Table 3 Studies evaluating the validity and/or reliability of paediatric a-priori diet quality indices (n = 37).
Index | Study | Validation and reliability | Academy QCC rating | |
|---|---|---|---|---|
1 | Australian child and adolescent recommended food score (ACARFS) | Marshall et al. (2012) [33] • Country: Australia • Age: 9-12y (μ 11.0, SD 1.1) • Sex: f 56.2% • Data collected: diet quality scores, food and nutrient intake and BMI • Data measurement: Cross-sectional | • Validity: Relative. • Reference standard: nutrient intakes and core food groups and demographics • Reliability: none. • Significant result (p <  0.001): ACARFS demonstrated statistically significant positive correlations with all vitamins and minerals tested. The strongest correlations were with vitamin C, β-carotene and fibre. ACARFS also had a moderately strong positive correlation with total energy. When the ACARFS was correlated with macronutrients adjusted for energy intake there was a positive correlation with protein. Weak negative correlation was found with total fat (P = 0.003) and SFA (P <  0.001). • The percent energy intake from SFA gave the least overall agreement of all the nutrients (κ = 0.13) and demonstrated ‘slight’ agreement, followed by riboflavin (κ = 0.36) which showed ‘fair’ agreement. Vitamin C (κ = 0.64), fibre (κ = 0.62) and β-carotene (κ = 0.62) had the strongest ‘substantial’ agreement. All other nutrients showed ‘moderate’ agreement (κ = 0.42–0.56). Within quartiles, fibre, vitamin C and β-carotene had the lowest percentages grossly misclassified. The strongest agreement amongst the quartiles was quartile one. • No association found between the ACARFS and percent energy intake from MUFA, PUFA, carbohydrate and sugar intake. • The percent energy intake from SFA gave the least overall agreement of all the nutrients (κ = 0.13) and demonstrated ‘slight’ agreement, followed by riboflavin (κ = 0.36) which showed ‘fair’ agreement. Vitamin C (κ = 0.64), fibre (κ = 0.62) and β-carotene (κ = 0.62) had the strongest ‘substantial’ agreement. All other nutrients showed ‘moderate’ agreement (κ = 0.42–0.56). | ∅ |
2 | Australian Recommended Food Scores for Pre-schoolers (ARFS-P) | Burrows et al. (2014) [19] • Country: Australia • Age: 2-5y • Sex: f 46% • Data collected: diet quality scores, food and nutrient intake • Data measurement: Cross-sectional | • Validity: Construct • Reference standard: nutrient intakes and core food groups, adjusted for total energy intakes and demographics • Reliability: none • Significant result (p < 0.05): positive association with protein, cholesterol, dietary fibre, vitamin A, beta-carotene, niacin equivalent, folate, vitamin C, Ca, Mg, K, P, Zn, vegetables, fruit, meat, and meat alternatives; and a negative association with carbohydrate, sugar sweetened drinks, packaged snacks, confectionary, take-away, and processed meats. • No association found with saturated fat, sugars, retinol, thiamine, riboflavin, Fe, Na, grains, dairy, baked sweet products, condiments, or sweet breakfast cereal. | + |
3 | Dietary Guideline Index for Children and Adolescents (DGI-CA) | Golley et al. (2015) [265] • Country: Australia • Age: 4-13y (grouped 4–8, 9–11, 12–13) • Sex: f 40% • Data collected: diet quality scores, food and nutrient intake and serum biomarkers • Data measurement: Validation study | • Validity: Concurrent/convergent • Reference standard: plasma dietary biomarkers and serum lipid concentrations via separate simple and multiple linear regression models, adjusted for demographic data • Reliability: none • Significant results (p < 0.05): Diet quality assessed by DGI-CA was a significant positive predictor of a-carotene, b-carotene, and n–3 FAs. Diet quality was inversely associated with lycopene and stearic acid (18:0) concentrations. • No association was found between diet quality and lutein and palmitic acid (16:0). DGI-CA had no association with lutein, a-tocopherol, n–6 FAs, myristic acid (14:0), pentadecanoic acid (15:0), palmitic acid, total cholesterol, cholesterol fractions, or triglycerides. | + |
4 | Obesity Protective Dietary Index (OPDI) | Spence et al. (2013) [43] • Country: Australia • Age: ~ 15 m • Sex: f 47% • Data collected: DQI score, nutrient intake and energy intake • Data measurement: Intervention, development and validation study | • Validity: Construct • Reference standard: Energy and nutrient intakes • Reliability: none • Significant results (P < 0.01): OPDI was positively correlated with intakes of energy (0.18), dietary fibre (0.55), b-carotene (0.51), and vitamin C (0.40). • No associations found between OPDI and intakes of saturated fat (20.02) or sodium (0.03). • When adjusted for energy intake, the correlations altered only for saturated fat (20.19) and sodium (20.11) and both were significant (P < 0.05). | ∅ |
5 | Short Food Frequency Questionnaire Diet Quality Index (sFFQ-DQI) | Kunaratnam et al. (2018) [45] • Country: Australia • Age: 2-5y • Sex: f 54.8% • Data collected: diet quality scores, anthropometry, food and nutrient intake and serum biomarkers of health and dietary exposure • Data measurement: cross-sectional validation study | • Validity: Comparative • Reference standard: sFFQ-DQI and the 3d-FR-DQI • Reliability: test-retest • Significant results (p < 0.05): There was a weak, but significant positive correlation between the sFFQ–DQI scores and 3d-FR–DQI scores. A positive mean difference occurred between sFFQ–DQI scores and 3d-FR–DQI scores and a significant positive trend indicating some bias between scores. Test-retest reliability of sFFQ–DQI scores and found no significant difference (p = 0.06) between mean total DQI scores. There was a high correlation between scores, Intraclass correlation (p < 0.001). | ∅ |
6 | Adapted Healthy Eating Index (adHEI) | Conceicao et al. (2018) [53] • Country: Brazil • Age: 1-2y • Sex: f 48.7% • Data collected: dietary scores and nutrient intake • Data measurement: Cross-sectional validation study | • Validity: Construct • Reference standards: adHEI components, diet quality, dietary energy, demographics • Reliability: internal consistency • Significant results (p < 0.05): The scores for adapted HEI components presented low correlations with energy intake, and correlation with individual food types was moderate, except in the case of milk and milk products. The correlations were negative for total fat, saturated fats, sodium, and cholesterol. The scores for the adapted HEI indicated a high positive correlation with dietary variety and vegetable consumption. For the other components of the index, the correlations ranged from moderate to low. | ∅ |
