Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol

Tsang, Kevin Cheuk Him, Pinnock, Hilary, Wilson, Andrew M., Salvi, Dario and Shah, Syed Ahmar (2022) Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol. BMJ Open, 12 (10). ISSN 2044-6055

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Abstract

Introduction: Supported self-management empowering people with asthma to detect early deterioration and take timely action reduces the risk of asthma attacks. Smartphones and smart monitoring devices coupled with machine learning could enhance self-management by predicting asthma attacks and providing tailored feedback. We aim to develop and assess the feasibility of an asthma attack predictor system based on data collected from a range of smart devices. Methods and analysis: A two-phase, 7-month observational study to collect data about asthma status using three smart monitoring devices, and daily symptom questionnaires. We will recruit up to 100 people via social media and from a severe asthma clinic, who are at risk of attacks and who use a pressurised metered dose relief inhaler (that fits the smart inhaler device). Following a preliminary month of daily symptom questionnaires, 30 participants able to comply with regular monitoring will complete 6 months of using smart devices (smart peak flow meter, smart inhaler and smartwatch) and daily questionnaires to monitor asthma status. The feasibility of this monitoring will be measured by the percentage of task completion. The occurrence of asthma attacks (definition: American Thoracic Society/European Respiratory Society Task Force 2009) will be detected by self-reported use (or increased use) of oral corticosteroids. Monitoring data will be analysed to identify predictors of asthma attacks. At the end of the monitoring, we will assess users’ perspectives on acceptability and utility of the system with an exit questionnaire. Ethics and dissemination: Ethics approval was provided by the East of England - Cambridge Central Research Ethics Committee. IRAS project ID: 285 505 with governance approval from ACCORD (Academic and Clinical Central Office for Research and Development), project number: AC20145. The study sponsor is ACCORD, the University of Edinburgh. Results will be reported through peer-reviewed publications, abstracts and conference posters. Public dissemination will be centred around blogs and social media from the Asthma UK network and shared with study participants.

Item Type: Article
Uncontrolled Keywords: asthma,health informatics,information technology,world wide web technology,medicine(all) ,/dk/atira/pure/subjectarea/asjc/2700
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups: Faculty of Medicine and Health Sciences > Research Groups > Respiratory and Airways Group
Faculty of Medicine and Health Sciences > Research Groups > Cardiovascular and Metabolic Health
Faculty of Medicine and Health Sciences > Research Centres > Population Health
Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health
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Depositing User: LivePure Connector
Date Deposited: 20 Oct 2022 11:30
Last Modified: 13 Nov 2023 17:53
URI: https://ueaeprints.uea.ac.uk/id/eprint/89244
DOI: 10.1136/bmjopen-2022-064166

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