• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of advannutSearch Advances in NutritionManuscript SubmissionSubscribe to Advances in NutritionView all articlesRead published version of paper
Adv Nutr. Jul 2011; 2(4): 295–303.
Published online Jun 28, 2011. doi:  10.3945/an.110.000166
PMCID: PMC3125679

Use of Dietary Indexes among Children in Developed Countries1,2


In this article, we review studies that have used dietary indexes to assess different aspects of diet in relation to health outcomes and sociodemographic factors in childhood populations of developed countries. Eighty-four papers published from 1980 to mid-2010 including 90 unique dietary indexes were reviewed. Seventy-two indexes were developed (or have been adapted) specifically for childhood populations; 38 of these were used to assess diet-disease associations, mostly of diet and obesity. In the majority of these studies, small inverse associations between dietary indexes and obesity indexes were shown. Children who were younger, female, and from high-income families had better dietary quality scores. Forty-nine indexes (of 90) were compared with other aspects of dietary intakes or behaviors, with correlations ranging from very low to modest (~r = 0.05–0.50). Only 2 validation studies compared an index with nutritional biomarkers, and correlations were quite weak for most plasma nutrients (P < 0.10). Overall, a large number of indexes have been created and used, but the majority of studies are descriptive. Fewer analytic studies on index-health associations have been performed, and most analyses insufficiently adjusted for confounders. Thus, prospective and intervention research in diverse populations is needed to further test these tools. In conclusion, indexes are potentially useful methods for dietary assessment, because they offer valuable information on overall dietary patterns in children. However, understanding the advantages and limitations when applying them in research and public health settings is important, and more research is needed to further develop their utility.


Analysis of overall dietary patterns is an approach that has been used in nutritional epidemiology, especially during recent years, and aims to measure the human diet as a whole (1, 2). Specifically, pattern analysis permits the assessment of the combined effect of the diet, including interactive and synergistic relationships among and between nutrients, foods, and eating habits.

Dietary indexes are one of the methodological tools that permit a holistic assessment of diet (35). A major advantage of using this approach over empirical methods such as principal components, cluster analysis, or reduced rank regression analyses is the ease of interpretability. Specifically, an index yields a summary score that represents the level of adherence to dietary recommendations and/or reflects the overall quality of diet. Thus, the results of dietary assessment via this method are more easily understood and interpreted by consumers and nondietetic health professionals (e.g. general practitioners and nurses) as well as by various stakeholders such as policy makers. Although the latter group may not have specialized nutrition- or health-related knowledge, they make decisions regarding public health policies and thus need to be provided with scientifically accurate, easily comprehensible information to correctly address population needs.

Dietary indexes have been widely used in adult populations (3, 4). As a result, some important relationships of diet to chronic diseases risk factors and mortality have been revealed (4). Several papers have also reviewed associations between diet indexes, diet quality, and disease (312). In children, however, the use of dietary indexes has been rather limited. Their application in developed countries has been aimed mainly at the assessment of diet quality (1317) and only recently at the investigation with diet-related diseases or chronic disease risk factors (1821). In contrast to developed countries, the development and use of dietary indexes for childhood populations in developing countries have generally been designed to assess nutritional adequacy and growth, which are the major nutritional concerns in those countries (22, 23). Although dietary indexes in developing countries have been reviewed (22, 23), to our knowledge, a comprehensive review of dietary indexes among children in developed countries has not yet been performed.

Because the use of dietary indexes is relatively new in nutritional epidemiology, criteria need to be developed that guide users in the selection of a suitable index. The first step is to evaluate the outcome for which the index was constructed to measure. Second, the evidence on which the index was developed needs to be critically assessed (5, 24, 25). The final step is to determine whether rigorous methods were used to create and evaluate the index.

Such techniques include the application of specific weights to the index’s components to reflect each component’s relative importance to what an index measures (26) and/or the assessment of the index’s performing characteristics (i.e. validity and reliability) (27). Finally, the discriminative power of an index may be influenced by the inherent characteristics of the study sample. It is thus important to recognize that certain population subgroups with distinct sociodemographic characteristics might exhibit significant differences in diet characteristics and thus the utility an index may be varied accordingly (28).

Therefore, the main objectives of this review were to present dietary indexes that have been developed and utilized for childhood populations in developed countries and discuss associations of these indexes with overall diet quality, sociodemographic characteristics, and health outcomes in children. We also examined the potential use of diet quality indexes and delineated the research implications and future research directions in this area.


An extensive search of the scientific databases PubMed, Scopus, ProQuest, Heal-Link, and HighWire, as well as Google, was performed. The following English key terms were used: “dietary indexes (indices)/index (es)/score(s),” “diversity indexes(indices)/index (es)/score(s)” combined with the following terms “child/children,” “obesity/weight/height/BMI/growth/anthropometric,” “asthma/blood pressure/blood lipids/LDL-cholesterol/HDL-cholesterol/TG/biomarkers/blood test/risk/chronic disease factors,” and “nutrient status/nutrition/diet quality/dietary patterns/habits.” In addition, links from the original Web sites and article reference lists were checked to identify additional articles of relevance. Publications were searched from January 1980 through August 2010.

Exclusion criteria for papers were: 1) indexes from nondeveloped countries; 2) indexes that were not examined in relation with either sociodemographic, dietary, anthropometric, disease risk factors, or health and disease outcomes; 3) publications in languages other than English; 4) studies where the sample included adults and no separate results were reported for children; 5) indexes that focused on other lifestyle factors (even if they included diet as well); 6) indexes that targeted clinical conditions and hospitalized children and were not applied in the general population; 7) nutrient density scores, because they do not evaluate total dietary patterns (29); and 8) publications that were available only in abstract form from conference proceedings.

Of note, there are several psychometric tools that measure behavioral aspects of diet and eating behavior, such as the Child Eating Behavior Questionnaire (30), Adolescent Food Habits Checklist (31), Dutch Eating Behavior Questionnaire (32), and the Three Factor Questionnaire (33). These tools are scored according to the definition given for dietary indexes (i.e. each answer is scored and the result of the evaluation is expressed in the format of an arithmetic score) and have been used to assess the relationship to dietary quality (31) and diet-related diseases (32). Thus, these tools could arguably be regarded as types of dietary indexes. No previous review of dietary indexes has included them (312). Therefore, we also largely omitted these publications in this review and included only those that specifically facilitated discussion about the possible applications and uses of dietary indexes in public health.

In total, we retrieved 128 articles; 84 of these met the inclusion criteria and were selected for review.

Data presentation and organization

Table 1 presents an overview of the indexes reviewed, grouped by intended use as reported in the paper. The number of indexes by each category along with general characteristics of their components and scoring system are summarized.

