Emma Wilkins1, Ariadni Aravani1, Amy Downing1, Adam Drewnowski2, Claire Griffiths3, Stephen Zwolinsky3, Mark Birkin4, Seraphim Alvanides5,6, Michelle A Morris7. 1. Leeds Institute for Data Analytics and School of Medicine, University of Leeds, Leeds, UK. 2. Center for Public Health Nutrition, University of Washington, Seattle, WA, USA. 3. School of Sport, Leeds Beckett University, Leeds, UK. 4. Leeds Institute for Data Analytics and School of Geography, University of Leeds, Leeds, UK. 5. Engineering and Environment, Northumbria University, Newcastle, UK. 6. GESIS-Leibniz Institute for the Social Sciences, Cologne, Germany. 7. Leeds Institute for Data Analytics and School of Medicine, University of Leeds, Leeds, UK. m.morris@leeds.ac.uk.
Abstract
BACKGROUND/ OBJECTIVE: Obesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and time-consuming to collect through traditional means. Use of 'big data' presents a potential solution to this challenge. Big data is defined by Delphi consensus as: always digital, has a large sample size, and a large volume or variety or velocity of variables that require additional computing power (Vogel et al. Int J Obes. 2019). 'Additional computing power' introduces the concept of big data analytics. The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered. METHODS AND RESULTS: Three case studies are presented. The first investigated the influence of the built environment on physical activity. It used spatial data on green spaces and exercise facilities alongside individual-level data on physical activity and swipe card entry to leisure centres, collected as part of a local authority exercise class initiative. The second used a variety of linked electronic health datasets to investigate associations between obesity surgery and the risk of developing cancer. The third used data on tax parcel values alongside data from the Seattle Obesity Study to investigate sociodemographic determinants of obesity in Seattle. CONCLUSIONS: The case studies demonstrated how big data could be used to augment traditional data to capture a broader range of variables in the obesity system. They also showed that big data can present improvements over traditional data in relation to size, coverage, temporality, and objectivity of measures. However, the case studies also encountered challenges or limitations; particularly in relation to hidden/unforeseen biases and lack of contextual information. Overall, despite challenges, big data presents a relatively untapped resource that shows promise in helping to understand drivers of obesity.
BACKGROUND/ OBJECTIVE:Obesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and time-consuming to collect through traditional means. Use of 'big data' presents a potential solution to this challenge. Big data is defined by Delphi consensus as: always digital, has a large sample size, and a large volume or variety or velocity of variables that require additional computing power (Vogel et al. Int J Obes. 2019). 'Additional computing power' introduces the concept of big data analytics. The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered. METHODS AND RESULTS: Three case studies are presented. The first investigated the influence of the built environment on physical activity. It used spatial data on green spaces and exercise facilities alongside individual-level data on physical activity and swipe card entry to leisure centres, collected as part of a local authority exercise class initiative. The second used a variety of linked electronic health datasets to investigate associations between obesity surgery and the risk of developing cancer. The third used data on tax parcel values alongside data from the Seattle Obesity Study to investigate sociodemographic determinants of obesity in Seattle. CONCLUSIONS: The case studies demonstrated how big data could be used to augment traditional data to capture a broader range of variables in the obesity system. They also showed that big data can present improvements over traditional data in relation to size, coverage, temporality, and objectivity of measures. However, the case studies also encountered challenges or limitations; particularly in relation to hidden/unforeseen biases and lack of contextual information. Overall, despite challenges, big data presents a relatively untapped resource that shows promise in helping to understand drivers of obesity.
Authors: Harry Rutter; Maira Bes-Rastrollo; Stefaan de Henauw; Marjaana Lahti-Koski; Susanna Lehtinen-Jacks; Dana Mullerova; Finn Rasmussen; Aila Rissanen; Tommy L S Visscher; Lauren Lissner Journal: Obes Facts Date: 2017-03-01 Impact factor: 3.942
Authors: Lynn D Silver; Shu Wen Ng; Suzanne Ryan-Ibarra; Lindsey Smith Taillie; Marta Induni; Donna R Miles; Jennifer M Poti; Barry M Popkin Journal: PLoS Med Date: 2017-04-18 Impact factor: 11.069