1. Introduction

Evidence suggests that adolescent eating habits are associated with diet-related chronic diseases in adulthood (Craigie et al., 2011; Cruz et al., 2018; Fardet & Boirie, 2014; Mikkilä et al., 2005; Movassagh et al., 2017); thus adolescents’ diet assessment is a matter of public health interest. However, challenges remain in identifying the best dietary assessment tool for this age group (Ochola & Masibo, 2014; Sherwood, 2009). Dietary intake is difficult to measure because any diet assessment method is subject to error (Shim et al., 2014). Various methods have been developed, ranging from relatively simple diet assessments, such as 24-hour recalls, to more complex approaches, measuring biomarkers of dietary intake in diverse biological samples (Shim et al., 2014).

Food Frequency Questionnaires (FFQ) have been widely used in several age groups, including adolescents, due to their advantages over other dietary measurement methods. In general, the FFQ assesses usual dietary intakes, it is cost-effective and time-saving, practical and economical for the collecting of dietary data, and is a modest burden on both interviewers and participants when compared to diet records (Shim et al., 2014).

When designing an FFQ for use among adolescents, there are several specific aspects of their cognitive development that should be considered, in addition to the recommended methodological aspects (Tabacchi et al., 2014). Several literature has shown that a shorter recall period, particularly when asking participants about their routines or certain frequent events, is preferable over a longer one (Althubaiti, 2016). Adolescents especially, have problems remembering their diet over a long period of time. Some reasons for this include unstructured eating patterns, snacking, meal skipping, and eating more frequent meals outside their homes (Livingstone et al., 2004; Truthmann et al., 2011). In addition, dietary assessment methods based on recall have a memory bias which increase as a function of time, because up to 30% memory on food consumption may be lost from previous day (Tabacchi et al., 2014). Previous studies report that foods consumed on a regular basis and main courses are more accurately reported than foods that are less often eaten (Speck et al., 2001; S. S. Wong et al., 2008). A systematic review of child/adolescent FFQ studies performed in several countries showed that the highest average variability correlation was obtained when the questionnaire measured a shorter time span (i.e. the previous week (Kolodziejczyk et al., 2012). On the other hand, it has been suggested that the recall period for previous months or years is strongly related to reporting current dietary intake (Cade et al., 2004).

Estimating the amount of food consumed is a complex cognitive task. It requires one to think abstractly about food while viewing generic food models of different volumes and dimensions or other tools, such as photographs of food. As a result, it is highly likely that the estimation of portion size is compatible with the perceptual and conceptual capacities of adolescents who have reached the stage of abstract reasoning, typically around 10 – 11 years of age (Dumontheil, 2014; Livingstone et al., 2004).

In spite of the potential pitfalls mentioned, different studies developed worldwide confirm that FFQ is a robust instrument for ranking adolescents according to energy and nutrient intake levels, regardless of the lack of demonstrated validity (Tabacchi et al., 2016). Most FFQs have been designed and widely used on adolescents in developed countries, but very little in Latin American countries (Brazil, Peru y Mexico) (Denova-Gutiérrez et al., 2016; Rodriguez et al., 2017; S. M. A. Matos, 2012). Recently, a semiquantitative FFQ was developed to use in a South American Youth/Child Cardiovascular and Environmental (SAYCARE) observational, multicenter, feasibility study, to assess dietary intake among children and adolescents aged 3 to 17 years from seven cities (Buenos Aires, Lima, Medellin, Montevideo, Santiago, Sao Paulo, and Teresina) in six South American countries (Saravia et al., 2018). To date, however, there are no other FFQs that target adolescents in other Latin American countries.

Unlike other dietary intake assessment methods, FFQs are dependent on each population group’s diet which is shaped by the dynamic interaction between individuals and their sociocultural environment over time (Sobal & Bisogni, 2009). This requires them to be specifically developed for each study group (Shim et al., 2014). Therefore, one cannot adapt an FFQ to be used in another context; although, it can be similar to the one originally created. The purpose of this study was to assess the validity and reliability of a FFQ designed for use with Costa Rican adolescents using 3-day food records as the reference method.

2. Methods

2.1 Design and development of the FFQ

Information on food consumption was collected using 3-day food records (3FR) from 133 adolescents (aged 13 to 18 years) who attended schools in urban and rural areas of the San José province (Fiatt & Romero, 2011). Nine high schools (6 urban and 3 rural) were selected using a proportional-size probability method (Alam, 2015) and participants from each school were selected through convenience sampling (Lohr, 2010).

Data from the 3FR collected on 2015 from 133 adolescents were analyzed to determine the average daily intake of energy and nutrients of each adolescent, using a program developed in Epi Info, version 7.0 (Centers for Disease Control and Prevention - CDC, 2009) and a food composition table developed in the School of Nutrition, University of Costa Rica, based mainly on information from the USA food composition database (United States Department of Agriculture - USDA, 2018). Then the following analysis was performed: a) frequency of consumption of 19 different food groups b) degree of association between consumption of each food group and average daily intake of energy and nutrients and c) percent contribution of each food group to average daily intake of energy and nutrients.

