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Home monitoring with connected mobile devices for asthma attack prediction with machine learning
Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK; Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK.
Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK; Norwich Medical School, University of East Anglia, Norwich, UK; Norwich University Hospital Foundation Trust, Colney Lane, Norwich, UK.
Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
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2023 (engelsk)Inngår i: Scientific Data, E-ISSN 2052-4463, Vol. 10, nr 1, artikkel-id 370Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Nature Publishing Group, 2023. Vol. 10, nr 1, artikkel-id 370
HSV kategori
Identifikatorer
URN: urn:nbn:se:mau:diva-61395DOI: 10.1038/s41597-023-02241-9ISI: 001003519300002PubMedID: 37291158Scopus ID: 2-s2.0-85161336943OAI: oai:DiVA.org:mau-61395DiVA, id: diva2:1775531
Tilgjengelig fra: 2023-06-27 Laget: 2023-06-27 Sist oppdatert: 2023-08-16bibliografisk kontrollert

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