What Are They Really Eating? A Review on New Approaches to Dietary Intake Assessment and Validation
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Assessing food and nutrient intakes is critical to evolving our understanding of diet-disease relationships and the refinement of nutrition guidelines to support healthy populations. The aims of this narrative review are to summarise recent advances in dietary assessment methodologies, with a particular focus on approaches using new technologies, as well as strategies to evaluate tools, and to provide directions for future research.
Technology as a mode to assess dietary intake has gained momentum in recent years, with the development of image-based methods and wearable devices, as well as the emergence of online methods of administering traditional paper-based approaches to dietary assessment. At the same time, there have been advances in the development of dietary biomarkers to evaluate measures of self-reported dietary intake. Common biomarkers, such as plasma carotenoids and red blood cell fatty acids, are still being utilised with new markers including urinary markers of sugar or wholegrain intake, skin carotenoids as a measure of fruit and vegetable intake. As well, the field of metabolomics shows promise.
Challenges remain in dietary intake assessment, and further efforts are required to refine and evaluate methods so that we can better understand diet-disease relationships and inform guidelines and interventions to promote health.
KeywordsDietary assessment Technology Validation Biomarkers eHealth mHealth
Compliance with Ethical Standards
Conflict of Interest
Megan E. Rollo, Rebecca L. Williams, Tracy Burrows, Sharon I. Kirkpatrick, Tamara Bucher and Clare E. Collins declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
- 1.Australian Institute of Health and Welfare. Risk factors contributing to chronic disease. In: Government A, editor.: AIHW; 2012.Google Scholar
- 4.Australian Government. Guideline development: Eat for Health; 2015. Available from: https://www.eatforhealth.gov.au/guidelines/guideline-development [cited 2016 June].
- 7.de Souza RJ, Mente A, Maroleanu A, Cozma AI, Ha V, Kishibe T, et al. Intake of saturated and trans unsaturated fatty acids and risk of all cause mortality, cardiovascular disease, and type 2 diabetes: systematic review and meta-analysis of observational studies. 2015.Google Scholar
- 10.Medina-Remon A, Casas R, Tressserra-Rimbau A, Ros E, Martinez-Gonzalez MA, Fito M, et al. Polyphenol intake from a Mediterranean diet decreases inflammatory biomarkers related to atherosclerosis: a sub-study of The PREDIMED trial. Br J Clin Pharmacol. 2016.Google Scholar
- 11.European Commission. World food consumption patterns—trends and drivers. Europa; 2015.Google Scholar
- 13.Gibson RS. Principles of nutritional assessment. USA: Oxford University Press; 2005.Google Scholar
- 14.National Institutes of Health. Dietary assessment primer: National Cancer Institute; 2014. Available from: http://dietassessmentprimer.cancer.gov [cited 2016 July].
- 15.• Hedrick VE, Dietrich AM, Estabrooks PA, Savla J, Serrano E, Davy BM. Dietary biomarkers: advances, limitations and future directions. Nutr J. 2012;11(1):1. This paper reviews existing literature on current biomarkers, including metabolomics, and discusses the limitations and future directions of the biomarker research.CrossRefGoogle Scholar
- 19.Archer E, Pavela G, Lavie CJ, editors. The inadmissibility of what we eat in America and NHANES dietary data in nutrition and obesity research and the scientific formulation of national dietary guidelines. Mayo Clin Proc. Elsevier; 2015.Google Scholar
- 23.• Subar AF, Freedman LS, Tooze JA, Kirkpatrick SI, Boushey C, Neuhouser ML, et al. Addressing Current Criticism Regarding the Value of Self-Report Dietary Data. J Nutr. 2015. This commentary provides a balanced discussion on the strengths and limitations of self-report dietary intake data and delivers recommendations for collecting, analysing and interpreting dietary intake data.Google Scholar
- 27.Beaton GH, Milner J, Corey P, McGuire V, Cousins M, Stewart E, et al. Sources of variance in 24-hour dietary recall data: implications for nutrition study design and interpretation. American J Clin Nutr (USA). 1979.Google Scholar
- 32.•• Gemming L, Utter J, Ni MC. Image-assisted dietary assessment: a systematic review of the evidence. J Acad Nutr Diet. 2015;115(1):64–77. This is a comprehensive review that identifies studies using or evaluating validated image-assisted methods of dietary assessment. The review reports on the benefits of using image-assisted methods for reducing dietary misreporting.Google Scholar
- 34.Innovation U. How mobile phones are changing the developing world UNICEF Connect2015. Available from: https://blogs.unicef.org/innovation/how-mobile-phones-are-changing-the-developing-world/ [cited 6 Aug 2016].