This analysis showed that 11 of the 19 food groups had a higher frequency of consumption, a higher degree of correlation with, and contributed by a larger percentage, to energy and nutrient intake. These 11 food groups contributed 78% of the average daily energy intake of the group of adolescents. Due to this finding, it was decided to include in the list of foods for the FFQ, all commonly consumed individual foods belonging to these 11 groups and in the case of the remaining 8 groups, more general items were included in the list (Supplementary materials: Food Frequency Questionnaire applied to Costa Rican adolescents).

In total, the FFQ included 183 foods and preparations and the following methods were used to estimate the portion size consumed: number of units, household measures, photos and for some foods, an average portion size was used. The questionnaire was designed to be administered in a face-to-face 30-minutes interview. The literature indicates higher correlation coefficients between the FFQ and reference method when the questionnaire is applied by interview than when it is self-administered (Cade et al., 2004). The reference period for reporting usual food intake was the week preceding the interview.

For this study, food consumption data from the FFQ was classified into 21 groups: dairy products, meats and eggs, processed meat, fish and seafood, legumes, non-starchy vegetables, starchy vegetables, fruits, breakfast cereals, pasta, rice, bread, tortillas, cookies, pastries and desserts, gelatin and ice cream, beverages with added sugars, pastries, soups, fast foods, and snacks. Consumption of alcoholic drinks, whole grains, and nuts were excluded because consumption among Costa Rican adolescents was negligible (5.98 g/d, 2.78 g/d, and 0.72 g/d, respectively).

2.2 Validation and reproducibility

2.2.1 Sample

On 2017, we recruited a convenience sample of 107 participants who consented to participate in a transversal study aimed to identify dietary intake and food sources of added sugars, which has been described previously (Monge-Rojas et al., 2022). The Bioethics Committee of the Costa Rican Institute for Research and Education on Nutrition and Health (INCIENSA) approved the study, and all guidelines for human subject research were strictly followed.

2.2.2 Dietary assessment

During a face-to-face interview, a previously trained nutritionist applied the FFQ to the adolescent and asked him/her if he/she had consumed each of the foods listed in the FFQ during the week prior to the interview. If the adolescent answered affirmatively, he/she was then asked how often it was eaten daily or weekly. It was an open-ended question because the frequency of consumption was not previously categorized. This allowed for declarative memory stimulation and would not influence the response. Allowing memory usage was proposed as one of the strategies to reduce recall bias that often occurs in retrospective study designs (Althubaiti, 2016). The adolescent might have also felt overwhelmed if he/she was subjected to multiple answer options simultaneously (Crone, 2017), potentially increasing the response bias.

To facilitate the adolescent with estimating the portion size consumed during the FFQ interview, he/she was presented with various alternatives: household measurements (e.g. cup, teaspoon, cup, glass, bottle), natural units (e.g. slice of cheese), tridimensional food models, and a booklet with three to six age-appropriate portion size photos of different foods commonly eaten in Costa Rica. Studies have shown a higher correlation coefficient between the FFQ and the reference method when subjects are able to describe their own portion size as opposed to not having a specified portion size listed on the questionnaire (would use average portion weights) (Cade et al., 2004; Kolodziejczyk et al., 2012). The 3FR was used as the reference method because it is considered a gold standard of accuracy among quantitative accounts of diet during a specific period, usually more than a 24 hour dietary recall (Rockett et al., 2003).

After concluding the FFQ interviews for all of the adolescents in one school, they gathered in a classroom and two nutritionists explained how to record their food consumption. 3FRs were completed on 2 weekdays (either Monday, Tuesday, Thursday, or Friday) and 1 weekend day (either Saturday or Sunday). Half of the participants from each high school were randomly asked to record the foods and drinks consumed on Thursday, Friday, and Saturday. The rest of the participants were asked to record their foods on Sunday, Monday, and Tuesday. This ensured that the data captured any variability in food consumption from weekday to weekend, and weighted food intake to represent weekly intake. Participants were asked to record detailed descriptions of all the foods and drinks consumed during the entire day and they also learned how to estimate portion sizes using the aforementioned booklet (that contains commonly consumed foods and preparations in Costa Rica), as well as kitchen measurement tools or volume and mass units. The students were asked to submit their 3FRs immediately after completion. On submission day, the nutritionists conducted a thorough review of the records with each participant during school hours. The nutritionists prompted the participants to provide information on commonly missed items or ingredients, inquired about missing details for the types of food or drinks consumed, and clarified any illegible items. Four weeks after the first FFQ interview (FFQ1), the adolescents were called again for another interview that followed the same methodology described (FFQ2). This four-week interval was chosen to minimize the variation in food intake response due to true changes over time.

