BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment

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Title: BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment
Authors: Diou, ChristosSarafis, IoannisPapapanagiotou, VasileiosNguyen, Thien-An NgocKechadi, TaharO'Donnell, ShaneDoyle, Gerardineet al.
Permanent link: http://hdl.handle.net/10197/12044
Date: 24-Jul-2020
Online since: 2021-03-12T12:28:44Z
Abstract: Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram.eu), a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants.
Funding Details: European Commission
Science Foundation Ireland
Funding Details: Insight Research Centre
Type of material: Conference Publication
Copyright (published version): 2020 IEEE
Keywords: Machine Learning & StatisticsObesityPediatricsSmartphonesDatamining
DOI: 10.1109/EMBC44109.2020.9175361
Other versions: https://embc.embs.org/2020/
Language: en
Status of Item: Peer reviewed
Is part of: 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2020
Conference Details: The 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), EMBS Virtual Academy, 20-24 July 2020
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Computer Science Research Collection
Geary Institute Research Collection
Insight Research Collection

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