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Clinical Benefits of a Randomized Allergy App Intervention in Grass Pollen Sufferers: A Controlled Trial

  • Caroline Holzmann
  • , Johannes Karg
  • , Matthias Reiger
  • , Rajiv Kharbal
  • , Paola Romano
  • , Sabrina Scheiwein
  • , Claudia Khalfi
  • , Anna Muzalyova
  • , Jens O. Brunner
  • , Gertrud Hammel
  • , Athanasios Damialis
  • , Claudia Traidl-Hoffmann
  • , María P. Plaza
  • , Stefanie Gilles
  • University Hospital of Augsburg
  • Helmholtz Zentrum München German Research Center for Environmental Health
  • Technical University of Denmark
  • Next Generation Technology
  • University Hospital Augsburg
  • Faculty of Sciences
  • Christine Kühne Center for Allergy Research and Education (CK-Care)

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: Symptom monitoring can improve adherence to daily medication. However, controlled clinical trials on multi-modular allergy apps and their various functions have been difficult to implement. The objective of this study was to assess the clinical benefit of an allergy app with varying numbers of functions in reducing symptoms and improving quality of (QoL) life in grass pollen allergic individuals. The secondary objective was to develop a symptom forecast based on patient-derived and environmental data. Methods: We performed a stratified, controlled intervention study (May–August 2023) with grass pollen allergic participants (N = 167) in Augsburg, Germany. Participants were divided into three groups, each receiving the same allergy app, but with increasing numbers of functions. Primary endpoint: rhinitis-related QoL; Secondary endpoints: symptom scores, relevant behavior, self-reported usefulness of the app, symptom forecast. Results: Rhinitis-related QoL was increased after the intervention, with no statistical inter-group differences. However, participants with access to the full app version, including a pollen forecast, took more medication and reported lower symptoms and social activity impairment than participants with access to a reduced-function app. Using an XGBoost multiclass classification model, we achieved promising results for predicting nasal (accuracy: 0.79; F1-score: 0.78) and ocular (accuracy: 0.82; F1-score: 0.76) symptom levels and derived feature importance using SHAP as a guidance for future approaches. Conclusion: Our allergy app with its high-performance pollen forecast, symptom diary, and general allergy-related information provides a clinical benefit for allergy sufferers. Reliable symptom forecasts may be created given high-quality and high-resolution data.

Original languageEnglish
Pages (from-to)1945-1955
Number of pages11
JournalAllergy: European Journal of Allergy and Clinical Immunology
Volume80
Issue number7
DOIs
StatePublished - Jul 2025
Externally publishedYes

Keywords

  • allergic rhinitis
  • allergy app
  • clinical study
  • pollen forecast
  • symptom forecast

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