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Validity of a food frequency questionnaire varied by age and body mass index
Journal of Clinical Epidemiology 59 (2006) 994–1001
Validity of a food frequency questionnaire varied by age and body mass index Laura Paalanena,*, Satu Ma¨nnisto¨a, Mikko J. Virtanena, Paul Knektb, Leena Ra¨sa¨nenc, Jukka Montonenb, Pirjo Pietinena a
Department of Health Promotion and Chronic Disease Prevention, National Public Health Institute, Helsinki, Finland b Department of Health and Functional Capacity, National Public Health Institute, Helsinki, Finland c Department of Applied Chemistry and Microbiology, Division of Nutrition, University of Helsinki, Helsinki, Finland Accepted 11 November 2005
Abstract Background and Objective: The validity of food frequency questionnaires (FFQs) in measuring food consumption and nutrient intake has to be assessed. The objective of this study was to assess the validity of a 128-item FFQ in specific subgroups of Finnish adults. Methods: The study included 294 subjects (137 men and 157 women). A 3-day food record was used as the reference method. Results: The mean intake of all nutrients except alcohol was higher measured with the FFQ than with the food records. In general, the Pearson correlations for energy adjusted nutrients between the FFQ and the food records were higher in women than in men. The correlations ranged from 0.14 (retinol) to 0.66 (fiber and alcohol) in men, and from 0.20 (long-chain n23 fatty acids) to 0.70 (alcohol) in women. The results in subgroups showed that measuring nutrient intakes is more difficult among younger (30–50 years) women and overweight men and women than among others. Conclusions: The study showed that the FFQ is a useful tool in epidemiologic studies in measuring the diet of Finnish adults given that the problems among specific subgroups are taken into account in interpretation. Ó 2006 Elsevier Inc. All rights reserved. Keywords: Age; Body mass index; Diet; Food frequency questionnaire; Validity
1. Introduction The food frequency questionnaire (FFQ) has been established as the primary method for estimating long-term food consumption in large epidemiologic studies. Structured dietary data is easy and inexpensive to process compared to data gathered with an open method. Furthermore, no interviewers are needed if the study subjects fill in the questionnaires themselves. However, the FFQ has its limitations. The information is based on the subjects’ memory, and because of the predefined food list, some information about the foods actually eaten may be missed. Therefore, it is essential to examine the validity of the FFQs against another dietary assessment method [1–10]. It has been recommended that in study populations including both genders, validity has to be examined separately for men and women [11]. In studies in which the correlations between the FFQ and food records (the reference
* Corresponding author. Tel.: 1358 9 47448617; fax: 1358 9 47448338. E-mail address: [email protected] (L. Paalanen). 0895-4356/06/$ – see front matter Ó 2006 Elsevier Inc. All rights reserved. doi: 10.1016/j.jclinepi.2006.01.002
method) were analyzed separately by gender, obvious differences were not observed [6,8,12]. However, in a Danish validation study, contrary to the researchers’ expectations, the correlations were mostly higher in men than in women [13]. In the Danish study the FFQ was slightly shorter than in the three other studies (92 vs. 116–350 foods). There is evidence that gender, age, and obesity are associated with underreporting energy intake [14–17]. In a Finnish study among subjects aged 25–64 years, underreporting was most common among women and overweight [body mass index (BMI) O25 kg/m2] subjects and among subjects older than 45 years [14]. Thus far, only a few validation studies for FFQs that measure the intake of a large variety of foods or nutrients with a food record as the reference method and including more than 100 subjects have shown results for subgroups other than by gender. Some validation studies have included only young [18] or elderly [19,20] subjects or subjects from a specific profession, for example, nurses [1] or tin miners [21]. A validation study that was part of a study on selenium intake in relation to health status in the United States found that the correlations did not notably differ between the subjects with a high
L. Paalanen et al. / Journal of Clinical Epidemiology 59 (2006) 994–1001
school education or less and the subjects with more than a high school education [6]. The study included 138 subjects with a mean age of 49 years. In another study in the United States, the correlations were reported separately for gender and race [22]. The correlations were higher for White subjects, and the correlations of Black men tended to be the lowest. The reference method was a combined 24-hr recall and food record and included food consumption data for 16 days (4 3 24-hr recall and 4 3 3-day food record). The objective of our study was to assess the validity of a semiquantitative FFQ among an adult Finnish population with a 3-day food record as the reference method. To deepen our knowledge on the possible effect of the characteristics of the subjects on reporting food consumption, we also examined whether there are differences in the validity by age or BMI. The FFQ was developed for the Health 2000 Survey in Finland.
2. Methods 2.1. Study design and subjects This validation study is a part of a nationally representative survey, the Health 2000 Survey (n 5 8,028), which was carried out at 160 study locations in Finland from fall 2000 to spring 2001 [23]. The survey consisted of various health interviews, self-administered questionnaires, and a comprehensive health examination. The main aim of the survey was to estimate health and functional capacity and to gather information on major diseases, their causes, and treatment circumstances. Some basic information was obtained from 93% of the subjects. Information on the subjects’ diet was obtained using a self-administered semiquantitative FFQ. A total of 6,771 subjects received the questionnaire at the health examination and subsequently filled it in at home. The questionnaire was then returned by mail to the National Public Health Institute (Helsinki, Finland) (n 5 6,373, 94% of the FFQs given out). Of the returned FFQs, 93 were blank and 282 were incompletely filled. Thus, 375 FFQs (6% of the FFQs given out) were excluded from the analyses, and consequently, the number of accepted FFQs in the survey was 5,998 (89% of the FFQs given out). The food records for the validation study were given out during September and October 2000 and during January and February 2001. The sample (n 5 470) for the validation study was chosen to represent the whole study sample by age and gender at eight study locations, which included both towns and rural municipalities. Of the original validation sample, 420 subjects (89%) participated in the Health 2000 Survey. At the health examination the subjects belonging to the validation sample were informed that they would receive a 3-day food record with a photographic picture booklet [2] after returning the FFQ, which was then to be completed at home and mailed back to the National
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Public Health Institute. The food records were not sent to those 35 subjects who participated in the health examination, but either declined to fill in the FFQ, did not return it, or returned it blank. Finally, 385 food records (82% of the original validation sample) were sent, of which 334 (87% of the food records sent) were returned. After the exclusion of the 19 blank food records, there were 315 food records (82% of the food records sent). In the analyses, an additional 21 subjects were excluded because of unidentified food records (the page containing the identification label had been removed) (1), pregnancy (1), incompletely filled food records (10), or incompletely filled FFQs (9). Thus, the final data of the validation study comprised 294 persons (137 men and 157 women, 63% of the original validation sample, 76% of the food records sent). The subjects in the validation study were between the ages of 30 and 79 years. The mean age of the men was 51.9 (SD 12.1) years and of the women 51.2 (SD 12.9) years. The mean height, weight, and body mass index of the men were 177 (SD 7.0) cm, 84.4 (SD 12.5) kg, and 27.0 (SD 3.7) kg/m2, respectively. For the women the figures were 163 (SD 6.6) cm, 71.6 (SD 12.7) kg, and 27.1 (SD 4.9) kg/m2. The final validation study population was compared with the entire Health 2000 Survey population. Men and women were relatively equally represented in the two study populations. The subjects in the validation study were slightly younger, however, than the subjects in the Health 2000 Survey. No one in the final validation study population was over 85 years of age. In the population of the Health 2000 Survey the proportion of at least 85 year olds was 5%. 2.2. FFQ The FFQ in the Health 2000 Survey was a modified version of two Finnish questionnaires used in earlier studies: the Alpha-Tocopherol Beta-Carotene Cancer Prevention Study (the ATBC Study), and the Kuopio Breast Cancer Study [2,4]. The FFQ was designed to cover the whole diet over the preceding 12 months, and the main purpose was to rank individuals according to their average food consumption and nutrient intake. The list of foods was based on the FFQ in the Kuopio Breast Cancer Study [4] and data collected from approximately 3,000 Finns in the 1997 National Dietary Survey [24]. To reflect the increased selection of foods available in the market, the number of food items was increased to 128 from the FFQ used in the Kuopio Breast Cancer Study. However, the number of food items queried in the present study was not increased beyond 130 foods, because a questionnaire with more items may result in the subjects experiencing boredom, which may then impair the accuracy of the answers [25]. The recipes for each food item in the FFQ were composed of one to seven food codes in the Finnish food composition database [26]. The food composition database comprises 1,341 recipes and 828 food items.
