Diet quality indices and their associations with health-related outcomes in children and adolescents: an updated systematic review

Background To describe a-priori diet quality indices used in children and adolescents, appraise the validity and reliability of these indices, and synthesise evidence on the relationship between diet quality and physical and mental health, and growth-related outcomes. Methods Five electronic databases were searched until January 2019. An a-priori diet quality index was included if it applied a scoring structure to rate child or adolescent (aged 0–18-years) dietary intakes relative to dietary or nutrient guidelines. Diagnostic accuracy studies and prospective cohort studies reporting health outcomes were appraised using the Academy of Nutrition and Dietetics Quality Criteria Checklist. Results From 15,577 records screened, 128 unique paediatric diet quality indices were identified from 33 countries. Half of the indices’ scores rated both food and nutrient intakes (n = 65 indices). Some indices were age specific: infant (< 24-months; n = 8 indices), child (2–12-years; n = 16), adolescent (13–18 years; n = 8), and child/adolescent (n = 14). Thirty-seven indices evaluated for validity and/or reliability. Eleven of the 15 indices which investigated associations with prospective health outcomes reported significant results, such as improved IQ, quality of life, blood pressure, body composition, and prevalence of metabolic syndrome. Conclusions Research utilising diet quality indices in paediatric populations is rapidly expanding internationally. However, few indices have been evaluated for validity, reliability, or association with health outcomes. Further research is needed to determine the validity, reliability, and association with health of frequently utilised diet quality indices to ensure data generated by an index is useful, applicable, and relevant. Registration PROSPERO number: CRD42018107630.


Background
The prevalence of non-communicable diseases (NCDs) including type 2 diabetes mellitus (T2DM), cardiovascular disease (CVD), and chronic respiratory disease experienced by children and adolescents aged 0 to 18-years is increasing [1,2]. Four hundred new cases of T2DM are diagnosed annually in Australians aged 10-24-years [3]. Hypertension, a risk factor of CVD, is present in 6-7% of children and adolescents in Australia, the United Kingdom, and the United States of America (USA) [4][5][6]. Of concern, NCDs adversely affect growth, development, and maturation in childhood and adolescence [7], leading to compromised adult health and reduced life expectancy [8]. Hence, the prevention of NCDs in childhood is a global priority, requiring a multi-pronged approach to address major NCD risk factors [9]. These risk factors include diet quality, healthcare access, and substance abuse, which affect physical growth and mental development [10], with poor diet quality identified as one of the largest contributors to the global burden of NCDs [11].
Diet quality is broadly defined as a dietary pattern or an indicator of variety across key food groups relative to those recommended in dietary guidelines [12]. High diet quality thereby reflects achieving more optimal nutrient intake profiles and a lower risk of diet-related NCDs [13]. Diet quality can be influenced by confounding factors, including cultural and food environment, socio-economic status, child and family food preferences, and nutrition recommendations relevant to age, sex, country, and/or culture of the individual [14]. Diet Quality Indices (DQIs) are assessment tools that can be used to quantify the overall quality of an individual's dietary intake by scoring food and/or nutrient intakes, and sometimes lifestyle factors, according to how closely they align with dietary guidelines [12]. There are a variety of DQIs which utilise a range of scoring matrices. Some use frequency of food or food group consumption, others use nutrient intakes which require estimation prior to scoring, and some include both.
Due to the link between dietary intake in childhood and NCDs in both childhood and adulthood, the accurate measurement of paediatric diet quality is essential both to understand current intakes as well as evaluate the effect of interventions [15,16]. Reflecting this need, the use of DQIs is increasing not only in research and epidemiology, but also in community health and clinical settings where DQIs may form part of dietary education and self-monitoring interventions [14,[17][18][19][20]. A systematic review of paediatric DQIs which included papers published up until October 2013 identified 80 individual DQIs used in paediatric population samples, some of which identified cross-sectional associations with growth and health outcomes such as body weight, early onset puberty, and blood pressure [14].
Given the increasing number of DQIs identified in the previous review used or created for research, the diversity in the tools, and the different settings, age groups, and countries they are used amongst, there is a need to update the previous systematic review to identify valid DQIs and their associations with health outcomes [14]. Therefore, the aims of this systematic review update are to; 1) summarise a-priori DQIs used in child and adolescents; 2) appraise the validity and reliability of paediatric diet quality indices; and 3) synthesise the evidence on the relationship between diet quality and physical health, mental health, and growth-related outcomes among paediatric samples.

