JMIR Publications

ABSTRACT

Background:

Mobile learning apps are increasingly used in medical education, but cross-national evidence on their market evolution and user satisfaction mechanisms remains limited.

Objective:

This study aimed to examine the longitudinal evolution of medical education mobile apps in China and the United States and identify app-related factors associated with user satisfaction and dissatisfaction.

Methods:

We conducted a longitudinal app store analysis of 467 medical education mobile apps and 232,598 user reviews from major Chinese and US app stores. Joinpoint regression was used to characterize temporal trends in app development. Latent Dirichlet Allocation topic modeling, with large language model-assisted topic labeling, was used to identify themes in user reviews. Tobit regression, Wald tests, and Kano model classification were used to examine asymmetric satisfaction mechanisms and classify user-perceived functional attributes.

Results:

Among the 232,598 analyzed reviews, 219,105 were rated 4 stars or higher, corresponding to an overall satisfaction rate of 94.19%. Satisfaction was lower in China than in the United States (128,018/139,416, 91.82% vs 91,087/93,182, 97.75%). The annual number of surviving medical education apps increased significantly in both countries, with average annual percentage changes of 41.69% in China and 41.17% in the United States. Major market inflection points occurred around 2017 and 2021-2022. User concerns also shifted over time. In China, the dominant review theme changed from clinical practicality in 2009-2017 (2735 reviews, 37.03%) to practice test functions in 2018-2025 (53,929 reviews, 40.85%). In the United States, user attention shifted from functional availability in 2009-2017 (20,736 reviews, 79.39%) to cost in 2018-2025 (27,401 reviews, 40.86%). Tobit regression and Kano classification showed that satisfaction-associated factors differed by country and developmental stage, with essential and charm factors changing dynamically over time.

Conclusions:

Large-scale app store reviews revealed distinct but partially converging patterns in the evolution and user satisfaction mechanisms of medical education mobile apps in China and the United States. These findings suggest that future medical education apps should prioritize reliable content, technical stability, affordable exam preparation, and personalized learning support. They may also inform the design of future AI-supported medical education platforms, while requiring further validation with learning-process and outcome data.