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The 128 food items were presented under 12 subgroups, for example, dairy products, vegetables, and fruits and berries. After each subgroup there were empty lines for subjects to add foods not listed in the questionnaire. The portion sizes were fixed and, if possible, specified using natural units (e.g., glass, slice). The nine frequency categories ranged from never or seldom to six or more times a day. The consumption of berries was recorded separately in winter and summer. The FFQ also contained additional questions about meal patterns, special diets, and the use of vitamin or mineral supplements. The food consumption and nutrient intakes were calculated by multiplying the frequency of food consumption by fixed portion sizes to obtain the weight of each listed food item consumed as an average per day. The FFQ was tested in two pilot studies. The reliability of the FFQ within 5–9 months was analyzed among 180 subjects, and the intraclass correlations (ICCs) for foods ranged from 0.16 for potato to 0.82 for milk in men and from 0.24 for poultry to 0.76 for fermented milk products in women (data not shown). The ICCs for nutrients ranged from 0.42 for retinol to 0.72 for riboflavin in men and from 0.22 for retinol to 0.66 for total fat and monounsaturated fatty acids in women. The reliability of the FFQ is comparable with results in earlier studies [2–4]. 2.3. Food records The food records were mostly completed within a month after the FFQ. However, the last food records were not completed until June, after a second reminder round. Thus, the food records cover the period from October 2000 to June 2001. Because the food records were mailed to the subjects, the instructions were given only in writing. The subjects were instructed to record everything they ate or drank during the 3 consecutive days after receiving the food record. A photographic picture booklet with 126 food items and mixed dishes was used to help in estimating portion sizes [27]. The diet records were coded by a nutrition student, and the nutrient calculations were carried out using a computer program developed at the National Public Health Institute [28]. The Finnish food composition database was used for the nutrient calculations [26], and losses due to cooking have been accounted for. All the subjects in the final sample had a complete 3-day food record. The food record data include weekdays and weekend days equally. In Finland, for example, alcohol consumption is concentrated on the weekends. However, in our study, measuring alcohol consumption with the food records is not a problem in that respect, because Saturdays and Sundays are well represented. The minimum and maximum intakes of energy as well as about 20 nutrients and the minimum and maximum consumptions of 20 foods were revised. None of the subjects had a mean daily energy intake during the 3-day period under 2,940 kJ (700 kcal) or more than 21,000 kJ (5,000 kcal).
2.4. Statistical methods All the analyses were computed separately for men and women. The means and standard deviations of food consumption and nutrient intake (untransformed data) were calculated from the FFQs and food records. The Pearson product-moment correlation was used to compare the two dietary assessment methods. In calculating the correlations the loge transformation was used to improve normality. The formula log(x 1 1) was used for alcohol and long-chain n23 fatty acids, because these nutrients included zero intakes. The nutrients were adjusted for each person’s energy intake with the residual method [29]. To compare the validity between subgroups, the Pearson correlation coefficients for energy-adjusted nutrient intakes were used [30]. The subjects were divided into two age groups (men and women separately) using the median age in the whole study population (50 years) as the cutoff point. The cutoff point for the BMI groups was the median among all the subjects (26.8 kg/m2). Sample-specific median cutoff points were used to assure that no preassumptions would affect the results.
3. Results In general, the mean nutrient intakes were higher measured with the FFQ than with the food records (Table 1). The only nutrient that was underestimated with the FFQ was alcohol. Overestimation was more prominent in women than in men. In women the intake of 17 of the 21 nutrients included in the validation study was over 40% higher measured with the FFQ than with the food records. The most overestimated nutrients were polyunsaturated fatty acids, long-chain n23 fatty acids (LC n23 FA), carotenoids, vitamin E, and vitamin C both in men and women. The difference in the intakes measured with the two methods was statistically significant (P ! .05) for all nutrients except retinol (data not shown). The FFQ appeared to over- or underestimate foods more than nutrients (Table 2). The most notable overestimates for foods were found for vegetables and soft drinks. In addition, the consumption of margarine was markedly overestimated in women. In men, the difference between the two methods was statistically significant (P ! .05) for all foods except wheat, butter, margarine, cheese, poultry, sausages, and coffee, and in women for all foods except berries, poultry, and sausages (data not shown). The Pearson correlation coefficients for unadjusted nutrients ranged from 0.20 for energy to 0.65 for alcohol in men, and from 0.07 for polyunsaturated fatty acids to 0.72 for alcohol in women (data not shown). The adjustment for energy improved the correlations slightly for the great majority of nutrients. After energy adjustment, the correlations ranged from 0.14 for retinol to 0.66 for dietary fiber and alcohol in men and from 0.20 for long-chain n23 fatty acids to 0.70 for alcohol in women (Table 1). In men,
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Table 1 Mean daily intakes of energy and nutrients based on food frequency questionnaire (FFQ) and energy-adjusted Pearson correlation coefficientsa (r) between daily intakes of nutrients based on FFQ and food records (FR) Men (n 5 137)
Women (n 5 157)
Nutrient
FFQb Mean (SD)
FFQ of FRc (%)
FFQ vs. FRd (r)
FFQb Mean (SD)
FFQ of FRc (%)
FFQ vs. FRd (r)
Energy (kcal) Protein (g) Carbohydrate (g) Sucrose (g) Dietary fiber (g) Total fat (g) Saturated fatty acids (g) Monounsaturated fatty acids (g) Polyunsaturated fatty acids (g) Long-chain n23 fatty acids (g) Alcohol (g) Retinol (mg) Carotenoids (mg) Vitamin D (mg) Vitamin E (mg TE) Thiamin (mg) Riboflavin (mg) Folate (mg) Vitamin C (mg) Calcium (mg) Magnesium (mg) Iron (mg) Protein, % of energy intake Carbohydrate, % of energy intake Fat, % of energy intake Alcohol, % of energy intake
2,446 102 262 51 25 102 39 32 15 0.84 7.8 1,056 11,044 7.8 14 1.7 2.4 327 115 1,338 460 16 17 44 37 2.3
116 119 114 116 123 123 116 124 141 149 64 122 148 121 142 121 121 118 159 120 119 130 102 97 107 60
d 0.27 0.49 0.47 0.66 0.35 0.41 0.37 0.24 0.35 0.66 0.14 0.20 0.36 0.22 0.26 0.63 0.41 0.36 0.46 0.54 0.44 d d d d
2,290 97 254 49 27 93 36 29 14 0.72 2.9 947 12,631 6.9 14 1.6 2.5 334 150 1,432 453 16 17 46 36 0.97
142 149 136 122 152 151 144 155 167 188 76 126 182 150 166 157 156 144 161 151 144 153 104 96 106 58
d 0.56 0.53 0.54 0.64 0.45 0.55 0.32 0.26 0.20 0.70 0.26 0.47 0.26 0.43 0.49 0.65 0.46 0.51 0.53 0.64 0.53 d d d d
(849) (36) (100) (28) (11) (39) (16) (13) (6.4) (0.62) (11) (789) (6,982) (4.3) (6.0) (0.60) (0.98) (129) (69) (608) (144) (5.6) (2.2) (6.0) (5.1) (3.1)
(709) (32) (81) (24) (10) (34) (15) (11) (4.8) (0.55) (3.7) (711) (8,041) (3.4) (5.0) (0.59) (0.92) (117) (84) (557) (131) (4.8) (2.3) (5.5) (4.8) (1.3)
Pe **
** * *
a
Based on loge transformed values. Observed intakes. c The difference in the mean intakes measured with the FFQ and the food records. d The correlations were calculated for energy-adjusted nutrient intakes. Therefore, the correlations for energy or nutrients as % of energy intake are not presented. e The significance of the difference in energy-adjusted correlations between men and women. * P ! .05. ** P !.01. b
the energy-adjustment improved the correlation by more than 0.20 for carbohydrate and magnesium, and in women for protein, fat, saturated fatty acids and vitamin E. The few decreases in the correlations due to energy adjustment were smaller than 0.10. Most correlations were higher among women than men both before and after energy adjustment. A more specific comparison of the energy-adjusted correlations between men and women revealed that the correlation for protein, carotenoids, vitamin E, and thiamin was statistically significantly (P ! .05) higher among women than men. For foods, the Pearson correlations ranged from 0.09 for beef to 0.89 for coffee in men and from 0.01 for oil to 0.85 for coffee in women (Table 2). The correlations for rye and beef were statistically significantly higher among women and the correlation for fermented milk products was higher among men. The adjustment for energy was not used in the case of foods. The Pearson correlation coefficients for energy-adjusted nutrients were compared in subgroups based on the
subjects’ age (<50 and O50 years) and BMI (!26.8 and >26.8 kg/m2) (Figs. 1–4). The nutrients in Figs. 1–4 are in the order of the difference in correlations between the two groups. Protein, carbohydrate, and magnesium were excluded from the figures because the correlations did not differ notably across the groups. Among men, the correlations did not seem to vary consistently by age (Fig. 1). However, the correlations for mono- and polyunsaturated fatty acids and total fat were notably higher in the younger age group, that is, between the ages of 30 and 50, while the correlations for carotenoids, folate, calcium, and thiamin were higher among men O50 years old. The difference was statistically significant (P ! .05) only for carotenoids and monounsaturated fatty acids. For women, the differences between the age groups were more evident (Fig. 2) with 14 of 18 nutrients having higher correlations among the older women. However, the difference was statistically significant only in case of polyunsaturated fatty acids and retinol.
