The Effects of Virtual Reality Telemedicine With Pediatric Patients Diagnosed With Posttraumatic Stress Disorder: Exploratory Research Method Case Report
Bibliographic record
Abstract
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".