7 | The Brazilian Healthy Eating Index-Revised (BHEI-R) | Toffano et al. (2018) [266] • Country: Brazil • Age: 9-13y • Sex: f 52.7% • Data collected: diet quality scores, food and nutrient intake and serum biomarkers of health and dietary exposure • Data measurement: Validation study | • Validity: Construct • Reference standard: BHEI-R dietary intake components, serum biomarkers and demographics • Reliability: none • Significant results (p < 0.04): Found between whole grains and 5 methyl tetrahydrofolate, vegetable and legumes intake were positively correlated with seven metabolites (LA, ALA, ARA, EPA, DHA, β-carotene and creatine). Dark green and orange vegetables (DGOV) and legumes were positively correlated with ALA, retinol, β-carotene, creatine DHA, retinol, β-carotene and S-adenosyl-homocysteine. Intake of total fruits positively correlated with LA, ALA, ARA, EPA, DHA and β-carotene. Whole fruits were only positively correlated with β-carotene and riboflavin. Milk and dairy were positively correlated with retinol and pyridoxal. Meat, eggs and legumes were positively correlated with ALA, DHA, and creatine. Negative significant correlations were found between saturated fat and retinol, and with α-tocopherol. • No significant associations (p ≥ 0.09): After adjusting results obtained for saturated fat with total cholesterol, no correlation was found for retinol or α-tocopherol. | + |
8 | Healthy nutrition score based on food intake for pre-schoolers (HNSP)a | Peng et al. (2015) [74] • Country: China • Age: pre-school children • Sex: Not specified • Data collected: food and nutrient intakes, serum biomarkers • Data measurement: Development and diagnostic study | • Validity: Construct • Reference standards: HNSP scores, nutrient intakes, serum nutrient levels and biochemical indicators • Reliability: none • Significant results (P ≤ 0.001): HNSP scores were positively associated with calcium, zinc, vitamin A, vitamin E, vitamin B1, vitamin B2 and vitamin C. • The Cronbach’s alpha score for the HNSP = 0.86, indicating good internal consistency. Inter-rater reliability and reproducibility, assessed via Cohen’s Kappa coefficient, scored 0.61, which indicates HNSP score had good reproducibility. • No significant results were seen between HNSP score and physical mass, BMI, age or age z-scores, blood biochemical indicators including haemoglobin or concentration of haemoglobin in red blood cells. | ∅ |
9 | Preschool dietary lifestyle index (PDL-index) | Manios et al. (2010) [121] Country: Greece • Age: 2-5y • Sex: f 48.5% • Data collected: BMI (OW & OB), food and nutrient intake • Data measurement: Development & validation study | • Validity: Construct • Reference standards: PDL index score and BMI score to validate associations between PDL-index score and BMI classifications. • Reliability: none • Significant results (P < 0.001): Consumption of vegetables, fruits, fish/seafood, unsaturated fats and white meats/legumes was significantly higher in participants belonging to the third tertile of the PDL-Index compared to those belonging to the lowest tertile. Red meat, sweets and grains was significantly lower in the third tertile compared to the first tertile. Total and saturated fat intake was significantly lower, while the protein and carbohydrate intake were significantly higher in the third compared to the first tertile. Participants who belonged to the third tertile of the PDL-Index were less likely to be OB or OW/OB compared to those who belonged to the first tertile.1/44 unit increase in score was associated with 5 and 3% lower odds of being OB and OW/OB, respectively. • No significant difference was detected in total energy intake across the tertiles of the index. No significant difference was detected in monounsaturated and polyunsaturated fat intake across the tertiles of the PDL-Index. The PDL-index was not strongly associated with fibre, zinc and riboflavin intake. | + |
10 | Healthy dietary-lifestyle index (HDL-index) | Manios et al. (2010) [130] • Country: Greece • Age: 10-12y • Sex: Not specified • Data collected: food and nutrient intake, medical examination including serum biomarkers of health (fasting glucose & fasting insulin) • Data measurement: Cross-sectional study | • Validity: Construct • Reference standard: diet quality, nutrient intake & insulin resistance and demographics • Reliability: none • Significant results (p < 0.001): Higher HLD-Index score was associated with lower proportion of children having intakes lower than EAR. Mean intake of fibre, calcium and vitamin K was significantly higher among schoolchildren in 3rd tertile of the index. Saturated fat intake was significantly lower among children with higher HLD-Index score (p = 0.029). 1/40 unit increase in the HLD-Index score was associated with almost 7% lower odds of being insulin resistant. The likelihood of being insulin resistant was almost 60% lower among participants with high HLD-Index score (3rd tertile) compared with those belonging to the 1st tertile. • No significant difference was detected in total, monosaturated and polysaturated fat, carbohydrate and protein intake across the tertiles of index. | ∅ |
