A Virtual Reality App Intervention to Improve Concussion Recognition and Reporting in Athletes Aged 9 to 12 Years: Development and Pilot Testing
Bibliographic record
Abstract
BACKGROUND: Existing concussion education programs for preteen athletes typically do not result in sustained improvements in concussion symptom recognition or reporting behaviors. Virtual reality (VR) technology offers an innovative tool that may improve concussion symptom recognition and reporting behaviors among preteen athletes. OBJECTIVE: We aimed to describe the design and development of a VR concussion education app, Make Play Safe (MPS), and present findings on the usability and preliminary efficacy of MPS in improving concussion recognition and reporting intentions among soccer athletes aged 9-12 years. METHODS: A collaborative user-centered design process was implemented to develop and evaluate MPS, a semi-immersive VR concussion education app designed to address two behavioral outcomes in preteen athletes aged 9-12 years: (1) recognizing concussion and (2) reporting concussion. The development of MPS occurred in three phases: (1) design and development, (2) usability testing, and (3) preliminary efficacy testing. During phase 1, consultations were completed with 6 experts. Additionally, 5 interviews with children who had a history of concussion were conducted to collect feedback about the proof of concept of MPS. During phase 2, a participatory workshop with 11 preteen athletes and a small group discussion with 6 parents and 2 coaches were conducted to explore the usefulness and acceptability of MPS from the perspective of end users. Finally, phase 3 included preliminary efficacy testing with 33 soccer athletes aged 9-12 years to examine changes in concussion-related knowledge, attitudes, and reporting intentions from pre- to postintervention. The data generated from each phase of this study informed the development of the final version of the proof of concept of the VR concussion education app, MPS. RESULTS: Experts positively rated the features of MPS and noted that the design and content were innovative and age-appropriate. Preteens with a history of concussion indicated the scenarios and symptoms portrayed in the app represented well what they experienced while concussed. Further, they stated that the app would be an engaging way for children to learn about concussions. The 11 healthy children in the workshop perceived the app positively, noting that the scenarios were informative and engaging. Results from preliminary efficacy testing revealed increases in many athletes' knowledge and reporting intentions from pre- to postintervention. Others demonstrated no significant changes or a decrease in knowledge, attitudes, or reporting intentions from pre- to postintervention. Group-level changes in concussion knowledge and intention to report concussions were statistically significant (P<.05), while changes in attitudes toward reporting concussions were not (P=.08). CONCLUSIONS: Results suggest VR technology may be an effective and efficient tool to equip preteen athletes with the requisite knowledge and skills to recognize and report future concussions. Further research is recommended to examine the use of VR as an effective strategy to improve concussion-reporting behaviors in preteen athletes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".