1. Introduction
The evaluation of relationships between health and intakes of single nutrients does not address the complexities of food and nutrient interactions in the human diet [
1]. A focus solely on nutrients does not allow for assessment of the cumulative impact of nutrient interactions from a range of foods on health outcomes over time. Individuals do not usually consume single foods, but combinations of several foods and beverages that contain both nutritive and non-nutritive substances [
2]. Given the complexity of assessing individual intakes, measurement of overall diet quality and variety by brief indices allows evaluation of several related aspects of dietary intake concurrently [
3], and may provide a better measure of usual dietary intake patterns [
4]. Diet quality refers to the nutritional adequacy of an individual’s dietary pattern and how closely this aligns with national dietary guidelines [
3,
5]. Scores or indexes of diet quality are being increasingly used in research as proxies for nutrient intakes, due to their lower researcher and respondent burden. The relationship between diet quality indices and nutritional adequacy, morbidity and mortality in adults has been reviewed [
3,
6]. This highlights that across these indices the risk for some health outcomes, including biomarkers of disease, incidence and risk of cardiovascular disease, some cancers and both cancer mortality and all-cause mortality can be quantified.
Diet quality indices have been derived by applying a scoring system to dietary intakes assessed by a variety of measures, including food frequency questionnaires (FFQ) and 24 h recalls. Indices are constructed by assigning higher scores within sub-scales based on more frequent or higher intakes of foods, nutrients, or both [
3]. Generally there are two types of diet quality scores. These are either food-based or nutrient-based. A food-based diet quality index considers the number of foods or food groups consumed in a given period and assigns points based on diversity and/or frequency of intake [
3,
5], however no consideration is usually given to the sources or intakes of nutrients. Food-based scores rely on food consumption data only, meaning they can be scored quickly, but they typically have a limited food list and so may not fully reflect overall variety of foods consumed. This may be particularly for some population sub-groups, such as specific ethnic groups where food items may not have been included in the original FFQ food list. In comparison, nutrient-based scores require the dietary intake record to be analysed first in order to derive nutrient intakes, form which the diet quality scores can be calculated. For this reason food-based scores may be preferable for clinical settings and education purposes as they are more easily adapted to this purpose [
3,
6]. Given differences in food supply, consumption patterns and nutrition recommendations, diet quality indices should be country-specific.
The aim of this study was to evaluate the reproducibility of the Australian Recommended Food Score (ARFS) and its validity against a food frequency questionnaire from which it is derived.
3. Results
A total of 96 participants, from 68 separate families, completed FFQs at baseline and 68 at follow-up. Of these 67 completed the survey in both administration rounds. Thirty one participants were male and 65 were female in the initial administration round and of these 20 males and 48 females remained for round 2.
Table 1 reports demographic and anthropometric variables for the two FFQ administration rounds and, by sex for
n = 67 participants,
Supplementary Table 2 provides details of all observations
n = 151. There were no significant differences by sex or by administration round in education, smoking habits and general health. While there were some significant differences in weight, height, BMI and waist by sex, there were no significant differences in these variables between the two administrations rounds.
Table 1.
Demographic and anthropometric data (151 observations on N = 67 participants (31 male) in 64 families). * Fisher’s exact test of homogeneity; † Wilcoxon rank-sum test for equality of populations; § No significant difference by gender in Round 1, Round 2 or in total according to the exact symmetry test of homogeneity for paired data; ** No significant difference by gender in Round 1, Round 2 or in total according to the Wilcoxon signed-rank test for equality of distributions on paired data.
Table 1.
Demographic and anthropometric data (151 observations on N = 67 participants (31 male) in 64 families). * Fisher’s exact test of homogeneity; † Wilcoxon rank-sum test for equality of populations; § No significant difference by gender in Round 1, Round 2 or in total according to the exact symmetry test of homogeneity for paired data; ** No significant difference by gender in Round 1, Round 2 or in total according to the Wilcoxon signed-rank test for equality of distributions on paired data.
