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| Name | Class |
|---|---|
| South London and Maudsley NHS Foundation Trust | OTHER |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| ILD | ILD, or lung fibrosis, is one of a spectrum of fibrotic diseases, associated with ageing, obesity, diabetes and pollution, that are responsible for ~45% of premature deaths in Western Europe. Of >90,000 patients in the United Kingdom with ILD, ~30,000 have idiopathic pulmonary fibrosis, idiopathic pulmonary fibrosis, the most severe form. idiopathic pulmonary fibrosis is a disease of unknown aetiology that is more frequent in males presenting mainly in the sixth and seventh decades of life. There is no cure and median survival, just 3-5 years following diagnosis is worse than for many cancers. | ||
| COPD | COPD is a common, long term condition of the lungs that is usually caused by cigarette smoking. In addition to daily symptoms and limitations in activities, patients are prone to developing chest infections called 'exacerbations'. Exacerbations are a significant problem: unpleasant for patients, and sometimes severe enough to cause hospital admission (and therefore National Health Service pressures) and death. | ||
| COVID-19 | Recovery from COVID19 has many unknowns, especially in the long term. Symptoms of COVID-19 have varied among those who have tested positive: some have displayed no symptoms, while others have developed severe pneumonia, progressing to lung injury and acute respiratory distress syndrome (ARDS) and, in the longer term, pulmonary fibrosis. Notably, the consequences of COVID-19 include effects on other organs including: heart, kidneys, and brain. |
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| Measure | Description | Time Frame |
|---|---|---|
| The feasibility of remote monitoring of patient symptoms and physiology using commercially available wearable sensors and questionnaires in patients with lung disease. | 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. | 6 months |
| Acceptability of remote monitoring system in patients with lung disease. | TAM-FF: Measure the impact of the technology being used and evaluate its acceptability, usability and performance. | 6 months |
| Quantification of symptoms using various symptom questionnaires and scales. |
| 6 months |
| Report longitudinal mental health symptoms measures as reported by GAD7 and PHQ8 associated with the three diseases. | 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. | 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Number of participants that experience one exacerbation within the stopping criteria for each group | Number of exacerbations that were detected by i) home-based spirometry ii) patient-reported outcome measure using mobile questionnaire iii) wearable data (Vivoactive 4). | 6 months |
| Establish whether subclinical exacerbations can be identified in patients with lung fibrosis, and if exacerbations can be detected earlier with home monitoring. |
| Measure | Description | Time Frame |
|---|---|---|
| Provide data for power calculations for a follow on study. | Power calculations will be centered around understanding the number of exacerbations according to sample size and duration | 6 months |
Inclusion Criteria:
Exclusion Criteria:
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Subjects with a diagnosis of IPF, COPD and COVID-19. Subjects that fit the in/exclusion criteria will be identified from the respiratory outpatients clinics at the UCLH and RFH
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| Name | Affiliation | Role |
|---|---|---|
| Amos Folarin | King's College London | Study Director |
| John Hurst | University College, London | Principal Investigator |
| Joanna Porter | University College, London | Principal Investigator |
| Malik Althobiani | University College, London | Study Chair |
| Yatharth Ranjan | King's College London | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Royal Free Hospital | London | United Kingdom | ||||
| University College London Hospital |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 34319235 | Result | Ranjan Y, Althobiani M, Jacob J, Orini M, Dobson RJ, Porter J, Hurst J, Folarin AA. Remote Assessment of Lung Disease and Impact on Physical and Mental Health (RALPMH): Protocol for a Prospective Observational Study. JMIR Res Protoc. 2021 Oct 7;10(10):e28873. doi: 10.2196/28873. |
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| ID | Term |
|---|---|
| D029424 | Pulmonary Disease, Chronic Obstructive |
| D017563 | Lung Diseases, Interstitial |
| D000086382 | COVID-19 |
| ID | Term |
|---|---|
| D008173 | Lung Diseases, Obstructive |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D002908 | Chronic Disease |
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| 6 months |
| The assessment of novel phone based tests (Audio, Breathing Tests see: non-questionnaire Active App tests) for remote monitoring of respiratory health. | 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 | 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 |
| 6 months |
| London |
| United Kingdom |
| D020969 |
| Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
| D014777 | Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |