Advertisement

Dietary Intake Assessment: From Traditional Paper-Pencil Questionnaires to Technology-Based Tools

  • Elske M. Brouwer-BrolsmaEmail author
  • Desiree Lucassen
  • Marielle G. de Rijk
  • Anne Slotegraaf
  • Corine Perenboom
  • Karin Borgonjen
  • Els Siebelink
  • Edith J. M. Feskens
  • Jeanne H. M. de Vries
Conference paper
  • 104 Downloads
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 554)

Abstract

Self-reported methods of recall and real-time recording are the most commonly used approaches to assess dietary intake, both in research as well as the health-care setting. The traditional versions of these methods are limited by various methodological factors and burdensome for interviewees and researchers. Technology-based dietary assessment tools have the potential to improve the accuracy of the data and reduce interviewee and researcher burden. Consequently, various research groups around the globe started to explore the use of technology-based tools. This paper provides an overview of the: (1) most-commonly used and generally accepted methods to assess dietary intake; (2) errors encountered using these methods; and (3) web-based and app-based tools (i.e., Compl-eatTM, Traqq, Dutch FFQ-TOOLTM, and “Eetscore”) that have been developed by researchers of the Division of Human Nutrition and Health of Wageningen University during the past years.

Keywords

Technology-based dietary intake assessment App Sensors Biomarkers FFQ Recall Food record Dietary history 

