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* School of Population Health, University of Queensland, Herston, Qld 4006, Australia and Queensland Institute of Medical Research, Herston, Qld 4029, Australia
3 To whom correspondence should be addressed. Email: g.marks{at}sph.uq.edu.au.
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ABSTRACT |
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 TOP ABSTRACT SUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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KEY WORDS: • food frequency questionnaire • weighed diet records • validation • relative validity • dietary assessment
There is an increasing need for reliable measurements of foods that are consumed as part of the usual diet. Valid estimates of the consumption of food items are the premise of dietary pattern characterizations, and intervention trials such as the CARET study showed that whole foods rather than individual nutrients may best indicate the potential role of the diet in disease prevention (1).
FFQ are widely used to investigate customary food intake over extended periods of time. Like all dietary methods, estimates derived from FFQ data suffer from random and systematic error and may not represent the "true" usual diet. Numerous factors may compromise the validity of food consumption estimates (2), but the effects of these on measurement error are generally assessed for nutrient intake estimates rather than food intake per se. As a result, the validity of food intake estimates derived from FFQ data is not well documented. Further, experience with validation of nutrient intake estimates showed that specific subject characteristics are frequently associated with measurement error (3,4). Knowledge of such factors may help improve the design of food intake instruments and provide a basis for more appropriate modeling of diet-disease relations.
Here we evaluate the relative validity of food intake estimates derived from an FFQ administered to participants in the Nambour Skin Cancer Prevention Trial (5). This field trial was conducted in an unselected adult population in Australia. One of the central objectives of the project was to examine the relation of dietary factors to development of actinic skin and eye disease (6). This study compares estimates of intake of food from the FFQ with those based on 12 d of weighed food records (WFR) over a 12-mo period for a randomly selected subsample of the Nambour study population. We estimated the relative bias and imprecision of food intake estimates, and assessed the extent to which selected demographic, anthropometric, and social characteristics of participants explain any difference between the 2 dietary methods.
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SUBJECTS AND METHODS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Also in 1992, a random sample of 168 participants was invited to participate in a validation study involving WFR. Participants were eligible to participate if they had completed the FFQ and remained an active participant in the trial (7). All participants provided written informed consent and the institutional ethics committee approved the study.
Administration of the food frequency questionnaire. The self-administered semiquantitative FFQ was adapted from the questionnaire used in the US Nurses' Health Study developed by Willett et al. (8,9). Revisions were made to ensure that the list of foods reflected the Australian diet according to the 1983 National Dietary Survey of Adults (10,11). For the Nambour Trial, further revisions were made to improve estimates of intake of antioxidant-rich foods (inclusion of major food sources, particularly vegetables and fruits).
The FFQ collected consumption information for 129 food items or food groups. Respondents were requested to recall how often, on average, they consumed a given amount of each food during the past 6 mo (judged appropriate for this population). The amounts were in household or common measures such as 1 slice, 1 tablespoon (15 mL), or 1 cup (250 mL), representing 1 standard serve for each food. Response options ranged from "Never" to "4+ times a day." For seasonal fruits and vegetables, participants were asked to indicate how often these foods were eaten in season. Additional information collected included cooking methods and specific types of oil, margarine, butter, cereals, and take-out foods eaten. The FFQ also collected information on brand, dosage, and frequency of use of dietary supplements. All FFQ data were double entered, and any discrepancies resolved by reference to the original forms.
Administration of the weighed food records. WFR participants completed 2 nonconsecutive days of food weighing every 2 mo over a period of 12 mo. Initial start days for data collection were randomly allocated among participants to ensure that each day of the week was equally represented and that the records for the sample were spaced evenly over the initial 2-mo block. For subsequent blocks, recording days were advanced by 1 d. If the days specified were unsuitable for participants, alternative days were determined to ensure an overall balance of week and weekend days.
Participants used 2-kg capacity digital scales in 2-g gradations to weigh all food and beverages consumed for the 2 recording days. Information on recipes and dietary supplements used was also recorded. A research dietitian collected the food diaries and reviewed the records with the participants to check for errors, omissions, or doubtful entries. Coding decisions were made by the research dietitians who checked all decisions for open-ended questions in the FFQ and checked a random 10% subsample of daily records for the WFR (error rate 0.7%).
