Constraint Therapy with and Without Virtual Reality for Children with Unilateral Cerebral Palsy: A Randomized Trial
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Cerebral palsy (CP) is the most common childhood motor disorder, with unilateral cerebral palsy (UCP) presenting with asymmetrical motor function that can cause decreased upper limb function. Constraint-Induced Movement Therapy (CIMT) is an evidence-based intervention that addresses upper limb functional limitations, but further study on combining interventions with CIMT is warranted. Combining CIMT with virtual reality (VR) is hypothesized to enhance engagement and therapeutic outcomes. This study compared the effectiveness of CIMT alone versus CIMT combined with VR (CIMT + VR) in improving upper limb function and occupational performance in children with UCP. METHODS: A blinded, randomized, controlled trial included children aged 5-13 years with Manual Ability Classification System (MACS) levels I-III. The participants were randomized into CIMT or CIMT + VR groups and completed a standardized 10-day camp protocol (60 h). Pre-intervention and post-intervention assessments included the Assisting Hand Assessment (AHA) and the Canadian Occupational Performance Measure (COPM). Secondary measures included acceptability ratings of VR devices and fidelity. RESULTS: Thirty-two participants, with a mean age of 9 years and 3 months (3 years 1 month), MACS I = 4, II = 20, and III = 8, completed this study. CIMT and CIMT + VR led to significant improvements in upper limb function, with no statistically significant differences between the groups in bilateral hand use and occupational performance. CONCLUSIONS: These findings reinforce the efficacy of CIMT while highlighting the potential of VR to enhance engagement when the child prefers to interact with the technology, underscoring the importance of individualized approaches that consider a child's preferences and responsiveness to different intervention modalities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".