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German Institute of Human Nutrition, Department of Epidemiology, Bergholz-Rehbruecke, Germany
2To whom correspondence should be addressed. E-mail: noethlin{at}mail.dife.de.
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ABSTRACT |
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 TOP ABSTRACT SUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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50y) or body mass index (BMI) (<26 kg/m2, 26 kg/m2). The FFQ inquired about both consumption frequency and portion size. Linear regression models for each food item were fit with intake (g/d) as dependent variables and frequency of intake as independent variables. The mean coefficient of determination (R2) for the different food items explained by frequency only was 84.0% (71.2–95.7%). The R2 for gender-, age- and BMI-specific frequencies of intake did not markedly alter the overall results. We conclude that the omission of individual portion size information would probably result in a notable reduction of interindividual variance. However, to reduce the respondents’ burden and to increase data completeness in self-administration in large epidemiologic studies, the assignment of a constant portion size seems to be adequate. The variance was not increased markedly when constant gender-, age- and BMI-specific portion sizes were applied, thus supporting the assignment of an overall portion size.
KEY WORDS: • food-frequency questionnaire • portion size • EPIC-Potsdam Study
The food-frequency questionnaire (FFQ)3 has become the primary instrument for dietary assessment in large-scale epidemiologic studies (1
,2
). The main reason for this development during the last decades is the agreement among scientists that FFQ data are sufficiently valid for etiologic studies (3
). This is accompanied by feasibility and budget considerations.
In studying diet and disease relationships, comparison of disease risk for quintiles of intake is often used as a measure of association (4
,5
). Therefore, the dietary assessment instrument has to rank individuals and reflect variance in food intake rather than estimate absolute amounts of individual intake. If the aim of dietary assessment by FFQ is to quantify intake, recent considerations have suggested conducting standardization studies as an integral part of dietary assessments (5
–8
). FFQ data are nevertheless sometimes used to assess mean values and distributions of nutrient intake among groups without standardization studies (9
). Whether the main focus of a FFQ is to provide information about quantitative nutrient or food intake or about variance in intake in a population is still under discussion (10
,11
).
Considerations about compliance with follow-up procedures and data completeness led us to the modification of the existing FFQ in the European Investigation into Cancer and Nutrition (EPIC)-Potsdam Study (12
). The revised instrument will be incorporated in the follow-up of the cohort study for repeated dietary exposure assessment in addition to the routine ascertainment of incident cases (13
).
In FFQ, information about amounts consumed is required to calculate daily intake. The two possibilities of incorporating this information into the analysis are either to ask the participants about individual portion sizes or to assign portion sizes.
With the aim of simplifying the FFQ, we evaluated the need to ask separate questions about portion size. Information on average portion sizes in FFQ can be asked in separate questions ("How often did you eat apples?" and "What was the usual portion size? (e.g., 0.5, 1)", be incorporated into the question about the consumption frequency of the food item itself ("How often did you eat one apple?") (semiquantitative questionnaire) or can be omitted ("How often did you eat apples?"). In the last two instances, assigned portion sizes are used in the analysis (14
). The assigned portion sizes can either be the same for all subjects or they can be chosen specifically for men and women or age groups, because amounts consumed differ by age and gender (15
).
We therefore investigated the effect of separate portion size questions on variance in reported food intake in our previous FFQ. Furthermore, we analyzed whether assignment of group-specific portion sizes in contrast to assignment of an overall portion size would increase interindividual variance in food intake.
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SUBJECTS AND METHODS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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The study population was the EPIC-Potsdam cohort, which is one of the two German cohorts contributing to the EPIC study, a multicenter cohort study into diet and chronic diseases, especially cancer (16
). A total of 27,548 study participants was recruited between 1994 and 1998. Recruitment procedures have been reported elsewhere (17
). Approval for all study procedures was given by the Ethical Committee of the State of Brandenburg, Germany, and written informed consent was obtained from all study participants.
