Skip to main content
Top
Published in: Current Nutrition Reports 4/2016

01-12-2016 | Public Health and Translational Medicine (MEJ Lean, Section Editor)

What Are They Really Eating? A Review on New Approaches to Dietary Intake Assessment and Validation

Authors: Megan E. Rollo, Rebecca L. Williams, Tracy Burrows, Sharon I. Kirkpatrick, Tamara Bucher, Clare E. Collins

Published in: Current Nutrition Reports | Issue 4/2016

Login to get access

Abstract

Purpose

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.

Recent Findings

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.

Summary

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.
Literature
1.
go back to reference Australian Institute of Health and Welfare. Risk factors contributing to chronic disease. In: Government A, editor.: AIHW; 2012. Australian Institute of Health and Welfare. Risk factors contributing to chronic disease. In: Government A, editor.: AIHW; 2012.
2.
go back to reference Reedy J, Krebs-Smith SM, Miller PE, Liese AD, Kahle LL, Park Y, et al. Higher diet quality is associated with decreased risk of all-cause, cardiovascular disease, and cancer mortality among older adults. J Nutr. 2014;144(6):881–9.CrossRefPubMedPubMedCentral Reedy J, Krebs-Smith SM, Miller PE, Liese AD, Kahle LL, Park Y, et al. Higher diet quality is associated with decreased risk of all-cause, cardiovascular disease, and cancer mortality among older adults. J Nutr. 2014;144(6):881–9.CrossRefPubMedPubMedCentral
3.
go back to reference Jacques PF, Tucker KL. Are dietary patterns useful for understanding the role of diet in chronic disease? Am J Clin Nutr. 2001;73(1):1–2.PubMed Jacques PF, Tucker KL. Are dietary patterns useful for understanding the role of diet in chronic disease? Am J Clin Nutr. 2001;73(1):1–2.PubMed
5.
go back to reference Risérus U, Willett WC, Hu FB. Dietary fats and prevention of type 2 diabetes. Prog Lipid Res. 2009;48(1):44–51.CrossRefPubMed Risérus U, Willett WC, Hu FB. Dietary fats and prevention of type 2 diabetes. Prog Lipid Res. 2009;48(1):44–51.CrossRefPubMed
6.
go back to reference Siri-Tarino PW, Sun Q, Hu FB, Krauss RM. Saturated fatty acids and risk of coronary heart disease: modulation by replacement nutrients. Current atherosclerosis reports. 2010;12(6):384–90.CrossRefPubMedPubMedCentral Siri-Tarino PW, Sun Q, Hu FB, Krauss RM. Saturated fatty acids and risk of coronary heart disease: modulation by replacement nutrients. Current atherosclerosis reports. 2010;12(6):384–90.CrossRefPubMedPubMedCentral
7.
go back to reference 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. 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.
8.
go back to reference Astrup A, Dyerberg J, Elwood P, Hermansen K, Hu FB, Jakobsen MU, et al. The role of reducing intakes of saturated fat in the prevention of cardiovascular disease: where does the evidence stand in 2010? Am J Clin Nutr. 2011;93(4):684–8.CrossRefPubMedPubMedCentral Astrup A, Dyerberg J, Elwood P, Hermansen K, Hu FB, Jakobsen MU, et al. The role of reducing intakes of saturated fat in the prevention of cardiovascular disease: where does the evidence stand in 2010? Am J Clin Nutr. 2011;93(4):684–8.CrossRefPubMedPubMedCentral
9.
go back to reference Malik VS, Popkin BM, Bray GA, Després J-P, Hu FB. Sugar-sweetened beverages, obesity, type 2 diabetes mellitus, and cardiovascular disease risk. Circulation. 2010;121(11):1356–64.CrossRefPubMedPubMedCentral Malik VS, Popkin BM, Bray GA, Després J-P, Hu FB. Sugar-sweetened beverages, obesity, type 2 diabetes mellitus, and cardiovascular disease risk. Circulation. 2010;121(11):1356–64.CrossRefPubMedPubMedCentral
10.
go back to reference 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. 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.
11.