11 | NutricheQ Tool | Rice et al. (2015) [87] • Country: Ireland • Age: 12-36 m • Sex: f 50% • Data collected: food and nutrient intake and anthropometric measurements • Data measurement: Validation study | • Validity: Concurrent • Reference standard: NutricheQ scores, nutrient density, anthropometrics and food group via analysis of covariance and demographics. • Reliability: test-retest • Significant results (p ≤ 0.05): Correlation analysis for section 1 revealed statistically significant, negative correlations between NutricheQ scores and seven nutrients (iron, vitamin D, zinc, thiamine, vitamin C, fibre, and saturated fat) and vegetables, the strongest correlation being for iron and vitamin D. Correlation analysis for section 2, statistically significant correlations were obtained for 14 nutrients (protein, fibre, SFA, non-milk sugars, Fe, Zn, Ca, riboflavin, folate, thiamine, P, K, carotene, and retinol) and for fruit and vegetables. When scores were combined (i.e. total score), similar statistically significant, weak correlations were maintained except for saturated fat and vitamin C. Analysis of energy-adjusted dietary intakes across the groups showed significant differences in mean daily intakes of most nutrients. Nutrient density was significantly lower for those with higher NutricheQ scores, ie. differences between the lowest and highest scoring groups were observed for dietary fibre, iron, vitamin D, and carotene patterns were supported by food group analysis where children in the highest scoring groups ate significantly less vegetables and vegetable dishes, fish/fish dishes and meat, and more non-milk beverages, processed foods and ‘sugars, confectionery, preserves and savoury snacks. • Levels of agreement for sensitivity SN and SP across a range of NutricheQ scores, ROC curves were generated based on high and moderate risk ratings, with an AUC for high risk of 85%, whereas the AUC for moderate risk was 76%. • Cronbach’s alpha subsequently returned a relatively low score of 0.5; however, it has been reported that values of 0.5 are satisfactory. | + |
Aramouny et al. (2018) [267] • Country: Lebanon • Age: μ: 22.2 m • Sex: f 45% • Data collected: DQI scores with age, gender, weight and BMI • Data measurement: Validation study | • Validity: Concurrent • Reference standard: NutricheQ questionnaire, average daily intake of nutrients • Reliability: none • Significant results (P < 0.05): Caffeine was positively associated with the NutrichQ score, the number of high-fat meats also was positively associated with the score. EPA was negatively associated with score, DHA was negatively associated with score, Fluoride and chromium were positively associated with the total score. Molybdenum was positively associated with risk score, soluble fibre was negatively associated with the score, lactose was positively associated with risk score. Lysine was negatively associated with risk score caffeine was positively associated with the score and fat was positively associated with total score. | ∅ | ||
12 | Healthy Eating Index for Malaysians (HEI-m) | Rezali et al. (2015) [175] • Country: Malaysia • Age: 13-16y • Sex: Not specified • Data collected: DQI score, food and nutrient intakes • Data measurement: Validation study | • Validity: Content • Reference standards: composite score of the HEI and adequacy of nutrient intakes • Reliability: none • Significant results (P < 0.05): The composite score of the HEI was significantly and positively correlated with adequacy of protein, calcium, thiamine, riboflavin, vitamin A, and vitamin C intakes, indicating that it can be used to assess diet quality. | ∅ |
13 | Diet quality score for preschool children | Voortman et al. (2016) [177] • Country: Netherlands • Age: 12-19 m • Sex: Not specified • Data collected: DQI score, food and nutrient intakes • Data measurement: Development and validation study | • Validity: Construct and predictive • Reference standards: five dietary patterns and each of the body composition measures, adjusted for energy intake and demographics • Reliability: none • Significant results (p < 0.05): ‘Health-conscious’ dietary pattern or a higher diet quality score at the age of 1 year was associated with a higher fat-free mass index at 6y -not associated with fat mass index or %BF. The first reduced-rank regression (RRR)-derived pattern, showed diet quality was positively correlated with FMI and FFMI, remained positively associated with both FMI and FFMI after adjustment for confounders and was also associated with a higher BF% and a higher android/gynoid ratio. The second RRR-pattern, showed diet quality was positively correlated with FFMI and inversely correlated with FMI, remained positively associated with FFMI (0.19 (95% CI 0.06; 0.32) SD for highest vs. lowest quartile) after adjustment, but was no longer significantly associated with FMI. • Non-significant results: Adherence to a ‘Western’ dietary pattern at the age of 1 year was not consistently associated with any of the body composition measures the age of 6y. | + |
14 | Diet quality score for school aged children | van der Velde et al. (2018) [178] • Country: Netherlands • Age: mean 8y • Sex: not specified • Data collected: diet quality score, food and nutrient intakes • Data measurement: Validation study | • Validity: Construct • Standard preferences: diet quality score, intake of nutrients and energy intake • Reliability: none • Significant results (p < 0.01): Positive correlation between the diet quality score and intakes of protein (mainly plant protein), dietary fibre, and n-3 fatty acids. The score was inversely correlated with intakes of saturated fat, and monosaccharides and disaccharides. The score was also positively correlated with intake of all of the examined micronutrients. | + |
15 | Dietary Index for a Child’s Eating (DICE) | Delshad et al. (2018) [268] • Country: New Zealand • Age: 2-8y • Sex: Not specified • Data collected: dietary scores and nutrient intakes • Data measurement: Validation study | • Validity: Relative and construct • Standard references: DICE and 4-day food record scores • Reliability: test-retest • Significant results (p < 0.05): A significant positive correlation was observed between the total scores for DICE and the 4DFR. The weighted ĸ-statistic demonstrated moderate agreement (ĸ = 0.49) between DICE and the 4DFR. Spearman’s correlation coefficients showed significant positive correlations between the DICE and 4DFR for servings of fruit, servings of vegetables, variety of vegetables, servings of bread and cereals, consumption of wholegrain products, servings of milk and milk products, servings of meat and its alternatives, number of meals and snacks, and fluid consumption. A significant and inverse correlation was found for low fat foods/snacks/drinks consumption. Higher intake of fibre, vitamin C, vitamin A, vitamin D, folate (p < 0.05), and calcium (p < 0.001) were associated with increasing tertiles of the DICE total score. There was no bias between the two methods; that is the difference in intake between the DICE and 4DFR did not alter across the mean intake • Non-significant results: The variety of fruits, low salt and low sugar foods/snacks/drinks components were not significantly correlated with the same components scores from the 4DFR. | ∅ |
