Skip to main content Accessibility help

Characteristics of European adults who dropped out from the Food4Me Internet-based personalised nutrition intervention

  • Katherine M Livingstone (a1), Carlos Celis-Morales (a1), Anna L Macready (a2), Rosalind Fallaize (a2), Hannah Forster (a3), Clara Woolhead (a3), Clare B O’Donovan (a3), Cyril FM Marsaux (a4), Santiago Navas-Carretero (a5) (a6), Rodrigo San-Cristobal (a6), Silvia Kolossa (a7), Lydia Tsirigoti (a8), Christina P Lambrinou (a8), George Moschonis (a8), Agnieszka Surwiłło (a9), Christian A Drevon (a10), Yannis Manios (a8), Iwona Traczyk (a9), Eileen R Gibney (a3), Lorraine Brennan (a3), Marianne C Walsh (a3), Julie A Lovegrove (a2), J Alfredo Martinez (a6), Wim HM Saris (a4), Hannelore Daniel (a7), Mike Gibney (a3) and John C Mathers (a1)...



To characterise participants who dropped out of the Food4Me Proof-of-Principle study.


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).


Seven recruitment sites: UK, Ireland, The Netherlands, Germany, Spain, Poland and Greece.


Adults aged 18–79 years (n 1607).


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).


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.


Corresponding author

* Corresponding author: Email


Hide All
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, 766781.
2. World Health Organization (2014) Global Status Report on Noncommunicable Diseases 2014. Geneva: WHO.
3. Sepúlveda, J & Murray, C (2014) The state of global health in 2014. Science 345, 12751278.
4. Hood, L & Friend, SH (2011) Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol 8, 184187.
5. Celis-Morales, C, Lara, J & Mathers, JC (2015) Personalising nutritional guidance for more effective behaviour change. Proc Nutr Soc 74, 130138.
6. Nielsen, DE & El-Sohemy, A (2014) Disclosure of genetic information and change in dietary intake: a randomized controlled trial. PLoS ONE 9, e112665.
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).
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, 11411147.
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, 912934.
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, 176181.
11. Bennett, GA & Jones, SE (1986) Dropping out of treatment for obesity. J Psychosom Res 30, 567573.
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, 19611969.
13. Davis, M & Addis, M (1999) Predictors of attrition from behavioral medicine treatments. Ann Behav Med 21, 339349.
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.
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.
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.
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.
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.
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.
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.
21. Food Standards Agency (2002) McCance and Widdowson’s The Composition of Foods, sixth summary edition ed. Cambridge: Royal Society of Chemistry.
22. Henry, CJK (2005) Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr 8, 11331152.
23. Institute of Medicine (2005) Dietary Reference Intakes for Energy, Carbohydrate, Fibre, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids. (accessed March 2015).
24. Institute of Medicine (2011) Dietary Reference Intakes Tables and Applications. (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.
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. (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.
28. European Commission (2015) European skills, competences, qualifications and occupations. (accessed April 2015).
29. European Commission (2015) Mean annual earnings by sex, age and occupation. (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, 782790.
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, 306321.
32. Tate, DF, Wing, RR & Winett, RA (2001) Using internet technology to deliver a behavioral weight loss program. JAMA 285, 11721177.
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, 320329.
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, 11321137.
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.
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).
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, 13211328.
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.
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, 280288.
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, 159165.
41. Davis, MJ & Addis, ME (1999) Predictors of attrition from behavioral medicine treatments. Ann Behav Med 21, 339349.
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, 759769.
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.
44. Cochrane, G (2008) Role for a sense of self-worth in weight-loss treatments: helping patients develop self-efficacy. Can Fam Physician 54, 543547.
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, 979986.
46. Schwarzer, R & Renner, B (2000) Social-cognitive predictors of health behavior: action self-efficacy and coping self-efficacy. Health Psychol 19, 487495.
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.
48. Macdiarmid, J & Blundell, J (1998) Assessing dietary intake: who, what and why of under-reporting. Nutr Res Rev 11, 231253.


Type Description Title
Supplementary materials

Livingstone supplementary material
Livingstone supplementary material 1

 Word (27 KB)
27 KB


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed