Skip to main content Accessibility help
×
×
Home

Personalised nutrition: the role of new dietary assessment methods

  • Hannah Forster (a1), Marianne C. Walsh (a1), Michael J. Gibney (a1), Lorraine Brennan (a1) (a2) and Eileen R. Gibney (a1)...

Abstract

Food records or diaries, dietary recalls and FFQ are methods traditionally used to measure dietary intake; however, advancing technologies and growing awareness in personalised health have heightened interest in the application of new technologies to assess dietary intake. Dietary intake data can be used in epidemiology, dietary interventions and in the delivery of personalised nutrition advice. Compared with traditional dietary assessment methods, new technologies have many advantages, including their ability to automatically process data and provide personalised dietary feedback advice. This review examines the new technologies presently under development for the assessment of dietary intakes, and their utilisation and efficacy for personalising dietary advice. New technology-based methods of dietary assessment can broadly be categorised into three key areas: online (web-based) methods, mobile methods and sensor technologies. Several studies have demonstrated that utilising new technologies to provide tailored advice can result in positive dietary changes and have a significant impact on selected nutrient and food group intakes. However, comparison across studies indicates that the magnitude of change is variable and may be influenced by several factors, including the frequency and type of feedback provided. Future work should establish the most effective combinations of these factors in facilitating dietary changes across different population groups.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Personalised nutrition: the role of new dietary assessment methods
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Personalised nutrition: the role of new dietary assessment methods
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Personalised nutrition: the role of new dietary assessment methods
      Available formats
      ×

Copyright

Corresponding author

* Corresponding author: Dr Eileen R. Gibney, email eileen.gibeny@ucd.ie

References

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 Organisation (2015) Obesity and overweight fact sheet. http://www.who.int/mediacentre/factsheets/fs311/en/ (accessed March 2015).
3. Celis-Morales, C, Livingstone, KM, Marsaux, CF 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.
4. Wright, JL, Sherriff, JL, Dhaliwal, SS et al. (2011) Tailored, iterative, printed dietary feedback is as effective as group education in improving dietary behaviours: results from a randomised control trial in middle-aged adults with cardiovascular risk factors. Int J Behav Nutr Phys Act 8, 43.
5. Brandt, CJ, Brandt, V, Pedersen, M et al. (2014) Long-term effect of interactive online dietician weight Loss Advice in General Practice (LIVA) protocol for a randomized controlled trial. Int J Family Med. Available at http://dx.doi.org/10.1155/2014/245347
6. Joost, HG, Gibney, MJ, Cashman, KD et al. (2007) Personalised nutrition: status and perspectives. Br J Nutr 98, 2631.
7. Ronteltap, A, van Trijp, H, Berezowska, A et al. (2013) Nutrigenomics-based personalised nutritional advice: in search of a business model?. Genes Nutr 8, 153163.
8. de Roos, B (2013) Personalised nutrition: ready for practice? Proc Nutr Soc 72, 4852.
9. Department of Health (1991) Dietary Reference Values for Food Energy and Nutrients for the United Kingdom: Report of the Panel on Dietary Reference Values of the Committee on Medical Aspects of Food Policy. London: The Stationary Office.
10. Lustria, ML, Cortese, J, Noar, SM et al. (2009) Computer-tailored health interventions delivered over the Web: review and analysis of key components. Patient Educ Couns 74, 156173.
11. Lustria, ML, Noar, SM, Cortese, J et al. (2013) A meta-analysis of web-delivered tailored health behavior change interventions. J Health Commun 18, 10391069.
12. Ezendam, NP, Brug, J & Oenema, A (2012) Evaluation of the Web-based computer-tailored FATaintPHAT intervention to promote energy balance among adolescents: results from a school cluster randomized trial. Arch Pediatr Adolesc Med 166, 248255.
13. Biro, G, Hulshof, KF, Ovesen, L et al. (2002) Selection of methodology to assess food intake. Eur J Clin Nutr 56, S25S32.
14. Thompson, FE, Subar, AF, Loria, CM et al. (2010) Need for technological innovation in dietary assessment. J Am Diet Assoc 110, 4851.
15. Kerr, DA, Pollard, CM, Howat, P et al. (2012) Connecting Health and Technology (CHAT): protocol of a randomized controlled trial to improve nutrition behaviours using mobile devices and tailored text messaging in young adults. BMC Public Health 12, 477.
16. Recio-Rodríguez, JI, Martín-Cantera, C, González-Viejo, N et al. (2014) Effectiveness of a smartphone application for improving healthy lifestyles, a randomized clinical trial (EVIDENT II): study protocol. BMC Public Health 14, 254.
17. Springvloet, L, Lechner, L, de Vries, H et al. (2015) Short- and medium-term efficacy of a web-based computer-tailored nutrition education intervention for adults including cognitive and environmental feedback: randomized controlled trial. J Med Internet Res 17, e23.
18. Illner, AK, Freisling, H, Boeing, H et al. (2012) Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. Int J Epidemiol 41, 11871203.
19. Labonte, ME, Cyr, A, Baril-Gravel, L et al. (2012) Validity and reproducibility of a web-based, self-administered food frequency questionnaire. Eur J Clin Nutr 66, 166173.
20. Matthys, C, Pynaert, I, De Keyzer, W et al. (2007) Validity and reproducibility of an adolescent web-based food frequency questionnaire. J Am Diet Assoc 107, 605610.
21. Touvier, M, Kesse-Guyot, E, Méjean, C et al. (2011) Comparison between an interactive web-based self-administered 24 h dietary record and an interview by a dietitian for large-scale epidemiological studies. Br J Nutr 105, 10551064.
22. Hutchesson, MJ, Rollo, ME, Callister, R et al. (2015) Self-monitoring of dietary intake by young women: online food records completed on computer or smartphone are as accurate as paper-based food records but more acceptable. J Acad Nutr Diet 115, 8794.
23. Ngo, J, Engelen, A, Molag, M et al. (2009) A review of the use of information and communication technologies for dietary assessment. Br J Nutr 101, S102S112.
24. Stumbo, PJ (2013) New technology in dietary assessment: a review of digital methods in improving food record accuracy. Proc Nutr Soc 72, 7076.
25. Kristal, AR, Kolar, AS, Fisher, JL et al. (2014) Evaluation of web-based, self-administered, graphical food frequency questionnaire. J Acad Nutr Diet 114, 613621.
