Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Technical Report
  • Published:

Epidemiology and Population Health

Creating a long-term future for big data in obesity research

International Journal of Obesity volume 43, pages 2587–2592 (2019)Cite this article

Subjects

Abstract

Big data are part of the future in obesity research. The ESRC funded Strategic Network for Obesity has together generated a series of papers, published in the International Journal for Obesity illustrating various aspects of their utility, in particular relating to the large social and environmental drivers of obesity. This article is the final part of the series and reflects upon progress to date and identifies four areas that require attention to promote the continued role of big data in research. We additionally include a ‘getting started with big data’ checklist to encourage more obesity researchers to engage with alternative data resources.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

References

  1. Morris MA, Birkin M. The ESRC Strategic Network for Obesity: tackling obesity with big data. Int J Obes (Lond). 2018;42:1948–50.

    Article  Google Scholar 

  2. Timmins KA, Green MA, Radley D, Morris MA, Pearce J. How has big data contributed to obesity research? A review of the literature. Int J Obes (Lond). 2018;42:1951–62.

    Article  Google Scholar 

  3. Morris MA, Wilkins E, Timmins KA, Bryant M, Birkin M, Griffiths C. Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map. Int J Obes (Lond). 2018;42:1963–76.

    Article  Google Scholar 

  4. Wilkins E, Alvanides S, Aravani A, Downing A, Drewnowski, Griffiths C, et al. Evidence from big data in obesity research: International case studies. Int J Obes. 2019. Forthcoming.

  5. Vogel C, Zwolinsky S, Griffiths C, Hobbs M, Henderson E, Wilkins E. A Delphi study to build consensus on the definition and use of big data in obesity research. Int J Obes (Lond). 2019. https://www.nature.com/articles/s41366-018-0313-9.

  6. Monsivais P, Francis O, Lovelace R, Chang M, Strachan E, Burgoine T. Data visualisation to support obesity policy: case studies of data tools for planning and transport policy in the UK. Int J Obes (Lond). 2018;42:1977–86.

    Article  Google Scholar 

  7. UKRI. Research and Innovation Infrastructure Roadmap. UKRI. 2019.

  8. UKRI. Consumer Data Research Support Service (CDRSS) [CDRC Phase 1]. UKRI. 2014.

  9. UKRI. Consumer Data Research Centre (CDRC) [CDRC Phase 2]. UKRI. 2019.

  10. Morris M, Wilkins E, Galazoula M, Clark S, Birkin M. Assessing diet in a university student population: A longitudinal novel data approach. TBC. 2019. Forthcoming.

  11. Alsharrah S, Coffee N, Alhuwail D, Daniel M. Spatial analysis of the built environment and food purchase behaviour patterns in the State of Kuwait. In: International Medical geography Symposium. Queenstown, New Zealand. 2019.

  12. ADRF. Network working group participants data sharing governance and management. ADRF. 2018. https://repository.upenn.edu/admindata_reports/2.

  13. CDRC. Consumer Data Research Centre. 2019. https://www.cdrc.ac.uk/.

  14. BLoG. Business and Local Government Data Research Centre. 2019. https://www.blgdataresearch.org/.

  15. UDBC. Urban Big Data Centre. 2019. https://www.ubdc.ac.uk/.

  16. HDR UK. Health Data Research UK. 2019. https://www.hdruk.ac.uk/.

  17. SAIL databank. SAIL Databank. 2019. https://saildatabank.com/.

  18. StatsNZ. Integrated data infrastructure. Government NZ. 2018. https://www.stats.govt.nz/integrated-data/integrated-data-infrastructure#data-in-idi.

  19. Ada Lovelace Institute. Ada Lovelace Institure. In. 2019. https://www.adalovelaceinstitute.org/.

  20. Nuffield Council on Bioethics. Biological and health data. 2019. http://nuffieldbioethics.org/project/biological-health-data.

  21. International Journal of Epidemiology. Cohort Profiles. 2019. https://academic.oup.com/ije/pages/Profiles.

  22. Edwards KL, Clarke GP. The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds, UK: SimObesity. Social Sci Med. 2009;69:1127–34.

    Article  Google Scholar 

  23. Hermann T, Gleckner W, Wasfi R, Thierry B, Kestens Y, Ross N. A pan-Canadian measure of active living environments using open data. Health Reports. 2019;30:16–25.

    PubMed  Google Scholar 

  24. Mah S, Fry R, Magliano D, Shaw J, Owen N, Bentley R, et al. Favourable active living environments and walking: findings from the AusDiab/Aus-ALE linkage initiative. In: International Medical Geography Symposium. Queenstown, New Zealand. 2019.