Participants with missing dietary data (n = 109) and body mass index [(BMI) = weight (kg)/height (m)2] (n = 212) were excluded from the analysis. The lower and upper 1% of participants were excluded also due to implausible energy intake in relation to energy requirement [energy intake (EI)/energy requirement (ER)] (n = 549). ER was calculated as basal metabolic rate x 1.35 (18
,19
). Lower and upper bounds were EI/ER 
0.31 and EI/ER 1.65, respectively. In total, data of 784 participants were excluded and data of 26,764 participants were available.
Food-frequency questionnaire.
The FFQ was designed and validated for the application in EPIC-Germany (12
,20
,21
). The FFQ consisted of a food list containing 148 food items accompanied by questions about preparation methods and preferred fat content of specific products (such as dairy and meat). For each food item, participants were asked about both frequency of consumption and average portion size. Frequency of intake was measured using a scale of 10 categories ranging from "never," "one time per month or less" to "five times per day or more." Photographs or, if available, household measures were used to define portion sizes for each food item. Respondents were either asked whether their average portion size was half, the same, double or three times the amount shown (relative portion sizes; 103 items), or respondents had to choose one out of three pictures showing different amounts of foods or dishes (absolute portion sizes; 38 items). The latter are used to visualize small, medium and large portions. The actual portion sizes were derived from a representative national nutrition survey (Nationale Verzehrsstudie). Daily consumed amount was calculated by multiplying frequency per day and portion size. In several cases of similar food items, portion size was obtained only for the generic item, resulting in 141 items used in this analysis. These 141 food items were divided into 24 food groups, each consisting of 1–24 food items (Table 1
).
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Mean food intake for men and women and for age (<50 y; 50 y) and BMI (<26 kg/m2; 26 kg/m2) groups were calculated. Median values for age and BMI were chosen as cut-off points. Gender, age and BMI were considered the most discriminating variables concerning food intake and were therefore focused on to potentially assign group-specific portion sizes.
We used the food group cereals as an example to analyze whether differences in mean intake could be either attributed to the variation in portion sizes or to the variation in consumption frequencies or both. We therefore compared the average reported portion sizes and the average reported consumption frequencies between men and women and according to age and BMI groups for five items. We chose the food group cereals as an example because of the heterogeneity in consumption patterns of the five items, i.e., consumption at various times a day and different percentage of nonconsumers.
To estimate the contribution of frequency of intake to variance in food intake, linear regression models for each food item were fit (model 1). Intake (Ii) (g/d) of item i (i = 1, ... 141) was used as the dependent variable. The independent variable was defined by frequency Fi of intake per day of food i (i = 1 ... 141). No intercept was allowed in the models because, by definition, daily intake has to be zero if frequency of intake is zero.
 | (1) |
We calculated the coefficient of determination, R2, to estimate the proportion of variance explained by frequency alone. Because the product of portion size and food intake completely explains variance of intake, the loss of variance, caused by omission of portion size information, is equal to 1 - R2. The single R2-values were combined in arithmetic means for food groups.
To analyze whether explained variance increases markedly by assignment of group-specific portion sizes, we fit linear regression models incorporating BMI-, gender- and age-specific frequencies of intake as independent variables. The number of independent variables equaled the number of groups under investigation in the different models. In detail, we used group-specific intake frequencies Fij. Here, Fij = Fj if the ith individual belongs to group j and Fij = 0 otherwise. In the case of two groups, e.g., men and women, the model equation has the form
 | (2) |
The two parameters ßI and ß2 can be interpreted as portion sizes of individuals of groups 1 and 2, respectively. Frequency of intake has been stratified in this way for gender, for BMI groups, for age groups and for combinations of age and gender, BMI and gender and of age, gender and BMI groups. To evaluate the effect of group-specific portion sizes, we compared R2 of model 2 with that of model 1. The difference in R2 would be the increase of explained variance attributed to the assignment of age-, gender- and BMI-specific portion sizes.
