Expert Involvement Predicts mHealth App Downloads: Multivariate Regression Analysis of Urology Apps

BackgroundUrological mobile medical (mHealth) apps are gaining popularity with both clinicians and patients. mHealth is a rapidly evolving and heterogeneous field, with some urology apps being downloaded over 10,000 times and others not at all. The factors that contribute to...

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Main Authors: Pereira-Azevedo, Nuno, Osório, Luís, Cavadas, Vitor, Fraga, Avelino, Carrasquinho, Eduardo, Cardoso de Oliveira, Eduardo, Castelo-Branco, Miguel, Roobol, Monique J
Format: Article
Language:English
Published: JMIR Publications 2016-07-01
Series:JMIR mHealth and uHealth
Online Access:http://mhealth.jmir.org/2016/3/e86/
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spelling doaj-508e00e14f1146ef8c6e1e6b4722b2462021-05-03T03:33:22ZengJMIR PublicationsJMIR mHealth and uHealth2291-52222016-07-0143e8610.2196/mhealth.5738Expert Involvement Predicts mHealth App Downloads: Multivariate Regression Analysis of Urology AppsPereira-Azevedo, NunoOsório, LuísCavadas, VitorFraga, AvelinoCarrasquinho, EduardoCardoso de Oliveira, EduardoCastelo-Branco, MiguelRoobol, Monique J BackgroundUrological mobile medical (mHealth) apps are gaining popularity with both clinicians and patients. mHealth is a rapidly evolving and heterogeneous field, with some urology apps being downloaded over 10,000 times and others not at all. The factors that contribute to medical app downloads have yet to be identified, including the hypothetical influence of expert involvement in app development. ObjectiveThe objective of our study was to identify predictors of the number of urology app downloads. MethodsWe reviewed urology apps available in the Google Play Store and collected publicly available data. Multivariate ordinal logistic regression evaluated the effect of publicly available app variables on the number of apps being downloaded. ResultsOf 129 urology apps eligible for study, only 2 (1.6%) had >10,000 downloads, with half having ≤100 downloads and 4 (3.1%) having none at all. Apps developed with expert urologist involvement (P=.003), optional in-app purchases (P=.01), higher user rating (P<.001), and more user reviews (P<.001) were more likely to be installed. App cost was inversely related to the number of downloads (P<.001). Only data from the Google Play Store and the developers’ websites, but not other platforms, were publicly available for analysis, and the level and nature of expert involvement was not documented. ConclusionsThe explicit participation of urologists in app development is likely to enhance its chances to have a higher number of downloads. This finding should help in the design of better apps and further promote urologist involvement in mHealth. Official certification processes are required to ensure app quality and user safety.http://mhealth.jmir.org/2016/3/e86/
collection DOAJ
language English
format Article
sources DOAJ
author Pereira-Azevedo, Nuno
Osório, Luís
Cavadas, Vitor
Fraga, Avelino
Carrasquinho, Eduardo
Cardoso de Oliveira, Eduardo
Castelo-Branco, Miguel
Roobol, Monique J
spellingShingle Pereira-Azevedo, Nuno
Osório, Luís
Cavadas, Vitor
Fraga, Avelino
Carrasquinho, Eduardo
Cardoso de Oliveira, Eduardo
Castelo-Branco, Miguel
Roobol, Monique J
Expert Involvement Predicts mHealth App Downloads: Multivariate Regression Analysis of Urology Apps
JMIR mHealth and uHealth
author_facet Pereira-Azevedo, Nuno
Osório, Luís
Cavadas, Vitor
Fraga, Avelino
Carrasquinho, Eduardo
Cardoso de Oliveira, Eduardo
Castelo-Branco, Miguel
Roobol, Monique J
author_sort Pereira-Azevedo, Nuno
title Expert Involvement Predicts mHealth App Downloads: Multivariate Regression Analysis of Urology Apps
title_short Expert Involvement Predicts mHealth App Downloads: Multivariate Regression Analysis of Urology Apps
title_full Expert Involvement Predicts mHealth App Downloads: Multivariate Regression Analysis of Urology Apps
title_fullStr Expert Involvement Predicts mHealth App Downloads: Multivariate Regression Analysis of Urology Apps
title_full_unstemmed Expert Involvement Predicts mHealth App Downloads: Multivariate Regression Analysis of Urology Apps
title_sort expert involvement predicts mhealth app downloads: multivariate regression analysis of urology apps
publisher JMIR Publications
series JMIR mHealth and uHealth
issn 2291-5222
publishDate 2016-07-01
description BackgroundUrological mobile medical (mHealth) apps are gaining popularity with both clinicians and patients. mHealth is a rapidly evolving and heterogeneous field, with some urology apps being downloaded over 10,000 times and others not at all. The factors that contribute to medical app downloads have yet to be identified, including the hypothetical influence of expert involvement in app development. ObjectiveThe objective of our study was to identify predictors of the number of urology app downloads. MethodsWe reviewed urology apps available in the Google Play Store and collected publicly available data. Multivariate ordinal logistic regression evaluated the effect of publicly available app variables on the number of apps being downloaded. ResultsOf 129 urology apps eligible for study, only 2 (1.6%) had >10,000 downloads, with half having ≤100 downloads and 4 (3.1%) having none at all. Apps developed with expert urologist involvement (P=.003), optional in-app purchases (P=.01), higher user rating (P<.001), and more user reviews (P<.001) were more likely to be installed. App cost was inversely related to the number of downloads (P<.001). Only data from the Google Play Store and the developers’ websites, but not other platforms, were publicly available for analysis, and the level and nature of expert involvement was not documented. ConclusionsThe explicit participation of urologists in app development is likely to enhance its chances to have a higher number of downloads. This finding should help in the design of better apps and further promote urologist involvement in mHealth. Official certification processes are required to ensure app quality and user safety.
url http://mhealth.jmir.org/2016/3/e86/
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