go back to reference European Commission. World food consumption patterns—trends and drivers. Europa; 2015. European Commission. World food consumption patterns—trends and drivers. Europa; 2015.
12.
go back to reference Mueller S, Szolnoki G. The relative influence of packaging, labelling, branding and sensory attributes on liking and purchase intent: consumers differ in their responsiveness. Food Qual Prefer. 2010;21(7):774–83.CrossRef Mueller S, Szolnoki G. The relative influence of packaging, labelling, branding and sensory attributes on liking and purchase intent: consumers differ in their responsiveness. Food Qual Prefer. 2010;21(7):774–83.CrossRef
13.
go back to reference Gibson RS. Principles of nutritional assessment. USA: Oxford University Press; 2005. Gibson RS. Principles of nutritional assessment. USA: Oxford University Press; 2005.
15.
go back to reference • 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.CrossRef • 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.CrossRef
16.
go back to reference Mishra G, McNaughton S, Bramwell G, Wadsworth M. Longitudinal changes in dietary patterns during adult life. Br J Nutr. 2006;96(04):735–44.PubMed Mishra G, McNaughton S, Bramwell G, Wadsworth M. Longitudinal changes in dietary patterns during adult life. Br J Nutr. 2006;96(04):735–44.PubMed
17.
go back to reference Popkin BM. Global nutrition dynamics: the world is shifting rapidly toward a diet linked with noncommunicable diseases. Am J Clin Nutr. 2006;84(2):289–98.PubMed Popkin BM. Global nutrition dynamics: the world is shifting rapidly toward a diet linked with noncommunicable diseases. Am J Clin Nutr. 2006;84(2):289–98.PubMed
18.
go back to reference Archer E, Hand GA, Blair SN. Validity of US nutritional surveillance: national health and nutrition examination survey caloric energy intake data, 1971–2010. PloS one. 2013;8(10):e76632.CrossRefPubMedPubMedCentral Archer E, Hand GA, Blair SN. Validity of US nutritional surveillance: national health and nutrition examination survey caloric energy intake data, 1971–2010. PloS one. 2013;8(10):e76632.CrossRefPubMedPubMedCentral
19.
go back to reference 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. 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.
20.
go back to reference Dhurandhar N, Schoeller D, Brown A, Heymsfield S, Thomas D, Sørensen T, et al. Energy balance measurement: when something is not better than nothing. Int J Obes (2005). 2015;39(7):1109–13.CrossRef Dhurandhar N, Schoeller D, Brown A, Heymsfield S, Thomas D, Sørensen T, et al. Energy balance measurement: when something is not better than nothing. Int J Obes (2005). 2015;39(7):1109–13.CrossRef
22.
go back to reference Mitka M. Do flawed data on caloric intake from NHANES present problems for researchers and policy makers? JAMA. 2013;310(20):2137–8.CrossRefPubMed Mitka M. Do flawed data on caloric intake from NHANES present problems for researchers and policy makers? JAMA. 2013;310(20):2137–8.CrossRefPubMed
23.
go back to reference • 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. • 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.
24.
go back to reference Neuhouser ML, Tinker L, Shaw PA, Schoeller D, Bingham SA, Van Horn L, et al. Use of recovery biomarkers to calibrate nutrient consumption self-reports in the women’s health initiative. Am J Epidemiol. 2008;167(10):1247–59.CrossRefPubMed Neuhouser ML, Tinker L, Shaw PA, Schoeller D, Bingham SA, Van Horn L, et al. Use of recovery biomarkers to calibrate nutrient consumption self-reports in the women’s health initiative. Am J Epidemiol. 2008;167(10):1247–59.CrossRefPubMed
25.
go back to reference O’Sullivan A, Gibney MJ, Brennan L. Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies. Am J Clin Nutr. 2011;93(2):314–21.CrossRefPubMed O’Sullivan A, Gibney MJ, Brennan L. Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies. Am J Clin Nutr. 2011;93(2):314–21.CrossRefPubMed
26.
27.
