Clinical study of an inhalation training and feedback device, user app, clinician portal and cloud based data analytics tool for self management and remote monitoring of respiratory conditions

Over 325M people around the world - and 5.4M in the UK - suffer from asthma, costing over $363B every year. Rising health inequalities, the coronavirus pandemic, growing patient burden and costs demand better asthma management. The correct use of inhaler devices and adherence to prescribed therapy are essential for optimal control, avoiding exacerbations, reducing mortality and healthcare costs.

A meta-analysis and systematic review (Chrystyn et al 2017) of inhaler use concluded that 86.6% patients make at least one error, while Usmani (2018) identified a significant association between inhaler errors, poor disease outcomes and greater health-economic burden.

'Smart inhalers' were developed to address this problem but have had limited impact due to functionality deficits (focused mainly on adherence rather than technique), high cost and slow adoption of digital support tools in chronic condition management. Climate concerns have also driven the need to find ways to reduce the global warming potential (GWP) of inhalers, in particular that of Metered Dose Inhalers.

In 2020-21 we designed, built and tested a first iteration, innovative digital platform, incorporating a data capture device (built to medical device standards), user App, clinician portal, and data analytics tool to address this need. It prompts user 'preventer' medication adherence and monitors the 5 key inhalation steps, with real-time correction feedback, aggregating clinical, behavioural and environmental (air quality) data, with AI driven analytics to support self management, remote clinician monitoring.

Optimising each medication dose will reduce the global warming potential of inhalers. The platform was successfully tested using synthetic data as a real-world data set that combines actual inhaler technique data with environmental data could not be identified.

This project proposes to undertake a randomised controlled clinical trial in 100 patients to evaluate the platform capability to improve asthma symptom control and to optimise medication usage, self management and remote monitoring via the recording and sharing of inhalation technique data.

The unique real patient dataset generated by this study (inhaler type, shake duration, time to dispense, time to inhalation, inhalation rate and volume inhaled, aggregated with air quality) will identify inhaler behaviour/technique-air quality-symptom relationships. This will permit more accurate platform capability to remotely detect and notify at-risk users (and their clinicians), improving self-management and lowering system costs via early and appropriate intervention.

We've co-designed the platform with support from Asthma UK- British Lung Foundation, and had user and expert clinician input into the study design.