Table 1.
Dietary indexes used among childhood populations in developed countries, grouped by intended use

Further details of the indexes reviewed here are presented in Supplemental Table 1. The acronym for the index and its full name are provided, alongside a brief description of the components, its scoring system, and the year of publication. Because there are several publications that may refer to the same index, Supplemental Table 1 presents the first use of a given index, with additional publications that employ the same index are provided as references in the table footnotes.

Key results of studies that have applied diet quality indexes are presented in Supplemental Table 2 and 3. Results were grouped into 2 tables based on whether the dietary index was examined in association with nutritional health, growth, diet quality, and/or socio-demographic factors (Supplemental Table 2) or diet-related diseases or chronic disease risk factors and biomarkers (Supplemental Table 3).

Results and discussion

Overview of findings

Ninety dietary indexes presented in 84 publications have been evaluated in this review (Table 1), because they met the published definition criteria for an index (5, 79, 11, 12). However, several of these indexes are subscales of some questionnaires [e.g. the Child Eating Behavior Questionnaire (96)] or are subindexes of an overall index [e.g. the Electronic Kids Index and its 3 subindexes (106)]. In addition, some are adaptations of indexes first developed for adults (such as the Mediterranean Diet Score by Trichopoulou et al. (107)], with different versions when applied to children (76, 77, 93, 95).

Four of the indexes measure overall dietary variety (59, 60, 61, 89). In this review, all of these have been counted as distinct indexes, because they meet the definition criteria of an index (5, 79, 11, 12) and our inclusion criteria. It should be noted that some publications evaluate more than one dietary index or several papers evaluate the same index; thus, 84 papers in total are presented in Supplemental Tables 1–3.

Seventy-two of the 90 indexes presented in Supplemental Table 1 were developed (or adapted) for use in childhood populations. Thirty-eight of these were used to examine diet-disease associations, mostly on diet and obesity. Only 18 of those were developed with the goal of assessing a specific diet-related disease or condition, as follows: 1) dental caries [one index (103)]; 2) obesity/overweight [16 indexes in total (96, 73, 104, 106)]; and 3) inflammation [one index (105)]. Of note, the 16 indexes that were developed with the aim of examining associations with obesity or overweight were from only 4 studies (96, 73, 104, 106). Specifically, one study used 8 scales of a single questionnaire [the Child Eating Behavior Questionnaire (96)]; 3 were parental feeding questionnaires scores from one study (104); 4 were the 3 subindexes and their overall index from another study (the Electronic Kids Dietary Index and its 3 subindexes) (106); and the last was a revised index [the Revised Overall Diet Quality Index for Children (73)]. The remaining indexes were originally developed to assess diet quality and food behaviors but were later applied to examining diet-disease relationships.

These observations perhaps suggest why some dietary indexes were more effective than others in showing associations of indexes with disease and risk factors. It may be partly due to the fact that most of the dietary indexes were initially constructed to assess overall diet quality and very few were actually constructed to assess the association with a specific diet-related health outcome. When indexes were specifically developed to address a certain health outcome, they were, in general, more likely to be significantly associated with that outcome. However, given that most studies are descriptive, it should be noted that the number of analytical studies (i.e. studies examining an association with an outcome) are limited and more research is needed.

Because the main purpose of dietary indexes is to summarize the overall diet quality or selected aspects of diet quality, we next discuss how effectively dietary indexes in children achieve this goal.

Are dietary indexes in children good indicators of overall dietary quality?

The majority of the 69 publications [46 (71%)] that assessed associations of dietary indexes in children with demographic and nutrient intakes or diet quality (Supplemental Table 1) reported significant associations. However, the majority of studies were cross-sectional. Diet-related characteristics included other than the index used diet quality markers (significant associations in 27 of 69 studies), food behaviors (significant associations in 14 of 69 studies), nutrition knowledge (significant associations in 3 of 69 studies), self-rated health status (1 of 69), and dietician’s rated diet quality (1 of 69). Other studies showed associations with plasma nutrients, mean adequacy ratio scores, and/or dietary intakes of nutrients and food groups. Finally, food beliefs and behaviors examined included neophobia, food variety, empirically derived dietary patterns (using principal components or cluster analyses), other dietary indexes, and meal habits. Correlations ranged mostly from very low (r < 0.10) to modest (r = 0.30–0.50).

A few patterns were observed when reviewing these studies. First, there was no clear tendency of stronger or weaker associations of dietary indexes to a specific diet-related characteristic. Interestingly, indexes derived from parentally administered tools (75, 98) did not show better results compared to indexes that were derived from child-administered tools. Surprisingly, no clear patterns emerged regarding associations across age groups. Dietary reporting errors and the possible effect of body weight on the association of indexes to diet-related characteristics was considered in fewer than 10 studies; 9 studies adjusted for BMI or obesity status (20, 44, 53, 66, 68, 83, 87, 88, 102) and 8 studies adjusted or excluded dietary misreporters (40, 51, 53, 58, 63, 64, 71, 93). However, results from studies that accounted for reporting errors or adjusted for obesity were not different from other studies that did not make those adjustments. Other differences across studies included heterogeneous populations, the number and type of variables used in adjustments, and the type of indexes used, which makes comparison across studies difficult. It is thus evident that more work is needed in this area. For example, using the same index and same adjustment variables in a number of studies that refer to same age groups will shed more light in this area.

Are dietary indexes associated with sociodemographic characteristics?

Sociodemographic factors are important correlates of diet quality in children (108110). It is therefore important for any dietary assessment tool, such as an index, to discriminate diet quality of groups or individuals across these characteristics. In this review, 54% percent of studies (37 of 69) reported significant associations between indexes and various sociodemographic variables, including age, gender, race/ethnicity, income, parental education, socio-economic status, place of living, and marital status of parents. Effect sizes (as determined by correlation coefficients) were mostly fair to low (r < 0.30). The most consistent associations appeared to be with age, family income, and gender. In general, children of younger ages (13 studies) and those coming from families with high incomes (3 studies) had significantly better scores. Also, girls often had higher quality scores compared to boys. Sixteen of 23 studies observed significant associations with diet indexes and gender, but correlations were generally modest (r < 0.30); only 3 of those 16 studies showed that males had better scores, whereas the remaining 13 showed better diet scores for females. There was no other evident pattern between index scores and socio-demographics in any of the age groups. Of note, only 23 of the 69 publications (presented in Supplemental Table 1) adjusted for potential confounders; thus, some findings may be spurious associations that need to be replicated with more rigorous analyses. That said, the direction of the findings were consistent with available evidence, especially income (111113), even though detected relationships were generally modest. In children, literature on the relationship of gender to diet quality are mixed (113117), although many studies usually are based on single food groups or nutrients and therefore comparability with those examining total diet quality is difficult. Besides the above factors, other sociodemographic and family-related factors were examined in a few studies, including ethnicity (5 studies), region of living (5 studies), type of school (2 studies), parental education (2 studies), and household type and parents’ divorce (1 study). Although additional research on the above factors would be helpful, including various other important variables, e.g. culture, occupation, family size, urban/rural environment, geographic characteristics, it would be useful to better understand correlates of high diet quality among children.

