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

Cross-Sectional Studies in Public Health (486) Information Quality in Digital Media (423) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1176) Information Seeking, Information Needs (600) Behavioural Surveillance for Public Health (322) Obesity and Nutrition as Public Health Problem (455) Instruments and Questionnaires for Nutrition and Food Intake (232)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54805, first published .
Laptop screen displaying "Nutrition" with icons of fruits and vegetables.

Prevalence and Correlates of Dietary and Nutrition Information Seeking Through Various Web-Based and Offline Media Sources Among Japanese Adults: Web-Based Cross-Sectional Study

Prevalence and Correlates of Dietary and Nutrition Information Seeking Through Various Web-Based and Offline Media Sources Among Japanese Adults: Web-Based Cross-Sectional Study

Authors of this article:

Kentaro Murakami1 Author Orcid Image ;   Nana Shinozaki2 Author Orcid Image ;   Tsuyoshi Okuhara3 Author Orcid Image ;   Tracy A McCaffrey4 Author Orcid Image ;   M Barbara E Livingstone5 Author Orcid Image
Article Authors Cited by (8) Tweetations (5) Metrics

Journals

  1. Murakami K, Shinozaki N, Okuhara T, McCaffrey T, Livingstone M. Self-perceived food literacy in relation to the quality of overall diet and main meals: A cross-sectional study in Japanese adults. Appetite 2024;196:107281 View
  2. Priya K, Alur S. Benchmarking nutrition facts panel label – a consumer ethics perspective using health belief model. Benchmarking: An International Journal 2025;32(4):1434 View
  3. Fappa E, Micheli M. Content accuracy and readability of dietary advice available on webpages: A systematic review of the evidence. Journal of Human Nutrition and Dietetics 2025;38(1) View
  4. Takefuji Y. Reevaluating feature importances in machine learning models for schizophrenia and bipolar disorder: The need for true associations. Brain, Behavior, and Immunity 2025;124:123 View
  5. O’Hara C, Kent G, Flynn A, Gibney E, Timon C. An Evaluation of ChatGPT for Nutrient Content Estimation from Meal Photographs. Nutrients 2025;17(4):607 View
  6. Murakami R, Nagao-Sato S, Akamatsu R, Kojima Y, Shimpo M. Frequency of Intake of a Japanese Well-Balanced Diet and Health Literacy in Clusters Based on Dietary Information Influencing Eating Habits. The Japanese Journal of Nutrition and Dietetics 2025;83(4):151 View
  7. Bolat Yilmaz A, Kenger E, Ozlu Karahan T, Saglam D, Bas M. Guideline Compliance of Artificial Intelligence–Generated Diet Plans After Bariatric Surgery: A Cross-Sectional Simulation Comparing ChatGPT-4o, DeepSeek and Grok-3. Nutrients 2025;17(24):3957 View
  8. Wang J, Li C, Dong X, Gao Z, Gibney E, Yang S, McGuinness L, Noronha N, Feeney E. Consumers Preferences for Presence‐Focused Nutrition Claims in China. International Journal of Consumer Studies 2026;50(3) View