Moving Towards a Medicine of Dance: A Scoping Review of Characteristics of Dance Interventions Targeting Older Adults and a Theoretical Framework
Bibliographic record
Abstract
BackgroundDance combines cultural and aesthetic elements with behaviors important for brain health, including physical activity, social engagement, and cognitive challenge. Therefore, dance could positively impact public health given the rapidly aging population, increasing incidence of Alzheimer's disease and related dementias, and lack of uptake of exercise in many older adults. Despite a high volume of literature, existing literature does not support evidence-based guidelines for dance to support healthy aging.ObjectiveTo conduct a scoping review of the dance intervention literature in older adults and provide information to facilitate a more consistent approach among scientists in designing dance interventions for older adults that stimulate physical and neurocognitive health adaptations.MethodsStudy characteristics (sample size, population, study design, outcomes, intervention details) were ascertained from 112 separate studies of dance reported in 127 papers that reported outcomes important for brain health (cardiorespiratory fitness, balance and mobility, cognition, mood, and quality of life).ResultsHigh heterogeneity across studies was evident. Class frequency ranged from < 1 to 5 classes per week, class length from 30-120 minutes, and intervention duration from 2 weeks to 18 months. Studies often did not randomize participants, had small (< 30) sample sizes, and used varied comparator conditions. Over 50 tests of cognition, 40 dance forms, and 30 tests of mobility were identified.ConclusionsBased on these results, important future directions are establishing common data elements, developing intervention mapping and mechanistic modeling, and testing dosing parameters to strengthen and focus trial design of future studies and generate evidence-based guidelines for dance.
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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 | 0.036 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.034 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".