The data from the FFQ and the 3FR were then gathered and entered into the software to assess the dietary composition of various foodstuffs. The software uses a food composition table developed in the School of Nutrition, University of Costa Rica, based mainly on information from the USA food composition database (United States Department of Agriculture - USDA, 2018). The software uses information about commonly consumed foods available in the food composition tables of the Institute of Nutrition for Central America and Panama and the USDA. It also includes updated information regarding the nutritional value of commonly consumed dishes and local fruits and vegetables. Nutritional information of new foods available in the domestic market, food reformulations introduced by the local food industry, and new dishes is also included. Therefore, an updated food composition database was used to reflect the foods and food compositions (including processed package and fortified foods) available for the year the data were collected. The coding procedure of the individual food items was carried out by qualified nutritionists who had been trained in food coding by the research team. The nutritionists followed an updated food codebook developed by the study researchers for each cohort and used it to classify and describe each food item. All coding decisions and food portion size determinations were reviewed with the research team and double-checked for accuracy.

2.3 Statistical analysis

Crude data from food and nutrient intake estimated with both the FFQ1 and the 3FR were analyzed using boxplot (outliers determined ±2 SD were removed before data analysis). The validity of the FFQ was assessed by comparing the intake of each nutrient/food group estimated from the FFQ with that estimated from the average of the 3FR. Because there were high correlations between total energy intake and individual nutrient intakes estimated by the 3FR and the FFQ, variables were adjusted for total energy intake using the residual model (Kabagambe et al., 2001; Willett, 2013). Mean values ± standard deviation (SD) of daily energy, nutrients, and food intake were calculated from the FFQ1 and 3FR. Wilcoxon signed-rank test was used to compare means values obtained from the FFQ1 and 3FR, as the data were not normally distributed. Wilcoxon signed rank test results with p-values > 0.05 indicate good agreement between two measures at group level, but p-values ≤ 0.05 indicate poor agreement (Lombard et al., 2015).

The Spearman correlation coefficient was determined to measure the strength and direction of the association between the two dietary assessment methods. Random within-subject errors in estimating the intake of foods and nutrients tend to attenuate correlations toward zero (Willett, 2013). Therefore, Spearman correlation coefficient was de-attenuated by removing the within-person variability for the 3FR data (Kabagambe et al., 2001; Willett, 2013). De-attenuated energy-adjusted correlations ≥ 0.50 indicate a good relationship; those from 0.20 to 0.49 suggest an acceptable relationship, and values < 0.20 indicate a weak or no relationship (Lombard et al., 2015).

Relative agreement between the 3FR and FFQ methods was tested using cross-classification of food and nutrient intake. The proportion of participants was classified by the two methods in the same/opposite tertile of the intake of nutrients and foods. A good agreement was considered if more than 50% of subjects were correctly classified and less than 10% of subjects were grossly misclassified (Masson et al., 2003). As has been suggested by Manson et al. (2003), the Fleiss and Cohen’s weighted Kappa (Kw) coefficient was used as a summary of cross-classification (Fleiss & Cohen, 1973). Values of Kw ≥ 0.61 indicate good agreement, from 0.20-0.60 acceptable agreement, and < 0.20 poor agreement (Lombard et al., 2015).

Bland-Altman method (Bland & Altman, 1999) was used to detect systematic differences between two methods. Traditional Bland-Altman plots show heteroscedasticity for most nutrient/food groups, with an increase in variability of the differences from FFQ and 3FR as the magnitude of the measurement increases. Giavarina (2015) suggests, in these cases, to express the differences of measurements under two methods as percentages of the values on the axis (i.e. proportionally to the magnitude of measurements) (Giavarina, 2015). Presenting the plots using the percentage of differences has the advantage that all the plots can be made with the same scale and are more comparable. The scale used in this study ranges from -200 to 200%.

For the reliability of the FFQ, Spearman’s correlation was used to assess the relationship between FFQ1 and FFQ2. Differences between mean food groups consumption and energy-adjusted mean nutrients intake between the FFQ1 and FFQ2 were also compared using the Wilcoxon signed-rank-test. Likewise, the proportion of participants classified by the first and second FFQ in the same/opposite tertile of the intake of nutrients and foods, and the weighted kappa was used to assess the agreement between FFQ1 and FFQ2.

3. Results

The sample for the validation of the FFQ comprised for 107 adolescents; 52.5% from urban areas and 66.3% girls, while the sample for determining reliability consisted of 80 adolescents, 54% from urban areas and 64% girls. Mean age was 14.7±1.5-year-old.

3.1 Relative validity (FFQ1 vs 3FR)

The FFQ overestimated most nutrients and food groups intake; except for cholesterol, niacin, vitamin E, sugary drinks, snacks, meat and eggs, processed meat, fat, soup, and refined bread and cookies. Energy-adjusted de-attenuated Spearman correlation between nutrient intake assessed by the FFQ and 3FR ranged from 0.121 (zinc) to 0.634 (thiamin); and all were statistically significant, except for zinc and carbohydrates. Regarding Spearman correlations, 5 out of 27 nutrients had good correlations, 20 nutrients showed acceptable values and only 2 had weak correlations. The proportion of adolescents classified in the same tertile of nutrient intake ranged from 41.3% for protein to 77.6% for vitamin D (Table 1).