go back to reference 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. 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.
28.
go back to reference Freedman LS, Schatzkin A, Midthune D, Kipnis V. Dealing with dietary measurement error in nutritional cohort studies. J Natl Cancer Inst. 2011;103(14):1086–92.CrossRefPubMedPubMedCentral Freedman LS, Schatzkin A, Midthune D, Kipnis V. Dealing with dietary measurement error in nutritional cohort studies. J Natl Cancer Inst. 2011;103(14):1086–92.CrossRefPubMedPubMedCentral
29.
go back to reference Prentice RL, Huang Y. Measurement error modeling and nutritional epidemiology association analyses. Can J Stat. 2011;39(3):498–509.PubMedPubMedCentral Prentice RL, Huang Y. Measurement error modeling and nutritional epidemiology association analyses. Can J Stat. 2011;39(3):498–509.PubMedPubMedCentral
30.
go back to reference Tooze JA, Kipnis V, Buckman DW, Carroll RJ, Freedman LS, Guenther PM, et al. A mixed effects model approach for estimating the distribution of usual intake of nutrients: the NCI method. Stat Med. 2010;29(27):2857–68.CrossRefPubMed Tooze JA, Kipnis V, Buckman DW, Carroll RJ, Freedman LS, Guenther PM, et al. A mixed effects model approach for estimating the distribution of usual intake of nutrients: the NCI method. Stat Med. 2010;29(27):2857–68.CrossRefPubMed
31.
go back to reference Illner A, Freisling H, Boeing H, Huybrechts I, Crispim S, Slimani N. Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. Int J Epidemiol. 2012;41(4):1187–203.CrossRefPubMed Illner A, Freisling H, Boeing H, Huybrechts I, Crispim S, Slimani N. Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. Int J Epidemiol. 2012;41(4):1187–203.CrossRefPubMed
32.
go back to reference •• 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. •• 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.
33.
go back to reference Stumbo PJ. New technology in dietary assessment: a review of digital methods in improving food record accuracy. Proc Nutr Soc. 2013;72(01):70–6.CrossRefPubMed Stumbo PJ. New technology in dietary assessment: a review of digital methods in improving food record accuracy. Proc Nutr Soc. 2013;72(01):70–6.CrossRefPubMed
35.
go back to reference Burrows TL, Khambalia AZ, Perry R, Carty D, Hendrie GA, Allman‐Farinelli MA, et al. Great ‘app‐eal’but not there yet: a review of iPhone nutrition applications relevant to child weight management. Nutr Diet. 2015;72(4):363–7.CrossRef Burrows TL, Khambalia AZ, Perry R, Carty D, Hendrie GA, Allman‐Farinelli MA, et al. Great ‘app‐eal’but not there yet: a review of iPhone nutrition applications relevant to child weight management. Nutr Diet. 2015;72(4):363–7.CrossRef
37.
go back to reference Burrows TL, Collins K, Watson J, Guest M, Boggess MM, Neve M, et al. Validity of the Australian recommended food score as a diet quality index for pre-schoolers. Nutr J. 2014;13:87.CrossRefPubMedPubMedCentral Burrows TL, Collins K, Watson J, Guest M, Boggess MM, Neve M, et al. Validity of the Australian recommended food score as a diet quality index for pre-schoolers. Nutr J. 2014;13:87.CrossRefPubMedPubMedCentral
38.
go back to reference Collins CE, Burrows TL, Rollo ME, Boggess MM, Watson JF, Guest M, et al. The comparative validity and reproducibility of a diet quality index for adults: the Australian recommended food score. Nutrients. 2015;7(2):785–98.CrossRefPubMedPubMedCentral Collins CE, Burrows TL, Rollo ME, Boggess MM, Watson JF, Guest M, et al. The comparative validity and reproducibility of a diet quality index for adults: the Australian recommended food score. Nutrients. 2015;7(2):785–98.CrossRefPubMedPubMedCentral
39.
go back to reference 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. 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.
40.
