,The dataset, Survey-SR, provides the nutrient data for assessing dietary intakes from the national survey What We Eat In America, National Health and Nutrition Examination Survey (WWEIA, NHANES). Historically, USDA databases have been used for national nutrition monitoring (1). Currently, the Food and Nutrient Database for Dietary Studies (FNDDS) (2), is used by Food Surveys Research Group, ARS, to process dietary intake data from WWEIA, NHANES. Nutrient values for FNDDS are based on Survey-SR. Survey-SR was referred to as the "Primary Data Set" in older publications. Early versions of the dataset were composed mainly of commodity-type items such as wheat flour, sugar, milk, etc. However, with increased consumption of commercial processed and restaurant foods and changes in how national nutrition monitoring data are used (1), many commercial processed and restaurant items have been added to Survey-SR. The current version, Survey-SR 2013-2014, is mainly based on the USDA National Nutrient Database for Standard Reference (SR) 28 (2) and contains sixty-six nutrientseach for 3,404 foods. These nutrient data will be used for assessing intake data from WWEIA, NHANES 2013-2014. Nutrient profiles were added for 265 new foods and updated for about 500 foods from the version used for the previous survey (WWEIA, NHANES 2011-12). New foods added include mainly commercially processed foods such as several gluten-free products, milk substitutes, sauces and condiments such as sriracha, pesto and wasabi, Greek yogurt, breakfast cereals, low-sodium meat products, whole grain pastas and baked products, and several beverages including bottled tea and coffee, coconut water, malt beverages, hard cider, fruit-flavored drinks, fortified fruit juices and fruit and/or vegetable smoothies. Several school lunch pizzas and chicken products, fast-food sandwiches, and new beef cuts were also added, as they are now reported more frequently by survey respondents. Nutrient profiles were updated for several commonly consumed foods such as cheddar, mozzarella and American cheese, ground beef, butter, and catsup. The changes in nutrient values may be due to reformulations in products, changes in the market shares of brands, or more accurate data. Examples of more accurate data include analytical data, market share data, and data from a nationally representative sample.,,

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Food consumption in terms of the Dietary Guidelines for Americans’ food groups is reported for all sources and the total U.S. population, as well as by food source, children age 2-19, and adults age 20 and older.

Recently published papers: We are what we eat?

This data section provides information about publicly available national surveys that include questions from the U.S. Food Security Survey Module. Information on each survey and directions for accessing data files are available in the documentation.

The Eating & Health (EH) Module of the American Time Use Survey (ATUS) collects data to analyze relationships among time use patterns and eating patterns, nutrition, and obesity; food and nutrition assistance programs; and grocery shopping and meal preparation.

,The average American’s diet does not align with the Dietary Guidelines for Americans (DGA) provided by the U.S. Department of Agriculture and the U.S. Department of Health and Human Services (2020). The present study aimed to compare fruit and vegetable consumption among those who had and had not heard of the DGA, identify characteristics of DGA users, and identify barriers to DGA use. A nationwide survey of 943 Americans revealed that those who had heard of the DGA ate more fruits and vegetables than those who had not. Men, African Americans, and those who have more education had greater odds of using the DGA as a guide when preparing meals relative to their respective counterparts. Disinterest, effort, and time were among the most cited reasons for not using the DGA. Future research should examine how to increase DGA adherence among those unaware of or who do not use the DGA.,Comparative analyses of fruit and vegetable consumption among those who were aware/unaware and use/do not use the DGA were completed using independent samples t tests. Fruit and vegetable consumption variables were log-transformed for analysis. Binary logistic regression was used to examine whether demographic features (race, gender, and age) predict DGA awareness and usage. Data were analyzed using SPSS version 28.1 and SAS/STAT® version 9.4 TS1M7 (2023 SAS Institute Inc).,

The data presented in this dataset is invaluable for monitoring the potential of the food supply to meet nutritional needs; for examining relationships between food supply nutrients and health; and for examining dietary trends of Americans. Additionally, food supply nutrient estimates reflect Federal enrichment and fortification standards and technological advances in the food industry and contribute to the Federal dietary guidance system. As such, these data are of interest to agricultural policymakers, economists, nutrition researchers, and nutrition and public health educators. Data are provided for the following nutrients and their food sources from the major food groups.

,Food Intakes Converted to Retail Commodities Databases (FICRCD) provide data for foods consumed in the United States national dietary intake surveys at the retail commodity level. The survey foods are converted into 65 retail-level commodities. The commodities are grouped into eight major categories: Dairy Products; Fats and Oils; Fruits; Grains; Meat, Poultry, Fish and Eggs; Nuts; Caloric Sweeteners; and Vegetables, Dry Beans and Legumes.,The Food Intakes Converted to Retail Commodities Databases were jointly developed by USDA's Agricultural Research Service (ARS) and Economic Research Service (ERS) for the following six surveys:,