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

Innovations and Technology for Healthy Eating Education (414) mHealth for Wellness, Behavior Change and Prevention (2872) Mobile Health (mhealth) (2941) Quality Evaluation and Descriptive Analysis/Reviews of Multiple Existing Mobile Apps (374) Usability of Apps and User Perceptions of mHealth (974) Instruments and Questionnaires for Nutrition and Food Intake (166)

Published on in Vol 23, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25160, first published .
User Perspectives of Diet-Tracking Apps: Reviews Content Analysis and Topic Modeling

User Perspectives of Diet-Tracking Apps: Reviews Content Analysis and Topic Modeling

User Perspectives of Diet-Tracking Apps: Reviews Content Analysis and Topic Modeling

Authors of this article:

Mila Zečević1 Author Orcid Image ;   Dejan Mijatović2 Author Orcid Image ;   Mateja Kos Koklič1 Author Orcid Image ;   Vesna Žabkar1 Author Orcid Image ;   Petar Gidaković1 Author Orcid Image
Article Authors Cited by (23) Tweetations (2) Metrics

Journals

  1. Wang T, Zheng X, Liang J, An K, He Y, Nuo M, Wang W, Lei J. Use of Machine Learning to Mine User-Generated Content From Mobile Health Apps for Weight Loss to Assess Factors Correlated With User Satisfaction. JAMA Network Open 2022;5(5):e2215014 View
  2. Nuo M, Zheng S, Wen Q, Fang H, Wang T, Liang J, Han H, Lei J. 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. Journal of Medical Internet Research 2023;25:e42856 View
  3. Fenton A, Cooper-Ryan A, Hardey M, Ahmed W. Football Fandom as a Platform for Digital Health Promotion and Behaviour Change: A Mobile App Case Study. International Journal of Environmental Research and Public Health 2022;19(14):8417 View
  4. An R, Perez-Cruet J, Wang J. We got nuts! use deep neural networks to classify images of common edible nuts. Nutrition and Health 2024;30(2):301 View
  5. Scarry A, Rice J, O' Connor E, Tierney A. Does the use of mobile applications or mobile health technology improve diet quality in adults? A protocol for a systematic literature review. HRB Open Research 2022;5:1 View
  6. van der Haar S, Raaijmakers I, Verain M, Meijboom S. Consumers’ needs in nutrition apps to start and maintain usage: a mixed methods study (Preprint). JMIR mHealth and uHealth 2022 View
  7. Kumar A, Bala P, Chakraborty S, Behera R. Exploring antecedents impacting user satisfaction with voice assistant app: A text mining-based analysis on Alexa services. Journal of Retailing and Consumer Services 2024;76:103586 View
  8. Chembakottu B, Li H, Khomh F. A large-scale exploratory study of android sports apps in the google play store. Information and Software Technology 2023;164:107321 View
  9. Alnooh G, Alessa T, Noorwali E, Albar S, Williams E, de Witte L, Hawley M. Identification of the Most Suitable Mobile Apps to Support Dietary Approaches to Stop Hypertension (DASH) Diet Self-Management: Systematic Search of App Stores and Content Analysis. Nutrients 2023;15(15):3476 View
  10. Uncovska M, Freitag B, Meister S, Fehring L. Rating analysis and BERTopic modeling of consumer versus regulated mHealth app reviews in Germany. npj Digital Medicine 2023;6(1) View
  11. Kheirinejad S, Visuri A, Suryanarayana S, Hosio S. Exploring mHealth applications for self-management of chronic low back pain: A survey of features and benefits. Heliyon 2023;9(6):e16586 View
  12. Theodore Armand T, Kim H, Kim J. Digital Anti-Aging Healthcare: An Overview of the Applications of Digital Technologies in Diet Management. Journal of Personalized Medicine 2024;14(3):254 View
  13. Wang T, Wang W, Feng J, Fan X, Guo J, Lei J. A novel user-generated content-driven and Kano model focused framework to explore the impact mechanism of continuance intention to use mobile APPs. Computers in Human Behavior 2024;157:108252 View
  14. An R, Perez-Cruet J, Wang X, Yang Y. Build Deep Neural Network Models to Detect Common Edible Nuts from Photos and Estimate Nutrient Portfolio. Nutrients 2024;16(9):1294 View
  15. Lozano C, Neubig K, Saha S, Broyles S, Apolzan J, Martin C. Validity of the PortionSize app compared to MyFitnessPal for accurately estimating intake: A randomized crossover laboratory-based evaluation. The American Journal of Clinical Nutrition 2024 View
  16. Grandinetti J. Gluten-free apps, disability, and travel: Developing a critical heuristic for the implementation of mobile apps. Mobile Media & Communication 2024 View
  17. Nellore N, Mishra T, Zimmer M. Unveiling User Perspectives: Exploring Themes in Femtech Mobile App Reviews for Enhanced Usability and Privacy. Proceedings of the ACM on Human-Computer Interaction 2024;8(MHCI):1 View
  18. Bhatnagr P, Rajesh A, Misra R. Understanding critical service factors in neobanks: crafting strategies through text mining. Journal of Modelling in Management 2024 View

Books/Policy Documents

  1. Kumar A, Singh A, Choi B. Intelligent Human Computer Interaction. View
  2. Gupta H, Jha S, Handa S, Gairola T. International Conference on Innovative Computing and Communications. View