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Comparison of a web-based food record tool and a food-frequency questionnaire and objective validation using the doubly labelled water technique in a Swedish middle-aged population

  • Sanna Nybacka (a1), Heléne Bertéus Forslund (a1), Elisabet Wirfält (a2), Ingrid Larsson (a3), Ulrika Ericson (a4), Eva Warensjö Lemming (a5), Göran Bergström (a6), Bo Hedblad (a7), Anna Winkvist (a1) and Anna Karin Lindroos (a5)...

Abstract

Two web-based dietary assessment tools have been developed for use in large-scale studies: the Riksmaten method (4-d food record) and MiniMeal-Q (food-frequency method). The aim of the present study was to examine the ability of these methods to capture energy intake against objectively measured total energy expenditure (TEE) with the doubly labelled water technique (TEEDLW), and to compare reported energy and macronutrient intake. This study was conducted within the pilot study of the Swedish CArdioPulmonary bioImage Study (SCAPIS), which included 1111 randomly selected men and women aged 50–64 years from the Gothenburg general population. Of these, 200 were enrolled in the SCAPIS diet substudy. TEEDLW was measured in a subsample (n 40). Compared with TEEDLW, both methods underestimated energy intake: −2·5 (sd  2·9) MJ with the Riksmaten method; −2·3 (sd 3·6) MJ with MiniMeal-Q. Mean reporting accuracy was 80 and 82 %, respectively. The correlation between reported energy intake and TEEDLW was r 0·4 for the Riksmaten method (P < 0·05) and r 0·28 (non-significant) for MiniMeal-Q. Women reported similar average intake of energy and macronutrients in both methods whereas men reported higher intakes with the Riksmaten method. Energy-adjusted correlations ranged from 0·14 (polyunsaturated fat) to 0·77 (alcohol). Bland–Altman plots showed acceptable agreement for energy and energy-adjusted protein and carbohydrate intake, whereas the agreement for fat intake was poorer. According to energy intake data, both methods displayed similar precision on energy intake reporting. However, MiniMeal-Q was less successful in ranking individuals than the Riksmaten method. The development of methods to achieve limited under-reporting is a major challenge for future research.

