A Virtual Reality Game to Change Sun Protection Behavior and Prevent Cancer: User-Centered Design Approach
Bibliographic record
Abstract
BACKGROUND: Public health sun safety campaigns introduced during the 1980s have successfully reduced skin cancer rates in Australia. Despite this success, high rates of sunburn continue to be reported by youth and young adults. As such, new strategies to reinforce sun protection approaches in this demographic are needed. OBJECTIVE: This study aims to develop a virtual reality (VR) game containing preventive skin cancer messaging and to assess the safety and satisfaction of the design based on end user feedback. METHODS: Using a two-phase design approach, we created a prototype VR game that immersed the player inside the human body while being confronted with growing cancer cells. The first design phase involved defining the problem, identifying stakeholders, choosing the technology platform, brainstorming, and designing esthetic elements. In the second design phase, we tested the prototype VR experience with stakeholders and end users in focus groups and interviews, with feedback incorporated into refining and improving the design. RESULTS: The focus groups and interviews were conducted with 18 participants. Qualitative feedback indicated high levels of satisfaction, with all participants reporting the VR game as engaging. A total of 11% (2/8) of participants reported a side effect of feeling nauseous during the experience. The end user feedback identified game improvements, suggesting an extended multistage experience with visual transitions to other environments and interactions involving cancer causation. The implementation of the VR game identified challenges in sharing VR equipment and hygiene issues. CONCLUSIONS: This study presents key findings highlighting the design and implementation approaches for a VR health intervention primarily aimed at improving sun protection behaviors. This design approach can be applied to other health prevention programs in the future.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".