A Delphi study to build consensus on the definition and use of big data in obesity research

作者：Christina Vogel, Stephen Zwolinsky, Claire Griffiths, Matthew Hobbs, Emily J Henderson, Emma L. Wilkins · 发表于：International Journal of Obesity · 年份：2019 · DOI：10.1038/s41366-018-0313-9 · 被引用次数：480 · 研究领域：Health, Environment, Cognitive Aging、Mobile Health and mHealth Applications、Meta-analysis and systematic reviews

BACKGROUND: 'Big data' has great potential to help address the global health challenge of obesity. However, lack of clarity with regard to the definition of big data and frameworks for effectively using big data in the context of obesity research may be hindering progress. The aim of this study was to establish agreed approaches for the use of big data in obesity-related research. METHODS: A Delphi method of consensus development was used, comprising three survey rounds. In Round 1, participants were asked to rate agreement/disagreement with 77 statements across seven domains relating to definitions of, and approaches to, using big data in the context of obesity research. Participants were also asked to contribute further ideas in relation to these topics, which were incorporated as new statements (n = 8) in Round 2. In Rounds 2 and 3 participants re-appraised their ratings in view of the group consensus. RESULTS: Ninety-six experts active in obesity-related research were invited to participate. Of these, 36/96 completed Round 1 (37.5% response rate), 29/36 completed Round 2 (80.6% response rate) and 26/29 completed Round 3 (89.7% response rate). Consensus (defined as > 70% agreement) was achieved for 90.6% (n = 77) of statements, with 100% consensus achieved for the Definition of Big Data, Data Governance, and Quality and Inference domains. CONCLUSIONS: Experts agreed that big data was more nuanced than the oft-cited definition of 'volume, variety and velocity', and includes...