Collection of health indicators. At baseline, trained research staff measured body weight and height using standard protocols. Information on age, sex, education, occupation, and medical condition was obtained by questionnaires (6). Participants were considered to have a medical condition if they answered "yes" to any of the conditions listed in the question "Have you ever been told by a doctor/nurse that you have: glaucoma, gallstones, high cholesterol, high triglycerides, diabetes/high blood sugar, high blood pressure/hypertension, angina, heart attack, stroke, cancer?"
Calculation of food intakes. Frequency of consumption of each food item in the FFQ was converted to intake in grams per day by multiplying the standard serving size of each food as specified in the FFQ by the following values for each frequency option: Never = 0; <1/mo = 0.02; 1–3/mo = 0.07; 1/wk = 0.14; 2–4/wk = 0.43; 5–6/ wk = 0.79; 1/d = 1.0; 2–3/d = 2.5; and 4+/d = 4. Seasonal foods were weighted according to the proportion of the year that each food was available. The intended use of the FFQ includes the association of food groups with skin cancer. For this reason, the 129 FFQ food items were reclassified into 37 food groups, including those that reflect dietary patterns that are hypothesized to modify the risk of skin cancers (see Supplemental Table 1). Daily grams of intake for individual FFQ items were summed to obtain daily intake of each food group. Data from WFR were entered into a database using Xyris Diet 1 Software (7,12). Individual food items were classified into 45 food groups. Only the 37 food groups that matched the FFQ were included in this analysis. The 8 nonmatching food groups included water (drinking or used in food preparation), drinking chocolate, organ meats other than liver, meat pastes, sauces, condiments, meal replacements (e.g., Sustagen®), and meat replacement foods. Daily grams of consumption of each food group were calculated by summing foods in each food group per day of WFR and obtaining the mean of all weighing days.
Statistical analyses. Mean, SD and median food group intakes (grams) were calculated for each dietary method. The intake estimates were not normally distributed.
Statistical analyses were performed in 2 phases. In the first phase, we compared agreement between the 2 methods by using the intake estimates in the form in which they would be used in future diet-disease analyses, i.e., (untransformed) grams of intake and quartiles of grams of intakes. For this comparison we used nonparametric methods including Spearman rank correlations and the Wilcoxon signed rank test for difference between paired observations. We compared the classification of intakes into quartiles by the 2 methods. The proportion of exact agreement, deviation by 1 quartile, and the proportion of grossly misclassified individuals (disagreement by 3 quartiles) were calculated. Finally, we calculated the median grams of food intake as determined using WFR for each FFQ quartile. These analyses compared the ranking of individuals by food intake.
In the second phase, agreement in estimating absolute estimates of intakes was assessed using parametric tests for which food group intake data were log-(natural) transformed (FFQlog and WFRlog) to achieve a normal distribution. The formula log(gram intake + 1) was used if food groups were not consumed by all participants. Paired t test (P = 0.05) and limits of agreement (LOA) described by Bland and Altman (13) with correction for a small sample size (n < 100) by Ludbrook (14) were used to quantify the degree of agreement/disagreement between the 2 dietary methods. Results of paired t tests indicate whether on average, the FFQ consistently over- or underestimated the WFR. The LOA is calculated as follows:
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The lower and upper boundaries of the LOA present the range in which 95% of the differences between the dietary methods were expected to lie. Mean differences and LOA were exponentiated to provide a ratio of the gram intakes estimated by FFQ relative to the WFR (13). Thus, a mean ratio of 1.10 and LOA of 0.85–1.40 indicate that, on average, FFQ overestimates WFR by 10% and that 95% of the differences range from 15% below to 40% above.
The difference in intakes (FFQlog – WFRlog) was plotted against the mean (FFQlog-adj + WFRlog-adj)/2 to determine whether the difference between the methods varied across the range of intakes. A regression line was fitted and the slope was tested for significant difference from 0 (P = 0.05). Slopes significantly different from 0 identified cases in which the difference between methods increased or decreased across the range of intakes.
