Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map

Michelle A. Morris* (Corresponding Author), Emma Wilkins, Kate A. Timmins, Maria Bryant, Mark Birkin, Claire Griffiths

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


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.
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.
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.
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.
Original languageEnglish
Pages (from-to)1963–1976
Number of pages14
JournalInternational Journal of Obesity
Early online date21 Sept 2018
Publication statusPublished - Dec 2018

Bibliographical note

The ESRC Strategic Network for Obesity was funded via Economic and Social Research Council grant number ES/N00941X/1. The authors would like to thank all of the network investigators ( and members ( for their participation in network meetings and discussion which contributed to the development of this paper.

Data Availability Statement

Electronic supplementary material The online version of this article
( contains supplementary
material, which is available to authorised users


  • Risk factors
  • Epidemiology


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