Although COPD cannot be cured, managing COPD can help reduce symptoms control, slows progression of the disease, and may improve the quality of life. Management of COPD becomes less effective when healthcare professionals fail to prescribe appropriate inhaled therapies, due to poor adherence to evidence-based guidelines and underdiagnosis, or when patients fail to adhere to prescribed treatment regimens.
my mhealth has developed an app called myCOPD to allow patients to record their COPD symptoms and medications, which in term supports them with their COPD. This app is now used by COPD patients across the UK, via a prescription from their healthcare professionals. my mHealth has now developed a research app MMH-LAB1 which, in the intervention arm, includes an algorithm to assist patients with understanding their level of symptom burden according to the severity of their daily symptoms and exacerbation risk. The algorithm then analyses current patient prescribed inhaled medication against those recommended by the GOLD 2023 report and informs the patient via the MMH-LAB1 app as to whether their medication needs to be changed by their healthcare provider. This trial will explore whether the introduction of the algorithm within the MMH-LAB1 app can be used as a safe, effective and acceptable tool to assist patients to achieve optimal guideline based inhaled therapy and therefore provide greater symptom control and lowered exacerbation risk for COPD patients.
Participants will be randomised to the MMH-LAB1 app - control arm (MMH-LAB\_Control\_1 where participants just record COPD symptoms and medications), or to the MMH\_LAB1 app - intervention arm (MMH-LAB1\_Test\_1 where participants record their COPD symptoms and medication plus receiving information from the algorithm, as to whether their medications are in guidance or not, and if not, be notified to visit their healthcare professional.
Adult patients over 18 years of age and able to give informed consent
A clinical diagnosis of COPD (all patients registered on the app have a confirmed diagnosis of COPD confirmed by their clinician before being prescribed the app).
A registered and activated user of the myCOPD app
One or more CAT questionnaire completed on the myCOPD app in the last 12 months
Cannot read or write in English as required to understand the trial materials and provide the trial data.
Has not opted in to the research agreement via MyCOPD
Parallel
MMH-LAB1 app
DeviceThis app is based on a COPD self management app, which they are already using and includes an algorithm which will notify participants if there is a discrepancy between the symptoms they report onto the app and their medication regime, and informs them to approach their healthcare provider for a medication review
To determine the safety, acceptability and efficacy of a digital treatment guidance algorithm for inhaled treatment in patients with COPD. The intention is to measure the impact of the algorithm in prompting patients to achieve optimal management, and t
Proportion (measured in %) of participants receiving guideline adherent treatment at Baseline and trial completion (Month 6). Range 0%-100%.
To assess effect of intervention on exacerbation frequency and patient wellness scores
• Change in COPD Assessment Test (CAT) score between Baseline and trial completion (Month 6) CAT test results 0 minimal symptom impact - Best and 40 maximum symptom impact All will be described in terms of: * Mean and SD * Median and range and will be reported as descriptive stats
To assess effect of the intervention on patients' adherence to guideline mandated treatment
\- Rate of indication of algorithm to change treatment (0 times to 150 times) • Rate if change in response to treatment change indication (0 times to 150 times) All measured via frequency and percentage and description of change. The combination of measurements will be reported as descriptive stats
To understand the utility of the intervention pathway in the pilot phase and to identify additional patient support mechanisms for the efficacy phase in order to maximise healthcare equality
\- % Patients receiving guideline adherent treatment at Baseline and trial completion (Month 6). 100% good. 0 % bad. Above described qualitatively and in terms of % and frequency
To assess the levels of app algorithm usage and engagement
-Frequency of app engagement * Rate of completion of endpoint data * Resource requirement to allow patients to act on intervention information Measured by frequency (numbers of participant who completed everything) % (0% of expected use bad and 100% of expected use good). This will be reported as descriptive stats.
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