Background Pollen allergies affect a significant proportion of the population globally. At present, Web-based tools such as pollen diaries and mobile apps allow for easy and fast documentation of allergic symptoms via the internet. Objective This study aimed to characterize the users of the Patient’s Hayfever Diary (PHD), a Web-based platform and mobile app, to apply different symptom score calculations for comparison, and to evaluate the contribution of organs and medications to the total score for the first time. Methods The PHD users were filtered with regard to their location in Austria and Germany, significant positive correlation to the respective pollen type (birch/grass), and at least 15 entries in the respective season. Furthermore, 4 different symptom score calculation methods were applied to the datasets from 2009 until 2018, of which 2 were raw symptom scores and 2 were symptom load index (normalized) calculations. Pearson correlation coefficients were calculated pairwise for these 4 symptom score calculations. Results Users were mostly male and belonged to the age groups of 21 to 40 years or ≥40 years. User numbers have increased in the last 5 years, especially when mobile apps were made available. The Pearson correlation coefficients showed a significant linear relationship above 0.9 among the 4 symptom score datasets and thus indicated no significant difference between the different methods of symptom score calculation. The nose contributed the most to the symptom score and determined about 40% of the score. Conclusions The exact method of calculation of the symptom score is not critical. All computation methods show the same behavior (increase/decrease during the season). Therefore, the symptom load index is a useful computation method in all fields exploring pollen allergy, and Web-based diaries are a globally applicable tool to monitor the effect of pollen on human health via electronically generated symptom data.

BackgroundOnline pollen diaries and mobile applications nowadays allow easy and fast documentation of pollen allergy symptoms. Such crowd-sourced symptom data provides insights into the development and the onset of a pollen allergy. Hitherto studies of the symptom load index (SLI) showed a discrepancy between the SLI and the total pollen amount of a season, but did not analyze the daily data.MethodsThe Patient’s Hayfever Diary (PHD) was used as data pool for symptom data. Symptom data of Vienna (Austria) was chosen as a large and local sample size within the study period of 2013 until 2017. The city was divided into three different areas based on equal population densities and different environmental factors. Correlation factors, regression lines, locally weighted smoothing (LOESS) curves and line plots were calculated to examine the data.ResultsDaily SLI and pollen concentration data correlates well and the progress of the SLI within a pollen season is mirrored by the pollen concentrations. The LOESS curves do not deviate much from the regression line and support the linearity of the symptom-pollen correlation on a daily basis. Seasonal SLI data does not follow the same pattern as the respective seasonal pollen indices. Results did not vary in the three areas within Vienna or when compared with the Eastern region of Austria showing no significant spatial variation of the SLI.DiscussionResults indicate a linear relationship of the SLI and pollen concentrations/seasonal polllen index (SPIn) on a daily basis for both in general and throughout the season, but not on a seasonal basis. These findings clarify the frequent misinterpretation of the SLI as index that is tightly connected to pollen concentrations, but reflects as well the seasonal variation of the burden of pollen allergy sufferers.ConclusionMore than just the seasonal pollen index has to be considered when the SLI of a selected pollen season has to be explained. Cross-reactivity to other pollen types, allergen content and air pollution could play a considerable role. The similar behavior of the SLI in Vienna and a whole region indicate the feasibility of a possible symptom forecast in future and justifies the use of a single pollen monitoring station within a city of the size of Vienna.

Pollen information as such is highly valuable and was considered so far as a self-evident good free for the public. The foundation for reliable and serious pollen information is the careful, scientific evaluation of pollen content in the air. However, it is essential to state and define now the requirements for pollen data and qualifications needed for institutions working with pollen data in the light of technical developments such as automated pollen counting and various political interests in aerobiology including attempts to finally acknowledge pollen and spores as relevant biological particles in the air worth being considered for pollution and health directives. It has to be emphasized that inadequate pollen forecasts are a considerable health risk for pollen allergy sufferers. Therefore, the responsibility of institutions involved in pollen monitoring and forecasting is high and should be substantiated with respective qualifications and know-how. We suggest here for the first time a portfolio of quality criteria and demand rigorous scientific monitoring and certification of such institutions in the interest and for the protection of persons affected by a pollen allergy.

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Digital health is an umbrella term which encompasses eHealth and benefits from areas such as advanced computer sciences. eHealth includes mHealth apps, which offer the potential to redesign aspects of healthcare delivery. The capacity of apps to collect large amounts of longitudinal, real‐time, real‐world data enables the progression of biomedical knowledge. Apps for rhinitis and rhinosinusitis were searched for in the Google Play and Apple App stores, via an automatic market research tool recently developed using JavaScript. Over 1500 apps for allergic rhinitis and rhinosinusitis were identified, some dealing with multimorbidity. However, only six apps for rhinitis (AirRater, AllergyMonitor, AllerSearch, Husteblume, MASK‐air and Pollen App) and one for rhinosinusitis (Galenus Health) have so far published results in the scientific literature. These apps were reviewed for their validation, discovery of novel allergy phenotypes, optimisation of identifying the pollen season, novel approaches in diagnosis and management (pharmacotherapy and allergen immunotherapy) as well as adherence to treatment. Published evidence demonstrates the potential of mobile health apps to advance in the characterisation, diagnosis and management of rhinitis and rhinosinusitis patients.