Home monitoring with connected mobile devices for asthma attack prediction with machine learning

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Tsang, Kevin C.H., Pinnock, Hilary, Wilson, Andrew M., Salvi, Dario and Shah, Syed Ahmar (2023) Home monitoring with connected mobile devices for asthma attack prediction with machine learning. Scientific Data, 10. ISSN 2052-4463

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Abstract

Monitoring asthma is essential for self-management. However, traditional monitoring methods require high levels of active engagement, and some patients may find this tedious. Passive monitoring with mobile-health devices, especially when combined with machine-learning, provides an avenue to reduce management burden. Data for developing machine-learning algorithms are scarce, and gathering new data is expensive. A few datasets, such as the Asthma Mobile Health Study, are publicly available, but they only consist of self-reported diaries and lack any objective and passively collected data. To fill this gap, we carried out a 2-phase, 7-month AAMOS-00 observational study to monitor asthma 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 collected a rich longitudinal dataset to explore the feasibility of passive monitoring and asthma attack prediction. This valuable anonymised dataset for phase-2 of the study (device monitoring) has been made publicly available. Between June-2021 and June-2022, in the midst of UK’s COVID-19 lockdowns, 22 participants across the UK provided 2,054 unique patient-days of data.

Item Type:	Article
Additional Information:	Funding Information: This work was supported by Asthma + Lung UK as part of the Asthma UK Centre for Applied Research grant number AUK-AC-2018-01. Support from Malmö University was co-funded by the Knowledge Foundation KK-stiftelsen. We thank all the participants of the AAMOS-00 study, this research would not be possible without their time and effort. We thank the AUKCAR PPI for their involvement in developing and analysing the study. We thank the Mobistudy team and Malmö University for their support with data collection. We thank Smart Respiratory Products Ltd for providing the Smart Peak Flow Meter and associated software. We thank FindAir for providing the FindAir ONE devices and FindAir’s API. We thank Ambee for providing the pollen data. We thank the Asthma + Lung UK and AUKCAR social media teams, Malcolm Marquette and the Norwich and Norfolk University Hospital for their assistance in participant recruitment. We thank Dr Sarah Brown (Edinburgh Innovations, University of Edinburgh, UK) for organising the contracts required for the study. We thank Aryelly Rodriguez (Edinburgh Clinical Trials Unit, University of Edinburgh, UK) for statistical advice in producing the anonymised AAMOS-00 dataset.
Uncontrolled Keywords:	statistics and probability,information systems,education,computer science applications,statistics, probability and uncertainty,library and information sciences ,/dk/atira/pure/subjectarea/asjc/2600/2613
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Centres > Population Health Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	29 Aug 2023 13:30
Last Modified:	19 Dec 2024 01:10
URI:	https://ueaeprints.uea.ac.uk/id/eprint/92946
DOI:	10.1038/s41597-023-02241-9

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