Trial reportAllergy2025
Clinical Benefits of a Randomized Allergy App Intervention in Grass Pollen Sufferers: A Controlled Trial.
Trial report in Allergy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Digital health applications, telemedicine and large language models in dermatology: results of a Germany-wide survey.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2026Article
- Evaluating the concordance of pollen forecasting apps against automated pollen monitoring: A single-site experience.The journal of allergy and clinical immunology. Global · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
backgroundSymptom 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.
methodsWe 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.
resultsRhinitis-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.
conclusionOur 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.