THE EFFECT OF SURGICAL MASKS ON OXYGEN SATURATION AND PERCEIVED EXERTION DURING RESISTANCE TRAINING

Objective: This investigation aimed to evaluate SpO2, and the rate of perceived exertion (RPE) derived from a strength training session in two distinct scenarios: normal condition versus the usage of surgical masks for COVID-19 prevention. Methods: Fifteen trained men (81.66 ± 8.37 kg;177.66 ± 6.31 cm;26.88 ± 5.55 years of age;12.17 ± 5.98 % fat;1.15 ± 0.19 kg/kg bench press relative strength/body weight) were selected, and they performed two test sessions to determine 10-RM loads for all exercises adjusted for 80%. The SpO2 measurement was verified immediately after each set for every exercise, and, concomitantly, the participants were asked to identify their RPE to provide a subjective measure of fatigue. In the first session, subjects performed the training routine using the SARS-CoV-2 protection surgical mask with a passive rest interval of 2 minutes, but the second was performed without wearing a surgical mask. Results: The SpO2 showed a difference (p = 0.03) under the condition curve with the mask (481.33 ± 3.04) versus without the mask (484.46 ± 5.96), with increments in SpO2 for the condition without the mask at different verification times (p = 0.039). Regarding the initial sets and exercises, there were no significant differences between the RPE values between the different conditions, that is, regardless of the mask use (p = 0.052). However, for the final exercises, significant differences were observed in the second set (PD, p = 0.01;LC, p = 0.02) and in the three sets of the TE exercise (p = 0.006). Conclusion: Overall, we found that the use of surgical masks reduces SpO2 and increases RPE in a strength training session. © 2022, Drustvo Pedagoga Tjelesne i Zdravstvene Kulture. All rights reserved.

Data-driven Mobility Analysis and Modeling: Typical and Confined Life of a Metropolitan Population

The idea of using mobile phone data to understand the impact of the Covid-19 pandemic and that of the sanitary constraints associated with it on human mobility imposed itself as evidence in most countries. This work uses spatiotemporal aggregated mobile phone data provided by a major French telecom operator, covering a geographical region centered on Paris for early 2020, i.e., periods before and during the first French lockdown. An essential property of this data is its fine-grained spatial resolution, which, to the best of our knowledge, is unique in the COVID-related mobility literature. Contrarily to regions or country-wide resolution, it describes population mobility flows among zones ranging from 0.025 km2 to 5.40 km2, corresponding to 326 aggregated zones over the total area of 93.76 km2 of the city of Paris. We perform a data-driven mobility investigation and modeling to quantify (in space and time) the population attendance and visiting flows in different urban areas. Second, when looking at periods both before and during the lockdown, we quantify the consequences of mobility restrictions and decisions on an urban scale. For this, per zone, we define a so-called signature, which captures behaviors in terms of population attendance in the corresponding geographical region (i.e., their land use) and allows us to automatically detect activity, residential, and outlier areas. We then study three different types of graph centrality, quantifying the importance of each zone in a time-dependent weighted graph according to the habits in the mobility of the population. Combining the three centrality measures, we compute per zone of the city, its impact-factor, and employ it to quantify the global importance of zones according to the population mobility. Our results firstly reveal the population's daily zone preferences in terms of attendance and mobility, with a high concentration on business and touristic zones. Second, results show that the lockdown mobility restrictions significantly reduced visitation and attendance patterns on zones, mainly in central Paris, and considerably changed the mobility habits of the population. As a side effect, most zones identified as mainly having activity-related population attendance in typical periods became residential-related zones during the lockdown, turning the entire city into a residential-like area. Shorter distance displacement restrictions imposed by the lockdown increased visitation to more "local" zones, i.e., close to the population's primary residence. Decentralization was also favored by the paths preferences of the still-moving population. On the other side, "jogging activities" allowing people to be outside their residences impacted parks visitation, increasing their visitation during the lockdown. By combining the impact factor and the signatures of the zones, we notice that areas with a higher impact factor are more likely to maintain regular land use during the lockdown.

Covid-19 time series prediction

The Artificial Neural Network (ANN) is a computer technique that uses a mathematical model to represent a simpler form of the biologic neural structure. It is formed by many processing units and its intelligent behavior comes from the iterations between these units. One application of the ANN is for time series prediction algorithms, where the network learns the behavior of time dependent data and it is able to predict future values. In this work, the ANN is applied in predicting the number of COVID-19 confirmed cases and deaths and also the future seven days for the time series of Brazil, Portugal and the United States. From the simulations it is possible to conclude that the prediction of confirmed cases and deaths from COVID-19 have been successfully made by the ANN. Overall, the ANN with a specific test set had a Mean Squared Error (MSE) 50% higher than the ANN with a random test set. The combination of the sigmoidal and linear activation functions and the Levenberg-Marquardt training function had the lowest MSE for all cases. © 2021 The Authors. Published by Elsevier B.V.