To identify factors associated with the validity of FFQ intake estimates, multivariable regression analysis was performed with the difference in log-transformed food intakes between dietary methods as the dependent variable and personal characteristics of participants as explanatory variables. Because of the dissimilarity in construct and error source of the 2 dietary methods, one would generally expect differences to be random relative to other factors. If any factors are associated with the differences, it would indicate that these factors are associated with the relative validity of the measures. Personal characteristics assessed included age (y), sex, BMI, education (school-leaving age), occupation (professionals, nonprofessionals), medical condition (yes, no), and use of dietary supplements (yes, no). Mean intakes were included in the model as a predictor of difference in intake estimates if the preliminary analyses (see above) showed that mean intakes were associated with the difference in intakes at P < 0.10. R2 was calculated to quantify the extent to which the explanatory variables accounted for total variation in the difference in intakes. All statistical tests were two-sided and a significance level of P < 0.05 was used. All analyses were performed using SAS v8.2 software(15).
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RESULTS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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The 96 subjects in the validation study, the 115 subjects who completed the WFR, and the 1621 Nambour Trial population did not differ in age, sex, BMI, education, or smoking status. There were significantly more regular users of dietary supplements in the validation study (45.8%) compared with the trial participants (32.7%) (P < 0.05). The proportion of professionals or participants with a medical condition did not differ.
The mean and median grams of food group intakes are presented in Table 1. For 21 of the 37 food groups, estimated intakes by the FFQ were significantly higher than those by WFR. WFR consumption estimates were significantly higher for 5 groups, whereas 11 groups did not differ. It is notable that mean FFQ intakes of all fruit and vegetable food groups (except legumes) were significantly higher, and the SD for most was also much larger.
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0.70 included all dairy products (unmodified and modified), alcoholic beverages, tea, and coffee. There were 10 food groups with correlations 
0.30; 5 of these were vegetable food groups. The strength of correlations was reflected in the comparison of median WFR intakes per FFQ quartile. Overall, median WFR intakes increased over increasing FFQ quartiles, with a more pronounced increase in intakes observed for correlations 0.50. The mean ratio (and 95% CI) of FFQ to WFR and LOA was similar to that presented in Table 1, with higher estimates for the FFQ than the WFR in a majority of the food groups (see Supplemental Table 2). FFQ estimations of 3 fruit food groups, 2 vegetable food groups, rice, and pasta and noodles were double the WFR estimates (mean ratio >2.00), whereas consumption of soft drinks was overestimated by 300%. Intakes of fats and oils, whole-meal and white bread, other cereals and cakes, and biscuits were underestimated by the FFQ. The most marked underestimation was of white bread; on average, the FFQ estimates were 75% below the WFR.
Although the population mean ratio of FFQ to WFR provides an estimate of over- or underestimation by the FFQ for the entire study population, the LOA provides information on the variability of estimates between the 2 methods at the individual level. For example, the mean ratio for meat showed overestimation by 41% (95% CI 17–71%), but the LOA indicated that for 95% of individuals, the differences ranged from underestimation at 0.22 of the WFR value to a 9-fold overestimation.
For 14 food groups, the differences in intakes varied significantly with the magnitude of the intakes. In 9 food groups, there was a negative association in which the differences were larger at lower levels of intake, i.e., individuals who do not consume these food groups regularly showed more error in their reporting of small amounts of intake compared who those who ate large amounts of the same food group. Differences in estimated intakes of fats and oils, modified and unmodified dairy, and all cereals and products increased significantly with the magnitude of intakes. For these food groups, the greatest error occurred in subjects who reported high levels of intake compared with nonregular consumers of the same food.
