The use of smart and mobile health monitoring devices has become very popular in the self-management of asthma. People using such solutions are provided with a completely new way of monitoring that does not interfere with their daily functioning. Data from such technologies make it possible to build a functional and safe system for people with asthma (Tsang et al., 2022 ). Such systems will be able to predict asthma attacks and reduce the need to enter large amounts of data.
Researchers from the University of Edinburgh have created a study entitled: Mobile device monitoring to inform the prediction of asthma attacks: the AAMOS-00 observational study”. The study aims to develop a method to accurately predict asthma attacks by using smart devices and symptom diaries . To conduct a measurable and accurate study, several entities have been involved. The researchers integrated the FindAir ONE device with their system to study the effects of this new data.
Individuals participating in the study will receive three monitoring devices. The first is the FindAir ONE device, a smart inhaler that attaches to a bottle of medication and collects data about every drug usage regarding the number of dosages and time. FindAir Asthma Diary App connects with FindAir ONE and provides patients with daily medication reminders, treatment overviews, and suggestions of their main asthma triggers. All provide more accurate asthma & COPD treatment and improve patient adherence. Another device is the Smart Asthma smart peak flow meter, which measures how fast you can blow air out of your lungs. The last device is the Xiaomi Band 3 smartwatch, which will collect heart rate, step count, and activity data. Other information like weather, pollen count, and air quality from Ambee and OpenWeatherMaps were integrated on one platform called Mobistudy – an open platform, that is used to conduct clinical research with the use of mobile devices [4,5]
The AAMOS-00 study led by researchers at the University of Edinburgh consists of two parts. The first requires participants to complete daily and weekly asthma questionnaires. The second part involves monitoring their symptoms using smart devices in addition to completing the questionnaires over six months. In the end, participants will be able to give their opinion about smart devices whether can be helpful for people with asthma in their everyday life. All of the information that will be collected using smart technology will allow for a safe system for people who struggle with asthma. The process will involve comparing data from the devices with data from a traditional symptom diary. This will serve to create systems that detect asthma attacks and reduce the need for a cumbersome data entry .
 Tsang KCH, Pinnock H, Wilson AM, Shah SA. Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review. J Asthma Allergy. 2022;15:855-873
 Salvi D, Olsson CM, Ymeri G, Carrasco-Lopez C, Tsang KC, Shah SA. “Mobistudy: mobile-based, platform-independent, multi-dimensional data collection for clinical studies.” In 11th International Conference on the Internet of Things. 2021 Nov 8 (pp. 219-222). https://doi.org/10.1145/3494322.3494363
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