Are dietary indexes associated with health and disease outcomes?

Thirty studies (using 40 indexes) reported associations between dietary indexes and diet-related diseases, disease risk factors, and/or biomarkers (Supplemental Table 2). All studies were cross-sectional. Most studies (22 of 30) examined relationships between dietary indexes and anthropometric factors (e.g. BMI, fat percentage, abdominal obesity). In 13 of 22 papers (67%), small but significant inverse associations with BMI or other obesity indexes were shown and the rest did not show any significant association. Almost one-half of the papers (12 of 30) presented associations of dietary indexes with other risk factors or health outcomes, as follows: blood pressure (n = 2; P < 0.05 for both), asthma and related symptoms (n = 5; P < 0.05 for all), blood lipids or inflammation factors (n = 1; P < 0.05), and dental caries (n = 2; P < 0.05 for 1 study). Of note, only 20 of those 30 publications (67%) adjusted for a few potential confounders, but many of these were not mulitivariable adjusted.

It is therefore evident that few studies have examined the associations between diet indexes and health/disease outcomes among children. Although the relationships observed here were weak to modest, the results were consistent with evidence from the available literature in adults on various outcomes (e.g. obesity, asthma, blood lipids, inflammation, insulin resistance, and blood pressure), as comprehensively reviewed by Kant (4).

Dietary indexes used in children: summary of strengths and limitations

Apart form the main observations above regarding associations of dietary indexes to diet-related characteristics, sociodemographic factors, and health outcomes, we noted a number of major strengths and weaknesses of the dietary indexes we reviewed. In our view, the 4 overall strengths of the extant literature include the following: 1) the ability to translate successfully a set of dietary guidelines or evidence into a single, comprehensible assessment tool that reflects adherence to a healthful diet prototype; 2) indexes were associated with a wide variety of dietary characteristics; 3) indexes were associated with important sociodemographic characteristics.; and 4) indexes were associated with some meaningful health outcomes. There are also a number of major weaknesses that we observed. There was a large variety of indexes used in the literature, with relatively few studies within any given age group. The quality of the primary dietary assessment tools used to derive the dietary indexes (e.g. FFQ, diet record, etc.) varied considerably, making comparisons across studies difficult. Most results were from descriptive analyses and most analytic studies showed weak associations, with limited if any adjustment for potential confounders. In addition, only a handful of studies compared different indexes to determine which was better associated with a given outcome; thus, it is unclear which indexes are better than others. Finally, only 2 validation studies were performed. It is to this final critical point, validation, that we now turn.

Are dietary indexes in children valid assessment tools?

Dietary indexes are a relatively new tool in nutritional epidemiology and very few validation studies have been performed in children, with modest results. Many studies (47 of 90 in Table 1 and Supplemental Table 1) simply compared the index with other aspects of diet measured using the same dietary assessment method. For example, 29 studies compared the index with mean adequacy ratio, nutrient intakes, food frequency intakes, or empirically derived dietary patterns. In other studies, indexes were compared with nutrition knowledge (n = 2), self-rated health status (n = 1), dietician’s rating (n = 1), dieting scale or other behavioral nutrition measures (n = 3), or menus developed by experts (n = 1). Four studies compared the index with an independent method. Two studies compared the index score using 2 different self-reported dietary assessment methods. Only 2 validation studies compared the index with nutritional plasma biomarkers (59, 90), considered an objective, gold standard of dietary validation, but correlations were quite weak for most nutrients (r < 0.10).

Across all of these studies, the magnitude of the effect size of the correlations was on average ~0.3–0.5. Some of these indexes have been compared with multiple dietary assessment methods over time. For example, the Dietary Variety Index created by Bordonada et al. (59) was validated using both nutrient biomarkers and reported food intakes. Another example is the Healthy Eating Index-2005 (8083), which has been compared with total energy intake (118) and expert-developed menus (27), although results in the latter study were not reported separately for children. The most extensively studied index is the (original) Healthy Eating Index (n = 15 studies), which has been compared with single nutrient intakes (n = 9), dietary patterns and food practices (n = 3), self-rated status (n = 1), and food frequency intakes (n = 5) in childhood populations from 3 different countries (United States, Greece, and Spain). In all of these reports, correlations were similar and ranged from ~0.10 to 0.70. Most correlations were of medium effect size (r = 0.30–0.50).

In summary, it is difficult to draw conclusions, because the reference methods used in the above studies vary and the number of studies conducted is so few. However, it seems that the magnitude of effect sizes (low- to medium-size correlations) is comparable to other similar studies using single nutrients and foods using FFQ in children (119, 120). Critically important, however, is the need for more validation studies that compare diet quality indexes in children with gold standard methods such as nutritional biomarkers.

Future research directions

Future attempts to develop new indexes should first focus on further improving the development and discriminative power of an index and second on testing and establishing its validity and reliability. Two main factors should be taken into consideration in working toward this direction. First, the application of more robust methods to create indexes and more rigorous methods to measure discriminative power may increase the content validity of those tools. Those methods may include: 1) applying specific weights to each component scale of the index on the basis of relative importance (24, 25, 27); 2) grouping index components according to the domain of the dietary behavior they assess (e.g. diet composition, beliefs, behaviors) (106); 3) conducting proper statistical tests to evaluate the discriminating characteristics and abilities of the index, such as sensitivity, specificity, c-statistic values, positive/negative predictive values, and others; and 4) performing additional tests beyond multivariate regression analyses to reveal any potential heterogeneity in index performance (106), including latent class analysis (106, 121), structural equation modeling (122), and data mining (123).

Second, future validation studies must use a superior method for comparison rather than relying upon internal, relative validity using the same dietary assessment method, as has been done in the vast majority of the studies reviewed here. Optimally, validation of dietary assessment tools should be evaluated with methods having uncorrelated errors, ideally nutritional biomarkers and/or a more accurate, gold standard method of self-report dietary assessment such as dietary records (124, 125). The collection of biomarkers is costly and may be prohibitive, and only 2 studies in this review used this method (59, 90). Comparison with a superior dietary assessment method, such as multiple days of diet records, may be more feasible and would also provide greater confidence in the soundness of these indexes. Also, additional studies are needed to establish the reliability (or reproducibility) of indexes over time to better understand how diet quality changes as children age and how to use diet indexes in longitudinal studies; only 3 studies (15, 35, 96) we reviewed examined the reproducibility of diet quality in children. Importantly, validity and reproducibility of any index should be tested in various racial/ethnic populations to have a clearer understanding of its utility alongside its discriminatory and predictive ability in diverse groups of children.

