Toward Systems Models for Obesity Prevention: A Big Role for Big Data.

Autor:	Tufford AR; Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands., Diou C; Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece., Lucassen DA; Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands., Ioakimidis I; Department of Biosciences and Nutrition, Karolinska Institute, Stockholm, Sweden., O'Malley G; W82GO Child and Adolescent Weight Management Service, Children's Health Ireland at Temple Street, Dublin, Ireland.; Division of Population Health Sciences, School of Physiotherapy, Royal College of Surgeons in Ireland University for Medicine and Health Sciences, Dublin, Ireland., Alagialoglou L; Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece., Charmandari E; Division of Endocrinology, Metabolism, and Diabetes, First Department of Pediatrics, National and Kapodistrian University of Athens Medical School, 'Aghia Sophia' Children's Hospital, Athens, Greece.; Division of Endocrinology and Metabolism, Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece., Doyle G; College of Business, University College Dublin, Dublin, Ireland.; Geary Institute for Public Policy, University College Dublin, Dublin, Ireland., Filis K; COSMOTE Mobile Telecommunications, Athens, Greece., Kassari P; Division of Endocrinology, Metabolism, and Diabetes, First Department of Pediatrics, National and Kapodistrian University of Athens Medical School, 'Aghia Sophia' Children's Hospital, Athens, Greece.; Division of Endocrinology and Metabolism, Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece., Kechadi T; CeADAR: Ireland's Centre for Applied AI, University College Dublin, Dublin 4, Ireland., Kilintzis V; Lab of Computing, Medical Informatics, and Biomedical Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece., Kok E; Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands., Lekka I; Lab of Computing, Medical Informatics, and Biomedical Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece., Maglaveras N; Lab of Computing, Medical Informatics, and Biomedical Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece., Pagkalos I; Department of Nutritional Sciences and Dietetics, School of Health Sciences, International Hellenic University, Thessaloniki, Greece., Papapanagiotou V; Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece., Sarafis I; Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece., Shahid A; CeADAR: Ireland's Centre for Applied AI, University College Dublin, Dublin 4, Ireland., van 't Veer P; Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands., Delopoulos A; Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece., Mars M; Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands.
Jazyk:	angličtina
Zdroj:	Current developments in nutrition [Curr Dev Nutr] 2022 Jul 30; Vol. 6 (9), pp. nzac123. Date of Electronic Publication: 2022 Jul 30 (Print Publication: 2022).
DOI:	10.1093/cdn/nzac123
Abstrakt:	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. (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society for Nutrition.)
Databáze:	MEDLINE
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