An Observational Pilot Study of a Tailored Environmental Monitoring and Alert System for Improved Management of Chronic Respiratory Diseases

Abstract

Mohammed Alotaibi,1 Fady Alnajjar,2 Badr A Alsayed,3 Tareq Alhmiedat,1,4 Ashraf M Marei,1,4 Anas Bushnag,1,4 Luqman Ali2 1Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, Saudi Arabia; 2Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University (UAEU), Al Ain, United Arab Emirates; 3Department of Internal Medicine Faculty of Medicine, University of Tabuk, Tabuk, Saudi Arabia; 4Faculty of Computers & Information Technology, University of Tabuk, Tabuk, Saudi ArabiaCorrespondence: Mohammed Alotaibi; Fady Alnajjar, Email [email protected]; [email protected]: Chronic lung-related diseases, with asthma being the most prominent example, characterized by diverse symptoms and triggers, present significant challenges in disease management and prediction of exacerbations across patients. This research aimed to devise a practical solution by introducing a personalized alert system tailored to individual lung function and environmental conditions, offering a holistic approach for the management of a range of chronic respiratory conditions.Methods: In response to these challenges, we developed a personalized alert system based on individual lung function tests conducted in diverse environmental conditions, as determined by air-quality sensors. Our research was substantiated through an observational pilot study involving twelve healthy participants. These participants were exposed to varying air quality, temperature, and humidity conditions, and their lung function, as indicated by peak expiratory flow (PEF) values, was monitored.Results: The study revealed pronounced variability in pulmonary responses across different environments. Leveraging these findings, we proposed a design of a personalized alarm system that monitors air quality in real-time and issues alerts under potentially unfavorable environmental conditions. Additionally, we investigated the use of basic machine learning techniques to predict PEF values in these varied environmental settings.Discussion: The proposed system offers a proactive approach for individuals, particularly those with asthma, to actively manage their respiratory health. By providing real-time monitoring and personalized alerts, it aims to minimize exposure to potential asthma triggers. Ultimately, our system seeks to empower individuals with the tools for timely intervention, potentially reducing discomfort and enhancing management of asthma symptoms.Keywords: respiratory health, real-time air-quality monitoring, lung functionality, alert system

Keywords

• respiratory health
• real-time air-quality monitoring
• lung functionality
• alert system