https://orcid.org/0000-0002-4817-3221
Figures
Abstract
This study aimed to validate a food frequency questionnaire for foods high in sodium (FFQ-FHS) in a population aged ≥18 years and to test its reproducibility. This cross-sectional study included 50 individuals (≥18 years) of both sexes. In addition to the FFQ-FHS, four 24-h dietary recalls (24hRs) were conducted and a socioeconomic and lifestyle questionnaire was administered. Two 24-h urinary excretions were collected for sodium analysis, and anthropometry was performed. For validation, the triad method was applied using the validity coefficient (ρ). For reproducibility, the intraclass correlation coefficient (ICC), 95% confidence interval, kappa coefficient, and Bland–Altman plots were used to check for agreement. The Kolmogorov-Smirnov test was used to verify the data distribution. The validity coefficients for daily energy-adjusted sodium intake were high for the 24hR (ρRAI = 0.85) and weak for the FFQ-FHS (FFQAI = 0.26) and biomarker (ρBAI = 0.20). The ICC values were 0.68 for unadjusted sodium and 0.54 for energy-adjusted sodium intake. The weighed Kappa scores were 0.49 (p<0.01) and 0.260 (p = 0.02) for unadjusted and adjusted sodium intake, respectively. Although the FFQ-FHS is reproducible, it is not valid for the assessment of sodium intake and cannot be the sole instrument used for this purpose.
Citation: Souza DS, Santos BI, Costa BM, Santos DM, Aragão LGS, Pires LV, et al. (2023) Food frequency questionnaire for foods high in sodium: Validation with the triads method. PLoS ONE 18(7): e0288123. https://doi.org/10.1371/journal.pone.0288123
Editor: Nicoletta Righini, Universidad de Guadalajara, MEXICO
Received: July 31, 2021; Accepted: June 20, 2023; Published: July 3, 2023
Copyright: © 2023 Souza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This study was partially financed by the following: 1. Scholarship awarded by the Fundação de Apoio à Pesquisa e a Inovação Tecnológica do Estado de Sergipe (Fapitec/SE/Brasil) [Research and Technological Innovation Support Foundation in the State of Sergipe] https://fapitec.se.gov.br/ 2. Financial resources granted by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES/Brasil) [Coordination for the Improvement of Higher Education Personnel], financing code 001, PROMOB, process no. 88881.157882/2017-01 https://www.gov.br/capes/pt-br The funders had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation. No additional external funding was received for the study.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Several studies [1–3] have developed food frequency questionnaires (FFQ) to estimate the sodium intake in different populations; however, validation is necessary. For this purpose, the FFQ should be compared with other methods of food intake assessment, such as food record (FR) and/or 24-hour dietary recall (24hR), which, although they are also prone to measurement errors, are considered reference methods. Biomarkers (Bs) are commonly used in validation studies because they have distinct and independent approaches from other methods that assess dietary intake. However, B have limitations in reflecting the intake of a particular nutrient over a short period. In addition, metabolic reactions, absorption, and excretion affect the B levels [4–7].
When nutrient intake is assessed using two dietary assessment tools and biological markers, the triad method is recommended because it estimates the validity coefficient between the unknown actual intake (AI) and the intake estimated using the FFQ, 24hR or FR, and B, allowing a triangular comparison between the different methods [4, 5, 8, 9]. The triad method is effective in comparing intake estimations using the instrument to be validated, the FFQ, the reference method (24hR or FR), and B. The proposed method assumes the existence of independent random errors in the three instruments and considers that each error is linearly related to the AI [5].
A systematic review investigated whether FFQs are reliable and valid for measuring the habitual sodium intake and concluded that the available instruments were inadequate because of the low agreement between intake measured by FFQ and 24-hour urinary excretion [10].
In Brazil, independent of the geographical region, the sodium intake of the population is high [11, 12] and corresponds to approximately twice the intake recommended by the World Health Organization (WHO) (2 g/day) [13]. Data from the 2008 to 2009 Household Budget Survey show that the contribution of processed and ultra-processed foods to the average sodium intake increased from that observed in the first edition of the survey (2002–2003) [11]. Despite this, studies seeking the validation of a tool to investigate sodium intake in the Brazilian population, especially in the northeast region, remain scarce. Thus, this study aimed to use the triad method to validate the food frequency questionnaire for foods high in sodium (FFQ-FHS) for Brazilian adults and determine its reproducibility.
