Skip to content

Opening book details…

Can I read Optimizing Twin Sampling Tube Stabilization Improves Quantitative Fit Test Results for Flat-fold Duckbill Filtering Facepiece Respirators on EtoBox?

Optimizing Twin Sampling Tube Stabilization Improves Quantitative Fit Test Results for Flat-fold Duckbill Filtering Facepiece Respirators by Daryl Lindsay Williams; Benjamin Kave; Charles Bodas; Fiona Begg; Megan Roberts; Irene Ng is a Medicine article available to read on EtoBox.

What is Optimizing Twin Sampling Tube Stabilization Improves Quantitative Fit Test Results for Flat-fold Duckbill Filtering Facepiece Respirators about?

Introduction: When performing quantitative fit testing (QNFT) on filtering facepiece respirators using an ambient aerosol technique, a twin sampling tube is connected between the condensation nuclei count machine and the probed respirator. To achieve high quality and repeatable QNFT results, robust sampling tube stabilization is required. Methods: In this prospective randomized crossover study, conducted in December 2021 to February 2022, we compared the commonly used hand-hold technique with the manufacturer-recommended lanyard technique in stabilizing the sampling tube during QNFT on a Halyard N95 respirator. Outcomes included QNFT pass rates, overall and individual fit factors, and concordance between the two techniques. Results: A total of 228 out of 316 participants (72.2%) passed the QNFT with the hand-hold technique, compared to the lanyard technique (166/316, 52%, P < .001). The most significant drop in the fit factors with the lanyard technique occurred during head movement side-to-side and up-and-down. The concordance between the 2 techniques was fair (Kappa coefficient = 0.39). Conclusion: Our study demonstrates that the method of sampling tube stabilization during QNFT

Who reads Optimizing Twin Sampling Tube Stabilization Improves Quantitative Fit Test Results for Flat-fold Duckbill Filtering Facepiece Respirators?

It is typically read by researchers, students, and practitioners in Medicine.

Author
Daryl Lindsay Williams; Benjamin Kave; Charles Bodas; Fiona Begg; Megan Roberts; Irene Ng
Publisher
Elsevier BV
Published
2023
Language
EN
Field
Medicine (Health Sciences)