Applications in public health settings

Dietary indexes have a number of potential uses and purposes in public health. For example, scores could be used to rank and/or compare within and between individuals or groups of individuals (such as school populations or certain subgroups within a population), with the goal of identifying individuals who have poorer dietary quality and hence need dietary counseling. They also may be helpful as monitoring tools to evaluate how well children comply with dietary recommendations, monitor changes in dietary patterns over time, or evaluate the effectiveness of public health nutrition programs. Moreover, individual components of an index can be used to determine specific areas and goals for improvement. As a result, dietary counseling and interventions may be better tailored to meet specific individual needs.

Additionally, some diet quality indexes that are formatted as questionnaires (17, 31, 106) can easily be used by nondietetic health professionals such as pediatricians, general practitioners, and nurses in their everyday clinical practice to guide nutrition counseling. Also, simple, user-friendly indexes, such as those based on key food intakes and/or dietary behaviors, may, e.g., potentially be used as educational and self-monitoring tools by parents or teachers. Several indexes reviewed here are readily available in electronic format, thus further easing their use. A good example of such an index is the KIDMED (Mediterranean Diet Quality Index in Children and Adolescents Index). KIDMED is an electronic diet quality index based on 16 yes/no questions aimed to evaluate adherence to the Mediterranean diet; the tool can be easily completed by children as young as 6 y old (17, 126). After completing the index, a child receives a score alongside personalized feedback and guidance designed to help the child improve his/her score, as needed; information is also provided to parents. Electronic indexes can be made even more attractive to children if they are based on interactive games, such as “Blast off” used in the USDA’s MyPyramid (127) and “Feed the Monster” used by the US Dairy Council (128). Finally, because most index scores are easily interpretable, they may be better poised than findings from single nutrient and food studies to inform policy makers and key stakeholders about overall diet quality of population subgroups.


In summary, the majority of studies reviewed had notable methodological weaknesses, but in general higher diet quality scores were associated with more favorable nutrient and food intakes, more healthful dietary behaviors, lower chronic disease risk factors, more favorable body weight, less obesity, and fewer asthma-related conditions among children in the developed world. In conclusion, dietary indexes are useful, practical tools for dietary assessment, because they offer valuable information on overall dietary patterns in children that are simple and comprehensible. However, certain measures and techniques may improve the diagnostic accuracy of these indexes and increase the robustness of results. Prospective and intervention research in diverse populations and additional validation studies are needed to strengthen the utility of these tools in understanding dietary quality in children and showing associations with meaningful health outcomes.


We thank Associate Professors Demosthenes Panagiotakos and Antonia-Leda Matalas of Harokopio University of Athens, Greece, who provided useful suggestions for the preparation of this manuscript. C.L. developed the concept for the article, conducted the literature search, and wrote the paper; P.K.N. supervised the work, helped draft the manuscript; and critically reviewed the paper. Both authors read and approved the final manuscript.


1 Author disclosures: C. Lazarou and P. K. Newby, no conflicts of interest.

Supplemental Tables 1–3 are available from the “Online Supporting Material” link in the online posting of the article and from the same link in the online table of contents at advances.nutrition.org.

