Design and baseline characteristics of the Food4Me study: a web-based randomised controlled trial of personalised nutrition in seven European countries
详细信息 查看全文
- 作者：Carlos Celis-Morales (1)
Katherine M. Livingstone (1)
Cyril F. M. Marsaux (2)
Hannah Forster (3)
Clare B. O’Donovan (3)
Clara Woolhead (3)
Anna L. Macready (4)
Rosalind Fallaize (4)
Santiago Navas-Carretero (5) (6)
Rodrigo San-Cristobal (5)
Silvia Kolossa (7)
Kai Hartwig (7)
Lydia Tsirigoti (8)
Christina P. Lambrinou (8)
George Moschonis (8)
Magdalena Godlewska (9)
Agnieszka Surwi??o (9)
Keith Grimaldi (10)
Jildau Bouwman (11)
E. J. Daly (12)
Victor Akujobi (12)
Rick O’Riordan (12)
Jettie Hoonhout (13)
Arjan Claassen (14)
Ulrich Hoeller (15)
Thomas E. Gundersen (16)
Siv E. Kaland (16)
John N. S. Matthews (17)
Yannis Manios (8)
Iwona Traczyk (9)
Christian A. Drevon (18)
Eileen R. Gibney (3)
Lorraine Brennan (3)
Marianne C. Walsh (3)
Julie A. Lovegrove (4)
J. Alfredo Martinez (5) (6)
Wim H. M. Saris (3)
Hannelore Daniel (7)
Mike Gibney (3)
John C. Mathers (1) - 关键词：Personalised nutrition ; Web ; based ; Phenotype ; Genotype ; Randomised controlled trial
- 刊名：Genes & Nutrition
- 出版年：2015
- 出版时间：January 2015
- 年：2015
- 卷：10
- 期：1
- 全文大小：476 KB
- 参考文献：1. Appel LJ, Brands MW, Daniels SR, Karanja N, Elmer PJ, Sacks FM (2006) Dietary approaches to prevent and treat hypertension—a scientific statement from the American Heart Association. Hypertension 47:296-08. doi:10.1161/01.hyp.0000202568.01167.b6 CrossRef
2. Baecke JAH, Burema J, Frijters JER (1982) A short questionnaire for the measurement of habitual physical-activity in epidemiological-studies. Am J Clin Nutr 36:936-42
3. Bonn SE, Lagerros YT, Balter K (2013) How valid are web-based self-reports of weight? J Med Internet Res 15. doi:10.2196/jmir.2393
4. Bonomi AG, Plasqui G, Goris AHC, Westerterp KR (2010) Estimation of free-living energy expenditure using a novel activity monitor designed to minimize obtrusiveness. Obesity 18:1845-851. doi:10.1038/oby.2010.34 CrossRef
5. Dyson PA et al (2011) Diabetes UK evidence-based nutrition guidelines for the prevention and management of diabetes. Diabet Med 28:1282-288. doi:10.1111/j.1464-5491.2011.03371.x CrossRef
6. Fallaize R, Macready AL, Butler LT, Ellis JA, Lovegrove JA (2013) An insight into the public acceptance of nutrigenomic-based personalised nutrition. Nutr Res Rev 26:39-8. doi:10.1017/s0954422413000024 CrossRef
7. Fallaize R et al (2014) Online dietary intake estimation: reproducibility and validity of the Food4Me Food Frequency Questionnaire against a 4-day weighed food record. J Med Internet Res 16. doi:10.2196/jmir.3355
8. FAO (2004) Human energy requirements. Report of a joint FAO/WHO/UNU expert consultation
9. Forster H et al (2014) Online dietary intake estimation: the Food4Me Food Frequency Questionnaire. J Med Internet Res 16:e150. doi:10.2196/jmir.3105 CrossRef
10. French SA, Jeffery RW, Wing RR (1994) Sex-differences among participants in a weight-control program. Addict Behav 19:147-58. doi:10.1016/0306-4603(94)90039-6 CrossRef
11. Gibney MJ, Walsh MC (2013) The future direction of personalised nutrition: my diet, my phenotype, my genes. Proc Nutr Soc 72:219-25. doi:10.1017/s0029665112003436 CrossRef
12. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U, Lancet Phys Activity Series W (2012) Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet 380:247-57. doi:10.1016/s0140-6736(12)60646-1 CrossRef
13. Hearty AP, McCarthy SN, Kearney JM, Gibney MJ (2007) Relationship between attitudes towards healthy eating and dietary behaviour, lifestyle and demographic factors in a representative sample of Irish adults. Appetite 48:1-1. doi:10.1016/j.appet.2006.03.329 CrossRef
14. Kodama S et al (2012) Effect of web-based lifestyle modification on weight control: a meta-analysis. Int J Obes 36:675-85. doi:10.1038/ijo.2011.121 CrossRef
15. Lassale C, Peneau S, Touvier M, Julia C, Galan P, Hercerg S, Kesse-Guyot E (2013) Validity of Web-based self-reported weight and height: results of the Nutrinet-Sante study. J Med Internet Res 15. doi:10.2196/jmir.2575
16. Marteau TM et al (2010) Effects of communicating DNA-based disease risk estimates on risk-reducing behaviours. Cochrane Database System Rev. doi:10.1002/14651858.CD007275.pub2
