Validation of the Brasil-Nutri software for 24-hour dietary recall using recovery biomarkers

ABSTRACT

Objective:  To evaluate measurement errors in duplicate 24-hour dietary recalls, guided by the Brasil-Nutri software, using recovery biomarkers for energy, protein, sodium, and potassium in a subsample of participants from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

Methods:  Apparently healthy men and women with stable body weight were included in the study. Two non-consecutive 24-hour dietary recalls, guided by the Brasil-Nutri software, were compared with recovery biomarkers of protein, potassium, and sodium obtained from two 24-hour urine samples. Total energy expenditure was assessed using the doubly labeled water method.

Results:  Eighty individuals, aged 43 to 72 years, participated in the study. Self-reported energy intake (2,146 kcal/day) was lower than the total energy expenditure estimated by the doubly labeled water method (2,526 kcal/day), with a mean difference of 380 kcal/day. Self-reported protein intake (94.9 g/day) was higher than the biomarker estimate (87.6 g/day). For sodium, mean self-reported intake was 2.9 g/day compared with 4.7 g/day estimated by the biomarker, while potassium intake was 2.9 g/day versus 3.0 g/day, respectively.

Conclusion:  The Brasil-Nutri software showed acceptable performance for estimating population mean intake of some nutrients; however, important measurement errors were observed, particularly for sodium intake. In addition, correlations with recovery biomarkers were low to moderate, indicating limitations in individual-level agreement.

Keywords:
Software; Diet surveys; Energy expenditure; Biomarker; Validation study

RESUMO

Objetivo:  Avaliar erros de medida em recordatórios alimentares de 24 horas duplicados, guiados pelo software Brasil-Nutri, utilizando biomarcadores de recuperação para energia, proteína, sódio e potássio em uma subamostra de participantes do Estudo Longitudinal de Saúde do Adulto (ELSA-Brasil).

Métodos:  Homens e mulheres aparentemente saudáveis e com peso corporal estável foram incluídos no estudo. Dois recordatórios alimentares de 24 horas não consecutivos, guiados pelo software Brasil-Nutri, foram comparados com biomarcadores de recuperação de proteína, potássio e sódio obtidos a partir de duas amostras de urina de 24 horas. O gasto energético total foi avaliado por meio do método da água duplamente marcada.

Resultados:  Oitenta indivíduos, com idade entre 43 e 72 anos, participaram do estudo. A ingestão energética autorreferida (2.146 kcal/dia) foi inferior ao gasto energético total estimado pelo método da água duplamente marcada (2.526 kcal/dia), com diferença média de 380 kcal/dia. A ingestão autorreferida de proteína (94,9 g/dia) foi superior à estimada pelo biomarcador (87,6 g/dia). Para o sódio, a ingestão média autorreferida foi de 2,9 g/dia em comparação com 4,7 g/dia estimados pelo biomarcador, enquanto a ingestão de potássio foi de 2,9 g/dia versus 3,0 g/dia, respectivamente.

Conclusão:  O software Brasil-Nutri apresentou desempenho aceitável para estimar a ingestão média populacional de alguns nutrientes; entretanto, erros de medida importantes foram observados, particularmente para a ingestão de sódio. Além disso, as correlações com os biomarcadores de recuperação foram baixas a moderadas, indicando limitações na concordância em nível individual.

Palavras-chave:
Software; Inquéritos sobre dietas; Gasto energético; Biomarcador; Estudo de validação

INTRODUCTION

Food intake is commonly assessed using self-report-based dietary methods that are subject to random and systematic errors¹. Understanding these errors is essential to interpret the results of studies in nutritional epidemiology or to correct for them. To evaluate the validity of dietary assessment methods, their estimates are compared with those obtained from reference methods with independent measurement errors²,³. Advances in dietary assessment technologies have reduced the time and costs required for data collection, coding, and data analysis. However, it is necessary to also validate these adapted methods⁴-⁹.

Brasil-Nutri is a computerized 24-hour dietary recall system developed to support national dietary surveys in Brazil, including the National Food Survey¹⁰-¹². The software includes an extensive food database based on previous national dietary surveys and epidemiological studies¹³.

Recovery biomarkers provide objective measures of dietary intake and are widely used in validation studies because their measurement errors are largely independent of self-reported methods¹. These biomarkers include 24-hour urinary excretion of nitrogen, sodium, and potassium, which can be used to estimate dietary protein and mineral intake¹⁴.

The aim of this study was to evaluate measurement errors in duplicate 24-hour dietary recalls collected using the Brasil-Nutri software by comparison with recovery biomarkers in a subsample of ELSA-Brasil participants. Energy intake was evaluated using the doubly labeled water (DLW) method, whereas protein, sodium, and potassium intakes were assessed using urinary recovery biomarkers. Performance was evaluated at both the population and individual levels.

METHODS

Study sample

The Longitudinal Study of Adult Health (ELSA-Brasil) is a multicenter cohort investigation comprising 15,105 participants, of both sexes, active and retired workers from six Brazilian states, with the main objective of investigating the incidence and risk factors for chronic non-communicable diseases¹⁵. The present study was carried out at the ELSA-Brasil research center in São Paulo, located at the Clinical and Epidemiological Research Center of the University Hospital of the University of São Paulo (CPCE/HU/USP).

Between August 2018 and December 2019, 101 adults and older adults of both sexes were recruited. Participant selection and exclusions are described in Figure 1. To validate the estimates of dietary protein, sodium, and potassium collected using the Brasil-Nutri software, 18 individuals were excluded due to missing dietary assessment data and/or incomplete 24-hour urine collection. Additionally, three participants without DLW information were excluded. Thus, the final analytical sample comprised 80 participants.

Figure 1
Volunteer recruitment flowchart.

All participants received detailed instructions about the collection protocol and signed an Informed Consent Form, indicating their intention to participate in this study. The study was approved by the Research Ethics Committee of the University Hospital of the University of São Paulo.

24-hour dietary recalls guided by the Brasil-Nutri software

Two non-consecutive 24-hour dietary recalls (24hR), with an interval of 3 to 4 weeks between assessments, were collected through the Brasil-Nutri software. The study protocol was designed so that the first 24hR interview was conducted face-to-face at the study center and the second, by telephone. However, for participants who had difficulty completing the telephone interview or presented limitations related to remote contact, the second interview was also conducted face-to-face in order to minimize losses to follow-up. The telephone interview strategy was adopted to evaluate the operational feasibility of remote dietary assessment, similarly to procedures used in large epidemiological studies. In addition, all participants completed an initial face-to-face interview with visual support for portion size estimation and household measures.