26. Carter, MC, Burley, VJ, Nykjaer, C et al. (2013) ‘My Meal Mate’ (MMM): validation of the diet measures captured on a smartphone application to facilitate weight loss. Br J Nutr 109, 539546.
27. Sun, M, Burke, L, Mao, Z et al. (2014) eButton: a wearable computer for health monitoring and personal assistance. Proc Des Autom Conf 2014, 16.
28. Schap, TE, Zhu, F, Delp, EJ et al. (2014) Merging dietary assessment with the adolescent lifestyle. J Hum Nutr Diet 27, S82S88.
29. Boushey, CJ, Kerr, DA, Wright, J et al. (2009) Use of technology in children's dietary assessment. Eur J Clin Nutr 63, S50S57.
30. 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.
31. Foster, E, Hawkins, A, Delve, J et al. (2014) Reducing the cost of dietary assessment: self-completed recall and analysis of nutrition for use with children (SCRAN24). J Hum Nutr Diet 27, S26S35.
32. Timon, CM, Astell, AJ, Hwang, F et al. (2015) The validation of a computer-based food record for older adults: the Novel Assessment of Nutrition and Ageing (NANA) method. Br J Nutr 113, 654664.
33. Forster, H, Fallaize, R, Gallagher, C et al. (2014) Online dietary intake estimation: the Food4Me food frequency questionnaire. J Med Internet Res 16, e150.
34. Hutchesson, MJ, Truby, H, Callister, R et al. (2013) Can a web-based food record accurately assess energy intake in overweight and obese women? A pilot study. J Hum Nutr Diet 26, S140S144.
35. Douglass, D, Islam, N, Baranowski, J et al. (2013) Simulated adaptations to an adult dietary self-report tool to accommodate children: impact on nutrient estimates. J Am Coll Nutr 32, 9297.
36. Biltoft-Jensen, A, Trolle, E, Christensen, T et al. (2014) WebDASC: a web-based dietary assessment software for 8–11-year-old Danish children. J Hum Nutr Diet 27, S43S53.
37. Moore, HJ, Hillier, FC, Batterham, AM et al. (2014) Technology-based dietary assessment: development of the Synchronised Nutrition and Activity Program (SNAP). J Hum Nutr Diet 27, S36S42.
38. Subar, AF, Kirkpatrick, SI, Mittl, B et al. (2012) The Automated Self-Administered 24-hour dietary recall (ASA24): a resource for researchers, clinicians, and educators from the National Cancer Institute. J Acad Nutr Diet 112, 11341137.
39. Arab, L, Tseng, CH, Ang, A et al. (2011) Validity of a multipass, web-based, 24-hour self-administered recall for assessment of total energy intake in blacks and whites. Am J Epidemiol 174, 12561265.
40. Liu, B, Young, H, Crowe, FL et al. (2011) Development and evaluation of the Oxford WebQ, a low-cost, web-based method for assessment of previous 24 h dietary intakes in large-scale prospective studies. Public Health Nutr 14, 19982005.
41. Kirkpatrick, SI, Subar, AF, Douglass, D et al. (2014) Performance of the automated self-administered 24-hour recall relative to a measure of true intakes and to an interviewer-administered 24-h recall. Am J Clin Nutr 100, 233240.
42. Arab, L, Wesseling-Perry, K, Jardack, P et al. (2010) Eight self-administered 24-hour dietary recalls using the Internet are feasible in African Americans and Whites: the energetics study. J Am Diet Assoc 110, 857864.
43. González Carrascosa, R, García Segovia, P & Martínez Monzó, J (2011) Paper and pencil vs online self-administered food frequency questionnaire (FFQ) applied to university population: a pilot study. Nutr Hosp 26, 13781384.
44. Beasley, JM, Davis, A & Riley, WT (2009) Evaluation of a web-based, pictorial diet history questionnaire. Public Health Nutr 12, 651659.
45. Vereecken, CA, De Bourdeaudhuij, I & Maes, L (2010) The HELENA online food frequency questionnaire: reproducibility and comparison with four 24-h recalls in Belgian-Flemish adolescents. Eur J Clin Nutr 64, 541548.
46. Beasley, J, Riley, W & Jean-Mary, J (2005) Accuracy of a PDA-based dietary assessment program. Nutrition 21, 672677.
47. Yon, BA, Johnson, RK, Harvey-Berino, J et al. (2007) Personal digital assistants are comparable to traditional diaries for dietary self-monitoring during a weight loss program. J Behav Med 30, 165175.
48. Fukuo, W, Yoshiuchi, K, Ohashi, K et al. (2009) Development of a hand-held personal digital assistant-based food diary with food photographs for Japanese subjects. J Am Diet Assoc 109, 12321236.
49. Sharp, DB & Allman-Farinelli, M (2014) Feasibility and validity of mobile phones to assess dietary intake. Nutrition 30, 12571266.
50. Weiss, R, Stumbo, PJ & Divakaran, A (2010) Automatic food documentation and volume computation using digital imaging and electronic transmission. J Am Diet Assoc 110, 4244.
51. Free, C, Phillips, G, Galli, L et al. (2013) The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med 10, e1001362.
