Actively Recruiting

Age: 18Years +
All Genders
ID07289100

Novel Sensors and Artificial Intelligence for Detection of Acute Pulmonary Exacerbations in Cystic Fibrosis and Non-Cystic Fibrosis Bronchiectasis

Led by Papworth Hospital NHS Foundation Trust · Updated on 2025-12-17

50

Participants Needed

1

Research Sites

4 weeks

Total Duration

On this page

Sponsors

P

Papworth Hospital NHS Foundation Trust

Lead Sponsor

L

LifeArc

Collaborating Sponsor

AI-Summary

What this Trial Is About

This research aims to explore how new sensors and artificial intelligence (AI) can detect acute pulmonary exacerbations (APEs) early in adults with cystic fibrosis (CF) or non-CF bronchiectasis. These conditions cause lung problems that worsen with sudden infections, leading to symptoms like coughing and breathlessness. The study focuses on measuring changes in body signals, such as heart rate, during chest infections treated with intravenous antibiotics, to better understand and detect these flare-ups. Participants will use novel sensors named OMED, ARIA, and ANNE that monitor physiological signs like heart rate, temperature, and cough. They will wear or use these sensors daily throughout their intravenous antibiotic treatment, usually lasting around two weeks. Participants may choose which sensors to try, and if a second infection occurs shortly after, sensor use continues during the new treatment. The study is observational and does not alter usual care. During the study, participants will record daily cough and wellness scores on a simple scale. Researchers will collect clinical data such as lung function tests, blood markers, and treatment details from routine care records. The main measurement is the change in sensor readings from the start to the end of antibiotic treatment. There are no extra follow-up visits, and safety monitoring includes checking for any technical issues with the equipment. Participation lasts up to about five weeks depending on treatment courses.

CONDITIONS

Brief Title

Novel Sensors and Artificial Intelligence for Detection of Acute Pulmonary Exacerbations in Cystic Fibrosis and Bronchiectasis

Who Can Participate

Age: 18Years +
All Genders

Eligibility Criteria

Eligible

You may qualify if you...

  • 18 years old or older
  • Diagnosis of cystic fibrosis based on genetic testing and/or sweat chloride levels or CT-confirmed diagnosis of non-cystic fibrosis bronchiectasis
  • Under the care of Royal Papworth Hospital
  • Starting intravenous antibiotics for treatment of acute pulmonary exacerbation as defined by the Hill Criteria
Not Eligible

You will not qualify if you...

  • Unable to provide written informed consent
  • Lung transplant recipients or on lung transplant waiting list
  • Use of long-term oxygen therapy or non-invasive ventilation to manage respiratory failure
  • Unwilling to consent to use of link-anonymised home monitoring data for research
  • Considered unsuitable for home monitoring by the investigator
  • Admitted for elective antibiotic treatment (not acute exacerbation treatment)

AI-Screening

AI-Powered Screening

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Your Study Journey

Screening

Duration - 2 to 4 weeks

Participants are screened for eligibility to participate in the trial.

1 visit (in-person or virtual) for consent and eligibility confirmation

Monitoring

Duration - Up to 5 weeks, depending on the duration of intravenous antibiotic treatment and any repeat courses within two weeks after treatment ends

Participants who are starting intravenous antibiotics for acute pulmonary exacerbation wear or use novel sensors daily to monitor physiological parameters and record coughing and wellness scores.

Daily use of sensors and daily self-reporting of coughing and wellness scores; ongoing contact by clinical research team by telephone, email, or hospital visit as preferred

Trial Site Locations

Total: 1 location

1

Royal Papworth Hospital NHS Foundation Trust

Cambridge, Cambridgeshire, United Kingdom, CB2 0AY

Actively Recruiting

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Research Team

L

Lucy E Gale

V

Victoria Hughes, PhD

How is the study designed?

Study Type

OBSERVATIONAL

Masking

N/A

Allocation

N/A

Model

N/A

Primary Purpose

N/A

Number of Arms

0

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