Materials and methods
Study design and participants
This was a cross-sectional study using a convenience sample. This study enrolled 50 adult Brazilian men and women. This sample size was considered the minimum sample size suggested for validation studies, as recommended by Cade et al. [14] and McLean et al. [10]. The study was approved by the Research Ethics Committee of the Federal University of Sergipe (process no. 1.875.674, CAAE no. 62502216.0.0000.5546).
Participants were recruited through social media and traditional methods (posters and e-mails) in 2018. Fifty male and female individuals aged between 18 and 60 years and belonged to the academic community of a Federal Public University (undergraduate students and staff personnel) were selected. We excluded pregnant and lactating women, vegetarians, individuals clinically diagnosed with non communicable chronic diseases or following special diets, individuals with identifiable cognitive deficits, and nutrition professionals were not included.
All four assistants were nutrition graduate students who received training and supervision from the nutritionists. The researcher and assistants conducted body measurements and interviews using a socioeconomic questionnaire. Data collection and interviews were carried out at the university nutrition school clinic. The researcher and assistants conducted four 24hRs and two FFQ-FHS surveys, in addition to the collection of two urine samples over a period of 6 months. Anthropometric measures were used to determine the nutritional status. The socioeconomic and demographic data were also collected. Fig 1 shows a schematic diagram of the study process.
Description of data collection process. SQ:sociodemographic questionnaire; 24hR:24-h dietary recall; FFQ-FHS:food frequency questionnaire for foods high in sodium; AA: anthropometric assessment.
The participants signed a free and informed consent form, allowing voluntary participation, followed by the application of the socioeconomic questionnaire, 24hR and FFQ-FHS, and anthropometric assessment. In the first and fourth interviews, the FFQ-FHS was completed after the 24hR via face-to-face interviews with the support of a food portion photographic book.
Food frequency questionnaire for foods high in sodium
The FFQ-FHS is a 55-item semiquantitative tool developed by Santos et al. [15] to assess the habitual sodium intake during the preceding 6-month period. Participants in this validation study completed two sets of FFQ-FHS, at the beginning and end of the semester, to assess for reproducibility.
The FFQ-FHS contain a list of foods that contain ≥400mg of sodium per 100g or ml, as defined by the National Health Surveillance [16] for high-sodium food. The list of items corresponds to the foods consumed by the local population, selected after applying the 24hR [15]. The foods and typical meals that make up the dietary pattern of the reference population or those that indicated a consumption rate of ≥5% in the northeast region [17] were also included in the FFQ-FHS. The standard portion size of each food or meal was defined according to the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística, IBGE) database of reference measurements and portion sizes of foods and dishes consumed in Brazil [18].
The food items in the FFQ-FHS are divided into seven groups (processed meats, canned and preserved foods, dairy products, bakery and pasta, salts and condiments, miscellaneous, and regional foods), four portion options (½ portion, 1 portion, 2 portions, and more portions–the interviewee should specify the amount consumed), and eight intake frequency categories (rarely/never, once a month, 2–3 times a month, once a week, 2–4 times a week, once daily, twice daily, and ≥3 times a day).
24-h dietary recall
Each volunteer answered three 24hR questions, with minimum and maximum intervals of 15 and 45 days, respectively, depending on the participants’ availability, including one day during the weekend. After 3 months, the fourth 24hR was carried out at the end of the period. The researcher and assistants were trained to conduct the four 24hRs using the multiple pass method, developed by the United States Department of Agriculture [19]. To facilitate the completion of all forms, a photographic manual of food quantification with preset portion sizes was used [20].
Added salt
In addition to the sodium estimate obtained during the 24hR, the amount of sodium in the added salt was considered. The participants were asked if they had the habit of adding salt to the dish and salad. If yes, they were asked whether commercial sachets or table salt was used. Considering that each sachet contained 1 g of salt, the units used were counted to estimate the total sodium intake. For those who confirmed the use of table salt, the estimated consumption was 1 g of added salt. This estimate was adopted because the amount of salt added to the table could not be quantified.