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L. Paalanen et al. / Journal of Clinical Epidemiology 59 (2006) 994–1001
Table 2 Mean daily consumption of foods based on food frequency questionnaire (FFQ) and Pearson correlation coefficientsa (r) between daily consumption of foods based on FFQ and food records (FR) Men (n 5 137)
Women (n 5 157)
Food group, g
FFQ Mean (SD)
FFQ of FRb (%)
FFQ vs. FR (r)
FFQ Mean (SD)
FFQ of FRb (%)
FFQ vs. FR (r)
Rye Wheat Potato Vegetablesd Fruit Berries Juice Butter Oil Margarine Milk Fermented milk products Cheese Beef Pork Poultry Sausages Fish Sugar and sweets Coffee Soft drinks
63 91 211 202 119 25 57 12 9.7 8.9 374 146 42 31 61 24 49 52 36 484 100
119 105 149 221 125 133 164 96 171 118 115 145 113 149 158 96 87 135 131 98 202
0.34 0.52 0.27 0.53 0.44 0.44 0.35 0.47 0.13 0.53 0.67 0.61 0.48 0.09 0.13 0.15 0.49 0.27 0.37 0.89 0.22
64 82 176 255 220 34 75 12 8.6 9.3 374 207 46 25 50 30 30 46 36 420 53
180 119 188 243 175 113 152 120 179 236 157 159 151 176 199 138 117 180 124 91 241
0.54 0.37 0.37 0.57 0.37 0.38 0.43 0.28 0.01 0.53 0.67 0.40 0.49 0.36 0.29 0.17 0.43 0.29 0.43 0.85 0.28
(43) (50) (137) (147) (122) (22) (90) (8.4) (5.0) (9.8) (303) (171) (43) (23) (37) (27) (40) (37) (25) (257) (159)
(37) (44) (96) (193) (191) (28) (93) (8.6) (3.9) (10) (288) (212) (35) (23) (32) (36) (37) (31) (27) (233) (98)
Pc *
* *
a
Based on loge transformed values. The difference in the mean consumption of foods measured with the FFQ and the food records. c The significance of the difference in correlations between men and women. d Excluding roots and legumes. * P ! .05. b
For both genders, most of the correlations were higher among subjects in the lower BMI group (BMI !26.8 kg/ m2) than in the upper BMI group (BMI >26.8 kg/m2) (Figs. 3 and 4). In men, the difference between BMI groups was statistically significant (P ! .05) for monounsaturated fatty acids, total fat, and alcohol and in women, for saturated fatty acids, total fat, riboflavin, calcium, and fiber. ≤5 years (men)
r 1.0
>50 years (men)
4. Discussion This validation study was a part of the Health 2000 Survey. The FFQ was specifically designed for the survey to assess the subjects’ diet during the previous year, and to classify the subjects according to food consumption and nutrient intake. The validation sample included both genders ≤ 50 years (women)
r
>50 years (women)
1.0
Carotenoids
Fat
Riboflavin
Thiamin
Iron
MUFA
Vitamin C
Sucrose
Folate
Vitamin D
SFA
Fiber
Alcohol
Calcium
Vitamin E
LC n-3 FA
Retinol
Fig. 1. Energy-adjusted Pearson correlation coefficients (r) between food frequency questionnaire and food records for <50-year-old (n 5 67) and O50-year-old (n 5 70) men.
PUFA
Iron
Alcohol
Retinol
Vitamin D
Sucrose
Riboflavin
Fiber
-0.2
LC n-3 FA
-0.2
Vitamin C
0.0
SFA
0.0
Vitamin E
0.2
Thiamin
0.2
Calcium
0.4
Folate
0.4
Fat
0.6
PUFA
0.6
MUFA
0.8
Carotenoids
0.8
Fig. 2. Energy-adjusted Pearson correlation coefficients (r) between food frequency questionnaire and food records for <50-year-old (n 5 81) and O50-year-old (n 5 76) women.
L. Paalanen et al. / Journal of Clinical Epidemiology 59 (2006) 994–1001 BMI<26.8 (men)
r
BMI≥26.8 (men)
1.0 0.8 0.6 0.4 0.2 0.0
Calcium
Vitamin C
Thiamin
SFA
Vitamin E
Vitamin D
Riboflavin
PUFA
Retinol
Folate
Sucrose
Iron
LC n-3 FA
Fiber
Carotenoids
Fat
Alcohol
MUFA
-0.2
Fig. 3. Energy-adjusted Pearson correlation coefficients (r) between food frequency questionnaire and food records for men in the lower (n 5 67) and upper (n 5 70) BMI group. The cutoff point was the median of BMI (26.8 kg/m2).
aged 30–79 years. Although the subjects had participated in a number of interviews and examinations in the main survey before the administration of the FFQs and food records, the response rate in the validation study was reasonably high (n 5 294, 63% of the original validation sample, 76% of the subjects to whom the food records were sent). To examine whether the participants and nonparticipants of the validation study differed according to their nutrient intake measured with the FFQ, the energy intake and the intake of fat, vitamin C, fiber, and alcohol were compared between these two groups. There were, however, no statistical differences between the participants and nonparticipants in their nutrient intakes (data not shown). Most FFQs have overestimated [2,5,8,31] and some have underestimated [3,7] nutrient intake and food consumption compared to food records. In this validation study, the BMI<26.8 (women)
r 1.0
BMI≥26.8 (women)
0.8 0.6 0.4 0.2 0.0
Carotenoids
LC n-3 FA
Sucrose
Retinol
Vitamin C
Vitamin D
Thiamin
Iron
Alcohol
Folate
Fiber
Vitamin E
PUFA
Calcium
MUFA
Fat
Riboflavin
SFA
-0.2
Fig. 4. Energy-adjusted Pearson correlation coefficients (r) between food frequency questionnaire and food records for women in the lower (n 5 80) and upper (n 5 77) BMI group. The cutoff point was the median of BMI (26.8 kg/m2).