16 | Complementary Feeding Utility Index (CFUI) | Golley et al. (2012) [80] • Country: UK • Age: 3y • Sex: Not specified • Data collected: diet quality score and nutrient intakes • Data measurement: Validation study | • Validity: Concurrent/convergent • Reference standards: CFUI, dietary intake, feeding behaviour • Reliability: none • Significant results (p < 0.01): Higher CFUI scores were associated with higher energy-adjusted intakes of polyunsaturated fat, carbohydrate, total sugar (including fruit sugar), fibre, non-starch polysaccharide, and folate. • Higher index scores were also associated with lower energy-adjusted intakes of protein, calcium, and iodine. | + |
Golley et al. (2013) [269] • Country: UK • Age: 7-8y • Sex: f 48.3% • Data collected: food and nutrient intakes, anthropometry, BP, lipids • Data measurement: observational prospective cohort and validation study | • Validity: Predictive • Reference Standards: CFUI score, dietary patterns, BP, blood cholesterol and demographics. • Reliability: none • Significant results (p < 0.001): Greater adherence to complementary feeding guidelines (i.e., higher CFUI score) was negatively associated with the processed dietary pattern and positively associated with the health-conscious dietary pattern at 7y. • In the unadjusted models, CFUI score was negatively associated with BMI and waist circumference however, in fully adjusted model, the point estimates for both associations were attenuated by about one-half and only a weak association with waist circumference remained (p = 0.046). Results were consistent when stratified by gender. Similar inverse associations were observed between CFUI score and both systolic and diastolic BP (p < 0.05). • Non-significant associations: CFUI score was not associated with total cholesterol or cholesterol fractions in either the unadjusted or fully adjusted models or the gender-stratified analyses. CFUI score was not associated with the traditional dietary pattern. Stratified for gender, CFUI was weakly associated with the traditional dietary pattern in boys (p = 0.008) but not girls (p = 0.91). | + | ||
17 | Healthy Eating Index-2005 (HEI-2005) | Kranz et al. (2013) [152] • Country: USA • Age: 2-18y • Sex: Not specified • Data collected: DQI scores, food and nutrient intakes • Data measurement: Validation study | • Validity: Content and construct • Reference standards: HEI-2005, nutrient intakes • Reliability: none • Significant results (p < 0.05): associations were seen between: dairy and whole grains, dairy and fruit, whole grains and total grains, whole grains and fruit, total grains and vegetables, and total grains and fruit. The RC-DQI Analysis of the correlation between component scores in the RC-DQI showed that all comparisons were positive. The correlations between the identical components, that is, RC-DQI dairy and HEI 2005 dairy, were positive. • Non-significant results: association between dairy and total grains, vegetables or whole grains and vegetables, and vegetables and fruit. | + |
18 | Healthy Eating Index (HEI) & Youth Healthy Eating Index (YHEI) | Hurley et al. (2009) [270] • Country: USA (African American Adolescents) • Age: 11-16y • Sex: f 49% • Data collected: DQI score, nutrient intake, BMI, %BF • Data measurement: Validation study | • Validity: Concurrent • Reference standards: HEI, YHEI and health indicators • Reliability: none • Significant results (p < 0.05): Both HEI and YHEI, had significant positive correlations between index scores, micronutrients and total energy intake. In the Challenge sample, the magnitude of the correlation was significantly higher for the HEI vs. YHEI for iron. Among Challenge participants, higher percent body fat and abdominal fat were associated with a lower overall HEI score. • Non-significant results: BMI and total HEI or YHEI scores were not significantly associated. However, the directions of the associations were consistent with our hypothesis. | + |
19 | Diet Quality Index for Preschool Children (DQI-CH) | Huybrechts et al. (2010) [46] • Country: Belgium • Age: 2.5–6.5y • Sex: Not specified • Data collected: DQI scores, nutrient intakes • Data measurement: Validation and reproducibility study | • Validity: Construct and relative • Reference standards: DQI scores, nutrient intakes and 3d estimated diet records • Reliability: test-retest • Significant results (p < 0.05): The dietary diversity score was positively associated with vitamin C, thiamine, riboflavin, Na, K, Ca, P, Mg and Zn intakes, total water, fibre, protein and SFA intakes. The dietary quality score is negatively associated with energy, MUFA and carbohydrate intakes, while it was positively associated with thiamine, riboflavin, K, Ca, P and Mg intake, and protein, water and fibre intakes. Dietary equilibrium score was inversely correlated with energy, total fat, carbohydrate, MUFA and PUFA intakes, while it was positively correlated with protein, fibre, water, riboflavin, Ca, P, Mg and Zn intakes. The meal index was positively associated with energy, PUFA, complex carbohydrates, fibre, Na, Fe and Mg intakes. • No significant differences in mean DQI scores for preschool children were found between repeated measurements in the reproducibility study • The validity correlation for the DQI score corrected for within-individual variability was 0·82. Pearsons correlations varied among the four main components of the DQI (from 0·39 to 0·74) | ∅ |
20 | Modified revised children’s diet quality index (M-RCDQI) | Keshani et al. (2018) [151] • Country: Iran • Age: 13-15y • Sex: f 46.7% • Data collected: DQI score & nutrient intakes • Data measurement: cross sectional | • Validity: Content • Reference standards: M-RCDQI diet quality components • Reliability: test-retest • Significant results (p < 0.03): • Adolescents’ diet quality had positive significant association with HBM constructs, cues to action and self-efficacy. For every unit increase in cues to action score, a 0.19 unit increase in M-RCDQI was predicted, holding all other variables constant. Evaluating the relationships between cues to action and M-RCDQI components, we found a positive, significant association between cues to action and fruit consumption. A negative significant association was observed between cues to action and total fat intake and linoleic acid. For every unit increase in cue to action score, a 0.62 unit decrease in fat intake was predicted, holding all other variables constant. Furthermore, self-efficacy had a direct significant association with dairy intake | + |
