Conditioning Methodologies for DanceSport: Lessons from Gymnastics, Figure Skating, and Concert Dance Research
Bibliographic record
Abstract
OBJECTIVES: Dancesport, the competitive branch of ballroom dancing, places high physiological and psychological demands on its practitioners, but pedagogical resources in these areas for this dance form are limited. Dancesport competitors could benefit from strategies used in other aesthetic sports. In this review, we identify conditioning methodologies from gymnastics, figure skating, and contemporary, modern, and ballet dance forms that could have relevance and suitability for dancesport training, and propose several strategies for inclusion in the current dancesport curriculum. METHODS: We reviewed articles derived from Google Scholar, PubMed, ScienceDirect, Taylor & Francis Online, and Web of Science search engines and databases, with publication dates from 1979 to 2013. The keywords included MeSH terms: dancing, gymnastics, physiology, energy metabolism, physical endurance, and range of motion. Out of 47 papers examined, 41 papers met the inclusion criteria (validity of scientific methods, topic relevance, transferability to dancesport, publication date). Quality and validity of the data were assessed by examining the methodologies in each study and comparing studies on similar populations as well as across time using the PRISMA 2009 checklist and flowchart. RESULTS: The relevant research suggests that macro-cycle periodization planning, aerobic and anaerobic conditioning, range of motion and muscular endurance training, and performance psychology methods have potential for adaptation for dancesport training. CONCLUSIONS: Dancesport coaches may help their students fulfill their ambitions as competitive athletes and dance artists by adapting the relevant performance enhancement strategies from gymnastics, figure skating, and concert dance forms presented in this paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| 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 | 0.001 |
| 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 teacher head, 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".