Study design
A systematic literature review was conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [21] and registered prospectively with the International Prospective Register of Systematic Reviews (PROSPERO number: CRD42018107630).

Search strategy
The search was designed as an update of the 2014 systematic review [14]. Medline (PubMed) and CINAHL were searched from 31 October 2013 to 11 January 2019. To broaden the search, the current review also searched Embase, Web of Science, and CENTRAL from database inception to 11 January 2019. The strategy used both controlled-vocabulary and keywords, and was designed for PubMed and translated for use in other databases using Polyglot Search Translator [22]. The translated search strategies were checked for accuracy by a librarian, and two authors (PD and SM), then further adapted for each database after examination of sensitivity and specificity by using a target of one eligible study per 100 records retrieved, with an estimated 150 eligible studies (Appendix). To support the systematic search update, snowball searching of reference lists of identified papers was conducted and the previous review [14] was examined to include any eligible studies the current search strategy didn't identify. Table 1 describes the eligibility criteria used to identify studies to answer the research questions; a study was included if it addressed one or more of the research questions. Studies published in English and Mandarin (translated to English by colleagues) were included. Studies published in other languages were included if they could be translated using Google translate [23]. For this review, a DQI was defined as any assessment tool which applied a quantitative score to food (i.e., frequency of consumption) or nutrient intake, where the scoring system reflected pre-defined national dietary or nutrient guideline/s (i.e., the DQI scoring system was developed a-priori). Diversity and variety indices that score or count the variety of foods consumed without regards to a dietary standard were excluded. Excluded lifestyle indices were any scoring system which had ≥2 scoring components on behaviours such as exercise, sedentary activities, or smoking.

Study selection and data extraction
Identified records were de-duplicated using Systematic Review Assistant-Deduplication [24] followed by a manual search in Endnote [25]. Titles and abstracts of papers were screened independently to assess their potential eligibility by two researchers (PD and SM) using Covidence [26], which further removed duplicates. The full texts of potentially eligible records were acquired and screened for eligibility by two researchers independently (PD and SM), with disagreements managed by consensus. Data were extracted from included papers by one researcher (PD) into three standardised tables; with random quality checks by a second researcher SM). For studies which measured prospective health-related outcomes, data were reported in their standard international units at baseline and followup, as well as mean change over time where possible.

Health-related outcomes
Any prospective outcome related to physical health, mental health, or growth was included if the variable was reported relative to DQI score or categories. Healthrelated outcomes used to describe the sample, but not linked to a DQI score were not considered. Healthrelated outcomes in adults were considered if they were related to a DQI assessment when the sample was aged < 18 years. In order to assess the ability of the DQI to predict health-related outcomes, outcomes were considered from 1-week after the DQI assessment with no further restriction on timeframe of follow-up. Health-related outcomes reported as the result of an intervention study were not considered as outcomes are likely to reflect the intervention rather than baseline diet quality.

Study quality
Any study which reported on the validity of a paediatric DQI or health-related outcomes was critically appraised using The Academy of Nutrition and Dietetics Quality Criteria Checklist (QCC) [27], independently by two authors (PD, SM, TB, or CC). Studies which reported the use of a DQI but didn't report validity, reliability, or health-related outcomes were not critically appraised as study quality was not relevant to research question 1. Any disagreements in study quality were settled by consensus. The Academy QCC is a critical appraisal tool suitable to evaluate the risk of bias for any study design, including diagnostic, intervention, or observational. The QCC rates the quality of the study as positive, negative, or neutral reflecting risk of bias in participant selection, generalisability, data collection, and analysis [27]. Studies found to have negative study quality were not excluded.

