ArticleDigital health
Ifood: Development and usability study of a social media-based applet for dietary monitoring.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Triggering just-in-time adaptive interventions based on real-time detection of daily-life stress: Methodological development and longitudinal multicenter evaluation.Behavior research methods · 2025Article
- Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions.Frontiers in nutrition · 2025Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Dietary monitoring is critical to maintaining human health. Social media platforms are widely used for daily recording and communication for individuals' diets and activities. The textual content shared on social media offers valuable resources for dietary monitoring. Objective: This study aims to describe the development of iFood, an applet providing personal dietary monitoring based on social media content, and validate its usability, which will enable efficient personal dietary monitoring. Methods: The process of the development and validation of iFood is divided into four steps: Diet datasets construction, diet record and analysis, diet monitoring applet design, and diet monitoring applet usability assessment. The diet datasets were constructed with the data collected from Weibo, Meishijie, and diet guidelines, which will be used as the basic knowledge for further model training in the phase of diet record and analysis. Then, the friendly user interface was designed to link users with backend functions. Finally, the applet was deployed as a WeChat applet and 10 users from the Beijing Union Medical College have been recruited to validate the usability of iFood. Results: Three dietary datasets, including User Visual-Textual Dataset, Dietary Information Expansion Dataset, and Diet Recipe Dataset have been constructed. The performance of 4 models for recognizing diet and fusing unimodality data was 40.43%(dictionary-based model), 18.45%(rule-based model), 59.95%(Inception-ResNet-v2), and 51.38% (K-nearest neighbor), respectively. Furthermore, we have designed a user-friendly interface for the iFood applet and conducted a usability assessment, which resulted in an above-average usability score. Conclusions: iFood is effective for managing individual dietary behaviors through its seamless integration with social media data. This study suggests that future products could utilize social media data to promote healthy lifestyles.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.