Using dance to increase physical activity: A systematic review of interventions
Bibliographic record
Abstract
Purpose: Reviews of physical activity interventions among children and adolescents span different settings of activity as well as types of activity. Dance may be one way to increase physical activity among youth. The goal of this study was to review physical activity interventions for youth using dance to understand both how dance is used as well as the outcomes of such interventions. Methods: Key databases (SportDiscus, PubMed, PsychInfo, CINAHL, and ERIC) were explored for studies using dance (in whole or part) as an intervention, published in English (peer-reviewed), between 2009-2014, and including an assessment of physical activity. Study quality was evaluated for included article. Relevant information was obtained from each included study. Results: Eight interventions (found in 10 papers) met the inclusion criteria. Study quality was 'weak' across all of the included studies. Different forms of dance were used across studies with many of the populations of youth being girls, overweight, and/or of an ethnic minority. The outcome (for physical activity) of the included interventions using dance was mixed. Conclusion: Dance is being used as a means to engage youth in physical activity. Study quality may be limiting what we know about the effectiveness of these interventions. Future studies should consider the appropriateness of dance interventions among other groups of children and adolescents including boys, those with disabilities, or cultural considerations.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".