Harvard College, Harvard University, Cambridge, Massachusetts.
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts.
J Occup Environ Hyg. 2022 Jan;19(1):23-34. doi: 10.1080/15459624.2021.2002342. Epub 2021 Dec 16.
Face mask usage is one of the most effective ways to limit SARS-CoV-2 transmission, but a mask is only useful if user compliance is high. Through anonymous surveys (n = 679), it was shown that mask discomfort is the primary source of noncompliance in mask wearing. Further, through these surveys, three critical predicting variables that dictate mask comfort were identified: air resistance, water vapor permeability, and face temperature change. To validate these predicting variables in a physiological context, experiments (n = 9) were performed to measure the respiratory rate and change in face temperature while wearing different types of three commonly used masks. Finally, using values of these predicting variables from experiments and the literature, and surveys asking users to rate the comfort of various masks, three machine learning algorithms were trained and tested to generate overall comfort scores for those masks. Although all three models performed with an accuracy of approximately 70%, the multiple linear regression model provides a simple analytical expression to predict the comfort scores for common face masks provided the input predicting variables. As face mask usage is crucial during the COVID-19 pandemic, the goal of this quantitative framework to predict mask comfort is hoped to improve user experience and prevent discomfort-induced noncompliance.
戴口罩是限制 SARS-CoV-2 传播的最有效方法之一，但只有用户高度遵守规定，口罩才有用。通过匿名调查（n=679），表明口罩不适是佩戴口罩不合规的主要原因。此外，通过这些调查，确定了三个决定口罩舒适度的关键预测变量：空气阻力、水蒸气透过率和面部温度变化。为了在生理环境中验证这些预测变量，进行了实验（n=9）来测量佩戴不同类型的三种常用口罩时的呼吸率和面部温度变化。最后，使用实验和文献中的这些预测变量的值，以及调查用户对各种口罩舒适度的评价，训练和测试了三种机器学习算法，以生成这些口罩的整体舒适度得分。虽然这三个模型的准确率都约为 70%，但多元线性回归模型提供了一个简单的分析表达式，可预测常见口罩的舒适度得分，前提是输入预测变量。由于在 COVID-19 大流行期间口罩的使用至关重要，因此该预测口罩舒适度的定量框架的目标是希望改善用户体验并防止因不适而导致的不合规。