Sodium excretion biomarker
A 24-h urine specimen was collected following the recommendation of McLean et al. [10] to determine the daily excretion of Na (spectrophotometry) and creatinine (Jaffé method). Between the third and the fourth 24hR, two 24-hr urine specimens were collected for urinary sodium analysis. The participants received instructions [21] on how to collect and deliver the biological materials. The participants received 2-L containers for 24-h urine collection.
The participant had to discard the first urine of the day and collect all urine from that moment on, including nocturnal and bath urine, until the next day at the same time as that during which the first urine was discarded. The collected urine was stored in a container and kept in a refrigerator during and after collection. The delivered material was identified and sent to the Clinical Laboratory of the Department of Nutrition of the UFS.
Considering the need for a minimum volume of urine to identify the excretion of metabolites, we used urine specimens with less than 400mL [22], or with a creatinine value <0.2 mmol/kg/day [23]. The results were expressed in mg/kg/day.
Sociodemographic data
A structured questionnaire was used to collect the sociodemographic information (sex, age, household per capita income, and educational level). The household per capita income was calculated by summing the monetary income reported and dividing by the number of all family members (minimum wage corresponding to USD $ 248.69).
Anthropometry
The assistants performed anthropometric measurements on the first day of the interviews. Standard and per protocol measurement techniques were used in this study. A digital platform scale (LIDER®, São Paulo, Brazil) was used to determine the body weight with a capacity of 200kg and a precision of 100g. Stature was measured using a portable 2-m stadiometer with a precision of 1 millimeter (Alturaexata®, Minas Gerais, Brazil). Waist circumference was measured in centimeters using inelastic anthropometric tape. Nutritional status was classified according to the body mass index based on the cut-off values proposed by the WHO [24].
Food composition analysis
The food nutrient composition was analyzed using NutWin® software (version 1.5.2.51). All items listed in the applied 24hR and FFQ-FHS were standardized and typed individually in duplicate. The IBGE [25] nutrient composition table and product labels (mean values of the three brands) were used.
Statistical analysis
The Kolmogorov-Smirnov test was used to determine normality of data distribution. Descriptive statistics was conducted for calculation of the measures of central tendency and dispersion or absolute or relative frequencies. Spearman’s correlation coefficients were used to determine agreement between the FFQ-FHS, 24hR, and B.
The FFQ-FHS validity was analyzed using the triad method, comparing the results of the FFQ-FHS survey, 24hR, and B assessment with the estimated AI. The validity coefficient was determined, which can vary from 0 to 1 and must not be negative. The correlations were considered low (r<0.3) or high (r>0.7) [5]. To check for agreement between FFQ-FHS, 24hR, and B, the weighted Kappa value was calculated using the daily energy-adjusted sodium intake categorized into tertiles.
For reproducibility, the correlation between the two FFQ-FHS surveys was assessed using the intraclass correlation coefficient (ICC) and a confidence interval of 95% (95% CI). The weighted kappa value was used to check the agreement of sodium classifications categorized into tertiles.
Bland–Altman [26] plots graphically demonstrate the level of agreement between the FFQ, 24hR, and B, and the reproducibility of the FFQ-FHS. The centerline indicates the bias. It is desirable that this line be positioned close to zero, which means that there is a small difference between the means of both instruments. The upper and lower limits represent the 95% CI [27]. Good agreement is also shown by a greater number of points concentrated around the centerline. In the plots, the disagreement is expressed by a great dispersion of points around the centerline; additionally, a proportional bias exists because the differences diminish as the means increase [26, 27].
The 24hR data were attenuated to correct for intra-individual variability using the Multiple Source Method [28]. The method estimates the individual usual intake based on the results of repeated measurements over a long period. The program estimates the probability of nutrient intake from the data provided and then estimates the usual intake of nutrients. The values obtained were multiplied with each other to obtain an estimate of the individual usual daily intake after removing the intra-individual variability. The significance level was set at p<0.05.