go back to reference Daugherty BL, Schap TE, Ettienne-Gittens R, Zhu FM, Bosch M, Delp EJ, et al. Novel technologies for assessing dietary intake: evaluating the usability of a mobile telephone food record among adults and adolescents. J Med Internet Res. 2012;14(2):e58.CrossRefPubMedPubMedCentral Daugherty BL, Schap TE, Ettienne-Gittens R, Zhu FM, Bosch M, Delp EJ, et al. Novel technologies for assessing dietary intake: evaluating the usability of a mobile telephone food record among adults and adolescents. J Med Internet Res. 2012;14(2):e58.CrossRefPubMedPubMedCentral
41.
go back to reference Zhu F, Bosch M, Khanna N, Boushey CJ, Delp EJ. Multiple hypotheses image segmentation and classification with application to dietary assessment. IEEE J Biomed Health Inform. 2015;19(1):377–88.CrossRefPubMedPubMedCentral Zhu F, Bosch M, Khanna N, Boushey CJ, Delp EJ. Multiple hypotheses image segmentation and classification with application to dietary assessment. IEEE J Biomed Health Inform. 2015;19(1):377–88.CrossRefPubMedPubMedCentral
42.
go back to reference Zhu F, Bosch M, Woo I, Kim S, Boushey CJ, Ebert DS, et al. The use of mobile devices in aiding dietary assessment and evaluation. IEEE J Sel Top Signal Process. 2010;4(4):756–66.CrossRefPubMedPubMedCentral Zhu F, Bosch M, Woo I, Kim S, Boushey CJ, Ebert DS, et al. The use of mobile devices in aiding dietary assessment and evaluation. IEEE J Sel Top Signal Process. 2010;4(4):756–66.CrossRefPubMedPubMedCentral
43.
go back to reference Harray AJ, Boushey CJ, Pollard CM, Delp EJ, Ahmad Z, Dhaliwal SS, et al. A novel dietary assessment method to measure a healthy and sustainable diet using the mobile food record: protocol and methodology. Nutrients. 2015;7(7):5375–95.CrossRefPubMedPubMedCentral Harray AJ, Boushey CJ, Pollard CM, Delp EJ, Ahmad Z, Dhaliwal SS, et al. A novel dietary assessment method to measure a healthy and sustainable diet using the mobile food record: protocol and methodology. Nutrients. 2015;7(7):5375–95.CrossRefPubMedPubMedCentral
44.
go back to reference 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.CrossRefPubMedPubMedCentral 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.CrossRefPubMedPubMedCentral
45.
go back to reference Rollo ME, Ash S, Lyons-Wall P, Russell AW. Evaluation of a mobile phone image-based dietary assessment method in adults with type 2 diabetes. Nutrients. 2015;7(6):4897–910.CrossRefPubMedPubMedCentral Rollo ME, Ash S, Lyons-Wall P, Russell AW. Evaluation of a mobile phone image-based dietary assessment method in adults with type 2 diabetes. Nutrients. 2015;7(6):4897–910.CrossRefPubMedPubMedCentral
46.
go back to reference Sun M, Burke LE, Mao ZH, Chen Y, Chen HC, Bai Y, et al. eButton: a wearable computer for health monitoring and personal assistance. Proc Des Autom Conf. 2014;2014:1–6.PubMedPubMedCentral Sun M, Burke LE, Mao ZH, Chen Y, Chen HC, Bai Y, et al. eButton: a wearable computer for health monitoring and personal assistance. Proc Des Autom Conf. 2014;2014:1–6.PubMedPubMedCentral
47.
go back to reference Sun M, Fernstrom JD, Jia W, Hackworth SA, Yao N, Li Y, et al. A wearable electronic system for objective dietary assessment. J Am Diet Assoc. 2010;110(1):45.CrossRefPubMedPubMedCentral Sun M, Fernstrom JD, Jia W, Hackworth SA, Yao N, Li Y, et al. A wearable electronic system for objective dietary assessment. J Am Diet Assoc. 2010;110(1):45.CrossRefPubMedPubMedCentral
48.
go back to reference Gemming L, Doherty A, Kelly P, Utter J, Ni MC. Feasibility of a SenseCam-assisted 24-h recall to reduce under-reporting of energy intake. Eur J Clin Nutr. 2013;67(10):1095–9.CrossRefPubMed Gemming L, Doherty A, Kelly P, Utter J, Ni MC. Feasibility of a SenseCam-assisted 24-h recall to reduce under-reporting of energy intake. Eur J Clin Nutr. 2013;67(10):1095–9.CrossRefPubMed
49.