The 24hR interview guided by the Brasil-Nutri software begins with questions regarding the habitual use of salt and sugar or sweeteners, nutritional supplements, and adherence to restrictive diet patterns or specific diet therapies. The respondent was then asked to mention all food and beverages consumed 24 hours before, the place and time of consumption, as well as details about each food and beverage, including the method of preparation, type of unit, and the amount of consumption in household measurements or standard units, illustrated by images on the computer screen¹⁶. Nutrient estimation was based on the Brazilian Food Composition Table (TBCA/USP)¹⁷-¹⁹. Sodium values available in the food composition database include intrinsic sodium, sodium added during food processing, and estimated sodium from culinary preparations.

Anthropometric and lifestyle data

Each participant completed a questionnaire on socioeconomic characteristics and lifestyle factors. Physical activity was classified according to the International Physical Activity Questionnaire (IPAQ) as light, moderate, or vigorous²⁰. Due to the small number of participants in the vigorous category, moderate and vigorous levels were combined. Social class was categorized in accordance with the Criteria for Economic Classification of the Brazilian Association of Research Companies, with class high having ≥45 points; class middle ≥29 and ≤44 points, and class low ≤28 points²¹.

Anthropometric measurements were performed duplicate, with the participants wearing light clothing, without shoes and adornments, and with empty pockets. Height, weight, and waist circumference were measured to the nearest of 0.1 cm, 0.1 kg, and 0.1 cm, respectively, with calibrated equipment at the beginning and end of the study. Average weight and height measurements were used to calculate the body mass index (BMI [kg/m²]) and classify the nutritional status of the individuals, following the criteria of the World Health Organization²² for adults and the Lipschitz criteria²³ for older people. Participants were classified as: 1) underweight with BMI<18.5 kg/m² for adults and BMI<22 kg/m² for older people; 2) normal weight (BMI 18.5–24.9 kg/m²) for adults and BMI<27 kg/m² for older people; and 3) overweight with BMI≥25 kg/m² for adults and BMI≥27 kg/m² for older participants. However, as only one participant was underweight, this category was combined with the normal-weight group.

Recovery biomarkers

Two 24-hour urine samples (24hU) were collected from each participant as part of the study protocol. The first 24hU sample was collected on the same day as the first 24hR assessment. The second 24hU sample was collected after completion of the two 24hR assessments, during a subsequent evaluation period. The average interval between urine collections was six months (minimum of one and maximum of 14 months). For the analyses, the mean of the two urine collections was used as the reference biomarker measure in order to reduce within-person variability and improve estimation of habitual intake.

24hU collections that had a collection interval of less than 22 hours or greater than 26 hours were considered invalid. Total urine collection time was calculated as the interval between the recorded start and end times of collection. Collections with a 24hU volume lower than 500 mL or with total creatinine excretion outside the expected range, <14.4 or >33.6 mg/kg for men and <10.8 or >25.2 mg/kg for women, as suggested by Pereira et al.²⁴, were also considered invalid.

The determination of urinary urea was performed by a kinetic method (urease and glutamate dehydrogenase), performed by the Cobas c501^® automated analyzer (Roche^®). Values of total urea excretion in 24h were converted to nitrogen (g/day), assuming that urea is a constant/stable portion (85%) of the total urinary nitrogen²⁵,²⁶. Next, protein intake was calculated by the following formula: protein intake = 6.25 * (urinary nitrogen (g/day) + 2), where the +2 represents extra-renal nitrogen losses (stool and skin) in g/day²⁷. Urinary potassium concentrations were measured using the ion-selective electrode technique. Dietary potassium intake was calculated by dividing urinary potassium excretion by 0.77. Urinary sodium concentrations were measured using the ion-selective electrode technique. Dietary sodium intake was calculated by dividing urinary sodium excretion by 0.86, as per Freedman et al.²⁸.

Total energy expenditure (TEE) over a 2-week period was assessed using the double labeled water (DLW) in 101 participants. The DLW protocol was repeated in 26 participants to evaluate reproducibility and improve the precision of TEE estimates. Doses were prepared with a ratio of 2 g of 10% ¹⁸O and 0.11 g of 99.8% deuterium oxide (²H₂O) per kilogram of estimated total body water²⁹. A baseline urine sample was collected before the participants ingested the DLW dose. After ingestion, a sample was collected every day for 14 days, preferably at the same time at the participants’ homes. On the 7th and 14th days of collection, the samples of the week were delivered to the laboratory, and the samples of the day were collected at the time of the interview at the investigation center. However, only samples of baseline and of days 1, 2, 3, 7, 8, 13, and 14 were analyzed. All samples were refrigerated until delivered for processing. Enrichment of ²H and ¹⁸O isotopes in urine samples was analyzed in triplicate using isotope ratio mass spectrometry (IRMS-GLS: ANCA 20-20, Europe Scientific, UK and ANCA 22-20, Sercon, UK) at the Laboratory of Isotope Ratio Mass Spectrometry, Faculty of Medicine of Ribeirão Preto, University of São Paulo, Brazil. The decay ratio values between ²H and ¹⁸O ranged from 1.01 to 1.07. Body weight was monitored throughout the DLW measurement period to ensure participants were not losing or gaining weight. Participants were considered weight-stable when the weight difference between days 1 and 14 was <1 kg.

Statistical analysis

Data referring to socioeconomic, anthropometric, and lifestyle characteristics were described by frequencies, and associations between categorical variables were tested using the chi-square or Fisher's exact test. The values of dietary variables and biomarkers were presented in crude form. Difference (dietary intake minus biomarker), ratio (dietary intake / biomarker) and relative bias {[(ln dietary intake / ln biomarker) × 100] − 100}, representing the percentage of misreporting at the population level, were estimated²⁸. Agreement between methods at both the group and individual levels, overall and stratified by sex, was evaluated using Bland–Altman plots³⁰. For the other analyses, the values obtained on two non-consecutive days were adjusted for the distribution of intake by intrapersonal variability, using the Multiple Source Method (MSM) software³¹. Correlation coefficients between dietary variables and their respective biomarkers to evaluate whether the method was able to rank individuals according to their intake were calculated using Pearson correlation coefficient, and Fisher's Z transformations were used to calculate 95% confidence intervals of the correlation coefficients. In all analyses, a significance level of 5% was considered, and the statistical treatment of the data was carried out using Stata^® software (version 14.0).

To interpret the validity of the dietary assessment method, different aspects of performance were considered. Agreement at the population level was evaluated by comparing mean dietary intake estimates with recovery biomarkers through differences, ratios, and relative bias. The ability to rank individuals according to intake was assessed using correlation coefficients between dietary estimates and biomarkers, with correlations classified as low (<0.30), moderate (0.30–0.50), or strong (>0.50). Agreement at the individual level was additionally evaluated using Bland–Altman plots and limits of agreement.