References

  1. 1.
    Carpenter, K.J.: The discovery of vitamin C. Ann. Nutr. Metab. 61, 259–264 (2012)CrossRefGoogle Scholar
  2. 2.
    Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet (London, England), vol. 390, pp. 2627–2642 (2017)Google Scholar
  3. 3.
    Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet (London, England), vol. 388, pp. 1545–1602 (2016)Google Scholar
  4. 4.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
    Brouwer-Brolsma, E.M., Sluik, D., Singh-Povel, C.M., Feskens, E.J.M.: Dairy shows different associations with abdominal and BMI-defined overweight: cross-sectional analyses exploring a variety of dairy products. Nutr. Metab. Cardiovasc. Dis. 28, 451–460 (2018)CrossRefGoogle Scholar
  9. 9.
    Brouwer-Brolsma, E.M., Sluik, D., Singh-Povel, C.M., Feskens, E.J.M.: Dairy product consumption is associated with pre-diabetes and newly diagnosed type 2 diabetes in the lifelines cohort study. Br. J. Nutr. 119, 442–455 (2018)CrossRefGoogle Scholar
  10. 10.
    Staudacher, H.M., Irving, P.M., Lomer, M.C.E., Whelan, K.: The challenges of control groups, placebos and blinding in clinical trials of dietary interventions. Proc. Nutr. Soc. 76, 203–212 (2017)CrossRefGoogle Scholar
  11. 11.
    Huybrechts, I., et al.: Global comparison of national individual food consumption surveys as a basis for health research and integration in national health surveillance programmes. Proc. Nutr. Soc. 76, 549–567 (2017)CrossRefGoogle Scholar
  12. 12.
    Willett, W.C.: Nutritional Epidemiology. Oxford University Press, Inc., New York (2013)Google Scholar
  13. 13.
    Moshfegh, A.J., et al.: The US department of agriculture automated multiple-pass method reduces bias in the collection of energy intakes. Am. J. Clin. Nutr. 88, 324–332 (2008)CrossRefGoogle Scholar
  14. 14.
    Blanton, C.A., Moshfegh, A.J., Baer, D.J., Kretsch, M.J.: The USDA automated multiple-pass method accurately estimates group total energy and nutrient intake. J. Nutr. 136, 2594–2599 (2006)CrossRefGoogle Scholar
  15. 15.
    Touvier, M., et al.: 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, 1055–1064 (2011)CrossRefGoogle Scholar
  16. 16.
    Subar, A.F., et al.: 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, 1134–1137 (2012)CrossRefGoogle Scholar
  17. 17.
    Foster, E., et al.: Validity and reliability of an online self-report 24-h dietary recall method (Intake24): a doubly labelled water study and repeated-measures analysis. J. Nutr. Sci. 8, e29 (2019)CrossRefGoogle Scholar
  18. 18.
    Arab, L., Wesseling-Perry, K., Jardack, P., Henry, J., Winter, A.: 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, 857–864 (2010)CrossRefGoogle Scholar
  19. 19.
    Greenwood, D.C., et al.: Validation of the Oxford WebQ online 24-hour dietary questionnaire using biomarkers. Am. J. Epidemiol. 188, 1858–1867 (2019)CrossRefGoogle Scholar
  20. 20.
    Wark, P.A., et al.: Validity of an online 24-h recall tool (myfood24) for dietary assessment in population studies: comparison with biomarkers and standard interviews. BMC Med. 16, 136 (2018)CrossRefGoogle Scholar
  21. 21.
    Timon, C.M., et al.: Comparison of a web-based 24-h dietary recall tool (Foodbook24) to an interviewer-led 24-h dietary recall. Nutrients 9, 425 (2017)CrossRefGoogle Scholar
  22. 22.
    Meijboom, S., et al.: Evaluation of dietary intake assessed by the Dutch self-administered web-based dietary 24-h recall tool (Compl-eatTM) against interviewer-administered telephone-based 24-h recalls. J. Nutr. Sci. 6, e49 (2017)CrossRefGoogle Scholar
  23. 23.
    Moran Fagundez, L.J., Rivera Torres, A., Gonzalez Sanchez, M.E., de Torres Aured, M.L., Perez Rodrigo, C., Irles Rocamora, J.A.: Diet history: method and applications. Nutr. Hosp. 31(Suppl 3), 57–61 (2015)Google Scholar
  24. 24.
    Bloemberg, B.P., Kromhout, D., Obermann-De Boer, G.L., Van Kampen-Donker, M.: The reproducibility of dietary intake data assessed with the cross-check dietary history method. Am. J. Epidemiol. 130, 1047–1056 (1989)CrossRefGoogle Scholar
  25. 25.
    van Rossum, C.T.M., et al.: The diet of the Dutch: Results of the first two years of the Dutch National Food Consumption Survey 2012–2016. Dutch Institute for Public Health and the Environment (2016)Google Scholar
  26. 26.
    Brouwer-Brolsma, E.M., et al.: A national dietary assessment reference database (NDARD) for the dutch population: rationale behind the design. Nutrients 9, 1136 (2017)CrossRefGoogle Scholar
  27. 27.
    Jenab, M., Slimani, N., Bictash, M., Ferrari, P., Bingham, S.A.: Biomarkers in nutritional epidemiology: applications, needs and new horizons. Hum. Genet. 125, 507–525 (2009)CrossRefGoogle Scholar
