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

Kevin Tsang, Hilary Pinnock, Andrew M. Wilson, Dario Salvi, Syed Ahmar Shah

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Article number370
JournalScientific Data
Volume10
DOIs
Publication statusPublished - 8 Jun 2023

Keywords / Materials (for Non-textual outputs)

  • Asthma
  • Communicable Disease Control
  • Computers, Handheld
  • Datasets as Topic
  • Humans
  • Machine Learning
  • Surveys and Questionnaires

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