Results
The sample included 60% of men, 84% of individuals with secondary education, and 62% of individuals whose household per capita income was lower than one minimum wage. The participants’ average age was 22.5 years. With regard to nutritional status, 60% of the patients had normal weight. The prevalence of overweight and obesity was 30% (Table 1).
Aracaju, SE, 2018 (n = 50).
A total of 47 and 36 specimens from the first and second urine collections were adequate according to the urine volume [22] and creatinine value [23].
The mean sodium intake and urinary excretion values are presented in Table 2. The estimated values were higher after administering the first set of FFQ-FHS compared with that after the second set. In both cases, the sodium intake was above the recommended level. The level of urinary sodium excretion exceeded the average dietary intake estimated by both the 24hR and FFQ-FHS survey. The mean sodium estimated in the four 24hRs was also higher than that in the two FFQ-FHS surveys.
Aracaju, SE, 2018 (n = 50).
The Spearman’s correlations and validity coefficients are shown in Table 3. A statistical significance was only observed in the unadjusted sodium correlation between the FFQ and 24hR (r = 0.386). The validity coefficient was high for the 24hR (r>0.7). The validity coefficient for unadjusted sodium considering the average urinary excretion could not be verified because of the negative rRB. The analysis of the correlation coefficients indicates that the particular properties of sodium make it difficult to estimate the consumption.
Aracaju, SE, 2018 (n = 50).
Classification by tertiles of energy-adjusted sodium intake using the three methods indicated a disagreement. The best agreement was observed between 24hR and B assessment (p = 0.271) (Table 4). A tertile analysis was performed to classify the individuals’ sodium consumption. The percentages indicated in the same tertile reveal the agreement of classification of the methods, whereas the opposite and adjacent tertiles indicate the degree of disagreement.
Aracaju, SE, 2018 (n = 50).
Bland–Altman plots (Fig 2) were constructed to assess the agreement between the FFQ, 24hR, and B, and the reproducibility of the FFQ-FHS. A disagreement was observed between the instruments evaluated. The reproducibility graphs indicated a moderate agreement between the first FFQ-FHS survey and second FFQ-FHS survey as most of the points were placed around the centerline.
Aracaju, SE, 2018 (n = 50). a) Food frequency questionnaire and 24-h dietary recall; b) 24-h dietary recall and average urinary excretion (n = 36); c) food frequency questionnaire and average urinary excretion (n = 36); d) agreement of the daily energy-adjusted sodium intake between the FFQ-FHS 1 and 2 *Absolute differences between values from the ordinate axes and mean intake of the abscissa axes. SD = standard deviation, AVG = average.
The correlation and reproducibility coefficients of FFQ-FHS were moderate. The classification by tertiles of sodium intake and energy-adjusted sodium as estimated by the FFQ-FHS survey showed the exact agreement rates between 50% and 66% (Table 5).
Aracaju, SE, 2018 (n = 50).
Discussion
The correlations and validity coefficients found in this study were ranked as low to moderate. The correlation of unadjusted sodium between the FFQ survey and 24hR was weak, but was significant (r = 0.386; p<0.01), which eventually lost significance after adjustment for energy intake. The minimum correlation was 0.4 for the relative validity of an FFQ [29]. FFQ validation studies using FR or 24hR as a reference method also found non-significant associations and, sometimes, negative associations. Sodium correlations varied from 0.07 to 0.43 [1, 30–32].
The validity coefficients for daily energy-adjusted intake were considered high for the 24hR (ρRAI = 0.85) and weak for the FFQ-FHS survey (ρFFQAI = 0.26) and B assessment (ρBAI = 0.20). The findings of this study were similar to those of Pereira et al. [1], in which the B assessment showed the weakest validity coefficient (ρBAI = 0.21), while the 24hR showed the highest validity coefficient (ρRAI = 0.56). The validity coefficients based on unadjusted sodium levels were not verifiable owing to the negative correlation. Negative correlations have been reported in previous studies [9, 33].
