The effects of VR-based multi-task sensorimotor intervention on motor performance in children with ADHD and DCD comorbidity
Bibliographic record
Abstract
Attention-Deficit/Hyperactivity Disorder (ADHD) and Developmental Coordination Disorder (DCD) are two prevalent neurodevelopmental disorders among children. Both of these diseases, occurring independently or in combination, can result in significant motor skill deficits. The purpose of this study was to observe the improvement in motor performance and skill acquisition of children with ADHD + DCD in comparison with their peers with ADHD or DCD, through repeated practice in the MTSI game. A total of 139 children (37 ADHD, 33 ADHD + DCD, 34 DCD, 35 TD: Typically Developing) participated in the MTSI (Multi-task Sensorimotor Intervention), which involved five sensorimotor intervention tasks. The change of motor performance scores provided by the MTSI system was assessed with repeated measurements, and the skill acquisition in gross & fine motor skills before and after intervention was analyzed with a mixed-design repeated measures ANOVA with post hoc analysis. All groups of children demonstrated a significant increase in motor performance during repeated practice in MTSI and displayed great improvements in gross and fine motor skills, with ADHD + DCD children benefiting more in the magnitude. Multi-task sensorimotor intervention (MTSI) can effectively improve gross and fine motor skills for children with ADHD or DCD, and particularly for those with ADHD and DCD comorbidity, with tailored, multidimensional intervention strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".