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. 2022 Oct 3;12(10):e064166.
doi: 10.1136/bmjopen-2022-064166.

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

Kevin Cheuk Him Tsang  1   2 , Hilary Pinnock  3 , Andrew M Wilson  3   4   5 , Dario Salvi  6 , Syed Ahmar Shah  3   2
Affiliations

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

Kevin Cheuk Him Tsang et al. BMJ Open. .

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.

Keywords: Asthma; Health informatics; Information technology; World Wide Web technology.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
AAMOS-00 system overview. AAMOS, Asthma Attack Management Online System.
Figure 2
Figure 2
Mobistudy system overview. API, Application Programming Interface.
Figure 3
Figure 3
Participant’s app home page. AAMOS, Asthma Attack Management Online System.
Figure 4
Figure 4
Questionnaire delivered by Mobistudy.
Figure 5
Figure 5
Smart peak flow meter task.
Figure 6
Figure 6
Smartwatch data.
Figure 7
Figure 7
Local weather data.
Figure 8
Figure 8
Exploring asthma attack prediction.
See this image and copyright information in PMC

References

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    1. Iacobucci G. Asthma deaths rise 33% in past decade in England and Wales. BMJ 2019;366:l5108. 10.1136/bmj.l5108 - DOI - PubMed
    1. Pinnock H, Parke HL, Panagioti M, et al. . Systematic meta-review of supported self-management for asthma: a healthcare perspective. BMC Med 2017;15:64. 10.1186/s12916-017-0823-7 - DOI - PMC - PubMed
    1. Scottish Intercollegiate Guidelines Network/ British Thoracic Society . SIGN 158 British guideline on the management of asthma. London/Edinburgh: BTS/SIGN, 2019
    1. Juniper EF, O'Byrne PM, Guyatt GH, et al. . Development and validation of a questionnaire to measure asthma control. Eur Respir J 1999;14:902–7. 10.1034/j.1399-3003.1999.14d29.x - DOI - PubMed

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