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.

中文翻译：

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来自肥胖研究大数据的证据：国际案例研究。

背景/目标 肥胖被认为是 100 多种不同因素的产物，作为一个复杂系统在多个层面相互作用。了解肥胖的驱动因素需要大量数据，而通过传统方式收集这些数据具有挑战性、成本高且耗时。“大数据”的使用为这一挑战提供了一个潜在的解决方案。Delphi 共识将大数据定义为：始终是数字的，具有大样本量，以及需要额外计算能力的大量变量或变化速度（Vogel et al. Int J Obes. 2019）。“附加计算能力”引入了大数据分析的概念。本文的目的是展示在英国经济和社会研究委员会 (ESRC) 肥胖战略网络举办的系列研讨会上介绍的国际研究案例研究。这些旨在深入了解如何在肥胖研究中使用大数据，以及遇到的具体好处、局限性和挑战。方法和结果 介绍了三个案例研究。第一个调查了建筑环境对身体活动的影响。它使用了绿色空间和运动设施的空间数据，以及关于体育活动和刷卡进入休闲中心的个人层面数据，这些数据是作为地方当局运动课程计划的一部分收集的。第二个使用各种链接的电子健康数据集来研究肥胖手术与患癌症风险之间的关联。第三个使用税包价值数据以及西雅图肥胖研究的数据来调查西雅图肥胖的社会人口决定因素。结论 案例研究展示了如何使用大数据来增强传统数据以捕获肥胖系统中更广泛的变量。他们还表明，大数据可以在规模、覆盖范围、时间性和措施的客观性方面比传统数据有所改进。然而，案例研究也遇到了挑战或局限性；特别是在隐藏/不可预见的偏见和缺乏上下文信息方面。总体而言，尽管面临挑战，