The journal of nutrition, health & aging

, Volume 19, Issue 6, pp 603–610 | Cite as

Relative validity of a diet history questionnaire against a four-day weighed food record among older men in Australia: The Concord Health and Ageing in Men Project (CHAMP)

  • Waern Rosilene
  • R. Cumming
  • T. Travison
  • F. Blyth
  • V. Naganathan
  • M. Allman-Farinelli
  • V. Hirani
Article

Abstract

Objectives

To evaluate the relative validity of the diet history questionnaire (DHQ) used in the Concord Health and Ageing in Men Project (CHAMP) against a four-day weighed food record (4dWFR) as the reference method.

Design and measurements

Detailed DHQ followed by a 4dWFR were completed between July 2012 and October of 2013. Setting: Burwood, Canada Bay and Strathfield in Sydney, Australia.

Participants

Fifty six community- dwelling men aged 75 years and over (mean=79 years).

Results

DHQ estimates of intakes were generally higher than estimates from 4dWFR. Differences between the two methods were generally less than 20% with the exception of ß-carotene (37%). Fixed and proportional biases were only present for retinol, ß-carotene, magnesium, phosphorus and percentage of energy from protein; however, 95% limits of agreement were in some cases wide. Pearson correlation coefficient of log-transformed unadjusted values ranged from 0.15 (zinc) to 0.70 (alcohol), and from 0.06 (iron) to 0.63 (thiamin) after energy-adjustment. Spearman’s correlation coefficients ranged from 0.16 (zinc) to 0.80 (alcohol) before energy adjustment, and from 0.15(zinc) to 0.81(alcohol) after energy adjustment.

Conclusion

Our findings suggest that the DHQ used in CHAMP to measure the nutritional intake of its participants is appropriate to this age group and provides reasonably similar results to the 4dWFR for the majority of nutrients analysed.

