Augmented Reality Improves Pediatric Mask Induction: A Prospective, Matched Case-Control Study.

Introduction: Pediatric perioperative anxiety is a significant problem during mask induction for general anesthesia. Immersive technologies, such as extended reality headsets, are a promising strategy for alleviating anxiety. Our primary aim was to investigate mask acceptance during inhalational induction utilizing augmented reality (AR). Methods: This was a prospective, matched case-control study at a quaternary academic hospital. Fifty pediatric patients using AR for mask induction were matched to 150 standard-of-care (SOC) controls. The primary outcome was measured with the Mask Acceptance Scale (MAS). Secondary outcomes of cooperation and emergent delirium (ED) were assessed. Results: MAS scores ≥2 occurred at 4% (95% CI [0, 9.4%]) with AR versus 19.3%, (95% CI [13%, 25.7%]) with SOC (RR 0.21, 95% CI [0.05, 0.84], P = .027). Ninety-eight percent of AR patients were cooperative versus 91.3% with SOC (P = .457). Zero percent had ED with AR versus 0.7% with SOC (P = 1.000). Conclusions: AR during mask induction improved mask acceptance compared to SOC. No relationship was observed between AR and cooperation or ED. Future research will investigate the integration of AR into clinical practice as a nonpharmacologic intervention.

Quantifying face mask comfort.

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.

Risk quantification for SARS-CoV-2 infection through airborne transmission in university settings.

The COVID-19 pandemic has significantly impacted learning as many institutions switched to remote or hybrid instruction. An in-depth assessment of the risk of infection that considers environmental setting and mitigation strategies is needed to make safe and informed decisions regarding reopening university spaces. A quantitative model of infection probability that accounts for space-specific parameters is presented to enable assessment of the risk in reopening university spaces at given densities. The model uses the fraction of the campus population that are viral shedders, room capacity, face covering filtration efficiency, air exchange rate, room volume, and time spent in the space as parameters to calculate infection probabilities in teaching spaces, dining halls, dorms, and shared bathrooms. The model readily calculates infection probabilities in various university spaces, with face covering filtration efficiency and air exchange rate being among the dominant variables. When applied to university spaces, this model demonstrated that, under specific conditions that are feasible to implement, in-person classes could be held in large lecture halls with an infection risk over the semester <1%. Meal pick-ups from dining halls and usage of shared bathrooms in residential dormitories among small groups of students could also be accomplished with low risk. The results of applying this model to spaces at Harvard University (Cambridge and Allston campuses) and Stanford University are reported. Finally, a user-friendly web application was developed using this model to calculate infection probability following input of space-specific variables. The successful development of a quantitative model and its implementation through a web application may facilitate accurate assessments of infection risk in university spaces. However, since this model is thus far unvalidated, validation using infection rate and contact tracing data from university campuses will be crucial as such data becomes available at larger scales. In light of the impact of the COVID-19 pandemic on universities, this tool could provide crucial insight to students, faculty, and university officials in making informed decisions.