Web-Based 24-h Dietary Recall: The SACANA Program

Part of the Springer Series on Epidemiology and Public Health book series (SSEH)


In research, dietary intake data are mainly assessed using food frequency questionnaire (FFQ) (semi-, quantitative), 24-h dietary recall (repeated, 24HDR) or food diary/records (repeated). In the USA, a Web-based automated, self-administered 24HDR was shown to be a low-cost method for collecting accurate dietary intake information, but no such instrument was available for Europe. In I.Family, we developed a Web-based automated, self-administered 24HDR for large-scale assessment of dietary intake data in children, adolescents and their families across Europe. The Self-Administered Children, Adolescents and Adults Nutrition Assessment (SACANA) program used in the I.Family study is able to assess the absolute nutrient and energy intake, the per cent contribution from foods and drinks to total energy and nutrient intake, as well as portion sizes and food groups among the children and their families. Further, place and time of all eating and snacking occasions during the past 24 h as well as eating in company and simultaneous eating activities (e.g., reading, TV watching) can be reported. The program collects self-reported dietary data in individuals from 11 years of age and above, with parental assistance at younger ages. In order to reduce errors in portion size estimation, in food composition tables and incomplete recalls the program offers features such as photo-assisted correct portion size estimation, multiple plausibility checks and reminding questions. The instrument was found to collect reproducible and valid data. SACANA is a reproducible, validated and suitable self-administered instrument for obtaining Web-based 24HDR data from children, adolescents and adults in large-scale studies across Europe.


Food Composition Tables (FCTs) Standard Food Groups Self-Administered Children Plausibility Checks Country-specific FCTs 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The development of instruments, the baseline data collection and the first follow-up work as part of the IDEFICS study ( were financially supported by the European Commission within the Sixth RTD Framework Programme Contract No. 016181 (FOOD). The most recent follow-up including the development of new instruments and the adaptation of previously used instruments was conducted in the framework of the I.Family study ( which was funded by the European Commission within the Seventh RTD Framework Programme Contract No. 266044 (KBBE 2010–14).

We thank all families for participating in the extensive examinations of the IDEFICS and I.Family studies. We are also grateful for the support from school boards, headmasters and communities.

We greatly appreciate the input of the following colleagues: Markus Modzelewski from Technologie-Zentrum Informatik und Informationstechnik, Bremen, Germany, Selim Cici, Claudia Brünings-Kuppe, and Timm Intemann from the Leibniz Institute for Prevention Research and Epidemiology—BIPS, Bremen, Germany.


