ESPE2018 Poster Presentations Fat, Metabolism and Obesity P3 (45 abstracts)
BigO: Big Data Against Childhood Obesity
Christos Diou a , Ioannis Ioakeimidis b , Evangelia Charmandari c, , Penio Kassari c, , Irini Lekka e , Monica Mars f , Cecilia Bergh g , Tahar Kechadi h , Gerardine Doyle h , Grace O’Malley i , Rachel Heimeier j , Anna Karin Lindroos k , Sofoklis Sotiriou l , Evangelia Koukoula m , Sergio Guillén n , George Lymperopoulos o , Nicos Maglaveras e & Anastasios Delopoulos a
aDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece; bDepartment of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden; cDivision of Endocrinology, Metabolism and Diabetes, First Department of Paediatrics, National and Kapodistrian University of Athens Medical School, “Aghia Sophia” Children’s Hospital, Athens, Greece; dDivision of Endocrinology and Metabolism, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece; eMedical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; fDepartment of Agrotechnology and Food Sciences, Wageningen University, Wageningen, Netherlands; gMando Group AB, Stockholm, Sweden; hUniversity College Dublin, Dublin, Ireland; iTemple Street Children’s University Hospital, Dublin, Ireland; jInternationalle Engelska Skolan I Sverige AB, Stockholm, Sweden; kNational Food Agency, Uppsala, Sweden; lEllinogermaniki Agogi, Athens, Greece; mEkpedeftiria N. Mpakogianni, Larissa, Greece; nMySphera – TSB RTLS, Valencia, Spain; oCosmote Mobile Telecommunications SA, Athens, Greece
Background: Childhood obesity is a major global and European public health problem. The need for community-targeted actions has long been recognized, however it has been prevented by the lack of monitoring and evaluation framework, and the methodological inability to objectively quantify the local community characteristics in a reasonable timeframe. Recent technological achievements in mobile and wearable electronics and Big Data infrastructures allow the engagement of European citizens in the data collection process.
Objective: BigO (bigoprogram.eu) is an EU-funded project that collects objective evidence on the causes of obesity in local communities and enables public health authorities to design effective interventions to prevent or combat obesity. BigO aims to redefine the way those strategies are designed and deployed.
Method: A novel technological platform will be built relying on big data analytics and visualization. The BigO platform will use sensor technologies to record children’s daily eating and physical activity behavior and correlate it with environment data from on-line sources. Widely spread sensors in smartphones or activity bracelets will be used, in combination with Mandometer®, a clinically validated device monitoring the rate of food intake. Data as a whole will include what and how children eat, how they move and sleep, along with characteristics of their urban, socioeconomic, commercial and school environment. Data driven analytics will then be employed to extract relationships between environment, personal behavior, obesity risk factors and obesity prevalence, and determine which particular local conditions are associated with the development of obesity in children of a specific region. BigO will engage children and adolescents aged 9-18 years from Greece, Sweden and Ireland, to share their data as citizen scientists. Moreover, age-matched obese children will be recruited from obesity clinics in these countries. The BigO consortium brings together 13 European partners from Greece, Sweden, Ireland, Spain and the Netherlands. The project started in December 2016 and aims to reach out to more than 25,000 children in its 4-year duration targeting the active participation of 9,000 volunteers.
Results: Comprehensive models of the obesity prevalence dependence matrix will be created, allowing, for the first time the data-driven effectiveness predictions about specific policies on a community and the real-time monitoring of the population response, supported by powerful real-time data visualizations.
Conclusion: BigO will provide an innovative suite, allowing the Public Health Authorities to evaluate their communities based on their obesity prevalence risk and to take local action, based on objective evidence.
Article tools
My recent searches
My recently viewed abstracts
Authors
ESPE Abstracts
© Bioscientifica 2024 |
Privacy policy |
Cookie settings
BiosciAbstracts
Bioscientifica Abstracts is the gateway to a series of products that provide a permanent, citable record of abstracts for biomedical and life science conferences.
Find out more