Effect of personalized nutrition on health-related behaviour change: evidence from the Food4me European randomized controlled trial

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Celis-Morales, C., Livingstone, K., Marsaux, C. F. M., Macready, A. L. ORCID: https://orcid.org/0000-0003-0368-9336, Fallaize, R., O’Donovan, C. B., Woolhead, C., Forster, H., Walsh, M. C., Navas-Carretero, S., San-Cristobal, R., Tsirigoti, L., Lambrinou, C. P., Mavrogianni, C., Moschonis, G., Kolossa, S., Hallmann, J., Godlewska, M., Surwiłło, A., Traczyk, I., Drevon, C. A., Bouwman, J., van Ommen, B., Grimaldi, K., Parnell, L. D., Matthews, J. N. S., Manios, Y., Daniel, H., Martinez, J. A., Lovegrove, J. A. ORCID: https://orcid.org/0000-0001-7633-9455, Gibney, E. R., Brennan, L., Saris, W. H. M., Gibney, M. and Mathers, J. C. (2017) Effect of personalized nutrition on health-related behaviour change: evidence from the Food4me European randomized controlled trial. International Journal of Epidemiology, 46 (2). pp. 578-588. ISSN 0300-5771 doi: 10.1093/ije/dyw186

Abstract/Summary

Background: Optimal nutritional choices are linked with better health, but many current interventions to improve diet have limited effect. We tested the hypothesis that providing personalized nutrition (PN) advice based on information on individual diet and lifestyle, phenotype and/or genotype would promote larger, more appropriate, and sustained changes in dietary behaviour. Methods: Adults from seven European countries were recruited to an internet-delivered intervention (Food4Me) and randomized to: (i) conventional dietary advice (control) or to PN advice based on: (ii) individual baseline diet; (iii) individual baseline diet plus phenotype (anthropometry and blood biomarkers); or (iv) individual baseline diet plus phenotype plus genotype (five diet-responsive genetic variants). Outcomes were dietary intake, anthropometry and blood biomarkers measured at baseline and after 3 and 6 months’ intervention. Results: At baseline, mean age of participants was 39.8 years (range 18–79), 59% of participants were female and mean body mass index (BMI) was 25.5 kg/m2. From the enrolled participants, 1269 completed the study. Following a 6-month intervention, participants randomized to PN consumed less red meat [-5.48 g, (95% confidence interval:- 10.8,-0.09), P=0.046], salt [-0.65 g, (-1.1,-0.25), P=0.002] and saturated fat [-1.14 % of energy, (-1.6,-0.67), P<0.0001], increased folate [29.6 mg, (0.21,59.0), P=0.048] intake and had higher Healthy Eating Index scores [1.27, (0.30, 2.25), P=0.010) than those randomized to the control arm. There was no evidence that including phenotypic and phenotypic plus genotypic information enhanced the effectiveness of the PN advice. Conclusions: Among European adults, PN advice via internet-delivered intervention produced larger and more appropriate changes in dietary behaviour than a conventional approach.

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Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/66441
Identification Number/DOI	10.1093/ije/dyw186
Refereed	Yes
Divisions	Interdisciplinary centres and themes > Food Chain and Health Life Sciences > School of Agriculture, Policy and Development > Department of Agri-Food Economics & Marketing Life Sciences > School of Chemistry, Food and Pharmacy > Department of Food and Nutritional Sciences > Human Nutrition Research Group Life Sciences > School of Psychology and Clinical Language Sciences > Nutrition and Health
Uncontrolled Keywords	Personalized nutrition, internet-based, randomized controlled trial, genotype, phenotype, obesity, diet, metabolic health
Publisher	Oxford University Press
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Funders:	European Commission	The sponsoring bodies who contributed funding for the creation of this item. Example: NERC Example: The Royal Society of Chemistry A pick list of funders may appear as you type in the funder's name in full or as an acronym. Select a correct match to complete the field or type in a new entry in full. For new entries, the full name is preferred.
Projects:	Food4Me Funded by: European Commission (FP7-KBBE-2010-4 265494 - £321,916) Local Lead (PI): Julie Lovegrove 1 April 2011 - 31 March 2015	Click Add to select your project (received at Reading) from an autocomplete list.

