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

mHealth for Wellness, Behavior Change and Prevention (2518) Instruments and Questionnaires for Nutrition and Food Intake (149) mHealth for Data Collection and Research (770) Usability of Apps and User Perceptions of mHealth (790)

Published on in Vol 9, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24467, first published .
The Human Factor in Automated Image-Based Nutrition Apps: Analysis of Common Mistakes Using the goFOOD Lite App

The Human Factor in Automated Image-Based Nutrition Apps: Analysis of Common Mistakes Using the goFOOD Lite App

The Human Factor in Automated Image-Based Nutrition Apps: Analysis of Common Mistakes Using the goFOOD Lite App

Authors of this article:

Maria F Vasiloglou 1 Author Orcid Image ;   Klazine van der Horst 2 Author Orcid Image ;   Thomai Stathopoulou 1 Author Orcid Image ;   Michael P Jaeggi 2 Author Orcid Image ;   Giulia S Tedde 2 Author Orcid Image ;   Ya Lu 1 Author Orcid Image ;   Stavroula Mougiakakou 1 Author Orcid Image
Article Authors Cited by (11) Tweetations (15) Metrics

Journals

  1. Vasiloglou M, Christodoulidis S, Reber E, Stathopoulou T, Lu Y, Stanga Z, Mougiakakou S. Perspectives and Preferences of Adult Smartphone Users Regarding Nutrition and Diet Apps: Web-Based Survey Study. JMIR mHealth and uHealth 2021;9(7):e27885 View
  2. Vasiloglou M, Marcano I, Lizama S, Papathanail I, Spanakis E, Mougiakakou S. Multimedia Data-Based Mobile Applications for Dietary Assessment. Journal of Diabetes Science and Technology 2023;17(4):1056 View
  3. Santo K. Can Digital Health Solutions Fill in the Gap for Effective Guideline Implementation in Cardiovascular Disease Prevention: Hope or Hype?. Current Atherosclerosis Reports 2022;24(9):747 View
  4. Martinon P, Saliasi I, Bourgeois D, Smentek C, Dussart C, Fraticelli L, Carrouel F. Nutrition-Related Mobile Apps in the French App Stores: Assessment of Functionality and Quality. JMIR mHealth and uHealth 2022;10(3):e35879 View
  5. Vasiloglou M. mHealth nutrition apps in dietary assessment. Public Health and Toxicology 2022;2(Supplement 1) View
  6. Hinojosa-Nogueira D, Ortiz-Viso B, Navajas-Porras B, Pérez-Burillo S, González-Vigil V, de la Cueva S, Rufián-Henares J. Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition. Nutrients 2023;15(2):276 View
  7. König L, Van Emmenis M, Nurmi J, Kassavou A, Sutton S. Characteristics of smartphone-based dietary assessment tools: a systematic review. Health Psychology Review 2022;16(4):526 View
  8. Yanai A, Uchiyama K, Suganuma S. Salt Reduction Using a Smartphone Application Based on an Artificial Intelligence System for Dietary Assessment in Patients with Chronic Kidney Disease: A Single-Center Retrospective Cohort Study. Kidney and Dialysis 2023;3(1):139 View
  9. He J, Lin L, Eicher-Miller H, Zhu F. Long-Tailed Food Classification. Nutrients 2023;15(12):2751 View
  10. Papathanail I, Abdur Rahman L, Brigato L, Bez N, Vasiloglou M, van der Horst K, Mougiakakou S. The Nutritional Content of Meal Images in Free-Living Conditions—Automatic Assessment with goFOODTM. Nutrients 2023;15(17):3835 View

Books/Policy Documents

  1. Panagiotou M, Papathanail I, Abdur Rahman L, Brigato L, Bez N, Vasiloglou M, Stathopoulou T, de Galan B, Pedersen-Bjergaard U, van der Horst K, Mougiakakou S. Computer Analysis of Images and Patterns. View