go back to reference Gemming L, Rush E, Maddison R, Doherty A, Gant N, Utter J, et al. Wearable cameras can reduce dietary under-reporting: doubly labelled water validation of a camera-assisted 24 h recall. Br J Nutr. 2015;113(2):284–91.CrossRefPubMed Gemming L, Rush E, Maddison R, Doherty A, Gant N, Utter J, et al. Wearable cameras can reduce dietary under-reporting: doubly labelled water validation of a camera-assisted 24 h recall. Br J Nutr. 2015;113(2):284–91.CrossRefPubMed
50.
go back to reference Kong F, He H, Raynor HA, Tan J. DietCam: multi-view regular shape food recognition with a camera phone. Pervasive and Mobile Computing. 2015;19:108–21.CrossRef Kong F, He H, Raynor HA, Tan J. DietCam: multi-view regular shape food recognition with a camera phone. Pervasive and Mobile Computing. 2015;19:108–21.CrossRef
51.
go back to reference Jia W, Chen H-C, Yue Y, Li Z, Fernstrom J, Bai Y, et al. Accuracy of food portion size estimation from digital pictures acquired by a chest-worn camera. Public Health Nutr. 2014;17(08):1671–81.CrossRefPubMed Jia W, Chen H-C, Yue Y, Li Z, Fernstrom J, Bai Y, et al. Accuracy of food portion size estimation from digital pictures acquired by a chest-worn camera. Public Health Nutr. 2014;17(08):1671–81.CrossRefPubMed
52.
go back to reference Martin CK, Nicklas T, Gunturk B, Correa JB, Allen HR, Champagne C. Measuring food intake with digital photography. J Hum Nutr Diet. 2014;27 Suppl 1:72–81.CrossRefPubMed Martin CK, Nicklas T, Gunturk B, Correa JB, Allen HR, Champagne C. Measuring food intake with digital photography. J Hum Nutr Diet. 2014;27 Suppl 1:72–81.CrossRefPubMed
54.
go back to reference Dong Y, Hoover A, Scisco J, Muth E. A new method for measuring meal intake in humans via automated wrist motion tracking. Appl Psychophysiol Biofeedback. 2012;37(3):205–15.CrossRefPubMedPubMedCentral Dong Y, Hoover A, Scisco J, Muth E. A new method for measuring meal intake in humans via automated wrist motion tracking. Appl Psychophysiol Biofeedback. 2012;37(3):205–15.CrossRefPubMedPubMedCentral
55.
go back to reference Salley JN, Hoover AW, Wilson ML, Muth ER. Comparison between human and bite-based methods of estimating caloric intake. J Acad Nutr Diet. 2016. Salley JN, Hoover AW, Wilson ML, Muth ER. Comparison between human and bite-based methods of estimating caloric intake. J Acad Nutr Diet. 2016.
57.
go back to reference Fontana JM, Higgins JA, Schuckers SC, Bellisle F, Pan Z, Melanson EL, et al. Energy intake estimation from counts of chews and swallows. Appetite. 2015;85:14–21.CrossRefPubMed Fontana JM, Higgins JA, Schuckers SC, Bellisle F, Pan Z, Melanson EL, et al. Energy intake estimation from counts of chews and swallows. Appetite. 2015;85:14–21.CrossRefPubMed
58.
go back to reference Sazonov ES, Fontana JM. A sensor system for automatic detection of food intake through non-invasive monitoring of chewing. IEEE Sens J. 2012;12(5):1340–8.CrossRefPubMedPubMedCentral Sazonov ES, Fontana JM. A sensor system for automatic detection of food intake through non-invasive monitoring of chewing. IEEE Sens J. 2012;12(5):1340–8.CrossRefPubMedPubMedCentral
59.
go back to reference Fontana JM, Farooq M, Sazonov E. Automatic ingestion monitor: a novel wearable device for monitoring of ingestive behavior. IEEE Trans Biomed Eng. 2014;61(6):1772–9.CrossRef Fontana JM, Farooq M, Sazonov E. Automatic ingestion monitor: a novel wearable device for monitoring of ingestive behavior. IEEE Trans Biomed Eng. 2014;61(6):1772–9.CrossRef
60.
go back to reference Farooq M, Chandler-Laney PC, Hernandez-Reif M, Sazonov E. Monitoring of infant feeding behavior using a jaw motion sensor. J Healthcare Eng. 2015;6(1):23–40.CrossRef Farooq M, Chandler-Laney PC, Hernandez-Reif M, Sazonov E. Monitoring of infant feeding behavior using a jaw motion sensor. J Healthcare Eng. 2015;6(1):23–40.CrossRef
61.
go back to reference Farooq M, Sazonov E. A novel wearable device for food intake and physical activity recognition. Sensors (Basel, Switzerland). 2016;16(7):1067.CrossRef Farooq M, Sazonov E. A novel wearable device for food intake and physical activity recognition. Sensors (Basel, Switzerland). 2016;16(7):1067.CrossRef
