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Epidemiology and Population Health

Non-traditional data sources in obesity research: a systematic review of their use in the study of obesogenic environments

Abstract

Background

The complex nature of obesity increasingly requires a comprehensive approach that includes the role of environmental factors. For understanding contextual determinants, the resources provided by technological advances could become a key factor in obesogenic environment research. This study aims to identify different sources of non-traditional data and their applications, considering the domains of obesogenic environments: physical, sociocultural, political and economic.

Methods

We conducted a systematic search in PubMed, Scopus and LILACS databases by two independent groups of reviewers, from September to December 2021. We included those studies oriented to adult obesity research using non-traditional data sources, published in the last 5 years in English, Spanish or Portuguese. The overall reporting followed the PRISMA guidelines.

Results

The initial search yielded 1583 articles, 94 articles were kept for full-text screening, and 53 studies met the eligibility criteria and were included. We extracted information about countries of origin, study design, observation units, obesity-related outcomes, environment variables, and non-traditional data sources used. Our results revealed that most of the studies originated from high-income countries (86.54%) and used geospatial data within a GIS (76.67%), social networks (16.67%), and digital devices (11.66%) as data sources. Geospatial data were the most utilised data source and mainly contributed to the study of the physical domains of obesogenic environments, followed by social networks providing data to the analysis of the sociocultural domain. A gap in the literature exploring the political domain of environments was also evident.

Conclusion

The disparities between countries are noticeable. Geospatial and social network data sources contributed to studying the physical and sociocultural environments, which could be a valuable complement to those traditionally used in obesity research. We propose the use of information available on the Internet, addressed by artificial intelligence-based tools, to increase the knowledge on political and economic dimensions of the obesogenic environment.

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Fig. 1: PRISMA flowchart of the literature search.
Fig. 2: Parallel categories diagram between the country of origin, the non-traditional data sources, and the ANGELO domains of the obesogenic environment.

Data availability

The datasets generated during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

This research was partially supported by the National Science and Technology Agency (FONCyT) grants PICT-2020-A-03283 and PICT-2019-2019-04594.

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JMWB was responsible for writing the main draft of the report, conducting the search, screening potentially eligible studies, extracting and analysing the data, interpreting the results, creating the results tables, and discussing studies. CN and EH were responsible for extracting articles, screening potentially eligible studies, and providing feedback on the report. SAP and LRA were responsible for the design of the review protocol and the arbitrating studies selected by the authors, contributing to the writing of the report and the table of results, analysing and interpreting the results, and discussing with other studies. All the authors critically read and approved the final manuscript.

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Correspondence to Laura Rosana Aballay.

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Wirtz Baker, J.M., Pou, S.A., Niclis, C. et al. Non-traditional data sources in obesity research: a systematic review of their use in the study of obesogenic environments. Int J Obes 47, 686–696 (2023). https://doi.org/10.1038/s41366-023-01331-3

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