1. Introduction
The Canadian Longitudinal Study on Aging (CLSA) is a national longitudinal study that will follow 50,000 men and women aged 45 to 85 years (y) at recruitment over a twenty-year period. The goal of the CLSA is to better understand the aging process and its determinants through the collection of information on biological, medical, psychological, social, lifestyle and economic aspects of people’s lives, and their changes over time [
1].
As an important lifestyle component, dietary data are being gathered at multiple time-points in the CLSA. Of interest were usual intakes of several key nutrients and foods of current concern in health promotion and chronic disease prevention in both younger and older adults (OA), and which have been the focus of population-based nutritional health promotion campaigns. They include intakes of total fat, and fatty acid classes (saturated, polyunsaturated, monounsaturated, omega-3 and
trans fatty acids), as well as dietary fibre, calcium, vitamin D, and consumption of fruits and vegetables. Because of interview time constraints and the challenging logistics related to administration of a full food frequency questionnaire (FFQ), a brief instrument was sought for assessment of usual frequency of consumption of these items. Existing dietary screeners that addressed the foods and nutrients of interest were initially considered, specifically, the Block Dietary Screener [
2], which ranks individuals with regard to their usual intakes of fat, fibre, calcium and vitamin D. Also of interest was the six-item Fruit and Vegetable Food Frequency Questionnaire developed by the Centers for Disease Control and Prevention (CDC) for the US Behavioral Risk Factor Surveillance System Survey (BRFSS [
3]). The BRFSS questionnaire collects intake frequency, as occasions per day, week or month, of six categories of vegetables and fruit [
4]. It has been used at the state level in the United States since 1990 [
5] and was incorporated into the Canadian Community Health Survey by Health Canada [
6]. The fruit and vegetable module has been validated against three 24-hour recalls (24HR) in a young adult population by trained dietitian interviewers, and results indicate that it can be used as a proxy for quantified intake in population groups [
7].
However, because of constraints set by developers on instrument modification (e.g., changing the food list), as well as handling and cost considerations related to their use, it was decided to adapt a previously validated Canadian FFQ [
8] as it best fit the needs of the CLSA and was specific to the population [
9]. This approach also allowed us to conserve the BRFSS in the new tool. Furthermore, it limited the additional work needed for instrument modification, database preparation and data entry and analysis software as we had already successfully used the full FFQ [
8] at recruitment into an ongoing cohort study on nutrition and healthy aging. The present paper describes the development, pretest and evaluation of the relative validity of the Short Diet Questionnaire (SDQ), developed to estimate usual consumption frequencies of fat, fibre, calcium and vitamin D, and fruit and vegetables.
3. Results
Average completion time for both pretest versions of the SDQ was approximately 14 minutes (data not shown). The “precise frequencies” version of the pretest SDQ was retained in line with pretest participants’ comments and to ensure consistency with diet modules based on the BRFSS previously used by Statistics Canada and to allow for easier comparison with other studies using this mode of questioning. The SDQ validation study was carried out among 396 NuAge participants (54.8% female) who had complete data in both the test and reference instruments (
Table 1). Most nutrient intakes and the number of servings of fruit and vegetables estimated from the SDQ were significantly lower than those estimated by the mean of three non-consecutive 24HR (
p < 0.05), with the exception of calcium and vitamin D which were significantly higher compared to the 24HR, and saturated and trans fat (both NS) (
Table 2). Spearman correlations between the SDQ and 24HR were low to moderate and statistically significant (
p < 0.01), ranging from 0.19 (cholesterol: 27 mg/day lower than the 24HR) to 0.45 (fruits and vegetables: 1 portion/day lower than the 24HR) (
Table 3). The arithmetic mean unadjusted Spearman correlation between the SDQ and the mean of three non-consecutive 24HR for the nine nutrients was 0.34, and it was 0.31 for the nine nutrients plus fruits and vegetables (data not shown). Finally, it can be seen in
Table 4 that for all nutrients combined, 33.9% were jointly classified into identical quartiles of the distribution, 72.8% into identical and contiguous quartiles, and only 6.7% were frankly misclassified. Similar cross-classification results were observed in men and in women and there were no gender-related differences (data not shown).
Table 1.
NuAge participants with complete data in both dietary data collection instruments, SDQ validation study (n = 396).
Table 1.
NuAge participants with complete data in both dietary data collection instruments, SDQ validation study (n = 396).
Sex | Age Group (years) | Total |
---|
n (%) |
---|
67–72 | 73–77 | 78–84 |
---|
Male | 65 (46.1) | 46 (41.1) | 68 (47.6) | 179 (45.2) |
Female | 76 (53.9) | 66 (58.9) | 75 (52.4) | 217 (54.8) |
All | 141 (35.6) | 112 (28.3) | 143 (36.1) | 396 (100) |
Table 2.