62.
go back to reference Jenab M, Slimani N, Bictash M, Ferrari P, Bingham SA. Biomarkers in nutritional epidemiology: applications, needs and new horizons. Hum Genet. 2009;125(5):507–25.CrossRefPubMed Jenab M, Slimani N, Bictash M, Ferrari P, Bingham SA. Biomarkers in nutritional epidemiology: applications, needs and new horizons. Hum Genet. 2009;125(5):507–25.CrossRefPubMed
63.
go back to reference Corella D, Ordovás JM. Biomarkers: background, classification and guidelines for applications in nutritional epidemiology. Nutr Hosp. 2015;31(s03):177–88.PubMed Corella D, Ordovás JM. Biomarkers: background, classification and guidelines for applications in nutritional epidemiology. Nutr Hosp. 2015;31(s03):177–88.PubMed
64.
go back to reference Burrows T, Truby H, Morgan P, Callister R, Davies P, Collins CE. A comparison and validation of child versus parent reporting of children’s energy intake using food frequency questionnaires versus food records: who’s an accurate reporter? Clin Nutr. 2013;32(4):613–8.CrossRefPubMed Burrows T, Truby H, Morgan P, Callister R, Davies P, Collins CE. A comparison and validation of child versus parent reporting of children’s energy intake using food frequency questionnaires versus food records: who’s an accurate reporter? Clin Nutr. 2013;32(4):613–8.CrossRefPubMed
65.
go back to reference Puiggròs F, Solà R, Bladé C, Salvadó M-J, Arola L. Nutritional biomarkers and foodomic methodologies for qualitative and quantitative analysis of bioactive ingredients in dietary intervention studies. J Chromatogr A. 2011;1218(42):7399–414.CrossRefPubMed Puiggròs F, Solà R, Bladé C, Salvadó M-J, Arola L. Nutritional biomarkers and foodomic methodologies for qualitative and quantitative analysis of bioactive ingredients in dietary intervention studies. J Chromatogr A. 2011;1218(42):7399–414.CrossRefPubMed
66.
go back to reference Collins CE, Burrows TL, Truby H, Morgan PJ, Wright IM, Davies PS, et al. Comparison of energy intake in toddlers assessed by food frequency questionnaire and total energy expenditure measured by the doubly labeled water method. J Acad Nutr Diet. 2013;113(3):459–63.CrossRefPubMed Collins CE, Burrows TL, Truby H, Morgan PJ, Wright IM, Davies PS, et al. Comparison of energy intake in toddlers assessed by food frequency questionnaire and total energy expenditure measured by the doubly labeled water method. J Acad Nutr Diet. 2013;113(3):459–63.CrossRefPubMed
67.
go back to reference Burrows TL, Martin RJ, Collins CE. A systematic review of the validity of dietary assessment methods in children when compared with the method of doubly labeled water. J Am Diet Assoc. 2010;110(10):1501–10.CrossRefPubMed Burrows TL, Martin RJ, Collins CE. A systematic review of the validity of dietary assessment methods in children when compared with the method of doubly labeled water. J Am Diet Assoc. 2010;110(10):1501–10.CrossRefPubMed
68.
go back to reference Freedman LS, Commins JM, Moler JE, Willett W, Tinker LF, Subar AF, et al. Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for potassium and sodium intake. Am J Epidemiol. 2015;181(7):473–87.CrossRefPubMedPubMedCentral Freedman LS, Commins JM, Moler JE, Willett W, Tinker LF, Subar AF, et al. Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for potassium and sodium intake. Am J Epidemiol. 2015;181(7):473–87.CrossRefPubMedPubMedCentral
69.
go back to reference Bingham SA. Urine nitrogen as a biomarker for the validation of dietary protein intake. J Nutr. 2003;133(3):921S–4S.PubMed Bingham SA. Urine nitrogen as a biomarker for the validation of dietary protein intake. J Nutr. 2003;133(3):921S–4S.PubMed
70.
