Abstract
This study assessed the correlation of N95 filtering facepiece respirator (FFR) fit between a Static Advanced Headform (StAH) and 10 human test subjects. Quantitative fit evaluations were performed on test subjects who made three visits to the laboratory. On each visit, one fit evaluation was performed on eight different FFRs of various model/size variations. Additionally, subject breathing patterns were recorded. Each fit evaluation comprised three two-minute exercises: “Normal Breathing,” “Deep Breathing,” and again “Normal Breathing.” The overall test fit factors (FF) for human tests were recorded. The same respirator samples were later mounted on the StAH and the overall test manikin fit factors (MFF) were assessed utilizing the recorded human breathing patterns. Linear regression was performed on the mean log10-transformed FF and MFF values to assess the relationship between the values obtained from humans and the StAH.
This is the first study to report a positive correlation of respirator fit between a headform and test subjects. The linear regression by respirator resulted in R2 = 0.95, indicating a strong linear correlation between FF and MFF. For all respirators the geometric mean (GM) FF values were consistently higher than those of the GM MFF. For 50% of respirators, GM FF and GM MFF values were significantly different between humans and the StAH. For data grouped by subject/respirator combinations, the linear regression resulted in R2 = 0.49. A weaker correlation (R2 = 0.11) was found using only data paired by subject/respirator combination where both the test subject and StAH had passed a real-time leak check before performing the fit evaluation. For six respirators, the difference in passing rates between the StAH and humans was < 20%, while two respirators showed a difference of 29% and 43%. For data by test subject, GM FF and GM MFF values were significantly different for 40% of the subjects. Overall, the advanced headform system has potential for assessing fit for some N95 FFR model/sizes.
ACKNOWLEDGMENTS
We wish to thank Andrew Viner and Craig Colton of 3M, Inc. and David Hanson of Hanson Robotics, Inc. for informative discussions on the subject matter.
FUNDING
This research was funded by the U.S. Department of Health and Human Services (HHS), the Office of Assistant Secretary for Preparedness and Response, Biomedical Advanced Research and Development Authority (BARDA) through an interagency agreement with the Air Force Research Laboratory (AFRL).
DISCLAIMER
The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the HHS, BARDA, NIOSH, Air Force Civil Engineer Center (AFCEC). or AFRL. Mention of company names or products does not constitute endorsement by HHS, BARDA, NIOSH, AFCEC, or AFRL.