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Updated: Jun 16, 2026

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
Published on: September 18, 2018
The assessment of individual usual food intake in large-scale prospective studies
Anne-Kathrin Illner1, Ute Nöthlings, Karen Wagner
1Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany. anne-kathrin.illner @ dife.de
Abstract:
Recent research has called into question the current practice to estimate individual usual food intake in large-scale studies. In such studies, usual food intake has been defined as diet over the past year. The aim of this review is to summarise the concepts of dietary assessment methods providing food intake data over this time period. A conceptualised framework is given to help researchers to understand the more recent developments to improve dietary assessment in large-scale prospective studies, and also to help to spot the gaps that need to be addressed in future methodological research. The conceptual framework illustrates the current options for the assessment of an individual's food consumption over 1 year. Ideally, a person's food intake on each day of this year should be assessed. Due to participants' burden, and organisational and financial constraints, however, the options are limited to directly requesting the long-term average (e.g. food frequency questionnaires), or selecting a few days with detailed food consumption measurements (e.g. 24-hour dietary recalls) or using snapshot techniques (e.g. barcode scanning of purchases). It seems necessary and important to further evaluate the performance of statistical modelling of the individual usual food intake from all available sources. Future dietary assessment might profit from the growing prominence of internet and telecommunication technologies to further enhance the available data on food consumption for each study participant. Research is crucial to investigate the performance of innovative assessment tools. However, the self-reported nature of the data itself will always lead to bias.
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