Belichtungen

Siegel, Steffen; Boehm, Gottfried; Brandstetter, Gabriele; Stiegler, Bernd; Hendricks, Peter S.; Rautert, Timm; Höhle, Frank; Gründig, Matthias; Goldbach, Philipp; Florath, P.; Roosen, Barbara; Wenzel, Jan; Struth, Thomas; Höfer, Candida; Fischer, Thomas; Dürrfeld, Eike Barbara; Stumpf, Sebastian; Sauer, Adrian; Jansen, Irina; Klemm, Sebasian Oliver; Huber, Evi Ziegler c/o EDV Fotowerk

doi:10.30965/9783846757086

Cited by 3 publications

(1 citation statement)

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“…The app store ratings were converted into Bayesian Ratings (Y3) since there is growing support for the use of Bayesian analysis to assess any user-generated content, such as ratings, games, and cases that take into account individual judgment [ 17 ]. Lastly, with a recent spotlight shone on questionable techniques for increasing an app’s number of ratings [ 18 ], any apps that received an unusually large number of ratings (ie, >3000 individual user ratings) were deemed outliers and eliminated from consideration.…”

Section: Methodsmentioning

confidence: 99%

Valuable Features in Mobile Health Apps for Patients and Consumers: Content Analysis of Apps and User Ratings

Mendiola¹,

Kalnicki²,

Lindenauer³

2015

JMIR mHealth uHealth

205

146

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BackgroundThe explosion of mobile phones with app capabilities coupled with increased expectations of the patient-consumers’ role in managing their care presents a unique opportunity to use mobile health (mHealth) apps.ObjectivesThe aim of this paper is to identify the features and characteristics most-valued by patient-consumers (“users”) that contribute positively to the rating of an app.MethodsA collection of 234 apps associated with reputable health organizations found in the medical, health, and fitness categories of the Apple iTunes store and Google Play marketplace was assessed manually for the presence of 12 app features and characteristics. Regression analysis was used to determine which, if any, contributed positively to a user’s rating of the app.ResultsAnalysis of these 12 features explained 9.3% (R 2=.093 n=234, P<.001) of the variation in an app’s rating, with only 5 reaching statistical significance. Of the 5 reaching statistical significance, plan or orders, export of data, usability, and cost contributed positively to a user’s rating, while the tracker feature detracted from it.ConclusionsThese findings suggest that users appreciate features that save time over current methods and identify an app as valuable when it is simple and intuitive to use, provides specific instructions to better manage a condition, and shares data with designated individuals. Although tracking is a core function of most health apps, this feature may detract from a user’s experience when not executed properly. Further investigation into mHealth app features is worthwhile given the inability of the most common features to explain a large portion of an app’s rating. In the future, studies should focus on one category in the app store, specific diseases, or desired behavior change, and methods should include measuring the quality of each feature, both through manual assessment and evaluation of user reviews. Additional investigations into understanding the impact of synergistic features, incentives, social media, and gamification are also warranted to identify possible future trends.

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Section: Methodsmentioning

confidence: 99%