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nutrient intakes were higher measured with the FFQ than with the 3-day food record. The FFQ overestimated the intakes in women more than in men compared to the intakes with the food records. However, the correlations for nutrients between the two methods were higher in women than in men, both before and after energy adjustment. Due to their greater health awareness, women may report their food consumption more similarly with the two methods than men. In this study, the true food consumption most likely lies somewhere between the figures measured with the two methods because subjects are prone to underestimate their food consumption when they keep food records [32–34]. In the case of food records, the proportion of underreporters increased from 27 to 42% among men and 33 to 46% among women during 10 years in Finland [14], and this trend has probably continued. Therefore, the apparent overestimation with the FFQ is presumably partly due to underreporting with the food records. The most overestimated nutrients were polyunsaturated fatty acids, long-chain n23 fatty acids, carotenoids, vitamin E, and vitamin C, which was most probably due to the striking overestimation of vegetable, oil, and margarine consumption. Furthermore, the overestimation of fish consumption in women was reflected as an overestimate of long-chain n23 fatty acid intake. The tendency to overestimate the consumption of vegetables, margarine and fish with the FFQ may partly be due to the subjects’ desire to achieve social acceptance by emphasizing the use of foods considered to be healthy. On the other hand, for example, margarine is typically a foodstuff easily forgotten when keeping a food record. In the FFQ, the use of margarine was asked separately, and this ensured that it was reported. In FFQ validation studies where a comparison between the genders has been carried out, the FFQ has overestimated the intakes more both in women compared to men [12] and vice versa [8]. In our study, the overestimates with the FFQ compared to the food records were more prominent among women than among men. The portion sizes were similar for both genders. Some of the predefined portion sizes may have been too large for women, which increased the difference between the two methods. Block et al. [9] examined the effect of portion size assumptions on questionnaire validity. The female subjects reported whether their normal portion size of each food item was small, medium, or large. The correlations were only slightly lower, when the analyses were repeated using standard portion sizes. In another study, the accuracy of a checklist method was found to be slightly less accurate with rather than without self-assessed portion sizes [35]. However, in a review comprising 227 FFQ validation studies, the correlations were observed to be slightly higher when the subjects were allowed to describe their own portion sizes (0.5–0.6) than with portion sizes specified on the questionnaire (0.4–0.5) [36]. In women, a great majority of the correlations were higher among older (O50 years) than younger women
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(<50 years). The dietary habits of older Finnish women are more regular, and therefore easier to report with the FFQ, while meal patterns among younger women are more irregular and complex. In men, the correlations for fat, monounsaturated fatty acids, and polyunsaturated fatty acids were higher among younger than older men. It is possible that the younger Finnish men cook more often than the older men, and hence, are more acquainted with different types of fat and can estimate their fat intake more specifically than the older men. In this study, the correlations were mostly lower in the upper BMI group (BMI >26.8 kg/m2), which might indicate a difficulty of overweight subjects to report their food consumption reliably. Furthermore, our results are in line with findings, which show that measuring fat intake may be especially difficult among overweight subjects [37]. In our study, the correlations for fat and fatty acids were notably lower in the upper BMI group for both genders. In addition, compared to other subjects, the intake of carotenoids and vitamin C was overestimated to a higher degree among subjects in the upper BMI group (data not shown), which might indicate a stronger tendency of overweight subjects to try and attain social acceptance by exaggerating the consumption of vegetables. The mean BMI of the men in this study was the same as in another Finnish study (27.0 kg/m2) [38]. The women in this study had a slightly higher BMI than in the other study (27.1 vs. 25.9 kg/m2). The correlations for energy-adjusted nutrients were preferred in this study, because in epidemiologic studies the nutrient intake independent of the total energy intake is of special interest [29]. However, the subjects’ BMI might be positively associated with energy intake, which could cause problems, when energy adjusted correlations are presented according to BMI groups. According to previous studies, the energy intake and BMI are not explicitly correlated [39,40], which holds true in our study as well. In our study, there was no correlation between the BMI and energy intake measured with the FFQ or food record: the correlation in men was between 20.11 and 20.20 and in women 0.07 analyzed with both methods. Thus, in this case, using the energy adjusted figures should not pose any problems. The correlations for food groups in our study were relatively low for foods consumed infrequently (fish and soft drinks). The reference time period in the FFQ was the preceding 12 months, while the food record covered only 3 days. The 3 consecutive days’ food record may not be sufficient to examine the use of infrequently consumed foods or the intake of certain nutrients on an individual level, and therefore leads to an underestimate of the questionnaire’s validity [41]. Repeating the food-recording period later would have improved the quality of the reference data and probably increased the apparent validity of the FFQ [42], but was not possible in this extensive population study setting. However, the food records in this study cover both weekdays and weekend days, and additionally, seasonal
variation is fairly well covered. The food records were collected during a time period from October to June, which is representative of the usual food intake in Finland. During the late summer months the food intake in Finland differs notably from the rest of the year. In this study, both the FFQs and the food records were returned by mail. The quality of the data could probably have been further improved by revising the food records and the FFQs with the respondents. However, instructions on filling in the FFQ were given by a nurse when the questionnaires were given out, and the food records were accompanied by a picture booklet to help the subjects estimate the portion sizes, which most probably improved the accuracy of the validation data. In summary, this study showed that the 128-item semiquantitative food frequency questionnaire is a useful tool in measuring the diet of Finnish adults. Even though it overestimated the absolute intakes, the correlations of the food and nutrient intakes between the two methods were in the same range as in earlier studies. The study also revealed that the validity was poorer among younger women and obese subjects, which should be noted in future studies examining diet–disease associations. The sociodemographic factors and their possible interactions affecting the validity of an FFQ should be subjected to further study in future.
Acknowledgments The authors thank all those who contributed to the data collection. Special thanks are due to Tommi Korhonen, M.Sc., and Heli Tapanainen, M.Sc., for assistance with the data management.
