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

Evaluation and Research Methodology for mHealth (263) Web-based and Mobile Health Interventions (4344) Mobile Health (mhealth) (3764) New Methods (1207) mHealth for Wellness, Behavior Change and Prevention (3507) Obesity and Nutrition as Public Health Problem (457)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52074, first published .
Hands holding a smartphone photographing a healthy salmon and broccoli meal.

Tailored Prompting to Improve Adherence to Image-Based Dietary Assessment: Mixed Methods Study

Tailored Prompting to Improve Adherence to Image-Based Dietary Assessment: Mixed Methods Study

Authors of this article:

Lachlan Lee1 Author Orcid Image ;   Rosemary Hall1 Author Orcid Image ;   James Stanley2 Author Orcid Image ;   Jeremy Krebs1 Author Orcid Image
Article Authors Cited by (3) Tweetations (10) Metrics

Journals

  1. Lee L, Bishop R, Stanley J. An automated image-based dietary assessment application: a pilot study. Journal of Nutritional Science 2025;14 View
  2. Rodríguez-Jiménez M, Martín-del-Campo-Becerra G, Sumalla-Cano S, Crespo-Álvarez J, Elio I. Image-Based Dietary Energy and Macronutrients Estimation with ChatGPT-5: Cross-Source Evaluation Across Escalating Context Scenarios. Nutrients 2025;17(22):3613 View
  3. Hinojosa-Nogueira D, Subiri-Verdugo A, Rodríguez-Muñoz A, Díaz-Perdigones C, García-López M, Tinahones F, Moreno-Indias I. Online surveys, a breakthrough in research, especially in nutrition and food sciences. Clinical Nutrition ESPEN 2026;73:103299 View