Estimated intakes of nutrients of interest from SDQ and mean of three non-consecutive 24HR, SDQ validation study (n = 396).
Table 2.
Estimated intakes of nutrients of interest from SDQ and mean of three non-consecutive 24HR, SDQ validation study (n = 396).
Nutrient and Dietary Variables | Dietary Assessment Method | p-Value 1 |
---|
SDQ | Mean of Three Non-Consecutive 24HR |
---|
Mean | SD | Mean | SD |
---|
Dietary fibre (g) | 15.4 | 6.2 | 19.6 | 7.7 | 0.0001 |
Calcium (mg) | 946 | 465 | 768 | 334 | 0.0001 |
Vitamin D (ug) | 5.70 | 2.91 | 5.06 | 3.87 | 0.003/0.0001 |
Total fat (g) | 63.7 | 25.5 | 69.5 | 26.2 | 0.0001 |
Cholesterol (mg) | 229 | 86 | 256 | 132 | 0.0001/0.006 |
Saturated fat (g) | 22.4 | 9.2 | 23.3 | 10.9 | 0.163/0.423 |
Monounsaturated fat (g) | 24.5 | 10.5 | 25.9 | 10.9 | 0.04/0.053 |
Polyunsaturated fat (g) | 11.4 | 5.0 | 13.6 | 5.7 | 0.0001 |
Trans fat (g) | 0.7 | 0.4 | 0.8 | 0.9 | 0.03/0.829 |
Number of servings of fruit and vegetables 2 | 4.5 | 1.9 | 5.5 | 3.4 | 0.0001 |
Table 3.
Associations between nutrient estimates from SDQ and mean of three non-consecutive 24HR, SDQ validation study (n = 396).
Table 3.
Associations between nutrient estimates from SDQ and mean of three non-consecutive 24HR, SDQ validation study (n = 396).
Nutrient and Dietary Variables | Spearman r 1,2 |
---|
Dietary fibre (g) | 0.34 |
Calcium (mg) | 0.41 |
Vitamin D (ug) | 0.33 |
Total fat (g) | 0.26 |
Cholesterol (mg) | 0.19 |
Saturated fat (g) | 0.30 |
Monounsaturated fat (g) | 0.28 |
Polyunsaturated fat (g) | 0.22 |
Trans fat (g) | 0.30 |
Number of servings of fruit and vegetables | 0.45 |
Table 4.
Cross-classification of nutrient estimates from SDQ and mean of three non-consecutive 24HR, SDQ validation study (n = 396).
Table 4.
Cross-classification of nutrient estimates from SDQ and mean of three non-consecutive 24HR, SDQ validation study (n = 396).
Nutrient and Dietary Variables | % in Identical Quartile | % in Identical and Contiguous Quartile | % in Opposite Quartile 1 |
---|
Dietary fibre (g) | 36.4 | 75.8 | 6.8 |
Calcium (mg) | 41.7 | 77.1 | 5.3 |
Vitamin D (ug) | 34.1 | 73.5 | 6.1 |
Total fat (g) | 29.5 | 71.4 | 7.6 |
Cholesterol (mg) | 30.8 | 68.7 | 9.8 |
Saturated fat (g) | 32.1 | 73.5 | 8.6 |
Monounsaturated fat (g) | 37.1 | 69.9 | 6.8 |
Polyunsaturated fat (g) | 29.8 | 68.9 | 6.1 |
Trans fat (g) | 36.1 | 73.7 | 6.6 |
Number of servings of fruit and vegetables 2 | 32.2 | 77 | 3.5 |
Mean % classification (10 nutrients plus fruit and vegetables) | 33.9 | 72.8 | 6.7 |
4. Discussion
Brief dietary measurement instruments have been developed to assess intakes of single nutrients or foods such as fat, fruits and vegetables and to examine relationships between certain dietary exposures and risk of chronic disease. Many have been compared to other dietary assessment measures with known validity [
15]. The present study reports on the relative validity of a 36-item frequency-based Short Diet Questionnaire compared to the mean of three non-consecutive, quantitative 24HR, developed for use in the population-based Canadian Longitudinal Study on Aging, compared to the mean of three non-consecutive, quantitative 24HR. While many brief instruments have focussed mainly on fat intakes or on fruit and vegetables [
15,
16], the SDQ was developed to estimate older adults’ usual consumption frequencies over a 12-month period of a set of key nutrients and foods that have been the focus of nutritional health promotion programmes targeting this segment of the population. Relative validation of the SDQ was carried out in a large sample (
n = 396) compared to other studies of this type, and the reference instrument was collected rigourously. To our knowledge, this is the first study of its type to be conducted among community-dwelling older adults. Consequently, while comparisons with other studies in this population group were not possible, because these older adults were cognitively intact there is no basis for expecting less accurate reporting of their intakes on either the test or reference instruments. Although the results showed some inconsistencies, where certain nutrients were underestimated while others were overestimated by the SDQ relative to the 24HR, others have observed that brief instruments tend to overestimate fat and underestimate fruits and vegetables [
17]. However, almost three-quarters of participants were cross-classified into the same section of the distribution by both test and reference instruments, providing evidence of the SDQ’s reasonable measurement properties.