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[24] FINDIET Study Group. The 1997 Dietary Survey of Finnish Adults. Publications of the National Public Health Institute B8/1998, Helsinki; 1998. [25] Willett WC. Nutritional epidemiology. 2nd ed. New York: Oxford University Press; 1998. [26] National Public Health Institute. Finnish food composition database. Release 2. Helsinki: National Public Health Institute; 2002. [27] Haapa E, Toponen T, Pietinen P, Ra¨sa¨nen L. Annoskuvakirja (In Finnish). Helsinki: The National Public Health Institute and Department of Nutrition, University of Helsinki; 1985. [28] Ma¨nnisto¨ S, Ovaskainen M-L, Valsta L. The National Findiet 2002 Study. Publications of the National Public Health Institute B3/2003. Helsinki; 2003. [29] Willett W, Stampfer MJ. Total energy intake: implications for epidemiologic analyses. Am J Epidemiol 1986;124:17–27. [30] Cohen J, Cohen P. Applied multiple regression/correlation analysis for the behavioural sciences. Hillsdale, NJ: Wiley; 1975. [31] Feskanich D, Rimm EB, Giovannucci EL, Colditz GA, Stampfer MJ, Litin LB, Willett WC. Reproducibility and validity of food intake measurements from a semiquantitative food frequency questionnaire. J Am Diet Assoc 1993;93:790–6. [32] Livingstone MB, Prentice AM, Strain JJ, Coward WA, Black AE, Barker ME, McKenna PG, Whitehead RG. Accuracy of weighed dietary records in studies of diet and health. BMJ 1990;300:708–12. [33] Mertz W, Tsui JC, Judd JT, Reiser S, Hallfrisch J, Morris ER, Steele PD, Lashley E. What are people really eating? The relation between energy intake derived from estimated diet records and intake determined to maintain body weight. Am J Clin Nutr 1991;54:291–5. [34] Vuckovic N, Ritenbaugh C, Taren DL, Tobar M. A qualitative study of participants’ experiences with dietary assessment. J Am Diet Assoc 2000;100:1023–8. [35] Bingham SA, Gill C, Welch A, Day K, Cassidy A, Khaw KT, Sneyd MJ, Key TJ, Roe L, Day NE. Comparison of dietary assessment methods in nutritional epidemiology: weighed records v. 24 h recalls, food-frequency questionnaires and estimated-diet records. Br J Nutr 1994;72:619–43. [36] Cade J, Thompson R, Burley V, Warm D. Development, validation and utilisation of food-frequency questionnairesda review. Public Health Nutr 2002;5:567–87. [37] Goris AH, Westerterp-Plantenga MS, Westerterp KR. Undereating and underrecording of habitual food intake in obese men: selective underreporting of fat intake. Am J Clin Nutr 2000;71:130–4. [38] Laatikainen T, Tapanainen H, Alfthan G, Salminen I, Sundvall J, Leiviska¨ J, Harald K, Jousilahti P, Salomaa V, Vartiainen E. FINRISKI 2002 Tutkimuksen toteutus ja tulokset 1. Perusraportti (in Finnish). Publications of the National Public Health Institute, B7/2003. Helsinki; 2003. [39] Baecke JA, van Staveren WA, Burema J. Food consumption, habitual physical activity, and body fatness in young Dutch adults. Am J Clin Nutr 1983;37:278–86. [40] Ballard-Barbash R, Graubard I, Krebs-Smith SM, Schatzkin A, Thompson FE. Contribution of dieting to the inverse association between energy intake and body mass index. Eur J Clin Nutr 1996;50: 98–106. [41] Block G. Human dietary assessment: methods and issues. Prev Med 1989;18:653–60. [42] Potosky AL, Block G, Hartman AM. The apparent validity of diet questionnaires is influenced by number of diet-record days used for comparison. J Am Diet Assoc 1990;90:810–3.
Validity of a food frequency questionnaire varied by age and body mass index Laura Paalanena,*, Satu Ma¨nnisto¨a, Mikko J. Virtanena, Paul Knektb, Leena Ra¨sa¨nenc, Jukka Montonenb, Pirjo Pietinena a
Department of Health Promotion and Chronic Disease Prevention, National Public Health Institute, Helsinki, Finland b Department of Health and Functional Capacity, National Public Health Institute, Helsinki, Finland c Department of Applied Chemistry and Microbiology, Division of Nutrition, University of Helsinki, Helsinki, Finland Accepted 11 November 2005
Abstract Background and Objective: The validity of food frequency questionnaires (FFQs) in measuring food consumption and nutrient intake has to be assessed. The objective of this study was to assess the validity of a 128-item FFQ in specific subgroups of Finnish adults. Methods: The study included 294 subjects (137 men and 157 women). A 3-day food record was used as the reference method. Results: The mean intake of all nutrients except alcohol was higher measured with the FFQ than with the food records. In general, the Pearson correlations for energy adjusted nutrients between the FFQ and the food records were higher in women than in men. The correlations ranged from 0.14 (retinol) to 0.66 (fiber and alcohol) in men, and from 0.20 (long-chain n23 fatty acids) to 0.70 (alcohol) in women. The results in subgroups showed that measuring nutrient intakes is more difficult among younger (30–50 years) women and overweight men and women than among others. Conclusions: The study showed that the FFQ is a useful tool in epidemiologic studies in measuring the diet of Finnish adults given that the problems among specific subgroups are taken into account in interpretation. Ó 2006 Elsevier Inc. All rights reserved. Keywords: Age; Body mass index; Diet; Food frequency questionnaire; Validity
1. Introduction The food frequency questionnaire (FFQ) has been established as the primary method for estimating long-term food consumption in large epidemiologic studies. Structured dietary data is easy and inexpensive to process compared to data gathered with an open method. Furthermore, no interviewers are needed if the study subjects fill in the questionnaires themselves. However, the FFQ has its limitations. The information is based on the subjects’ memory, and because of the predefined food list, some information about the foods actually eaten may be missed. Therefore, it is essential to examine the validity of the FFQs against another dietary assessment method [1–10]. It has been recommended that in study populations including both genders, validity has to be examined separately for men and women [11]. In studies in which the correlations between the FFQ and food records (the reference
* Corresponding author. Tel.: 1358 9 47448617; fax: 1358 9 47448338. E-mail address: [email protected] (L. Paalanen). 0895-4356/06/$ – see front matter Ó 2006 Elsevier Inc. All rights reserved. doi: 10.1016/j.jclinepi.2006.01.002
method) were analyzed separately by gender, obvious differences were not observed [6,8,12]. However, in a Danish validation study, contrary to the researchers’ expectations, the correlations were mostly higher in men than in women [13]. In the Danish study the FFQ was slightly shorter than in the three other studies (92 vs. 116–350 foods). There is evidence that gender, age, and obesity are associated with underreporting energy intake [14–17]. In a Finnish study among subjects aged 25–64 years, underreporting was most common among women and overweight [body mass index (BMI) O25 kg/m2] subjects and among subjects older than 45 years [14]. Thus far, only a few validation studies for FFQs that measure the intake of a large variety of foods or nutrients with a food record as the reference method and including more than 100 subjects have shown results for subgroups other than by gender. Some validation studies have included only young [18] or elderly [19,20] subjects or subjects from a specific profession, for example, nurses [1] or tin miners [21]. A validation study that was part of a study on selenium intake in relation to health status in the United States found that the correlations did not notably differ between the subjects with a high
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school education or less and the subjects with more than a high school education [6]. The study included 138 subjects with a mean age of 49 years. In another study in the United States, the correlations were reported separately for gender and race [22]. The correlations were higher for White subjects, and the correlations of Black men tended to be the lowest. The reference method was a combined 24-hr recall and food record and included food consumption data for 16 days (4 3 24-hr recall and 4 3 3-day food record). The objective of our study was to assess the validity of a semiquantitative FFQ among an adult Finnish population with a 3-day food record as the reference method. To deepen our knowledge on the possible effect of the characteristics of the subjects on reporting food consumption, we also examined whether there are differences in the validity by age or BMI. The FFQ was developed for the Health 2000 Survey in Finland.