| Round 1 | Round 2 |
---|
Male N = 20 | Female N = 47 | p * | Male N = 20 | Female N = 47 | p * |
---|
N (%) | N (%) | N (%) | N (%) |
---|
Education § |
Year 10 | 1 (5%) | 3 (6%) | | 1 (5%) | 3 (6%) | |
Year 12 | 1 (5%) | 7 (15%) | | 1 (5%) | 7 (15%) | |
Trade | 3 (15%) | 1 (2%) | | 5 (25%) | 1 (2%) | |
Certificate | 4 (20%) | 11 (23%) | | 2 (10%) | 11 (23%) | |
Degree | 5 (25%) | 13 (28%) | | 3 (15%) | 14 (30%) | |
Postgrad | 6 (30%) | 12 (26%) | | 8 (40%) | 11 (23%) | |
Total | 20 | 47 | 0.44 | 20 | 47 | 0.03 |
Smoked within 10yrs § |
Yes | 2 (10%) | 2 (4%) | | 3 (15%) | 3 (6%) | |
No | 18 (90%) | 45 (96%) | | 17 (85%) | 44 (94%) | |
Total | 20 | 47 | 0.58 | 20 | 47 | 0.35 |
Current Smoker § |
Yes | 1 (5%) | 0 (0%) | | 1 (5%) | 0 (0%) | |
No | 19 (95%) | 47 (100%) | | 19 (95%) | 47 (100%) | |
Total | 20 | 47 | 0.30 | 20 | 47 | 0.30 |
General Health § |
Excellent | 3 (33%) | 6 (29%) | | 1 (14%) | 8 (35%) | |
Very Good | 2 (22%) | 11 (52%) | | 5 (71%) | 11 (48%) | |
Good | 4 (44%) | 4 (19%) | | 1 (14%) | 4 (17%) | |
Fair/Poor | 0 (0%) | 0 (0%) | | 0 (0%) | 0 (0%) | |
Total | 9 | 21 | 0.25 | 7 | 23 | 0.62 |
| Median (IQR) | Median (IQR) | p † | Median (IQR) | Median (IQR) | p † |
Age (years) | 43.6 (41–47) | 41.3 (38–45) | 0.03 | 44.2 (41–47) | 41.9 (39–46) | 0.07 |
Height (cm) ** | 179 (174–182) | 165 (162–170) | <0.01 | 179 (172–183) | 164 (162–169) | <0.01 |
Weight (kg) ** | 81.7 (74–89) | 64.9 (60–72) | <0.01 | 81.6 (74–91) | 65.0 (60–73) | <0.01 |
BMI (kg/m2) ** | 25.7 (24–28) | 23.5 (22–26) | 0.06 | 26.8 (23–28) | 23.5 (22–26) | 0.12 |
Waist (cm) ** | 90.3 (84–98) | 80.8 (74–86) | <0.01 | 91.4 (85–99) | 80.4 (75–87) | <0.01 |
Table 2 reports the median FFQ nutrient intakes and the proportion of the sample by sex who met the Recommended Dietary Intake (RDI) targets. These results confirm that the sample is representative of the Australian adult population, having similar nutrient profiles as the last Australian National Nutrition Survey [
28].
Table 2.
Comparison of adult nutrient intakes, as assessed by the Australian Eating Survey (AES) food frequency questionnaire (FFQ), to Australian Recommended Dietary Intakes (RDI), Adequate Intake (AI) and upper limit, by gender.
Table 2.
Comparison of adult nutrient intakes, as assessed by the Australian Eating Survey (AES) food frequency questionnaire (FFQ), to Australian Recommended Dietary Intakes (RDI), Adequate Intake (AI) and upper limit, by gender.
