Children With Developmental Coordination Disorder Play Active Virtual Reality Games Differently Than Children With Typical Development
Bibliographic record
Abstract
BACKGROUND: Active virtual reality gaming (AVG) may be useful for children with developmental coordination disorder (DCD) to practice motor skills if their movement patterns are of good quality while engaged in AVG. OBJECTIVE: This study aimed to examine: (1) the quality of motor patterns of children with DCD participating in AVG by comparing them with children with typical development (TD) and (2) whether differences existed in the motor patterns utilized with 2 AVG types: Sony PlayStation 3 Move and Microsoft Xbox 360 Kinect. DESIGN: This was a quasi-experimental, biomechanical laboratory-based study. METHODS: Twenty-one children with DCD, aged 10 to 12 years, and 19 age- and sex-matched children with TD played a match of table tennis on each AVG type. Hand path, wrist angle, and elbow angle were recorded using a motion analysis system. Linear mixed-model analyses were used to determine differences between DCD and TD groups and Move and Kinect AVG type for forehands and backhands. RESULTS: Children with DCD utilized a slower hand path speed (backhand mean difference [MD]=1.20 m/s; 95% confidence interval [95% CI]=0.41, 1.98); greater wrist extension (forehand MD=34.3°; 95% CI=22.6, 47.0); and greater elbow flexion (forehand MD=22.3°; 95% CI=7.4, 37.1) compared with children with TD when engaged in AVG. There also were differences in movement patterns utilized between AVG types. LIMITATIONS: Only simple kinematic measures were compared, and no data regarding movement outcome were assessed. CONCLUSIONS: If a therapeutic treatment goal is to promote movement quality in children with DCD, clinical judgment is required to select the most appropriate AVG type and determine whether movement quality is adequate for unsupervised practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".