Literature Cited

1. Newby PK, Tucker KL. Empirically derived eating patterns using factor or cluster analysis: a review. Nutr Rev. 2004;62:177–203 [PubMed]
2. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13:3–9 [PubMed]
3. Kant AK. Indexes of overall diet quality: a review. J Am Diet Assoc. 1996;96:785–91 [PubMed]
4. Kant AK. Dietary patterns and health outcomes. J Am Diet Assoc. 2004;104:615–35 [PubMed]
5. Michels KB, Schulze MB. Can dietary patterns help us detect diet-disease associations? Nutr Res Rev. 2005;18:241–8 [PubMed]
6. Bach A, Serra-Majem L, Carrasco JL, Roman B, Ngo J, Bertomeu I, Obrador B. The use of indexes evaluating the adherence to the Mediterranean diet in epidemiological studies: a review. Public Health Nutr. 2006;9:132–46 [PubMed]
7. Fransen HP, Ocké MC. Indices of diet quality. Curr Opin Clin Nutr Metab Care. 2008;11:559–65 [PubMed]
8. Waijers PM, Feskens EJ, Ocké MC. A critical review of predefined diet quality scores. Br J Nutr. 2007;97:219–31 [PubMed]
9. Wirt A, Collins CE. Diet quality: what is it and does it matter? Public Health Nutr. 2009;12:2473–92 [PubMed]
10. Serra-Majem L, Bes-Rastrollo M, Román-Viñas B, Pfrimer K, Sánchez-Villegas A, Martínez-González MA. Dietary patterns and nutritional adequacy in a Mediterranean country. Br J Nutr. 2009;101 Suppl 2:S21–8 [PubMed]
11. Arvaniti F, Panagiotakos DB. Healthy indexes in public health practice and research: a review. Crit Rev Food Sci Nutr. 2008;48:317–27 [PubMed]
12. Kourlaba G, Panagiotakos DB. Dietary quality indices and human health: a review. Maturitas. 2009;62:1–8 [PubMed]
13. Krebs-Smith SM, Clark LD. Validation of a nutrient adequacy score for use with women and children. J Am Diet Assoc. 1989;89:775–83 [PubMed]
14. Cox DR, Skinner JD, Carruth BR, Moran J III, Houck KS. A Food Variety Index for Toddlers (VIT): development and application. J Am Diet Assoc. 1997;97:1382–6 [PubMed]
15. Cusatis DC, Chinchilli VM, Johnson-Rollings N, Kieselhorst K, Stallings VA, Lloyd T. Longitudinal nutrient intake patterns of US adolescent women: the Penn State Young Women's Health Study. J Adolesc Health. 2000;26:194–204 [PubMed]
16. Falciglia GA, Couch SC, Gribble LS, Pabst SM, Frank R. Food neophobia in childhood affects dietary variety. J Am Diet Assoc. 2000;100:1474–81 [PubMed]
17. Serra-Majem L, Ribas L, Ngo J, Ortega RM, Garcia A, Perez-Rodrigo C, Aranceta J. Food, youth and the Mediterranean diet in Spain. Development of KIDMED, Mediterranean Diet Quality Index in children and adolescents. Public Health Nutr. 2004;7:931–5 [PubMed]
18. Feskanich D, Rockett HR, Colditz GA. Modifying the Healthy Eating Index to assess diet quality in children and adolescents. Am Diet Assoc. 2004;104:1375–83 [PubMed]
19. Kranz S, Findeis JL, Shrestha SS. Use of the Revised Children's Diet Quality Index to assess preschooler's diet quality, its sociodemographic predictors, and its association with body weight status. J Pediatr (Rio J). 2008;84:26–34 [PubMed]
20. Jacka FN, Kremer PJ, Leslie ER, Berk M, Patton GC, Toumbourou JW, Williams JW. Associations between diet quality and depressed mood in adolescents: results from the Australian Healthy Neighbourhoods Study. Aust N Z J Psychiatry. 2010;44:435–42 [PubMed]
21. Lazarou C, Panagiotakos DB, Matalas AL. Foods E-KINDEX: a dietary index associated with reduced blood pressure levels among young children: the CYKIDS study. J Am Diet Assoc. 2009;109:1070–5 [PubMed]
22. Ruel MT. Operationalizing dietary diversity: a review of measurement issues and research priorities. J Nutr. 2003;133:S3911–26 [PubMed]
23. Ruel MT. Is dietary diversity an indicator of food security or dietary quality? A review of measurement issues and research needs. Food Nutr Bull. 2003;24:231–2 [PubMed]
24. Jones JM. Development of a nutritional screening or assessment tool using a multivariate technique. Nutrition. 2004;20:298–306 [PubMed]
25. Kourlaba G, Panagiotakos D. The number of index components affects the diagnostic accuracy of a diet quality index: the role of intracorrelation and intercorrelation structure of the components. Ann Epidemiol. 2009;19:692–700 [PubMed]
26. Kourlaba G, Panagiotakos D. The diagnostic accuracy of a composite index increases as the number of partitions of the components increases and when specific weights are assigned to each component. J Appl Stat. 2010;37:537–54
27. Guenther PM, Reedy J, Krebs-Smith SM, Reeve BB, Basiotis PP. Development and EVALU ation of the Healthy Eating Index-2005: Technical Report. Center for Nutrition Policy and Promotion, USDA; 2007. [cited 2007 Sep 2010]. Available from: http://www.cnpp.usda.gov/HealthyEatingIndex.htm
28. WHO. Addressing the socioeconomic determinants of healthy eating habits and physical activity levels among adolescents. [cited 2006 Sep 2010]. Available from: http://www.euro.who.int/__data/assets/pdf_file/0005/98231/e89375.pdf.
29. Drewnowski A. Concept of a nutritious food: toward a nutrient density score. Am J Clin Nutr. 2005;82:721–32 [PubMed]
30. Carnell S, Wardle J. Measuring behavioural susceptibility to obesity: validation of the child eating behaviour questionnaire. Appetite. 2007;48:104–13 [PubMed]
31. Johnson F, Wardle J, Griffith J. The Adolescent Food Habits Checklist: reliability and validity of a measure of healthy eating behaviour in adolescents. Eur J Clin Nutr. 2002;56:644–9 [PubMed]
32. Vissers D, Devoogdt N, Gebruers N, Mertens I, Truijen S, Van Gaal L. Overweight in adolescents: differences per type of education. Does one size fit all? J Nutr Educ Behav. 2008;40:65–71 [PubMed]
33. Stunkard AJ, Messick S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J Psychosom Res. 1985;29:71–83 [PubMed]
34. Knol LL, Haughton B, Fitzhugh EC. Food group adherence scores assess food patterns compared to US Department of Agriculture Food Guide. J Am Diet Assoc. 2006;106:1201–8 [PubMed]
35. Huybrechts I, Vereecken C, De Bacquer D, Vandevijvere S, Van Oyen H, Maes L, Vanhauwaert E, Temme L, De Backer G, et al. Reproducibility and validity of a diet quality index for children assessed using a FFQ. Br J Nutr. 2010;104:135–44 [PubMed]
36. Mills BC, Rahardjo VC. Food behaviors of young children: cognitive and attitudinal factors. Early Child Dev Care. 1987;29:321–30
37. Nader PR, Sallis JF, Patterson TL, Abramson IS, Rupp JW, Senn KL, Atkins CJ, Roppe BE, Morris JA, et al. A family approach to cardiovascular risk reduction: results from the San Diego Family Health Project. Health Educ Q. 1989;16:229–44 [PubMed]
