Paper accepted: Towards Systems Models for Obesity Prevention: A Big Role for Big Data

Just before the summer holidays, we received the news that our opinion paper was accepted. With this paper, we contribute to the academic discussion on system models and the use of Big Data in obesity prevention. In the paper, we compare traditional epidemiologic vs. emerging big data approaches that are used in obesity research. We describe research questions, needs, and outcomes of three broad research domains: eating behavior, social food environments, and the built environment.

In this paper, the BigO project is used as an illustration. The BigO project used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learnings on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise.

We advocate that 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 accepted pdf manuscripts can be found here. This version is the version prior to copyediting or typesetting so it can still change.