2. Methods 2.1. Study design and subjects This validation study is a part of a nationally representative survey, the Health 2000 Survey (n 5 8,028), which was carried out at 160 study locations in Finland from fall 2000 to spring 2001 [23]. The survey consisted of various health interviews, self-administered questionnaires, and a comprehensive health examination. The main aim of the survey was to estimate health and functional capacity and to gather information on major diseases, their causes, and treatment circumstances. Some basic information was obtained from 93% of the subjects. Information on the subjects’ diet was obtained using a self-administered semiquantitative FFQ. A total of 6,771 subjects received the questionnaire at the health examination and subsequently filled it in at home. The questionnaire was then returned by mail to the National Public Health Institute (Helsinki, Finland) (n 5 6,373, 94% of the FFQs given out). Of the returned FFQs, 93 were blank and 282 were incompletely filled. Thus, 375 FFQs (6% of the FFQs given out) were excluded from the analyses, and consequently, the number of accepted FFQs in the survey was 5,998 (89% of the FFQs given out). The food records for the validation study were given out during September and October 2000 and during January and February 2001. The sample (n 5 470) for the validation study was chosen to represent the whole study sample by age and gender at eight study locations, which included both towns and rural municipalities. Of the original validation sample, 420 subjects (89%) participated in the Health 2000 Survey. At the health examination the subjects belonging to the validation sample were informed that they would receive a 3-day food record with a photographic picture booklet [2] after returning the FFQ, which was then to be completed at home and mailed back to the National
995
Public Health Institute. The food records were not sent to those 35 subjects who participated in the health examination, but either declined to fill in the FFQ, did not return it, or returned it blank. Finally, 385 food records (82% of the original validation sample) were sent, of which 334 (87% of the food records sent) were returned. After the exclusion of the 19 blank food records, there were 315 food records (82% of the food records sent). In the analyses, an additional 21 subjects were excluded because of unidentified food records (the page containing the identification label had been removed) (1), pregnancy (1), incompletely filled food records (10), or incompletely filled FFQs (9). Thus, the final data of the validation study comprised 294 persons (137 men and 157 women, 63% of the original validation sample, 76% of the food records sent). The subjects in the validation study were between the ages of 30 and 79 years. The mean age of the men was 51.9 (SD 12.1) years and of the women 51.2 (SD 12.9) years. The mean height, weight, and body mass index of the men were 177 (SD 7.0) cm, 84.4 (SD 12.5) kg, and 27.0 (SD 3.7) kg/m2, respectively. For the women the figures were 163 (SD 6.6) cm, 71.6 (SD 12.7) kg, and 27.1 (SD 4.9) kg/m2. The final validation study population was compared with the entire Health 2000 Survey population. Men and women were relatively equally represented in the two study populations. The subjects in the validation study were slightly younger, however, than the subjects in the Health 2000 Survey. No one in the final validation study population was over 85 years of age. In the population of the Health 2000 Survey the proportion of at least 85 year olds was 5%. 2.2. FFQ The FFQ in the Health 2000 Survey was a modified version of two Finnish questionnaires used in earlier studies: the Alpha-Tocopherol Beta-Carotene Cancer Prevention Study (the ATBC Study), and the Kuopio Breast Cancer Study [2,4]. The FFQ was designed to cover the whole diet over the preceding 12 months, and the main purpose was to rank individuals according to their average food consumption and nutrient intake. The list of foods was based on the FFQ in the Kuopio Breast Cancer Study [4] and data collected from approximately 3,000 Finns in the 1997 National Dietary Survey [24]. To reflect the increased selection of foods available in the market, the number of food items was increased to 128 from the FFQ used in the Kuopio Breast Cancer Study. However, the number of food items queried in the present study was not increased beyond 130 foods, because a questionnaire with more items may result in the subjects experiencing boredom, which may then impair the accuracy of the answers [25]. The recipes for each food item in the FFQ were composed of one to seven food codes in the Finnish food composition database [26]. The food composition database comprises 1,341 recipes and 828 food items.
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The 128 food items were presented under 12 subgroups, for example, dairy products, vegetables, and fruits and berries. After each subgroup there were empty lines for subjects to add foods not listed in the questionnaire. The portion sizes were fixed and, if possible, specified using natural units (e.g., glass, slice). The nine frequency categories ranged from never or seldom to six or more times a day. The consumption of berries was recorded separately in winter and summer. The FFQ also contained additional questions about meal patterns, special diets, and the use of vitamin or mineral supplements. The food consumption and nutrient intakes were calculated by multiplying the frequency of food consumption by fixed portion sizes to obtain the weight of each listed food item consumed as an average per day. The FFQ was tested in two pilot studies. The reliability of the FFQ within 5–9 months was analyzed among 180 subjects, and the intraclass correlations (ICCs) for foods ranged from 0.16 for potato to 0.82 for milk in men and from 0.24 for poultry to 0.76 for fermented milk products in women (data not shown). The ICCs for nutrients ranged from 0.42 for retinol to 0.72 for riboflavin in men and from 0.22 for retinol to 0.66 for total fat and monounsaturated fatty acids in women. The reliability of the FFQ is comparable with results in earlier studies [2–4]. 2.3. Food records The food records were mostly completed within a month after the FFQ. However, the last food records were not completed until June, after a second reminder round. Thus, the food records cover the period from October 2000 to June 2001. Because the food records were mailed to the subjects, the instructions were given only in writing. The subjects were instructed to record everything they ate or drank during the 3 consecutive days after receiving the food record. A photographic picture booklet with 126 food items and mixed dishes was used to help in estimating portion sizes [27]. The diet records were coded by a nutrition student, and the nutrient calculations were carried out using a computer program developed at the National Public Health Institute [28]. The Finnish food composition database was used for the nutrient calculations [26], and losses due to cooking have been accounted for. All the subjects in the final sample had a complete 3-day food record. The food record data include weekdays and weekend days equally. In Finland, for example, alcohol consumption is concentrated on the weekends. However, in our study, measuring alcohol consumption with the food records is not a problem in that respect, because Saturdays and Sundays are well represented. The minimum and maximum intakes of energy as well as about 20 nutrients and the minimum and maximum consumptions of 20 foods were revised. None of the subjects had a mean daily energy intake during the 3-day period under 2,940 kJ (700 kcal) or more than 21,000 kJ (5,000 kcal).
2.4. Statistical methods All the analyses were computed separately for men and women. The means and standard deviations of food consumption and nutrient intake (untransformed data) were calculated from the FFQs and food records. The Pearson product-moment correlation was used to compare the two dietary assessment methods. In calculating the correlations the loge transformation was used to improve normality. The formula log(x 1 1) was used for alcohol and long-chain n23 fatty acids, because these nutrients included zero intakes. The nutrients were adjusted for each person’s energy intake with the residual method [29]. To compare the validity between subgroups, the Pearson correlation coefficients for energy-adjusted nutrient intakes were used [30]. The subjects were divided into two age groups (men and women separately) using the median age in the whole study population (50 years) as the cutoff point. The cutoff point for the BMI groups was the median among all the subjects (26.8 kg/m2). Sample-specific median cutoff points were used to assure that no preassumptions would affect the results.
