The aim of this project is to continue developing digital tools that help us predict when people living with chronic obstructive pulmonary disease (COPD) are likely to become unwell and support them in managing their condition.
Three million people in the UK live with COPD and many of them experience acute lung attacks or disease exacerbations. COPD exacerbations are a leading cause of death and disability and one of the most common reasons for an emergency admission to hospital.
In the first SPACE study, we wanted to understand how accurate an exacerbation tool needs to be to be useful for users of the myCOPD app. The second SPACE study aimed to understand whether the tool we developed is acceptable to myCOPD users and how regularly they might use it.
In the third SPACE study we will send sensing devices such as smartwatches and smart spirometers to study participants. Participants will then use these devices together with the myCOPD app. We will collect the data generated by these devices as well as gathering data patients enter manually into the app. We will combine this with machine learning algorithms to generate an exacerbation prediction model which could be used in the real-world.
This work builds on our findings from the second SPACE study, where we found that myCOPD patient users will engage in the daily use of sensing technologies with minimal issues and/or burden. Our aim for this project is to develop an exacerbation support tool that contains both an exacerbation prediction model and an exacerbation warning system.