The Food Recommender app collects data on anthropometrics, such as height, weight, hip, waist and upper arm circumference as well as the frequency and amount of the food intake of households. Depending on sex and age of the households’ family members, the app calculates their optimal intake of macro- and micronutrients and compares it to the actual intake. According to the differences of this target-performance comparison, the app recommends up to three food items each to increase or decrease in the household’s usual diet. The aim is to individually educate and enhance households concerning their usual diet and to reduce malnutrition.

The app was developed by the Chair of Nutrition Physiology in cooperation with the Chair of Applied Informatics – Cooperative Systems at the Technical University of Munich (TUM) and is currently used in a nutritional intervention project in Benin. Although the Benin government provides nutritional information in a “Guide alimentaire du Benin”, the population still suffers especially from Vitamin A and iron deficiency. Researchers of the TUM and the Institut National des Recherches Agricoles du Bénin (INRAB) study weather personalized household-based nutritional recommendations lead to a better dietary intake than non-personalized information. Covering all eight agricultural zones in Benin, a total of 720 households are visited by enumerators to conduct this study. In a balanced design and several rounds, households receive either personalized recommendations or general advice of the Guide alimentaire.