Exposure assessment and data-analysis

Within this theme focus is on design, evaluation, and implementation of novel (e.g. web-based) dietary assessment tools. Furthermore, we focus on methodologies to take measurement error in dietary assessment into account when estimating associations between diet and disease. In addition, we implement novel data analysis techniques in observational and experimental studies and develop methods to combine data from different sources.


Research of the Division of Human Nutrition in this field focusses on high quality dietary data collection and advanced data analysis methods. Developments in the fields of ICT and ‘omics’ offer opportunities to innovate dietary exposure assessment and can lead to efficient methods that generate valid results with impact on public policy. This is complemented by statistical methods supporting the development of new dietary assessment methods, including prediction models. Furthermore, development of statistical methods to correct diet-disease associations for measurement error is a central issue. Lastly, our research targets estimation of outcomes such as population attributable risks or gains in healthy life expectancy. 

Research focus

Exposure assessment for epidemiology and public health

There is great need for innovation to make current state-of-the-art dietary assessment future-proof for applications in large scale epidemiological and public health research and advice in clinical practice. Technological innovations have the potential to decrease measurement error and enrich the assessment with information on e.g. eating behaviour and the food environment. Diet as a whole should be increasingly studied using multi-dimensional patterns based on arrays of variables with multiple assessment methods, including objective biomarkers of intake. Opportunities should not be missed to integrate these new methods in large studies with concomitantly running validation studies.
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Biostatistical modelling for nutrition

Statistical methods are indispensable for high quality research. Sometimes standard methods are sufficient, but in other cases methods have to be specifically tailored to a particular research question, e.g. in handling measurement error in dietary assessment and in combining intake data from different assessment methods. Furthermore, in order to give advice on nutrition, the positive effects of a food need to be balanced against its disadvantages. This requires mathematical modelling and quantification of effects.
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Current key projects