Toward Systems Models for Obesity Prevention: A Big Role for Big Data.
behavior
big data
built environment
childhood obesity
monitoring systems
physical activity
systems models
Journal
Current developments in nutrition
ISSN: 2475-2991
Titre abrégé: Curr Dev Nutr
Pays: United States
ID NLM: 101717957
Informations de publication
Date de publication:
Sep 2022
Sep 2022
Historique:
received:
08
11
2021
revised:
24
03
2022
accepted:
28
07
2022
entrez:
26
9
2022
pubmed:
27
9
2022
medline:
27
9
2022
Statut:
epublish
Résumé
The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project "BigO: Big data against childhood obesity" used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions.
Identifiants
pubmed: 36157849
doi: 10.1093/cdn/nzac123
pii: nzac123
pmc: PMC9492244
doi:
Types de publication
Journal Article
Langues
eng
Pagination
nzac123Informations de copyright
© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society for Nutrition.
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