Intake per day | Male (N = 31) | Female (N = 65) |
---|
Meeting | RDI/AI | Median | Meeting RDI | RDI/AI | Median | Meeting RDI |
---|
Protein (g) | 64 | 124.54 | 96% | 46 | 92.25 | 95% |
Fiber (g) AI | 30 | 37.95 | 73% | 25 | 28.41 | 70% |
Vitamin A (µg) | 900 | 1323.77 | 88% | 700 | 1198.36 | 87% |
Thiamine (mg) | 1.2 | 2.27 | 90% | 1.1 | 1.6 | 84% |
Riboflavin (mg) | 1.3 | 3.24 | 100% | 1.1 | 2.42 | 97% |
Niacin equiv. (mg) | 16 | 56.95 | 100% | 14 | 43.28 | 100% |
Folate (µg) | 420 | 468.17 | 65% | 420 | 341.22 | 31% |
Vitamin C (mg) | 45 | 198.1 | 100% | 45 | 174.38 | 98% |
Calcium (mg) | 1000 | 1375.59 | 71% | 1000 | 1172.81 | 70% |
Iron (mg) | 8 | 19.09 | 100% | 18 | 13.95 | 37% |
Magnesium (mg) | 420 | 540.95 | 75% | 320 | 411.14 | 80% |
Phosphorus(mg) | 1000 | 2132.67 | 100% | 1000 | 1642.88 | 95% |
Potassium(mg) AI | 3800 | 4447.83 | 73% | 2800 | 3681.6 | 79% |
Zinc (mg) | 14 | 16.44 | 67% | 8 | 13.14 | 82% |
Exceeding | Upper Limit | Median | Exceeding Upper limit | Upper Limit | Median | Exceeding Upper limit |
Sodium(mg) | 920 | 2768.22 | 100% | 920 | 2161.33 | 97% |
% E Saturated fat | 10 | 11 | 71% | 10 | 13 | 79% |
3.1. FFQ Reproducibility
Table 3 lists medians, correlations and intraclass correlation coefficients (ICC) for FFQ food group and nutrient intakes. Since observations for both administration rounds need to be present to estimate correlation between then, the number of observations available for use was only 67. When calculating the ICC however, all observations from both rounds can be utilized, thus the sample size was 163. The median correlation for nutrients was 0.72 (95% CI 0.51–0.92), which was attained by both thiamin and riboflavin. The least correlated was the percent energy (%E) from protein 0.49 (95% CI 0.19–0.78), and the most highly correlated was carbohydrate 0.83, (95% CI 0.68–0.98). We can expect tighter confidence intervals when using this approach. The median ICC was thiamin 0.73 (95% CI 0.55–0.80). The lowest ICC was the percent energy (% E) from protein 0.50 (95% CI 0.33–0.58), and the highest ICC was vitamin C, 0.88 (95% CI 0.92–0.93).
Data summarising the ARFS component subscales, the medians percentage energy from FFQ food groups are presented in
Table 4. The median correlation was 0.66 (95% CI 0.48–0.84), which was attained by meat. The lowest correlation was for packaged snacks 0.52 (95% CI 0.32–0.72), and the most strongly correlated was for breakfast cereal 0.83, (95% CI 0.57–1.0). The median ICC was grains 0.62 (95% CI 0.53–0.70), with the lowest for condiments 0.44 (95% CI 0.28–0.61), and the highest for vegetables, 0.84% (95% CI 0.79–0.89).
Table 3.
Reproducibility of Food Frequency Questionnaire (FFQ) nutrients: Median, interquartile range (IQR) and correlation, with 95% confidence interval, between round 1 and round 2.
Table 3.
Reproducibility of Food Frequency Questionnaire (FFQ) nutrients: Median, interquartile range (IQR) and correlation, with 95% confidence interval, between round 1 and round 2.
| Round 1 N = 96 | Round 2 N = 67 | Correlation N = 67 | ICC N = 163 |
---|
Nutrients/day | Median | IQR | Median | IQR | ρ | 95% CI | ICC | 95% CI |
Energy | | | | | | | | |
Energy (kJ) | 9601 | (8024–11501) | 8938 | (7298–11085) | 0.81 | (0.67, 0.96) | 0.85 | (0.80, 0.89) |
Protein (g) | 101 | (82–125) | 96.5 | (77.3–124.8) | 0.65 | (0.46, 0.84) | 0.70 | (0.62, 0.77) |
Total fat (g) | 75.5 | (62.6–85.2) | 73.6 | (53.5–89.8) | 0.71 | (0.49, 0.93) | 0.69 | (0.61, 0.78) |
Saturated fat (g) | 30.1 | (25.0–35.9) | 30.6 | (20.7–34.8) | 0.67 | (0.43, 0.90) | 0.65 | (0.55, 0.76) |
