Morris, MA orcid.org/0000-0002-9325-619X, Wilkins, E, Timmins, KA et al. (3 more authors) (2018) Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map. International Journal of Obesity, 42. pp. 1963-1976. ISSN 0307-0565
Abstract
Background: Obesity research at a population level is multifaceted and complex. This has been characterised in the UK by the Foresight obesity systems map, identifying over 100 variables, across seven domain areas which are thought to influence energy balance, and subsequent obesity. Availability of data to consider the whole obesity system is traditionally lacking. However, in an era of big data, new possibilities are emerging. Understanding what data are available can be the first challenge, followed by an inconsistency in data reporting to enable adequate use in the obesity context. In this study we map data sources against the Foresight obesity system map domains and nodes and develop a framework to report big data for obesity research. Opportunities and challenges associated with this new data approach to whole systems obesity research are discussed.
Methods: Expert opinion from the ESRC Strategic Network for Obesity was harnessed in order to develop a data source reporting framework for obesity research. The framework was then tested on a range of data sources. In order to assess availability of data sources relevant to obesity research, a data mapping exercise against the Foresight obesity systems map domains and nodes was carried out.
Results: A reporting framework was developed to recommend the reporting of key information in line with these headings: Background; Elements; Exemplars; Content; Ownership; Aggregation; Sharing; Temporality (BEE-COAST). The new BEE-COAST framework was successfully applied to eight exemplar data sources from the UK. 80% coverage of the Foresight obesity systems map is possible using a wide range of big data sources. The remaining 20% were primarily biological measurements often captured by more traditional laboratory based research.
Conclusions: Big data offer great potential across many domains of obesity research and need to be leveraged in conjunction with traditional data for societal benefit and health promotion.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > Institute of Molecular Medicine (LIMM) (Leeds) > Section of Translational Medicine (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Inst of Clinical Trials Research (LICTR) (Leeds) |
Funding Information: | Funder Grant number ESRC ES/N00941X/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Jul 2018 14:13 |
Last Modified: | 25 Jun 2023 21:26 |
Status: | Published |
Publisher: | Springer Nature |
Identification Number: | 10.1038/s41366-018-0184-0 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:133313 |