  1. Banca Dati di Composizione degli Alimenti per Studi Epidemiologici in Italia (BDA). Food composition database for epidemiological studies in Italy. Milan, Italy: European Institute of Oncology. 2015. Accessed 27 Mar 2018.
  2. Bel-Serrat S, Mouratidou T, Pala V, Huybrechts I, Börnhorst C, Fernandez-Alvira JM, et al. Relative validity of the children’s eating habits questionnaire-food frequency section among young European children: the IDEFICS study. Public Health Nutr. 2014;17(2):266–76.CrossRefGoogle Scholar
  3. Belgian Federal Public Service. La Table Belge de composition des aliments. Nubel. 2016. Accessed 27 Mar 2018.
  4. Börnhorst C, Bel-Serrat S, Pigeot I, Huybrechts I, Ottavaere C, Sioen I, et al. IDEFICS consortium. Validity of 24-h recalls in (pre-)school aged children: comparison of proxy-reported energy intakes with measured energy expenditure. Clin Nutr. 2014;33(1):79–84.CrossRefGoogle Scholar
  5. Börnhorst C, Huybrechts I, Hebestreit A, Vanaelst B, Molnar D, Bel-Serrat S, et al. IDEFICS consortium. Diet-obesity associations in children: approaches to counteract attenuation caused by misreporting. Public Health Nutr. 2013;16(2):256–66.CrossRefGoogle Scholar
  6. Cameron ME, van Staveren WA. Manual on methodology for food consumption studies (Oxford Medical Publications). New York: Oxford University Press; 1988.Google Scholar
  7. De Henauw S, Brants HA, Becker W, Kaic-Rak A, Ruprich J, Sekula W, et al. EFCOSUM Group. Operationalization of food consumption surveys in Europe: recommendations from the European food consumption survey methods (EFCOSUM) project. Eur J Clin Nutr. 2002;56(Suppl 2):S75–88.CrossRefGoogle Scholar
  8. El Centro de Enseñanza Superior de Nutrición y Dietética (CESNID). Tablas de composición de alimentos del CESNID. Barceolona: Edicions Universitat de Barcelona; 2003.Google Scholar
  9. European Food Information Resource (EuroFIR). Food composition databases. 2018. Accessed 6 Apr 2018.
  10. Hebestreit A, Barba G, De Henauw S, Eiben G, Hadjigeorgiou C, Kovacs E, et al. IDEFICS consortium. Cross-sectional and longitudinal associations between energy intake and BMI z-score in European children. Int J Behav Nutr Phys Act. 2016;13(1):23.CrossRefGoogle Scholar
  11. Hebestreit A, Börnhorst C, Barba G, Siani A, Huybrechts I, Tognon G, et al. Associations between energy intake, daily food intake and energy density of foods and BMI z-score in 2-9-year-old European children. Eur J Nutr. 2014a;53(2):673–81.CrossRefGoogle Scholar
  12. Hebestreit A, Börnhorst C, Pala V, Barba G, Eiben G, Veidebaum T, et al. IDEFICS consortium. Dietary energy density in young children across Europe. Int J Obes (Lond). 2014b;38(Suppl 2):S124–34.CrossRefGoogle Scholar
  13. Hoffmann K, Boeing H, Dufour A, Volatier JL, Telman J, Virtanen M, et al. Estimating the distribution of usual dietary intake by short-term measurements. Eur J Clin Nutr. 2002;56:S53–62.CrossRefGoogle Scholar
  14. Hunsberger M, Mehlig K, Börnhorst C, Hebestreit A, Moreno L, Veidebaum T, et al. Dietary carbohydrate and nocturnal sleep duration in relation to children’s BMI: findings from the IDEFICS study in eight European countries. Nutrients. 2015;7(12):10223–36.CrossRefGoogle Scholar
  15. Intemann T, Pigeot I, De Henauw S, Eiben G, Lissner L, Krogh V, et al.; I.Family consortium. Urinary sucrose and fructose to validate self-reported sugar intake in children and adolescents: results from the I.Family study. Eur J Nutr. 2018. (Epub 2018 Mar 6).
  16. Jahns L, Arab L, Carriquiry A, Popkin BM. The use of external within-person variance estimates to adjust nutrient intake distributions over time and across populations. Public Health Nutr. 2005;8(1):69–76.CrossRefGoogle Scholar
  17. Jenab M, Slimani N, Bictash M, Ferrari P, Bingham SA. Biomarkers in nutritional epidemiology: applications, needs and new horizons. Hum Genet. 2009;125(5–6):507–25.CrossRefGoogle Scholar
  18. Kipnis V, Midthune D, Buckman DW, Dodd KW, Guenther PM, Krebs-Smith SM, et al. Modeling data with excess zeros and measurement error: application to evaluating relationships between episodically consumed foods and health outcomes. Biometrics. 2009;65(4):1003–10.MathSciNetCrossRefGoogle Scholar
  19. Lanfer A, Hebestreit A, Ahrens W, Krogh V, Sieri S, Lissner L, et al. IDEFICS consortium. Reproducibility of food consumption frequencies derived from the children’s eating habits questionnaire used in the IDEFICS study. Int J Obes (Lond). 2011;35(Suppl 1):S61–8.CrossRefGoogle Scholar
  20. Livingstone MB, Robson PJ. Measurement of dietary intake in children. Proc Nutr Soc. 2000;59(2):279–93.CrossRefGoogle Scholar
  21. Max Rubner-Institut. Bundeslebensmittelschlüssel des Bundesministeriums für Ernährung, Landwirtschaft und Verbraucherschutz. Federal Ministry of Food and Agriculture. 2016. Accessed 27 Mar 2018.
  22. McCance RA, Widdowson EM. The composition of foods, vol. 6. Cambridge, London: The Royal Society of Chemistry and the Food Standards Agency; 2002.Google Scholar
  23. Menzies IS. Urinary excretion of sugars related to oral administration of disaccharides in adult coeliac disease. Clin Sci. 1972;42(4):18P.CrossRefGoogle Scholar
  24. Minister of Food and Agriculture. The Norwegian food composition table. 2015. Accessed 27 Mar 2018.
  25. Nakamura H, Tamura Z. Gas chromatographic analysis of mono- and disaccharides in human blood and urine after oral administration of disaccharides. Clin Chim Acta. 1972;39(2):367–81.CrossRefGoogle Scholar
  26. National Institute for Health and Welfare. Fineli, national food composition database in Finland. 2016. Accessed 27 Mar 2018.
  27. Pala V, Sieri S, Palli D, Salvini S, Berrino F, Bellegotti M, et al. Diet in the Italian EPIC cohorts: presentation of data and methodological issues. Tumori. 2003;89(6):594–607.CrossRefGoogle Scholar
  28. Papoutsou S, Briassoulis G, Hadjigeorgiou C, Savva SC, Solea T, Hebestreit A, et al. The combination of daily breakfast consumption and optimal breakfast choices in childhood is an important public health message. Int J Food Sci Nutr. 2014;65(3):273–9.CrossRefGoogle Scholar
  29. Svensson A, Larsson CL, Eiben G, Lanfer A, Pala V, Hebestreit A, et al. IDEFICS consortium. European childrens’s intake of sugars and sugar-rich foods and drinks on weekdays versus weekends - the IDEFICS study. Eur J Clin Nutr. 2014;68(7):822–8.CrossRefGoogle Scholar
  30. Swedish Food Administration. The Swedish food composition database. National Food Agency. 2016. Accessed 27 Mar 2018.
  31. Swiss Society for Nutrition SSN. Swiss food composition table. Federal Food Safety and Veterinary Office FSVO. 2015. Accessed 27 Mar 2018.
  32. Tognon G, Moreno LA, Mouratidou T, Veidebaum T, Molnar D, Russo P, et al. IDEFICS consortium. Adherence to a Mediterranean-like dietary pattern in children from eight European countries. The IDEFICS study. Int J Obes (Lond). 2014;38(Suppl 2):S108–14.CrossRefGoogle Scholar
  33. Tooze JA, Midthune D, Dodd KW, Freedman LS, Krebs-Smith SM, Subar AF, et al. A new statistical method for estimating the usual intake of episodically consumed foods with application to their distribution. J Am Diet Assoc. 2006;106(10):1575–87.CrossRefGoogle Scholar
  34. Willett WC. Nutritional epidemiology, monographs in epidemiology in biostatistics. 2nd ed. New York: Oxford University Press; 1998.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Leibniz Institute for Prevention Research and Epidemiology—BIPSBremenGermany
  2. 2.University of GothenburgGothenburgSweden
  3. 3.Fondazione IRCCS Istituto Nazionale Dei TumoriMilanItaly

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