Do Infield Softball Masks Effectively Reduce Facial Fracture Risk?


Journal

Annals of biomedical engineering
ISSN: 1573-9686
Titre abrégé: Ann Biomed Eng
Pays: United States
ID NLM: 0361512

Informations de publication

Date de publication:
Feb 2019
Historique:
received: 18 04 2018
accepted: 27 09 2018
pubmed: 27 10 2018
medline: 11 5 2019
entrez: 27 10 2018
Statut: ppublish

Résumé

Infield softball masks are intended to reduce facial fracture risk, but are rarely worn. The objective of this study was to evaluate the effectiveness of infield masks' ability to attenuate facial fracture risk over a range of designs and materials. To simulate batted ball impacts, a customized pitching machine was used to propel softballs at 24.6 ± 0.51 m/s. The balls impacted locations centered over the maxilla and zygoma bones of a FOCUS headform. The FOCUS headform was attached to a 50th percentile Hybrid III neck and secured to a slider table. Facial fracture risk of each facial bone was compared between masks and impact locations using peak resultant forces. Analysis of these data showed that the mask material and the distance between the mask and the impacted facial bone were key factors in determining a mask's performance. The effectiveness of masks varied. It was found that a metal mask with a separation distance ≥ 35 mm away from the maxilla and ≥ 25 mm away from the zygoma best reduced facial fracture risk for these test configurations. Plastic masks performed worse because they excessively deformed allowing ball contact with the face. This study assesses various mask designs for their ability to reduce facial fracture and suggests design recommendations based on the impact configurations tested.

Identifiants

pubmed: 30362083
doi: 10.1007/s10439-018-02144-6
pii: 10.1007/s10439-018-02144-6
pmc: PMC6342899
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

453-463

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Auteurs

Tyler P Morris (TP)

Virginia Polytechnic Institute and State University, Blacksburg, VA, USA. tytymo95@vt.edu.

Ryan A Gellner (RA)

Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.

Steven Rowson (S)

Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.

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Classifications MeSH