ArticleEmpirical software engineering2022
A large scale analysis of mHealth app user reviews.
Article in Empirical software engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The trial behind it
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Who cites it
22 citing papers in PubMed, 67 citations in OpenAlex.
- What Users Say About Reimbursable Digital Therapeutics in Germany: Large-Scale App Store Review Analysis Using a Large Language Model.JMIR human factors · 2026Article
- Value in app store metadata and user reviews: A dual perspective on quality of Parkinson's and dementia apps.PLOS digital health · 2026Article
- Advancing foundational models in digital health technology adoption: A systematic literature review of multidisciplinary factors.PLOS digital health · 2026Article
- User-Reported Issues With Mental Health Apps: Machine-Assisted Topic Analysis of Social Media Posts.JMIR mHealth and uHealth · 2026Article
- Patient-Centered Lupus Erythematosus Mobile Apps: Systematic Search and Cross-Sectional Evaluation by Patients and Physicians.JMIR mHealth and uHealth · 2026Article
- Identifying and Evaluating Patient-Centered Mobile and Web Apps for Patients With Chronic Spontaneous Urticaria: Systematic Search and Content Analysis.JMIR mHealth and uHealth · 2026Article
- Designing interfaces for digital physical ability self-assessment: a user-centered iterative approach.Frontiers in digital health · 2026Article
- User Satisfaction With Pregnancy Management Apps in Mainland China: User-Generated Content Analysis and Text Mining Study.Journal of medical Internet research · 2025Article
- User Perceptions of E-Cigarette Cessation Apps: Content Analysis of App Reviews.Journal of medical Internet research · 2025Article
- Exploring Heart Disease-Related mHealth Apps in India: Systematic Search in App Stores and Metadata Analysis.Journal of medical Internet research · 2025Article
- Methodological Challenges in Randomized Controlled Trials of mHealth Interventions: Cross-Sectional Survey Study and Consensus-Based Recommendations.Journal of medical Internet research · 2024Article
- When and Why Adults Abandon Lifestyle Behavior and Mental Health Mobile Apps: Scoping Review.Journal of medical Internet research · 2024Article
- Establishing an Evaluation Indicator System for User Satisfaction With Hypertension Management Apps: Combining User-Generated Content and Analytic Hierarchy Process.Journal of medical Internet research · 2024Article
- Evaluating the Usability of mHealth Apps: An Evaluation Model Based on Task Analysis Methods and Eye Movement Data.Healthcare (Basel, Switzerland) · 2024Article
- Patient-Centered Chronic Wound Care Mobile Apps: Systematic Identification, Analysis, and Assessment.JMIR mHealth and uHealth · 2024Article
- MyTrack+: Human-centered design of an mHealth app to support long-term weight loss maintenance.Frontiers in digital health · 2024Article
- Exploring Generation Z and Young Millennials' Perspectives of a Spiritual Self-Care App and Their Spiritual Identity (Skylight): Qualitative Semistructured Interview Study.JMIR formative research · 2023Article
- Association Between the Characteristics of mHealth Apps and User Input During Development and Testing: Secondary Analysis of App Assessment Data.JMIR mHealth and uHealth · 2023Article
- Rating analysis and BERTopic modeling of consumer versus regulated mHealth app reviews in Germany.NPJ digital medicine · 2023Article
- Willingness to Use Mobile Health Devices in the Post-COVID-19 Era: Nationwide Cross-sectional Study in China.Journal of medical Internet research · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.