38. Murphy SP, Castillo RO, Martorell R, Mendoza F. An evaluation of food group intakes by Mexican-American children. J Am Diet Assoc. 1990;90:388–93 [PubMed]
39. Vartiainen E, Tossavainen K, Viri L, Niskanen E, Puska P. The North Karelia Youth Programs. Ann N Y Acad Sci. 1991;623:332–49 [PubMed]
40. Alexy U, Sichert-Hellert W, Kersting M, Lausen B, Schöch G. Development of scores to measure the effects of nutrition counselling on the overall diet: a pilot study in children and adolescents. Eur J Nutr. 1999;38:196–200 [PubMed]
41. Lee Y, Mitchell DC, Smiciklas-Wright H, Birch LL. Diet quality, nutrient intake, weight status, and feeding environments of girls meeting or exceeding recommendations for total dietary fat of the American Academy of Pediatrics. Pediatrics. 2001;107:E95. [PMC free article] [PubMed]
42. Dwyer J, Cosentino C, Li D, Feldman H, Garceau A, Stevens M, Perry C, Hoelscher D, Webber LS, et al. Evaluating school-based interventions using the Healthy Eating Index. J Am Diet Assoc. 2002;102:257–9 [PubMed]
43. Lino M, Basiotis PP, Gerrior SA, Carlson A. The quality of young children’s diets. Fam Econ Nutr Rev. 2002;14:52–60
44. Rodríguez-Artalejo F, García EL, Gorgojo L, Garcés C, Royo MA, Martín Moreno JM, Benavente M, Macías A, De Oya M, et al. Consumption of bakery products, sweetened soft drinks and yogurt among children aged 6–7 years: association with nutrient intake and overall diet quality. Br J Nutr. 2003;89:419–29 [PubMed]
45. Royo-Bordonada MA, Gorgojo L, Martín-Moreno JM, Garcés C, Rodríguez-Artalejo F, Benavente M, Mangas A, de Oya M. Investigators of the Four Provinces Study Spanish children's diet: compliance with nutrient and food intake guidelines. Eur J Clin Nutr. 2003;57:930–9 [PubMed]
46. Royo-Bordonada MA, Garcés C, Gorgojo L, Martín-Moreno JM, Lasunción MA, Rodríguez-Artalejo F, Fernández O, de Oya M. Four Provinces Study Saturated fat in the diet of Spanish children: relationship with anthropometric, alimentary, nutritional and lipid profiles. Public Health Nutr. 2006;9:429–35 [PubMed]
47. Griel AE, Eissenstat B, Juturu V, Hsieh G, Kris-Etherton PM. Improved diet quality with peanut consumption. J Am Coll Nutr. 2004;23:660–8 [PubMed]
48. Powers SW, Patton SR, Rajan S. A comparison of food group variety between toddlers with and without cystic fibrosis. J Hum Nutr Diet. 2004;17:523–7 [PubMed]
49. Knol LL, Haughton B, Fitzhugh EC. Dietary patterns of young, low-income US children. J Am Diet Assoc. 2005;105:1765–73 [PubMed]
50. Goodwin DK, Knol LL, Eddy JM, Fitzhugh EC, Kendrick OW, Donahue RE. The relationship between self-rated health status and the overall quality of dietary intake of US adolescents. J Am Diet Assoc. 2006;106:1450–3 [PubMed]
51. LaRowe TL, Moeller SM, Adams AK. Beverage patterns, diet quality, and body mass index of US preschool and school-aged children. J Am Diet Assoc. 2007;107:1124–33 [PubMed]
52. Hurley KM, Oberlander SE, Merry BC, Wrobleski MM, Klassen AC, Black MM. The healthy eating index and youth healthy eating index are unique, nonredundant measures of diet quality among low-income, African American adolescents. J Nutr. 2009;139:359–64 [PMC free article] [PubMed]
53. Manios Y, Kourlaba G, Kondaki K, Grammatikaki E, Birbilis M, Oikonomou E, Roma-Giannikou E. Diet quality of preschoolers in Greece based on the Healthy Eating Index: the GENESIS study. J Am Diet Assoc. 2009;109:616–23 [PubMed]
54. Angelopoulos P, Kourlaba G, Kondaki K, Fragiadakis GA, Manios Y. Assessing children's diet quality in Crete based on Healthy Eating Index: The Children Study. Eur J Clin Nutr. 2009;63:964–9 [PubMed]
55. Nunn ME, Braunstein NS, Krall Kaye EA, Dietrich T, Garcia RI, Henshaw MM. Healthy eating index is a predictor of early childhood caries. J Dent Res. 2009;88:361–6 [PMC free article] [PubMed]
56. Gittelsohn J, Vijayadeva V, Davison N, Ramirez V, Cheung LW, Murphy S, Novotny R. A food store intervention trial improves caregiver psychosocial factors and children's dietary intake in Hawaii. Obesity (Silver Spring). 2010;18 Suppl 1:S84–90 [PubMed]
57. Kourlaba G, Kondaki K, Grammatikaki E, Roma-Giannikou E, Manios Y. Diet quality of preschool children and maternal perceptions/misperceptions: the GENESIS study. Public Health. 2009;123:738–42 [PubMed]
58. Wang Y, Jahns L, Tussing-Humphreys L, Xie B, Rockett H, Liang H, Johnson L. Dietary intake patterns of low-income urban African-American adolescents. J Am Diet Assoc. 2010;110:1340–5 [PMC free article] [PubMed]
59. Royo-Bordonada MA, Gorgojo L, Ortega H, Martín-Moreno JM, Lasunción MA, Garcés C, Gil A, Rodríguez-Artalejo F, de Oya M. Investigators of the Four Provinces StudyGreater dietary variety is associated with better biochemical nutritional status in Spanish children: the Four Provinces Study. Nutr Metab Cardiovasc Dis. 2003;13:357–64 [PubMed]
60. Falciglia GA, Troyer AG, Couch SC. Dietary variety increases as a function of time and influences diet quality in children. J Nutr Educ Behav. 2004;36:77–83 [PubMed]
61. Knol LL, Haughton B, Fitzhugh EC. Food insufficiency is not related to the overall variety of foods consumed by young children in low-income families. J Am Diet Assoc. 2004;104:640–4 [PubMed]
62. Kranz S, Siega-Riz AM, Herring AH. Changes in diet quality of American preschoolers between 1977 and 1998. Am J Public Health. 2004;94:1525–30 [PMC free article] [PubMed]
63. Serra-Majem L, Ribas L, García A, Pérez-Rodrigo C, Aranceta J. Nutrient adequacy and Mediterranean Diet in Spanish school children and adolescents. Eur J Clin Nutr. 2003;57 Suppl 1:S35–9 [PubMed]
64. Kontogianni MD, Vidra N, Farmaki AE, Koinaki S, Belogianni K, Sofrona S, Magkanari F, Yannakoulia M. Adherence rates to the mediterranean diet are low in a representative sample of Greek children and adolescents. J Nutr. 2008;138:1951–6 [PubMed]
65. Mariscal-Arcas M, Rivas A, Velasco J, Ortega M, Caballero AM, Olea-Serrano F. Evaluation of the Mediterranean Diet Quality Index (KIDMED) in children and adolescents in Southern Spain. Public Health Nutr. 2009;12:1408–12 [PubMed]
66. Lazarou C, Kalavana T. Urbanization influences dietary habits of Cypriot children: the CYKIDS study. Int J Public Health. 2009;54:69–77 [PubMed]
67. Lazarou C, Panagiotakos DB, Matalas AL. Lifestyle factors are determinants of children's blood pressure levels: the CYKIDS study. J Hum Hypertens. 2009;23:456–63 [PubMed]
68. Lazarou C, Panagiotakos DB, Matalas AL. Level of adherence to the Mediterranean diet among children from Cyprus: the CYKIDS study. Public Health Nutr. 2009;12:991–1000 [PubMed]