Energy-adjusted de-attenuated Spearman correlation between food groups consumption assessed by the FFQ and the reference method ranged from 0.024 (fat) to 0.839 (breakfast cereal). The correlation was not statistically significant for vegetables, starchy vegetables, fat, pasta, fish and seafood, refined bread and cookies, pastries, and ice cream. Six out of 21 food groups presented good correlations, while seven and eight food groups had acceptable and weak correlations, respectively. The proportion of adolescents classified in the same tertile of food group consumption ranged from 31.0% for pastries to 70.0% for breakfast cereal (Table 2). Analysis of Bland-Altman plots, using a range from -200 to 200%, showed that for most of the nutrients intake and food groups consumption have a constant bias, except for the soups, with a higher bias for lower values (Figures 1 and 2 show depicted bias between FFQ and 3FR for some examples of nutrients and food groups, according to Bland-Altman plots).

In general, FFQ overestimates 3FR, with an average overestimation of 40.2% for foods and 38.8% for nutrients (Tables 1 and 2). FFQ-derived estimates for some food groups are much higher than 3FR: processed meat (82.0%), fish (60.9%), vegetables (74.4%), fruits (87.4%), and fast foods (92.1%) (Table 2). Confidence limits for most food groups are very wide, except for sugar-sweetened beverages. An element that makes these confidence limits so wide is the fact that several individuals registered no consumption in the 3FR, while reporting a different value in the FFQ. On the other hand, three nutrients present more than 50% overestimation for the FFQ: vitamin C (73.5%), vitamin A (52.8%) and vitamin D (55.7%) (Table 1).

The cross-classification (more than 50% of subjects were correctly classified and less than 10% of subjects grossly misclassified) was good for 24 of 26 nutrients and for 12 of the 21 food groups (Tables 3 and 4). However, cross-classification of data is limited in that the percentage of the agreement includes chance agreement (Lombard et al., 2015). Nevertheless, the weighted kappa that excludes chance agreement showed an acceptable discriminant ability of the FFQ to categorize individuals into broad nutrient intake (except for protein and cholesterol) and food groups categories (dairy products, white rice, beans, vegetables, fruits and fruit juice, sugary drinks, breakfast cereal, candies and sweets, snacks, fast foods, fat, and ice cream).

3.2 Test-Retest reliability (FFQ1 vs. FFQ2)

Mean food consumption and mean energy and nutrients intake from FFQ1 and FFQ2 are reported in Tables 3 and 4. In general terms, both means of food consumption and nutrients intake obtained in FFQ1 were significantly higher than those obtained in FFQ2. Energy-adjusted de-attenuated Spearman correlation ranged between 0.182 for vitamin E (p < 0.05) to 0.570 for magnesium (p < 0.05), and between 0.003 for ice cream (p > 0.05) to 0.626 for beans (p < 0.05). Most of nutrients had acceptable correlations (21 out of 27), while in the rest of the nutrients the correlation was good (5 out of 27) or weak (1 out of 27). On the other hand, most food groups (12 out of 21) had good correlations and only three food groups showed weak correlations. As expected, the FFQ is unable to assess absolute dietary intakes, however, the analysis of the cross-classification and the Kw, confirm that FFQ is able to categorize and rank individuals either by their usual frequency of food consumption or nutrient.

4. Discussion

A systematic literature review on validation and reproducibility of dietary assessment methods in adolescents published in 2013, evidenced that almost all studies were considered valid and reproducible when using either Pearson’s or Spearman’s correlation coefficient as the only validity criterion (Tabacchi et al., 2014). Nevertheless, the use of correlation analysis in assessing the validity of a questionnaire has been questioned, because it measures the strength of association between two variables, rather than agreement (Ambrosini et al., 2009; Cade et al., 2002; Chinn, 1990; Fraser et al., 1998). In addition, Lombard et al. (2015) noted that one to three statistical tests may not be sufficient to provide a comprehensive view of the various facets of FFQ validity and that only the application and interpretation of multiple statistical tests (i.e. correlation coefficient, paired t-test/Wilcoxon signed-rank test, percent difference, cross-classification, weighted kappa coefficient, and Bland-Altman analysis) support conclusions regarding the validation for dietary intake assessment methods. Among the statistical tests proposed by Lombard et al. (2015), a p-value > 0.05 is required in the Bland-Altman analysis to obtain a good outcome in the validation phase related to the presence, direction, and extent of bias at the group level; however, the results of the Bland-Altman analysis in this study showed p-values ≤ 0.05, indicating a poor outcome.

