Characteristics of European adults who dropped out from the Food4Me Internet-based personalised nutrition intervention
Livingstone, K., Celis-Morales, C., Macready, A. L.  ORCID: https://orcid.org/0000-0003-0368-9336, Fallaize, R., Forster, H., Woolhead, C., O'Donovan, C. B., Marsaux, C. F. M., Navas-Carretero, S., San-Cristobel, R., Kolossa, S., Tsirigoti, L., Lambrinou, C. P., Moschonis, G., Surwiłło, A., Drevon, C. A., Manios, Y., Traczyk, I., Gibney, E. R., Brennan, L. et al, Walsh, M. C., Lovegrove, J. A. ORCID: https://orcid.org/0000-0001-7633-9455, Martinez, J. A., Saris, W. H. M., Daniel, H., Gibney, M. and Mathers, J. C.
(2017)
Characteristics of European adults who dropped out from the Food4Me Internet-based personalised nutrition intervention.
Public Health Nutrition, 20 (1).
pp. 53-63.
ISSN 1368-9800
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1017/S1368980016002020 Abstract/SummaryObjective To characterise participants who dropped out of the Food4Me Proof-of-Principle study.
Design The Food4Me study was an Internet-based, 6-month, four-arm, randomised controlled trial. The control group received generalised dietary and lifestyle recommendations, whereas participants randomised to three different levels of personalised nutrition (PN) received advice based on dietary, phenotypic and/or genotypic data, respectively (with either more or less frequent feedback).
Setting Seven recruitment sites: UK, Ireland, The Netherlands, Germany, Spain, Poland and Greece.
Subjects Adults aged 18–79 years (n 1607).
Results A total of 337 (21 %) participants dropped out during the intervention. At baseline, dropouts had higher BMI (0·5 kg/m2; P<0·001). Attrition did not differ significantly between individuals receiving generalised dietary guidelines (Control) and those randomised to PN. Participants were more likely to drop out (OR; 95 % CI) if they received more frequent feedback (1·81; 1·36, 2·41; P<0·001), were female (1·38; 1·06, 1·78; P=0·015), less than 45 years old (2·57; 1·95, 3·39; P<0·001) and obese (2·25; 1·47, 3·43; P<0·001). Attrition was more likely in participants who reported an interest in losing weight (1·53; 1·19, 1·97; P<0·001) or skipping meals (1·75; 1·16, 2·65; P=0·008), and less likely if participants claimed to eat healthily frequently (0·62; 0·45, 0·86; P=0·003).
Conclusions Attrition did not differ between participants receiving generalised or PN advice but more frequent feedback was related to attrition for those randomised to PN interventions. Better strategies are required to minimise dropouts among younger and obese individuals participating in PN interventions and more frequent feedback may be an unnecessary burden.
Downloads per month over past year
| 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. |
|---|
| Date Deposited: | 15 Aug 2016 14:25 | Date item deposited into CentAUR |
|---|
| Last Modified: | 09 Jun 2024 05:44 | Date item last modified |
|---|
1. Ng, M, Fleming, T, Robinson, M et al. (2014) Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384, 766–781. [PubMed] [CrossRef] [Google Scholar]
2. World Health Organization (2014) Global Status Report on Noncommunicable Diseases 2014. Geneva: WHO. [Google Scholar]
3. Sepúlveda, J & Murray, C (2014) The state of global health in 2014. Science 345, 1275–1278. [PubMed] [CrossRef] [Google Scholar]
4. Hood, L & Friend, SH (2011) Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol 8, 184–187. [PubMed] [CrossRef] [Google Scholar]
5. Celis-Morales, C, Lara, J & Mathers, JC (2015) Personalising nutritional guidance for more effective behaviour change. Proc Nutr Soc 74, 130–138. [PubMed] [CrossRef] [Google Scholar]
6. Nielsen, DE & El-Sohemy, A (2014) Disclosure of genetic information and change in dietary intake: a randomized controlled trial. PLoS ONE 9, e112665. [PubMed] [CrossRef] [Google Scholar]
7. Livingstone, KM, Celis-Morales, C, Navas-Carretero, S et al. (2016) Effect of an Internet-based, personalized nutrition randomized trial on dietary changes associated with the Mediterranean diet: the Food4Me Study. Am J Clin Nutr (Epublication ahead of print version). [Google Scholar]
8. Roumen, C, Feskens, EJM, Corpeleijn, E et al. (2011) Predictors of lifestyle intervention outcome and dropout: the SLIM study. Eur J Clin Nutr 65, 1141–1147. [PubMed] [CrossRef] [Google Scholar]
