Automatic market research of mobile health apps for the self-management of allergic rhinitis

Aram Antó, Bernardo Sousa-Pinto, Wienczyslawa Czarlewski, Oliver Pfaar, Sinthia Bosnic-Anticevich, Ludger Klimek, Paolo Matricardi, Salvatore Tripodi, Joao A. Fonseca, Josep M. Antó, Jean Bousquet

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
23 Downloads (Pure)

Abstract

Background: Only a small number of apps addressing allergic rhinitis (AR) patients have been evaluated. This makes their selection difficult. We aimed to introduce a new approach to market research for AR apps, based on the automatic screening of Apple App and Google Play stores.

Methods: A JavaScript programme was devised for automatic app screening, and applied in a market assessment of AR self-management apps. We searched the Google Play and Apple App stores of three countries (USA, UK and Australia) with the following search terms: "hay fever", "hayfever", "asthma", "rhinitis", "allergic rhinitis". Apps were eligible if symptoms were evaluated. Results obtained with the automatic programme were compared to those of a blinded manual search. As an example, we used the search to assess apps that can be used to design a combined medication score for AR.

Results: The automatic search programme identified 39 potentially eligible apps out of a total of 1593 retrieved apps. Each of the 39 apps was individually checked, with 20 being classified as relevant. The manual search identified 19 relevant apps (out of 6750 screened apps). Combining both methods, a total of 21 relevant apps were identified, pointing to a sensitivity of 95% and a specificity of 99% for the automatic method. Among these 21 apps, only two could be used for the combined symptom-medication score for AR.

Conclusions: The programmed algorithm presented herein is able to continuously retrieve all relevant AR apps in the Apple App and Google Play stores, with high sensitivity and specificity. This approach has the potential to unveil the gaps and unmet needs of the apps developed so far.
Original languageEnglish
Pages (from-to)1195-1207
Number of pages13
JournalClinical and Experimental Allergy
Volume52
Issue number10
DOIs
Publication statusPublished - Oct 2022
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • allergic rhinitis
  • app
  • automatic search
  • Google
  • Apple
  • JavaScript
  • Google, Apple

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