For most nutrients, observed FFQ-derived nutrients were higher compared to those of the 3FR. Similar findings have been evidenced in previous studies. (Brunner et al., 2001; Eng & Moy, 2011), particularly for FFQs that have more than (Klipstein-Grobusch et al., 1998), as was the case here. The Bland-Altman analysis shows discrepancies between the two methods, both for nutrients and food groups intake. FFQ overestimates 3FR, with an average overestimation of 40.2% for foods and 38.8% for nutrients. The overestimation of nutrient intake by the FFQ may reflect the fact that the 3FR underestimates many food groups (Kaczkowski et al., 2000). It is likely that some food items on the FFQ may not have been consumed during the 3FR and this may contribute to the evidenced difference. The variations between the two instruments in terms of the method of data collection and the manner to transform the self-reported food items into nutrients may explain these dissimilarities in estimates.

The higher consumption of dietary fiber and vitamins determined by the FFQ could be related to the variety of fruits and vegetables included in the FFQ, which provides more selection options, compared to the 3FR. In fact, in the FFQ, 36 food items were used to describe the groups of fruits, vegetables, and legumes which could explain the overestimation of vitamin C and folate equivalents intakes in the FFQ (despite an acceptable agreement for the classification). Another possible explanation for the large differences in average nutrients and food intakes between the compared methods is the underreporting bias that interferes with the food intake data obtained from food records (Andersen et al., 2005; Chinnock, 2006). Chinnock (2006) analyzed data from 60 Costa Rican adults and observed that food intake data collected with estimated food records were underestimated for energy, nutrients, and food groups, when compared to the results obtained from a weighed food record. Andersen et al. (2005) observed that energy intake estimated with a pre-coded food diary was 34% lower than the estimated energy expenditure among Norwegian adolescents. Possible underreporting in the food records and/or overreporting in the FFQ may have influenced the results observed in this study.

Although in this study some good correlations and weighted kappa between FFQ-derived intakes and the 3FR were observed, they are likely to be underestimates of the correlations between the FFQ and real intake (Sauvageot et al., 2013). Therefore, as already stated in similar studies by other researchers (Tabacchi et al., 2016; J. E. Wong et al., 2012), the designed FFQ for Costa Rican adolescents is an inappropriate tool to estimate absolute levels of nutrients intake and food groups consumption, when considering the bias and low precision observed.

Concerning the results on cross-classification into tertiles, the FFQ performed well. After energy adjustment, 19/25 nutrients obtained a weighted kappa coefficient above 0.20 and the average correct classification rate was about 50%. These percentages of agreement were comparable to those of previous studies, which compared their FFQ with dietary records (Fernández-Ballart et al., 2010; Khairunnisa Mohamed et al., 2018; J. E. Wong et al., 2012), confirming that the FFQ designed to Costa Rican adolescents is a reasonable tool to categorize adolescents into broad ranges of dietary intakes and could be used to evaluate dietary patterns in large populations of adolescents and to study the association between eating habits and cardiovascular risk factors, such as overweight/obesity. Consistent with the findings of this study, a meta-analysis of the validity of the FFQ targeting adolescents concluded that the FFQ is a suitable tool for data collection and could only be used to classify adolescents in terms of energy and nutrients intake and food groups consumption (Tabacchi et al., 2016).

This study has several strengths: 1) The FFQ was designed based on studies on the dietary intake of Costa Rican adolescents determined by 3FR, 2) the validity of the FFQ was carried out through the 3FR, which is the dietary assessment method that has the least correlated errors with the FFQ, since it is independent of memory and the size of the portion is estimated at the moment of consumption (Rankin et al., 2010; Willett, 2013), 3) the sample size was adequate for the FFQ validation study, since a sample size between 50-100 individuals has been suggested as the minimum required for an FFQ validation and reproducibility studies (Cade et al., 2004; Willett, 2013). Some limitations of this study include: 1) the effect of sex was not removed by conducting separate analyses for boys and girls; however, very few FFQ validation studies provide this type of data separately, 2) the 3FR applied in a single week is likely not to be as representative of long-term dietary habits. However, given that the adolescents have problems remembering their diet over a long period of time (Livingstone et al., 2004; Truthmann et al., 2011), it may be better to run the FFQ over several weeks spread over a year to capture seasonal variations, 3) food record was performed only on 3 days and not on 7 or more days, which may limit the capture of greater variability in food consumption and nutrient intake. Nevertheless, according to the literature (Ortega, 2015), 3 days of recordings is reasonable because declining accuracy of recording with increasing fatigue and boredom have been noticed with longer records.

Supplementary materials

Acknowledgments

Acknowledgements

The authors are grateful to Alejandra Abarca-Rojas, Graciela Méndez-Rojas, Angie Morera-Quesada, and Katherine Vindas-Chichilla for their support in data analysis.

