ArticleInternational journal of obesity (2005)2019
A Delphi study to build consensus on the definition and use of big data in obesity research.
Article in International journal of obesity (2005), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 202 papers, 6 of them syntheses that pooled it.
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Who cites it
202 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Ethical issues raised by artificial intelligence and big data in population health: a scoping review.Frontiers in sociology · 2025Pooled it
- Developing quality indicators for cross-sectoral psycho-oncology in Germany: combining the RAND/UCLA appropriateness method with a Delphi technique.BMC health services research · 2023Pooled it
- Altering product placement to create a healthier layout in supermarkets: Outcomes on store sales, customer purchasing, and diet in a prospective matched controlled cluster study.PLoS medicine · 2021Pooled it
- Improving nutritional status among urban poor children in sub-Saharan Africa: An evidence-informed Delphi-based consultation.Maternal & child nutrition · 2021Pooled it
- A systematic review of the influences of food store product placement on dietary-related outcomes.Nutrition reviews · 2020Pooled it
- Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.RMD open · 2019Pooled it
- Management of patients with hypoparathyroidism: a Delphi consensus analysis.Journal of endocrinological investigation · 2026Article
- Modified Delphi Consensus Recommendations for the Management of Psoriasis in Asia-Pacific.Dermatology and therapy · 2026Article
- A modified Delphi consensus on fusion (laparoscopic and robotic) approaches to colorectal surgery in Asia-Pacific.Journal of gastroenterology · 2026Article
- Article
- World Allergy Organization (WAO) Consensus on the use and practice of precautionary allergen labelling: The international ACT-UP! e-Delphi.The World Allergy Organization journal · 2026Review
- Development of a robotic training curriculum for visceral and gastrointestinal surgical trainees: an international Delphi study.Surgical endoscopy · 2026Article
- Development of patient-reported experience and patient-reported outcome measurement tool for abortion care in Ethiopia: a mixed-method study.Reproductive health · 2026Article
- Practice Areas, Skills, and Core Competencies of Advanced Practice Physiotherapists Working in Tertiary Care in Germany: Protocol for a 3-Round Delphi Study to Establish Expert Consensus.JMIR research protocols · 2026Article
- Development of a robotic training curriculum for visceral and gastrointestinal surgical trainees: an international Delphi study.The British journal of surgery · 2026Article
- Building a consumer health informatics introductory course consensus curriculum: an eDelphi study.JAMIA open · 2026Article
- Development of a Robotic Training Curriculum for Visceral and Gastrointestinal Surgical Trainees: An International Delphi Study.United European gastroenterology journal · 2026Article
- European Expert Consensus on Essential Variables for Teledermatological Assessment of Skin Tumours.Acta dermato-venereologica · 2026Article
- Building consensus on what to include in public health expenditures: A modified Delphi study in Canada.Canadian journal of public health = Revue canadienne de sante publique · 2026Article
- International consensus on airway clearance techniques for patients hospitalised with acute exacerbations of COPD: a European Delphi study.ERJ open research · 2026Article
142 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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.
methodsA 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.
resultsNinety-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.
conclusionsExperts agreed that big data was more nuanced than the oft-cited definition of 'volume, variety and velocity', and includes quantitative, qualitative, observational or intervention data from a range of sources that have been collected for research or other purposes. Experts repeatedly called for third party action, for example to develop frameworks for reporting and ethics, to clarify data governance requirements, to support training and skill development and to facilitate sharing of big data. Further advocacy will be required to encourage organisations to adopt these roles.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.