Physical fitness assessment tools for children with developmental coordination disorder and their feasibility for low-income settings: A systematic review
Bibliographic record
Abstract
Background: This study systematically reviewed the literature on physical fitness assessment tools for children with developmental coordination disorder compared with typically developing children aged 7 to 10 and analyzed the feasibility of these tools for use in low-income settings. Methods: Searches were conducted in the PubMed, Scopus, Web of Science, and EBSCO/RIC databases. The Newcastle - Ottawa Quality Assessment Scale assessed the methodological quality of the studies, and a checklist adapted from COSMIN assessed the feasibility of the instruments. Results: From 8470 studies initially retrieved, 21 were included in this systematic review. The most assessed physical fitness components in children with developmental coordination disorder compared with typically developing children were cardiorespiratory fitness and muscle strength. Most studies had high methodological quality. The shuttle run (cardiorespiratory fitness) and handgrip dynamometer (muscle strength) were the most used tools. The PERF-FIT, long jump, and 6-min walk test were considered the most feasible tools for low-income settings, while the incremental treadmill test was deemed the least feasible. Conclusion: The findings evidenced several viable tools for testing physical fitness in children with DCD compared to typically developing peers from low-income countries. The most viable, as PERF-FIT, long jump and 6-min walk test should be used on large scale in low-income settings.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".