21 | Finnish Children Healthy Eating Index (FCHEI) | Kyttälä et al. (2014) [104] • Country: Finnland • Age: 1, 3 & 6y • Sex: Not specified • Data collected: energy intake, energy adjusted intakes of SFA, MUFA, PUFA, dietary fibre and sugars, as well as absolute intakes of vitamin D and E • Data measurement: development and validation study | • Validity: Relative • Reference standards: diet quality score and nutrient intakes • Reliability: none • Significant results (p < 0.04): High amounts of sugar’ correlated positively with the scores of ‘vegetables, fruits and berries’, ‘oils and margarine’ and ‘skimmed milk’ at 1y, 3y and 6y. The score of ‘fish and fish dishes’ correlated positively with the scores of ‘vegetables, fruits and berries’ among the 3y and ‘oils and margarine’ among 6y. In all ages, energy adjusted intakes of SFA and sugars decreased across ascending quartiles of the FCHEI scores. Further, the energy density of the diet was lower among those 3y and 6y who belonged to the higher FCHEI quartiles. Strong inverse correlations of SFA, sugars and energy density of the diet with the FCHEI scores indicate that a higher FCHEI reflects a healthier diet. Energy-adjusted intakes of PUFA and dietary fibre, as well as absolute intakes of vitamins D and E, increased across ascending quartiles of the FCHEI scores in all age groups. Energy-adjusted intakes of PUFA and dietary fibre had strong positive correlations with the FCHEI scores. Absolute intakes of vitamin D and vitamin E correlated positively with the FCHEI. | + |
22 | Electronic Kids Dietary Index (E-KINDEX) | Lazarou et al. (2011) [271] • Country: Cyprus • Age: 9-13y • Sex: f 58.64% • Data collected: DQI scores, nutrient intake and body composition • Data measurement: Development study | • Validity: Predictive • Reference standards: E-KINDEX score, BMI classification and waist circumference • Reliability: none • Significant results (p < 0.001): Each 1 SD increase in the E-KINDEX score was associated with a 2.31 ± 0.23 kg/m2 decrease in BMI, a 2.23 ± 0.35 decrease in calculated %BF, and a 2.16 ± 0.61 cm decrease in WC. Significant and consistent inverse associations between the E-KINDEX score and BMI, %BF, WC, and generalized Obesity were observed in all models. • Overall, the diagnostic ability of the score appears more effective in screening for OB than for OW status in this sample. Compared with children belonging to the lowest E-KINDEX category those with scores in the second, third, and fourth categories had, on average, a 73, 76, and 85% decreased likelihood of being OW/OB, respectively. Children with scores that fell into the second, third, and fourth categories were, respectively, 62, 78, and 86% less likely to exhibit WC ≥ 75th percentile Being classified in the highest scored category was associated with an 84% decreased likelihood of an increase in BMI greater than 3 kg/m2 in 1 year (OR, 0.16; 95% CI, 0.04–0.74). | ∅ |
23 | Food Index (FI) | Magriplis et al. (2015) [122] • Country: Greece • Age: 10-12y • Sex: Not specified • Data collected: DQI scores, weight, BMI, %BF • Data measurement: Cross-sectional study | • Validity: Construct • Reference standards: FI score, percentage of body fat %, fat mass, BMI • Reliability: none • Significant results (p < 0.05): A difference was found in the gender’s mean BMI, WC and in total Energy intake. Difference was found assessing BMI categories between boys and girls, ~ 57% of boys versus 60% of girls being under- or normal-weight; 30% boys versus 29% girls were OW; and 13% boys versus 11% girls were OB. A borderline difference between BMI categories and age groups was found. • Associations were found between total food score and BMI, and their WC, in a crude analysis. When stratified by gender, the association remained significant for both genders for BMI (boys: −0.058 ± 0.03, 95% CI: −0.012, −0.001; girls: −0.06 ± 0.04, 95% CI: −0.016, −0.004) but only for girls in the case of WC (boys: −0.075 ± 0.04, 95% CI: −0.158, 0.008; girls: −0.098 ± 0.01, 95% CI: −0.177, −0.019). With every unit increase in the FI score the children were − 0.057 times less likely to be OW or OB and 0.08 less likely to have a high WC. The strength of the association remained significant in both the cases, when adjusted for confounders. BMI category increases the total FI score is lower than the median FI score. Gender, age and inactivity provided significant results. Sensitivity analysis that tested the probability of children being OW/OB with the total FI score showed that as the FI total increases in the 25% randomly selected GRECO sample, the probability of OW/OB decreases significantly. • Non-significant results: Total energy intake is entered BMI categories have no significant association with the dichotomized FI score. | + |
24 | Infant and Child Feeding Index (ICFI). | Moursi et al. (2009) [272] • Country: Madagascar • Age: 6-23 m • Sex: Not specified • Data collected: DQI scores, nutrient intake, energy intake, Length-for-age score, • Data measurement: validation study | • Validity: Concurrent and construct • ICFI scores, mean micronutrient density adequacy and energy intake • Reliability: none • Significant results (p < 0.0001): Complementary food energy intake increased with age. MMDA also increased with age. Both energy intake from complementary food and mean micronutrient density adequacy were positively correlated with ICFI across all age groups. Contrastingly, mean ICFI decreased with age and was the lowest for children between 12 m and 24 m of age. Both energy intake from complementary food and MMDA were positively correlated with ICFI across all age groups. Breastfeeding was overall significantly associated with LAZ with a .0.16 z-score difference in favour of non-breast-fed children. Dietary diversity was associated with LAZ when all age groups were combined with higher dietary diversity translating into better mean LAZ. There was a strong difference of 0.45 z-score when moving from medium to high frequency of feeding in 9–11 m children (P.0.01), but differences became marginally significant when all age groups were combined. • Non-significant results: There was no association between either WAZ or WLZ and ICFI after adjustment for specific confounders. Although statistically significant associations occurred between the ICFI and LAZ in the univariate analysis (P.0.002), it did not remain significant after adjustment • The exception to that was the 6–8 m age group for which there was a .0.65 LAZ difference for children with high ICFI compared to those with low ICFI (P.0.02). | + |
