Current Evidence of the Application of Music in Tai Chi Exercise: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Music has frequently been used in movement exercises to enhance health benefits. However, scientific evidence regarding the application of music to Tai Chi practice is limited. OBJECTIVE: This scoping review aims to understand how music has been used in Tai Chi practice and whether music could be applied to Tai Chi practice to help optimize its benefits. METHODS: PubMed, CINAHL, CNKI, and Weipu databases were searched. We included studies that compare Tai Chi practice experience or health outcomes between individuals practicing Tai Chi with music and those practicing Tai Chi without music. Studies published through September 2022 were identified. Two researchers (YD and YH) independently performed study selection and data extraction. Thematic analysis was used to summarize and categorize the findings of the included studies. RESULTS: Seven studies were included in this review. All 7 included studies are experimental studies. Practicing Tai Chi with music might lead to positive perceptions of Tai Chi practice (eg, motivation, concentration, enjoyment, compliance, and performance) and higher evaluations of Tai Chi instructional quality, especially for Tai Chi beginners. The effects of incorporating music into Tai Chi practice on health outcomes are inconclusive due to the heterogeneities of the sample size, and the intervention components, lengths, and frequencies of the included studies. CONCLUSIONS: Applying music to Tai Chi practice may result in positive Tai Chi practice experience and adherence, particularly for beginners, which could help improve the dissemination and implementation of Tai Chi interventions for public health. However, whether applying music to Tai Chi practice leads to synergetic effects on health outcomes needs further investigation.
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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 | 0.021 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".