Evidence from big data in obesity research: international case studies

Wilkins, Emma; Aravani, Ariadni; Downing, Amy; Drewnowski, Adam; Griffiths, Claire; Zwolinsky, Stephen; Birkin, Mark; Alvanides, Seraphim; Morris, Michelle A.

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%T Evidence from big data in obesity research: international case studies
%A Wilkins, Emma
%A Aravani, Ariadni
%A Downing, Amy
%A Drewnowski, Adam
%A Griffiths, Claire
%A Zwolinsky, Stephen
%A Birkin, Mark
%A Alvanides, Seraphim
%A Morris, Michelle A.
%J International Journal of Obesity
%P 1028-1040
%V 44
%D 2020
%K Big Data
%@ 1476-5497
%~ GESIS
%> https://nbn-resolving.org/urn:nbn:de:0168-ssoar-70720-3
%X Obesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and time-consuming to collect through traditional means. Use of 'big data' presents a potential solution to this challenge. Big data is defined by Delphi consensus as: always digital, has a large sample size, and a large volume or variety or velocity of variables that require additional computing power (Vogel et al. Int J Obes. 2019). 'Additional computing power' introduces the concept of big data analytics. The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered.
%C USA
%G en
%9 journal article
%W GESIS - http://www.gesis.org
%~ SSOAR - http://www.ssoar.info