The Effects of Virtual Reality Telemedicine With Pediatric Patients Diagnosed With Posttraumatic Stress Disorder: Exploratory Research Method Case Report
Notice bibliographique
Résumé
BACKGROUND: Trauma-focused cognitive behavioral therapy (TF-CBT) strategies are common interventions to treat child trauma and a posttraumatic stress disorder (PTSD) diagnosis in children with histories of sexual and physical abuse. With the advent of COVID-19, the disruption of child development combined with intense exposure to technology and screen time indicate a need for delivering other novel approaches to treat pediatric PTSD. Virtual reality (VR) has been used with evidence-based TF-CBT as an intervention in lab-based settings, but never as telehealth. Such technologies, including a VR head-mounted device (HMD) programmed with novel TheraVR software, for psychotherapy and treating trauma-related symptoms could redefine how pediatric populations respond to treatment. OBJECTIVE: The aim of this exploratory single-case study was to reflect symptom improvement and patient engagement using VR as telehealth. METHODS: The patient was a 10-year-old girl of Middle Eastern descent diagnosed with trauma and comorbid medical conditions. The patient was in divorced joint parental custody and a Child Protective Services report was made with referral for therapy. Night terrors, hallucinations, depression, anxiety, isolation, and encopresis symptoms were assessed at the beginning of treatment. Clinical analysis met the criteria for a diagnosis of early onset PTSD, which was treated over the course of 7 months using TF-CBT. A cross-analysis design was used to compare improved effectiveness in treatment and patient outcomes when moving from delivery of care with telehealth using desktop and tablet synchronous technology to 2D VR desktop telehealth with TheraVR software and subsequently HMD VR telehealth with TheraVR software. Sessions were conducted in private practice providing psychotherapy for remote patient care, collateral care with the family, and coordination of clinical care with the patient's pediatrician. Safety and protocols for reducing triggers were clinically monitored by the provider. RESULTS: Over the course of treatment, and moving from standard telehealth to 2D VR to TheraVR with a standalone HMD, there was a significant reduction in PTSD symptoms. The transfer from using the standard video conferencing with face-to-face video to using customizable avatar technology with an assigned scene environment presented an increase in patient retention and follow-through with the treatment goals. The continuous use of delivery of care using VR with the TheraVR software demonstrated breakthrough clinical observations where the patient devised her own interventions for coping with mood, emotional regulation, and negative cognitive processes using the 10 different VR environments. CONCLUSIONS: This study shows the potential efficacy in using VR specifically for younger populations as a better modality of pediatrics care, while improving engagement with the provider through telehealth. These findings suggest the value of further research through larger clinical trials including pediatric patients diagnosed with severe trauma or trauma-related symptoms to assess the effectiveness of TheraVR software.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».