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1000
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Abstract/Summary
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Background!#!The Apple and Google app stores offer a wide range of health apps. It is still a challenge to find valuable and qualified apps.!##!Objective!#!Can German language apps be identified using the 'semiautomated retrospective app store analysis' (SARASA) method for the field of rheumatology?!##!Material and method!#!The SARASA is a semiautomated method to select and characterize apps listed in the app store. After the first application in February 2018 SARASA was applied again to the Apple app store in February 2020.!##!Results!#!In February 2018 it was possible to acquire metadata for 103,046 apps and in February 2020 data for 94,735 apps that were listed in the category 'health and fitness' or 'medicine' in Apple's app store frontend for Germany. After applying the search terms 59 apps with a German language app description were identified for the field of rheumatology in 2018 and 53 apps in 2020. For these, more detailed manual reviews seem worthwhile. In 2018, the apps found were more likely to address patients than physicians and this was more balanced in 2020. In addition, it became apparent that for certain diseases there was no app developer activity. The percentage breakdown of matches by search term revealed substantial fluctuations in the app market when comparing 2018 to 2020.!##!Discussion!#!The SARASA method provides a useful tool to identify apps from app stores that meet predefined, formal criteria. Subsequent manual checks of the quality of the contents are still necessary. Further development of the SARASA method and consensus and standardization of quality criteria are worthwhile. Quality criteria should be considered for offers of mobile health apps in app stores.
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1000
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Hinweis
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DeepGreen-ID: 46077d47c0ed4c48be66b3c8ce7961cd ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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