RESUMO
Introduction: More than 12% of COVID-19 hospitalized patients develop Generalized Anxiety Disorder (GAD) after discharged. High frequency band percentage of heart rate variability (hfHRV) is a reliable indicator of efficient functional coupling between autonomic branches across high-demanding adaptive situations. Objective(s): To compare hfHRV among post-hospitalized COVID-19 survivors by level of GAD. Method(s): We conducted an observational study with 211 post-COVID-19 participants (63.7% males;47.6y +/-14.3), 3 months after discharged. We registered their hfHRV with a computerized biofeedback equipment throughout four conditions: open-eyes (C1);closed-eyes (C2);closed-eyes+natural-relaxation (C3);and closed-eyes+deep-breathing (C4) (2.5 minutes per condition). Participants were classified into 3 categories using General Anxiety Disorder Scale (GAD-7): low anxiety (n=174, 67.5%, 47.2 yo +/-13.4;G1);moderate anxiety (n=24, 66.5%, 47.3 yo +/-15.3;G2) and severe anxiety (n=13, 60.5%, 46.1 yo +/-9;G3). Statistical analysis were performed with SPSS v28. Result(s): hfHRV percentage is higher at C3 in G1 (G1: 29.5 +/-21.1, G2: 21.1 +/-17.1, G3: 20.0 +/-20.4;p = 0.01). G3 display a 30% decrease in hfHRV during this condition in contrast with G1 (p = 0.006). Percentage of hfHRV in G1 (C1: 31 +/-22.6, C2: 29.2 +/-23.6, C4: 24.3 +/-20.7), and G3 (C1: 29.7 +/-22.8;C2: 27.9 +/-17.6;C4: 20 +/-20) didn't show any significant differences. Conclusion(s): C3 involve an adaptive challenge that demands an effective sympathetic-parasympathetic regulation. An increase in hfHRV during C3 in G1, indicates that the group with low anxiety exhibit a more effective psychophysiological adaptive feature than G2 and G3: a potential protective factor from GAD.
RESUMO
A new and deadly virus known as SARS-CoV-2, which is responsible for the coronavirus disease (COVID-19), is spreading rapidly around the world causing more than 4 million deaths. Hence, there is an urgent need to find new and innovative ways to reduce the likelihood of infection. One of the most common ways of catching the virus is by being in contact with droplets delivered by a sick person. The risk can be reduced by wearing a face mask as suggested by the World Health Organization (WHO), especially in closed environments such as classrooms, hospitals, and supermarkets. However, people hesitate to use a face mask leading to an increase in the risk of spreading the disease, moreover when the face mask is used, sometimes it is worn in the wrong way. In this work, an autonomic face mask detection system with deep learning and powered by the image tracking technique used for the augmented reality development is proposed as a mechanism to request the correct use of face masks to grant access to people to critical areas. To achieve this, a machine learning model based on Convolutional Neural Networks was built on top of an IoT framework to enforce the correct use of the face mask in required areas as it is requested by law in some regions. © 2021 Sociedad Mexicana de Ingenieria Biomedica. All rights reserved.