Data availability statement:

The data described in the manuscript can be made available upon request, pending application and approval by the chair of the steering committee for the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) Brasil-Nutri validation study.

RESULTS

Among the 80 study participants, 43 were women and 37 were men. Most (86.3%) were aged between 43 and 59 years. More than half had completed a university degree or postgraduate studies (56.2%), 62.5% belonged to the middle socioeconomic class, 55.0% reported a light level of physical activity, and 63.8% were classified as overweight (Table 1).

Table 1
Socioeconomic and lifestyle characteristics of a sub-sample from the Longitudinal Study of Adult Health (ELSA-Brasil) used for validation of the Brasil-Nutri software.

		Total (n=80)		Men (n=37)		Women (n=43)		p*
		N	%	n	%	N	%
Age group
	Adult	69	86.3	32	86.5	37	86.1	0.955
	Older people	11	13.7	5	13.5	6	13.9
Education
	High school or less	35	43.8	19	51.4	16	37.2	0.204
	University degree/Postgraduate	45	56.2	18	48.6	27	62.8
Social class
	High	17	21.2	7	18.9	10	23.3	0.787
	Top	50	62.5	23	62.2	27	62.8
	Low	13	16.2	7	18.9	6	13.9
Body mass index
	Normal weight	29	36.2	9	24.3	20	46.5	0.040
	Overweight	51	63.8	28	75.7	23	53.5
Physical activity
	Light	44	55.0	19	51.4	25	58.1	0.543
	Moderate to vigorous	36	45.0	18	48.6	18	41.9

• Displayed values are count (frequency)
• ^*
  p-value relative to the comparison between sexes according to the chi-square test.
• Adult: 43 to 59 years old; older people are aged 60 years or more.
  Social class according to the criteria of the Brazilian Association of Research Companies (ABEP): high is greater than or equal to 45 points; top is greater than or equal to 29 and less than or equal to 44 points; low is less than or equal to 28 points.
  Body mass index: normal weight is less than 25 kg/m² for adults and less than 27 kg/m² for older people; overweight is greater than or equal to 25 kg/m² for adults and greater than or equal to 27 kg/m² for older people
  Light physical activity: those who did not perform any physical activity for at least 10 continuous minutes during the week or who performed physical activity but insufficiently to be classified as active for not meeting the recommendations regarding frequency (greater than or equal to five days per week) or duration (greater than or equal to 150 minutes per week). Moderate-to-vigorous physical activity: those who met the recommendations of greater than or equal to three days per week and greater than or equal to 20 minutes per session of vigorous activity; greater than or equal to five days per week and greater than or equal to 30 minutes per session of moderate activity or walking; or greater than or equal to five days per week and greater than or equal to 150 minutes with any added activity.

Table 2 presents dietary intake estimates for energy, protein, sodium, and potassium, their respective recovery biomarkers, and the correlations between the two methods. Mean self-reported energy intake (2,146.1 kcal/day) was lower than TEE estimated by DLW (2,526.5 kcal/day), resulting in a mean difference of 380.3 kcal/day. Relative bias indicated underestimation of energy intake in the total sample (-1.8%), as well as among men (-1.6%) and women (-2.0%).

Table 2
Dietary and biomarker energy, protein calculated from nitrogen, sodium, and potassium based on two days of 24-hourR recalls and two 24-hour urine collections or doubly labeled water in the Brasil-Nutri validation study.

Variables		Total (n=80)		Men (n=37)		Women (n=43)
		Mean	95%CI	Mean	95%CI	Mean	95%CI
Energy
	Diet (kcal/day)	2,146.1	(1,995.7 to 2,296.5)	2,413.9	(2,179 to 2,648.9)	1,915.6	(1,742.7 to 2,088.6)
	Biomarker (kcal/day)	2,526.5	(2,316.1 to 2,736.9)	2,834.9	(2,489.5 to 3,180.4)	2,261.1	(2,021.8 to 2,500.4)
	Difference (kcal/day)	-380.3	(-617.8 to -142.9)	-421.0	(-809.5 to -32.56)	-345.4	(-649.2 to -41.6)
	Ratio	0.9	(0.8 to 1.0)	0.9	(0.8 to 1.1)	0.9	(0.8 to 1.0)
	Relative bias (%)	-1.8	(-3.0 to -0.6)	-1.6	(-3.5 to 0.3)	-2.0	(-3.7 to -0.4)
	Crude correlation	0.15	(-0.07 to 0.36)	0.15	(-0.19 to 0.45)	-0.02	(-0.32 to 0.28)
Protein
	Diet (g/day)	94.9	(87.2 to 102.7)	111.5	(100.4 to 122.7)	80.6	(71.5 to 89.8)
	Biomarker (g/day)	87.6	(83.2 to 92.1)	98.1	(91.6 to 104.6)	78.7	(73.8 to 83.5)
	Difference (g/day)	7.3	(0.7 to 13.9)	13.5	(2.6 to 24.3)	1.9	(-6.1 to 10.1)
	Ratio	1.1	(1.0 to 1.1)	1.2	(1.0 to 1.3)	1.0	(0.9 to 1.1)
	Relative bias (%)	0.8	(-0.7 to 2.42)	2.3	(0.1 to 4.5)	-0.4	(-2.7 to 1.8)
	Crude correlation	0.6	(0.39 to 0.69)	0.4	(0.10 to 0.65)	0.5	(0.23 to 0.69)
	Deattenuated correlation1	0.6	(0.46 to 0.74)	0.3	(0.02 to 0.59)	0.4	(0.06 to 0.59)
Sodium
	Diet (g/day)	2.9	(2.7 to 3.3)	3.5	(3.0 to 3.9)	2.6	(2.3 to 2.9)
	Biomarker (g/day)	4.7	(4.4 to 5.0)	5.6	(5.1 to 6.1)	3.9	(3.6 to 4.2)
	Difference (g/day)	-1.7	(-2.1 to -1.3)	-2.1	(-2.8 to -1.4)	-1.4	(-1.7 to -0.9)
	Ratio	0.7	(0.6 to 0.8)	0.7	(0.6 to 0.8)	0.7	(0.6 to 0.8)
	Relative bias (%)	-30.7	(-37.1 to -24.3)	-27.7	(-37.5 to -18.0)	-33.2	(-42.1 to -24.4)
	Crude correlation	0.37	(0.16 to 0.55)	0.12	(-0.21 to 0.43)	0.27	(-0.03 to 0.53)
	Deattenuated Correlation1	0.52	(0.33 to 0.66)	0.02	(-0.31 to 0.34)	0.11	(-0.19 to 0.39)
Potassium
	Diet (g/day)	2.9	(2.7 to 3.1)	3.2	(2.9 to 3.5)	2.6	(2.3 to 2.9)
	Biomarker (g/day)	3.0	(2.9 to 3.2)	3.2	(2.9 to 3.5)	2.9	(2.6 to 3.1)
	Difference (g/day)	-0.2	(-0.4 to 0.1)	-0.0	(-0.3 to 0.3)	-0.3	(-0.6 to 0.1)
	Ratio	0.9	(0.9 to 1.1)	1.0	(0.9 to 1.1)	0.9	(0.8 to 1.1)
	Relative bias (%)	-3.7	(-11.9 to 4.6)	-0.1	(-10.6 to 10.3)	-6.7	(-19.6 to 6.1)
	Crude correlation	0.28	(0.06 to 0.47)	0.53	(0.24 to 0.73)	0.01	(-0.29 to 0.31)
	Deattenuated Correlation1	0.33	(0.12 to 0.51)	0.34	(0.02 to 0.60)	0.19	(-0.11 to 0.47)