Polyunsat. Fat (g) | 9.7 | (7.52–10.98) | 9.15 | (7.26–11.91) | 0.76 | (0.58, 0.94) | 0.69 | (0.63, 0.76) |
Monounsat. Fat (g) | 27.8 | (22.9–31.7) | 27.3 | (19.6–35.5) | 0.73 | (0.52, 0.93) | 0.72 | (0.65, 0.79) |
Cholesterol (mg) | 283 | (224–360) | 252 | (211–329) | 0.66 | (0.45, 0.87) | 0.70 | (0.60, 0.80) |
Carbohydrate (g) | 262 | (217–341) | 243 | (192–337) | 0.83 | (0.68, 0.98) | 0.85 | (0.81, 0.89) |
Sugars (g) | 141 | (100–182) | 119 | (97–168) | 0.82 | (0.68, 0.95) | 0.83 | (0.77, 0.90) |
Alcohol (g) | 12 | (1.6–20.3) | 8.14 | (1.58–14.29) | 0.79 | (0.64, 0.95) | 0.82 | (0.75, 0.90) |
Nutrients | | | | | | | | |
Fiber (g) | 30.5 | (23.8–37.4) | 29.7 | (23.9–35.6) | 0.76 | (0.65, 0.87) | 0.79 | (0.70, 0.87) |
Vitamin A (µg) | 1228 | (1004–1511) | 1225 | (970–1667) | 0.62 | (0.36, 0.87) | 0.69 | (0.55, 0.83) |
Retinol (µg) | 297 | (227–410) | 317 | (214–480) | 0.69 | (0.42, 0.97) | 0.67 | (0.57, 0.76) |
Beta-carotene(µg) | 5316 | (3997–6581) | 5122 | (3824–6959) | 0.61 | (0.35, 0.88) | 0.72 | (0.54, 0.89) |
Thiamin (mg) | 1.77 | (1.41–2.21) | 1.74 | (1.38–2.16) | 0.72 | (0.52, 0.92) | 0.73 | (0.66, 0.80) |
Riboflavin (mg) | 2.59 | (2.10–3.19) | 2.54 | (2.06–3.34) | 0.72 | (0.51, 0.92) | 0.72 | (0.66, 0.78) |
Niacin (mg) | 45.3 | (38.8–55.7) | 43.8 | (36.4–54.7) | 0.70 | (0.51, 0.88) | 0.74 | (0.67, 0.81) |
Vitamin C (mg) | 184 | (140–235) | 167 | (133–213) | 0.81 | (0.61, 0.101) | 0.88 | (0.82, 0.93) |
Folate (µg) | 372 | (288–455) | 357 | (279–459) | 0.78 | (0.62, 0.93) | 0.80 | (0.74, 0.85) |
Calcium (mg) | 1200 | (949–1413) | 1205 | (903–1603) | 0.72 | (0.55, 0.89) | 0.71 | (0.63, 0.79) |
Iron (mg) | 15.1 | (11.5–18.1) | 14.3 | (11.2–17.7) | 0.75 | (0.60, 0.91) | 0.76 | (0.70, 0.83) |
Magnesium (mg) | 450 | (371–531) | 430 | (344–541) | 0.83 | (0.70, 0.95) | 0.85 | (0.80, 0.90) |
Phosphorus(mg) | 1743 | (1421–2148) | 1704 | (1273–2256) | 0.73 | (0.57, 0.89) | 0.75 | (0.68, 0.82) |
Potassium(mg) | 3881 | (3247–4610) | 3730 | (3093–4580) | 0.73 | (0.58, 0.88) | 0.78 | (0.72, 0.83) |
Sodium(mg) | 2272 | (1783–2846) | 2313 | (1765–2865) | 0.76 | (0.58, 0.93) | 0.80 | (0.75, 0.85) |
Zinc (mg) | 13.9 | (11.3–17.2) | 13.5 | (11.1–16.4) | 0.71 | (0.54, 0.87) | 0.75 | (0.68, 0.81) |
Water (mL) | 3469 | (2977–4024) | 3388 | (2987–3837) | 0.80 | (0.64, 0.96) | 0.87 | (0.83, 0.91) |
Percent Energy | | | | | | | | |
Protein | 18 | (16.0–20.0) | 18 | (16.0–20.0) | 0.49 | (0.19, 0.78) | 0.50 | (0.33, 0.68) |
Carbohydrate | 47.5 | (44.0–52.5) | 48 | (43.0–52.0) | 0.68 | (0.50, 0.87) | 0.66 | (0.58, 0.74) |
Total Fats | 30 | (27.0–33.0) | 30 | (28.0–34.0) | 0.64 | (0.47, 0.81) | 0.60 | (0.50, 0.69) |
Saturated Fat | 12 | (11.0–14.0) | 12 | (11.0–14.0) | 0.64 | (0.42, 0.85) | 0.63 | (0.55, 0.72) |
Alcohol | 4 | (0.50–6.00) | 2 | (1.00–5.00) | 0.77 | (0.63, 0.91) | 0.78 | (0.71, 0.85) |
Percent Fat | | | | | | | | |
Saturated | 45 | (42.0–49.0) | 45 | (42.0–48.0) | 0.72 | (0.56, 0.89) | 0.73 | (0.64, 0.81) |
Polyunsaturated | 14 | (12.0–15.5) | 15 | (13.0–16.0) | 0.80 | (0.59, 0.102) | 0.76 | (0.68, 0.84) |
Monounsaturated | 41 | (39.0–43.0) | 41 | (39.0–42.0) | 0.57 | (0.36, 0.79) | 0.64 | (0.51, 0.76) |
3.2. ARFS Reproducibility
The median correlation between the two rounds for ARFS food groups was 0.66 (95% CI 0.48–0. 84), which was attained by meat (
Table 4). The lowest correlation was for was vegetables, 0.59 (95% CI 0.34–0.83), and the strongest for ARFS total score, 0.83 (95% CI 0.68–0.98). Similarly, the median ICC was thiamin 0.69 (95% CI 0.55–0.80). The lowest ICC was for meat, 0.62 (95% CI 0.51–0.73), and the highest ICC was for ARFS total score, 0.87 (95% CI 0.83–0.90).
