AAMOS-00 Study: Predicting Asthma Attacks Using Connected Mobile Devices and Machine Learning

Dataset

Abstract

Monitoring asthma condition is essential to asthma self-management. However, traditional methods of monitoring require high levels of active engagement and patients may regard this level of monitoring as tedious. Passive monitoring with mobile health devices, especially when combined with machine learning, provides an avenue to dramatically reduce management burden. However, data for developing machine learning algorithms are scarce, and gathering new data is expensive. A few asthma mHealth datasets are publicly available, but lack objective and passively collected data which may enhance asthma attack prediction systems.

To fill this gap, we carried out the 2-phase, 7-month AAMOS-00 observational study to collect data about asthma status using three smart monitoring devices (smart peak flow meter, smart inhaler, smartwatch), and daily symptom questionnaires. Combined with localised weather, pollen, and air quality reports, we have collected a rich longitudinal dataset to explore the feasibility of passive monitoring and asthma attack prediction. Conducting phase 2 of device monitoring over 12 months, from June 2021 to June 2022 and during the COVID-19 pandemic, 22 participants across the UK provided 2,054 unique patient-days of data. This valuable anonymised dataset has been made publicly available with the consent of participants.

Ethics approval was provided by the East of England - Cambridge Central Research Ethics Committee. IRAS project ID: 285505 with governance approval from ACCORD (Academic and Clinical Central Office for Research and Development), project number: AC20145. The study sponsor was ACCORD, the University of Edinburgh.

The anonymised dataset was produced with statistical advice from Aryelly Rodriguez - Statistician, Edinburgh Clinical Trials Unit, University of Edinburgh.

Protocol: "Predicting asthma attacks using connected mobile devices and machine learning; the AAMOS-00 observational study protocol" - BMJ Open, DOI: 10.1136/bmjopen-2022-064166

Data Citation

Tsang, Kevin CH; Pinnock, Hilary; Wilson, Andrew M; Salvi, Dario; Shah, Syed Ahmar. (2022). AAMOS-00 Study: Predicting Asthma Attacks Using Connected Mobile Devices and Machine Learning, 2021-2022 [dataset]. University of Edinburgh. Edinburgh Medical School. Usher Institute. https://doi.org/10.7488/ds/3775.
Date made available26 Oct 2022
PublisherEdinburgh DataShare
Temporal coverage24 Jun 2021 - 2 Jun 2022
Geographical coverageUK,UNITED KINGDOM

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