• Displayed values are means and 95% confidence intervals (95%CI); Difference: dietary intake – biomarker; Ratio: dietary intake divided by biomarker; Relative bias: {[(ln dietary intake / ln biomarker) × 100] − 100}.
• ^*
  Dietary and biomarker measurements were obtained from 24-hour recalls and 24-hour urine collections, respectively.
• ¹
  Correlation with removal of intrapersonal variability by the Multiple Source Method (MSM).

Mean self-reported protein intake (94.9 g/day) was higher than the biomarker estimate (87.6 g/day), corresponding to a mean difference of 7.3 g/day. Among men, mean self-reported protein intake was 111.5 g/day compared with 98.1 g/day estimated by the biomarker, whereas among women the corresponding values were 80.6 g/day and 78.7 g/day. The correlation coefficient between protein intake and its biomarker was 0.60 (95%CI 0.39–0.69), remaining unchanged after adjustment for within-person variability (r = 0.60; 95%CI 0.46–0.74). For sodium, mean self-reported intake was 2.9 g/day, whereas the biomarker estimate was 4.7 g/day, resulting in a mean difference of 1.7 g/day. For potassium, mean self-reported intake was 2.9 g/day compared with 3.0 g/day estimated by the biomarker, corresponding to a mean difference of 0.2 g/day.

Table 3 presents the mean differences between dietary intake estimates and recovery biomarkers according to socioeconomic, anthropometric, and lifestyle characteristics. No statistically significant differences were observed across the evaluated groups. Nevertheless, underestimation was observed for energy, sodium, and potassium intake in most subgroups. For energy intake, greater underestimation was observed among older adults, overweight participants, and those reporting moderate-to-vigorous physical activity. A distinct pattern was observed across socioeconomic groups, with overreporting among participants in the lowest socioeconomic class and underreporting among those in the middle and highest classes. For sodium and potassium, differences between dietary estimates and biomarkers were relatively consistent across subgroups, although larger discrepancies were observed among older adults. In contrast, protein intake tended to be overestimated among adults, participants from the lowest socioeconomic class, overweight individuals, and those reporting light physical activity.

Table 3
Means and 95% confidence intervals of the difference between dietary and biomarker estimates for energy, protein, sodium, and potassium in the Brasil-Nutri software validation study.

Characteristics		Energy (kcal) difference		Protein (g) difference		Sodium (g) difference		Potassium (g) difference
Age group
	Adult	-321.3	(-586.9 to -55.8)	7.5	(0.0 to 14.9)	-1.7	(-2.0 to -1.2)	-0.1	(-0.3 to 0.2)
	Older people	-750.7	(-1,229.1 to -272.4)	6.1	(-8.4 to 20.7)	-2.2	(-3.2 to -0.8)	-0.6	(-1.4 to 0.2)
Education
	High school or less	-488.4	(-882.4 to -4.3)	6.9	(-2.8 to 16.7)	-1.9	(-2.6 to -1.2)	-0.2	(-0.5 to 0.2)
	University degree/Postgraduate	-327.5	(-596.7 to -58.3)	7.6	(-1.8 to 16.9)	-1.6	(-2.0 to -1.1)	-0.1	(-0.5 to 0.2)
Social class
	High	-493.8	(-1004.6 to 16.8)	0.8	(-5.6 to 7.2)	-1.5	(-2.2 to -0.9)	-0.6	(-1.0 to -0.1)
	Top	-449.6	(-760.2 to -139.1)	8.8	(-1.1 to 18.7)	-1.7	(-2.2 to -1.2)	-0.1	(-0.4 to 0.2)
	Low	34.4	(-592.1 to 661.0)	9.9	(-5.0 to 24.8)	-1.8	(-3.0 to -0.6)	0.1	(-0.5 to 0.7)
Body mass index
	Normal weight	-185.0	(-429.4 to 59.3)	1.9	(-10.7 to 14.7)	-1.5	(-1.9 to -0.9)	-0.5	(-0.9 to 0)
	Overweight	-491.5	(-839.6 to -143.3)	10.3	(2.6 to 17.9)	-1.8	(-2.4 to -1.3)	0.0	(-0.2 to 0.3)
Physical activity
	Light	-276.6	(-547.2 to -6.0)	8.6	(-0.9 to 18.1)	-1.6	(-2.1 to -1.1)	-0.2	(-0.5 to 0.2)
	Moderate to vigorous	-507.2	(-930.5 to -83.9)	5.6	(-3.9 to 15.1)	-1.8	(-2.4 to -1.3)	-0.1	(-0.5 to 0.2)

• ^*
  Negative values indicate underestimation.
• Adult: 43 to 59 years old; older people are aged 60 years or more.
  Social class according to the criteria of the Brazilian Association of Research Companies (ABEP): high is greater than or equal to 45 points; top is greater than or equal to 29 and less than or equal to 44 points; low is less than or equal to 28 points.
  Body mass index: normal weight is less than 25 kg/m2 for adults and less than 27 kg/m2 for older people; overweight is greater than or equal to 25 kg/m2 for adults and greater than or equal to 27 kg/m2 for older people
  Light physical activity: those who did not perform any physical activity for at least 10 continuous minutes during the week or who performed physical activity but insufficiently to be classified as active for not meeting the recommendations regarding frequency (greater than or equal to five days per week) or duration (greater than or equal to 150 minutes per week). Moderate-to-vigorous physical activity: those who met the recommendations of greater than or equal to three days per week and greater than or equal to 20 minutes per session of vigorous activity; greater than or equal to five days per week and greater than or equal to 30 minutes per session of moderate activity or walking; or greater than or equal to five days per week and greater than or equal to 150 minutes with any added activity.