Keywords

Validity weighed food record diet history questionnaire elderly men 

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References

  1. 1.
    Year Book Australia. Australia: Australian Bureau of Statistics; 2012 [updated 21 January 201310/02/2014]; Available from: http://www.abs.gov.au/ausstats/abs.Google Scholar
  2. 2.
    Mathers JC. Nutrition and ageing: knowledge, gaps and research priorities. Proceedings of the Nutrition Society. 2013;72(02):246–50.PubMedCrossRefGoogle Scholar
  3. 3.
    Van Staveren WA, De Groot LC, Blauw YH, Van der Wielen RP. Assessing diets of elderly people: problems and approaches. The American Journal of Clinical Nutrition. 1994;59(1):221S–3S.Google Scholar
  4. 4.
    Livingstone MB, Prentice AM, Strain JJ, Coward WA, Black AE, Barker ME, et al. Accuracy of weighed dietary records in studies of diet and health. BMJ. 1990;300(6726):708–12.PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Margetts B, Nelson M. Design concepts in nutritional epidemiology. 2nd ed: Oxford University Press; 1997.CrossRefGoogle Scholar
  6. 6.
    Nes M, Van Staveren WA, Zajkas G, Inelmen EM, Moreiras-Varela O. Validity of the dietary history method in elderly subjects. Euronut SENECA investigators. European Journal of Clinical Nutrition. 1991;45 Suppl 3:97–104.PubMedGoogle Scholar
  7. 7.
    Van Staveren W, Burema J, Livingstone M, Van Den Broek T, Kaaks R. Evaluation of the dietary history method used in the SENECA Study. European journal of clinical nutrition. 1996;50:S47–55.PubMedGoogle Scholar
  8. 8.
    Pedersen AN, Fagt S, Ovesen L, Schroll M. Quality control including validation in dietary surveys of elderly subjects. The validation of a dietary history method (the SENECA-method) used in the 1914-population study in Glostrup of Danish men and women aged 80 years. The journal of nutrition, health & aging. 2001;5(4):208–16.Google Scholar
  9. 9.
    Gibson RS. Principles of nutritional assessment: Oxford University Press; 2005.Google Scholar
  10. 10.
    Lee-Han H, McGuire V, Boyd N. A review of the methods used by studies of dietary measurement. Journal of Clinical Epidemiology. 1989;42(3):269–79.PubMedCrossRefGoogle Scholar
  11. 11.
    Bingham SA. The dietary assessment of individuals; methods, accuracy, new techniques and recommendations 1987.Google Scholar
  12. 12.
    Visser M, De Groot LCPGM, Deurenberg P, Van Staveren WA. Validation of dietary history method in a group of elderly women using measurements of total energy expenditure. British Journal of Nutrition. 1995;74(06):775–85.PubMedGoogle Scholar
  13. 13.
    McNeill G, Winter J, Jia X. Diet and cognitive function in later life: a challenge for nutrition epidemiology. European Journal of Clinical Nutrition. 2009;63 Suppl 1:S33–7.PubMedCrossRefGoogle Scholar
  14. 14.
    Hankin JH. Development of a diet history questionnaire for studies of older persons. American Journal of Clinical Nutrition. 1989;50(5 Suppl):1121–7; discussion 231–5.PubMedGoogle Scholar
  15. 15.
    Shahar S, Earland J, Abdulrahman S. Validation of a dietary history questionnaire against a 7-D weighed record for estimating nutrient intake among rural elderly Malays. Malaysian journal of nutrition. 2000;6(1):33–44.PubMedGoogle Scholar
  16. 16.
    Willett W. Nutritional epidemiology: Oxford University Press; 1998.CrossRefGoogle Scholar
  17. 17.
    Cumming RG, Handelsman D, Seibel MJ, Creasey H, Sambrook P, Waite L, et al. Cohort Profile: The Concord Health and Ageing in Men Project (CHAMP). International Journal of Epidemiology. 2009;38(2):374–8.PubMedCrossRefGoogle Scholar
  18. 18.
    Burke BS. The dietary history as a tool in research. J Am Diet Assoc. 1947;23(12):1041–6.Google Scholar
  19. 19.
    Williams T. This=That: a life-size photo guide to food serves: revised and expanded 2013.Google Scholar
  20. 20.
    Henderson L, Gregory J, Swan G, Britain G, Britain G. The national diet & nutrition survey: adults aged 19 to 64 years: HM Stationery Office; 2002.Google Scholar
  21. 21.
    Mendez MA, Popkin BM, Buckland G, Schroder H, Amiano P, Barricarte A, et al. Alternative methods of accounting for underreporting and overreporting when measuring dietary intake-obesity relations. American Journal of Epidemiology. 2011;173(4):448–58.PubMedCentralPubMedCrossRefGoogle Scholar
  22. 22.
    McCrory MA, Hajduk CL, Roberts SB. Procedures for screening out inaccurate reports of dietary energy intake. Public Health Nutrition. 2002;5(6A):873–82.PubMedCrossRefGoogle Scholar
  23. 23.
    Black AE. Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations. International Journal of Obesity & Related Metabolic Disorders: Journal of the International Association for the Study of Obesity. 2000;24(9):1119–30.CrossRefGoogle Scholar
  24. 24.