Table 3 shows the associations between personal characteristics of individuals and the difference in estimated intake between the 2 methods. The difference in reported intakes was larger among women than men for fats and oils, modified dairy, meat (including processed), poultry and seafood, a number of vegetable food groups, including green leafy vegetables, all vegetables and fruits, rice, pasta and noodles, and all cereals and products, whereas the difference was smaller for the intake of tea. The presence of any medical condition was significantly associated with increased difference in reported intakes of meat, cruciferous vegetables, pasta and noodles, and tea and decreased difference for all cereals and products. For example, the ratio of intakes of meat estimated by FFQ to WFR was 1.51 times greater (exponential of 0.412) in participants with medical condition than in those with no medical condition. Smaller differences in estimated intakes of other vegetables, all vegetables, and other fruits were noted for participants who reported taking dietary supplements, whereas differences were larger for alcoholic beverages and nuts. Although only the difference in intakes of other vegetables, all vegetables, and all vegetables and fruit decreased significantly with the use of dietary supplements, the association between dietary supplements and intake differences in almost all of the other vegetable and fruit food groups was in the same direction. Age was significantly associated with green leafy vegetables, other vegetables, and all cereals and products. However, the direction of the associations was not consistent. BMI was not significantly associated with difference in intakes of any food group, but the majority of associations was positive, i.e., an increasing difference between the 2 dietary methods with increasing age or BMI.
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DISCUSSION |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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To our knowledge, there is no other Australian study that presented food-based validity of FFQ to which we can compare our results. Compared with food-based validation studies in other countries, the strength of the correlations obtained in this study is comparable to studies in the United States (16), Mali (17), Guatemala (18), and Japan (19); with the exception of the United States (Nurses' Health Study), all samples included both men and women. The correlation coefficients in our study were better than those reported for Chinese miners by Forman et al. (20), and were generally not as good as correlations observed in France (21) (nursing staff), Sweden (22) (population based), Finland (23) (pregnant women), and Shanghai/ China (24) (women). Importantly, the higher correlation coefficients were generally observed in single-sex studies. A correlation coefficient of 0.3 is a level at which attenuation is so severe that it would be difficult to detect associations (25). The correlations 0.3 in our study were for poultry, eggs, 5 vegetable groups, rice, pasta and noodles, and nuts.
The ranking of individuals in terms of quartiles was assessed by comparing the quartile allocation of intake estimates from FFQ with that of the WFR. Studies on diet and disease associations frequently divide food intake into quartiles. Overall, the pattern of results was similar to that of the correlations, but poor agreement and gross misclassification were not as extensive as suggested by the correlation results. Exact agreement in the allocation of food intakes by the FFQ and WFR ranged from 26 to 63%, and agreement within 1 quartile ranged from 60 to 93%, results comparable to those reported in other food-based studies (17,19,24). The poorest agreement was observed for eggs, cruciferous vegetables, "other vegetables," rice, and nuts, with agreement for these food groups little different from that expected in random allocation. Median WFR intake estimates generally showed increasing trends over increasing FFQ quartiles, although this was not monotonic for all food groups.
The poor agreement found for vegetables, assessed as a food group or as individual foods, was also reported by studies in the United States (16), Guatemala (18), China (20) (miners), and France (21) (nursing staff), but not by others, i.e., Mali (17), Japan (19), Sweden (22) (population based), and Finland (23) (pregnant women). Nevertheless, Cade et al. (25) noted in their review of validation studies for FFQ that mean correlations between FFQ and reference methods are usually lowest for vegetables, explaining that misreporting of vegetables can occur for a number of reasons including double counting of items and social desirability bias. For the other foods with poor agreement, the results from other studies are more varied. For example, studies have tended to report higher correlations for eggs than we observed, e.g., >0.4 in the United States (16) and Guatemala (18).
Agreement in the estimation of absolute intake was assessed in the limits of agreement analysis. Key findings were that on average the FFQ overestimated the intake of most foods, which was also reported in other validation studies (17,23,24), with large overestimations for intake of the fruit and vegetable groups. This is reflected also at the individual level, with 4 of the 37 food groups having 95% lower limits 0.05, and 12 having upper limits >20; there were very broad ranges for many foods, but a general trend for overestimation. The differences between the FFQ and WFR intake estimates varied significantly with magnitude of intake estimates for 14 food groups, with 9 showing a negative association and 5 showing a positive association. There was no clear pattern concerning which food groups had a negative, positive, or no association with magnitude of intake.
