Observational studyJournal of medical Internet research2020
Translating the Burden of Pollen Allergy Into Numbers Using Electronically Generated Symptom Data From the Patient's Hayfever Diary in Austria and Germany: 10-Year Observational Study.
Observational study in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Interpretable machine learning classification of cedar and cypress pollen on routine Durham slides for environmental exposure assessment.Frontiers in allergy · 2026Article
- A global survey addressing sustainability of pollen monitoring.The World Allergy Organization journal · 2024Article
- Pollen grading prediction scale for patients with Artemisia pollen allergy in China: A 3-day moving predictive model.Clinical and translational allergy · 2023Article
- Real-world data using mHealth apps in rhinitis, rhinosinusitis and their multimorbidities.Clinical and translational allergy · 2022Article
- Ecological momentary assessment of outcomes in allergic rhinitis and chronic rhinosinusitis: A review.International forum of allergy & rhinology · 2022Review
- Validation Parameters of Patient-Generated Data for Digitally Recorded Allergic Rhinitis Symptom and Medication Scores in the @IT.2020 Project: Exploratory Study.JMIR mHealth and uHealth · 2022Article
- Digital health for allergen immunotherapy.Allergologie select · 2022Review
- Heterogeneous validity of daily data on symptoms of seasonal allergic rhinitis recorded by patients using the e-diary AllergyMonitor®.Clinical and translational allergy · 2021Article
- Variability of grass pollen allergy symptoms throughout the season: Comparing symptom data profiles from the Patient's Hayfever Diary from 2014 to 2016 in Vienna (Austria).The World Allergy Organization journal · 2021Article
- [Analysis of sensitization characteristics of artemisia pollen in the Inner Mongolian grassland region of China].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2020Article
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Authors and funding
5 authors.
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Abstract
backgroundPollen 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.
objectiveThis 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.
methodsThe 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.
resultsUsers 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.
conclusionsThe 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.
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