Table 4.
Reproducibility of The Australian Recommended Food Score (ARFS) components and the AES FFQ percentage of energy (%E) from core and non-core food groups: Median, interquartile range (IQR) and correlation between rounds.
Table 4.
Reproducibility of The Australian Recommended Food Score (ARFS) components and the AES FFQ percentage of energy (%E) from core and non-core food groups: Median, interquartile range (IQR) and correlation between rounds.
Scores | Round 1 N = 96 | Round 2 N = 67 | Correlation N = 67 | ICC N = 163 |
---|
ARFS (max avail. score) | Median | IQR | Median | IQR | ρ | 95% CI | ICC | 95% CI |
ARFS total(73) | 36 | (32.0–42.5) | 35 | (31.0–41.0) | 0.83 | (0.68, 0.98) | 0.87 | (0.83, 0.90) |
Vegetables(21) | 14 | (12.0–16.0) | 13 | (11.0–15.0) | 0.59 | (0.34, 0.83) | 0.69 | (0.58, 0.80) |
Fruit(12) | 7 | (4.0–8.0) | 6 | (4.0–8.0) | 0.64 | (0.47, 0.81) | 0.68 | (0.61, 0.75) |
Meat(7) | 2 | (2.0–3.0) | 2 | (1.0–3.0) | 0.66 | (0.48, 0.84) | 0.62 | (0.51, 0.73) |
Meat alternatives(6) | 2 | (1.0–3.0) | 2 | (1.0–3.0) | 0.78 | (0.62,0. 93) | 0.79 | (0.72, 0.86) |
Grains(13) | 6 | (4.0–7.0) | 6 | (5.0–7.0) | 0.64 | (0.48, 0.80) | 0.68 | (0.59, 0.77) |
Dairy(11) | 5 | (3.0–6.0) | 5 | (4.0–6.0) | 0.77 | (0.63, 0.91) | 0.79 | (0.73, 0.84) |
Extras(2) | 1 | (0.0–1.0) | 1 | (0.0–1.0) | 0.65 | (0.44, 0.85) | 0.66 | (0.56, 0.76) |
%E from food groups | | | | | | | | |
FFQ CORE | 67.5 | (58.0–76.0) | 69 | (60.0–75.0) | 0.71 | (0.51, 0.91) | 0.76 | (0.68, 0.85) |
Vegetables | 8 | (6.0–11.0) | 8 | (6.0–10.0) | 0.79 | (0.66, 0.93) | 0.84 | (0.79, 0.89) |
Fruit | 8 | (5.0–11.5) | 8 | (5.0–11.0) | 0.60 | (0.46, 0.74) | 0.57 | (0.39, 0.74) |
Meat | 11.5 | (8.0–15.0) | 11 | (7.0–14.0) | 0.53 | (0.15, 0.91) | 0.52 | (0.31, 0.74) |
Meat alternatives | 4 | (2.0–7.0) | 5 | (2.0–7.0) | 0.53 | (0.26, 0.80) | 0.57 | (0.42, 0.71) |
Grains | 22 | (15.0–27.0) | 22 | (18.0–25.0) | 0.60 | (0.45, 0.76) | 0.62 | (0.53, 0.70) |
Dairy | 9 | (7.0–14.0) | 11 | (7.0–16.0) | 0.54 | (0.32, 0.76) | 0.52 | (0.39, 0.64) |
FFQ NON-CORE | 32.5 | (24.0–42.0) | 31 | (25.0–40.0) | 0.71 | (0.51, 0.91) | 0.77 | (0.69, 0.84) |
Sweet drinks, fruit juice | 1 | (0.0–4.0) | 1 | (0.0–4.0) | 0.78 | (0.59, 0.97) | 0.78 | (0.70, 0.87) |