Results
Of 15,577 records identified in the search, 4896 were duplicates. After title and abstract screening, 312 full texts were assessed against the eligibility criteria, with 132 papers included, including 22 identified through snowball searching (Fig. 1). The main reasons for exclusion were use of a non-apriori diversity or variety index (n = 127), study design (n = 48), or study outcomes (n = 48). From the 132 included studies, 81 diet quality indices were identified by the current search strategy in addition to those identified in the original systematic review [14]. Of the 80 indices described in the Table 1 Eligibility criteria of original studies included in this review according to the population, indicator, comparator, outcomes, and study design (PICOS) format.

Inclusion criteria Exclusion criteria
Population children and adolescents aged 0-18 years old or sample mean age of ≤18 years old DQI applied to household or menu Indicator a 1) Reported the development of an a-priori DQI, 2) Assessed the validity or reliability of an apriori DQI, and/or 3) Reported prospective health-outcomes according to an a-priori DQI DQI reflecting only part of a guideline (e.g. fruit/vegetables only), DQI was not apriori (e.g. diet diversity scores or food variety scores which do not score according to a pre-established diet or nutrient guideline), or lifestyle indices.
Comparator Not applicable Not applicable Outcomes a 1) Scoring structure and characteristics 2) Concurrent, predictive b , or content validity; inter-rater reliability 3) Physical health, mental health, or growthrelated outcomes Physical health, mental health, or growth-related outcomes measured crosssectionally It should be acknowledged that there is overlap between these two eligibility criteria. Prospective health outcomes are frequently used as a measure of predictive validity. Any instance where prospective health outcomes were examined for the purposes of evaluating predictive validity was eligible for inclusion in aim 2 and included in this study as assessing DQI validity original review [14], 47 were eligible in the current review update and were primarily identified from the current search strategy but was supported by the snowball search ( Fig. 1), leading to a combined total of 128 unique indices designed for and/or used among children and adolescents. Of these, 39 included papers had evaluated the validity and/or reliability of 37 DQIs, while 12 evaluated the association of 12 DQIs with prospective health outcomes.
Characteristics of diet quality indices developed for or used in paediatric samples The 128 DQIs were developed across 33 countries, with most being designed for the USA (n = 23), Australia (n = 16), Germany (n = 11), and Brazil (n = 8) ( Table 2). There were 23 DQIs created outside of the USA such as Australia, Belgium, Canada, and Gaza with scoring methods based on the Dietary Guidelines for Americans (Table 2). Very few indices were identified in developing countries (n = 7) [262]. Those identified were from India, Indonesia, and Guatemala [134,138,141] and were typically brief tools more appropriate for field work, assessing frequency of consumption or dietary patterns and used dietary guidelines from other countries such as the USA to assess diet quality [134,138,141]. Thirteen (10%) DQIs were adaptations of the Diet Quality Index (DQI) [250], and 22 (17%) were adaptations of the Health Eating Index (HEI) [227]. These adaptions reflected changes to the scoring system to be more applicable to different countries or age groups. Four identified DQIs were designed for adults and subsequently used among children and adolescents without being adapted [89,106,127,250].

Not specified
No alternative methods such as study specific questionnaires or multiple day food diaries or records (n = 23) ( Table 2).
A number of studies utilised information from the same datasets, such as data from the National Health and Nutrition Examination Survey (NHANES) prospective population surveillance in the USA, or the Healthy Lifestyle by Nutrition in Adolescence (HELENA) in Europe [263,264].

The quality and strength of papers identified
Of the 39 papers assessing validity and/or reliability of 37 DQIs, 22 papers had positive study quality, while 17 papers had neutral study quality (Table 3). Of the papers assessing the relationship with health-related outcomes, 10 papers had positive study quality and two papers had neutral study quality ( Table 4). None of papers evaluated had a negative study quality. The most prevalent reasons for papers to be downgraded to neutral study quality was due to authors not reporting the eligibility criteria of participants, sampling method, or reasons for attrition.
Significant associations were found between high diet quality and serum vitamin D (β = 0.005, 95% CI = 0·002, 0·008, p < 0.0001), holo-transcobalamin (an indicator of B12) (β = 1·005, 95% CI = 1·002, 1·007, p = 0.0002), n-3 FS status (β = 0·376, 95% CI = 0·105, 0·646, p < 0·007) [88], and serum vitamin A (r = 0.128, p = 0.004) [74]. In adjusted models there     • 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.  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 Nutri-cheQ 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. 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.  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.   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   Table 3 Studies evaluating the validity and/or reliability of paediatric a-priori diet quality indices (n = 37). (Continued)
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.

