UNASSIGNED: The average American household\'s diet and food purchasing patterns are out of sync with federal recommendations. Researchers have connected this with the large and growing rates of obesity, diabetes, and other diet-related ailments in the U.S. Restaurant food has been discussed a potential contributor to unhealthful diets, as it is often calorically dense. We investigate the association between household access to restaurants and diet quality using USDA FoodAPS data and NPD ReCount data.
UNASSIGNED: We define radii around households to measure restaurant outlet counts and apply a regression analysis incorporating household characteristics.
UNASSIGNED: We find that neither restaurant counts nor openings share many statistically or economically significant associations with average dietary quality. Household characteristics and demographics are far more powerful in explaining variation in diet quality.
UNASSIGNED: Our findings align with the large and growing body of empirical research that suggests that personal preferences and other household characteristics are more important than the food environment in explaining food choices and diet quality. Given the extant research on the importance of access to large supermarkets, our results suggest that access to food retailers is more important in explaining diet quality than access to restaurants.

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.

Deep learning models can recognize the food item in an image and derive their nutrition information, including calories, macronutrients (carbohydrates, fats, and proteins), and micronutrients (vitamins and minerals). This technology has yet to be implemented for the nutrition assessment of restaurant food. In this paper, we crowdsource 15,908 food images of 470 restaurants in the Greater Hartford region on Tripadvisor and Google Place. These food images are loaded into a proprietary deep learning model (Calorie Mama) for nutrition assessment. We employ manual coding to validate the model accuracy based on the Food and Nutrient Database for Dietary Studies. The derived nutrition information is visualized at both the restaurant level and the census tract level. The deep learning model achieves 75.1% accuracy when compared with manual coding. It has more accurate labels for ethnic foods but cannot identify portion sizes, certain food items (e.g., specialty burgers and salads), and multiple food items in an image. The restaurant nutrition (RN) index is further proposed based on the derived nutrition information. By identifying the nutrition information of restaurant food through crowdsourced food images and a deep learning model, the study provides a pilot approach for large-scale nutrition assessment of the community food environment.