Effect of Virtual Reality Technology on Attention and Motor Ability in Children With Attention-Deficit/Hyperactivity Disorder: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Attention-deficit/hyperactivity disorder (ADHD) is one of the common neurodevelopmental disorders in children and virtual reality (VR) has been used in the diagnosis and treatment of ADHD. Objective: This paper aims to systematically evaluate the effect of VR technology on the attention and motor ability of children with ADHD. Methods: The intervention method of the experimental group was VR technology, while the control group adopted non-VR technology. The population was children with ADHD. The outcome indicators were attention and motor abilities. The experimental design was randomized controlled trial. Two researchers independently searched PubMed, Cochrane Library, Web of Science, and Embase for randomized controlled trials related to the effect of VR technology on ADHD children's attention and motor ability. The retrieval date was from the establishment of each database to January 4, 2023. The PEDro scale was used to evaluate the quality of the included literature. Stata (version 17.0; StataCorp LLC) was used for effect size combination, forest map-making, subgroup analyses, sensitivity analyses, and publication bias. GRADEpro (McMaster University and Evidence Prime Inc) was used to evaluate the level of evidence quality. Results: A total of 9 literature involving 370 children with ADHD were included. VR technology can improve ADHD children's attention (Cohen d=-0.68, 95% CI -1.12 to -0.24; P<.001) and motor ability (Cohen d=0.48, 95% CI 0.16-0.80; P<.001). The intervention method and diagnosis type for VR technology had a moderating effect on the intervention' impact on children's attention (P<.05). The improvement in children's attention by "immersive" VR technology was statistically significant (Cohen d=-1.05, 95% CI -1.76 to -0.34; P=.004). The improvement of children's attention by "nonimmersive" VR technology was statistically significant (Cohen d=-0.28, 95% CI -0.55 to -0.01; P=.04). VR technology had beneficial effects on both children with an "informal diagnosis" (Cohen d=-1.47, 95% CI -2.35 to -0.59; P=.001) and those with a "formal diagnosis" (Cohen d=-0.44, 95% CI -0.85 to -0.03; P=.03). Conclusions: VR technology can improve attention and motor ability in children with ADHD. Immersive VR technology has the best attention improvement effect for informally diagnosed children with ADHD.https://www.crd.york.ac.uk/PROSPERO/.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".