References

Alam, M. (2015). Selection of the Samples with Probability Proportional to Size. Science Journal of Applied Mathematics and Statistics, 3(5), 230. https://doi.org/10.11648/j.sjams.20150305.13

Althubaiti, A. (2016). Information bias in health research: Definition, pitfalls, and adjustment methods. Journal of Multidisciplinary Healthcare, 211. https://doi.org/10.2147/JMDH.S104807

Ambrosini, G. L., De Klerk, N. H., O’Sullivan, T. A., Beilin, L. J., & Oddy, W. H. (2009). The reliability of a food frequency questionnaire for use among adolescents. European Journal of Clinical Nutrition, 63(10), 1251–1259. https://doi.org/10.1038/ejcn.2009.44

Andersen, L. F., Pollestad, M. L., Jacobs, D. R., Løvø, A., & Hustvedt, B.-E. (2005). Validation of a pre-coded food diary used among 13-year-olds: Comparison of energy intake with energy expenditure. Public Health Nutrition, 8(8), 1315–1321. https://doi.org/10.1079/PHN2005751

Bland, J. M., & Altman, D. G. (1999). Measuring agreement in method comparison studies. Statistical Methods in Medical Research, 8(2), 135–160. https://doi.org/10.1177/096228029900800204

Brunner, E., Stallone, D., Juneja, M., Bingham, S., & Marmot, M. (2001). Dietary assessment in Whitehall II: Comparison of 7 d diet diary and food-frequency questionnaire and validity against biomarkers. British Journal of Nutrition, 86(3), 405–414. https://doi.org/10.1079/BJN2001414

Cade, J., Burley, V. J., Warm, D. L., Thompson, R. L., & Margetts, B. M. (2004). Food-frequency questionnaires: A review of their design, validation and utilisation. Nutrition Research Reviews, 17(1), 5–22. https://doi.org/10.1079/NRR200370

Cade, J., Thompson, R., Burley, V., & Warm, D. (2002). Development, validation and utilisation of food-frequency questionnaires – a review. Public Health Nutrition, 5(4), 567–587. https://doi.org/10.1079/PHN2001318

Centers for Disease Control and Prevention - CDC. (2009). Statistical epidemiology software: Epi Info [Software] (7.0) [Computer software]. https://www.cdc.gov/epiinfo

Chinn, S. (1990). The assessment of methods of measurement. Statistics in Medicine, 9(4), 351–362. https://doi.org/10.1002/sim.4780090402

Chinnock, A. (2006). Validation of an estimated food record. Public Health Nutrition, 9(7), 934–941. https://doi.org/10.1017/PHN2005922

Craigie, A. M., Lake, A. A., Kelly, S. A., Adamson, A. J., & Mathers, J. C. (2011). Tracking of obesity-related behaviours from childhood to adulthood: A systematic review. Maturitas, 70(3), 266–284. https://doi.org/10.1016/j.maturitas.2011.08.005

Crone, E. A. (2017). The adolescent brain: Changes in learning, decision-making and social relations. Routledge, Taylor & Francis Group.

Cruz, F., Ramos, E., Lopes, C., & Araújo, J. (2018). Tracking of food and nutrient intake from adolescence into early adulthood. Nutrition, 55–56, 84–90. https://doi.org/10.1016/j.nut.2018.02.015

Denova-Gutiérrez, E., Ramírez-Silva, I., Rodríguez-Ramírez, S., Jiménez-Aguilar, A., Shamah-Levy, T., & Rivera-Dommarco, J. A. (2016). Validity of a food frequency questionnaire to assess food intake in Mexican adolescent and adult population. Salud Pública de México, 58(6), 617. https://doi.org/10.21149/spm.v58i6.7862

Dumontheil, I. (2014). Development of abstract thinking during childhood and adolescence: The role of rostrolateral prefrontal cortex. Developmental Cognitive Neuroscience, 10, 57–76. https://doi.org/10.1016/j.dcn.2014.07.009

Eng, J. Y., & Moy, F. M. (2011). Validation of a food frequency questionnaire to assess dietary cholesterol, total fat and different types of fat intakes among Malay adults. Asia Pacific Journal of Clinical Nutrition, 20(4), 639–645.

Fardet, A., & Boirie, Y. (2014). Associations between food and beverage groups and major diet-related chronic diseases: An exhaustive review of pooled/meta-analyses and systematic reviews. Nutrition Reviews, 72(12), 741–762. https://doi.org/10.1111/nure.12153

Fernández-Ballart, J. D., Piñol, J. L., Zazpe, I., Corella, D., Carrasco, P., Toledo, E., Perez-Bauer, M., Martínez-González, M. Á., Salas-Salvadó, J., & Martín-Moreno, J. M. (2010). Relative validity of a semi-quantitative food-frequency questionnaire in an elderly Mediterranean population of Spain. British Journal of Nutrition, 103(12), 1808–1816. https://doi.org/10.1017/S0007114509993837

Fiatt, A., & Romero, A. (2011). Desarrollo de un Cuestionario de Frecuencia de Consumo de Alimentos para adolescentes en Costa Rica, 2010 (Tesis de Grado). Universidad de Costa Rica.