To investigate the cumulative effect of a predefined portion size vs. variable portion sizes on the nutrient intake, we analyzed data of our FFQ validation study (21
). The procedure of the validation was reported elsewhere. In brief, 134 participants (75 men and 59 women) completed 10–12 24-h dietary recalls throughout a year and filled in the FFQ at the end of this year. We simulated the assignment of predefined portion sizes by replacing all absolute portion sizes (i.e., small, medium, large) by the medium portion sizes and compared nutrient intake derived from FFQ correlated with 24-h recalls with simulated FFQ data with 24-h recalls. Spearman correlation coefficients are given for the comparison between nutrient intakes according to the mean of 24-h recalls, FFQ with variable portion sizes (original data) and FFQ with assigned portion sizes (simulated data). We used SAS for Windows V8 (SAS Institute, Cary, NC) to conduct all statistical analyses.
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RESULTS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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), the men were older with a mean age of 52 y and had a higher mean BMI (27 kg/m2). Women had a mean age of 49 y and a BMI of 26 kg/m2.
Mean intake of food groups and 95% confidence limits (CL) are presented in Table 2
. Food consumption showed significant gender differences in all food groups except for sweets and cheese. Cereals, coffee, tea, fruits, vegetables, milk, milk products and soft drinks were consumed in higher amounts by women, whereas men consumed higher amounts of potatoes, meat, soup, desserts, bread, fats, eggs, salty snacks, sweets, legumes, fish, cake, alcoholic beverages, processed meat, spreads, cheese, sauce and nuts. Consumption of most of the food groups was different for participants with a BMI 26 kg/m2 and those with a BMI <26 kg/m2. No differences according to BMI were observed for coffee, tea, fruits, milk, milk products, cheese, sauce and nuts. In most cases, the amounts consumed were higher in individuals with higher BMI. However, compared with participants with lower BMI, consumption of cereals, fats, salty snacks, sweets, cake, spreads was lower in this group. Comparison of age groups revealed significantly different amounts consumed of all food groups except for bread, coffee, tea, alcoholic beverages, vegetables and milk and milk products. Gender was the most discriminating variable with regard to the mean food consumption. Percentage of nonconsumers varied across food items. In general, across all food groups, the proportion of nonconsumers was 21.5% on average, with a range of 94.1% (vegetarian spreads) to 0.2% (boiled potatoes).
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). Mean portion sizes and mean consumption frequencies differed between men and women for all food items of this food group. Differences in reported portion size and reported consumption frequency according to BMI and age groups were also observed, but these were not as consistent as for gender. Taking all 15 comparisons into account, 12 portion sizes differed and 14 consumption frequencies showed differences. Because both portion sizes and frequencies of intake contributed to variation, we separated out the variance due to portion size and frequency.
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, explained variance of food intake is shown for all food groups. On average, 84.0% of variance in food intake was explained by frequency of intake solely (model 1), with the highest values for French fries (95.7%) and the lowest for nuts (71.2%). In general, compared with other food groups, the proportion of variance in intake explained only by frequency of intake was greatest for potatoes. Variance in intake of nuts was explained least by frequency alone. The range of the single R2-values for items of one food group in most cases was rather small. Loss of variance due to omission of portion size information ranged between 4.3 and 28.8%.
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Table 5
shows correlation coefficients of nutrient intake between the original FFQ data and the 24-h recall, and the simulated data and 24-h recalls of 134 participants of the validation study. The range of the correlation coefficients was r = 0.36 and r = 0.62 with a mean value of r = 0.52 for FFQ vs. 24-h recall and r = 0.38 to r = 0.56 with a mean of r = 0.48 for simulated FFQ vs. 24-h recall.
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DISCUSSION |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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40 years ago (22
). Although we determined that amounts consumed showed gender differences and varied across age and BMI groups, calculations that allowed group-specific portion sizes according to gender, age or BMI did not markedly improve explained variance. The assignment of portion sizes compared with inquiring about variable portion sizes resulted in a moderate decrease in correlation coefficients for nutrient intake between FFQ and repeated 24-h recalls. A limitation of our study in this context was that information on portion sizes and frequency of intake was assessed simultaneously. If necessary, participants could interchange larger portion size with a higher frequency of intake and vice versa. We do not know how frequently participants made use of such substitutions. However, we assume that substitution was rarely present because portion size and frequency options were very detailed. Therefore, these data may be a good approximation of real effects. A further limitation concerns our study population, which was a selected sample out of the general population, probably with higher motivation for epidemiologic research. Generalizability of our results is therefore limited. Results may not be valid for other populations with other dietary habits.