go back to reference Subar AF, Kipnis V, Troiano RP, Midthune D, Schoeller DA, Bingham S, et al. Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN study. Am J Epidemiol. 2003;158(1):1–13.CrossRefPubMed Subar AF, Kipnis V, Troiano RP, Midthune D, Schoeller DA, Bingham S, et al. Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN study. Am J Epidemiol. 2003;158(1):1–13.CrossRefPubMed
71.
go back to reference Burrows TL, Hutchesson MJ, Rollo ME, Boggess MM, Guest M, Collins CE. Fruit and vegetable intake assessed by food frequency questionnaire and plasma carotenoids: a validation study in adults. Nutrients. 2015;7(5):3240–51.CrossRefPubMedPubMedCentral Burrows TL, Hutchesson MJ, Rollo ME, Boggess MM, Guest M, Collins CE. Fruit and vegetable intake assessed by food frequency questionnaire and plasma carotenoids: a validation study in adults. Nutrients. 2015;7(5):3240–51.CrossRefPubMedPubMedCentral
72.
go back to reference Burrows TL, Warren JM, Colyvas K, Garg ML, Collins CE. Validation of overweight children’s fruit and vegetable intake using plasma carotenoids. Obesity. 2009;17(1):162–8.CrossRefPubMed Burrows TL, Warren JM, Colyvas K, Garg ML, Collins CE. Validation of overweight children’s fruit and vegetable intake using plasma carotenoids. Obesity. 2009;17(1):162–8.CrossRefPubMed
73.
go back to reference Burrows T, Berthon B, Garg ML, Collins CE. A comparative validation of a child food frequency questionnaire using red blood cell membrane fatty acids. Eur J Clin Nutr. 2012;66(7):825–9.CrossRefPubMed Burrows T, Berthon B, Garg ML, Collins CE. A comparative validation of a child food frequency questionnaire using red blood cell membrane fatty acids. Eur J Clin Nutr. 2012;66(7):825–9.CrossRefPubMed
74.
go back to reference 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. 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.
75.
go back to reference Davy BM, Jahren AH, Hedrick VE, Comber DL. Association of δ 13 C in fingerstick blood with added-sugar and sugar-sweetened beverage intake. J Am Diet Assoc. 2011;111(6):874–8.CrossRefPubMedPubMedCentral Davy BM, Jahren AH, Hedrick VE, Comber DL. Association of δ 13 C in fingerstick blood with added-sugar and sugar-sweetened beverage intake. J Am Diet Assoc. 2011;111(6):874–8.CrossRefPubMedPubMedCentral
76.
go back to reference Kuhnle GGC. Nutritional biomarkers for objective dietary assessment. J Sci Food Agric. 2012;92(6):1145–9.CrossRefPubMed Kuhnle GGC. Nutritional biomarkers for objective dietary assessment. J Sci Food Agric. 2012;92(6):1145–9.CrossRefPubMed
78.
go back to reference 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. 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.
79.
go back to reference Choy K, Nash SH, Kristal AR, Hopkins S, Boyer BB, O’Brien DM. The carbon isotope ratio of alanine in red blood cells is a new candidate biomarker of sugar-sweetened beverage intake. J Nutr. 2013;143(6):878–84.CrossRefPubMedPubMedCentral Choy K, Nash SH, Kristal AR, Hopkins S, Boyer BB, O’Brien DM. The carbon isotope ratio of alanine in red blood cells is a new candidate biomarker of sugar-sweetened beverage intake. J Nutr. 2013;143(6):878–84.CrossRefPubMedPubMedCentral
80.
go back to reference van Dam RM, Hu FB. Are alkylresorcinols accurate biomarkers for whole grain intake? Am J Clin Nutr. 2008;87(4):797–8.PubMed van Dam RM, Hu FB. Are alkylresorcinols accurate biomarkers for whole grain intake? Am J Clin Nutr. 2008;87(4):797–8.PubMed
81.
go back to reference Ermakov IV, Gellermann W. Optical detection methods for carotenoids in human skin. Arch Biochem Biophys. 2015;572:101–11.CrossRefPubMed Ermakov IV, Gellermann W. Optical detection methods for carotenoids in human skin. Arch Biochem Biophys. 2015;572:101–11.CrossRefPubMed
82.