69. Lazarou C, Panagiotakos DB, Matalas AL. Physical activity mediates the protective effect of the Mediterranean diet on children's obesity status: the CYKIDS study. Nutrition. 2010;26:61–7 [PubMed]
70. Kontogianni MD, Farmaki AE, Vidra N, Sofrona S, Magkanari F, Yannakoulia M. Associations between lifestyle patterns and body mass index in a sample of Greek children and adolescents. J Am Diet Assoc. 2010;110:215–21 [PubMed]
71. Veugelers PJ, Fitzgerald AL, Johnston E. Dietary intake and risk factors for poor diet quality among children in Nova Scotia. Can J Public Health. 2005;96:212–6 [PubMed]
72. Mariscal-Arcas M, Romaguera D, Rivas A, Feriche B, Pons A, Tur JA, Olea-Serrano F. Diet quality of young people in southern Spain evaluated by a Mediterranean adaptation of the Diet Quality Index-International (DQI-I). Br J Nutr. 2007;98:1267–73 [PubMed]
73. Kranz S, Hartman T, Siega-Riz AM, Herring AH. A diet quality index for American preschoolers based on current dietary intake recommendations and an indicator of energy balance. J Am Diet Assoc. 2006;106:1594–604 [PubMed]
74. Cheng G, Gerlach S, Libuda L, Kranz S, Günther AL, Karaolis-Danckert N, Kroke A, Buyken AE. Diet quality in childhood is prospectively associated with the timing of puberty but not with body composition at puberty onset. J Nutr. 2010;140:95–102 [PubMed]
75. Randall Simpson JA, Keller HH, Rysdale LA, Beyers JE. Nutrition Screening Tool for Every Preschooler (NutriSTEP): validation and test-retest reliability of a parent-administered questionnaire assessing nutrition risk of preschoolers. Eur J Clin Nutr. 2008;62:770–80 [PubMed]
76. Chatzi L, Apostolaki G, Bibakis I, Skypala I, Bibaki-Liakou V, Tzanakis N, Kogevinas M, Cullinan P. Protective effect of fruits, vegetables and the Mediterranean diet on asthma and allergies among children in Crete. Thorax. 2007;62:677–83 [PMC free article] [PubMed]
77. Garcia-Marcos L, Canflanca IM, Garrido JB, Varela AL, Garcia-Hernandez G, Grima FG, Gonzalez-Diaz C, Carvajal-Uruena I, Arnedo-Pena A, et al. Relationship of asthma and rhinoconjunctivitis with obesity, exercise and Mediterranean diet in Spanish schoolchildren. Thorax. 2007;62:503–8 [PMC free article] [PubMed]
78. Castro-Rodriguez JA, Garcia-Marcos L, Alfonseda Rojas JD, Valverde-Molina J, Sanchez-Solis M. Mediterranean diet as a protective factor for wheezing in preschool children. J Pediatr. 2008;152:823–8, 828.e1–2 [PubMed]
79. Gonzalez Barcala FJ, Pertega S, Bamonde L, Garnelo L, Perez Castro T, Sampedro M, Sanchez Lastres J, San Jose Gonzalez MA, Lopez Silvarrey A. Mediterranean diet and asthma in Spanish schoolchildren. Pediatr Allergy Immunol. 2010;21:1021–7 [PubMed]
80. Freedman LS, Guenther PM, Krebs-Smith SM, Dodd KW, Midthune D. A population's distribution of Healthy Eating Index-2005 component scores can be estimated when more than one 24-hour recall is available. J Nutr. 2010;140:1529–34 [PMC free article] [PubMed]
81. Fungwe T, Guenther PM, Juan WJ, Hiza H, Lino M.The quality of children’s diets in 2003–04 as measured by the Healthy Eating Index-2005 Nutrition Insight 43. [cited 2009 Jul 2010]. Available from: http://www.cnpp.usda.gov/Publications/NutritionInsights/Insight43.pdf.
82. Guenther PM, Juan WJ, Lino M, Hiza H, Fungwe T, Lucas R. Diet quality of low-income and higher income Americans in 2003–04 as measured by the Healthy Eating Index-2005. Nutrition Insight 42 [cited 2008 Aug 2010]. Available from: http://www.cnpp.usda.gov/Publications/NutritionInsights/Insight42.pdf
83. Beydoun MA, Wang Y. Parent-child dietary intake resemblance in the United States: evidence from a large representative survey. Soc Sci Med. 2009;68:2137–44 [PMC free article] [PubMed]
84. Lee MS, Lai CJ, Yang FY, Su HH, Yu HL, Wahlqvist ML. A global overall dietary index: ODI-R revised to emphasize quality over quantity. Asia Pac J Clin Nutr. 2008;17 Suppl 1:82–6 [PubMed]
85. Vereecken CA, Rossi S, Giacchi MV, Maes L. Comparison of a short food-frequency questionnaire and derived indices with a seven-day diet record in Belgian and Italian children. Int J Public Health. 2008;53:297–305 [PubMed]
86. Sabbe D, De Bourdeaudhuij I, Legiest E, Maes L. A cluster-analytical approach towards physical activity and eating habits among 10-year-old children. Health Educ Res. 2008;23:753–62 [PubMed]
87. Woodruff SJ, Hanning RM, Lambraki I, Storey KE, McCargar L. Healthy Eating Index-C is compromised among adolescents with body weight concerns, weight loss dieting, and meal skipping. Body Image. 2008;5:404–8 [PubMed]
88. Woodruff SJ, Hanning RM, McGoldrick K, Brown KS. Healthy eating index-C is positively associated with family dinner frequency among students in grades 6–8 from Southern Ontario, Canada. Eur J Clin Nutr. 2010;64:454–60 [PubMed]
89. Falciglia GA, Horner SL, Liang J, Couch SC, Levin LS. Assessing dietary variety in children: development and validation of a predictive equation. J Am Diet Assoc. 2009;109:641–7 [PubMed]
90. Kleiser C, Mensink GB, Scheidt-Nave C, Kurth BM. HuSKY: a healthy nutrition score based on food intake of children and adolescents in Germany. Br J Nutr. 2009;102:610–8 [PubMed]
91. Kleiser C, Mensink GB, Neuhauser H, Schenk L, Kurth BM. Food intake of young people with a migration background living in Germany. Public Health Nutr. 2010;13:324–30 [PubMed]
92. Magarey A, Golley R, Spurrier N, Goodwin E, Ong F. Reliability and validity of the Children's Dietary Questionnaire: a new tool to measure children's dietary patterns. Int J Pediatr Obes. 2009;4:257–65 [PubMed]
93. Martínez E, Llull R, Del Mar Bibiloni M, Pons A, Tur JA. Adherence to the Mediterranean dietary pattern among Balearic Islands adolescents. Br J Nutr. 2010;103:1657–64 [PubMed]
94. Nagel G, Weinmayr G, Kleiner A, Garcia-Marcos L, Strachan DP. ISAAC Phase Two Study Group Effect of diet on asthma and allergic sensitisation in the International Study on Allergies and Asthma in Childhood (ISAAC) Phase Two. Thorax. 2010;65:516–22 [PubMed]
95. Tarabusi V, Cavazza C, Pasqui F, Gambineri A, Pasquali R. Quality of diet, screened by the Mediterranean diet quality index and the evaluation of the content of advanced glycation end products, in a population of high school students from Emilia Romagna. Mediterr J Nutr Metab. 2010;3:153–7
96. Wardle J, Guthrie CA, Sanderson S, Rapoport L. Development of the Children's Eating Behaviour Questionnaire. J Child Psychol Psychiatry. 2001;42:963–70 [PubMed]
97. Wardle J, Carnell S, Haworth CM, Farooqi IS, O'Rahilly S, Plomin R. Obesity-associated genetic variation in FTO is associated with diminished satiety. J Clin Endocrinol Metab. 2008;93:3640–3 [PubMed]
