Design Strategies for Virtual Reality Interventions for Managing Pain and Anxiety in Children and Adolescents: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) technology has been explored in the health sector as a novel tool for supporting treatment side effects, including managing pain and anxiety. VR has recently become more available with the launch of low-cost devices and apps. OBJECTIVE: This study aimed to provide an updated review of the research into VR use for pain and anxiety in pediatric patients undergoing medical procedures. Specifically, we wanted to gain an understanding of the techniques and goals used in selecting or designing VR apps in this context. METHODS: We performed a scoping review. To identify relevant studies, we searched three electronic databases. Two authors screened the titles and abstracts for relevance and eligibility criteria. RESULTS: Overall, 1386 articles published between 2013 and 2018 were identified. In total 18 articles were included in the review, with 7 reporting significant reduction in pediatric pain or anxiety, 3 testing but finding no significant impact of the VR apps employed, and the rest not conducting any test of significance. We identified 9 articles that were based on VR apps specifically designed and tailored for pediatric patients. The findings were analyzed to develop a holistic model and describe the product, experience, and intervention aspects that need to be considered in designing such medical VR apps. CONCLUSIONS: VR has been demonstrated to be a viable choice for managing pain and anxiety in a range of medical treatments. However, commercial products lack diversity and meaningful design strategies are limited beyond distraction techniques. We propose future VR interventions to explore skill-building goals in apps characterized by dynamic feedback to the patient and experiential and product qualities that enable them to be an active participant in managing their own care. To achieve this, design must be part of the development.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".