Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/137233
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Type: Journal article
Title: A large scale analysis of mHealth app user reviews
Author: Haggag, O.
Grundy, J.
Abdelrazek, M.
Haggag, S.
Citation: Empirical Software Engineering: an international journal, 2022; 27(7):1-53
Publisher: Springer Verlag
Issue Date: 2022
ISSN: 1382-3256
1573-7616
Statement of
Responsibility: 
Omar Haggag, John Grundy, Mohamed Abdelrazek, Sherif Haggag
Abstract: The global mHealth app market is rapidly expanding, especially since the COVID-19 pandemic. However, many of these mHealth apps have serious issues, as reported in their user reviews. Better understanding their key user concerns would help app developers improve their apps’ quality and uptake. While app reviews have been used to study user feedback in many prior studies, many are limited in scope, size and/or analysis. In this paper, we introduce a very large-scale study and analysis of mHealth app reviews. We extracted and translated over 5 million user reviews for 278 mHealth apps. These reviews were then classified into 14 different aspects/categories of issues reported. Several mHealth app subcategories were examined to reveal differences in significant areas of user concerns, and to investigate the impact of different aspects of mhealth apps on their ratings. Based on our findings, women’s health apps had the highest satisfaction ratings. Fitness activity tracking apps received the lowest and most unfavourable ratings from users. Over half of users who reported troubles leading them to uninstall mHealth apps gave a 1-star rating. Half of users gave the account and logging aspect only one star due to faults and issues encountered while registering or logging in. Over a third of users who expressed privacy concerns gave the app a 1-star rating. However, only 6% of users gave apps a one-star rating due to UI/UX concerns. 20% of users reported issues with handling of user requests and internationalisation concerns. We validated our findings by manually analysing a sample of 1,000 user reviews from each investigated aspect/category. We developed a list of recommendations for mHealth apps developers based on our user review analysis.
Keywords: mHealth apps
User reviews
App store
Google play
Classification
Analysis
Recommendations
Rights: © The author(s). 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/.
DOI: 10.1007/s10664-022-10222-6
Grant ID: http://purl.org/au-research/grants/arc/FL190100035
http://purl.org/au-research/grants/arc/DP200100020
Published version: http://dx.doi.org/10.1007/s10664-022-10222-6
Appears in Collections:Computer Science publications

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