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Evaluation of a semiautomated App Store analysis for the identification of health apps for cardiac arrhythmias

BACKGROUND: Smartphone apps are increasingly utilised by patients and physicians for medical purposes. Thus, numerous applications are provided on the App Store platforms. OBJECTIVES: The aim of the study was to establish a novel, expanded approach of a semiautomated retrospective App Store analysis...

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Autores principales: Lawin, Dennis, von Jan, Ute, Pustozerov, Evgenii, Lawrenz, Thorsten, Stellbrink, Christoph, Albrecht, Urs-Vito
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Medizin 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10462560/
https://www.ncbi.nlm.nih.gov/pubmed/37380893
http://dx.doi.org/10.1007/s00399-023-00947-2
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author Lawin, Dennis
von Jan, Ute
Pustozerov, Evgenii
Lawrenz, Thorsten
Stellbrink, Christoph
Albrecht, Urs-Vito
author_facet Lawin, Dennis
von Jan, Ute
Pustozerov, Evgenii
Lawrenz, Thorsten
Stellbrink, Christoph
Albrecht, Urs-Vito
author_sort Lawin, Dennis
collection PubMed
description BACKGROUND: Smartphone apps are increasingly utilised by patients and physicians for medical purposes. Thus, numerous applications are provided on the App Store platforms. OBJECTIVES: The aim of the study was to establish a novel, expanded approach of a semiautomated retrospective App Store analysis (SARASA) to identify and characterise health apps in the context of cardiac arrhythmias. MATERIALS AND METHODS: An automated total read-out of the “Medical” category of Apple’s German App Store was performed in December 2022 by analysing the developer-provided descriptions and other metadata using a semiautomated multilevel approach. Search terms were defined, based on which the textual information of the total extraction results was automatically filtered. RESULTS: A total of 435 of 31,564 apps were identified in the context of cardiac arrhythmias. Of those, 81.4% were found to deal with education, decision support, or disease management, and 26.2% (additionally) provided the opportunity to derive information on heart rhythm. The apps were intended for healthcare professionals in 55.9%, students in 17.5%, and/or patients in 15.9%. In 31.5%, the target population was not specified in the description texts. In all, 108 apps (24.8%) provided a telemedicine treatment approach; 83.7% of the description texts did not reveal any information on medical product status; 8.3% of the apps indicated that they have and 8.0% that they do not have medical product status. CONCLUSION: Through the supplemented SARASA method, health apps in the context of cardiac arrhythmias could be identified and assigned to the target categories. Clinicians and patients have a wide choice of apps, although the app description texts do not provide sufficient information about the intended use and quality.
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spelling pubmed-104625602023-08-30 Evaluation of a semiautomated App Store analysis for the identification of health apps for cardiac arrhythmias Lawin, Dennis von Jan, Ute Pustozerov, Evgenii Lawrenz, Thorsten Stellbrink, Christoph Albrecht, Urs-Vito Herzschrittmacherther Elektrophysiol Original Contributions BACKGROUND: Smartphone apps are increasingly utilised by patients and physicians for medical purposes. Thus, numerous applications are provided on the App Store platforms. OBJECTIVES: The aim of the study was to establish a novel, expanded approach of a semiautomated retrospective App Store analysis (SARASA) to identify and characterise health apps in the context of cardiac arrhythmias. MATERIALS AND METHODS: An automated total read-out of the “Medical” category of Apple’s German App Store was performed in December 2022 by analysing the developer-provided descriptions and other metadata using a semiautomated multilevel approach. Search terms were defined, based on which the textual information of the total extraction results was automatically filtered. RESULTS: A total of 435 of 31,564 apps were identified in the context of cardiac arrhythmias. Of those, 81.4% were found to deal with education, decision support, or disease management, and 26.2% (additionally) provided the opportunity to derive information on heart rhythm. The apps were intended for healthcare professionals in 55.9%, students in 17.5%, and/or patients in 15.9%. In 31.5%, the target population was not specified in the description texts. In all, 108 apps (24.8%) provided a telemedicine treatment approach; 83.7% of the description texts did not reveal any information on medical product status; 8.3% of the apps indicated that they have and 8.0% that they do not have medical product status. CONCLUSION: Through the supplemented SARASA method, health apps in the context of cardiac arrhythmias could be identified and assigned to the target categories. Clinicians and patients have a wide choice of apps, although the app description texts do not provide sufficient information about the intended use and quality. Springer Medizin 2023-06-28 2023 /pmc/articles/PMC10462560/ /pubmed/37380893 http://dx.doi.org/10.1007/s00399-023-00947-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Contributions
Lawin, Dennis
von Jan, Ute
Pustozerov, Evgenii
Lawrenz, Thorsten
Stellbrink, Christoph
Albrecht, Urs-Vito
Evaluation of a semiautomated App Store analysis for the identification of health apps for cardiac arrhythmias
title Evaluation of a semiautomated App Store analysis for the identification of health apps for cardiac arrhythmias
title_full Evaluation of a semiautomated App Store analysis for the identification of health apps for cardiac arrhythmias
title_fullStr Evaluation of a semiautomated App Store analysis for the identification of health apps for cardiac arrhythmias
title_full_unstemmed Evaluation of a semiautomated App Store analysis for the identification of health apps for cardiac arrhythmias
title_short Evaluation of a semiautomated App Store analysis for the identification of health apps for cardiac arrhythmias
title_sort evaluation of a semiautomated app store analysis for the identification of health apps for cardiac arrhythmias
topic Original Contributions
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10462560/
https://www.ncbi.nlm.nih.gov/pubmed/37380893
http://dx.doi.org/10.1007/s00399-023-00947-2
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