    Goldberg G, Black A, Jebb S, Cole T, Murgatroyd P, Coward W, et al. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. European Journal of Clinical Nutrition. 1991;45(12):569–81.Google Scholar
  25. 25.
    Washburn RA, Smith KW, Jette AM, Janney CA. The physical activity scale for the elderly (PASE): Development and evaluation. Journal of Clinical Epidemiology. 1993;46(2):153–62.PubMedCrossRefGoogle Scholar
  26. 26.
    AUSNUT2007—Australian Food, Supplement and Nutrient Database for Estimation of Population Nutrient Intakes. Canberra: Food Standards Australia New Zealand; [cited 2014 05/03/14]; Available from: http://www.foodstandards.gov.au/consumerinformation/ausnut2007/.Google Scholar
  27. 27.
    Sobolewski R, Cunningham J, Mackerras D. Which Australian food composition database should I use? Nutrition & Dietetics. 2010;67(1):37–40.CrossRefGoogle Scholar
  28. 28.
    Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples). Biometrika. 1965;52(3–4):591–611.CrossRefGoogle Scholar
  29. 29.
    Team RC. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.Google Scholar
  30. 30.
    Martin Bland J, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet. 1986;327(8476):307–10.CrossRefGoogle Scholar
  31. 31.
    Wood SN. Generalized Additive Models: An Introduction with R: Chapman and Hall/CRC.; 2006.Google Scholar
  32. 32.
    Hastie TJ TR. Generalized additive models. London: Chapman and Hall 1990.Google Scholar
  33. 33.
    Ludbrook J. Special article comparing methods of measurement. Clinical and Experimental Pharmacology and Physiology. 1997;24(2):193–203.PubMedCrossRefGoogle Scholar
  34. 34.
    Ludbrook J. Statistical Techniques For Comparing Measurers And Methods Of Measurement: A Critical Review. Clinical and Experimental Pharmacology and Physiology. 2002;29(7):527–36.PubMedCrossRefGoogle Scholar
  35. 35.
    Ludbrook J. Linear regression analysis for comparing two measurers or methods of measurement: but which regression? Clinical and Experimental Pharmacology and Physiology. 2010;37(7):692–9.PubMedCrossRefGoogle Scholar
  36. 36.
    Paalanen L, Mannisto S, Virtanen MJ, Knekt P, Rasanen L, Montonen J, et al. Validity of a food frequency questionnaire varied by age and body mass index. Journal of Clinical Epidemiology. 2006;59(9):994–1001.PubMedCrossRefGoogle Scholar
  37. 37.
    Mahalko JR, Johnson L, Gallagher S, Milne D. Comparison of dietary histories and seven-day food records in a nutritional assessment of older adults. The American journal of clinical nutrition. 1985;42(3):542–53.PubMedGoogle Scholar
  38. 38.
    Mares-Perlman J, Klein B, Klein R, Ritter L, Fisher M, Freudenheim J. A Diet History Questionnaire Ranks Nutrient Intakes in Middle-Aged and Older Men and Women Similarly to Multiple Food Records. The Journal of Nutrition. 1993;123(3):489–501.PubMedGoogle Scholar
  39. 39.
    Lassale C, Guilbert C, Keogh J, Syrette J, Lange K, Cox DN. Estimating food intakes in Australia: validation of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) food frequency questionnaire against weighed dietary intakes. Journal of Human Nutrition and Dietetics. 2009;22(6):559–66.PubMedCrossRefGoogle Scholar
  40. 40.
    Hebert JR, Clemow L, Pbert L, Ockene IS, Ockene JK. Social desirability bias in dietary self-report may compromise the validity of dietary intake measures. International Journal of Epidemiology. 1995;24(2):389–98.PubMedCrossRefGoogle Scholar
  41. 41.
    Martin Bland J. How can I decide the sample size for a study of agreement between two methods of measurement? [updated 12 january, 200406 march 2014]; Available from: http://www-users.york.ac.uk/~mb55/meas/sizemeth.htm.Google Scholar
  42. 42.
    Cade J, Thompson R, Burley V, Warm D. Development, validation and utilisation of food-frequency questionnaires–a review. Public Health Nutrition. 2002;5(04):567–87.PubMedCrossRefGoogle Scholar
  43. 43.
    Serra-Majem L, Frost Andersen L, Henríque-Sánchez P, Doreste-Alonso J, Sánchez-Villegas A, Ortiz-Andrelluchi A, et al. Evaluating the quality of dietary intake validation studies. British Journal of Nutrition. 2009;102(SupplementS1):S3–S9.PubMedCrossRefGoogle Scholar

Copyright information

© Serdi and Springer-Verlag France 2015

Authors and Affiliations

  • Waern Rosilene
    • 1
    • 2
    • 3
  • R. Cumming
    • 2
    • 3
  • T. Travison
    • 4
  • F. Blyth
    • 1
  • V. Naganathan
    • 1
  • M. Allman-Farinelli
    • 5
  • V. Hirani
    • 1
  1. 1.Centre for Education and Research on Ageing, Concord HospitalUniversity of SydneySydneyAustralia
  2. 2.School of Public HealthUniversity of SydneySydneyAustralia
  3. 3.ARC Centre of Excellence in Population Ageing ResearchUniversity of SydneySydneyAustralia
  4. 4.Institute for Ageing Research, Hebrew Senior LifeHarvard Medical SchoolBostonUSA
  5. 5.School of Molecular BioscienceUniversity of SydneySydneyAustralia

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