Timmins, K, Green, MA, Radley, D et al. (2 more authors) (2018) How has big data contributed to obesity research? A review of the literature. International Journal of Obesity, 42 (12). pp. 1951-1962. ISSN 0307-0565
Abstract
There has been growing interest in the potential of ‘big data’ to enhance our understanding in medicine and public health. Although there is no agreed definition of big data, accepted critical components include greater volume, complexity, coverage and speed of availability. Much of these data are ‘found’ (as opposed to ‘made’), in that they have been collected for non-research purposes but could include valuable information for research. The aim of this paper is to review the contribution of ‘found’ data to obesity research to date, and describe the benefits and challenges encountered.
A narrative review was conducted to identify and collate peer-reviewed research studies. Database searches conducted up to September 2017 found original studies using a variety of data types and sources. These included: retail sales, transport, geospatial, commercial weight management data, social media, and smartphones and wearable technologies.
The narrative review highlights the variety of data uses in the literature: describing the built environment, exploring social networks, estimating nutrient purchases or assessing the impact of interventions. The examples demonstrate four significant ways in which ‘found’ data can complement conventional ‘made’ data: firstly, in moving beyond constraints in scope (coverage, size, and temporality); secondly, in providing objective, quantitative measures; thirdly, in reaching hard to-access population groups; and lastly in the potential for evaluating real-world interventions.
Alongside these opportunities, ‘found’ data come with distinct challenges, such as: ethical and legal questions around access and ownership; commercial sensitivities; costs; lack of control over data acquisition; validity; representativeness; finding appropriate comparators; and complexities of data processing, management and linkage.
Despite widespread recognition of the opportunities, the impact of ‘found’ data on academic obesity research has been limited. The merit of such data lies not in their novelty, but in the benefits they could add over and above, or in combination with, conventionally collected data.
Despite widespread recognition of the opportunities, the impact of ‘found’ data on academic obesity research has been limited. The merit of such data lies not in their novelty, but in the benefits they could add over and above, or in combination with, conventionally collected data
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2018. 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. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > Institute of Molecular Medicine (LIMM) (Leeds) > Section of Translational Medicine (Leeds) |
Funding Information: | Funder Grant number ESRC ES/N00941X/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Jul 2018 14:23 |
Last Modified: | 25 Jun 2023 21:26 |
Status: | Published |
Publisher: | Nature Publishing Group |
Identification Number: | 10.1038/s41366-018-0153-7 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:133316 |