go back to reference Pezdirc K, Hutchesson MJ, Whitehead R, Ozakinci G, Perrett D, Collins CE. Fruit, vegetable and dietary carotenoid intakes explain variation in skin-color in young Caucasian women: a cross-sectional study. Nutrients. 2015;7(7):5800–15.CrossRefPubMedPubMedCentral Pezdirc K, Hutchesson MJ, Whitehead R, Ozakinci G, Perrett D, Collins CE. Fruit, vegetable and dietary carotenoid intakes explain variation in skin-color in young Caucasian women: a cross-sectional study. Nutrients. 2015;7(7):5800–15.CrossRefPubMedPubMedCentral
83.
go back to reference Gibbons H, Brennan L. Metabolomics as a tool in the identification of dietary biomarkers. Proc Nutr Soc. 2016; FirstView:1–12. Gibbons H, Brennan L. Metabolomics as a tool in the identification of dietary biomarkers. Proc Nutr Soc. 2016; FirstView:1–12.
84.
go back to reference Hanhineva K, Lankinen MA, Pedret A, Schwab U, Kolehmainen M, Paananen J, et al. Nontargeted metabolite profiling discriminates diet-specific biomarkers for consumption of whole grains, fatty fish, and bilberries in a randomized controlled trial. J Nutr. 2015;145(1):7–17.CrossRefPubMed Hanhineva K, Lankinen MA, Pedret A, Schwab U, Kolehmainen M, Paananen J, et al. Nontargeted metabolite profiling discriminates diet-specific biomarkers for consumption of whole grains, fatty fish, and bilberries in a randomized controlled trial. J Nutr. 2015;145(1):7–17.CrossRefPubMed
85.
go back to reference Brennan L. Metabolomics in nutrition research: current status and perspectives. Biochem Soc Trans. 2013;41(2):670–3.CrossRefPubMed Brennan L. Metabolomics in nutrition research: current status and perspectives. Biochem Soc Trans. 2013;41(2):670–3.CrossRefPubMed
86.
go back to reference Adeva MM, Calvino J, Souto G, Donapetry C. Insulin resistance and the metabolism of branched-chain amino acids in humans. Amino Acids. 2012;43(1):171–81.CrossRefPubMed Adeva MM, Calvino J, Souto G, Donapetry C. Insulin resistance and the metabolism of branched-chain amino acids in humans. Amino Acids. 2012;43(1):171–81.CrossRefPubMed
87.
go back to reference • 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. • 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.
88.
go back to reference Collins CE, Boggess MM, Watson JF, Guest M, Duncanson K, Pezdirc K, et al. Reproducibility and comparative validity of a food frequency questionnaire for Australian adults. Clin Nutr. 2014;33(5):906–14.CrossRefPubMed Collins CE, Boggess MM, Watson JF, Guest M, Duncanson K, Pezdirc K, et al. Reproducibility and comparative validity of a food frequency questionnaire for Australian adults. Clin Nutr. 2014;33(5):906–14.CrossRefPubMed
89.
go back to reference 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. 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.
90.
go back to reference 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. 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.
91.
go back to reference 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. 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.
Metadata
Title
What Are They Really Eating? A Review on New Approaches to Dietary Intake Assessment and Validation
Authors
Megan E. Rollo
Rebecca L. Williams
Tracy Burrows
Sharon I. Kirkpatrick
Tamara Bucher
Clare E. Collins
Publication date
01-12-2016
Publisher
Springer US
Published in
Current Nutrition Reports / Issue 4/2016
Electronic ISSN: 2161-3311
DOI
https://doi.org/10.1007/s13668-016-0182-6

Other articles of this Issue 4/2016

Current Nutrition Reports 4/2016 Go to the issue

Public Health and Translational Medicine (MEJ Lean, Section Editor)

Beyond BMI: How to Capture Influences from Body Composition in Health Surveys

Public Health and Translational Medicine (MEJ Lean, Section Editor)

Personalized Health, eLearning, and mHealth Interventions to Improve Nutritional Status

Diabetes and Obesity (MC de Oliveira Otto, Section Editor)

Mapping of Reviews on Breastfeeding and Obesity Risk in Children

Diabetes and Obesity (MC de Oliveira Otto, Section Editor)

The Role of Sleep Duration on Energy Balance: an Update