In addition to the calculation of correlation and validity coefficients, the use of different analyses to estimate the validity of an instrument is important. Although the triad method was chosen for this study, other statistical tests were conducted to ensure the greater reliability of results, such as the weighted Kappa agreement and Bland–Altman [26] plots. Furthermore, the recommendations for carrying out an FFQ validation study to assess the sodium intake were followed according to McLean et al.’s study [10].
According to Kaaks [4], the FFQ survey and 24hR exhibit similar errors, which overestimate the correlation and, consequently, the validity coefficient. On the contrary, the correlation between the FFQ survey and B assessment was weak owing to the intra-individual influence; the B reflects the short-term intake, restricted to a short period of time, while the FFQ assesses the long-term intake. Strong correlations (r>0.7) produce high validity coefficients, whereas weak correlations (r<0.3) generate undesirable validity coefficients [4].
The quantification of sodium intake remains a challenging task. In addition to its great variability, sodium metabolic balance is sensitive to changes inherent to each individual [34]. Even for individuals who constantly consume salt (6, 9, and 12 g/day), urinary sodium excretion will not remain constant. The variation in excretion is a reflection of the biological rhythm; that is, salt intake does not necessarily reflect the corresponding excretion [34].
A single 24-h urine specimen is probably inadequate for validating the FFQ [10]. A single 24-h urine specimen, not with standing whether properly collected, cannot determine a 3-g difference in daily salt intake [34]. To assess for sodium intake, the 24-h urine collection should be repeated 2–7 times [10]. In the literature, urinary excretion tests to assess sodium intake varied from those using a single sample [1, 35] to six collected specimens [36]. Regardless of the number of repetitions of urinary excretion to assess for sodium intake, the correlations remained weak. The need for more urinary excretion repetitions can justify the difficulty in validating the instrument.
Other studies corroborate that the correlations between urinary excretion and 24hR/FR are stronger than those between FFQ survey and urinary excretion [23, 36]. Day et al. [36] used six 24-h urine specimens to measure the sodium, potassium, and nitrogen intakes. The FFQ survey and 7-day FR underestimated the sodium intake, and the correlation between urinary excretion and the FFQ survey (r = 0.13) was lower than that with the FR(r = 0.36). Some studies reported that the correlations between dietary intake assessment methods are stronger than those between methods using Bs (urinary sodium excretion) because the different dietary intake assessment tools have correlating and systematic errors, which are different and independent from the errors inherent to a B analysis [4, 35, 36].
Tangney et al. [32] also reported the underestimation of sodium consumption. The food items in the assessment tool did not identify the main sources of sodium. A dietary assessment tool can never be expected to capture 100% of the sodium intake, but it is expected to provide a reliable estimate [32]. The selection of foods that comprise the FFQ for habitual sodium intake must include foods and/or dishes usually consumed by the population, considering their contribution to sodium intake, as well as processed and ultra-processed foods, because it is believed that the higher sodium intake is attributed to the consumption of these food items [11]. According to Cooper et al. [37], one of the limitations in the development of the Sodium AnaLysis Tool (SALT) was the absence of ethnic dishes, which are typical preparations of the study population. This may have interfered with the responses of individuals who consumed food items other than those listed in the instrument. Inclusion of these food items remains a challenge as the use of an extensive list can make the interview tiring and diminish the quality of the interviewee’s response, while the use of a short list makes it difficult to identify the variability of individual diets [38].
Dietary sodium has intrinsic peculiarities. The assessment of dietary sodium intake using an FFQ is limited by the fact that the list of food items with high sodium content is primarily composed of processed and ultra-processed foods, in which the in natura or minimally processed foods that comprise the main meals, lunch and dinner, of a Brazilians’ diet are not represented. In addition to the sodium present in foods, it is necessary to consider the amount of salt as a culinary ingredient and the added salt at the table.
With regard to the study limitations, the 24hR was performed prior to the FFQ-FHS survey, which was in disagreement with the recommendations of some authors [14]. According to Willet [29], the time and effort undertaken by the research participants to complete the records and dietary recalls, either by direct weighing or by recording what was consumed, may influence the completion of subsequent FFQ. However, if the FFQ is administered prior to the reference method (24hR/FR), these records can be influenced by the FFQ responses and do not reflect the reality [29]. In addition, McLean et al. [10] recommended that in order to assess the sodium intake, the 24-h urinary excretion test should be used as a reference tool; thus, the order of administration of FFQ and 24hR would not be important in this case.
