Effectiveness and User Experience of Immersive Virtual Reality in Cognitive Rehabilitation for Attention-Deficit/Hyperactivity Disorder: Systematic Review
Bibliographic record
Abstract
Background: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by difficulties in attention, impulsivity, and hyperactivity. These difficulties can result in pervasive and longstanding psychological distress and social, academic, and occupational impairments. Objective: This systematic review aims to investigate the effectiveness and user experience (ie, safety, usability, acceptability, and attrition) outcomes of immersive virtual reality (VR) interventions for cognitive rehabilitation in people with ADHD and identify research gaps and avenues for future research in this domain. Methods: Peer-reviewed journal articles that appraised the treatment impact of any immersive VR-based intervention on cognitive abilities in people of all ages with ADHD were eligible for inclusion. The following databases were searched up until November 2024: Cochrane Library, IEEE Explore Digital Library, PsycINFO, PubMed, Scopus, and Web of Science. Records were screened on title and abstract information after deduplication, leading to full-text appraisal of the remaining records. Findings from eligible articles were extracted into a standardized coding sheet before being tabulated and reported with a narrative synthesis. Results: Out of 1046 records identified, 15 articles met the inclusion criteria. Immersive VR-based interventions for people with ADHD were generally effective in improving cognitive abilities, such as attention, memory, and executive functioning. User experience outcomes were also generally positive, with low levels of simulator sickness and minimal attrition reported during VR-based treatment. Conclusions: Immersive VR-based interventions hold promise for effectively, safely, and rapidly treating cognitive deficits in children and adults with ADHD. However, more studies are required to examine their longitudinal impact beyond treatment cessation.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".