We reported previously that there were sex differences in the extent of underreporting of energy intakes for FFQ and WFR in this study group (7). Using cut-off values based on the ratio of energy intake to basal metabolic rate, as described by Goldberg et al. (26), the extent of underreporting was highest among women using the WFR, 
2 times that observed for women using the FFQ, or for men using either method. In spite of recognized weaknesses, the WFR is still regarded as the method of choice to use as the reference method in validation studies of this type. Bingham et al. (27) showed in an evaluation of 7 dietary assessment methods in comparison with several biomarkers of dietary intake that WFR were consistently more strongly associated with the biological markers than were the other methods. The authors concluded that WFR "remain the most accurate measure of dietary intake." Cade et al. (28) in a recent review of FFQ also suggest that WFR should be the first method of choice in validation studies; a major advantage is that the main sources of errors are different for the 2 methods, and unlikely to be correlated (correlated errors can lead to overestimation of validity by some measures). Thus, measurement errors in the WFR likely contributed to the results observed in our study.
Nevertheless, the effect of sex on measures of agreement is illustrated with the observation above that FFQ validations in single-sex studies tend to have higher correlations. It was suggested that the FFQ format contributes to sex differences, and particularly that the treatment of portion sizes in data collection contributes to both the sex differences observed between FFQ and WFR and the underreporting. Subar et al. (29) compared the validity in estimation of nutrient intakes for 3 FFQ formats and found that the Willett instrument tends to underestimate the nutrient intakes of men and overestimate those of women. They attribute it to the same portion sizes being assigned to men and women, as applied in this study. In a recent review of the design, validation, and utilization of FFQ, Cade et al. (28) reported that in studies in which portion sizes are self-defined, there tended to be differences in portion size between men and women, and further that correlations in validity studies tended to be highest when subjects were able to describe their own portion sizes.
The poor performance of vegetables across measures of agreement is a matter of particular concern because of our interest in examining their potential role in cancer etiology. For all vegetable groups, intakes were estimated by the FFQ to be 2 times those found with WFR. Correlations were modest, ranging from 0.08 to 0.40, and were generally stronger for fruits. This is consistent with other studies in which levels of agreement between the FFQ and other dietary assessment methods were generally found to be poor for vegetables and fruits (30). Reasons for this are not well established.
The multivariable modeling in the limits of agreement analysis shows that the models for fats and oils, poultry, seafood, various vegetable groups, rice, pasta and noodles, and all cereals and products explained 25% of the variation in difference between FFQ and WFR. Sex was a significant explanatory variable for most of these. Of the other food groups with particularly poor performance, the model explained 10% of the variation in difference.
These findings have important implications for modeling diet-disease relations. The significant association between personal characteristics and difference for most food groups raises the possibility of differential bias and misclassification. Adjusting intake estimates for these characteristics will improve the validity of the model. One might expect this to be particularly appropriate for the vegetable groups, for which the performance of the FFQ is otherwise poor, and a reasonably large proportion of variation in difference is explained by the models. This would also suggest that different subgroups of the study population may need different FFQ to accurately measure dietary intake; this remains to be confirmed by further investigation.
This is the first study we know of that directly assessed the association between personal characteristics and measurement errors in FFQ food intake estimates. It is widely acknowledged that a number of factors such as gender, age, and socioeconomic factors may be associated with the validity of dietary estimates (3). Our study assessed the extent to which they affect measurement error in the Nambour study population. Of all the personal characteristics studied, sex was most commonly associated with intake estimate errors for food groups; the presence of a medical condition and dietary supplement intake were also associated for some food groups. The findings highlight the need to assess FFQ validity in a sample that is representative of the overall population in which the FFQ will be used, with a sample size that is large enough to assess differences among subgroups.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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2 Supplemental Tables 1–2 are available as Online Supporting Material with the online posting of this paper at www.nutrition.org.
Manuscript received 23 August 2005. Initial review completed 19 September 2005. Revision accepted 22 November 2005.
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LITERATURE CITED |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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