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.

[journal article]

dc.contributor.author	Wilkins, Emma	de
dc.contributor.author	Aravani, Ariadni	de
dc.contributor.author	Downing, Amy	de
dc.contributor.author	Drewnowski, Adam	de
dc.contributor.author	Griffiths, Claire	de
dc.contributor.author	Zwolinsky, Stephen	de
dc.contributor.author	Birkin, Mark	de
dc.contributor.author	Alvanides, Seraphim	de
dc.contributor.author	Morris, Michelle A.	de
dc.date.accessioned	2020-11-27T08:32:47Z
dc.date.available	2020-12-14T08:32:47Z
dc.date.issued	2020	de
dc.identifier.issn	1476-5497	de
dc.identifier.uri	https://www.ssoar.info/ssoar/handle/document/70720
dc.description.abstract	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.	de
dc.language	en	de
dc.subject.ddc	Sozialwissenschaften, Soziologie	de
dc.subject.ddc	Social sciences, sociology, anthropology	en
dc.subject.ddc	Medicine and health	en
dc.subject.ddc	Medizin und Gesundheit	de
dc.subject.other	Big Data	de
dc.title	Evidence from big data in obesity research: international case studies	de
dc.description.review	begutachtet (peer reviewed)	de
dc.description.review	peer reviewed	en
dc.source.journal	International Journal of Obesity
dc.source.volume	44	de
dc.publisher.country	USA
dc.subject.classoz	Medizin, Sozialmedizin	de
dc.subject.classoz	Erhebungstechniken und Analysetechniken der Sozialwissenschaften	de
dc.subject.classoz	Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods	en
dc.subject.classoz	Medicine, Social Medicine	en
dc.subject.thesoz	Gesundheitsverhalten	de
dc.subject.thesoz	Datengewinnung	de
dc.subject.thesoz	adipositas	en
dc.subject.thesoz	demographic factors	en
dc.subject.thesoz	körperliche Bewegung	de
dc.subject.thesoz	Fettsucht	de
dc.subject.thesoz	Datenqualität	de
dc.subject.thesoz	cause	en
dc.subject.thesoz	data quality	en
dc.subject.thesoz	soziale Faktoren	de
dc.subject.thesoz	Ursache	de
dc.subject.thesoz	demographische Faktoren	de
dc.subject.thesoz	data capture	en
dc.subject.thesoz	health behavior	en
dc.subject.thesoz	social factors	en
dc.subject.thesoz	physical exercise	en
dc.identifier.urn	urn:nbn:de:0168-ssoar-70720-3
dc.rights.licence	Deposit Licence - Keine Weiterverbreitung, keine Bearbeitung	de
dc.rights.licence	Deposit Licence - No Redistribution, No Modifications	en
ssoar.contributor.institution	GESIS	de
internal.status	formal und inhaltlich fertig erschlossen	de
internal.identifier.thesoz	10045563
internal.identifier.thesoz	10040547
internal.identifier.thesoz	10055811
internal.identifier.thesoz	10043547
internal.identifier.thesoz	10045241
internal.identifier.thesoz	10060687
internal.identifier.thesoz	10040663
internal.identifier.thesoz	10086741
dc.type.stock	article	de
dc.type.document	journal article	en
dc.type.document	Zeitschriftenartikel	de
dc.source.pageinfo	1028-1040	de
internal.identifier.classoz	50100
internal.identifier.classoz	10105
internal.identifier.journal	1880
internal.identifier.document	32
internal.identifier.ddc	610
internal.identifier.ddc	300
dc.identifier.doi	https://doi.org/10.1038/s41366-020-0532-8	de
dc.description.pubstatus	Postprint	en
dc.description.pubstatus	Postprint	de
internal.identifier.licence	3
internal.identifier.pubstatus	2
internal.identifier.review	1
ssoar.wgl.collection	true	de
internal.pdf.wellformed	true
internal.pdf.encrypted	false

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Erhebungstechniken und Analysetechniken der Sozialwissenschaften
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Medizin, Sozialmedizin
Medicine, Social Medicine

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