Fleiss, J. L., & Cohen, J. (1973). The Equivalence of Weighted Kappa and the Intraclass Correlation Coefficient as Measures of Reliability. Educational and Psychological Measurement, 33(3), 613–619. https://doi.org/10.1177/001316447303300309

Fraser, G. E., Lindsted, K. D., Knutsen, S. F., Beeson, W. L., Bennett, H., & Shavlik, D. J. (1998). Validity of Dietary Recall Over 20 Years among California Seventh-day Adventists. American Journal of Epidemiology, 148(8), 810–818. https://doi.org/10.1093/oxfordjournals.aje.a009703

Giavarina, D. (2015). Understanding Bland Altman analysis. Biochemia Medica, 25(2), 141–151. https://doi.org/10.11613/BM.2015.015

Kabagambe, E. K., Baylin, A., Allan, D. A., Siles, X., Spiegelman, D., & Campos, H. (2001). Application of the Method of Triads to Evaluate the Performance of Food Frequency Questionnaires and Biomarkers as Indicators of Long-term Dietary Intake. American Journal of Epidemiology, 154(12), 1126–1135. https://doi.org/10.1093/aje/154.12.1126

Kaczkowski, C. H., Jones, P. J. H., Feng, J., & Bayley, H. S. (2000). Four-Day Multimedia Diet Records Underestimate Energy Needs in Middle-Aged and Elderly Women as Determined by Doubly-Labeled Water. The Journal of Nutrition, 130(4), 802–805. https://doi.org/10.1093/jn/130.4.802

Khairunnisa Mohamed, Tin Su Tin, Muhammad Yazid Jalaludin, Nabilla Al-Sadat, & Hazreen Abdul Majid. (2018). Comparative validity of a Food Frequency Questionnaire (MyUM Adolescent FFQ) to estimate the habitual dietary intake of adolescents in Malaysia. Asia Pacific Journal of Clinical Nutrition, 27(4). https://doi.org/10.6133/apjcn.022018.03

Klipstein-Grobusch, K., Den Breeijen, J., Goldbohm, R., Geleijnse, J., Hofman, A., Grobbee, D., & Witteman, J. (1998). Dietary assessment in the elderly: Validation of a semiquantitative food frequency questionnaire. European Journal of Clinical Nutrition, 52(8), 588–596. https://doi.org/10.1038/sj.ejcn.1600611

Kolodziejczyk, J. K., Merchant, G., & Norman, G. J. (2012). Reliability and Validity of Child/Adolescent Food Frequency Questionnaires That Assess Foods and/or Food Groups. Journal of Pediatric Gastroenterology & Nutrition, 55(1), 4–13. https://doi.org/10.1097/MPG.0b013e318251550e

Livingstone, M. B. E., Robson, P. J., & Wallace, J. M. W. (2004). Issues in dietary intake assessment of children and adolescents. British Journal of Nutrition, 92(S2), S213–S222. https://doi.org/10.1079/BJN20041169

Lohr, S. L. (2010). Sampling: Design and analysis (2nd ed). Brooks/Cole.

Lombard, M. J., Steyn, N. P., Charlton, K. E., & Senekal, M. (2015). Application and interpretation of multiple statistical tests to evaluate validity of dietary intake assessment methods. Nutrition Journal, 14(1), 40. https://doi.org/10.1186/s12937-015-0027-y

Masson, L., MCNeill, G., Tomany, J., Simpson, J., Peace, H., Wei, L., Grubb, D., & Bolton-Smith, C. (2003). Statistical approaches for assessing the relative validity of a food-frequency questionnaire: Use of correlation coefficients and the kappa statistic. Public Health Nutrition, 6(3), 313–321. https://doi.org/10.1079/PHN2002429

Mikkilä, V., Räsänen, L., Raitakari, O. T., Pietinen, P., & Viikari, J. (2005). Consistent dietary patterns identified from childhood to adulthood: The Cardiovascular Risk in Young Finns Study. British Journal of Nutrition, 93(6), 923–931. https://doi.org/10.1079/BJN20051418

Monge-Rojas, R., Vargas-Quesada, R., Colón-Ramos, U., & Chinnock, A. (2022). Dietary Intake and Sources of Added Sugars in Various Food Environments in Costa Rican Adolescents. Nutrients, 14(5), 959. https://doi.org/10.3390/nu14050959

Movassagh, E., Baxter-Jones, A., Kontulainen, S., Whiting, S., & Vatanparast, H. (2017). Tracking Dietary Patterns over 20 Years from Childhood through Adolescence into Young Adulthood: The Saskatchewan Pediatric Bone Mineral Accrual Study. Nutrients, 9(9), 990. https://doi.org/10.3390/nu9090990

Ochola, S., & Masibo, P. K. (2014). Dietary Intake of Schoolchildren and Adolescents in Developing Countries. Annals of Nutrition and Metabolism, 64(Suppl. 2), 24–40. https://doi.org/10.1159/000365125

Ortega, R. M. (2015). Métodos de evaluación de la ingesta actual: Registro o diario dietético. Nutrición Hospitalaria, 3, 38–45. https://doi.org/10.3305/nh.2015.31.sup3.8749