25 | Healthy Eating Index (HEI) for Brazilians | Rauber et al. (2014) [50] • Country: Brazil • Age: 3-4y & 7-8y • Sex: f 43% • Data collected: DQI scores, serum biomarkers • Data measurement: Development and validity study | • Validity: Construct • Reference standards: HEI scores and HEI components, energy, and nutrients • Reliability: none • Significant results (p < 0.05): At 3-4y, the food groups and dietary variety increased across the HEI score quartiles (from the lowest to the highest), except for the milk group, whereas intake of total fat, saturated fat, and sodium decreased. At 7-8y, food groups and dietary variety increased across the HEI score quartiles, whereas total fat, saturated fat, and sodium intake decreased. Contrary to expectations, cholesterol intake was positively correlated to the HEI score. The selected nutrients correlated to the HEI score, except for vitamin B12 at 3-4y, energy and carbohydrates at 7-8y, and calcium in both age groups. | ∅ |
26 | Diet Quality Index Score (DQIS) | Rios et al. (2016) [200] • Country: Puerto Rico • Age: 0-24 m • Sex: f 46% • Data collected: DQI score and BMI • Data measurement: Cross-sectional study | • Validity: Relative • DQIS categories and weight status and demographics • Reliability: none • Significant results it was found a trend, between DQIS categories and weight status, in which those categorized as having ‘Poor’ diets had two-fold higher odds of Excessive weight compared to those categorized as having ‘Excellent’ diets, after controlling for caregiver’s age and education (OR 2.01; 95% CI: 0.85, 5.18). | + |
27 | Menzies remote short-item dietary assessment tool (MRSDAT) | Rohit et al. (2018) [30] • Country: Australia (remote aboriginal communities) • Age: 18-54 m • Sex: Not specified • Data collected: ability and ease of completing index & DQI scores • Data measurement: development and validation study | • Validity: None • Reference standards: Diet scores and nutrient intakes • Reliability: Test-retest • Significant results: Test–retest analysis showed good-to-very good agreement between participant responses for 20 of the 24 items tested (0.63–0.88). The four items that showed weak agreement (0.13–0.50) were for questions regarding homemade freshly squeezed juice, red meat serve size, offal consumption and the frequency of consuming confectionery (chips, chocolates and ice creams). The MRSDAT was then modified to address these issues. | ∅ |
28 | Menzies remote short-item dietary assessment tool (MRSDAT) | Tonkin et al. (2018) [28] • Country: Australia (remote aboriginal communities) • Age: 6-24 m • Sex: f 50% • Data collected: DQI score from MRSDAT • Data measurement: Validation study | • Validity: Relative • Reference standards: MRSDAT scores, 24-h recalls • Reliability: none • Significant results: Relative to the 24-h recalls, the MRSDAT had higher estimates across all food groups, except fruit. While the median reported intakes for vegetables differed by only 0.04 servings between the two methods, and breads and cereals differed by 1.19 servings per day, Wilcoxon signed-rank test only showed the meat and vegetable intakes to be significantly different (p < 0.001 and p = 0.04, respectively). • Significant results (p < 0.05): Small bias reflects that the MRSDAT-estimated DGI-CA scores were both higher and lower to a similar degree compared with those derived from 24-h recalls. Secondary analyses showed that the MRSDAT-estimated DGI-CA scores were higher compared with 24-h recalls for all participants. Secondary analyses of individual dietary indicators showed significantly higher scores for meat and wholegrain indicators, and significantly lower dietary variety scores, when estimated by the MRSDAT compared with scores derived from 24-h recalls. For the meat indicator score, this bias was proportional; with the increasing indicator score, the difference between the MRSDAT and 24-h recalls scores was reduced. Regression for the wholegrain indicator showed a borderline-significant proportional bias in the opposite direction and this was also the case for the breads and cereals indicator; with increasing indicator scores, the difference between MRSDAT and 24-h recall derived scores increased. Given discretionary indicator is negatively scored, lower MRSDAT discretionary indicator scores are consistent with the MRSDAT, tending to estimate higher intakes of all foods. Kappa showed there was moderate agreement between methods for determining whether a child is still breastfed. | ∅ |
