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Contact the study team using the details below to take part. If there are no contact details below please ask your doctor in the first instance.
Ms
Kate
Martinson
km3@soton.ac.uk
Jackie
Seely
j.seely@soton.ac.uk
Prof
Nick
Francis
nick.francis@soton.ac.uk
Mrs
Kate
Martinson
k.martinson@soton.ac.uk
Acute upper respiratory infectionsInfluenza and pneumoniaOther acute lower respiratory infections
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Background
Respiratory Tract Infections (RTIs) are the most common cause of illness and the most common reason patients consult a GP. The illness they cause is usually mild, but in some cases can become severe, and occasionally can lead to death. Around half of all antibiotic prescriptions are for RTIs. Most people with an RTI get better without needing treatment. However, we need to notice quickly when people are getting seriously ill. If we do not, the effect on them and on healthcare services can be large. Doctors have rules and tests that help them identify patients who are more likely to need treatment, but these do not work well for every patient. Also, they are not useful for helping patients manage their own illness. Using machine learning (AI systems) to analyse breathing and speech sounds automatically could be a game-changer. Firstly, it could reassure many patients that they do not need to see a doctor. Secondly, it could reduce prescriptions for antibiotics by identifying patients who will get better on their own. Identifying patients at higher risk could also reduce hospital admissions, cases of severe illness and the number who die. All these effects would reduce pressure on the NHS. We already know that some signs, such as breathing faster, can tell us whether an RTI is getting worse, and we know we can measure these signs by recording the sound of the breath. We know that RTIs also affect breathing pattern, the sound of speech and trying to breathe when speaking. We believe that other breathing sounds and patterns are also likely to change with RTI severity, and this is something we want to explore in this project.
Aim
We aim to find information in sound recordings of breathing, cough and speech which changes in a way we can predict as a person gets sicker or recovers.
Methods
Our project will ask volunteers to use an app to collect speech and breathing sound data. One group will be asked to make a recording when they are healthy and then record again if they get an RTI (every day until recovered). Another group will be recruited from participating general practices when they consult with an RTI, and will be asked to record sounds every day until they have recovered. The app will also collect other health information from them, such as their symptoms, any medication they take and any other illness they may have. The machine learning system will process the data to predict whether they are getting better or worse and rate its own confidence in its prediction. Participants will also record if they see their doctor for treatment or had to go to hospital. This will allow us to assess the quality of the advice from the machine learning system.
Anticipated impact and dissemination
We hope to be able to develop a machine learning system that can assess if someone with an RTI should see their doctor for advice or can expect to get better without treatment. Our results will be published in scientific journals, presented and discussed at a dissemination event that relevant stakeholders will be invited to, and distributed to other relevant stakeholders. If shown to be promising, we will seek further funding to evaluate use of the app to guide self-management and/or clinical care of RTIs in the community.
Start dates may differ between countries and research sites. The research team are responsible for keeping the information up-to-date.
The recruitment start and end dates are as follows:
Observational type: Cohort study;
You can take part if:
You may not be able to take part if:
1. Known to have a chronic voice disorder. 2. Currently pregnant. 3. Has already participated in the study (as part of cohort 1 or cohort 2). 4. Unable to complete the study procedures in English.
Below are the locations for where you can take part in the trial. Please note that not all sites may be open.
Ms
Kate
Martinson
km3@soton.ac.uk
Jackie
Seely
j.seely@soton.ac.uk
Mrs
Kate
Martinson
k.martinson@soton.ac.uk
Prof
Nick
Francis
nick.francis@soton.ac.uk
The study is sponsored by University of Southampton and funded by Engineering and Physical Sciences Research Council (EPSRC) .
Your feedback is important to us. It will help us improve the quality of the study information on this site. Please answer both questions.
Read full details
for Trial ID: CPMS 59221
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