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Supporting self-management of COPD and asthma

Lead Research Organisation: University of Bristol
Department Name: Electrical and Electronic Engineering

Abstract

The objective of the project is to predict exacerbations in COPD and asthma using both supervised and unsupervised learning. Initially, the project will involve identifying what data needs to be collected and what data patients are comfortable having collected from them. Data will be collected at the individual patient level such as home monitoring sensors, activity data, cough frequency, symptom reporting and medication and from the population level such as citywide air quality, met office, traffic, pollen count, linked to primary care clinical records and secondary care admissions data and postcode. If possible, another objective is to clinically validate the effectiveness of the algorithm for predicting exacerbation.

Exacerbations are a period of symptom worsening that are difficult to predict and can cause a person's condition to worsen, to develop complications, require emergency care and can be fatal. There is a clinical need to effectively predict exacerbations in people with COPD and asthma so interventions can be applied early to prevent the severe consequences of exacerbation.

There are many novel aspects of this project including defining what data patients are comfortable having collected, combining patient-level and population-level data, implementing predictive algorithms to be used over long periods of time and prediction of an exacerbation.

The methodologies to be included in this project involve co-design, primary data collection, secondary data collection and training, validation and testing of a predictive algorithm using supervised and unsupervised learning.

Planned Impact

Impact on Health and Care
The CDT primarily addresses the most pressing needs of nations such as the UK - namely the growth of expenditure on long term health conditions. These conditions (e.g. diabetes, depression, arthritis) cost the NHS over £70Bn a year (~70% of its budget). As our populations continue to age these illnesses threaten the nation's health and its finances.

Digital technologies transforming our world - from transport to relationships, from entertainment to finance - and there is consensus that digital solutions will have a huge role to play in health and care. Through the CDT's emphasis on multidisciplinarity, teamwork, design and responsible innovation, it will produce future leaders positioned to seize that opportunity.

Impact on the Economy
The UK has Europe's 2nd largest medical technology industry and a hugely strong track record in health, technology and societal research. It is very well-placed to develop digital health and care solutions that meet the needs of society through the creation of new businesses.

Achieving economic impact is more than a matter of technology. The CDT has therefore been designed to ensure that its graduates are team players with deep understanding of health and social care systems, good design and the social context within which a new technology is introduced.

Many multinationals have been keen to engage the CDT (e.g. Microsoft, AstraZeneca, Lilly, Biogen, Arm, Huawei ) and part of the Director's role will be to position the UK as a destination for inwards investment in Digital Health. CDT partners collectively employ nearly 1,000,000 people worldwide and are easily in a position to create thousands of jobs in the UK.

The connection to CDT research will strongly benefit UK enterprises such as System C and Babylon, along with smaller companies such as Ayuda Heuristics and Evolyst.

Impact on the Public
When new technologies are proposed to collect and analyse highly personal health data, and are potentially involved in life or death decisions, it is vital that the public are given a voice. The team's experience is that listening to the public makes research better, however involving a full spectrum of the community in research also has benefits to those communities; it can be empowering, it can support the personal development of individuals within communities who may have little awareness of higher education and it can catalyse community groups to come together around key health and care issues.

Policy Makers
From the team's conversations with the senior leadership of the NHS, local leaders of health and social care transformation (see letters from NHS and Bristol City Council) and national reports, it is very apparent that digital solutions are seen as vital to the delivery of health and care. The research of the CDT can inform policy makers about the likely impact of new technology on future services.

Partner organisation Care & Repair will disseminate research findings around independent living and have a track record of translating academic research into changes in practice and policy.

Carers UK represent the role of informal carers, such as family members, in health and social care. They have a strong voice in policy development in the UK and are well-placed to disseminate the CDTs research to policy makers.

STEM Education
It has been shown that outreach for school age children around STEM topics can improve engagement in STEM topics at school. However female entry into STEM at University level remains dramatically lower than males; the reverse being true for health and life sciences. The CDT outreach leverages this fact to focus STEM outreach activities on digital health and care, which can encourage young women into computer science and impact on the next generation of women in higher education.

For academic impact see "Academic Beneficiaries" section.

People

ORCID iD

Henry Glyde (Student)

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023704/1 31/03/2019 29/09/2027
2275456 Studentship EP/S023704/1 30/09/2019 29/09/2023 Henry Glyde
 
Description The main achievement of this award was successfully completing my doctoral research and earning my PhD. This research contributed to the development of a support tool within the myCOPD app, designed to predict flare-ups (exacerbations) in people with chronic obstructive pulmonary disease (COPD). The project explored how data from patients using the myCOPD app-such as their symptom reports and medication use-could be analysed using machine learning to identify early warning signs of worsening health.

By studying real-world patient data, I found that those experiencing frequent exacerbations tended to be in higher-risk COPD groups and were more engaged with digital health tools. Machine learning models applied to this data demonstrated the potential to predict exacerbations, though accuracy needs improvement.

A key part of the research involved understanding patient perspectives. One study found that some patients recognised the limitations of current prediction models, highlighting the need for further refinements and clear communication to build trust in digital health predictions. Another study revealed that many patients were open to using wearable sensors to help predict exacerbations, though maintaining long-term engagement with devices like digital spirometers could be challenging.

Overall, the research shows that using digital health tools and machine learning could help predict COPD flare-ups, but further work is needed to improve accuracy and ensure patients are willing and able to use these technologies effectively.
Exploitation Route The outcomes of this research provide a foundation for advancing digital tools to predict COPD exacerbations. Insights from this work can help improve existing COPD management apps, enhancing early warning systems for patients and healthcare providers. The predictive models developed can be refined with additional data and improved algorithms to increase accuracy and real-world applicability. Future studies could explore clinical implementation through pilot studies or larger trials to assess effectiveness in improving patient outcomes. Understanding patient engagement with digital health tools also informs the design of more user-friendly systems, supporting long-term adoption. Collaboration with industry, healthcare providers, and researchers could further develop AI-driven decision support tools, extending the impact beyond COPD to broader respiratory disease management.
Sectors Digital/Communication/Information Technologies (including Software)

Healthcare

 
Description myCOPD modelling 
Organisation My mhealth Limited
Country United Kingdom 
Sector Public 
PI Contribution Data insights and machien learning models of myCOPD data.
Collaborator Contribution Access to myCOPD datasets and myCOPD users.
Impact PhD thesis, Heliyon paper, ERS congress poster presentation, PPI focus groups
Start Year 2021
 
Description Digital Health Public Engagement Experience with We the Curious 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Public/other audiences
Results and Impact Digit Health Week at Bristol's We The Curious where members of the public, mainly family's enagged in making monsters and disucssing the idea of predicting scary things from happening like asthma attacks.
Year(s) Of Engagement Activity 2021