29 | Children’s Index of Diet Quality (CIDQ). | Röytiö et al. (2015) [100] • Country: Finland • Age: 2–6 • Sex: f 52% • Data collected: DQI scores • Data measurement: development and validation study | • Validity: Concurrent • Reference standards: CIDQ cut off scores and nutrient intake values • Reliability: none • Higher CIDQ scores were related to higher proportions of energy from protein (P = 0.001) and carbohydrates (P = 0.005) and lower proportions of energy from fat (P = 0.001), SFA (P = 0.001) and saccharose (P = 0.007). Higher intake of fibre (P = 0.001) and decreased intake of cholesterol (P = 0.001) were also associated with greater index scores and thus a good-quality diet. Of the several calculated intakes of different vitamins and minerals, higher intakes of Fe (P = 0.02), vitamin C (P = 0.001), vitamin E (P = 0.02) and folate (P = 0.001) were related with higher CIDQ points. Intakes of Ca and vitamins C and E increased from the lowest index group to the moderate and further to the highest group, which reflected healthier diet quality. The intake of SFA (E%) decreased when moving from the lowest group to the moderate and highest groups. Intake of MUFA did not change according to the three diet quality categories. • Analysis of the biochemical markers demonstrated that higher CIDQ scores were associated with clinical biomarkers that are connected with health, such as cholesterol (P = 0.008) and vitamin C (P = 0.008) concentrations. The children in the highest CIDQ group, which described good diet quality, had the lowest serum total cholesterol (P = 0.008) and LDL cholesterol (P = 0.02) concentrations and these concentrations increased significantly when moving down to moderate and low diet quality index scores. However, the same was detected also for HDL cholesterol (P = 0.01) concentrations. Vitamin C concentration increased significantly from the lowest to the highest diet quality category (p = 0·008). • Children’s BMI was not was not associated with the CIDQ score (r = 0·03, P = 0·65). The proportion of children with overweight (BMI ≥ 25·0 kg/m2) was 22·8%, 20·3% and 20·0% in the CIDQ score categories of poor (< 10 points), moderate (10·0–13·9 points) and good (≥14 points) diet quality, respectively (P = 0·86). No association was observed between the number of fulfilled criteria of healthy diet and overweight. The proportions of children with overweight was 24·1%, 20·4% and 18·9% when zero to two, three or four, or five or six criteria were fulfilled, respectively (P = 0·70). | + |
30 | The Healthy Eating Preference Index (HEPI) | Sharafi et al. (2015) [240] • Country: USA • Age: 2-5y • Sex: f 48% • Data collected: DQI scores, nutrient intake, energy intake and BMI • Data measurement: Validation | • Validity: Construct, predictive and concurrent • Reference standards: components of HEPI and energy intake and demographics • Reliability: Internal consistency • Significant results (p < 0.05): All HEI components showed weak-to-strong significant associations with energy intake, except nonsignificant associations for whole and refined grains. PCA analysis of HEPI components showed multiple dimensions with adequate internal consistency (a = 0.74). HEI only approached adequate internal consistency (a = 0.45). Liking/intake discordance for high-fat/sweet/salty foods also predicted BMI percentiles highest percentiles were observed in the high/low group, whereas the lowest percentiles were in the low/low group. ANCOVA showed significant effects of ratio group on BMI percentiles. Pre-schoolers in the highest ratio grouping had the lowest BMI percentiles. Ratio groupings also predicted carotenoid status pre-schoolers liking a healthy diet equal or above the pleasurable activities had the highest carotenoid status versus those liking a healthy diet half as much as the pleasurable activities. Similarly, the ratio groupings were formed for liking of high-fat/sweet/salty foods to pleasurable activities (each group included at least 20% of pre-schoolers), with a significant main effect on BMI percentiles. When HEPI and HEI were combined into a latent dietary quality variable, the best model fit with stronger associations was observed. Hierarchical regression analysis showed that only the HEPI significantly explained BMI percentile as an alternative or added-value predictor. Although the HEPI and HEI were significantly correlated, discord was observed in 40% of pre-schoolers. A similar pattern of association and discord was noted for high-fat/sweet/salty foods. • Non-significant results: HEPI components showed associations with energy intake (Pearson’s rs, < 0.12). HEI did not significantly predict BMI percentiles. | ∅ |
31 | Probability of adequate nutrient intake (PANDiet) score | Verger et al. (2016) [107] • Country: UK • Age: 12-18 m • Sex: f 49.4% • Data collected: DQI scores, food intake, nutrient intake and energy intake • Data measurement: validation study | • Validity: Content and construct • Reference standards: PANDiet score and its components • Reliability: none • Significant results (p < 0.05): The mean probabilities for avoiding excessive Na and SFA intakes were very low: 0·13 (SE 0·01) and 0·12 (SE 0·01), respectively. The Spearman correlation between the PANDiet score and energy intake was very weak. The lower the PANDiet score, the higher the intakes of whole milk, sugar, preserves and confectionery, burgers, kebabs, sausages, meat pies and pastries, biscuits and soft drinks and the lower the intakes of vegetables, fruits, and formula. PANDiet scores were significantly different across the four groups but energy intakes did not differ. Compared with other groups, the children in the YCF+/CIF− and YCF+/CIF+ groups had better nutrient adequacy for SFA, PUFA, vitamin D, Zn, Fe and Cu. The intakes of vegetables, fruit, fish and water were not significantly different between the four groups. | + |
32 | Diet quality index for adolescents (DQI-A) | Vyncke et al. (2013) [88] • Country: ten European cities b • Age: 12.5–17.5y • Sex: f 52.3% • Data collected: DQI scores, food and nutrient intake, serum biomarkers • Data measurement: Validation study | • Validity: Construct • Reference standards: DQI-A score, food and nutrient intakes, serum biomarkers and nutritional status • Reliability: none • Significant results (p > 0·0005): A strong positive association between the DQI-A score and water intake was observed. Soft drinks, fruit juices and alcoholic beverages had significant negative associations with the DQI-A. DQI-A score and bread/cereals had a positive association. Milk and cheese were positively associated with the DQI-A score, and animal fat and vegetable fat showed a small, however, significant positive association with DQI-A. No significant relation was present with meat, fish, eggs and substitutes. All non-recommended (energy-dense and low-nutritious) foods showed a significant negative association with the DQI-A score. A positive association was observed between the DQI-A and water and fibre intake, and a negative relationship was found with total energy intake. Polysaccharides were positively related to the dietary quality, whilst intake of mono- and disaccharides showed a negative relationship. Minerals Na, K, Cl, Ca, Mg, Zn, F, I, P, Mn were positively associated with the DQI-A score. Intake of vitamins, thiamine, riboflavin, pantothenic acid, pyridoxine, biotin, folic acid, cobalamin, retinol equivalents, vitamin D and vitamin K showed a significant positive association with the calculated index. • Non-significant results: but no significant association between DQI-A and potatoes and grains. No significant association was seen between DQI-A and protein intake or fat intake. Fe and Cu were not associated with the DQI-A score. Vitamins niacin, vitamin C and vitamin E, did not show a significant positive association with the calculated index. | + |