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 zscore 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).  Table 3 Studies evaluating the validity and/or reliability of paediatric a-priori diet quality indices (n = 37). (Continued)

Index
Study Validation and reliability Academy QCC rating • Data measurement: Development and validity study 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. 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 MRSDATestimated 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. Table 3 Studies evaluating the validity and/or reliability of paediatric a-priori diet quality indices (n = 37). (Continued)

Index
Study Validation and reliability Academy QCC rating 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 24h 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. 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).   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 nonrecommended (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. 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.    [269].
In adjusted models, significant inverse associations were found between diet quality and waist circumference (β =  (Table 4). Significant inverse associations were found between diet quality and HbA1c levels in youth with type 1 diabetes (β = − 0.2, SE = 0.07, p = 0.0063). There was no association between diet quality and HbA1c in youth type 2 diabetes; however, there was a significant association for improved systolic blood pressure (β = −2.02, SE = 0.97, p = 0.0406) [273,274].
In addition to the above; three studies used prospective health outcomes to evaluate the predictive validity of DQIs (Table 3). The CFUI was associated with improved BMI, waist circumference, and blood pressure [269]; the E-KINDEX was associated with improved BMI, total body fat, and waist circumference [271]; and the Diet Quality Score for Preschool Children was associated with improved fat-free mass and fat mass [177].