Agreement analyses based on Bland–Altman plots demonstrated substantial variability between dietary estimates and recovery biomarkers at the individual level, particularly for energy and sodium intake (Supplementary Figure 1). Potassium showed comparatively narrower limits of agreement, whereas sodium exhibited the greatest dispersion of differences between methods.

DISCUSSION

This study evaluated measurement errors in 24-hour dietary recalls guided by the Brasil-Nutri software using recovery biomarkers in participants from the ELSA-Brasil study. At the population level, the performance of the method varied according to the nutrient evaluated, with overestimation for protein and underestimation for sodium and potassium when compared with recovery biomarkers. In addition, low to moderate correlations were observed between dietary estimates and biomarkers, particularly for energy intake, indicating limited agreement at the individual level. These findings suggest that, although the software may provide reasonable estimates for some nutrients at the population level, important measurement errors remain and should be carefully considered when interpreting the results.

An increasing number of studies have proposed technological innovations for collecting 24hR, including the use, for example, of smartphone applications and digital images, in order to overcome the logistical challenges in epidemiological studies⁵-⁹,³²,³³. Previous validation studies of online dietary assessment tools such as Oxford WebQ and MyFood24 have also reported measurement errors when compared with recovery biomarkers, including overestimation of some nutrients³²,³³. Other studies that compared the estimates of protein, sodium, and potassium obtained by 24hR with urinary biomarkers described a tendency to underestimate the intake of the three nutrients³⁴-³⁶. The present study showed an overestimation for protein (94.9 g versus 87.6 g) and underestimation for sodium (2.9 g versus 4.7 g) and potassium (2.9 g versus 3.0 g). In contrast to the underestimation commonly described for dietary intake assessment methods, protein intake was slightly overestimated in the present study. One possible explanation is that protein-rich foods are generally easier to identify and quantify during dietary recalls than nutrients such as sodium, which depend heavily on food preparation practices and discretionary salt use. In addition, the relatively high educational level of the study population may have contributed to more accurate reporting of main meals and protein-containing foods.

Potassium showed comparatively smaller differences and narrower limits of agreement than sodium. This finding may be related to the fact that potassium intake is largely derived from minimally processed foods such as fruits, vegetables, and legumes, which are generally easier to report and less influenced by discretionary preparation practices.

A North American study that used recovery biomarkers as the reference method and included only women reported a deattenuated correlation coefficient of 0.54 for energy-adjusted protein intake assessed using the Automated Self-Administered 24-Hour Dietary Recall (ASA24)³⁷, which is comparable to the correlation observed in the present study (r=0.60)³⁷. The correlation coefficients observed herein were comparable to those reported in previous validation studies using urinary biomarkers³⁸. For potassium, deattenuated correlations were 0.34 for men and 0.19 for women. For sodium, the corresponding values were 0.02 and 0.11, respectively. Although low to moderate, these correlations indicate that the dietary recalls were able to rank individuals according to intake, which is an important characteristic for epidemiological studies.

The substantial underestimation observed for sodium intake represents one of the main findings of this study and highlights an important limitation of the method. Estimating sodium intake through self-reported dietary assessment remains one of the greatest challenges in dietary assessment due to difficulties in quantifying discretionary salt added during food preparation and consumption, as well as the variability of sodium content in processed foods. In addition, sodium intake presents considerable intraindividual variability, which may further reduce agreement between dietary recalls and recovery biomarkers. Body weight has consistently been identified as a predictor of measurement bias in self-reported dietary assessment methods, with a higher degree of underreporting, especially of energy, associated with overweight and obesity³⁹-⁴⁵. Mercado et al.³⁸ described a greater underreporting of sodium intake in 24hR collected by the traditional method among overweight and obese individuals. In the present study, sodium estimates obtained by the 24hR compared with its biomarker were underestimated by 1.7 g for the total population and by 2.1 g and 1.4 g for men and women, respectively. Furthermore, underestimation was greater among overweight participants compared with normal-weight participants for this nutrient specifically (1.8 g versus 1.5 g). However, it is important to highlight that individuals with obesity were excluded from the present study as a deliberate methodological strategy to minimize factors previously associated with systematic misreporting in dietary assessment studies. Previous studies comparing dietary recalls with urinary biomarkers have also demonstrated low agreement and substantial underestimation for sodium intake at the individual level⁴⁶-⁴⁹.

Comparisons with studies involving obese populations should therefore be interpreted with caution; they were included herein primarily to contextualize patterns of dietary misreporting described in the literature. The applicability of the present findings to individuals with obesity remains uncertain and should be further investigated in future validation studies that include more heterogeneous populations.

Another important aspect is that estimating discretionary salt intake remains inherently challenging in dietary surveys, even when specific questions regarding added salt are included. In the Brasil-Nutri software, additional questions related to food preparation and seasoning, including salt addition, were systematically applied to all reported food items. Nevertheless, participants may fail to report salt added to salads and home-prepared foods. These factors may have contributed to the substantial underestimation observed for sodium intake in the present study.

Freedman et al. pooled data from several validation studies and reported energy underreporting ranging from 6% to 28%, depending on the study population and sex²⁸. In studies with the ASA 24, the estimated energy intake was lower than the TEE for both men and women⁵⁰,⁵¹. In the present study, the underreporting of energy intake with two 24hR using the Brasil-Nutri software was 1.6% for men and 2.0% for women. The underestimation observed for energy intake may be partially explained by omissions of foods perceived as unhealthy, difficulties in recalling snacks and foods consumed outside the home, and challenges in estimating portion sizes. In addition, energy intake is particularly susceptible to social desirability bias and selective underreporting, even among apparently healthy individuals.

The study has several strengths, including the use of strict participant selection criteria designed to minimize factors associated with dietary misreporting, such as chronic diseases, use of drugs/supplements, or with physiological conditions that could compromise the validity of biomarkers or the reporting of consumption; the use of recovery biomarkers for protein, sodium, and potassium validation; and rigorous quality control procedures throughout urine collection, storage, and laboratory analysis. Although the selected sample limits external validity, the inclusion of apparently healthy individuals without obesity or chronic diseases was a deliberate methodological strategy to minimize factors associated with systematic misreporting already described in the literature. This approach allowed evaluation of the software performance under more controlled conditions before broader application in more heterogeneous populations. Therefore, the findings should not be directly extrapolated to the Brazilian population as a whole. Another limitation is that the second biomarker assessment was performed during a later evaluation period, with an average interval of six months between urine collections. In addition, portion-size estimation during telephone interviews was not supported by on-screen food images.

