%0 Generic
%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.
%K Gesundheitsverhalten
%K Datengewinnung
%K körperliche Bewegung
%K Fettsucht
%K Datenqualität
%K soziale Faktoren
%K Ursache
%K demographische Faktoren
%K Big Data
%D 2020
%X Postprint
%X begutachtet (peer reviewed)
%X In: International Journal of Obesity ; 44 (2020) ; 1028-1040
%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.
%U http://slubdd.de/katalog?TN_libero_mab2
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