Completed Project: AAMOS-00

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

Collecting data

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.

Key People

Publications

Tsang, K.C.H., Pinnock, H., Wilson, A.M., Salvi, D., Olsson, C.M., Shah, S.A.

Compliance and Usability of an Asthma Home Monitoring System.

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.

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

Sci Data 2023;10:370. doi:10.1038/s41597-023-02241-9 

Tsang KCH, Pinnock H, Wilson AM, Salvi D, Shah SA.

AAMOS-00 Study: Predicting Asthma Attacks Using Connected Mobile Devices and Machine Learning, 2021-2022 [dataset].

University of Edinburgh, Edinburgh Medical School, Usher Institute. 2022. https://doi.org/10.7488/ds/3775. 

Tsang KCH, Pinnock H, Wilson AM, et al.

Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol.

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.

Mobistudy: mobile-based, platform-independent, multi-dimensional data collection for clinical studies.

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

Contact us

If you have any questions about this study, get in touch:

Kevin Tsang

Dr Ahmar Shah

Partners

We thank Mobistudy for their support with data collection.	Image
We thank Smart Respiratory Products Ltd for providing the Smart Peak Flow Meter and associated software.	Image
We thank FindAir for providing the FindAir ONE devices and FindAir's API.	Image
We thank Ambee for providing the pollen data.	Image

Funding

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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