  28. 28.
    Brevik, A., Andersen, L.F., Karlsen, A., Trygg, K.U., Blomhoff, R., Drevon, C.A.: Six carotenoids in plasma used to assess recommended intake of fruits and vegetables in a controlled feeding study. Eur. J. Clin. Nutr. 58, 1166–1173 (2004)CrossRefGoogle Scholar
  29. 29.
    Al-Delaimy, W.K., et al.: Plasma carotenoids as biomarkers of intake of fruits and vegetables: individual-level correlations in the European Prospective Investigation into Cancer and Nutrition (EPIC). Eur. J. Clin. Nutr. 59, 1387–1396 (2005)CrossRefGoogle Scholar
  30. 30.
    Saadatian-Elahi, M., et al.: Plasma phospholipid fatty acid profiles and their association with food intakes: results from a cross-sectional study within the European prospective investigation into cancer and nutrition. Am. J. Clin. Nutr. 89, 331–346 (2009)CrossRefGoogle Scholar
  31. 31.
    Brouwer-Brolsma, E.M., et al.: Combining traditional dietary assessment methods with novel metabolomics techniques: present efforts by the food biomarker alliance. Proc. Nutr. Soc. 76, 619–627 (2017)CrossRefGoogle Scholar
  32. 32.
    Trijsburg, L., et al.: Comparison of duplicate portion and 24 h recall as reference methods for validating a FFQ using urinary markers as the estimate of true intake. Br. J. Nutr. 114, 1304–1312 (2015)CrossRefGoogle Scholar
  33. 33.
    Cade, J., Thompson, R., Burley, V., Warm, D.: Development, validation and utilisation of food-frequency questionnaires - a review. Public Health Nutr. 5, 567–587 (2002)CrossRefGoogle Scholar
  34. 34.
    Kristal, A.R., et al.: Evaluation of web-based, self-administered, graphical food frequency questionnaire. J. Acad. Nutr. Diet. 114, 613–621 (2014)CrossRefGoogle Scholar
  35. 35.
    Fallaize, R., et al.: 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 (2014)CrossRefGoogle Scholar
  36. 36.
    Labonte, M.E., Cyr, A., Baril-Gravel, L., Royer, M.M., Lamarche, B.: Validity and reproducibility of a web-based, self-administered food frequency questionnaire. Eur. J. Clin. Nutr. 66, 166–173 (2012)CrossRefGoogle Scholar
  37. 37.
    Wise, A., Birrell, N.M.: Design and analysis of food frequency questionnaires–review and novel method. Int. J. Food Sci. Nutr. 53, 273–279 (2002)CrossRefGoogle Scholar
  38. 38.
    Molag, M.: Towards transparent development of food frequency questionnaires. Scientific basis of the Dutch FFQ-TOOLTM: a computer system to generate, apply and process FFQs. Division of Human Nutrition and Health. Ph.D. Wageningen University, Wageningen (2010)Google Scholar
  39. 39.
    Cameron, M.E., van Staveren, W.A.: Manual on Methodology for Food Consumption Studies. Methods for Data Collection at an Individual Level. Oxford University Press, Oxford (1988)Google Scholar
  40. 40.
    Eldridge, A.L., et al.: Evaluation of new technology-based tools for dietary intake assessment-an ILSI Europe dietary intake and exposure task force evaluation. Nutrients 11, 55 (2018)CrossRefGoogle Scholar
  41. 41.
    Boushey, C.J., Spoden, M., Zhu, F.M., Delp, E.J., Kerr, D.A.: New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods. Proc. Nutr. Soc. 76, 283–294 (2017)CrossRefGoogle Scholar
  42. 42.
    Trijsburg, L., et al.: BMI was found to be a consistent determinant related to misreporting of energy, protein and potassium intake using self-report and duplicate portion methods. Public Health Nutr. 20, 598–607 (2017)CrossRefGoogle Scholar
  43. 43.
    Dutch Institute for Public Health and the Environment, R.: NEVO-tabel. Nederlands Voedingsstoffenbestand 2011. Voedingscentrum (2011)Google Scholar
  44. 44.
    Gijsbers, L., Ding, E.L., Malik, V.S., de Goede, J., Geleijnse, J.M., Soedamah-Muthu, S.S.: Consumption of dairy foods and diabetes incidence: a dose-response meta-analysis of observational studies. Am. J. Clin. Nutr. 103, 1111–1124 (2016)CrossRefGoogle Scholar
  45. 45.
    Schwingshackl, L., et al.: Food groups and risk of hypertension: a systematic review and dose-response meta-analysis of prospective studies. Adv. Nutr. 8, 793–803 (2017). (Bethesda, Md.)CrossRefGoogle Scholar
  46. 46.
    Kipnis, V., et al.: Structure of dietary measurement error: results of the OPEN biomarker study. Am. J. Epidemiol. 158, 14–21 (2003). Discussion 22-16CrossRefGoogle Scholar
  47. 47.
    Cade, J.E., et al.: DIET@NET: best practice guidelines for dietary assessment in health research. BMC Med. 15, 202 (2017)CrossRefGoogle Scholar
  48. 48.
    Eussen, S.J., et al.: A national FFQ for the Netherlands (the FFQ-NL1.0): development and compatibility with existing Dutch FFQs. Public Health Nutr. 21, 2221–2229 (2018)CrossRefGoogle Scholar
