METHODS: We combined reports of symptoms relating to the lungs, nose and eyes entered in AirRater, with daily concentrations of the most abundant local pollen taxa (n=13), air quality, and meteorological variables, between September 2015 and July 2017. Negative binomial regression models were constructed to evaluate the associations between symptoms and environmental variables while controlling for changes in app user behaviour, and season.
RESULTS: A total of 3,443 individuals used the AirRater app during the study. There were 12,396 individual symptom reports, with 9,245 relating to the nose or eyes, and 3,151 reports related to the lungs. Registered users with a known history of either allergic rhinitis or asthma provided 84% of all reports. We found that the most important pollen taxa associated with symptoms were grasses, conifers, and native Australian flowering species. Particulate matter (PM2.5), relative humidity, rainfall, and temperature were important environmental predictors of symptoms.
CONCLUSIONS: We demonstrate a novel approach to understanding the epidemiology of asthma and allergic rhinitis using smartphone technology. Our results document specific environmental drivers of respiratory symptoms, highlighting the importance of local plant species. Our future goals are to use AirRater to generate of personalised profile of environmental triggers for individual users and to incorporate early warning predictive systems.