Hostname: page-component-76d6cb85b7-92wsb Total loading time: 0 Render date: 2026-07-12T19:39:20.961Z Has data issue: false hasContentIssue false
  • English
  • Français

A comparison of selected nutrient intakes derived from three diet assessment methods used in a low-fat maintenance trial

Published online by Cambridge University Press:  01 September 1998

James R Hebert ,
Thomas G Hurley ,
David E Chiriboga  and
Jeanine Barone
James R Hebert*
Affiliation:
Division of Preventive and Behavioral Medicine, Department of Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
Thomas G Hurley
Affiliation:
Division of Preventive and Behavioral Medicine, Department of Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
David E Chiriboga
Affiliation:
Department of Surgery, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
Jeanine Barone
Affiliation:
Currently affiliated with theUniversity of Californiaat Berkeley Wellness Letter, 632 Broadway, New York, NY 10012, USA
*
*Corresponding author: E-mail Hebert@ummed.edu
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.
Objective:

In the vast majority of surveys and research in humans, dietary data are obtained from self-reports: recalls; records; or historical methods, usually food frequency questionnaires (FFQ). This study provides a rare opportunity to compare data derived from all three methods.

Design:

A crossover study of dietary fat in which data were collected using an average of 11.4 food records and 11.7 24-h diet recalls. Using simple subtraction and correlation, energy and nutrient intakes derived from the three methods were compared to each other and with those derived from a single FFQ. Analysis of variance was used to evaluate sources of variability in nutrient intakes estimated from the individual days of records and recalls.

Setting:

An independent, free-standing medical research institute.

Subjects:

13 men who were compliant with study procedures.

Results:

FFQ-derived estimates of energy and nutrient intake were highest (e.g. 1967 kcal versus 1858 kcal and 1936 kcal for the records and recalls, respectively). Mean differences in energy and nutrient intakes and their variances were lowest and correlation coefficients highest in comparing the records and recalls (e.g. for fat the mean difference was 5.0 g, and r=0.85). Analysis of variance of individual days of record- and recall-derived datd (n=300) revealed that there was no effect due to either method (record or recall) or the sequence of administration.

Conclusions:

Results of this study indicate that the FFQ overestimated dietary intake. Energy and nutrient results obtained from the records and recalls were interchangeable. However, based on smaller SDs around the means, it appears that the recalls may perform slightly better in estimating dietary intake in groups such as these well-educated, highly compliant men.

Keywords

Diet surveysDietary assessment methodsNutrient intakeVariabilityValidityReliabilityCalibration

Information

Type
Research Article
Information
Public Health Nutrition , Volume 1 , Issue 3 , September 1998 , pp. 207 - 214
Copyright
Copyright © The Nutrition Society 1998

