Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Lung Functionality-Centric Air Quality Alert System: An Observational Pilot Study

Version 1 : Received: 29 July 2023 / Approved: 1 August 2023 / Online: 1 August 2023 (11:50:19 CEST)
Version 2 : Received: 5 August 2023 / Approved: 7 August 2023 / Online: 8 August 2023 (07:12:49 CEST)

How to cite: Alotaibi, M.; Alnajjar, F.; Alsayed, B.A.; Alhmiedat, T.; Marie, A.; Bushnag, A.; Ali, L. Lung Functionality-Centric Air Quality Alert System: An Observational Pilot Study. Preprints 2023, 2023080024. https://doi.org/10.20944/preprints202308.0024.v2 Alotaibi, M.; Alnajjar, F.; Alsayed, B.A.; Alhmiedat, T.; Marie, A.; Bushnag, A.; Ali, L. Lung Functionality-Centric Air Quality Alert System: An Observational Pilot Study. Preprints 2023, 2023080024. https://doi.org/10.20944/preprints202308.0024.v2

Abstract

Asthma is a chronic respiratory disease that impairs breathing. Management of asthma presents a significant challenge due to its inherent variability; that is, its symptoms can substantially differ among individuals, thereby complicating the prediction and management of exacerbations. Furthermore, individuals with asthma often have unique triggers that precipitate symptoms or attacks. The identification of these triggers can often prove to be a challenging, and at times, an impractical attempt. To address this, our research proposes a practical, personalized alert system, predicated on individual lung function tests conducted under varying environmental conditions classified by air-quality sensors. To validate this concept, we conducted an observational pilot study involving healthy individuals. We recruited twelve healthy participants and monitored their responses across a broad spectrum of environments, characterized by varying air quality, temperature, and humidity conditions. The lung function for each participant, assessed using peak expiratory flow (PEF) values, was recorded in each of these environments. Our results highlighted substantial variability in pulmonary responses to different environments. Utilizing these insights, we proposed a personalized alarm system that provides real-time air-quality monitoring and issues alerts when environmental conditions may potentially become unfavorable. We also explored the feasibility of employing basic machine learning techniques to predict PEF values in the aforementioned environmental conditions. This proposed system has the potential to empower individuals in actively safeguarding their respiratory health and mitigating discomfort caused by environmental conditions, especially in cases of asthma patients. By enabling timely and personalized interventions, the system aims to provide individuals with the necessary tools to minimize exposure to asthma’s possible triggers.

Keywords

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

Subject

Public Health and Healthcare, Public Health and Health Services

Comments (1)

Comment 1
Received: 8 August 2023
Commenter: Fady Alnajjar
Commenter's Conflict of Interests: Author
Comment: 1- Based on the comments, I want to change the title to a shorter and clearer one. Please change the title to: "Lung Functionality-Centric Air Quality Alert System: An Observational Pilot Study".
2- To avoid copyright issues, I want to remove Figure 1. 
I have updated all necessary changes in the updated word file. 
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