In this study, both instruments were administered during in-person interviews, and not self-reported, using a photographic manual as an aid for completion of the 24hR and a specific album of photos showing the portion size of the FFQ-FHS items. As a positive aspect, the average dietary sodium intake was obtained by conducting a 24hR four times, which exceeds number of 24hRs performed in diverse validation studies (three repetitions of the 24hR or FR) [1, 9, 23]. The 24-h urinary excretion test was then repeated. Nonetheless, owing to the population profile, there was considerable sample loss, and a large portion of the urine specimens were collected on the weekend. This might have led to the misrepresentation of sodium that is usually consumed during the week. Because it is difficult to collect, the participants may not have collected all urine samples voided in 24 h, although they received oral and written instructions.
Currently, the existing FFQs are not valid for dietary sodium assessment because they show poor agreement. Thus, 24-h urine remains the gold standard for estimating the intake of this nutrient [10]. Perhaps the validation of an FFQ as the only tool for estimating sodium intake is not the best method. The singularities of this nutrient, with a high influence of intra-individual variability, made it difficult or even impossible to assess the actual intake [34].
The FFQ-FHS was demonstrated to be reproducible over a period of 6 months, with unadjusted correlation coefficients (0.686) and energy-adjusted ones (0.541) considered acceptable. Cooper et al. [37] also found a significant correlation between these two SALT applications (p = 0.001).
According to Cade et al. [14], the reproducibility of a short FFQ can be determined by considering the unadjusted data. Usually, the correlations of instruments re-administered within longer intervals, 6 months to 1 year apart, are lower than those of instruments re-administered within a shorter interval.
Hence, the FFQ-FHS should be used as a complement to other methods of assessing sodium intake. The combined use of the FFQ and 24hR enables a better detailing of the eating habits and may be especially useful for identifying the foods that are not consumed for many days and, therefore, might have been totally forgotten during the 24hR [29].
In conclusion, despite the administration of four 24hRs and the repeated 24-h urinary sodium excretion, the instrument was not considered valid for the assessment of sodium intake and cannot be used alone for this purpose; however, it may be convenient as a complement to the reference method. The FFQ-FHS was reproducible over a 6-month period.
Acknowledgments
We thank all the participants who agreed to participate in this study. We also thank TMSV, GBC, RKFS, ACOB, Hypertension Nutrition Care Group (Grupo de Atendimento Nutricional em Hipertensão, GANuH), and Nutrition Laboratory (Laboratório de Nutrição, LABNUT) for assistance in collecting data and analyzing biological samples.
References
- 1. Pereira TSS, Cade NV, Mill JG, Sichieri R, Molina MD. Use of the method of triads in the validation of sodium and potassium intake in the Brazilian longitudinal study of adult health (ELSA-Brasil). PLOS ONE. 2016;11: e0169085. pmid:28030625
- 2. Miranda Alatriste PV, Ramírez EC, Guillén Hernández M, Rivera Mancía S, Cartas Rosado R, Espinosa-Cuevas Á, et al. Development of a screening tool for a high sodium intake in an adult Mexican population. Nutr hosp. 2018;35: 1163–1173. pmid:30307301
- 3. Gallani MC, Proulx-Belhumeur A, Almeras N, Després JP, Doré M, Giguère JF. Development and validation of a salt food frequency questionnaire (FFQ-Na) and a Discretionary Salt Questionnaire (DSQ) for the evaluation of salt intake among French-Canadian population. Nutrients. 2020;13: 105. pmid:33396829
- 4. Kaaks RJ. Biochemical markers as additional measurements in studies of the accuracy of dietary questionnaire measurements: conceptual issues. Am J Clin Nutr. 1997;65(suppl): 1232S–1239S. pmid:9094927
- 5. Ocké MC, Kaaks RJ. Biochemical markers as additional measurements in dietary validity studies: application of the method of triads with examples from the European Prospective Investigation into Cancer and Nutrition. Am J Clin Nutr. 1997;65(4)(suppl): 1240S–1245S. pmid:9094928
- 6. Kaaks R, Ferrari P, Ciampi A, Plummer M, Riboli E. Uses and limitations of statistical accounting for random error correlations, in the validation of dietary questionnaire assessments. Public Health Nutr. 2002;5: 969–976. pmid:12638598
- 7.