Several researchers tried to measure the effect of portion size questions on food intake data by calculating correlation coefficients. Correlations between the same FFQ, inquiring about portion size or applying standard portion sizes, showed values around 0.9 for nutrients (23
). In a Danish study, FFQ data with and without individually estimated portion sizes were compared with weighed diet records (24
). Mean correlation coefficients for food groups and nutrients changed only slightly, indicating that little extra information could be obtained by additional questions about portion size. Clapp et al. (15
) found correlation coefficients of 0.73 to 0.92 for retinol and folacin, respectively. Our findings correspond to these results.
Stratification of standard portion sizes according to age and gender has been suggested and is already in practice (15
,25
). In a recent validation study, the authors stated that low correlation coefficients for nutrient intake could be due to assignment of an overall portion size instead of gender-specific portion sizes (26
). However, our empirical results regarding the assignment of group-specific portion sizes indicated a minor benefit with regard to variance in food intake. Furthermore, Willett (11
) pointed out that models of disease and diet relationships would always be adjusted for age and gender to account for the confounding effect.
The collection of valid individual portion size data requires the individuals to be able to estimate the amounts consumed correctly. However, this seems to be a questionable postulate. A study to validate individual portion size estimates compared FFQ using photos to 14-d weighed food records and revealed only a small relationship between estimated and measured portion size (27
). Participants selecting small portion sizes seemed to underestimate and those selecting large portion sizes seemed to overestimate amounts actually consumed (27
,28
).
The existence of a usual portion size for an individual is a further assumption that is implicitly made when inquiring about consumed amounts. The data of the proportion of intra- and interperson variability of portion sizes shed doubts on this concept. In a study by Hunter et al. (29
), the intra-individual variability in food intake in 61 of 68 items exceeded the interindividual variability. The ratio of intra-individual to interindividual variance was 3.4 on average, indicating a smaller contribution of interindividual variance to total variance in food intake. In another study, variance ratios ranged from 0.67 to 1.60 (27
).
The inclusion of separate questions inquiring about portion sizes in a FFQ introduces one additional question for each food item into the questionnaire (14
), and thus expands the length of the FFQ. In addition to the accuracy of information on food and nutrient intake, questionnaire length has to be considered and, consequently, respondent burden. Questionnaires extended in length by extra nondietary questions and portion size questions resulted in a 20% higher total nonresponse rate compared with short forms, whereas the inclusion of portion size questions alone was not significantly associated with the nonresponse rate (30
). A short FFQ including 97 items without questions on portion size except for a few items resulted in a 20-min completion time (31
), and response rates for a semiquantitative FFQ were higher than for questionnaires inquiring about portion size (26
). However, Subar et al. (32
), who designed a questionnaire to be cognitively easier for study participants, concluded that shorter questionnaires are not always better in large-scale epidemiologic settings.
In general, information about portion sizes in FFQ is important in measuring variance in food intake, and our findings might have different implications in different research contexts. However, depending on the purpose of the data, the omission of separate portion size questions in favor of a semiquantitative FFQ can be of advantage, especially in large epidemiologic studies in which a questionnaire should be kept simple. Because group-specific portion sizes did not markedly increase explained variance, the assignment of an overall portion size is recommended.
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FOOTNOTES |
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3 Abbreviations used: BMI, body mass index; CL, confidence limits; EI, energy intake; EPIC, European Investigation into Cancer and Nutrition; ER, energy requirement; FFQ, food-frequency questionnaire.
Manuscript received 23 August 2002. Initial review completed 24 September 2002. Revision accepted 4 November 2002.
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LITERATURE CITED |
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