Effectiveness of Tai Chi for Health Promotion of Older Adults: A Scoping Review of Meta-Analyses
Bibliographic record
Abstract
Background. Tai Chi is a form of exercise that is accessible to people from different socioeconomic backgrounds, making it a potentially valuable activity for health promotion of older adults. Purpose. The objective of this scoping review was to summarize the current knowledge about the effectiveness of Tai Chi for older adults across a range of general health outcomes from published, peer reviewed, unique meta-analyses. Methods. Meta-analyses were retrieved from Medline, Embase, AMED, CINAHL, SPORTDiscus, PsychINFO, Web of Science, PubMed Health, and the Cochrane Library from database inception to late August 2019. Multistage deduplication and screening processes identified eligible full-length meta-analyses. Two people independently appraised 27 meta-analyses based on the GRADE system and organized results into 3 appendices subsequently collated into heterogeneous, statistically significant, and statistically insignificant tables. Results. “High” and “moderate” quality evidence extracted from these meta-analyses demonstrated that practicing Tai Chi can significantly improve balance, cardiorespiratory fitness, cognition, mobility, proprioception, sleep, and strength; reduce the incidence of falls and nonfatal stroke; and decrease stroke risk factors. Conclusions. Health care providers can now recommend Tai Chi with high level of certainty for health promotion of older adults across a range of general health outcomes for improvement of overall well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".