References

1Buzzard, IM, Faucett, CL, Jeffery, RW, et al. Monitoring dietary change in a low-fat diet intervention study: advantages of using 24-hour dietary recalls vs food records. J. Am. Diet. Assoc. 1996; 96: 547–9.Google Scholar
2Willett, WC. Nutritional Epidemiology. Oxford: Oxford University Press, 1990.Google Scholar
3Bazzarre, TL, Yuhas, JA. Comparative evaluation of methods of collecting food intake data for cancer epidemiology studies. Nutr. Cancer 1983; 5: 201–14.CrossRefGoogle ScholarPubMed
4Bingham, SA, Gill, C, Welch, A, et al. Comparison of dietary assessment methods in nutritional epidemiology: weighted records v. 24h recalls, food-frequency questionnaires and estimated-diet records. Br. J. Nutr. 1994; 72: 619–43.Google Scholar
5Livingstone, MBE. Assessment of food intakes: are we measuring what people eat? Br. J. Biomed. Sci. 1994; 52: 58–67.Google Scholar
6Hebert, JR, Miller, DR. Methodologic considerations for investigating the diet-cancer link. Am. J. Clin. Nutr. 1988; 47: 1068–77.CrossRefGoogle ScholarPubMed
7Gibson, RS. Principles of Nutrititional Assesstment. New York: Oxford University Press, 1990.Google Scholar
8Potosky, A, Block, G, Hartman, A. The apparent validity of diet questionniares is influenced by number of diet-record days used for comparison. J. Am. Diet. Assoc. 1990; 90: 810–13.CrossRefGoogle Scholar
9Smith, AF. Cognitive psychological issues of relevance to the validity of dietary reports. Eur. J. Clin. Nutr. 1993; 47, suppl 2: S6–S18.Google Scholar
10Last, JM. ed. A Dictionary of Epidemiology. 2nd edn. New York: Oxford University Press, 1988.Google Scholar
11Hebert, JR, Ockene, IS, Hurley, TG, Luippold, R, Well, AD, Harmatz, MG. Development and testing of a seven-day dietary recall. J. Clin. Epidemiol. 1997; 50: 925–37.Google Scholar
12Rothenberg, E. Validation of the food frequency questionnaire with the 4-day record method and analysis of 24-h urinary nitrogen. Eur. J. Clin. Nutr. 1994; 48: 725–35.Google ScholarPubMed
13Hebert, JR, Clemow, L, Pbert, L, Ockene, IS, Ockene, JK. Social desirability and approval biases in dietary self-report may profoundly compromise the validity of diet-disease studies. Int. J. Epidemiol. 1995; 24: 389–98.Google Scholar
14Black, AE, Coldberg, GR, Jebb, SA, Livingstone, MBE, Cole, TJ, Prentice, AM. Critical evaluation of energy intake data using fundamental principles of energy physiology. 2. evaluating the results of published surveys. Eur. J. Clin. Nutr. 1991; 45: 583–99.Google ScholarPubMed
15McConaghy, J. Adults' belief about the determinants of successful dietary change. Comm. Health Studies. 1989; 13: 492–502.Google Scholar
16Naslund, GK, Fredrikson, M, Hellenius, ML, de Faire, U. Determinants of compliance in men enrolled in a diet and exercise intervention trial: a randomized, controlled study. Patient Edu. & Counsel. 1996; 29: 247–56.Google Scholar
17Barone, J, Hebert, JR, Reddy, MM. Dietary fat and natural killer cell activity. Am. J. Clin. Nutr. 1989; 50: 861–7.Google Scholar
18Hebert, JR, Barone, J, Reddy, MM, Backlund, JYC. Natural killer cell activity in a longitudinal dietary fat intervention trial. Clin. Immunol. Immunopathol. 1990; 54: 103–16.Google Scholar
19Hebert, JR, Miller, DR, Barone, J, Richie, JJ, Reddy, M. Erythrocyte membrane fatty acids and natural killer cell activity in a longitudinal intervention trial. Immunol. Infect. Dis. 1991; 1: 341–8.Google Scholar
20Block, G, Dresser, CM, Hartman, AM, Carroll, MD. Nutrient sources in the American diet: quantitative data from the NHANES I1 survey: 11. macronutrients and fats. Am. J. Epidemiol. 1985; 122: 27–39.CrossRefGoogle Scholar
21Us Dept of Agriculture. USDA Nutrient Data Base for Standard Reference Release 5. Hyattsville, MD: USDA, 1985.Google Scholar
22Engle, A, Hebert, JR, Reddy, BS. Relationships between food consumption and dietary intake among healthy volunteers and implications for meeting dietary goals. J. Am. Diet. Assoc. 1990; 90: 526–33.CrossRefGoogle ScholarPubMed
23SAS. SAS User's Guide. Cary, NC: SAS Institute, 1997.Google Scholar
24SAS. SAS User's Guide-Statistics Version 6.11. Cary, NC: SAS Institute, 1997.Google Scholar
25Beaton, GH, Milner, J, Corey, P, et al. Source of variance in 24-hour dietary recall data: implications for nutrition study design and interpretation. Am. J. Clin. Nutr. 1979; 32: 2546–59.CrossRefGoogle ScholarPubMed
26Beaton, GH, Milner, J, McGuire, V, Feather, TE, Little, JA. Source of variance in 24-hour dietary recall data: implications for nutrition study design and interpretation. Carbohydrate sources, vitamins. and minerals. Am. J. Clin. Nutr. 1983; 37: 986–95.Google Scholar
27Delcourt, C. Cubeau, J, Balkau, B, Papoz, L. Limitations of the correlation coefficient in the validation of diet assessment methods. Epidemiology 1994; 5: 518–24.Google Scholar
28Willett, WC, Sampson, L, Stampfer, MJ, et al. Reproducibility and validity of a semiquantitative food frequency questionnaire. Am. J. Epidemiol. 1985; 122: 51–65.Google Scholar
29Block, G, Woods, M. Potosky, A, Clifford, C. Validation of a self-administered diet history questionnaire using multiple diet records. J. Clin. Epidemiol. 1990; 43: 1327–35.CrossRefGoogle ScholarPubMed
30Willett, WC, Reynolds, RD, Cottrell-Hoehner, S, Sampson, L, Browne, ML. Validation of a semi-quantitative food frequency questionnaire: comparison with a 1-year record. J. Am. Diet. Assoc. 1987; 87: 43–7.CrossRefGoogle ScholarPubMed
31Morgan, KJ, Johnson, SR, Rizek, RL, Reese, R, Stampley, GL. Collection of food intake data: an evaluation of methods. J. Am. Diet. Assoc. 1987; 87: 888–36.Google Scholar
32Posner, BM, Martin-Munley, SS, Smigelski, C, et al. Comparison of techniques for estimating nutrient intake: the Framingham Study. Epidemiology 1992; 3: 171–7.CrossRefGoogle ScholarPubMed
33Guilland, JC, Aubert, R, Lhuissier, M, et al. Computerized analysis of food records: role of coding and food composition database. Eur. J. Clin. Nutr. 1993; 47: 445–53.Google Scholar
34Harlow, BL, Cramer, DW, Geller, J, Willett, WC, Bell, DA, Welch, WR. The influence of lactose consumption on the association of oral contraceptive use and ovarian cancer risk. Am. J. Epidemiol. 1991; 134: 445–53.CrossRefGoogle ScholarPubMed
35Heben, JR, Stoddard, AM, Harris, DR, et al. Measuring the effect of a worksite-based nutrition intervention on food consumption. Ann. Epidemiol. 1993; 3: 629–35.Google Scholar
36Kristal, AR, Shattuck, AL, Williams, A. Food Frequency Questionnaires for Diet Intervention Research. 17th National Nutrient Databank Conference, Baltimore. MD, 7-9 June, 1992. Washington, DC: International Life Sciences Institute, 1994: 110–25.Google Scholar
37Suitor, CJW, Gardner, J, Willett, WC. A comparison of food frequency and diet recall methods in studies of nutrient intake of low-income pregnant women. J. Am. Diet. Assoc. 1989; 89: 1786–94.CrossRefGoogle ScholarPubMed
38Heben, JR, Ma, Y, Clemow, L, et al. Gender differences in social desirability and social approval bias in dietary self-report. Am. J. Epidemiol. 1997; 146: 1046–55.Google Scholar