33 | Diet Quality Index for NZ adolescents (NZDQI-A) | Wong et al. (2013) [192] • Country: New Zealand • Age: 14-18y • Sex: f 61% • Data collected: DQI scores, nutrient intakes • Data measurement: Development and validation | • Validity: Construct and relative • Reference standards: NZDQI-A, nutrient intakes and 4DFR • Reliability: test-retest • Significant results (p < 0.05): Comparing nutrient intakes across the thirds of NZDQI-A score, those in the top third had higher intakes of iron and lower intakes of total fat, SFA and MUFA. Higher total scores were also associated with higher total sugars and fructose in the trend analysis. NZDQI-A had a fair internal consistency in measuring diet quality. The NZDQI-A total score derived from the repeated FQs showed good reproducibility, with reliability coefficients ranging from 0.32 to 0.67 for the individual components. Test-retest reliability was highest for fruit, but lowest for the meat component. | ∅ |
34 | Healthy Dietary Habits Score for Adolescents (HDHS-A) ratios | Wong et al. (2014) [194] • Country: New Zealand • Age: 15-18y • Sex: f 53% • Data collected: DQI scores, nutrient intakes, nutrient outputs, anthropometric data and serum biomarkers • Data measurement: development and validation | • Validity: Construct • Reference standards: HDHS-A scores, 24-h nutrient intakes, nutritional biomarkers and demographics. • Reliability: Internal • Significant results (p < 0.05): HDHS-A score was negatively associated with energy intake; all nutrients were adjusted for total energy intake. Higher relative intakes of protein, dietary fibre, PUFA, and lactose and lower intakes of sucrose were associated with increasing thirds of HDHS-A. Associations in the expected directions were also found with most micronutrient intakes, urinary sodium excretion, and whole-blood, serum, and RBC folate concentrations. The items in the HDHS-A had low intercorrelations. Correlations between individual items with the total score were highest for intake of potato and root vegetable fries, followed by item soft drink/energy drink consumption. Overall indicating the HDHS-A index had good internal reliability. | ∅ |
35 | Norwegian Adolescent Diet Score | Handeland et al. (2016) [195] • Country: Norway • Age: 14-15y • Sex: f 52.5% • Data collected: DQI score • Data measurement: Development and reliability | • Validity: None • Reference standards: diet score and components • Reliability: test-retest • Significant results (p < 0.001): The real percentage agreement for the Diet Score (87.6%) and the indicators (74.0–91.6%) exceeded expected agreement for all parameters, and Cohen’s k was > 0.4 for all parameters, except red meat (k = 0.249). | + |
36 | Unnamed Diet quality index for muti-ethnic Asian toddlers | Chen et al. (2019) [172] • Country: Singapore • Age: 18 m • Sex: f 48.5% • Data collected: DQI score, food and nutrient intakes, energy intakes • Data measurement: Development and validation study | • Validity: Construct • Reference standards: DQI scores, National recommended food group scores, food intake and demographics • Reliability: none • Significant results (p < 0.001): Those in the high DQI tertile were more likely to meet the recommended servings of the basic food groups, as compared with those in the low score tertile; significant for all basic food groups, except total milk and dairy products (p = 0.26). Increasing trends of participants meeting recommendation for whole grains intake and moderation of foods high in sugar across tertiles. Those in the high score tertile tended to meet the RDA of dietary fibre, protein, calcium and vitamin A, compared to the low tertile, but no significant association was observed for the AMDR of macronutrients (carbohydrates, total fat and saturated fat) and RDA of iron. When nutrients were modelled as continuous variables, we observed that toddlers in the high score tertile had a lower proportion of energy intake from carbohydrates and a higher proportion of energy intake from protein. When DQI was modelled as a continuous variable for the abovementioned analyses, similar associations were observed. Both FFQ and 24-h recall data, we observed higher DQI-24 h score across tertiles of DQI-FFQ score. Macronutrient intakes estimated from 24-h recall, toddlers in the high DQI-FFQ score tertile had a lower proportion of energy intake from carbohydrates and a higher proportion of energy intake from protein • Non-significant results: No significant associations observed for dietary fats (total, saturated, monounsaturated and polyunsaturated fat), iron and calcium. High DQI tertile did not meet the recommended servings for total milk and dairy products, as compared with those in the low score tertile; significant for all basic food groups, except (p = 0.26). There was no significant association observed for toddlers in the high DQI-FFQ score tertile and the proportion of energy from total dietary fats. | + |
37 | The Chinese Children Dietary Index (CCDI) | Cheng et al. (2016) [73] • Country: China • Age: 7-15y • Sex: f 49% • Data collected: DQI score, energy intake, food and nutrient intake, anthropometry, physical activity levels • Data measurement: Development and validation study | • Validity: Relative • Reference standards: CCDI score, BMI, inactivity and dietary intake and demographics: • Reliability: none • Significant results (p < 0.05): Positive correlations of the CCDI with majority of nutrient adequacy ratios and mean adequacy ratios was demonstrated. Whole grain intake and frequency of fried foods were not significantly associated with the CCDI. | + |