Use of Biofeedback-Based Virtual Reality in Pediatric Perioperative and Postoperative Settings: Observational Study
Bibliographic record
Abstract
BACKGROUND: Biofeedback-based virtual reality (VR-BF) is a novel, nonpharmacologic method for teaching patients how to control their breathing, which in turn increases heart rate variability (HRV) and may reduce pain. Unlike traditional forms of biofeedback, VR-BF is delivered through a gamified virtual reality environment, increasing the accessibility of biofeedback. This is the first study to systematically integrate VR-BF use in the pediatric perioperative setting, with the ultimate goal of evaluating the efficacy of VR-BF to reduce pain, anxiety, and opioid consumption once feasibility and acceptability have been established. OBJECTIVES: The primary objective was to develop a clinical trial protocol for VR-BF use in the pediatric perioperative setting, including preoperative education and training, and postoperative application of VR-BF in children undergoing surgery. A secondary objective was to evaluate the patient and parent experience with VR-BF. METHODS: A total of 23 patients (12-18 years of age) scheduled for surgery at Nationwide Children's Hospital were recruited using purposive sampling. Following training, participants independently completed a daily, 10-minute VR-BF session for 7 days before surgery and during their inpatient stay. Participants could use VR-BF up to 2 weeks after hospital discharge. Patient- and session-level data of VR-BF usage and achievement of target HRV parameters were measured to identify the optimal frequency and duration of sessions before and after surgery for this population. Standardized questionnaires and semistructured interviews were conducted to obtain qualitative information about patients' experiences with VR-BF. RESULTS: Patient-level data indicated that the highest odds of achieving 1 session under target HRV parameters was after 4 sessions (odds ratio [OR] 5.1 for 4 vs 3 sessions, 95% CI 1.3-20.6; OR 16.6 for 3 vs 2 sessions, 95% CI 1.2-217.0). Session-level data showed that a session duration of 9 to 10 minutes provided the greatest odds of achieving 1 session under target HRV parameters (OR 1.3 for 9 vs 8 min, 95% CI 1.1-1.7; OR 1.4 for 8 vs 7 min, 95% CI 1.1-1.8; OR 1 for 10 vs 9 min, 95% CI 0.9-1.2). Qualitative data revealed patient satisfaction with the VR-BF technology, particularly in managing perioperative stress (17/20, 85%). Few patients reported VR-BF as beneficial for pain (8/20, 40%). CONCLUSIONS: Children and adolescents undergoing surgery successfully learned behavioral strategies with VR-BF with 10-minute sessions once daily for 5 days. To integrate VR-BF as a therapeutic intervention in a subsequent clinical trial, patients will be instructed to complete three 10-minute sessions a day for 7 days after surgery. TRIAL REGISTRATION: ClinicalTrials NCT04943874; https://clinicaltrials.gov/ct2/show/NCT04943874.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".