support integration of dance into PD treatment programs.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".