Using Crowdsourced Food Image Data for Assessing Restaurant Nutrition Environment: A Validation Study

• Published in: Nutrients Vol. 15; no. 19; p. 4287
• Main Authors: Lyu, Weixuan, Seok, Nina, Chen, Xiang, Xu, Ran
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 08.10.2023; MDPI
• Subjects: Biomarkers; Calories; Consumers; cost effectiveness; Crowdsourcing; Customer services; Datasets; Deep learning; Expenditures; Food; Fruits; Nutrition; Restaurants; Social networks; Type 2 diabetes; Validity; Connecticut; United States > US
• ISSN: 2072-6643; 2072-6643
• DOI: 10.3390/nu15194287

Crowdsourced online food images, when combined with food image recognition technologies, have the potential to offer a cost-effective and scalable solution for the assessment of the restaurant nutrition environment. While previous research has explored this approach and validated the accuracy of food image recognition technologies, much remains unknown about the validity of crowdsourced food images as the primary data source for large-scale assessments. In this paper, we collect data from multiple sources and comprehensively examine the validity of using crowdsourced food images for assessing the restaurant nutrition environment in the Greater Hartford region. Our results indicate that while crowdsourced food images are useful in terms of the initial assessment of restaurant nutrition quality and the identification of popular food items, they are subject to selection bias on multiple levels and do not fully represent the restaurant nutrition quality or customers’ dietary behaviors. If employed, the food image data must be supplemented with alternative data sources, such as field surveys, store audits, and commercial data, to offer a more representative assessment of the restaurant nutrition environment.