An Engineering Approach to Personal Protective Equipment: An analysis of the PPE, non-compliance issues and the methodology to predict non-compliance.
Date
2024-05
Authors
Mgaedeh, Fatima
Major Professor
Advisor
Stone, Richard T.
De Brabanter, Kris M.
Gilbert, Stephen
Mackenzie, Cameron
Min, Kyung J
Committee Member
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Altmetrics
Abstract
This dissertation focuses on Personal Protective Equipment (PPE) within the healthcare sector, particularly the emerging practice of double masking and compliance with its use.
The dissertation begins with a comprehensive literature review to map out the existing research on PPE, focusing on its adverse effects, double masking, and compliance. Extensive studies have shown that face masks negatively impact psychological, physiological, visual, motor, and cognitive functions, including changing breathing rates, raising blood pressure, and hindering communication. The review further revealed that these adverse effects, coupled with organizational issues, significantly drive PPE noncompliance in healthcare settings. The review also highlighted the emergent practice of double masking, recommended by healthcare organizations as a method to enhance protectiveness during the pandemic despite the potential for increased discomfort and non-compliance.
Consequently, the Second part of the dissertation presented an investigation into the impact of various mask configurations, including single and double masking, on human performance. This analysis demonstrated that wearing any form of face mask—be it a singular or doubled—resulted in higher errors during cognitive evaluation tests compared to a control scenario without a mask. Furthermore, this study highlighted that the practice of double masking significantly heightened the levels of perceived difficulty, discomfort, and anxiety among participants, mainly after they engaged in tasks requiring both motor and cognitive effort. This practice could lead to PPE non-compliance to relieve discomfort and increased thermal stress.
Therefore, the third study within this dissertation set out to define and measure non-compliance by creating a unique within-subject experimental simulation specific to a healthcare setting. A predictive model for non-compliance was constructed, identifying the key variables contributing to non-compliance called MPENC (Model of predicting personal protective equipment non-compliance). While PPE use and workload were significant predictors, the experience of discomfort and thermal burden emerged as the most critical factors in predicting non-compliance instances.
This work could be applied to healthcare organizations such as the CDC and OSHA as it provides insight into enhancing compliance with PPE by considering the contributing factors.
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Type
dissertation