Packaged snacks | 1 | (0.5–3.5) | 1 | (0.0–3.0) | 0.52 | (0.32, 0.72) | 0.56 | (0.38, 0.74) |
Confectionary | 4 | (2.0–7.0) | 3 | (1.0–6.0) | 0.63 | (0.47, 0.78) | 0.54 | (0.41, 0.67) |
Baked sweet products | 4 | (2.0–7.0) | 3 | (2.0–7.0) | 0.76 | (0.62, 0.90) | 0.72 | (0.58, 0.85) |
Take-away | 6 | (4.0–8.0) | 6 | (4.0–8.0) | 0.77 | (0.53, 0.100) | 0.76 | (0.69, 0.84) |
Condiments | 2 | (1.0–3.5) | 2 | (1.0–5.0) | 0.60 | (0.38, 0.82) | 0.44 | (0.28, 0.61) |
Processed fatty meats | 2 | (1.0–3.0) | 2 | (1.0–3.0) | 0.69 | (0.50, 0.88) | 0.57 | (0.39, 0.76) |
Breakfast cereal | 7 | (4.0–10.0) | 8 | (5.0–11.0) | 0.83 | (0.57, 0.100) | 0.70 | (0.58, 0.82) |
Meat meals with veg. | 7 | (4.5–10.0) | 6 | (4.0–9.0) | 0.52 | (0.19, 0.85) | 0.54 | (0.36, 0.71) |
Meat meals no veg. | 1 | (0.0–2.0) | 1 | (0.0–1.0) | 0.53 | (0.26, 0.79) | 0.54 | (0.43, 0.65) |
3.3. Validity between ARFS and FFQ
Table 5 summarises the correlations between the ARFS sub-scale components and FFQ nutrients adjusted for total FFQ energy, significant at the 5% level. Negative correlations were found for % energy from saturated fat and ARFS total score and ARFS components of fruit, vegetables and grains, this is likely as foods high in SFA are not accounted for in ARFS so as the total ARFS increases intake of SFA decreases. ARFS was highly correlated with FFQ nutrient intakes, particularly for fiber, 0.38 (95% CI 0.27–0.49); vitamin A, 0.45 (95% CI 0.23–0.61); beta-carotene, 0.51 (95% CI 0.34–0.69); and vitamin C, 0.53 (95% CI 0.37–0.67). There were also strong correlations with mineral intakes, particularly calcium, 0.23 (95% CI 0.10–0.46); magnesium, 0.30 (95% CI 0.21–0.40); and potassium, 0.32 (95% CI 0.23–0.40) (See
Supplementary Figure 1).
Table 5.
Correlations between the Australian Recommended Food Score (ARFS) and the Australian Eating Survey (AES) FFQ components, adjusted for total FFQ energy, significant at the 5% level. Shaded cells are negative correlations.
Table 5.
Correlations between the Australian Recommended Food Score (ARFS) and the Australian Eating Survey (AES) FFQ components, adjusted for total FFQ energy, significant at the 5% level. Shaded cells are negative correlations.
