Utilization of a smartphone-based app for N95 respirator fit recommendations: A feasibility study assessing app respirator selection accuracy and usability compared to standard fit test processes.

Authors

Keith Dellagrotta, Hilary Richards, Jesse Chang, Tina Henderson, Dietrich Kessee, Linette Leadon, Jim Mathis, Matthew Lee Berkheiser

Published in

Journal of occupational and environmental hygiene. Pages 1-8. Sep 29, 2026. Epub Sep 29, 2026.

Abstract

Traditional quantitative N95 respirator fit testing methods are resource-intensive and may not optimize acceptable fit across diverse facial structures due to underlying variables in respirator design. This feasibility study evaluated the potential of integrating a novel smartphone-based app into existing fit testing workflows as a potential precursor to fully virtual fit testing processes. A feasibility study was conducted at the University of Texas MD Anderson Cancer Center involving 41 healthcare workers undergoing routine N95 respirator fit testing. Participants used a smartphone app that captured facial measurements via selfie photos and recommended respirators from a library of six possible options currently in use at the institution. These recommendations were compared with outcomes from standard quantitative fit testing using a PortaCount Respirator Fit Tester 8308 device. User experience and workflow integration were assessed. The app successfully recommended at least one properly fitting respirator for 40 of 41 participants (97.56% success rate). Users rated the app as easy to use (mean score 4.5 of 5), expressed confidence in using it (mean score 4.2 of 5), and indicated interest in frequent use (mean score 4.3 of 5). The app integrated smoothly into existing fit testing protocols. This feasibility study demonstrates the potential of integrating a smartphone-based app into N95 respirator fit testing procedures. The promising performance, positive user experience, and seamless workflow integration support expansion to a larger study to develop and assess more precise prediction models. Ultimately, this approach could lead to a fully virtual fit testing method, pending improved recommendation precision and regulatory approval. This innovation has the potential to significantly streamline respirator fit testing in healthcare settings, improving efficiency and compliance while maintaining safety standards.

PMID:
42811692
Bibliographic data and abstract were imported from PubMed on 30 Sep 2026.

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