Effects of 10 Weeks Classical Chinese Dance Training on Flexibility, Balance and Muscle Strength of the Elderly
Bibliographic record
Abstract
Abstract Purpose This study aimed to explore the effectiveness of Classical Chinese Dance as a sports intervention for enhancing the physical capabilities of older adults. Methods Twenty-four participants were divided into experimental group ( N = 12, age 69.0 ± 1.5 years, body mass index [BMI] 22.2 ± 0.6 kg/m 2 ) and control group ( N = 12, age 69.1 ± 1.8 years, BMI 23.5 ± 0.8 kg/m 2 ). The Classical Chinese Dance intervention lasted for 10 weeks, with 90 min per class twice a week. Two-way analysis of variance was used to assess the effect of the training intervention on flexibility, balance, and muscle strength in the experimental and control groups. Results The experimental group (EG) significantly outperformed the control group (CG) in the Biceps Curl Test at both time points (EG Pre-test: 26.8 ± 4.8(t), Post-test: 30.5 ± 5.0(t); CG Pre-test: 20.8 ± 4.3(t), Post-test: 22.8 ± 6.4(t), p <.01) and in the Chair Sit-and-Reach Test at post-test ( p =.017). EG showed significant within-group improvements in upper-body strength, cardiorespiratory endurance, and agility, while CG declined in static balance (Pre-test: 0.9 ± 0.6, Post-test: 2.4 ± 2.5, p =.008) and agility (Pre-test: 4.9 ± 1.2, Post-test: 4.4 ± 1.3, p <.001). No significant changes were observed in Chair Stand or Grip Strength across the groups or times. Conclusion Classical Chinese Dance significantly enhances functional fitness in older adults, and barre training provides unique benefits for upper limb strength. Its adaptable intensity underscores its potential as an effective and accessible intervention for older adults.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".