Cardoso MA. Desenvolvimento, validação e aplicações de Questionários de freqüência alimentar em estudos epidemiológicos. In: Kac G, Sichieri R, Gigante DP, editors. Epidemiologia nutricional. Rio de Janeiro: Editora Fiocruz/Atheneu; 2007.
- 8. Da Silva DCG, Segheto W, de Lima MFC, Pessoa MC, Pelúzio MCG, Marchioni DML, et al. Using the method of triads in the validation of a food frequency questionnaire to assess the consumption of fatty acids in adults. J Hum Nutr Diet. 2018;31: 85–95. pmid:28744912
- 9.
Yokota RTC. Validação do questionário de freqüência de consumo de ácidos graxos com o uso de biomarcadores e aplicação do método das tríades. 119f. [Dissertação de mestrado]. Brasília: Universidade de Brasília; 2008.
- 10. McLean RM, Farmer VL, Nettleton A, Cameron CM, Cook NR, Campbell NRC, et al. Assessment of dietary sodium intake using a food frequency questionnaire and 24-hour urinary sodium excretion: a systematic literature review. J ClinHypertens (Greenwich). 2017;19: 1214–1230. pmid:29220554
- 11. Sarno F, Claro RM, Levy RB, Bandoni DH, Monteiro CA. Estimativa de consumo de sódio pela população brasileira, 2008–2009. Rev Saúde Pública. 2013;47: 571–578. pmid:24346570
- 12. Mill JG, Malta DC, Machado ÍE, Pate A, Pereira CA, Jaime PC, et al. Estimation of salt intake in the Brazilian population: results from the 2013 National Health Survey. Rev Bras Epidemiol. 2019;22Suppl: E190009.SUPL.2. pmid:31596380
- 13.
World Health Organization. Guideline. Sodium intake for adults and children. Available from:https://apps.who.int/iris/bitstream/handle/10665/77985/9789241504836_eng.pdf?sequence=1. Genebra. 6. World Health Organization; 2012. p. 86.
- 14. Cade J, Thompson R, Burley V, Warm D. Development, validation and utilisation of food-frequency questionnaires–a review. Public Health Nutr. 2002;5: 567–587. pmid:12186666
- 15. Santos TJFd dos Souza DS, Vasconcelos NLD Oliveira JVC, Voci SM Pires LV, et al. Instrumento para análise de consumo de alimentos com alto teor de sódio. BJDV. 2021;7: 19908–19920.
- 16. Agência nacional de vigilância Sanitária (ANVISA). Resolução–RDC n. 24, de 15 de junho de 2010. [Acesso em 2018 abr 15]. Disponível em. Availablefrom: http://portal.anvisa.gov.br/documents/.
- 17.
Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa de orçamentos familiares 2008–2009: análise do consumo alimentar pessoal no Brasil. Rio de Janeiro: IBGE: Coordenação de Trabalho e Rendimento; 2011a. 150p.
- 18.
Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa de orçamentos familiares 2008–2009: tabela de medidas referidas para alimentos consumidos no Brasil. Rio de Janeiro: IBGE, Coordenação de Trabalho e Rendimento; 2011b. 540p.
- 19. Raper N, Perloff B, Ingwersen L, Steinfeldt L, Anand J. An overview of USDA’s Dietary Intake Data System. Journal of Food Composition and Analysis. 2004;17(3–4): 545–555.
- 20.
Crispim SP, Fisberg RM, Marchioni DML, Steluti J. Manual fotográfico de quantificaçãoalimentar. 1st ed. Curitiba: Universidade Federal do Paraná; 2017. 147p.
- 21.
Sociedade Brasileira de Patologia Clínica/Medicina Laboratorial (SBPC/ML). Recomendações da Sociedade Brasileira de Patologia Clínica/Medicina Laboratorial (SBPC/ML): coleta e preparo da amostra biológica. Barueri, SP: Manole: Minha Editora. 2014: 487p.
