ReviewInternational journal of obesity (2005)2018
Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map.
Review in International journal of obesity (2005), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- A systematic review of supermarket automated electronic sales data for population dietary surveillance.Nutrition reviews · 2022Pooled it
- Economic Evaluation of NHS England's Type 2 Diabetes Path to Remission Pilot Scheme: Evidence From Real-World Data and Patient Simulation Modelling.PharmacoEconomics - open · 2026Article
- Understanding overweight and obesity subgroups: a cluster analysis of data from the UK Yorkshire Health Study.BMC public health · 2025Article
- Article
- Real-life experiments in supermarkets to encourage healthy dietary-related behaviours: opportunities, challenges and lessons learned.The international journal of behavioral nutrition and physical activity · 2023Article
- A systems science approach to identifying data gaps in national data sources on adolescent suicidal ideation and suicide attempt in the United States.BMC public health · 2023Article
- Can We Deliver Person-Centred Obesity Care Across the Globe?Current obesity reports · 2022Review
- A scoping review of systems approaches for increasing physical activity in populations.Health research policy and systems · 2022Article
- Toward Systems Models for Obesity Prevention: A Big Role for Big Data.Current developments in nutrition · 2022Article
- Machine learning to predict in-hospital mortality among patients with severe obesity: Proof of concept study.Obesity science & practice · 2022Article
- Pathways to suicide or collections of vicious cycles? Understanding the complexity of suicide through causal mapping.Social network analysis and mining · 2022Article
- A foresight whole systems obesity classification for the English UK biobank cohort.BMC public health · 2022Article
- Effect of a 1-Year Controlled Lifestyle Intervention on Body Weight and Other Risk Markers (the Healthy Lifestyle Community Programme, Cohort 2).Obesity facts · 2022Article
- Understanding Barriers to Novel Data Linkages: Topic Modeling of the Results of the LifeInfo Survey.Journal of medical Internet research · 2021Article
- Progress Towards Using Linked Population-Based Data For Geohealth Research: Comparisons Of Aotearoa New Zealand And The United Kingdom.Applied spatial analysis and policy · 2021Article
- Establishing consensus on key public health indicators for the monitoring and evaluating childhood obesity interventions: a Delphi panel study.BMC public health · 2020Article
- Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data.International journal of obesity (2005) · 2020Article
- Public preferences regarding data linkage for research: a discrete choice experiment comparing Scotland and Sweden.BMC medical informatics and decision making · 2020Article
- Evidence from big data in obesity research: international case studies.International journal of obesity (2005) · 2020Article
- A Delphi study to build consensus on the definition and use of big data in obesity research.International journal of obesity (2005) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundObesity research at a population level is multifaceted and complex. This has been characterised in the UK by the Foresight obesity systems map, identifying over 100 variables, across seven domain areas which are thought to influence energy balance, and subsequent obesity. Availability of data to consider the whole obesity system is traditionally lacking. However, in an era of big data, new possibilities are emerging. Understanding what data are available can be the first challenge, followed by an inconsistency in data reporting to enable adequate use in the obesity context. In this study we map data sources against the Foresight obesity system map domains and nodes and develop a framework to report big data for obesity research. Opportunities and challenges associated with this new data approach to whole systems obesity research are discussed.
methodsExpert opinion from the ESRC Strategic Network for Obesity was harnessed in order to develop a data source reporting framework for obesity research. The framework was then tested on a range of data sources. In order to assess availability of data sources relevant to obesity research, a data mapping exercise against the Foresight obesity systems map domains and nodes was carried out.
resultsA reporting framework was developed to recommend the reporting of key information in line with these headings: Background; Elements; Exemplars; Content; Ownership; Aggregation; Sharing; Temporality (BEE-COAST). The new BEE-COAST framework was successfully applied to eight exemplar data sources from the UK. 80% coverage of the Foresight obesity systems map is possible using a wide range of big data sources. The remaining 20% were primarily biological measurements often captured by more traditional laboratory based research.
conclusionsBig data offer great potential across many domains of obesity research and need to be leveraged in conjunction with traditional data for societal benefit and health promotion.
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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.