| ARFS Total | ARFS Veg | ARFS Fruit | ARFS Meat | ARFS Meat Alt | ARFS Grains | ARFS Dairy | ARFS Extra |
---|
Protein (g) | | | | 0.19 | | 0.10 | 0.22 | |
Saturated fat (g) | | −0.09 | −0.13 | | | | 0.14 | |
Cholesterol (mg) | | | | 0.26 | | | 0.21 | |
Carbohydrate (g) | | | | −0.09 | −0.09 | | −0.07 | |
Sugars (g) | | | 0.15 | | −0.14 | | | |
Fiber (g) | 0.38 | 0.31 | 0.37 | | 0.25 | 0.16 | | |
Vitamin A (µg) | 0.45 | 0.38 | 0.37 | | 0.29 | | | |
Retinol (µg) | | | | | | | | |
Beta-carotene(µg) | 0.51 | 0.43 | 0.47 | | 0.30 | | | |
Thiamine (mg) | | | | | | | | 0.17 |
Riboflavin (mg) | 0.16 | | | | | 0.14 | 0.24 | |
Niacin equiv. (mg) | 0.12 | | | 0.20 | | 0.09 | 0.12 | |
Folate (µg) | 0.27 | 0.20 | 0.19 | | 0.15 | 0.17 | | |
Vitamin C (mg) | 0.53 | 0.49 | 0.51 | 0.16 | 0.22 | | | |
Calcium (mg) | 0.23 | | | | | 0.15 | 0.40 | |
Iron (mg) | 0.12 | | | | | 0.13 | | |
Magnesium (mg) | 0.30 | 0.20 | 0.22 | | 0.19 | 0.18 | 0.15 | −0.10 |
Phosphorus (mg) | 0.32 | 0.24 | 0.28 | 0.15 | 0.13 | 0.12 | 0.20 | −0.13 |
Potassium(mg) | 0.32 | | | 0.09 | | 0.14 | 0.27 | −0.07 |
Sodium(mg) | | | −0.12 | | | | | 0.13 |
Zinc (mg) | | | | 0.13 | | | 0.17 | |
% E Saturated Fat | −0.23 | −0.22 | −0.29 | | | −0.20 | | 0.18 |
Table 6 displays the correlations between the ARFS components and FFQ nutrients, adjusted for total FFQ energy, significant at the 5% level. There were significant, strong correlations between the corresponding ARFS and FFQ food groups vegetables, fruit, meat, meat alternatives, grains and dairy (0.50, 0.68, 0.42, 0.56, 0.28, 0.46, respectively) (See
Supplementary Figure 2).
ARFS was strongly positively correlated with FFQ %E food group intakes, particularly for fruit, 0.38 (95% CI 0.27–0.49); vegetable, 0.45 (95% CI 0.23–0.61), meat alternatives, 0.51 (95% CI 0.34–0.69); and dairy, 0.53 (95% CI 0.37–0.67). There were also strong correlations with mineral intakes, particularly calcium, 0.23 (95% CI 0.10–0.46); magnesium, 0.30 (95% CI 0.21–0.40); and potassium, 0.32 (95% CI 0.23–0.40).
Table 6.
Correlations between the Australian Recommended Food Score (ARFS) and the Australian Eating Survey (AES) FFQ food groups, adjusted for total FFQ energy, significant at the 5% level. Light grey shaded cells are those with the same group in row and column where positive correlation would be anticipated. Dark grey shaded cells are negative correlations.
Table 6.
Correlations between the Australian Recommended Food Score (ARFS) and the Australian Eating Survey (AES) FFQ food groups, adjusted for total FFQ energy, significant at the 5% level. Light grey shaded cells are those with the same group in row and column where positive correlation would be anticipated. Dark grey shaded cells are negative correlations.
Percentage of Energy From | ARFS Total | ARFS Veg | ARFS Fruit | ARFS Meat | ARFS Meat Alt | ARFS Grains | ARFS Dairy | ARFS Extra |
---|
CORE | 0.31 | 0.30 | 0.32 | | 0.25 | | | −0.26 |
Vegetables | 0.22 | 0.50 | 0.20 | | | | | |
Fruit | 0.37 | 0.33 | 0.68 | | | | | |
Meat | | | | 0.42 | −0.30 | | | |
Meat alternatives | 0.31 | 0.28 | 0.23 | | 0.56 | | | |
Grains | | | | | | 0.28 | | |
Dairy | 0.23 | | | | | 0.21 | 0.46 | |
NON-CORE | −0.31 | −0.30 | −0.32 | | −0.25 | | | 0.26 |
Sweet drinks, fruit juice | −0.25 | −0.27 | −0.18 | | −0.19 | | | |
Packaged snacks | | −0.20 | | | −0.17 | | | |
Confectionary | | −0.25 | | | −0.23 | | | |
Baked sweet products | | | −0.24 | −0.26 | | | | 0.19 |
Take-away | −0.29 | −0.26 | −0.25 | | | −0.26 | | |
Condiments | | | | | 0.21 | | | 0.40 |
Processed fatty meats | | | −0.26 | | −0.27 | | | |
Breakfast cereal | | | | | | 0.23 | | −0.21 |
Meat meals with vegetables | | | | 0.32 | −0.26 | | | |
Meat meals without vegetables | | | | | | | | 0.15 |
4. Discussion
The reproducibility and comparative validity of the ARFS in Australian adults was assessed in the current study by comparing food and nutrient intake data from the AES FFQ over two administration rounds five months apart, to estimate intra-class correlation coefficients (ICC). The reproducibility of the ARFS was confirmed as shown by ICCs for each nutrient assessed as being similar to those for the AES FFQ. The median ICC for ARFS nutrients was 0.66 (0.48–0.84) was similar when compared with the median ICC for FFQ nutrients 0.72 (0.51–0.92). These results confirm that the ARFS can be used when a brief evaluation of overall diet quality is required and with the advantages of considerably lower participant and researcher burden compared to other methods of dietary intake assessment.
