Wilkins, E, Aravani, A orcid.org/0000-0002-3976-7888, Downing, A orcid.org/0000-0002-0335-7801 et al. (6 more authors) (2020) Evidence from big data in obesity research: International case studies. International Journal of Obesity, 44 (5). pp. 1028-1040. ISSN 0307-0565
Abstract
Background/objective
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.
Methods and results
Three case studies are presented. The first investigated the influence of the built environment on physical activity. It used spatial data on green spaces and exercise facilities alongside individual-level data on physical activity and swipe card entry to leisure centres, collected as part of a local authority exercise class initiative. The second used a variety of linked electronic health datasets to investigate associations between obesity surgery and the risk of developing cancer. The third used data on tax parcel values alongside data from the Seattle Obesity Study to investigate sociodemographic determinants of obesity in Seattle.
Conclusions
The case studies demonstrated how big data could be used to augment traditional data to capture a broader range of variables in the obesity system. They also showed that big data can present improvements over traditional data in relation to size, coverage, temporality, and objectivity of measures. However, the case studies also encountered challenges or limitations; particularly in relation to hidden/unforeseen biases and lack of contextual information. Overall, despite challenges, big data presents a relatively untapped resource that shows promise in helping to understand drivers of obesity.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s), under exclusive licence to Springer Nature Limited 2020. This is an author produced version of a paper published in International Journal of Obesity. Uploaded in accordance with the publisher's self-archiving policy. |
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) |
Funding Information: | Funder Grant number ESRC (Economic and Social Research Council) ES/N00941X/1 ESRC (Economic and Social Research Council) ES/S007164/1 Cancer Research UK C23434/A23706 ESRC (Economic and Social Research Council) ES/L011891/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Dec 2019 10:23 |
Last Modified: | 18 Dec 2020 13:28 |
Status: | Published |
Publisher: | Springer Nature |
Identification Number: | 10.1038/s41366-020-0532-8 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154851 |