Deposit Details

Date Deposited:	31 Aug 2016 10:18	Date item deposited into CentAUR
Last Modified:	22 Mar 2025 02:31	Date item last modified

References

1. WHO. Global Health Risk: Mortality and Burden of Disease Attributable to Selected Major Risks. Geneva: World Health Organization, 2009. 2. Ezzati M, Riboli E. Global health behavioural and dietary risk factors for noncommunicable diseases. N Engl J Med 2013; 369:954–64. 3. WHO. Primary Health Care: Now More Than Ever. Geneva: World Health Organization, 2008. 4. National Health Service. Live Well. 2014. http://www.nhs.uk/Livewell/Goodfood/Pages/Healthyeating.aspx (6 March 2015, date last accessed). 5. Celis-Morales C, Lara J, Mathers JC. Personalising nutritional guidance for more effective behaviour change. Proc Nutr Soc 2015;74:130–38. 6. Fallaize R, Macready AL, Butler LT, Ellis JA, Lovegrove JA. An insight into the public acceptance of nutrigenomic-based personalised nutrition. Nutr Res Rev 2013;26:39–48. 7. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015;30:3. 8. Marteau TM, French DP, Griffin SJ et al. Effects of communicating DNA-based disease risk estimates on risk-reducing behaviours. Cochrane Database Syst Rev 2010;10:CD007275. 9. Di Cesare M, Khang Y-H, Asaria P et al. Inequalities in noncommunicable diseases and effective responses. Lancet 2013;381:585–97. 10. Seybert H, Loof A. Internet Usage in 2010 – Households and Individuals. Eurostat 2010. ec.europa.eu/eurostat/product?lang=en&mode=view&code=KS (20 January 2016, date last accessed). 11. Celis-Morales C, Livingstone KM, Marsaux CFM et al. Design and baseline characteristics of the Food4Me study: a web-based randomised controlled trial of personalised nutrition in seven European countries. Genes Nutr 2014;10:1–13. 12. Wei LJ, Lachin JM. Properties of t3he urn randomization in clinical-trials. Control Clin Trials 1988;9:345–64. 13. Forster H, Fallaize R, Gallagher C et al. Online dietary intake estimation: the Food4Me food frequency questionnaire. J Med Internet Res 2014;16:e150–e. 14. Fallaize R, Forster H, Macready AL et al. Online dietary intake estimation: reproducibility and validity of the Food4Me Food Frequency Questionnaire against a 4-day weighed food record. J Med Internet Res 2014;16(18):e190. 15. Guenther PM, Casavale KO, Reedy J et al. Update of the Healthy Eating Index: HEI-2010. J Acad Nutr Dietet 2013;113:569–80. 16. Baecke JAH, Burema J, Frijters JER. A short questionnaire for the measurement of habitual physical activity in epidemiological-studies. Am J Clin Nutr 1982;36:936–42. 17. Livingstone K, Celis-Morales C, Navas-Carretero S et al. Profile of European adults interested in internet-based personalised nutrition: the Food4Me study. Eur J Nutr 2016;55:759–69. 18. Kodama S, Saito K, Tanaka S et al. Effect of web-based lifestyle modification on weight control: a meta-analysis. Int J Obes (Lond) 2012;36:675–85. 19. McBride CM, Bryan AD, Bray MS, Swan GE, Green ED. Health behaviour change: can genomics improve behavioural adherence? Am J Public Health 2012;102:401–05. 20. Joost H-G, Gibney MJ, Cashman KD et al. Personalised nutrition: status and perspectives. Br J Nutr 2007;98:26–31. 21. Hietaranta-Luoma H-L, Tahvonen R, Iso-Touru T, Puolijoki H, Hopia A. An intervention study of individual, apoE genotypebased dietary and physical activity advice: impact on health behaviour. J Nutrigenet Nutrigenom 2014;7:161–74. 22. Bloss CS, Wineinger NE, Darst BF, Schork NJ, Topol EJ. Impact of direct-to-consumer genomic testing at long term follow-up. J Med Genet 2013;50:393–400. 23. Nielsen DE, El-Sohemy A. Disclosure of genetic information and change in dietary intake: a randomized controlled trial. Plos One 2014;9:e112665. 24. Frayling TM, Timpson NJ, Weedon MN et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 2007; 316:889–94. 25. Meisel SF, Beeken RJ, van Jaarsveld CH, Wardle J. Genetic susceptibility testing and readiness to control weight: Results from a randomized controlled trial. Obesity (Silver Spring) 2015; 23:305–12. 26. Yetter G, Capaccioli K. Differences in responses to Web and paper surveys among school professionals. Behav Res Methods 2010;42:266–72. 27. Bloss CS, Schork NJ, Topol EJ. Effect of direct-to-consumer genomewide profiling to assess disease risk. N Engl J Med 2011;364:524–34. 28. Celis-Morales C, Livingstone KM, Woolhead C et al. How reliable is internet-based self-reported identity, socio-demographic and obesity measures in European adults? Genes Nutr 2015;10:476.

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