The Brasil-Nutri software demonstrated acceptable performance for estimating mean population intake of some nutrients; however, important measurement errors remained, particularly for sodium intake. Correlations with recovery biomarkers were low to moderate, suggesting limitations in individual-level agreement. These findings support the use of the method with caution in population studies, while highlighting the need for further refinement, especially for sodium estimation.

ETHICS COMMITTEE APPROVAL:

This study was approved by the Research Ethics Committee of the University Hospital, University of São Paulo (CAAE: 79758417.2.00000.0076; Technical opinion No. 2385410).

FUNDING:

The São Paulo Research Foundation (FAPESP; processes No. 2016/20054-6 and No. 2018/08319-0) and the Brazilian National Research Council (CNPq; process No. 142983/2018-7) provided financial support for the study.

Supplementary Material

supplementary material

REFERENCES

• ¹ Corella D, Ordovás J. Biomarkers: background, classification and guidelines for applications in nutritional epidemiology. Nutr Hosp 2015; 31 Suppl 3: 177-88. https://doi.org/10.3305/nh.2015.31.sup3.8765
  » https://doi.org/10.3305/nh.2015.31.sup3.8765
• ² Kirkpatrick SI, Baranowski T, Subar AF, Tooze JA, Frongillo EA. Best Practices for Conducting and Interpreting Studies to Validate Self-Report Dietary Assessment Methods. J Acad Nutr Diet 2019; 119(11):1801-16. https://doi.org/10.1016/j.jand.2019.06.010
  » https://doi.org/10.1016/j.jand.2019.06.010
• ³ Thompson FE, Kirkpatrick SI, Subar AF, Reedy J, Schap TE, Wilson MM, et al. The National Cancer Institute's Dietary Assessment Primer: A Resource for Diet Research. J Acad Nutr Diet 2015; 115(12): 1986-95. https://doi.org/10.1016/j.jand.2015.08.016
  » https://doi.org/10.1016/j.jand.2015.08.016
• ⁴ Arab L, Wesseling-Perry K, Jardack P, Henry J, Winter A. Eight self-administered 24-hour dietary recalls using the Internet are feasible in African Americans and Whites: the energetics study. J Am Diet Assoc 2010; 110(6): 857-64. https://doi.org/10.1016/j.jada.2010.03.024
  » https://doi.org/10.1016/j.jada.2010.03.024
• ⁵ Thompson FE, Subar AF, Loria CM, Reedy JL, Baranowski T. Need for technological innovation in dietary assessment. J Am Diet Assoc 2010; 110(1): 48-51. https://doi.org/10.1016/j.jada.2009.10.008
  » https://doi.org/10.1016/j.jada.2009.10.008
• ⁶ Illner AK, Freisling H, Boeing H, Huybrechts I, Crispim SP, Slimani N. Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. Int J Epidemiol 2012; 41(4):1187-203. https://doi.org/10.1093/ije/dys105
  » https://doi.org/10.1093/ije/dys105
• ⁷ Crispim SP, Nicolas G, Casagrande C, Knaze V, Illner AK, Huybrechts I, Slimani N. Quality assurance of the international computerised 24 h dietary recall method (EPIC-Soft). Br J Nutr 2014; 111(3): 506-15. https://doi.org/10.1017/S0007114513002766
  » https://doi.org/10.1017/S0007114513002766
• ⁸ Bel-Serrat S, Knaze V, Nicolas G, Marchioni DM, Steluti J, Mendes A, et al. Adapting the standardised computer- and interview-based 24 h dietary recall method (GloboDiet) for dietary monitoring in Latin America. Public Heal Nutr 2017; 20(16): 2847-58. https://doi.org/10.1017/S1368980017001872
  » https://doi.org/10.1017/S1368980017001872
• ⁹ Steluti J, Crispim SP, Araujo MC, Peralta AM, Sichieri R, Yokoo EM, et al. Technology in Health: Brazilian version of the GloboDiet program for dietary intake assessment in epidemiological studies. Rev Bras Epidemiol 2020; 23: e200013. https://doi.org/10.1590/1980-549720200013
  » https://doi.org/10.1590/1980-549720200013
• ¹⁰ IBGE: Instituto Brasileiro de Geografia e Estatística [Internet]. Pesquisa de Orçamentos Familiares: 2008-2009. Análise do Consumo Alimentar Pessoal no Brasil. Rio de Janeiro; 2011.
• ¹¹ Instituto Brasileiro de Geografia e Estatística. Pesquisa de orçamentos familiares 2017-2018: análise do consumo alimentar pessoal no Brasil. Rio de Janeiro; 2020.
• ¹² Moshfegh AJ, Rhodes DG, Baer DJ, Murayi T, Clemens JC, Rumpler WV, et al. The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes. Am J Clin Nutr 2008; 88(2): 324-32. https://doi.org/10.1093/ajcn/88.2.324
  » https://doi.org/10.1093/ajcn/88.2.324
• ¹³ Bloch KV, Szklo M, Kuschnir MC, Abreu G de A, Barufaldi LA, Klein CH, et al. The Study of Cardiovascular Risk in Adolescents--ERICA: rationale, design and sample characteristics of a national survey examining cardiovascular risk factor profile in Brazilian adolescents. BMC Public Health 2015; 15: 94. https://doi.org/10.1186/s12889-015-1442-x
  » https://doi.org/10.1186/s12889-015-1442-x
• ¹⁴ Jenab M, Slimani N, Bictash M, Ferrari P, Bingham SA. Biomarkers in nutritional epidemiology: applications, needs and new horizons. Hum Genet. 2009; 125(5-6):507-25. https://doi.org/10.1007/s00439-009-0662-5
  » https://doi.org/10.1007/s00439-009-0662-5
• ¹⁵ Aquino EML, Barreto SM, Bensenor IM, Carvalho MS, Chor D, Duncan BB, et al. Brazilian Longitudinal Study of Adult Health (ELSA-Brasil): Objectives and Design. Am J Epidemiol 2012; 175(4): 315-24. https://doi.org/10.1093/aje/kwr294
  » https://doi.org/10.1093/aje/kwr294
• ¹⁶ Barufaldi LA, Abreu GA, Veiga GV, Sichieri R, Kuschnir MCC, Cunha DB, et al. Software to record 24-hour food recall: application in the Study of Cardiovascular Risks in Adolescents. Rev Bras Epidemiol 2016; 19: 464-8.
