Accepted for/Published in: JMIR Biomedical Engineering
Date Submitted: Mar 21, 2021
Date Accepted: Oct 12, 2021
Date Submitted to PubMed: Nov 24, 2021
Atmosome: A Comprehensive Approach to Understanding the Personal Atmospheric Exposome
ABSTRACT
Background:
Modern environmental health research extensively focuses on outdoor air pollutants and their effects on public health. However, research on monitoring and enhancing individual indoor air quality is lacking. The field of exposomics encompasses the totality of human environmental exposures and its effects on health. A subset of this exposome deals with atmospheric exposure, termed the “atmosome.” The atmosome plays a pivotal role in health and has significant effects on DNA, metabolism, skin integrity, and lung health.
Objective:
The aim of this work is to develop a low-cost, comprehensive measurement system for collecting and analyzing atmosomic factors. The research explores the significance of the atmosome in personalized and preventive care for public health.
Methods:
An Internet of Things (IoT) microcontroller-based system is introduced and demonstrated. The system collects real-time indoor air quality data and posts it to the Cloud for immediate access.
Results:
The experimental results yield an accuracy of over 90% in terms of air quality data 1 measurements when compared to precalibrated commercial devices and demonstrate a direct correlation between lifestyle events and air quality.
Conclusions:
Quantifying the individual atmosome is a monumental step in advancing personalized health, medical research and epidemiological research. The two main goals in this work are to present the atmosome as a measurable concept and to demonstrate how to implement it using low-cost electronics. By enabling atmosome measurements at a massive scale, this work also opens up potential new directions for public health research. Researchers will now have the data to model the impact of indoor air pollutants on the health of individuals, communities, and specific demographics, leading to novel approaches for predicting and preventing diseases.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.