3. Results In general, the mean nutrient intakes were higher measured with the FFQ than with the food records (Table 1). The only nutrient that was underestimated with the FFQ was alcohol. Overestimation was more prominent in women than in men. In women the intake of 17 of the 21 nutrients included in the validation study was over 40% higher measured with the FFQ than with the food records. The most overestimated nutrients were polyunsaturated fatty acids, long-chain n23 fatty acids (LC n23 FA), carotenoids, vitamin E, and vitamin C both in men and women. The difference in the intakes measured with the two methods was statistically significant (P ! .05) for all nutrients except retinol (data not shown). The FFQ appeared to over- or underestimate foods more than nutrients (Table 2). The most notable overestimates for foods were found for vegetables and soft drinks. In addition, the consumption of margarine was markedly overestimated in women. In men, the difference between the two methods was statistically significant (P ! .05) for all foods except wheat, butter, margarine, cheese, poultry, sausages, and coffee, and in women for all foods except berries, poultry, and sausages (data not shown). The Pearson correlation coefficients for unadjusted nutrients ranged from 0.20 for energy to 0.65 for alcohol in men, and from 0.07 for polyunsaturated fatty acids to 0.72 for alcohol in women (data not shown). The adjustment for energy improved the correlations slightly for the great majority of nutrients. After energy adjustment, the correlations ranged from 0.14 for retinol to 0.66 for dietary fiber and alcohol in men and from 0.20 for long-chain n23 fatty acids to 0.70 for alcohol in women (Table 1). In men,
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Table 1 Mean daily intakes of energy and nutrients based on food frequency questionnaire (FFQ) and energy-adjusted Pearson correlation coefficientsa (r) between daily intakes of nutrients based on FFQ and food records (FR) Men (n 5 137)
Women (n 5 157)
Nutrient
FFQb Mean (SD)
FFQ of FRc (%)
FFQ vs. FRd (r)
FFQb Mean (SD)
FFQ of FRc (%)
FFQ vs. FRd (r)
Energy (kcal) Protein (g) Carbohydrate (g) Sucrose (g) Dietary fiber (g) Total fat (g) Saturated fatty acids (g) Monounsaturated fatty acids (g) Polyunsaturated fatty acids (g) Long-chain n23 fatty acids (g) Alcohol (g) Retinol (mg) Carotenoids (mg) Vitamin D (mg) Vitamin E (mg TE) Thiamin (mg) Riboflavin (mg) Folate (mg) Vitamin C (mg) Calcium (mg) Magnesium (mg) Iron (mg) Protein, % of energy intake Carbohydrate, % of energy intake Fat, % of energy intake Alcohol, % of energy intake
2,446 102 262 51 25 102 39 32 15 0.84 7.8 1,056 11,044 7.8 14 1.7 2.4 327 115 1,338 460 16 17 44 37 2.3
116 119 114 116 123 123 116 124 141 149 64 122 148 121 142 121 121 118 159 120 119 130 102 97 107 60
d 0.27 0.49 0.47 0.66 0.35 0.41 0.37 0.24 0.35 0.66 0.14 0.20 0.36 0.22 0.26 0.63 0.41 0.36 0.46 0.54 0.44 d d d d
2,290 97 254 49 27 93 36 29 14 0.72 2.9 947 12,631 6.9 14 1.6 2.5 334 150 1,432 453 16 17 46 36 0.97
142 149 136 122 152 151 144 155 167 188 76 126 182 150 166 157 156 144 161 151 144 153 104 96 106 58
d 0.56 0.53 0.54 0.64 0.45 0.55 0.32 0.26 0.20 0.70 0.26 0.47 0.26 0.43 0.49 0.65 0.46 0.51 0.53 0.64 0.53 d d d d
(849) (36) (100) (28) (11) (39) (16) (13) (6.4) (0.62) (11) (789) (6,982) (4.3) (6.0) (0.60) (0.98) (129) (69) (608) (144) (5.6) (2.2) (6.0) (5.1) (3.1)
(709) (32) (81) (24) (10) (34) (15) (11) (4.8) (0.55) (3.7) (711) (8,041) (3.4) (5.0) (0.59) (0.92) (117) (84) (557) (131) (4.8) (2.3) (5.5) (4.8) (1.3)
Pe **
** * *
a
Based on loge transformed values. Observed intakes. c The difference in the mean intakes measured with the FFQ and the food records. d The correlations were calculated for energy-adjusted nutrient intakes. Therefore, the correlations for energy or nutrients as % of energy intake are not presented. e The significance of the difference in energy-adjusted correlations between men and women. * P ! .05. ** P !.01. b
the energy-adjustment improved the correlation by more than 0.20 for carbohydrate and magnesium, and in women for protein, fat, saturated fatty acids and vitamin E. The few decreases in the correlations due to energy adjustment were smaller than 0.10. Most correlations were higher among women than men both before and after energy adjustment. A more specific comparison of the energy-adjusted correlations between men and women revealed that the correlation for protein, carotenoids, vitamin E, and thiamin was statistically significantly (P ! .05) higher among women than men. For foods, the Pearson correlations ranged from 0.09 for beef to 0.89 for coffee in men and from 0.01 for oil to 0.85 for coffee in women (Table 2). The correlations for rye and beef were statistically significantly higher among women and the correlation for fermented milk products was higher among men. The adjustment for energy was not used in the case of foods. The Pearson correlation coefficients for energy-adjusted nutrients were compared in subgroups based on the
subjects’ age (<50 and O50 years) and BMI (!26.8 and >26.8 kg/m2) (Figs. 1–4). The nutrients in Figs. 1–4 are in the order of the difference in correlations between the two groups. Protein, carbohydrate, and magnesium were excluded from the figures because the correlations did not differ notably across the groups. Among men, the correlations did not seem to vary consistently by age (Fig. 1). However, the correlations for mono- and polyunsaturated fatty acids and total fat were notably higher in the younger age group, that is, between the ages of 30 and 50, while the correlations for carotenoids, folate, calcium, and thiamin were higher among men O50 years old. The difference was statistically significant (P ! .05) only for carotenoids and monounsaturated fatty acids. For women, the differences between the age groups were more evident (Fig. 2) with 14 of 18 nutrients having higher correlations among the older women. However, the difference was statistically significant only in case of polyunsaturated fatty acids and retinol.
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Table 2 Mean daily consumption of foods based on food frequency questionnaire (FFQ) and Pearson correlation coefficientsa (r) between daily consumption of foods based on FFQ and food records (FR) Men (n 5 137)
Women (n 5 157)
Food group, g
FFQ Mean (SD)
FFQ of FRb (%)
FFQ vs. FR (r)
FFQ Mean (SD)
FFQ of FRb (%)
FFQ vs. FR (r)
Rye Wheat Potato Vegetablesd Fruit Berries Juice Butter Oil Margarine Milk Fermented milk products Cheese Beef Pork Poultry Sausages Fish Sugar and sweets Coffee Soft drinks
63 91 211 202 119 25 57 12 9.7 8.9 374 146 42 31 61 24 49 52 36 484 100
119 105 149 221 125 133 164 96 171 118 115 145 113 149 158 96 87 135 131 98 202
0.34 0.52 0.27 0.53 0.44 0.44 0.35 0.47 0.13 0.53 0.67 0.61 0.48 0.09 0.13 0.15 0.49 0.27 0.37 0.89 0.22
64 82 176 255 220 34 75 12 8.6 9.3 374 207 46 25 50 30 30 46 36 420 53
180 119 188 243 175 113 152 120 179 236 157 159 151 176 199 138 117 180 124 91 241
0.54 0.37 0.37 0.57 0.37 0.38 0.43 0.28 0.01 0.53 0.67 0.40 0.49 0.36 0.29 0.17 0.43 0.29 0.43 0.85 0.28
(43) (50) (137) (147) (122) (22) (90) (8.4) (5.0) (9.8) (303) (171) (43) (23) (37) (27) (40) (37) (25) (257) (159)
(37) (44) (96) (193) (191) (28) (93) (8.6) (3.9) (10) (288) (212) (35) (23) (32) (36) (37) (31) (27) (233) (98)
Pc *
* *
a
Based on loge transformed values. The difference in the mean consumption of foods measured with the FFQ and the food records. c The significance of the difference in correlations between men and women. d Excluding roots and legumes. * P ! .05. b
For both genders, most of the correlations were higher among subjects in the lower BMI group (BMI !26.8 kg/ m2) than in the upper BMI group (BMI >26.8 kg/m2) (Figs. 3 and 4). In men, the difference between BMI groups was statistically significant (P ! .05) for monounsaturated fatty acids, total fat, and alcohol and in women, for saturated fatty acids, total fat, riboflavin, calcium, and fiber. ≤5 years (men)
r 1.0
>50 years (men)
4. Discussion This validation study was a part of the Health 2000 Survey. The FFQ was specifically designed for the survey to assess the subjects’ diet during the previous year, and to classify the subjects according to food consumption and nutrient intake. The validation sample included both genders ≤ 50 years (women)
r
>50 years (women)
1.0
Carotenoids
Fat
Riboflavin
Thiamin
Iron
MUFA
Vitamin C
Sucrose
Folate
Vitamin D
SFA
Fiber
Alcohol
Calcium
Vitamin E
LC n-3 FA
Retinol
Fig. 1. Energy-adjusted Pearson correlation coefficients (r) between food frequency questionnaire and food records for <50-year-old (n 5 67) and O50-year-old (n 5 70) men.
PUFA
Iron
Alcohol
Retinol
Vitamin D
Sucrose
Riboflavin
Fiber
-0.2
LC n-3 FA
-0.2
Vitamin C
0.0
SFA
0.0
Vitamin E
0.2
Thiamin
0.2
Calcium
0.4
Folate
0.4
Fat
0.6
PUFA
0.6
MUFA
0.8
Carotenoids
0.8
Fig. 2. Energy-adjusted Pearson correlation coefficients (r) between food frequency questionnaire and food records for <50-year-old (n 5 81) and O50-year-old (n 5 76) women.
