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Journal of Allergy and Clinical Immunology ; 151(2):AB158, 2023.
Article in English | EMBASE | ID: covidwho-2245747


Rationale: Asthma remains a significant comorbidity among children with food allergy (FA). Longitudinal data on the course of asthma in this population, particularly during the COVID-19 pandemic, is lacking. This study aims to describe asthma management and control among children with FA during the COVID-19 pandemic. Methods: Children with FA (≤12 years old at enrollment) were enrolled into FORWARD, a prospective, observational cohort study. Data from participants with FA and asthma who completed a 12-month and 24-month post-enrollment asthma therapy assessment were included (n=125). Surveys were administered between January 2019 - July 2022, which includes the onset and duration of COVID-19. Responses to the same questions at the two time points were analyzed using tests of exact symmetry. Results: Compared to the 12-month survey, caregivers at the 24-month survey more frequently reported that their children were not using their inhaler for quick relief (1.6% vs. 9.4%, p = 0.008) and were using their medication incorrectly (3.2% vs. 8.7%, p = 0.003). They less frequently reported that they were unsure whether their medications were useful (3.2% vs. 0.0%, p = 0.016). A similar distribution was observed when non-Hispanic Black and non-Hispanic White participants were compared. No significant differences were evident when comparing symptoms. Conclusions: The symptom burden of asthma remained stable even during the pandemic. However, during this time, children with asthma were less likely to need a rescue inhaler and to be adherent to their maintenance regimen. Further longitudinal research on asthma management is necessary to better understand the potential impact of COVID-19.

12th International Conference on Ambient Systems, Networks and Technologies ; 184:524-531, 2021.
Article in English | Web of Science | ID: covidwho-1353997


In the face of the COVID-19 pandemic and the absence of a vaccine or an effective treatment against the virus, the available studies show that today, the most effective measure for prevention continues to be social distancing. In this sense, in this article, we focus implementing an IoT-based System for safer mobility in the age of COVID-19 using machine learning called SafeMobility. This system has been designed to monitor in real-time the social distancing between people and control the capacity in common interior spaces via a multilayer architecture that integrates IoT, fog, and cloud solutions. To control the capacity safely, we have detected the location of people using machine learning models. We have trained and evaluated these models from a data set containing the RSSI signals of the different surrounding WiFi networks obtained via a portable IoT device. Besides, this portable device integrated with a high precision laser sensor has also been used to detect the distance between people, thus avoiding potential infections. Also, we have exploited the advantages of fog computing to perform data processing and analysis in a fog node using the machine learning model that presented the highest accuracy in the evaluation. In case of non-compliance with the allowed social distance or the established peak capacity, alert messages are sent via a lightweight and optimal protocol in using IoT applications. A web application hosted on a cloud server receives the information from the fog node in real-time and dynamically displays the congestion sites in the environment. Our experiments demonstrate the effectiveness of the system to determine the position of the people with an accuracy of 91%. (C) 2021 The Authors. Published by Elsevier B.V.