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

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dc.contributor.author Antó, Aram
dc.contributor.author Sousa Pinto, Bernardo
dc.contributor.author Czarlewski, Wienczyslawa
dc.contributor.author Pfaar, Oliver
dc.contributor.author Bosnic-Anticevich, Sinthia
dc.contributor.author Klimek, Ludger
dc.contributor.author Matricardi, Paolo
dc.contributor.author Tripodi, Salvatore
dc.contributor.author Fonseca, Joao A.
dc.contributor.author Antó i Boqué, Josep Maria
dc.contributor.author Bousquet, Jean
dc.date.accessioned 2022-06-10T05:55:23Z
dc.date.available 2022-06-10T05:55:23Z
dc.date.issued 2022
dc.identifier.citation Antó A, Sousa-Pinto B, Czarlewski W, Pfaar O, Bosnic-Anticevich S, Klimek L, Matricardi P, Tripodi S, Fonseca JA, Antó JM, Bousquet J. Automatic market research of mobile health apps for the self-management of allergic rhinitis. Clin Exp Allergy. 2022 Oct;52(10):1195-207. DOI: 10.1111/cea.14135
dc.identifier.issn 0954-7894
dc.identifier.uri http://hdl.handle.net/10230/53440
dc.description.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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Wiley
dc.relation.ispartof Clin Exp Allergy. 2022 Oct;52(10):1195-207
dc.rights © 2022 The Authors. Clinical & Experimental Allergy published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.title Automatic market research of mobile health apps for the self-management of allergic rhinitis
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1111/cea.14135
dc.subject.keyword Google
dc.subject.keyword Apple
dc.subject.keyword JavaScript
dc.subject.keyword Allergic rhinitis
dc.subject.keyword App
dc.subject.keyword Automatic search
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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