L. Paalanen et al. / Journal of Clinical Epidemiology 59 (2006) 994–1001 BMI<26.8 (men)
r
BMI≥26.8 (men)
1.0 0.8 0.6 0.4 0.2 0.0
Calcium
Vitamin C
Thiamin
SFA
Vitamin E
Vitamin D
Riboflavin
PUFA
Retinol
Folate
Sucrose
Iron
LC n-3 FA
Fiber
Carotenoids
Fat
Alcohol
MUFA
-0.2
Fig. 3. Energy-adjusted Pearson correlation coefficients (r) between food frequency questionnaire and food records for men in the lower (n 5 67) and upper (n 5 70) BMI group. The cutoff point was the median of BMI (26.8 kg/m2).
aged 30–79 years. Although the subjects had participated in a number of interviews and examinations in the main survey before the administration of the FFQs and food records, the response rate in the validation study was reasonably high (n 5 294, 63% of the original validation sample, 76% of the subjects to whom the food records were sent). To examine whether the participants and nonparticipants of the validation study differed according to their nutrient intake measured with the FFQ, the energy intake and the intake of fat, vitamin C, fiber, and alcohol were compared between these two groups. There were, however, no statistical differences between the participants and nonparticipants in their nutrient intakes (data not shown). Most FFQs have overestimated [2,5,8,31] and some have underestimated [3,7] nutrient intake and food consumption compared to food records. In this validation study, the BMI<26.8 (women)
r 1.0
BMI≥26.8 (women)
0.8 0.6 0.4 0.2 0.0
Carotenoids
LC n-3 FA
Sucrose
Retinol
Vitamin C
Vitamin D
Thiamin
Iron
Alcohol
Folate
Fiber
Vitamin E
PUFA
Calcium
MUFA
Fat
Riboflavin
SFA
-0.2
Fig. 4. Energy-adjusted Pearson correlation coefficients (r) between food frequency questionnaire and food records for women in the lower (n 5 80) and upper (n 5 77) BMI group. The cutoff point was the median of BMI (26.8 kg/m2).
999
nutrient intakes were higher measured with the FFQ than with the 3-day food record. The FFQ overestimated the intakes in women more than in men compared to the intakes with the food records. However, the correlations for nutrients between the two methods were higher in women than in men, both before and after energy adjustment. Due to their greater health awareness, women may report their food consumption more similarly with the two methods than men. In this study, the true food consumption most likely lies somewhere between the figures measured with the two methods because subjects are prone to underestimate their food consumption when they keep food records [32–34]. In the case of food records, the proportion of underreporters increased from 27 to 42% among men and 33 to 46% among women during 10 years in Finland [14], and this trend has probably continued. Therefore, the apparent overestimation with the FFQ is presumably partly due to underreporting with the food records. The most overestimated nutrients were polyunsaturated fatty acids, long-chain n23 fatty acids, carotenoids, vitamin E, and vitamin C, which was most probably due to the striking overestimation of vegetable, oil, and margarine consumption. Furthermore, the overestimation of fish consumption in women was reflected as an overestimate of long-chain n23 fatty acid intake. The tendency to overestimate the consumption of vegetables, margarine and fish with the FFQ may partly be due to the subjects’ desire to achieve social acceptance by emphasizing the use of foods considered to be healthy. On the other hand, for example, margarine is typically a foodstuff easily forgotten when keeping a food record. In the FFQ, the use of margarine was asked separately, and this ensured that it was reported. In FFQ validation studies where a comparison between the genders has been carried out, the FFQ has overestimated the intakes more both in women compared to men [12] and vice versa [8]. In our study, the overestimates with the FFQ compared to the food records were more prominent among women than among men. The portion sizes were similar for both genders. Some of the predefined portion sizes may have been too large for women, which increased the difference between the two methods. Block et al. [9] examined the effect of portion size assumptions on questionnaire validity. The female subjects reported whether their normal portion size of each food item was small, medium, or large. The correlations were only slightly lower, when the analyses were repeated using standard portion sizes. In another study, the accuracy of a checklist method was found to be slightly less accurate with rather than without self-assessed portion sizes [35]. However, in a review comprising 227 FFQ validation studies, the correlations were observed to be slightly higher when the subjects were allowed to describe their own portion sizes (0.5–0.6) than with portion sizes specified on the questionnaire (0.4–0.5) [36]. In women, a great majority of the correlations were higher among older (O50 years) than younger women
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(<50 years). The dietary habits of older Finnish women are more regular, and therefore easier to report with the FFQ, while meal patterns among younger women are more irregular and complex. In men, the correlations for fat, monounsaturated fatty acids, and polyunsaturated fatty acids were higher among younger than older men. It is possible that the younger Finnish men cook more often than the older men, and hence, are more acquainted with different types of fat and can estimate their fat intake more specifically than the older men. In this study, the correlations were mostly lower in the upper BMI group (BMI >26.8 kg/m2), which might indicate a difficulty of overweight subjects to report their food consumption reliably. Furthermore, our results are in line with findings, which show that measuring fat intake may be especially difficult among overweight subjects [37]. In our study, the correlations for fat and fatty acids were notably lower in the upper BMI group for both genders. In addition, compared to other subjects, the intake of carotenoids and vitamin C was overestimated to a higher degree among subjects in the upper BMI group (data not shown), which might indicate a stronger tendency of overweight subjects to try and attain social acceptance by exaggerating the consumption of vegetables. The mean BMI of the men in this study was the same as in another Finnish study (27.0 kg/m2) [38]. The women in this study had a slightly higher BMI than in the other study (27.1 vs. 25.9 kg/m2). The correlations for energy-adjusted nutrients were preferred in this study, because in epidemiologic studies the nutrient intake independent of the total energy intake is of special interest [29]. However, the subjects’ BMI might be positively associated with energy intake, which could cause problems, when energy adjusted correlations are presented according to BMI groups. According to previous studies, the energy intake and BMI are not explicitly correlated [39,40], which holds true in our study as well. In our study, there was no correlation between the BMI and energy intake measured with the FFQ or food record: the correlation in men was between 20.11 and 20.20 and in women 0.07 analyzed with both methods. Thus, in this case, using the energy adjusted figures should not pose any problems. The correlations for food groups in our study were relatively low for foods consumed infrequently (fish and soft drinks). The reference time period in the FFQ was the preceding 12 months, while the food record covered only 3 days. The 3 consecutive days’ food record may not be sufficient to examine the use of infrequently consumed foods or the intake of certain nutrients on an individual level, and therefore leads to an underestimate of the questionnaire’s validity [41]. Repeating the food-recording period later would have improved the quality of the reference data and probably increased the apparent validity of the FFQ [42], but was not possible in this extensive population study setting. However, the food records in this study cover both weekdays and weekend days, and additionally, seasonal
variation is fairly well covered. The food records were collected during a time period from October to June, which is representative of the usual food intake in Finland. During the late summer months the food intake in Finland differs notably from the rest of the year. In this study, both the FFQs and the food records were returned by mail. The quality of the data could probably have been further improved by revising the food records and the FFQs with the respondents. However, instructions on filling in the FFQ were given by a nurse when the questionnaires were given out, and the food records were accompanied by a picture booklet to help the subjects estimate the portion sizes, which most probably improved the accuracy of the validation data. In summary, this study showed that the 128-item semiquantitative food frequency questionnaire is a useful tool in measuring the diet of Finnish adults. Even though it overestimated the absolute intakes, the correlations of the food and nutrient intakes between the two methods were in the same range as in earlier studies. The study also revealed that the validity was poorer among younger women and obese subjects, which should be noted in future studies examining diet–disease associations. The sociodemographic factors and their possible interactions affecting the validity of an FFQ should be subjected to further study in future.
Acknowledgments The authors thank all those who contributed to the data collection. Special thanks are due to Tommi Korhonen, M.Sc., and Heli Tapanainen, M.Sc., for assistance with the data management.
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