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Wirtz Baker JM, Pou SA, Niclis C, Haluszka E, Aballay LR. Non-traditional data sources in obesity research: a systematic review of their use in the study of obesogenic environments. Int J Obes (Lond) 2023:10.1038/s41366-023-01331-3. [PMID: 37393408 DOI: 10.1038/s41366-023-01331-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/01/2023] [Accepted: 06/21/2023] [Indexed: 07/03/2023]
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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Affiliation(s)
- Julia Mariel Wirtz Baker
- Health Sciences Research Institute (INICSA), National Council of Scientific and Technical Research (CONICET), Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
- Human Nutrition Research Centre (CenINH), School of Nutrition, Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
| | - Sonia Alejandra Pou
- Health Sciences Research Institute (INICSA), National Council of Scientific and Technical Research (CONICET), Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
- Human Nutrition Research Centre (CenINH), School of Nutrition, Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
| | - Camila Niclis
- Health Sciences Research Institute (INICSA), National Council of Scientific and Technical Research (CONICET), Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
- Human Nutrition Research Centre (CenINH), School of Nutrition, Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
| | - Eugenia Haluszka
- Health Sciences Research Institute (INICSA), National Council of Scientific and Technical Research (CONICET), Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
- Human Nutrition Research Centre (CenINH), School of Nutrition, Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
| | - Laura Rosana Aballay
- Human Nutrition Research Centre (CenINH), School of Nutrition, Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina.
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Lam TM, Wang Z, Vaartjes I, Karssenberg D, Ettema D, Helbich M, Timmermans EJ, Frank LD, den Braver NR, Wagtendonk AJ, Beulens JWJ, Lakerveld J. Development of an objectively measured walkability index for the Netherlands. Int J Behav Nutr Phys Act 2022; 19:50. [PMID: 35501815 PMCID: PMC9063284 DOI: 10.1186/s12966-022-01270-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 03/10/2022] [Indexed: 12/03/2022] Open
Abstract
Background Walkability indices have been developed and linked to behavioural and health outcomes elsewhere in the world, but not comprehensively for Europe. We aimed to 1) develop a theory-based and evidence-informed Dutch walkability index, 2) examine its cross-sectional associations with total and purpose-specific walking behaviours of adults across socioeconomic (SES) and urbanisation strata, 3) explore which walkability components drive these associations. Methods Components of the index included: population density, retail and service density, land use mix, street connectivity, green space, sidewalk density and public transport density. Each of the seven components was calculated for three Euclidean buffers: 150 m, 500 m and 1000 m around every 6-digit postal code location and for every administrative neighbourhood in GIS. Componential z-scores were averaged, and final indices normalized between 0 and 100. Data on self-reported demographic characteristics and walking behaviours of 16,055 adult respondents (aged 18–65) were extracted from the Dutch National Travel Survey 2017. Using Tobit regression modelling adjusted for individual- and household-level confounders, we assessed the associations between walkability and minutes walking in total, for non-discretionary and discretionary purposes. By assessing the attenuation in associations between partial indices and walking outcomes, we identified which of the seven components drive these associations. We also tested for effect modification by urbanization degree, SES, age and sex. Results In fully adjusted models, a 10% increase in walkability was associated with a maximum increase of 8.5 min of total walking per day (95%CI: 7.1–9.9). This association was consistent across buffer sizes and purposes of walking. Public transport density was driving the index’s association with walking outcomes. Stratified results showed that associations with minutes of non-discretionary walking were stronger in rural compared to very urban areas, in neighbourhoods with low SES compared to high SES, and in middle-aged (36–49 years) compared to young (18–35 years old) and older adults (50–65 years old). Conclusions The walkability index was cross-sectionally associated with Dutch adult’s walking behaviours, indicating its validity for further use in research. Supplementary Information The online version contains supplementary material available at 10.1186/s12966-022-01270-8.
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Affiliation(s)
- Thao Minh Lam
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health research institute, Boelelaan 1089a, 1081HV, Amsterdam, Netherlands. .,Upstream Team, Vrije Universiteit, Amsterdam, Netherlands.
| | - Zhiyong Wang
- Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, Princetonlaan 8a, 3584, Utrecht, CB, Netherlands
| | - Ilonca Vaartjes
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.,Global Geo Health Data Center, University Medical Center Utrecht & Utrecht University, Utrecht, Netherlands
| | - Derek Karssenberg
- Global Geo Health Data Center, University Medical Center Utrecht & Utrecht University, Utrecht, Netherlands.,Department of Physical Geography, Utrecht University, Princetonlaan 8a, 3584, Utrecht, CB, Netherlands
| | - Dick Ettema
- Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, Princetonlaan 8a, 3584, Utrecht, CB, Netherlands
| | - Marco Helbich
- Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, Princetonlaan 8a, 3584, Utrecht, CB, Netherlands
| | - Erik J Timmermans
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Lawrence D Frank
- Department of Urban Studies and Planning, UC San Diego, La Jolla, San Diego, USA.,Urban Design 4 Health, Inc, Rochester, NY, USA
| | - Nicolette R den Braver
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health research institute, Boelelaan 1089a, 1081HV, Amsterdam, Netherlands.,Upstream Team, Vrije Universiteit, Amsterdam, Netherlands
| | - Alfred J Wagtendonk
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health research institute, Boelelaan 1089a, 1081HV, Amsterdam, Netherlands.,Upstream Team, Vrije Universiteit, Amsterdam, Netherlands
| | - Joline W J Beulens
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health research institute, Boelelaan 1089a, 1081HV, Amsterdam, Netherlands.,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.,Upstream Team, Vrije Universiteit, Amsterdam, Netherlands
| | - Jeroen Lakerveld
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health research institute, Boelelaan 1089a, 1081HV, Amsterdam, Netherlands.,Upstream Team, Vrije Universiteit, Amsterdam, Netherlands
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