Automatic Image Recognition Meal Reporting Among Young Adults: Randomized Controlled Trial
The AIR group outperformed the VIR group concerning accuracy and time efficiency for overall dish reporting within the meal testing scenario. While further technological enhancement may be required, artificial intelligence vision technology integration into existing mobile apps holds promise. Our results provide evidence-based contributions to the integration of automatic image recognition technology into existing apps in terms of user interaction efficacy and overall ease of use. Further empirical work is required, including full-scale randomized controlled trials and assessments of user perception under various conditions.
Authors
- Denisa Irianti (ORCID: https://orcid.org/0000-0002-2272-9666)
- Hsin‐Yun Chen (ORCID: https://orcid.org/0000-0002-5256-4559)
- Yu‐Sheng Lin (ORCID: https://orcid.org/0000-0002-7654-6558)
- Mekhla Sarkar (ORCID: https://orcid.org/0000-0002-6374-2584)
- Prasan Kumar Sahoo (ORCID: https://orcid.org/0000-0003-3496-1195)
- Sherry Yueh‐Hsia Chiu (ORCID: https://orcid.org/0000-0002-7207-7088)
- Ying‐Chieh Liu (ORCID: https://orcid.org/0000-0003-1876-7632)
- Chien‐Hung Chen (ORCID: https://orcid.org/0000-0002-7087-733X)
Institutions
- Chang Gung University of Science and Technology (TW)
- Chang Gung University (TW)
- Chang Gung Memorial Hospital (TW)
- Institute for Information Industry (TW)
- Taoyuan Chang Gung Memorial Hospital (TW)
- Kaohsiung Chang Gung Memorial Hospital (TW)
- National Yang Ming Chiao Tung University (TW)
Publication Details
- Journal
- JMIR mhealth and uhealth
- Published
- 2025-06-06
- DOI
- https://doi.org/10.2196/60070
- Primary Topic
- Nutritional Studies and Diet
- Type
- article
- Field-Weighted Citation Impact
- 0.00