ARFS was found to be highly correlated with FFQ nutrient intakes, particularly fiber, vitamin A, beta-carotene, and vitamin C. There were also strong positive correlations with mineral intakes for calcium, magnesium and potassium. These results indicate that the ARFS reflects the intake of a variety of nutrients which are known to be associated with health outcomes. These results are similar to a larger validation study in 6542 adults by Toft
et al. [
29], that used an FFQ to validate a food based diet quality score for fiber and vitamin C. However correlations in our study were higher for calcium (0.23), magnesium (0.30) and vitamin A (0.45) [
29]. In the present study there were significant and positive correlations between the corresponding ARFS sub-scale score and the corresponding FFQ food groups of vegetables, fruit, meat and vegetarian alternatives, grains and dairy. This was not completely expected as although the ARFS score is based on sub-set of FFQ questions, only nutrient dense foods and drink are included. The approach to scoring is also different with the ARFS being a simple count based on foods usually consumed at least weekly, while the FFQ incorporates the total number of daily serves, portion size and nutrient content. Similar correlations have been previously found between fruits and vegetables assessed by FFQ and diet quality scores [
29]. Toft
et al. [
29] found correlations with grams of fruit (
r 0.55) and vegetables (
r 0.48) and in the current study,
r = 0.68 and 0.50 respectively. These results suggest that the ARFS does reflect intakes across a variety of nutrients. In addition to food groups and that the foods included in the ARFS are representative of the AES.
The correlation coefficients from the current study are comparable to those found in the validation study conducted in children and adolescents [
9]. This was anticipated given that adult AES FFQ was modified from the child and adolescent version [
9] and both studies had a similar design. When the results of the current analysis are compared to those in children and adolescents, which reported a median energy adjusted correlation between FFQ and food records of 0.32, the median correlation of all nutrients in the current study are stronger at 0.72, suggesting that frequency based on weekly consumption of a range of nutrient-dense foods is a stronger predictor of nutrient intakes in adults compared to children.
For dietary instruments to be used to examine associations between diet and disease outcomes, it is suggested that correlations between the instrument and the reference method need to be in the range of at least 0.3 or 0.4 [
30]. The current study found correlations significantly greater than 0.3 for all nutrients indicating that the ARFS is an appropriate tool to assess dietary patterns and that it has the potential to be used to evaluate relationships between diet and health status.
The ARFS food based diet quality score accounts for diet variety, particularly fruits and vegetables and assesses the healthiness of diet in relation to National Dietary guidelines however does not account for non-core foods. The ARFS has application as a brief tool to assess overall diet quality and provide a cross-sectional snapshot of dietary intake in relation to dietary guidelines and dietary compliance however may not be sensitive to detect change over time.
A general limitation of validation studies is that the results are not necessarily transferable to other populations. This is generally due to the dietary assessment method such as an FFQ being based on the local food supply and portion size data from national-level surveys [
31]. A sample size of at least 50 is desirable for each demographic group [
32], and ideally between 100 and 200 participants [
33]. Although the sample size in the present study was adequate at the group level it was inadequate to confirm validity and reproducibility for subsets based on age, ethnicity or BMI category. The current sample included 65 female (68%) and therefore results likely to represent females as sub-category, but not males, however all participants are parents of primary school aged children so more likely to reflect a younger age group of adults. Performance of the AES FFQ also needs to be evaluated in populations of varying socioeconomic status and ethnicity. Strengths of the current study include that data were screened for implausible intakes. The reporting period of the FFQ was the previous six months so is likely to reflect differing intake due to seasonality. Lastly, by using statistical methods appropriate for repeated measures and correlated data, that is bootstrapped ICC, strong correlations were revealed.