52. Kong, F & Tan, J (2012) DietCam: automatic dietary assessment with mobile camera phones. Pervasive Mob. Comput. 8, 147163.
53. Nielsen (2013) The Mobile Consumer. A Global Snapshot. http://www.nielsen.com/content/dam/corporate/uk/en/documents/Mobile-Consumer-Report-2013.pdf (accessed March 2015).
54. Jospe, MR, Fairbairn, KA, Green, P et al. (2015) Diet app use by sports dietitians: a survey in five countries. JMIR Mhealth Uhealth 3, e7.
55. Turner-McGrievy, GM, Beets, MW, Moore, JB et al. (2013) Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program. J Am Med Inf Assoc 20, 513518.
56. Gemming, L, Utter, J & Ni Mhurchu, C (2015) Image-assisted dietary assessment: a systematic review of the evidence. J Acad Nutr Diet 115, 6477.
57. Khanna, N, Boushey, CJ, Kerr, D et al. (2010) An Overview of The Technology Assisted Dietary Assessment Project at Purdue University. Proceedings of the IEEE Int. Symp. on Multimedia, pp. 290–295.
58. Martin, CK, Nicklas, T, Gunturk, B et al. (2014) Measuring food intake with digital photography. J Hum Nutr Diet 27, S72S81.
59. Martin, CK, Correa, JB, Han, H et al. (2012) Validity of the Remote Food Photography Method (RFPM) for estimating energy and nutrient intake in near real-time. Obesity 20, 891899.
60. Zhu, F, Bosch, M, Woo, I et al. (2010) The use of mobile devices in aiding dietary assessment and evaluation. IEEE J Sel Top Signal Process 4, 756766.
61. O'Loughlin, G, Cullen, SJ, McGoldrick, A et al. (2013) Using a wearable camera to increase the accuracy of dietary analysis. Am J Prev Med 44, 297301.
62. Sternfeld, B, Block, C, Quesenberry, CP et al. (2009) Improving diet and physical activity with ALIVE: a worksite randomized trial. Am J Prev Med 36, 475483.
63. Ambeba, EJ, Ye, L, Sereika, SM et al. (2015) The use of mHealth to deliver tailored messages reduces reported energy and fat intake. J Cardiovasc Nurs 30, 3543.
64. Maes, L, Cook, TL, Ottovaere, C et al. (2011) Pilot evaluation of the HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) Food-O-Meter, a computer-tailored nutrition advice for adolescents: a study in six European cities. Public Health Nutr 14, 12921302.
65. Kroeze, W, Oenema, A, Campbell, M et al. (2008) The efficacy of Web-based and print-delivered computer-tailored interventions to reduce fat intake: results of a randomized, controlled trial. J Nutr Educ Behav 40, 226236.
66. Oenema, A, Tan, F & Brug, J (2005) Short-term efficacy of a web-based computer-tailored nutrition intervention: main effects and mediators. Ann Behav Med 29, 5463.
67. Kroeze, W, Oenema, A, Dagnelie, PC et al. (2008) Examining the minimal required elements of a computer-tailored intervention aimed at dietary fat reduction: results of a randomized controlled dismantling study. Health Educ Res 23, 880891.
68. Brug, J (1999) Dutch research into the development and impact of computer-tailored nutrition education. Eur J Clin Nutr 53, S78S82.
69. Harris, J, Felix, L, Miners, A et al. (2011) Adaptive e-learning to improve dietary behaviour: a systematic review and cost-effectiveness analysis. Health Technol Assess 15, 1160.
70. Capacci, S, Mazzocchi, M, Shankar, B et al. (2012) Policies to promote healthy eating in Europe: a structured review of policies and their effectiveness. Nutr Rev 70, 188200.
71. Noar, S, Harrington, N, Van Stee, S et al. (2011) Tailored health communication to change lifestyle behaviours. Am. J. Lifestyle Med 5, 112122.
72. Block, G, Sternfeld, B, Block, CH et al. (2008) Development of Alive! (A Lifestyle Intervention Via Email), and its effect on health-related quality of life, presenteeism, and other behavioral outcomes: randomized controlled trial. J Med Internet Res 10, e43.
73. Brug, J & van Assema, P (2000) Differences in use and impact of computer-tailored dietary fat-feedback according to stage of change and education. Appetite 34, 285293.
74. Celis-Morales, C, Lara, J & Mathers, JC (2014) Personalising nutritional guidance for more effective behaviour change. Proc Nutr Soc 12, 19.
75. Patrick, K, Calfas, KJ, Norman, GJ et al. (2011) Outcomes of a 12-month web-based intervention for overweight and obese men. Ann Behav Med 42, 391401.
76. Tate, DF, Jackvony, EH & Wing, RR (2006) A randomized trial comparing human e-mail counseling, computer-automated tailored counseling, and no counseling in an Internet weight loss program. Arch Intern Med 166, 16201625.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Proceedings of the Nutrition Society
  • ISSN: 0029-6651
  • EISSN: 1475-2719
  • URL: /core/journals/proceedings-of-the-nutrition-society
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords

Metrics

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