9. Moroshko, I, Brennan, L & O’Brien, P (2011) Predictors of dropout in weight loss interventions: a systematic review of the literature. Obes Rev 12, 912–934. [PubMed] [CrossRef] [Google Scholar]
10. Huisman, S, Maes, S, De Gucht, VJ et al. (2010) Low goal ownership predicts drop-out from a weight intervention study in overweight patients with type 2 diabetes. Int J Behav Med 17, 176–181. [PubMed] [CrossRef] [Google Scholar]
11. Bennett, GA & Jones, SE (1986) Dropping out of treatment for obesity. J Psychosom Res 30, 567–573. [PubMed] [CrossRef] [Google Scholar]
12. Dalle Grave, R, Calugi, S, Molinari, E et al. (2005) Weight loss expectations in obese patients and treatment attrition: an observational multicenter study. Obes Res 13, 1961–1969. [PubMed] [CrossRef] [Google Scholar]
13. Davis, M & Addis, M (1999) Predictors of attrition from behavioral medicine treatments. Ann Behav Med 21, 339–349. [PubMed] [CrossRef] [Google Scholar]
14. Colombo, O, Ferretti, VV, Ferraris, C et al. (2014) Is drop-out from obesity treatment a predictable and preventable event? Nutr J 13, 13. [PubMed] [CrossRef] [Google Scholar]
15. Groeneveld, I, Proper, K, van der Beek, A et al. (2009) Factors associated with non-participation and drop-out in a lifestyle intervention for workers with an elevated risk of cardiovascular disease. Int J Behav Nutr Phys Act 6, 80. [PubMed] [CrossRef] [Google Scholar]
16. Williams, G, Hamm, MP, Shulhan, J et al. (2014) Social media interventions for diet and exercise behaviours: a systematic review and meta-analysis of randomised controlled trials. BMJ Open 4, e003926. [PubMed] [CrossRef] [Google Scholar]
17. Shuger, S, Barry, V, Sui, X et al. (2011) Electronic feedback in a diet- and physical activity-based lifestyle intervention for weight loss: a randomized controlled trial. Int J Behav Nutr Phys Act 8, 41. [PubMed] [CrossRef] [Google Scholar]
18. Celis-Morales, C, Livingstone, KM, Marsaux, CFM et al. (2015) Design and baseline characteristics of the Food4Me study: a web-based randomised controlled trial of personalised nutrition in seven European countries. Genes Nutr 10, 450. [PubMed] [CrossRef] [Google Scholar]
19. Forster, H, Fallaize, R, Gallagher, C et al. (2014) Online dietary intake estimation: the Food4Me food frequency questionnaire. J Med Internet Res 16, e150. [PubMed] [CrossRef] [Google Scholar]
20. Fallaize, R, Forster, H, Macready, AL 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, e190. [PubMed] [CrossRef] [Google Scholar]
21. Food Standards Agency (2002) McCance and Widdowson’s The Composition of Foods, sixth summary edition ed. Cambridge: Royal Society of Chemistry. [Google Scholar]
22. Henry, CJK (2005) Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr 8, 1133–1152. [PubMed] [Google Scholar]
23. Institute of Medicine (2005) Dietary Reference Intakes for Energy, Carbohydrate, Fibre, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids. http://www.nap.edu/openbook.php?isbn=0309085373 (accessed March 2015).
24. Institute of Medicine (2011) Dietary Reference Intakes Tables and Applications. http://www.iom.edu/Activities/Nutrition/SummaryDRIs/DRI-Tables.aspx (accessed March 2015).
25. World Health Organization (2007) Protein and Amino Acid Requirements in Human Nutrition. Report of a Joint WHO/FAO/UNU Expert Consultation. WHO Technical Report Series no. 935. Geneva: WHO. [Google Scholar]
26. World Health Organization & Food and Agriculture Organization of the United Nations (2010) Report of an expert consultation on fats and fatty acids in human nutrition. http://www.who.int/nutrition/publications/nutrientrequirements/fatsandfattyacids_humannutrition/en/ (accessed March 2016).
27. Celis-Morales, C, Livingstone, K, Woolhead, C et al. (2015) How reliable is internet-based self-reported identity, socio-demographic and obesity measures in European adults? Genes Nutr 10, 476. [PubMed] [Google Scholar]
28. European Commission (2015) European skills, competences, qualifications and occupations. https://ec.europa.eu/esco/web/guest/hierarchybrowser/-/browser/Occupation (accessed April 2015).
29. European Commission (2015) Mean annual earnings by sex, age and occupation. http://ec.europa.eu/eurostat/web/products-datasets/-/earn_ses_agt28 (accessed March 2015).
30. Clark, F, Jackson, J, Carlson, M et al. (2012) Effectiveness of a lifestyle intervention in promoting the well-being of independently living older people: results of the Well Elderly 2 Randomised Controlled Trial. J Epidemiol Community Health 66, 782–790. [PubMed] [CrossRef] [Google Scholar]
31. Neve, M, Morgan, PJ, Jones, PR et al. (2010) Effectiveness of web-based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with meta-analysis. Obes Rev 11, 306–321. [PubMed] [CrossRef] [Google Scholar]
32. Tate, DF, Wing, RR & Winett, RA (2001) Using internet technology to deliver a behavioral weight loss program. JAMA 285, 1172–1177. [PubMed] [CrossRef] [Google Scholar]
33. Harvey-Berino, J, Pintauro, S, Buzzell, P et al. (2004) Effect of internet support on the long-term maintenance of weight loss. Obes Rev 12, 320–329. [CrossRef] [Google Scholar]
34. Grossi, E, Dalle Grave, R, Mannucci, E et al. (2006) Complexity of attrition in the treatment of obesity: clues from a structured telephone interview. Int J Obes (Lond) 30, 1132–1137. [PubMed] [CrossRef] [Google Scholar]
35. Wanner, M, Martin-Diener, E, Bauer, G et al. (2010) Comparison of trial participants and open access users of a web-based physical activity intervention regarding adherence, attrition, and repeated participation. J Med Internet Res 12, e3. [PubMed] [CrossRef] [Google Scholar]
36. Susin, N, Boff, RdM, Ludwig, MWB et al. (2015) Predictors of adherence in a prevention program for patients with metabolic syndrome. J Health Psychol (Epublication ahead of print version). [PubMed] [Google Scholar]
37. Mesas, AE, Guallar-Castillón, P, León-Muñoz, LM et al. (2012) Obesity-related eating behaviors are associated with low physical activity and poor diet quality in Spain. J Nutr 142, 1321–1328. [PubMed] [CrossRef] [Google Scholar]
38. Bukman, AJ, Teuscher, D, Feskens, EJM et al. (2014) Perceptions on healthy eating, physical activity and lifestyle advice: opportunities for adapting lifestyle interventions to individuals with low socioeconomic status. BMC Public Health 14, 1036. [PubMed] [CrossRef] [Google Scholar]
39. Graffagnino, CL, Falko, JM, La Londe, M et al. (2006) Effect of a community-based weight management program on weight loss and cardiovascular disease risk factors. Obesity (Silver Spring) 14, 280–288. [PubMed] [CrossRef] [Google Scholar]
40. Gripeteg, L, Karlsson, J, Torgerson, J et al. (2010) Predictors of very-low-energy diet outcome in obese women and men. Obes Facts 3, 159–165. [PubMed] [Google Scholar]
41. Davis, MJ & Addis, ME (1999) Predictors of attrition from behavioral medicine treatments. Ann Behav Med 21, 339–349. [PubMed] [CrossRef] [Google Scholar]
42. Livingstone, K, Celis-Morales, C, Navas-Carretero, S et al. (2016) Profile of European adults interested in internet-based personalised nutrition: the Food4Me study. Eur J Nutr 55, 759–769. [PubMed] [CrossRef] [Google Scholar]
43. Post, A, Gilljam, H, Bremberg, S et al. (2012) Psychosocial determinants of attrition in alongitudinal study of tobacco use in youth. Scientific World Journal 2012, 654030. [PubMed] [Google Scholar]
44. Cochrane, G (2008) Role for a sense of self-worth in weight-loss treatments: helping patients develop self-efficacy. Can Fam Physician 54, 543–547. [PubMed] [Google Scholar]
45. Mutsaerts, MAQ, Kuchenbecker, WKH, Mol, BW et al. (2013) Dropout is a problem in lifestyle intervention programs for overweight and obese infertile women: a systematic review. Hum Reprod 28, 979–986. [PubMed] [CrossRef] [Google Scholar]
46. Schwarzer, R & Renner, B (2000) Social-cognitive predictors of health behavior: action self-efficacy and coping self-efficacy. Health Psychol 19, 487–495. [PubMed] [CrossRef] [Google Scholar]
47. Pursey, K, Burrows, LT, Stanwell, P et al. (2014) How accurate is web-based self-reported height, weight, and body mass index in young adults? J Med Internet Res 16, e4. [PubMed] [CrossRef] [Google Scholar]
48. Macdiarmid, J & Blundell, J (1998) Assessing dietary intake: who, what and why of under-reporting. Nutr Res Rev 11, 231–253. [Google Scholar] University Staff: Request a correction | Centaur Editors: Update this record |
Lists
Lists