  49. 49.
    Sluik, D., et al.: A national FFQ for the Netherlands (the FFQ-NL 1.0): validation of a comprehensive FFQ for adults. Br. J. Nutr. 116, 913–923 (2016)CrossRefGoogle Scholar
  50. 50.
    van Lee, L., et al.: Evaluation of a screener to assess diet quality in the Netherlands. Br. J. Nutr. 115, 517–526 (2016)CrossRefGoogle Scholar
  51. 51.
    Health Council of the Netherlands: Guidelines for a healthy diet 2006. Health Council of the Netherlands (2006)Google Scholar
  52. 52.
    Health Council of the Netherlands: Guidelines for a healthy diet 2015. Health Council of the Netherlands (2015)Google Scholar
  53. 53.
    Looman, M., et al.: Development and evaluation of the Dutch Healthy Diet index 2015. Public Health Nutr. 20, 2289–2299 (2017)CrossRefGoogle Scholar
  54. 54.
  55. 55.
    Bi, Y., Lv, M., Song, C., Xu, W., Guan, N., Yi, W.: AutoDietary: a wearable acoustic sensor system for food intake recognition in daily life. IEEE Sens. J. 16, 806–816 (2016)CrossRefGoogle Scholar
  56. 56.
    Lopez-Meyer, P., Schuckers, S., Makeyev, O., Sazonov, E.: Detection of periods of food intake using support vector machines. In: Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Annual Conference 2010. IEEE Engineering in Medicine and Biology Society, pp. 1004–1007 (2010)Google Scholar
  57. 57.
    Olubanjo, T., Moore, E., Ghovanloo, M.: Detecting food intake acoustic events in noisy recordings using template matching. In: 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 388–391 (2016)Google Scholar
  58. 58.
    Päßler, S., Fischer, W.: Acoustical method for objective food intake monitoring using a wearable sensor system. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, pp. 266–269 (2011)Google Scholar
  59. 59.
    Ye, X., Chen, G., Gao, Y., Wang, H., Cao, Y.: Assisting food journaling with automatic eating detection. In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, San Jose (2016)Google Scholar
  60. 60.
    Ye, X., Chen, G., Cao, Y.: Automatic Eating Detection using head-mount and wrist-worn accelerometers. In: 2015 17th International Conference on E-health Networking, Application and Services (HealthCom), pp. 578–581 (2015)Google Scholar
  61. 61.
    Amft, O., Junker, H., Troster, G.: Detection of eating and drinking arm gestures using inertial body-worn sensors. In: Ninth IEEE International Symposium on Wearable Computers (ISWC 2005), pp. 160–163 (2005)Google Scholar
  62. 62.
    Farooq, M., Fontana, J.M., Sazonov, E.: A novel approach for food intake detection using electroglottography. Physiol. Meas. 35, 739–751 (2014)CrossRefGoogle Scholar
  63. 63.
    Kalantarian, H., Alshurafa, N., Sarrafzadeh, M.: A wearable nutrition monitoring system. In: 2014 11th International Conference on Wearable and Implantable Body Sensor Networks, pp. 75–80 (2014)Google Scholar
  64. 64.
    Amft, O., Kusserow, M., Troster, G.: Bite weight prediction from acoustic recognition of chewing. IEEE Trans. Bio-med. Eng. 56, 1663–1672 (2009)CrossRefGoogle Scholar
  65. 65.
    Sun, M., et al.: eButton: a wearable computer for health monitoring and personal assistance. In: Proceedings of the Design Automation Conference, pp. 1–6 (2014)Google Scholar
  66. 66.
    Puri, M., Zhiwei, Z., Yu, Q., Divakaran, A., Sawhney, H.: Recognition and volume estimation of food intake using a mobile device. In: 2009 Workshop on Applications of Computer Vision (WACV), pp. 1–8 (2009)Google Scholar
  67. 67.
    Jia, W., et al.: Accuracy of food portion size estimation from digital pictures acquired by a chest-worn camera. Public Health Nutr. 17, 1671–1681 (2014)CrossRefGoogle Scholar
  68. 68.
    Shang, J., et al.: A pervasive dietary data recording system. In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 307–309 (2001)Google Scholar
  69. 69.
    Michielsen, C., Almanza-Aguilera, E., Brouwer-Brolsma, E.M., Urpi-Sarda, M., Afman, L.A.: Biomarkers of food intake for cocoa and liquorice (products): a systematic review. Genes Nutr. 13, 22 (2018)CrossRefGoogle Scholar
  70. 70.
    Michielsen, C., Hangelbroek, R.W.J., Feskens, E.J.M., Afman, L.A.: Disentangling the effects of monounsaturated fatty acids from other components of a mediterranean diet on serum metabolite profiles: a randomized fully controlled dietary intervention in healthy subjects at risk of the metabolic syndrome. Mol. Nutr. Food Res. 63, e1801095 (2019)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2020

Authors and Affiliations

  1. 1.Division of Human Nutrition and HealthWageningen UniversityWageningenThe Netherlands

Personalised recommendations