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Perceived barriers to the uptake of personalised nutrition: a comparison between European countries

Lookup NU author(s): Professor Lynn Frewer, Dr Sharron Kuznesof

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Abstract

Personalised nutrition is a relatively new field of research aiming to provide personalised dietary advice, which can be based on an individuals genotype, phenotype, dietary and lifestyle data. According to this approach, dietary recommendations are tailored to meet personal nutritional needs. The goal of this analysis is to explore differences in perceived barriers to the uptake of personalised nutrition between consumers in different European countries. Data for this research were collected in February and March 2013, using on-line survey methodology. A total of 9381 participants from 9 European countries (Germany, Greece, Ireland, Poland, Portugal, Spain, the Netherlands, the UK, and Norway)participated. Perceived barriers for the adoption of personalised nutrition were measured using 18 items for which responses were on a five-point scale where respondents had to indicate their level of concern regarding various circumstances that could potentially prevent them from taking up personalised nutrition. Results showed significant differences between the 9 European countries in perceptions of barriers to the uptake of personalised nutrition. In some countries, like Greece, Spain and Germany, trust barrier was dominant while in other (e.g. Poland and Ireland) family and social barriers were deemed more important. This implies that policies targeted at promoting adoption of personalised nutrition need to be adapted for each country


Publication metadata

Author(s): Markovina J, Stewart-Knox B, Frewer LJ, Gibney M, Almeida MD, Rankin A, Kuznesof S, Poinhos R, Fischer ARH

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 11th NuGOweek 2014 conference “Nutrigenomics of Foods”

Year of Conference: 2014

Pages: 117

Publisher: Wageningen Academic Publishers

URL: http://prodinra.inra.fr/ft?id=97903AB3-67BA-4F73-A159-EA8331E594A7


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