• ¹⁷ Tabela Brasileira de Composição de Alimentos [Internet]. Campinas: Nepa-Unicamp; 2005 [cited on Aug 12, 2022]. Available at: https://nepa.unicamp.br/wp-content/uploads/sites/27/2023/10/taco_4_edicao_ampliada_e_revisada.pdf
  » https://nepa.unicamp.br/wp-content/uploads/sites/27/2023/10/taco_4_edicao_ampliada_e_revisada.pdf
• ¹⁸ University of São Paulo. Food Research Center. Brazilian Food Composition Table [Internet]. 2017 [cited on Aug 12, 2022]. Available at: http://www.fcf.usp.br/tbca
  » http://www.fcf.usp.br/tbca
• ¹⁹ Giuntini EB, Coelho KS, Grande F, Marchioni DML, De Carli E, Sichieri R, et al. Brazilian Nutrient Intake Evaluation Database: An essential tool for estimating nutrient intake data. J Food Compos Anal 2019; 83: 103286.
• ²⁰ Matsudo S, Araújo T, Matsudo V, Andrade D, Andrade E, Oliveira LC, et al. Questionário Internacional de Atividade Física (IPAQ): estudo de validade e reprodutibilidade no Brasil. Rev Bras Ativ Fís Saúde 2001; 6(2): 5-18. https://doi.org/10.12820/rbafs.v.6n2p5-18
  » https://doi.org/10.12820/rbafs.v.6n2p5-18
• ²¹ Associação Brasileira de Empresas de Pesquisa. Critério de Classificação Econômica Brasil. ABEP; 2018.
• ²² World Health Organization. Physical status: The use and interpretation of anthropometry. Genebra: World Health Organization; 1995.
• ²³ Lipschitz D. Screening for nutritional status in the elderly. Prim Care 1994; 21(1): 55-67. PMID: 8197257
• ²⁴ Pereira T, Cade N, Mill J, Sichieri R, Molina M. 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(12): e0169085. https://doi.org/10.1371/journal.pone.0169085
  » https://doi.org/10.1371/journal.pone.0169085
• ²⁵ Bingham SA, Luben R, Welch A, Wareham N, Khaw KT, Day N. Are imprecise methods obscuring a relation between fat and breast cancer? Lancet 2003; 362(9379): 212-4. https://doi.org/10.1016/S0140-6736(03)13913-X
  » https://doi.org/10.1016/S0140-6736(03)13913-X
• ²⁶ Kroke A, Klipstein-Grobusch K, Voss S, Möseneder J, Thielecke F, Noack R, Boeing H. Validation of a self-administered food-frequency questionnaire administered in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study: comparison of energy, protein, and macronutrient intakes estimated with the doubly labeled water, urinary nitrogen, and repeated 24-h dietary recall methods. Am J Clin Nutr 1999; 70(4): 439-47. https://doi.org/10.1093/ajcn/70.4.439
  » https://doi.org/10.1093/ajcn/70.4.439
• ²⁷ Isaksson B. Urinary nitrogen output as a validity test in dietary surveys. Am J Clin Nutr 1980; 33(1): 4-5. https://doi.org/10.1093/ajcn/33.1.4
  » https://doi.org/10.1093/ajcn/33.1.4
• ²⁸ Freedman LS, Commins JM, Moler JE, Arab L, Baer DJ, Kipnis V, et al. Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake. Am J Epidemiol 2014; 180(2): 172-88. https://doi.org/10.1093/aje/kwu116
  » https://doi.org/10.1093/aje/kwu116
• ²⁹ Ferrioli E, Cruz BM, Pfrimer K. Use of Light Isotopes in Nutritional Sciences. In: Dutra JE, Marchini S Nutritional Sciences: Learning to Learn. 2nd ed. São Paulo: Sarvier 2008; 23: 443-64.
• ³⁰ Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 1(8476): 307-10. PMID: 2868172
• ³¹ Harttig U, Haubrock J, Knüppel S, Boeing H, EFCOVAL Consortium. The MSM program: web-based statistics package for estimating usual dietary intake using the Multiple Source Method. Eur J Clin Nutr 2011; 65 Suppl 1: S87-91. https://doi.org/10.1038/ejcn.2011.92
  » https://doi.org/10.1038/ejcn.2011.92
• ³² Greenwood DC, Hardie LJ, Frost GS, Alwan NA, Bradbury KE, Carter M, et al. Validation of the Oxford WebQ Online 24-Hour Dietary Questionnaire Using Biomarkers. Am J Epidemiol 2019; 188(10): 1858-67. https://doi.org/10.1093/aje/kwz165
  » https://doi.org/10.1093/aje/kwz165
• ³³ Wark PA, Hardie LJ, Frost GS, Alwan NA, Carter M, Elliott P, et al. Validity of an online 24-h recall tool (myfood24) for dietary assessment in population studies: comparison with biomarkers and standard interviews. BMC Med 2018; 16(1): 136. https://doi.org/10.1186/s12916-018-1113-8
  » https://doi.org/10.1186/s12916-018-1113-8
• ³⁴ Crispim SP, de Vries JH, Geelen A, Souverein OW, Hulshof PJ, Lafay L, et al. Two non-consecutive 24 h recalls using EPIC-Soft software are sufficiently valid for comparing protein and potassium intake between five European centres--results from the European Food Consumption Validation (EFCOVAL) study. Br J Nutr 2011; 105(3): 447-58. https://doi.org/10.1017/S0007114510003648
  » https://doi.org/10.1017/S0007114510003648
• ³⁵ Freisling H, van Bakel MM, Biessy C, May AM, Byrnes G, Norat T, et al. Dietary reporting errors on 24 h recalls and dietary questionnaires are associated with BMI across six European countries as evaluated with recovery biomarkers for protein and potassium intake. Br J Nutr 2012; 107(6): 910-20. https://doi.org/10.1017/S0007114511003564
  » https://doi.org/10.1017/S0007114511003564
• ³⁶ Rhodes DG, Murayi T, Clemens JC, Baer DJ, Sebastian RS, Moshfegh AJ. The USDA Automated Multiple-Pass Method accurately assesses population sodium intakes. Am J Clin Nutr 2013; 97(5): 958-64. https://doi.org/10.3945/ajcn.112.044982
  » https://doi.org/10.3945/ajcn.112.044982
• ³⁷ Yuan C, Spiegelman D, Rimm EB, Rosner BA, Stampfer MJ, Barnett JB, et al. Relative Validity of Nutrient Intakes Assessed by Questionnaire, 24-Hour Recalls, and Diet Records as Compared With Urinary Recovery and Plasma Concentration Biomarkers: Findings for Women. Am J Epidemiol 2018; 187(5): 1051-63. https://doi.org/10.1093/aje/kwx328
