Exercise training to improve balance ability for individuals with Down Syndrome: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Down syndrome (DS) is associated with exhibit specific balance problems due to inter alia deficits in the postural control system and hypotonia. One approach to reducing balance impairments in this population is exercise training. This study presents a systematic review and meta-analysis of the effects of exercise training designed to improve balance ability in people with DS. A search for relevant articles was carried out on seven electronic databases: MEDLINE, PubMed, Cochrane Library, Google Scholar, Scopus, PEDro, and Web of Science. This systematic review was carried out between 2010 and 2022. Utilizing a set of predetermined inclusion and exclusion criteria, the studies were selected and their methodology was assessed using the PEDro scale. Data analyses were performed using the CMA v3 random effects model. In total, 514 articles were screened, and the data from 15 randomized controlled trials (RCTs) involving individuals with DS were subjected to a meta-analysis. The results showed that exercise training was effective in improving balance (ES: 1.20, 95% CIs: 0.95 to 1.53, p = 0.00). Despite the small number of studies, the findings suggest that exercise training might improve balance in children and young people with DS. In conclusion, exercise training is highly recommended for people with DS, to improve their balance and prevent falling risk.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.021 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".