17. McCance RAW (2002) McCance and Widdowson’s the composition - 作者单位：Carlos Celis-Morales (1)
Katherine M. Livingstone (1)
Cyril F. M. Marsaux (2)
Hannah Forster (3)
Clare B. O’Donovan (3)
Clara Woolhead (3)
Anna L. Macready (4)
Rosalind Fallaize (4)
Santiago Navas-Carretero (5) (6)
Rodrigo San-Cristobal (5)
Silvia Kolossa (7)
Kai Hartwig (7)
Lydia Tsirigoti (8)
Christina P. Lambrinou (8)
George Moschonis (8)
Magdalena Godlewska (9)
Agnieszka Surwi??o (9)
Keith Grimaldi (10)
Jildau Bouwman (11)
E. J. Daly (12)
Victor Akujobi (12)
Rick O’Riordan (12)
Jettie Hoonhout (13)
Arjan Claassen (14)
Ulrich Hoeller (15)
Thomas E. Gundersen (16)
Siv E. Kaland (16)
John N. S. Matthews (17)
Yannis Manios (8)
Iwona Traczyk (9)
Christian A. Drevon (18)
Eileen R. Gibney (3)
Lorraine Brennan (3)
Marianne C. Walsh (3)
Julie A. Lovegrove (4)
J. Alfredo Martinez (5) (6)
Wim H. M. Saris (3)
Hannelore Daniel (7)
Mike Gibney (3)
John C. Mathers (1)
1. Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
2. Department of Human Biology, NUTRIM School for Nutrition, Toxicology and Metabolism, Maastricht University Medical Centre, Maastricht, The Netherlands
3. UCD Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Republic of Ireland
4. Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research, University of Reading, Reading, UK
5. Department of Nutrition, Food Science and Physiology, University of Navarra, Pamplona, Spain
6. CIBER Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
7. Biochemistry Unit, ZIEL Research Center of Nutrition and Food Sciences, Technische Universit?t München, Munich, Germany
8. Department of Nutrition and Dietetics, Harokopio University, Athens, Greece
9. National Food and Nutrition Institute (IZZ), Warsaw, Poland
10. Eurogenetica Ltd, 7 Salisbury Road, Burnham-on-Sea, UK
11. Microbiology and Systems Biology Group, TNO, Utrechtseweg 48, 3704 HE, Zeist, The Netherlands
12. Crème Global, Trinity Technology and Enterprise Campus, Grand Canal Quay, Dublin 2, Ireland
13. Experiences Research Department, Philips Research, High Tech Campus 34, 5656 AE, Eindhoven, The Netherlands
14. Software Products and Systems, PGI, Philips Innovation Services, High Tech Campus 33, 5656 AE, Eindhoven, The Netherlands
15. DSM Nutritional Products, Analytical Research Centre, Basel, Switzerland
16. Vitas AS, Gaustadalleen 21, 0349, Oslo, Norway
17. School of Mathematics and Statistics, Newcastle University, Newcastle, UK
18. Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway - ISSN：1865-3499
文摘
Improving lifestyle behaviours has considerable potential for reducing the global burden of non-communicable diseases, promoting better health across the life-course and increasing well-being. However, realising this potential will require the development, testing and implementation of much more effective behaviour change interventions than are used conventionally. Therefore, the aim of this study was to conduct a multi-centre, web-based, proof-of-principle study of personalised nutrition (PN) to determine whether providing more personalised dietary advice leads to greater improvements in eating patterns and health outcomes compared to conventional population-based advice. A total of 5,562 volunteers were screened across seven European countries; the first 1,607 participants who fulfilled the inclusion criteria were recruited into the trial. Participants were randomly assigned to one of the following intervention groups for a 6-month period: Level 0—control group—receiving conventional, non-PN advice; Level 1—receiving PN advice based on dietary intake data alone; Level 2—receiving PN advice based on dietary intake and phenotypic data; and Level 3—receiving PN advice based on dietary intake, phenotypic and genotypic data. A total of 1,607 participants had a mean age of 39.8?years (ranging from 18 to 79?years). Of these participants, 60.9?% were women and 96.7?% were from white-European background. The mean BMI for all randomised participants was 25.5?kg?m?, and 44.8?% of the participants had a BMI?≥?5.0?kg?m?. Food4Me is the first large multi-centre RCT of web-based PN. The main outcomes from the Food4Me study will be submitted for publication during 2015.