A Virtual Reality App Intervention to Improve Concussion Recognition and Reporting in Athletes Aged 9 to 12 Years: Development and Pilot Testing
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».