- 22.
Nemer ASA, Neves FJ, Ferreira JES. Manual de Solicitação e Interpretação de Exames Laboratoriais. 1ed. Revinter: 2010.
- 23. Charlton KE, Steyn K, Levitt NS, Jonathan D, Zulu JV, Nel JH. Development and validation of a short questionnaire to assess sodium intake. Public Health Nutr. 2008;11: 83–94. pmid:17582243
- 24.
Brasil. Ministério da Saúde. Orientações para a coleta e análise de dados antropométricos em serviços de saúde: norma Técnica do Sistema de Vigilância Alimentar e Nutricional–SISVAN. Brasília: Ministério da Saúde; 2011.
- 25.
Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa de orçamentos familiares 2008–2009: tabelas de composição nutricional dos alimentos consumidos no Brasil. Rio de Janeiro: IBGE: Coordenação de Trabalho e Rendimento; 2011c. 345p.
- 26. Bland JM, Altman DG. Comparing methods of measurement: why plotting difference against standard method is misleading. Lancet. 1995;346: 1085–1087. pmid:7564793
- 27. Aoun C, Bou Daher R, El Osta N, Papazian T, Khabbaz LR. Reproducibility and relative validity of a food frequency questionnaire to assess dietary intake of adults living in a Mediterranean country. PLOS ONE. 2019;14: e0218541. pmid:31206566
- 28.
German Institute of Human Nutrition Potsdam-Rehbrücke–DIfE, Department of Epidemiology. The Multiple Source Method (MSM)(version 1.0.1). [accessed 2019 jun 07].Available from: https://msm.dife.de/.
- 29.
Willett WC. Nutritional. Epidemiology. 3. ed. New York: Oxford University Press. 2012: 552p.
- 30. Xu L J Dibley MJ, D’Este C. Reliability and validity of a food-frequency questionnaire for Chinese postmenopausal women. Public Health Nutr. 2004;7: 91–98. pmid:14972077
- 31. Talegawkar SA, Tanaka T, Maras JE, Ferrucci L, Tucker KL. Validation of nutrient intake estimates derived using a semi-quantitative ffq against 3 day diet records in the Baltimore longitudinal study of aging. J Nutr Health Aging. 2015;19: 994–1002. pmid:26624210
- 32. Tangney CC, Rasmussen HE, Richards C, Li M, Appelhans BM. Evaluation of a brief sodium screener inTwo samples. Nutrients. 2019;11: 166–173. pmid:30646541
- 33. Mcnaughton SA, Marks GC, Gaffney P, Williams G, Green A. Validation of a food-frequency questionnaire assessment of carotenoid and vitamin E intake using weighed food records and plasma biomarkers: the method of triads model. Eur J Clin Nutr. 2005;59: 211–218. pmid:15483635
- 34. Lerchl K, Rakova N, Dahlmann A, Rauh M, Goller U, Basner M, et al. Agreement between 24-hour salt ingestion and sodium excretion in a controlled environment. Hypertension. 2015;66: 850–857. pmid:26259596
- 35. Lassale C, Guilbert C, Keogh J, Syrette J, Lange K, Cox DN. Estimating food intakes in Australia: validation of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) food frequency questionnaire against weighed dietary intakes. J Hum Nutr Diet. 2009;22: 559–566. pmid:19735348
- 36. Day N, McKeown N, Wong M, Welch A, Bingham S. Epidemiological assessment of diet: a comparison of a 7-day diary with a food frequency questionnaire using urinary markers of nitrogen, potassium and sodium. Int J Epidemiol. 2001;30: 309‐317. pmid:11369735
- 37. Cooper M, Simpson JR, Klutka R. Development and validation of a sodium AnaLysis tool (SALT). Nutr J. 2020;19: 55. pmid:32539782
- 38.
Thompson FE, Subar AF. Dietary assessment methodology. In: Coulston AM, Boushey CJ, Ferruzzi MG, editors (Orgs.). Nutrition in the prevention and treatment of disease. 3. ed. San Diego: Academic Press; 2013.