This paper is in the following e-collection/theme issue:

Mobile Health (mhealth) (3464) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1911) Machine Learning (2076) Sleep Monitoring, Sleep Quality, Sleep Disorders (268)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42856, first published .
Mining the Influencing Factors and Their Asymmetrical Effects of mHealth Sleep App User Satisfaction From Real-world User-Generated Reviews: Content Analysis and Topic Modeling

Mining the Influencing Factors and Their Asymmetrical Effects of mHealth Sleep App User Satisfaction From Real-world User-Generated Reviews: Content Analysis and Topic Modeling

Mining the Influencing Factors and Their Asymmetrical Effects of mHealth Sleep App User Satisfaction From Real-world User-Generated Reviews: Content Analysis and Topic Modeling

Authors of this article:

Mingfu Nuo1 Author Orcid Image ;   Shaojiang Zheng2, 3 Author Orcid Image ;   Qinglian Wen4 Author Orcid Image ;   Hongjuan Fang5 Author Orcid Image ;   Tong Wang6 Author Orcid Image ;   Jun Liang7, 8 Author Orcid Image ;   Hongbin Han1, 9 Author Orcid Image ;   Jianbo Lei10, 11, 12 Author Orcid Image
Article Authors Cited by (9) Tweetations (6) Metrics

Journals

  1. He Y, Zhu W, Wang T, Chen H, Xin J, Liu Y, Lei J, Liang J. Mining User Reviews From Hypertension Management Mobile Health Apps to Explore Factors Influencing User Satisfaction and Their Asymmetry: Comparative Study. JMIR mHealth and uHealth 2024;12:e55199 View
  2. Luo Q, Yang C, Yang J. Predicting counseling behavioral propensity based on temporal return visits patterns and current perceived intensity with chronic conditions management. Information Processing & Management 2025;62(3):104024 View
  3. Pallesen S, Vedaa Ø, Sunde E, Harris A, Waage S, Saxvig I, Zhang J, Bjorvatn B. The use and subjective experience of sleep apps and their relationship with personality characteristics among young adults. Frontiers in Sleep 2025;4 View
  4. Grljević O, Marić M, Božić R. Exploring Mobile Application User Experience Through Topic Modeling. Sustainability 2025;17(3):1109 View
  5. Yang L, Weng B, Xu X, Huang Z, Ding R, Si M, Fu Y, Zhu Y, Jiang Y, Rao B, Zhang X, Zhou Q, Lin S, Guo Y, Xie X. Functional characteristics of sleep monitoring devices in China: A real-world cross-sectional study. DIGITAL HEALTH 2025;11 View
  6. Liang J, Fang Q, Jiao X, Xiang P, Ma J, Zhang Z, Liu Y, He Y, Li Y, He Z, Lei J. Approved trends and product characteristics of digital therapeutics in four countries. npj Digital Medicine 2025;8(1) View
  7. He Y, Ye L, He X, Chen J, Wang T, Qiao L, Pu H, Li Y, Wang Y, Jiao X, Fang Q, Ma J, Xing M, Hu Y, Zhou T, Liang J, Lei J, Star X Z. Exploring patient satisfaction and its influencing factors in Chinese internet hospitals: An analysis using two-factor theory and Kano model based on user-generated contents. DIGITAL HEALTH 2025;11 View
  8. Alzate M, Vidaurreta-Apesteguia P, Morales-Garzón A, Gutiérrez-Batista K. Forecasting user perceptions of mHealth apps: AI-driven insights from large-scale user-generated content. Technological Forecasting and Social Change 2026;223:124427 View

Dissertations

  1. . Leveraging LLMs for the Analysis of Mobile App User Feedback: In-Depth Evaluation of User Perspectives on AI-Enabled Mobile Apps. View