COPD Exacerbation
Conditions
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.
Interventions
OTHERObservational
Observational
Sponsors
Chelsea and Westminster NHS Foundation Trust
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 120 Years
Healthy volunteers
No
Inclusion criteria
* 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.
Exclusion criteria
* 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| To assess the volume and quality of the data collected in terms of: | 3 months | Total number of subjects: number of subjects who completed the on boarding stage of the study |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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 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 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 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 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. |
Countries
United Kingdom
Outcome results
None listed