Physiological and Environmental Data in a Remote Setting to Predict Exacerbation Events in Patients With Chronic Obstructive Pulmonary Disease
- Conditions
- COPD Exacerbation
- Registration Number
- NCT06118632
- Lead Sponsor
- Chelsea and Westminster NHS Foundation Trust
- Brief Summary
The study plans to monitor around 300 people from different hospitals with COPD for a period of 3 months after they are discharged from the hospital using a smartphone app and a Fitbit device. This device can passively track certain health metrics; this way the research team can research whether it is possible to identify the early warning signs of a decline in health by using these ongoing measurements of vital signs and symptoms. This could allow doctors to intervene early and potentially prevent further deterioration in health decline and hospital admission altogether.
The study seeks to investigate how similar these physiological measurements are when collected in the real world rather than just in the hospital setting, and what influence environmental factors have on a patient's health and experience of their condition.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 300
- Aged 18 or over.
- Diagnosis of COPD, currently admitted to hospital and clinically stable with a confirmed acute exacerbation of COPD.
- Ownership of a smartphone (iOS version 13 or above, Android version 8 or above).
- Able to provide informed consent to participate in study.
- Patients who require less than 24 hours in hospital at initial visit.
- Patients deemed unlikely to cooperate with study requirements.
- Patients with implantable devices.
- Patient not felt to be suitable for research enrolment by admitting clinical team.
- Patients requiring non-invasive ventilation or deemed to have a life-expectancy of less than 90 days following discharge.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method To assess the volume and quality of the data collected in terms of: 3 months Similarity of collected data distribution to expected data distribution, where expected data distribution will be determined based on literature and similarity of the two distributions evaluated by a suitable statistical technique (e.g., Kolmogorov-Smirnov test)
- Secondary Outcome Measures
Name Time Method To assess the relationship between patient-generated data gathered from smartphone and connected devices and conventional clinical measures at point of readmission. 3 months The prediction of physiological measures at readmission (e.g., pulse rate, respiratory rate, pH, FBC, CRP, and CXR appearance) can be addressed as a regression task and evaluated with metrics such as root mean squared error (RMSE).
To ascertain whether marked physiological events can be detected using smartphone and connected device sensors in a remote setting. 3 months Using clinical endpoints such as exacerbation events and readmission to predict exacerbation episodes
To assess the change in passively generated data at the time of further community intervention (HCP review and/or prescription for corticosteroids or antibiotics). 3 months Acquired physiological and environmental data before and after community intervention will be compared using appropriate statistical tests to identify whether effects of these interventions were detectable in the acquired physiological data. We will also attempt to use machine learning models for the classification tasks of predicting corticosteroids, antibiotics, or HCP review outcome) using the physiological and environmental data in the time window prior to the specified community intervention outcome.
To assess the relationship between patient-generated data gathered from smartphone and connected devices and patient reported functional status. 3 months The prediction of reported outcome measures (CAT; EQ-5D; SGRQ-C) can also be addressed as a regression task, evaluated with RMSE, as detailed above.
To evaluate the usability and acceptability of patient-generated data gathered from smartphone and connected devices in a remote setting in patients with COPD. 3 months Summary of the outcomes measured in the HCP mHealth app usability questionnaire (MAUQ) and other app analytics will be generated using standard summary statistics measures (mean/median, standard deviation, confidence intervals). This data will be assessed in relation to app and usage analytics such as compliance rate, drop-out rate, and device wear time.
Trial Locations
- Locations (4)
Chelsea and Westminster Hospital NHS Foundation Trust
🇬🇧London, United Kingdom
Stoke Mandeville Hospital
🇬🇧Aylesbury, United Kingdom
Royal Sussex County Hospital
🇬🇧Brighton, United Kingdom
Nottingham University Hospitals
🇬🇧Nottingham, United Kingdom