While correlations between the key nutrients and foods estimated by the SDQ and the reference method (means of three non-consecutive 24HR) were quite modest, they were similar to those found in the literature for some of these variables. For example, a 43-item FFQ (Healthy Doc) administered by Spencer
et al. [
17] to 88 medical students estimated intakes of fruit and vegetables at 3.8 servings per day, only slightly lower than the 4.3 servings from the mean of five 24HR, with a Pearson correlation of 0.50. Using the 19-item NCI Fruit and Vegetable Screener (FVS) in an age and ethnically diverse sample of 590 adults, Greene
et al. [
18] obtained significant Pearson correlations ranging from 0.31 to 0.47 for men, and 0.43 to 0.63 for women between the FVS and multiple 24HR, depending on the sub-sample and version of their screener. Our results lined up closely to these findings, with 4.5 servings of fruits and vegetables per day estimated from the SDQ, compared to 5.5 servings daily from the three 24HR, and a significant, positive unadjusted Spearman rank correlation of 0.45.
Associations on fat from the SDQ and 24HR were also similar to those of Spencer
et al. [
17], with an unadjusted Spearman correlation of 0.26 between total fat estimated from the SDQ and the mean of the three 24HR, compared to the Spencer study which reported an adjusted, deattenuated Pearson correlation of 0.36 for fat between their brief FFQ and the mean of five 24HR.
Since the SDQ was not designed to assess the whole diet, we could not calculate energy intakes or estimate the percent of energy from fat. However, Thompson
et al. [
19] obtained a deattenuated Pearson correlation of 0.36 for percent energy from lipids from the 16-question NCI percentage of energy from fat short instrument (PFat) compared to multiple 24HR, suggesting that we can expect correlations between short diet questionnaires and quantitative, multiple 24HR to be in this modest range, similar to the present study.
It is difficult to contextualize results from this study with others, because of the heterogeneity of short dietary instruments and validation studies in the literature, including differing reference timeframes (for example the FVS asks for consumption frequencies over the last month), the use of implicit or explicit portion sizes in addition to frequency in some instruments [
18], reporting on comparisons to “multiple” 24HR without specifying the number, and highly divergent samples and sample sizes. Although some studies have used a measurement error model to deattenuate correlations between the short dietary instrument and the reference measure, we have presented raw, unadjusted correlations from the SDQ and 24HR which fall into same range as adjusted correlations.
The study has limits. First, it must be acknowledged that all self-report dietary assessment methods are fraught with error. However, despite their age, the study participants were cognitively intact, which precludes expectation of poor results in this sample. Second, frequency-based dietary assessment instruments and quantitative tools such as 24HR call upon a different set of cognitive processes in order to respond to the food consumption questions. Consequently, respondents may have had difficulties with the notion of frequency on the SDQ, or could have forgotten to report some foods eaten on the 24HR assessment despite interviewer-prompts and cues on the 24HR, thus compounding errors and attenuating associations between the two instruments. Third, to permit calculation of point estimates for the validation analyses, SDQ portion sizes were imputed using medium portions from the parent NuAge FFQ nutrient database for all respondents, which may have induced a “regression to the mean” bias in comparing SDQ results to those from the 24HR, the “true” reference intakes. Fourth, the modest correlation coefficients could have been inflated due to the comparison of two potentially error-prone instruments, the SDQ and 24HR. Furthermore, this was a sub-study carried out within an ongoing cohort study, and certain participants reported confusion during the SDQ telephone interviews because the SDQ reminded them of the full FFQ that they had completed earlier in the study. In addition, based on comments noted by interviewers suggesting some uncertainty as to whether certain participants were aware that they had consumed fortified foods, the accuracy of their responses on consumption of omega-3 fatty acid or calcium-fortified foods may be questioned. Finally, because they had agreed to participate in the SDQ validation study, these respondents may have been particularly interested in diet, and thus not a representative sample.