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Copyright

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Corresponding author

* Corresponding author: S. Nybacka, fax +46 736877771, email sanna.nybacka@gu.se

References

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1. Paeratakul, S, Popkin, BM, Kohlmeier, L, et al. (1998) Measurement error in dietary data: implications for the epidemiologic study of the diet–disease relationship. Eur J Clin Nutr 52, 722727.
2. Day, NE, Wong, MY, Bingham, S, et al. (2004) Correlated measurement error – implications for nutritional epidemiology. Int J Epidemiol 33, 13731381.
3. Margetts, BM & Nelson, M (1997) Design Concepts in Nutritional Epidemiology. Oxford: Oxford University Press.
4. Barrett-Connor, E (1991) Nutrition epidemiology: how do we know what they ate? Am J Clin Nutr 54, 1 Suppl., 182s187s.
5. Gibson, RS (2005) Principles of Nutritional Assessment. New York: Oxford University Press.
6. Champagne, CM, Han, H, Bajpeyi, S, et al. (2013) Day-to-day variation in food intake and energy expenditure in healthy women: the Dietitian II Study. J Acad Nutr Diet 113, 15321538.
7. Tarasuk, V & Beaton, GH (1991) The nature and individuality of within-subject variation in energy intake. Am J Clin Nutr 54, 464470.
8. Subar, AF, Dodd, KW, Guenther, PM, et al. (2006) The Food Propensity Questionnaire: concept, development, and validation for use as a covariate in a model to estimate usual food intake. J Am Diet Assoc 106, 15561563.
9. 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, Suppl. 1, 4353.
10. 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.
11. Baranowski, T, Islam, N, Douglass, D, et al. (2014) Food intake recording software system, version 4 (FIRSSt4): a self-completed 24-h dietary recall for children. J Hum Nutr Diet 27, Suppl. 1, 6671.
12. Lu, C, Pearson, M, Renker, S, et al. (2006) A novel system for collecting longitudinal self-reported dietary consumption information: the Internet Data Logger (iDL). J Expo Sci Environ Epidemiol 16, 427433.
13. Christensen, SE, Moller, E, Bonn, SE, et al. (2013) Two new meal- and web-based interactive food frequency questionnaires: validation of energy and macronutrient intake. J Med Internet Res 15, e109.
14. Christensen, SE, Moller, E, Bonn, SE, et al. (2014) Relative validity of micronutrient and fiber intake assessed with two new interactive meal- and Web-based food frequency questionnaires. J Med Internet Res 16, e59.
15. Warensjo Lemming, E, Nalsen, C, Becker, W, et al. (2015) Relative validation of the dietary intake of fatty acids among adults in the Swedish National Dietary Survey using plasma phospholipid fatty acid composition. J Nutr Sci 4, e25.
16. Nybacka, S, Lindroos, AK, Wirfält, E, et al. (2016) Carotenoids and alkylresorcinols as objective biomarkers of diet quality when assessing the validity of a web-based food record tool and a food frequency questionnaire in a middle-aged population. BMC Nutr 2, 53.
17. Bergström, G, Berglund, G, Blomberg, A, et al. (2015) The Swedish CArdioPulmonary BioImage Study: objectives and design. J Intern Med 278, 645659.
18. Schoeller, DA (1988) Measurement of energy expenditure in free-living humans by using doubly labeled water. J Nutr 118, 12781289.
19. Slinde, F, Ellegard, L, Gronberg, AM, et al. (2003) Total energy expenditure in underweight patients with severe chronic obstructive pulmonary disease living at home. Clin Nutr 22, 159165.
20. International Dietary Energy Consultancy Group (1990) The doubly-labelled water method for measuring energy expenditure: technical recommendations for use in humans: A consensus report by the IDECG Working Group (NAHRES-4). http://www.iaea.org/inis/collection/NCLCollectionStore/_Public/21/093/21093729.pdf(accessed August 2016).
22. Livsmedelsverket (2014) Matvanekollen. http://www7.slv.se/Matdagbok/LoggaInPublikUndersokning.aspx (accessed November 2014).
23. Almqvist, C, Adami, HO, Franks, PW, et al. (2011) LifeGene – a large prospective population-based study of global relevance. Eur J Epidemiol 26, 6777.
24. Black, AE & Cole, TJ (2000) Within- and between-subject variation in energy expenditure measured by the doubly-labelled water technique: implications for validating reported dietary energy intake. Eur J Clin Nutr 54, 386394.
25. Willett, W (2013) Nutritional Epidemiology. Oxford: Oxford University Press.
26. Bland, JM & Altman, DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet i, 307310.
27. Ocke, MC & Kaaks, RJ (1997) Biochemical markers as additional measurements in dietary validity studies: application of the method of triads with examples from the European Prospective Investigation into Cancer and Nutrition. Am J Clin Nutr 65, 4 Suppl., 1240s1245s.
28. Ferrari, P, Kaaks, R & Riboli, E (2000) Variance and confidence limits in validation studies based on comparison between three different types of measurements. J Epidemiol Biostat 5, 303313.
30. Freedman, LS, Commins, JM, Moler, JE, et al. (2014) Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake. Am J Epidemiol 180, 172188.
31. Hartman, AM, Brown, CC, Palmgren, J, et al. (1990) Variability in nutrient and food intakes among older middle-aged men: implications for design of epidemiologic and validation studies using food recording. Am J Epidemiol 132, 9991012.
32. Basiotis, PP, Welsh, SO, Cronin, FJ, et al. (1987) Number of days of food intake records required to estimate individual and group nutrient intakes with defined confidence. J Nutr 117, 16381641.
33. Seale, JL & Rumpler, WV (1997) Comparison of energy expenditure measurements by diet records, energy intake balance, doubly labeled water and room calorimetry. Eur J Clin Nutr 51, 856863.
34. Tomoyasu, NJ, Toth, MJ & Poehlman, ET (1999) Misreporting of total energy intake in older men and women. J Am Geriatr Soc 47, 710715.
35. Kroke, A, Klipstein-Grobusch, K, Voss, S, et al. (1999) Validation of a self-administered food-frequency questionnaire administered in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study: comparison of energy, protein, and macronutrient intakes estimated with the doubly labeled water, urinary nitrogen, and repeated 24-h dietary recall methods. Am J Clin Nutr 70, 439447.
36. 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, Suppl. 1, 140144.
37. Livingstone, MB & Black, AE (2003) Markers of the validity of reported energy intake. J Nutr 133, Suppl. 3, 895s920s.
38. Martin, LJ, Su, W, Jones, PJ, et al. (1996) Comparison of energy intakes determined by food records and doubly labeled water in women participating in a dietary-intervention trial. Am J Clin Nutr 63, 483490.
39. Goran, MI & Poehlman, ET (1992) Total energy expenditure and energy requirements in healthy elderly persons. Metabolism 41, 744753.
40. Bathalon, GP, Tucker, KL, Hays, NP, et al. (2000) Psychological measures of eating behavior and the accuracy of 3 common dietary assessment methods in healthy postmenopausal women. Am J Clin Nutr 71, 739745.
41. Kipnis, V, Subar, AF, Midthune, D, et al. (2003) Structure of dietary measurement error: results of the OPEN biomarker study. Am J Epidemiol 158, 1421; discussion 2–6.
42. Bingham, SA, Gill, C, Welch, A, et al. (1994) Comparison of dietary assessment methods in nutritional epidemiology: weighed records v. 24 h recalls, food-frequency questionnaires and estimated-diet records. Br J Nutr 72, 619643.
43. Liu, K, Stamler, J, Dyer, A, et al. (1978) Statistical methods to assess and minimize the role of intra-individual variability in obscuring the relationship between dietary lipids and serum cholesterol. J Chronic Dis 31, 399418.
44. Beaton, GH, Milner, J, Corey, P, et al. (1979) Sources of variance in 24-hour dietary recall data: implications for nutrition study design and interpretation. Am J Clin Nutr 32, 25462559.
45. Pakseresht, M & Sharma, S (2010) Validation of a culturally appropriate quantitative food frequency questionnaire for Inuvialuit population in the Northwest Territories, Canada. J Hum Nutr Diet 23, Suppl. 1, 7582.
46. Deschamps, V, de Lauzon-Guillain, B, Lafay, L, et al. (2009) Reproducibility and relative validity of a food-frequency questionnaire among French adults and adolescents. Eur J Clin Nutr 63, 282291.
47. Reinivuo, H, Hirvonen, T, Ovaskainen, ML, et al. (2010) Dietary survey methodology of FINDIET 2007 with a risk assessment perspective. Public Health Nutr 13, 915919.

Keywords

Comparison of a web-based food record tool and a food-frequency questionnaire and objective validation using the doubly labelled water technique in a Swedish middle-aged population

  • Sanna Nybacka (a1), Heléne Bertéus Forslund (a1), Elisabet Wirfält (a2), Ingrid Larsson (a3), Ulrika Ericson (a4), Eva Warensjö Lemming (a5), Göran Bergström (a6), Bo Hedblad (a7), Anna Winkvist (a1) and Anna Karin Lindroos (a5)...

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