Chronic Obstructive Pulmonary Disease, Covid19, Interstitial Lung Disease
Conditions
Brief summary
This cohort study aims to use the open-source RADAR-base mHealth platform to collect and analyze datasets associated with lung disease. This will include continuous data collected from wearable devices (e.g. heart rate, oxygen saturation, respiratory rate), including pulse oximeters, spirometer, mobile phones, digital tests, and smart phone symptom questionnaires.
Interventions
None listed
Sponsors
South London and Maudsley NHS Foundation Trust
University College, London
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
No
Inclusion criteria
* 20 patients with a diagnosis of COPD * 20 patients with a diagnosis of interstitial lung disease * 20 a clinical diagnosis of COVID-19 (within 4-13 weeks of enrolment) who either and report symptoms interfering with day to day activity present for more than 28 days following the onset of COVID-19 * Prior mobile phone use * Willingness to use monitoring devices and complete study questionnaires. * History of exacerbation (COPD: 2 or more exacerbations in last 1 yr)
Exclusion criteria
* Non English language Speaker * Lack of physical capability to take part e.g. Heart Failure * Pregnancy * Lack of capability to consent
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The assessment of novel phone based tests (Audio, Breathing Tests see: non-questionnaire Active App tests) for remote monitoring of respiratory health. | 6 months | The ubiquity of smartphones presents an opportunity to use the phone itself as a health measuring tool for both applications. Active audio tasks such as pronouncing sustained vowels or counting from 1 to 20 will provide additional information on voice production dynamics that might be affected by lung disorder symptoms.Voice production tasks via the phone. These tasks will assess change in the phonatory respiratory system |
| Quantification of symptoms using various symptom questionnaires and scales. | 6 months | 1. Epworth Sleepiness Scale (Used to diagnose obstructive sleep apnea(OSA). 2. STOPBang Questionnaire (Used to diagnose obstructive sleep apnea(OSA).) 3. MRC Breathlessness (Dyspnoea scale that evaluates theimpact of breathlessness on daily activity) 4. St. George's Respiratory Questionnaire (SGRQ) 5. Pittsburgh Sleep Quality Index (PSQI) Sleep scoring questionnaire 6. Visual Analogue Scale (VAS) Cough |
| Report longitudinal mental health symptoms measures as reported by GAD7 and PHQ8 associated with the three diseases. | 6 months | Impact of disease on mood and wellbeing and quality of life using generalised anxiety disorder assessment (GAD-7) from 0 to 21, and depression scale of the Patient Health Questionnaire (PHQ8), weekly for 6 months. |
| Fatigue is the major reported symptom for those experiencing long COVID. A range of modalities for evaluating fatigue are included 1) Garmin Body Battery value and 2) Fatigue Severity Scale (FSS), continuous/weekly respectively, duration of study | 6 months | 1. Fatigue Severity Scale (FSS) is A 9-item questionnaire used to measure fatigue in people with chronic diseases. 2. Garmin Body Battery value from the wearable (Garmin, Vivoactive 4). It uses activity, heart rate, and stress to estimate participant energy. |
| The feasibility of remote monitoring of patient symptoms and physiology using commercially available wearable sensors and questionnaires in patients with lung disease. | 6 months | Feasibility will be measured by recruitment, retention rate, completion of data, and drop-out rates at end of the study. (e.g. participants screened for study eligibility and enrollment were documented. Also, reasons for non-participation and completion of the study were recorded). Compliance using components of the RADAR-base system. |
| Acceptability of remote monitoring system in patients with lung disease. | 6 months | TAM-FF: Measure the impact of the technology being used and evaluate its acceptability, usability and performance. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Number of participants that experience one exacerbation within the stopping criteria for each group | 6 months | Number of exacerbations that were detected by i) home-based spirometry ii) patient-reported outcome measure using mobile questionnaire iii) wearable data (Vivoactive 4). |
| Establish whether subclinical exacerbations can be identified in patients with lung fibrosis, and if exacerbations can be detected earlier with home monitoring. | 6 months | Detecting exacerbation/symptom e.g. changes in wearable data (e.g. HR, SpO2, Activity) during the reported period of exacerbation( A real-time algorithm will be included to predict exacerbations with patients notified with the Exacerbation Rating Scale (ERS) to confirm the prediction at or close to the time of the event), detecting exacerbation prior to or after the reported period of exacerbation (e.g. signal that may precede participant awareness of the exacerbation/symptom), detecting subclinical exacerbations in patients with lung fibrosis, tracking self-reported symptoms and outcomes (including precursors presymptomatic signal) and their frequency |
Other
| Measure | Time frame | Description |
|---|---|---|
| Provide data for power calculations for a follow on study. | 6 months | Power calculations will be centered around understanding the number of exacerbations according to sample size and duration |
Countries
United Kingdom
Outcome results
None listed