Discussion
This review summarises 128 unique a-priori DQIs used in children and adolescents internationally; however, only 30% were assessed for validity and reliability, from which two were found to require refinement [151,266] to achieve suitable accuracy and reliability. Additionally, only 15 DQIs were tested for association with prospective health outcomes; finding associations between high diet quality and improved nutrient status, IQ, body composition, risk of metabolic syndrome, blood pressure, HbA1c, mental-health related quality of life, and premenopausal breast cancer.
This systematic review update identified 81 novel paediatric a-priori DQIs (from 157 publication), a 172% increase over 7 years from the 47 identified in the original systematic review [14]. This steep increase in the development and use of DQIs demonstrates that this approach to assessing diet quality is well-utilised within research in children and adolescents internationally. The USA, Australia, Germany, and Brazil appear to be leading the development of paediatric DQIs, together producing 45% of all paediatric DQIs. Beyond these four countries, the vast majority of other DQIs were from other developed countries, possibly reflecting this review's eligibility criteria. Dietary assessment in developing countries are often focused on assessing growth in an environment characterised by a high prevalence of undernutrition, and and is assessed using non-a-priori diet diversity indices (DDIs), diet diversity scores (DDSs), and food variety scores (FVSs) [14,138,167,224] of which there were 127 excluded from this review (Fig. 1).
There were significant variations in DQIs methods. Simpler scoring methods awarded and summed points for foods which were or were not consumed over a specific frequency. This simple food-based scoring method reduces burden on both researchers, clinicians, and individual users as they can be easily applied to clinical practice. Food-based DQIs included the KIDMED, DGI-CA and ACARFS [33,35,214]. More complex DQI scoring methods involved quantification of nutrient intakes from reported food intakes which then undergoes a further step of calculating nutrient intakes  Adjusted models: no significant association. Timing of puberty: Adjusted models: scores positively associated with timing of puberty (9.2, 95% CI 9.0, 9.4, p = 0.02). RC-DQI score not associated with relative to age-specific dietary guidelines or energy intake, which make such scores less applicable to the clinical setting or for individual use [141]. DQIs with complex nutrientbased scoring approaches included the NIS [114] and the NQI [115], with DQIs which used a combination of food and nutrient-based scoring methods being more common, such as the ARFS-P [19] and the DGI [36], which embody the same limitations as nutrient-only scoring methods. Of concern, only 29% of the 128 unique DQIs identified were evaluated for validity and/or reliability, and only 12% evaluated associations with prospective health outcomes. Of the 35 DQIs which were evaluated for validity, 34 were stated to be validated tools by authors; however, due to inconsistent methodological approaches the validity of the DQIs could not be consistently evaluated. Only five DQIs (5%; DQI-A [88], diet quality score for preschool children [177], CFUI [269], E-KINDEX [75] and HNSP [74]) were both evaluated for validity and found to be positively associated with prospective nutrient biomarkers, blood pressure, IQ, and body composition. This suggests these DQIs are the most rigorous in terms of accuracy, reliability, and relevance to health. While the use of DQIs to measure the diet quality of children and adolescents is a highly utilised assessment method, further research is required to address the current paucity of evaluation studies of currently available tools.
Further, the large number of new yet non-validated paediatric a-priori DQIs suggests new DQIs are developed prior to evaluating existing DQIs, and therefore may have been unnecessary. The use of DQIs which have not been rigorously developed and evaluated may compromise the research in which they were used and lead to inaccurate and/or unreliable results. This is particularly the case for DQIs which were developed specifically to evaluate outcomes of a particular study, where the development of the tool was minimally described and not intended for re-use or replication; therefore, limiting confidence in the study results. -Canada -Range: 8-10y Baseline: μ9.6(SD: 0.9) Follow up: μ 11.6(SD: 0.9); 45%F -Dataset: QUALITY (QUebec Adipose and Lifestyle InvesTigation in Youth) study + Anthropometry: Adjusted models: DQI-I score was negatively associated with lower gain in CFMI (β = − 0·08; 95% CI − 0·17, − 0·003) and %BF (β = − 0·55; 95% CI −1·08, − 0·02). Approximately half of the identified DQIs were modified forms of the DQI or HEI [227,250]. However, only 16 of these modified DQIs were validated in the new population (e.g. age, culture, country) group, where the remaining studies assumed validity based upon the tool being valid in the original population. Non-validated tools, even if adapted from a valid tool, should be used with caution as the modified DQI may not accurately assess diet quality or be appropriately extrapolated to the diet and cultural context of the new population sample. This is particularly the case for modified DQIs in which the scoring system was still based on national dietary guidelines of the original country (e.g. The USA), and not the new population (e.g. Brazil, Canada) [50,59,70]. Similar cautions should apply for DQIs such as the Healthy Diet Indicator and the Alternative Healthy Eating Index used in paediatric populations that were designed for adults as these indices may not accurately assess children and adolescent's diet quality [89,245].
A factor that varied between papers was the method of dietary data collection, with some DQIs able to be calculated using a variety of dietary assessment methods such as the Diet Quality Index -International [252]. This variety is a strength as it allows flexibility in the application of DQIs in future research and clinical practice. A 24-h recall was the most frequently used dietary assessment tool; however, it is unclear if the 24-h recalls were repeated over several days to improve its accuracy in reporting usual intake. Although most remaining DQIs used FFQs, a substantial number of papers did not use validated methods to collect dietary data [39]. There should also be a caution for the use of single 24-h recalls in studies with small sample sizes or in clinical practice as this one-off measure does not accurately represent usual dietary intake. Although a DQI may be valid, the method of dietary intake assessment must also be accurate and relevant if results are to be interpreted with confidence.

Limitations and future directions
The present review may be limited by publication bias, particularly in the fields of a null or negative result relating to the validity of DQIs and their association with health-related outcomes; however, publication bias was unable to be assessed as funnel plots were not able to be generated. Although this review reported validity, reliability, and associations with health-related outcomes; it did not evaluate other aspects of assessment tool utility such as sensitivity to change and participant burden nor did it evaluate the validity and reliability of dietary intake assessment methods.
Limitations in the existing literature highlight the need for future research to validate existing paediatric a-priori DQIs and to test their associations with prospective health-related outcomes. This will allow determination of the effect of diet quality during childhood and adolescence on physical health, mental health, and growth which is of increasing importance as the prevalence of diet-related NCDs continues to rise. The application of any DQI should appropriately assess dietary intake using validated methodology and researchers developing new DQIs should ensure that tools reflect indicators of alignment with an appropriate national dietary guideline or nutrient target specific to the culture, country, and agegroup of the intended population, and rigorously describe the tools development, scoring method, and validation procedures. Researchers should consider applying existing valid DQIs to their data and undertaking reliability and validity studies in their population groups. For research reporting associations with health-related outcomes, researchers should fully describe the demographic and medical characteristics of the sample, information about dataset used, and transparently detail the results.

