Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition
Abstract
:1. Introduction
2. Materials and Methods
2.1. Development and Characteristics of the S4H APP
2.2. APP Architecture
2.3. Sources of Data
- User data: We store the main aspects of the user in a MySQL database, including information about biometrics, restrictions and behavior. This information allows us to calculate the nutrient levels we are aiming to recommend. At the same time, it also lets us filter several items in the dietary database that are not suited for the user, either due to age (as coffee or tea recipes in children) or to food allergies (not recommending milk-based products to users allergic to milk proteins). Other than hard restrictions, we also allow the users to check whether they dislike or like specific products and recipes in the dataset. The presence of preferences allows us to choose one recipe over the other when suggesting the menu. In this section, the user’s clinical history is also considered. Currently only the weight section (normal weight, overweight and obesity) and the allergy and intolerances are operational. This section can also take into account pathologies such as diabetes and hypertension. However, these options are currently inoperative as these options are not necessary for the target population of the nutritional interventions performed along the Stance4Health research project.
- Nutritional references: These data are summarized in tables that are used as rules to generate a healthy menu. They contain recommendations for a Mediterranean and sustainable diet [26]. The EU Dietary Reference Values (DRVs) are the reference values on which the nutritional recommendations of the APP are based [27]. This module, therefore, establishes basic rules such as portions of food groups needed per week, or micronutrient intake for a healthy diet.
- Ingredients data: The S4H APP contains multiple nutrient information from many stakeholders. Moreover, some of these data could be updated as new analysis and reviews are made. For that reason, nutritional information for every single ingredient is stored as another set of MySQL tables. Specifically, for this APP, we used the S4H food composition database (FCDB) developed within the framework of the project [28]. In summary, the S4H FCDB consists of more than 2600 foods with nutritional information on approximately 880 elements, including bioactive compounds. However, multiple elements can differ from one country to another. Moreover, several products are not consumed raw, but they undergo some kind of thermal processing. For that reason, our dataset contains a Branded Food Products Database consisting of food from supermarkets and hypermarkets of different countries (this is likely to be a significant percentage of the food already purchased and consumed by consumers). We specifically have detailed data from three different countries: Spain (with 89,385 foods products) provided by AECOC (Spanish Association of Manufacturers and Distributors), Germany (with 211,014 foods products) provided by ATRIFY and Greece (with 3312 foods products) provided by researchers [29]. We also included 670 different items from fast food restaurants obtained from the publications of the restaurant chains. These fast food items could be a recipe themselves, but as it is rare to solely eat one of them, we stored them as ingredients, to give the user more flexibility when entering the different menus which they could have eaten.
- Recipe data: S4H APP, unlike recent approaches in food recommendations, follows recipe-centered meal planning. This means that our system recommends to the user a specific recipe for a specific time of the day. Unlike a single combination of ingredients, recipes give ingredients a context/relationship and a procedure. This allows the user to know “what to eat” and “how to cook it”. This selection has its drawbacks, too, which are analyzed in the discussion section. The recipes were reviewed by inhabitants from each country: it started with the analysis of more than 150,000 recipes from all countries to obtain a set of some 20,000 appropriate recipes (in terms of nutritional value, cultural traditions and diversity in all the possible meal plans). This dataset of recipes was then evaluated in terms of its ingredients’ names, weight, retention factors and yield factors according to the cooking technique described for each recipe. Finally, we obtained the nutritional composition of each recipe [28]. Additionally, users can create their own recipes, with ingredients from the ingredients database. Those users’ recipes will only be available for the users that have created them.
- Expert knowledge: Despite recent trends in computational nutrition (that aims to provide automatic recommendations), having a source of expert knowledge has been proven as an excellent way to manage/rank all the nutritional goals and levels. Within this source, the APP includes:
- Food constraints related to health issues.
- Food levels related to age and biometrics.
- Food patterns that ensure a diverse diet.
- How several factors affect the previous items such as meal distribution, physical exercise and portion size.
2.4. Physical Activity
2.5. Portion Sizes
2.6. Additional Data Sources: Images, Barcodes and User Interaction
- Allows the user to see how a recipe looks when finished.
- Allows the user to identify ingredients easily.
- Allows the user to quickly check if they are using the correct product.
2.7. Generator
2.8. Gut Microbiota Module
2.9. Search Engine
- Camera-based interactions were primarily developed to allow users to have a quick interaction with commercial products as they may be the main source of deviation from the diet. This can be achieved through a comprehensive database linked to the commercial barcodes in the system. A user can easily scan a barcode with their smartphone and automatically find the product data in the APP database.
- Text-based interactions are based on similarity metrics of the text-chains introduced.
- Voice-based interaction runs on Google Voice recognition API.
2.10. Wearable
2.11. Shopping List and GPS Module
2.12. APP Testing and Validation
2.13. Security and Ethical Aspects of APP
2.14. Statistical Analysis
3. Results
3.1. Integration between Modules
3.2. Testing the APP with Real Subjects
4. Discussion
4.1. Comparison of S4H APP with the Current Situation
4.2. S4H APP Security and Quality
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Kcal | Carbohydrates (g) | Fats (g) | Proteins (g) | |
---|---|---|---|---|
Average | −111.7 | −14.6 | −0.1 | −4.0 |
Standard deviation | 273.2 | 45.1 | 20.5 | 17.1 |
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Hinojosa-Nogueira, D.; Ortiz-Viso, B.; Navajas-Porras, B.; Pérez-Burillo, S.; González-Vigil, V.; de la Cueva, S.P.; Rufián-Henares, J.Á. Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition. Nutrients 2023, 15, 276. https://doi.org/10.3390/nu15020276
Hinojosa-Nogueira D, Ortiz-Viso B, Navajas-Porras B, Pérez-Burillo S, González-Vigil V, de la Cueva SP, Rufián-Henares JÁ. Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition. Nutrients. 2023; 15(2):276. https://doi.org/10.3390/nu15020276
Chicago/Turabian StyleHinojosa-Nogueira, Daniel, Bartolomé Ortiz-Viso, Beatriz Navajas-Porras, Sergio Pérez-Burillo, Verónica González-Vigil, Silvia Pastoriza de la Cueva, and José Ángel Rufián-Henares. 2023. "Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition" Nutrients 15, no. 2: 276. https://doi.org/10.3390/nu15020276