Comparing a Virtual Reality–Based Simulation App (VR-MRI) With a Standard Preparatory Manual and Child Life Program for Improving Success and Reducing Anxiety During Pediatric Medical Imaging: Randomized Clinical Trial
Notice bibliographique
Résumé
Background The experience of undergoing magnetic resonance imaging (MRI) can be anxiety provoking, particularly for pediatric patients and their families. Alternative methods to improve success and experiences without the use of sedation are needed. Objective This study aims to compare the effectiveness of a virtual reality (VR)—based simulation app (VR-MRI) with a standard preparatory manual (SPM) and a hospital-based Child Life Program (CLP) on success and anxiety during a simulated pediatric MRI scan. Our secondary aim is to compare caregivers’ reported anxiety, procedural data, caregiver usability, child satisfaction, and fun. Methods This unblinded, randomized, triple-arm clinical trial involved 92 children aged 4-13 years and their caregivers. Recruitment was conducted through posters, public libraries, community centers, and social media. At a 2-hour session, participants were instructed to prepare for a simulated MRI head scan using one of three randomly assigned preparation materials: the VR-MRI app, SPM, or the CLP. Data were collected before preparation, during a simulated MRI head scan, and after the simulated scan. The primary outcomes were the success of the simulated MRI scan (MoTrak head motion tracking system), and child-reported anxiety (Venham picture test). We secondarily measured caregivers’ reported anxiety (short State-Trait Anxiety Inventory), procedural data (minutes), usability (Usefulness, Satisfaction, and Ease of Use Questionnaire), and child-reported satisfaction and fun (visual analog scales). Results A total of 84 participants were included in the final analysis (VR-MRI: 30/84, 36%; SPM: 24/84, 29%; and CLP: 30/84, 36%). There were no clinically significant differences between the groups in terms of success during the MRI simulation (P=.27) or the children’s reported anxiety at any timepoint (timepoint 1, P=.99; timepoint 2, P=.008; timepoint 3, P=.10). Caregivers reported being significantly more anxious after preparing with the manual than caregivers in the other 2 groups (P<.001). Child and caregiver anxiety had a significant relationship, increasing together with moderate effect (r84=0.421; P<.001). Participants using VR-MRI took the most time to prepare (P<.001) and participants using the manual took the least time (P<.001). No statistically significant relationships were found between time preparing and time completing the simulated assessment (P=.13). There were no differences found in ease of use (P=.99), ease of learning (P=.48), and usefulness (P=.11) between the groups; however, caregivers reported being significantly more satisfied with the VR-MRI app and CLP than SPM (P<.001). Children reported the most satisfaction with the CLP (P<.001). There were no differences in how much fun the preparation materials were perceived to be (P=.37). Conclusions Digital preparation experiences using VR-based media could be a viable solution to improve the success of nonsedated MRI scans, with outcomes comparable with hospital-based in-person preparatory programs. Future research should focus on validating the results in a real MRI setting. Trial Registration Clinicaltrials.gov NCT03931382; https://clinicaltrials.gov/ct2/show/NCT03931382
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».