- 36.National Cancer Institute. Automated Self-Administered 24-Hour (ASA24®) Dietary Assessment Tool: NCI, Division of Cancer Control & Population Sciences; 2016. Available from: http://epi.grants.cancer.gov/asa24/ [cited 2016 August].
- 39.Khanna N, Boushey CJ, Kerr D, Okos M, Ebert DS, Delp EJ, editors. An overview of the technology assisted dietary assessment project at Purdue University. Multimedia (ISM), 2010 I.E. International Symposium on; 2010: IEEE.Google Scholar
- 44.Kerr DA, Harray AJ, Pollard CM, Dhaliwal SS, Delp EJ, Howat PA, et al. The connecting health and technology study: a 6-month randomized controlled trial to improve nutrition behaviours using a mobile food record and text messaging support in young adults. Int J Behav Nutr Phys Act. 2016;13:52.CrossRefPubMedPubMedCentralGoogle Scholar
- 53.Lunden I. 6.1B Smartphone Users Globally By 2020, Overtaking Basic Fixed Phone Subscriptions: Techcrunch; 2015. Available from: http://techcrunch.com/2015/06/02/6-1b-smartphone-users-globally-by-2020-overtaking-basic-fixed-phone-subscriptions/ [cited 4 May 2016].
- 55.Salley JN, Hoover AW, Wilson ML, Muth ER. Comparison between human and bite-based methods of estimating caloric intake. J Acad Nutr Diet. 2016.Google Scholar
- 74.Yeung EH, Saudek CD, Jahren AH, Kao WHL, Islas M, Kraft R, et al. Evaluation of a novel isotope biomarker for dietary consumption of sweets. Am J Epidemiol. 2010:kwq247.Google Scholar
- 78.O’Brien DM, Black JA, Niles K, Schoeller D. The breath carbon isotope ratio is a promising biomarker of added sugar intake. FASEB J. 2016;30(1 Supplement):43.5.Google Scholar
- 83.Gibbons H, Brennan L. Metabolomics as a tool in the identification of dietary biomarkers. Proc Nutr Soc. 2016; FirstView:1–12.Google Scholar
- 87.• O’Gorman A, Brennan L. Metabolomic applications in nutritional research: a perspective. J Sci Food Agric. 2015;95(13):2567–70. This paper provides a summary of the application of metabolomics to the field of nutrition including biomarker identification, diet-related diseases and nutritional interventions.Google Scholar
- 89.Freedman LS, Commins JM, Moler JE, Arab L, Baer DJ, Kipnis V, et al. Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake. Am J Epi. 2014;80(2):172–188.Google Scholar
- 90.Lara JJ, Scott JA, Lean MEJ. Intentional mis-reporting of food consumption and its relationship with body mass index and psychological scores in women. J Human Nutr and Diet. 2004;17(3):209–218.Google Scholar
- 91.Lachat C, Hawwash D, Ocké MC, Berg C, Forsum E, Hörnell A, et al. Strengthening the reporting of observational studies in epidemiology - Nutritional Epidemiology (STROBE-nut): an extension of the STROBE statement. PLoS Med. 2016;13(6):e1002036.Google Scholar