  » https://doi.org/10.1093/aje/kwx328
• ³⁸ Mercado CI, Cogswell ME, Valderrama AL, Wang CY, Loria CM, Moshfegh AJ, et al. Difference between 24-h diet recall and urine excretion for assessing population sodium and potassium intake in adults aged 18–39 y. Am J Clin Nutr 2015; 101(2): 376-86. https://doi.org/10.3945/ajcn.113.081604
  » https://doi.org/10.3945/ajcn.113.081604
• ³⁹ Mendez MA, Wynter S, Wilks R, Forrester T: Under- and overreporting of energy related to obesity, lifestyle factors and food group intakes in Jamaican adults. Public Health Nutr 2004; 7(1): 9-19. https://doi.org/10.1079/phn2003508
  » https://doi.org/10.1079/phn2003508
• ⁴⁰ Lissner L, Troiano R, Midthune D, Heitmann BL, Kipnis V, Subar AF, et al. OPEN about obesity: recovery biomarkers, dietary reporting errors and BMI. Int J Obes 2007; 31(6): 956-61. https://doi.org/10.1038/sj.ijo.0803527
  » https://doi.org/10.1038/sj.ijo.0803527
• ⁴¹ Moshfegh AJ, Rhodes DG, Baer DJ, Murayi T, Clemens JC, Rumpler WV, et al. The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes. Am J Clin Nutr 2008; 88(2): 324-32. https://doi.org/10.1093/ajcn/88.2.324
  » https://doi.org/10.1093/ajcn/88.2.324
• ⁴² Freisling H, van Bakel MM, Biessy C, May AM, Byrnes G, Norat T, et al. Dietary reporting errors on 24 h recalls and dietary questionnaires are associated with BMI across six European countries as evaluated with recovery biomarkers for protein and potassium intake. Br J Nutr 2012; 107(6): 910-20. https://doi.org/10.1017/S0007114511003564
  » https://doi.org/10.1017/S0007114511003564
• ⁴³ Mossavar-Rahmani Y, Shaw PA, Wong WW, Sotres-Alvarez D, Gellman MD, Horn LV, et al. Prentice, Applying Recovery Biomarkers to Calibrate Self-Report Measures of Energy and Protein in the Hispanic Community Health Study/Study of Latinos. Am J Epidemiol 2015; 181(12): 996-1007. https://doi.org/10.1093/aje/kwu468
  » https://doi.org/10.1093/aje/kwu468
• ⁴⁴ Trijsburg L, Geelen A, Hollman P, Hulshof P, Feskens E, Veer PV, et al. BMI was found to be a consistent determinant related to misreporting of energy, protein and potassium intake using self-report and duplicate portion methods. Public Health Nutr 2017; 20(4): 598-607. https://doi.org/10.1017/S1368980016002743
  » https://doi.org/10.1017/S1368980016002743
• ⁴⁵ Wehling H, Lusher J. People with a body mass index ≥30 under-report their dietary intake: A systematic review. J Health Psychol. 2019; 24(14): 2042-59. https://doi.org/10.1177/1359105317714318
  » https://doi.org/10.1177/1359105317714318
• ⁴⁶ McLean RM. Measuring population sodium intake: a review of methods. Nutrients 2014; 6(11): 4651-62. https://doi.org/10.3390/nu6114651
  » https://doi.org/10.3390/nu6114651
• ⁴⁷ Cogswell ME, Maalouf J, Elliott P, Loria CM, Patel S, Bowman BA. Use of Urine Biomarkers to Assess Sodium Intake: Challenges and Opportunities. Annu Rev Nutr 2015; 35: 349-87. https://doi.org/10.1146/annurev-nutr-071714-034322
  » https://doi.org/10.1146/annurev-nutr-071714-034322
• ⁴⁸ De Keyzer W, Dofková M, Lillegaard IT, De Maeyer M, Andersen LF, Ruprich J, et al. Reporting accuracy of population dietary sodium intake using duplicate 24 h dietary recalls and a salt questionnaire. Br J Nutr 2015; 113(3): 488-97. https://doi.org/10.1017/S0007114514003791
  » https://doi.org/10.1017/S0007114514003791
• ⁴⁹ McLean RM, Farmer VL, Nettleton A, Cameron CM, Cook NR, Campbell NRC; TRUE Consortium (International Consortium for Quality Research on Dietary Sodium/Salt). Assessment of dietary sodium intake using a food frequency questionnaire and 24-hour urinary sodium excretion: a systematic literature review. J Clin Hypertens (Greenwich) 2017; 19(12): 1214-30. https://doi.org/10.1111/jch.13148
  » https://doi.org/10.1111/jch.13148
• ⁵⁰ Park Y, Dodd KW, Kipnis V, Thompson FE, Potischman N, Schoeller DA, et al. Comparison of self-reported dietary intakes from the Automated Self-Administered 24-h recall, 4-d food records, and food-frequency questionnaires against recovery biomarkers. Am J Clin Nutr 2018; 107(1): 80-93. https://doi.org/10.1093/ajcn/nqx002
  » https://doi.org/10.1093/ajcn/nqx002
• ⁵¹ Subar AF, Potischman N, Dodd KW, Thompson FE, Baer DJ, Schoeller DA, et al. Performance and feasibility of recalls completed using the automated self-administered 24-hour dietary assessment tool in relation to other self-report tools and biomarkers in the interactive diet and activity tracking in AARP (IDATA) Study. J Acad Nutr Diet 2020; 120(11): 1805-20. https://doi.org/10.1016/j.jand.2020.06.015
  » https://doi.org/10.1016/j.jand.2020.06.015

CORRESPONDING AUTHOR:

Dirce Maria Lobo Marchioni. Avenida Doutor Arnaldo, 715, Cerqueira César, CEP: 01246-904, São Paulo (SP), Brazil. E-mail: [e-mail]

CONFLICT OF INTERESTS:

nothing to declare.

ASSOCIATED EDITOR:

Luana Patricia Marmitt https://orcid.org/0000-0003-0526-7954

SCIENTIFIC EDITOR:

Francisco Chiaravalloti Neto https://orcid.org/0000-0003-2686-8740

Data availability

The data described in the manuscript can be made available upon request, pending application and approval by the chair of the steering committee for the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) Brasil-Nutri validation study.

Publication Dates

• Publication in this collection
  19 Oct 2026
• Date of issue
  2026

History

• Received
  20 Feb 2026
• Reviewed
  15 June 2026
• Accepted
  03 Aug 2026