Asthma is characterized by rapid changes in the calibre of the airways. These changes are initiated by exposure to irritants such as allergens, pollutants, environmental tobacco smoke and viruses and modified by personal characteristics of the individual as well as their adherence to therapy.
While it has been possible to record lung function and collect data on environmental factors, it has been difficult to precisely record adherence to inhaler therapy. This research team have developed a unique technology that records precisely when and how well an individual has used their inhaler. We propose to study the interactions of digitally recorded lung function with both digitally recorded preventer medication adherence and daily environmental and respiratory viruses prevalence data, obtained already from several hundred patients with severe asthma in two prospective clinical studies.
To do this we will identify a single measure of lung function that describes all the possible changes in lung function that occur in asthma such as unstable periods, exacerbations or when the lung function stabilises. We will then assess the modifying effect of changes in medication adherence, environmental factors, season, exposure to viruses on this measure. To answer these questions we shall analyse a dataset of longitudinally monitored lung function to explore methods that describe aspects of lung function in a unified measure/scale and to develop threshold values that describe clinically meaningful events, such as exacerbations or improvement in lung function.
Then using this measure of lung function we will model the impact of variations in preventer medication adherence and other clinically meaningful asthma precipitants such as seasonal changes, pollution or virus and in different phenotypes of patients with asthma.
