Completed Project: AAMOS-00
Mobile device monitoring to inform prediction of asthma attacks: an observational study
Smart monitoring, mHealth and machine learning
Smart monitoring devices and mobile-health (mHealth) technologies are used more and more to help with asthma self-management. These technologies, including smartwatches and smartphones, give new ways for people with asthma to monitor their condition with the least interruption to their lives. Smart devices can replace the burden of daily monitoring, helping people to look after their asthma so that they manage their treatment and avoid attacks. Examples are smart inhalers and smart watches.
When mHealth is combined with tailored feedback, this can replace the burdensome task of daily monitoring, leading to a better level of care for patients and thus less asthma attacks and a peace of mind.
Our study consists of:
- Phase 1 - daily questionnaire monitoring for one month
- Phase 2 - smart device monitoring for six months
To develop a useful and safe system for people with asthma, we need to collect information using new smart technologies alongside the traditional daily symptom and peak flow diary. We can then compare the two sets of readings to develop systems that detect worsening asthma using smart devices and potentially reducing the need for burdensome data entry.
The aim of this study is to collect these two sets of data from about 30 people for 6 months. We’ll use the findings to develop a method that can accurately predict an asthma attack smart devices and symptom diaries. In the future this could be used in a connected asthma system to help people look after their asthma and avoid troublesome attacks.
Syed Ahmar Shah
|Asthma UK Centre for Applied Research PhD student||Chancellor's Fellow|
|Based at: University of Edinburgh||Based at: University of Edinburgh|
|Kevin's PhD Profile||Ahmar's Profile|
Lead: Optimising management of asthma attacks
Lead: Postgraduate Training, Network Coordinator
|Lead: Preventing asthma attacks|
|Based at: University of Edinburgh||Based at: University of East Anglia|
|Hilary's Profile||Andrew's Profile|
Tsang, K.C.H., Pinnock, H., Wilson, A.M., Salvi, D., Olsson, C.M., Shah, S.A.
In: Tsanas, A., Triantafyllidis, A. (eds) Pervasive Computing Technologies for Healthcare. PH 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 488. Springer, Cham. doi:10.1007/978-3-031-34586-9_9
Tsang, K.C.H., Pinnock, H., Wilson, A.M., Salvi, D., Shah, S.A.
Sci Data 2023;10:370. doi:10.1038/s41597-023-02241-9
Tsang KCH, Pinnock H, Wilson AM, Salvi D, Shah SA.
University of Edinburgh, Edinburgh Medical School, Usher Institute. 2022. https://doi.org/10.7488/ds/3775.
Tsang KCH, Pinnock H, Wilson AM, et al.
BMJ Open 2022;12:e064166. doi: 10.1136/bmjopen-2022-064166
Salvi, D, Olsson CM, Ymeri G, Carrasco-López C, Tsang KCH , and Shah SA.
In 11th International Conference on the Internet of Things (IoT ’21), November 8–12, 2021, St.Gallen, Switzerland. ACM, New York, NY, USA, 4 pages. doi.org/10.1145/3494322.3494363
If you have any questions about this study, get in touch:
|We thank Mobistudy 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.
This work is funded by Asthma UK as part of the Asthma UK Centre for Applied Research [AUK-AC-2012-01 and AUK-AC-2018-01].
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