  25. Kikuchi H, Nakaya T, Hanibuchi T, Fukushima N, Amagasa S, Oka K, et al. Objectively measured neighborhood walkability and change in physical activity in older Japanese adults: a five-year cohort study. Int J Environ Res Public Health. 2018;15:1814.

    Article  Google Scholar 

  26. Humanitarian Open Street Map Team. HOT is an international team dedicated to humanitarian action and community development through open mapping. Humanitarian Open Street Map Team. 2019.

  27. Missing Maps. Putting the World’s Vulnerable People on the Map. Missing Maps. 2019.

  28. Quebec InterUniversity Centre for Social Statistitcs. 2019. https://www.ciqss.org/en.

  29. Urban Data Centres. CBS Urban Data Centres: local policy insights. Urban Data Centres. 2017.

  30. Urban Data Centres. Identifying population movements using anonymised telephone data. Urban Data Centres. 2019.

  31. LIDA. LifeInfo Survey. LIDA. 2019.

  32. Wright J, Small N, Raynor P, Tuffnell D, Bhopal R, Cameron N, et al. Cohort Profile: the Born in Bradford multi-ethnic family cohort study. Int J Epidemiol. 2013;42:978–91.

    Article  Google Scholar 

  33. Fraser A, Macdonald-Wallis C, Tilling K, Boyd A, Golding J, Davey Smith G, et al. Cohort Profile: the Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort. Int J Epidemiol. 2013;42:97–110.

    Article  Google Scholar 

  34. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779.

    Article  Google Scholar 

  35. O’Reilly D, Rosato M, Catney G, Johnston F, Brolly M. Cohort description: the Northern Ireland Longitudinal Study (NILS). Int J Epidemiol. 2012;41:634–41.

    Article  Google Scholar 

  36. National Center for Health Statistics. Data linkage. National Center for Health Statistics. 2019.

  37. National Center for Health Statistics. National Health and Nutrition Examination survey: NCHS factsheet. National Center for Health Statistics. 2017.

  38. Rehm CD, Moudon AV, Hurvitz PM, Drewnowski A. Residential property values are associated with obesity among women in King County, WA, USA. Social Sci Med. 2012;75:491–5.

    Article  Google Scholar 

  39. Sabel C. BERTHA: the Danish Big Data Centre for environment and health. In: Lecture CB, (ed), 2019.

  40. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10:37–48.

    Article  CAS  Google Scholar 

  41. Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS ONE. 2017;12:e0174944.

    Article  Google Scholar 

  42. Bonabeau E. Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci USA. 2002;99:7280–7.

    Article  CAS  Google Scholar 

  43. Zhang J, Tong L, Lamberson PJ, Durazo-Arvizu RA, Luke A, Shoham DA. Leveraging social influence to address overweight and obesity using agent-based models: the role of adolescent social networks. Social Sci Med. 2015;125:203–13.

    Article  CAS  Google Scholar 

  44. Beheshti R, Jalalpour M, Glass TA. Comparing methods of targeting obesity interventions in populations: An agent-based simulation. SSM Popul Health. 2017;3:211–8.

    Article  Google Scholar 

  45. Global Obesity Prevention Center. Virtual population obesity prevention (VPOP) labs. Global Obesity Prevention Center. 2019. https://www.jhsph.edu/departments/international-health/the-globe/summer-2015/gopc-vpop.html.

  46. Economic and Social Research Council. Big data network. Economic and Social Research Council. 2019. https://esrc.ukri.org/research/our-research/big-data-network/.

Download references

Acknowledgements

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 (www.cdrc.ac.uk/research/obesity/investigators/) and members (www.cdrc.ac.uk/research/obesity/network-members/) for their participation in network meetings and discussion that contributed to the development of this paper. Thanks to the editorial team at the International Journal of Obesity for their support and encouragement in producing this series of papers.

Author information

Authors and Affiliations

  1. Leeds Institute for Data Analytics & School of Geography, University of Leeds, Leeds, LS2 9JT, United Kingdom

    Mark Birkin

  2. Leeds Institute for Data Analytics & School of Medicine, University of Leeds, Leeds, LS2 9JT, United Kingdom

    Emma Wilkins & Michelle A. Morris

Authors
  1. Mark Birkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emma Wilkins
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michelle A. Morris
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michelle A. Morris.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Birkin, M., Wilkins, E. & Morris, M.A. Creating a long-term future for big data in obesity research. Int J Obes 43, 2587–2592 (2019). https://doi.org/10.1038/s41366-019-0477-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-019-0477-y

Search

Advanced search

Quick links