Implications for practice
DQIs present an important opportunity to measure the quality of the total diet of individuals and groups. The current review can be used as a resource to assist health professionals in identifying relevant and valid DQIs for their clinical setting. When selecting a DQI, health professionals should consider: i) whether the DQI demonstrated validity and/or reliability, ii) does the DQI reflect a nutritional reference standard which is relevant to the population in which it will be applied, iii) can the DQI be easily calculated in the clinical setting, and finally iv) can the DQI be calculated by a dietary assessment method which can be performed efficiently in the clinical setting? Although it would be ideal to select a DQI which is associated with prospective health outcomes; due to the paucity of research in this area, this is not yet a feasible consideration.

Conclusion
Research examining diet quality among children and adolescents is of increasing interest globally. However, few indices have been evaluated for validity or reliability or examined for a relationship with prospective health outcomes. Rigorously developed DQIs which have been evaluated have shown good validity, reliability, and association with a range of physical and mental health outcomes. Longitudinal studies are needed to determine the ability of diet quality indices to predict optimal growth and dietrelated health-related outcomes among children and adolescents. (MH "Child+") OR (MH "infant+") OR (MH "adolescent+") OR (MH "minors+") OR TI Child* OR TI infant* OR TI toddler* OR TI adolescent* OR TI minor* OR TI youth* OR TI teen* OR TI pre-teen OR TI kid* OR AB Child* OR AB infant* OR AB toddler* OR AB adolescent* OR AB minor* OR AB pre-teen AB kid* AND TI DQI OR TI DQI-I OR TI "Healthy Eating Index" OR TI HEI OR TI YHEI OR TI "Recommended Food Score" OR TI RFS OR TI "variety score" OR TI "variety ind*" OR TI "diversity score" OR TI "diversity ind*" OR TI DDI OR TI ACARFS OR TI KIDMED OR TI "DGI CA" OR TI FVI OR TI KINDEX OR TI AMQI OR TI "diet* quality" OR TI "dietary variety" OR AB DQI OR AB DQI-I OR AB "Healthy Eating Index" OR AB HEI OR AB YHEI OR AB "Recommended Food Score" OR AB RFS OR AB "variety score" OR AB "variety ind*" OR AB "diversity score" OR AB "diversity ind*" OR AB DDI OR AB ACARFS OR AB KIDMED OR AB "DGI CA" OR AB FVI OR AB KINDEX OR AB AMQI OR AB "diet* quality" OR AB "dietary variety" ti,ab OR teen*:ti,ab OR pre-teen:ti,ab OR kid*:ti,ab)) AND ((DQI:ti,ab OR DQI-I:ti,ab OR "Healthy Eating Index":ti,ab OR HEI:ti,ab OR YHEI:ti,ab OR "Recommended Food Score":ti,ab OR RFS: ti,ab OR "variety score":ti,ab OR "variety ind*":ti,ab OR "diversity score":ti,ab OR "diversity ind*":ti,ab OR DDI:ti,ab OR ACARFS:ti,ab OR KIDMED:ti,ab OR DGI CA:ti,ab OR FVI:ti,ab OR KINDEX:ti,ab OR AMQI:ti,ab OR "diet quality":ti,ab OR "dietary quality":ti,ab OR "dietary variety":ti,ab)) From inception-11 January 2019 4329 (trials) Web of Science (Child* OR child OR infant* OR infant OR toddler* OR adolescent* OR adolescent OR minor* OR minors OR youth* OR teen* OR pre-teen OR kid*) AND (DQI OR DQI-I OR "Healthy Eating Index" OR HEI OR YHEI OR "Recommended Food Score" OR RFS OR "variety score" OR "variety ind*" OR "diversity score" OR "diversity ind*" OR DDI OR ACARFS OR KIDMED OR DGI CA OR FVI OR KINDEX OR AMQI OR "diet quality" OR "dietary quality" OR "diet variety") From inception-11 January 2019