The European Commission funded project Stance4Health (S4H) aims to develop a complete personalised nutrition service. In order to succeed, sources of information on nutritional composition and other characteristics of foods need to be as comprehensive as possible. Food composition tables or databases (FCT/FCDB) are the most commonly used tools for this purpose. The aim of this study is to describe the harmonisation efforts carried out to obtain the Stance4Health FCDB. A total of 10 FCT/FCDB were selected from different countries and organizations. Data were classified using FoodEx2 and INFOODS tagnames to harmonise the information. Hazard analysis and critical control points analysis was applied as the quality control method. Data were processed by spreadsheets and MySQL. S4H’s FCDB is composed of 880 elements, including nutrients and bioactive compounds. A total of 2648 unified foods were used to complete the missing values of the national FCDB used. Recipes and dishes were estimated following EuroFIR standards via linked tables. S4H’s FCDB will be part of the smartphone app developed in the framework of the Stance4Health European project, which will be used in different personalized nutrition intervention studies. S4H FCDB has great perspectives, being one of the most complete in terms of number of harmonized foods, nutrients and bioactive compounds included.

Access to good nutritional health is one of the principal objectives of current society. Several e-services offer dietary advice. However, multifactorial and more individualized nutritional recommendations should be developed to recommend healthy menus according to the specific user’s needs. In this article, we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. Specific menus were generated for each user based on their preferences and nutritional requirements. These menus were evaluated by comparing their nutritional content versus the nutrient composition retrieved from dietary records. The generated menus showed great similarity to those obtained from the user dietary records. Furthermore, the generated menus showed less variability in micronutrient amounts and higher concentrations than the menus from the user records. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the users. The presented system is a good tool for the generation of menus that are adapted to the user characteristics and a starting point to nutritional interventions.

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BACKGROUND Due to its influence on health, access to a good diet is one of the great objectives of the health services in many countries. There are many electronic services that offer advice on diet, recommending specific foods or recipes. Additionally, as information processing techniques advance, options to tackle a multi-factorial and individualized nutritional recommendation also increase in number, allowing the recommendation of complete menus taking into account several parameters. OBJECTIVE In this article we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. METHODS The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. From these, a user profile was developed with their nutritional requirements and menus were generated for 2 weeks. These menus were evaluated by comparing their mean nutritional content versus the mean nutrient composition retrieved from dietary records. RESULTS The generated menus showed great similarity to those obtained from the user dietary questionnaires. However, the generated menus showed less variability regarding the amounts of micronutrients reached. This lower variability was accompanied by higher quantities of most of the micronutrients tested in the user menus. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the biometric parameters provided by the users. CONCLUSIONS The presented system is a good tool for the generation of menus that are adapted to the user characteristics. It is therefore a starting point to carry out nutritional interventions where it is necessary to control the amount of nutrients. These nutritional interventions will be important to validate other modules of the system. CLINICALTRIAL ISRCTN63745549 INTERNATIONAL REGISTERED REPORT RR2-https:// doi.org/10.3390/foods11101480

Access to good nutritional health is one of the principal objectives of current society. Several e-services offer dietary advice. However, multifactorial and more individualized nutritional recommendations should be developed to recommend healthy menus according to the specific user's needs. In this article we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. Specific menus were generated for each user based on their preferences and nutritional requirements. These menus were evaluated by comparing their nutritional content versus the nutrient composition retrieved from dietary records. The generated menus showed great similarity to those obtained from the user dietary records. Furthermore, the generated menus showed less variability in micronutrient amounts and higher concentrations than the menus from the user records. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the users. The presented system is a good tool for the generation of menus that are adapted to the user characteristics and a starting point to nutritional interventions.