ArticleInternational journal of environmental research and public health2022
A Study of Mobile Medical App User Satisfaction Incorporating Theme Analysis and Review Sentiment Tendencies.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 33 citations in OpenAlex.
- From Comparison to Confidence: The Dove Self-Esteem Project and the Transformation of Beauty Perceptions on Social Media.Behavioral sciences (Basel, Switzerland) · 2025Article
- The Use of Mobile Health Care Among Medical Professionals in the Sichuan-Chongqing Region: Cross-Sectional Survey Study.JMIR human factors · 2024Article
- Assessment of Eye Care Apps for Children and Adolescents Based on the Mobile App Rating Scale: Content Analysis and Quality Assessment.JMIR mHealth and uHealth · 2024Article
- Determinants of multimodal fake review generation in China's E-commerce platforms.Scientific reports · 2024Article
- Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic.International journal of environmental research and public health · 2022Article
- Consumers' Opinions towards Public Health Effects of Online Games: An Empirical Study Based on Social Media Comments in China.International journal of environmental research and public health · 2022Article
- Medical service quality evaluation based on LDA and sentiment analysis: Examples of seven chronic diseases.Digital healthArticle
- Effect of patient satisfaction on the utilization of mHealth services by patients with chronic disease.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mobile medicine plays a significant role in optimizing medical resource allocation, improving medical efficiency, etc. Identifying and analyzing user concern elements from active online reviews can help to improve service quality and enhance product competitiveness in a targeted manner. Based on the latent Dirichlet allocation (LDA) topic model, this study carries out a topic-clustering analysis of users' online comments and builds an evaluation index system of mobile medical users' satisfaction by using grounded theory. After that, the evaluation information of users is obtained through an emotional analysis of online comments. Then, in order to fully consider the uncertainty of decision makers' evaluations, rough number theory and the fuzzy comprehensive evaluation method are used to confirm the conclusions of experts and indicators and to evaluate the satisfaction of mobile medical users. The empirical results show that users are satisfied with the service quality and content quality of mobile medical apps, and less satisfied with the management and technology qualities. Therefore, this paper puts forward corresponding countermeasures from the aspects of strengthening safety supervision, strengthening scientific research, strengthening information audit, attaching importance to service quality management and strengthening doctors' sense of gain. This study uses text mining for index extraction and satisfaction analysis of online reviews to quantitatively evaluate user satisfaction with mobile medical apps, providing a reference for the improvement of mobile medical apps. However, there are still certain shortcomings in the current study, and subsequent studies can screen false reviews for a deeper study of online review information.
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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.