98. Sleddens EF, Kremers SP, Thijs C. The Children's Eating Behaviour Questionnaire: factorial validity and association with Body Mass Index in Dutch children aged 6–7. Int J Behav Nutr Phys Act. 2008;20;5:49. [PMC free article] [PubMed]
99. Viana V, Sinde S, Saxton JC. Children's Eating Behaviour Questionnaire: associations with BMI in Portuguese children. Br J Nutr. 2008;100:445–50 [PubMed]
100. Ayala GX, Baquero B, Arredondo EM, Campbell N, Larios S, Elder JP. Association between family variables and Mexican American children's dietary behaviors. J Nutr Educ Behav. 2007;39:62–9 [PubMed]
101. Turconi G, Guarcello M, Maccarini L, Cignoli F, Setti S, Bazzano R, Roggi C. Eating habits and behaviors, physical activity, nutritional and food safety knowledge and beliefs in an adolescent Italian population. J Am Coll Nutr. 2008;27:31–43 [PubMed]
102. Yannakoulia M, Papanikolaou K, Hatzopoulou I, Efstathiou E, Papoutsakis C, Dedoussis GV. Association between family divorce and children's BMI and meal patterns: the GENDAI Study. Obesity (Silver Spring). 2008;16:1382–7 [PubMed]
103. Kristoffersson K, Axelsson P, Birkhed D, Bratthall D. Caries prevalence, salivary Streptococcus mutans and dietary scores in 13-year-old Swedish schoolchildren. Community Dent Oral Epidemiol. 1986;14:202–5 [PubMed]
104. Carnell S, Wardle J. Associations between multiple measures of parental feeding and children's adiposity in United Kingdom preschoolers. Obesity (Silver Spring). 2007;15:137–44 [PubMed]
105. Lazarou C, Panagiotakos DB, Chrysohoou C, Andronikou C, Matalas AL. C-Reactive protein levels are associated with adiposity and a high inflammatory foods index in mountainous Cypriot children. Clin Nutr. 2010;29:779–83 [PubMed]
106. Lazarou C, Panagiotakos DB, Spanoudis G, Matalas AL. E-KINDEX, a dietary screening tool to assess children’s obesogenic dietary habits. J Am Coll Nutr (in press) [PubMed]
107. Trichopoulou A, Costacou T, Bamia C, Trichopoulos D. Adherence to a Mediterranean diet and survival in a Greek population. N Engl J Med. 2003;348:2599–608 [PubMed]
108. Crawford PB, Obarzanek E, Schreiber GM, Barrier P, Goldman S, Frederick MM, Sabry ZI. The effect of race, household income, and parental education on nutrient intakes of 9- and 10-year-old girls. Ann Epidemiol. 1995;5:360–8 [PubMed]
109. Patrick H, Nicklas TA. A review of family and social determinants of children's eating patterns and diet quality. J Am Coll Nutr. 2005;24:83–92 [PubMed]
110. Neumark-Sztainer D, Hannan PJ, Story M, Croll J, Perry C. Family meal patterns: associations with sociodemographic characteristics and improved dietary intake among adolescents. J Am Diet Assoc. 2003;103:317–22 [PubMed]
111. Ambrosini GL, Oddy WH, Robinson M, O'Sullivan TA, Hands BP, de Klerk NH, Silburn SR, Zubrick SR, Kendall GE, et al. Adolescent dietary patterns are associated with lifestyle and family psycho-social factors. Public Health Nutr. 2009;12:1807–15 [PubMed]
112. Riediger ND, Shooshtari S, Moghadasian MH. The influence of sociodemographic factors on patterns of fruit and vegetable consumption in Canadian adolescents. J Am Diet Assoc. 2007;107:1511–8 [PubMed]
113. WHO. Addressing the socioeconomic determinants of healthy eating habits and physical activity levels among adolescents. [cited 2006 Jul 2010.] Available from: http://www.euro.who.int/__data/assets/pdf_file/0005/98231/e89375.pdf.
114. Ambrosini GL, de Klerk NH, O'Sullivan TA, Beilin LJ, Oddy WH. The reliabilityof a food frequency questionnaire for use among adolescents. Eur J Clin Nutr. 2009;63:1251–9 [PubMed]
115. Klein EG, Lytle LA, Chen V. Social ecological predictors of the transition to overweight in youth: results from the Teens Eating for Energy and Nutrition at Schools (TEENS) study. J Am Diet Assoc. 2008;108:1163–9 [PMC free article] [PubMed]
116. Lazarou C, Panagiotakos DB, Kouta C, Matalas AL. Dietary and other lifestyle characteristics of Cypriot school children: results from the nationwide CYKIDS study. BMC Public Health. 2009;9:147. [PMC free article] [PubMed]
117. de Gouw L, Klepp KI, Vignerová J, Lien N, Steenhuis IH, Wind M. Associations between diet and (in)activity behaviours with overweight and obesity among 10–18-year-old Czech Republic adolescents. Public Health Nutr. 2010;13:1701–7 [PubMed]
118. Guenther PM, Reedy J, Krebs-Smith SM, Reeve BB. Evaluation of the Healthy Eating Index-2005. J Am Diet Assoc. 2008;108:1854–64 [PubMed]
119. McPherson RS, Hoelscher DM, Alexander M, Scanlon KS, Serdula MK. Dietary assessment methods among school-aged children: validity and reliability. Prev Med. 2000;31:S11–33
120. Cullen KW, Watson K, Zakeri I. Relative reliability and validity of the Block Kids Questionnaire among youth aged 10 to 17 years. J Am Diet Assoc. 2008;108:862–6 [PubMed]
121. Patterson BH, Dayton CM, Graubard BI. Latent class analysis of complex sample survey data: application to dietary data. J Am Stat Assoc. 2002;97:721–41
122. MacIntyre UE, Venter CS, Vorster HH, Steyn HS. A combination of statistical methods for the analysis of the relative validation data of the quantitative food frequency questionnaire used in the THUSA study. Transition, Health and Urbanisation in South Africa. Public Health Nutr. 2001;4:45–51 [PubMed]
123. Hearty AP, Gibney MJ. Analysis of meal patterns with the use of supervised data mining techniques–artificial neural networks and decision trees. Am J Clin Nutr. 2008;88:1632–42 [PubMed]
124. Cade J, Thompson R, Burley V, Warm D. Development, validation and utilization of food-frequency questionnaires: a review. Public Health Nutr. 2002;5:567–87 [PubMed]
125. Willett WC. Nutritional epidemiology. 2nd ed New York: Oxford University Press; 1998
126. Mediterranean Diet Quality Index in children and adolescents. [cited 2002 Apr 2009]. Available from: http://fdmed.org/04_alimentaciones.asp?idioma=eng.
127. My Pyramid Blast-off. [cited 2009 Aug 2010]. Available from: http://www.fns.usda.gov/tn/Resources/game/BlastOff_Game.html.
128. Explore the world of nutrition with nutrition explorations: activities, Monster nutrition. [cited 2002 Aug 2010]. Available from: http://www.nutritionexplorations.org/kids/activities/monster2.asp.

Articles from Advances in Nutrition are provided here courtesy of American Society for Nutrition
PubReader format: click here to try


Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...


Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...