RECEIVER: Digital Service Model for Chronic Obstructive Pulmonary Disease (COPD)

Summary

Participation Requirements

Description

Patients will be screened from emergency attendance or admission at South and North Sector (Queen Elizabeth University Hospital, and Glasgow Royal Infirmary) and from referrals to the COPD clinical team at these sites. Patients meeting inclusion criteria will be approached and offered enrolment to t...

Patients will be screened from emergency attendance or admission at South and North Sector (Queen Elizabeth University Hospital, and Glasgow Royal Infirmary) and from referrals to the COPD clinical team at these sites. Patients meeting inclusion criteria will be approached and offered enrolment to the study. Recruitment and consent timings will be individualised to be most efficient and least burdensome for patients. For some patients it will be appropriate to do this immediately to avoid burden of repeated attendances; for some patients, delay and consideration may be appropriate; for some patients the enrolment and engagement may be a staged process (consent at time of hospital attendance, study commence at follow up home or clinic visit etc). Patients recruited will receive support information and assistance with login setups for the digital service components. Literature with frequently asked questions (FAQs), and team contacts for service support are available for throughout the study. Patients enrolled will be asked, and prompted with text notifications, to complete daily short structured COPD symptom questionnaire. There are a small number of additional questions on a weekly basis, with quality of life questions completed once every 28 days. Patients recruited will have a "Fitbit" wristband wearable to monitor physiology. Patients with hypercapnic respiratory failure will additionally be on home non-invasive ventilation (NIV) treatment - this is part of their routine clinical care rather than a study intervention. However, the study patient resource and messaging system will be used to gather information and support this treatment. Selected patients, who are recruited during hospital admission or attendance and will be attending outpatient clinic follow up, will undergo exploratory physiology measurements - parasternal electromyography (EMG) (similar to electrocardiography (ECG) recording, takes ~20 minutes with breathing manoeuvres), oscillometry (a breathing test involving 10 resting non-effortful breaths blown into the medical device), home pollution monitoring (a pack which rests in patients bedroom +/- tube placed outside house) for 7 days - alongside routine clinical care at baseline and 3 monthly intervals. Patients will have linked access from the patient resource to curated information about COPD diagnosis, and all aspects of management. Specific prompts about management - e.g. timing to make appointment for annual flu vaccination - will be provided through platform-text notifications. Self-management content of the resource will potentially be further developed over iterations within the study; any change in content of patient materials would be advised as a protocol amendment. Patients will be able to message the clinical team using the patient portal. This supplements existing availability of answer phone contact details provided as part of routine clinical care. Automatic messages will notify patients that this is not for emergency contact, and that replies should be expected within Mon-Fri working hours, by next working day. This messaging system will be used to support self management, home oxygen and home NIV treatment initiation and monitoring, and practical aspects such as appointment scheduling and equipment consumable replenishment. The clinical team will be able to access the data from the patients symptom diaries, wearable and NIV physiology directly - asynchronously, rather than delayed acquisition of this data at a clinical contact. This data visualisation will support routine clinical care, and better inform unscheduled advice contacts from patients (e.g. help determine significance of apparent worsening symptoms). This data will be subject to machine-learning analysis, which will evaluate secondary endpoints, as per protocol.

Tracking Information