Rankin, D., Hanekom, S., Wright, H., & MacIntyre, U. (2010). Dietary assessment methodology for adolescents: A review of reproducibility and validation studies. South African Journal of Clinical Nutrition, 23(2), 65–74. https://doi.org/10.1080/16070658.2010.11734284

Rockett, H. R. H., Berkey, C. S., & Colditz, G. A. (2003). Evaluation of dietary assessment instruments in adolescents. Current Opinion in Clinical Nutrition and Metabolic Care, 6(5), 557–562. https://doi.org/10.1097/00075197-200309000-00009

Rodriguez, C., Smith, E., Villamor, E., Zavaleta, N., Respicio-Torres, G., Contreras, C., Perea, S., Jimenez, J., Tintaya, K., Lecca, L., Murray, M., & Franke, M. (2017). Development and Validation of a Food Frequency Questionnaire to Estimate Intake among Children and Adolescents in Urban Peru. Nutrients, 9(10), 1121. https://doi.org/10.3390/nu9101121

S. M. A. Matos, M. S. P., C. A. S. T. Santos, S. D’Innocenzo, A. M. O. Assis, L. S. Dourado, N. S. Oliveira, L. C. Rodrigues y M. L. Barreto,-. (2012). Validation of a food frequency questionnaire for children and adolescents aged 4 to 11 years living in Salvador, Bahia. Nutrición Hospitalaria, 4, 1114–1119. https://doi.org/10.3305/nh.2012.27.4.5883

Saravia, L., González-Zapata, L. I., Rendo-Urteaga, T., Ramos, J., Collese, T. S., Bove, I., Delgado, C., Tello, F., Iglesia, I., Gonçalves Sousa, E. D., De Moraes, A. C. F., Carvalho, H. B., & Moreno, L. A. (2018). Development of a Food Frequency Questionnaire for Assessing Dietary Intake in Children and Adolescents in South America: Development of a Food Frequency Questionnaire. Obesity, 26, S31–S40. https://doi.org/10.1002/oby.22114

Sauvageot, N., Alkerwi, A., Albert, A., & Guillaume, M. (2013). Use of food frequency questionnaire to assess relationships between dietary habits and cardiovascular risk factors in NESCAV study: Validation with biomarkers. Nutrition Journal, 12(1), 143. https://doi.org/10.1186/1475-2891-12-143

Sherwood, N. E. (2009). Diet Assessment in Children and Adolescents. In E. Jelalian & R. G. Steele (Eds.), Handbook of Childhood and Adolescent Obesity (pp. 73–89). Springer US. https://doi.org/10.1007/978-0-387-76924-0_6

Shim, J.-S., Oh, K., & Kim, H. C. (2014). Dietary assessment methods in epidemiologic studies. Epidemiology and Health, 36, e2014009. https://doi.org/10.4178/epih/e2014009

Sobal, J., & Bisogni, C. A. (2009). Constructing food choice decisions. Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine, 38 Suppl 1, S37-46. https://doi.org/10.1007/s12160-009-9124-5

Speck, B. J., Bradley, C. B., Harrell, J. S., & Belyea, M. J. (2001). A food frequency questionnaire for youth: Psychometric analysis and summary of eating habits in adolescents. Journal of Adolescent Health, 28(1), 16–25. https://doi.org/10.1016/S1054-139X(00)00171-3

Tabacchi, G., Amodio, E., Di Pasquale, M., Bianco, A., Jemni, M., & Mammina, C. (2014). Validation and reproducibility of dietary assessment methods in adolescents: A systematic literature review. Public Health Nutrition, 17(12), 2700–2714. https://doi.org/10.1017/S1368980013003157

Tabacchi, G., Filippi, A. R., Amodio, E., Jemni, M., Bianco, A., Firenze, A., & Mammina, C. (2016). A meta-analysis of the validity of FFQ targeted to adolescents. Public Health Nutrition, 19(7), 1168–1183. https://doi.org/10.1017/S1368980015002505

Truthmann, J., Mensink, G. B., & Richter, A. (2011). Relative validation of the KiGGS Food Frequency Questionnaire among adolescents in Germany. Nutrition Journal, 10(1), 133. https://doi.org/10.1186/1475-2891-10-133

United States Department of Agriculture - USDA. (2018). Data Laboratory [Base de datos] [dataset]. USDA. http://fnic.nal.usda.gov

Willett, W. (2013). Nutritional Epidemiology (Third edition). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199754038.001.0001

Wong, J. E., Parnell, W. R., Black, K. E., & Skidmore, P. M. (2012). Reliability and relative validity of a food frequency questionnaire to assess food group intakes in New Zealand adolescents. Nutrition Journal, 11(1), 65. https://doi.org/10.1186/1475-2891-11-65

Wong, S. S., Boushey, C. J., Novotny, R., & Gustafson, D. R. (2008). Evaluation of a Computerized Food Frequency Questionnaire to Estimate Calcium Intake of Asian, Hispanic, and Non-Hispanic White Youth. Journal of the American Dietetic Association, 108(3), 539–543. https://doi.org/10.1016/j.jada.2007.12.006

IR