AI4FoodDB: a database for personalized e-Health nutrition and lifestyle through wearable devices and artificial intelligence

被引：1

作者：

Romero-Tapiador, Sergio ^{[1
]}

Lacruz-Pleguezuelos, Blanca ^{[2
]}

Tolosana, Ruben ^{[1
]}

Freixer, Gala ^{[3
]}

Daza, Roberto ^{[1
]}

Fernandez-Diaz, Cristina M. ^{[3
]}

Aguilar-Aguilar, Elena ^{[3
,4
]}

Fernandez-Cabezas, Jorge ^{[3
]}

Cruz-Gil, Silvia ^{[5
]}

Molina, Susana ^{[3
]}

Crespo, Maria Carmen ^{[3
]}

Laguna, Teresa ^{[2
]}

Marcos-Zambrano, Laura Judith ^{[2
]}

Vera-Rodriguez, Ruben ^{[1
]}

Fierrez, Julian ^{[1
]}

de Molina, Ana Ramirez ^{[3
]}

Ortega-Garcia, Javier ^{[1
]}

Espinosa-Salinas, Isabel ^{[3
]}

Morales, Aythami ^{[1
]}

Pau, Enrique Carrillo de Santa ^{[2
]}

机构：

[1] Univ Autonoma Madrid, Biometr & Data Pattern Analyt Lab, Calle Francisco Tomas & Valiente,11,Campus Cantobl, Madrid 28049, Spain

[2] CEI UAM CSIC, IMDEA Food Inst, Precis Nutr & Canc Res Program, Computat Biol Grp, Carretera Cantoblanco 8, Madrid 28049, Spain

[3] CEI UAM CSIC, IMDEA Food Inst, GENYAL Platform Nutr & Hlth, Carretera Cantoblanco 8, Madrid 28049, Spain

[4] Univ Europea Madrid, Fac Biomed & Hlth Sci, Dept Nursing & Nutr, Calle Tajo S-N, Villaviciosa De Odon 28670, Madrid, Spain

[5] CEI UAM CSIC, IMDEA Food Inst, Mol Oncol & Nutr Genom Canc Grp, Carretera Cantoblanco 8, Madrid 28049, Spain

来源：

DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION | 2023年 / 2023卷

关键词：

MEDICINE;

D O I：

10.1093/database/baad049

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The increasing prevalence of diet-related diseases calls for an improvement in nutritional advice. Personalized nutrition aims to solve this problem by adapting dietary and lifestyle guidelines to the unique circumstances of each individual. With the latest advances in technology and data science, researchers can now automatically collect and analyze large amounts of data from a variety of sources, including wearable and smart devices. By combining these diverse data, more comprehensive insights of the human body and its diseases can be achieved. However, there are still major challenges to overcome, including the need for more robust data and standardization of methodologies for better subject monitoring and assessment. Here, we present the AI4Food database (AI4FoodDB), which gathers data from a nutritional weight loss intervention monitoring 100 overweight and obese participants during 1 month. Data acquisition involved manual traditional approaches, novel digital methods and the collection of biological samples, obtaining: (i) biological samples at the beginning and the end of the intervention, (ii) anthropometric measurements every 2 weeks, (iii) lifestyle and nutritional questionnaires at two different time points and (iv) continuous digital measurements for 2 weeks. To the best of our knowledge, AI4FoodDB is the first public database that centralizes food images, wearable sensors, validated questionnaires and biological samples from the same intervention. AI4FoodDB thus has immense potential for fostering the advancement of automatic and novel artificial intelligence techniques in the field of personalized care. Moreover, the collected information will yield valuable insights into the relationships between different variables and health outcomes, allowing researchers to generate and test new hypotheses, identify novel biomarkers and digital endpoints, and explore how different lifestyle, biological and digital factors impact health. The aim of this article is to describe the datasets included in AI4FoodDB and to outline the potential that they hold for precision health research.

引用

页数：15

共 1 条

[1] Living lab AI4U-artificial intelligence for personalized digital mental health promotion and prevention in youth
Rauschenberg, C.
Goetzl, C.
Schick, A.
Koppe, G.
Durstewitz, D.
Krumm, S.
Reininghaus, U.
[J]. EUROPEAN JOURNAL OF PUBLIC HEALTH, 2021, 31

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