1. No category

N U TR ITI O N RE S EA R CH 3 4 ( 2 01 4 ) 7 4 –8 4
Available online at www.sciencedirect.com
ScienceDirect
www.nrjournal.com
Children's nutrient intake variability is affected by age and
body weight status according to results from a Brazilian
multicenter study
Michelle A. de Castro a,⁎, Eliseu Verly-Jr. b , Mauro Fisberg c , Regina M. Fisberg d
a
Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil
Department of Epidemiology, Institute of Social Medicine, State University of Rio de Janeiro, Rio de Janeiro, Brazil
c
Department of Pediatrics, Federal University of São Paulo, São Paulo, Brazil
d
Department of Nutrition, School of Public Health, University de São Paulo, Sao Paulo, Brazil
b
ARTI CLE I NFO
A BS TRACT
Article history:
A major challenge in nutritional studies focusing on children is estimating “true” intake
Received 21 May 2013
because the type and amount of foods eaten change throughout growth and development,
Revised 18 September 2013
thereby affecting the variability of intake. The present study investigated the hypothesis
Accepted 20 September 2013
that age and body weight status affect the ratio of the within- and between-subject variation
of intakes (VR) as well as the number of days of dietary assessment (D) of energy and
Keywords:
nutrients. A total of 2,981 Brazilian preschoolers aged 1–6 years were evaluated in a cross-
Children
sectional study. Weighed food records and estimated food records were used to assess
Diet
dietary intake inside and outside of school. Within- and between-subject variations of
Nutrition assessment
intakes were estimated by multilevel regression models. VR and D were calculated
Variation
according to age group and body weight status. VR ranged from 1.17 (calcium) to 8.70 (fat)
Multilevel analysis
in the 1- to 2-year-old group, and from 1.47 (calcium) to 8.95 (fat) in the 3- to 6-year-old
group. Fat, fiber, riboflavin, folate, calcium, phosphorus, and iron exhibited greater VR and D
in the 3- to 6-year-old group. For energy, carbohydrates, and protein, both within- and
between-subject variation increased with increasing age. In both body weight groups,
calcium showed the lowest VR. Fat showed the highest VR in nonoverweight/obese children
(9.47), and fiber showed the highest VR in overweight/obese children (8.74). For most
nutrients, D = 7 was sufficient to correctly rank preschoolers into tertiles of intake. In
conclusion, age and body weight status affected the within- and between-subject variation
and the VR of energy and nutrient intakes among Brazilian preschool children.
© 2014 Elsevier Inc. All rights reserved.
Abbreviations: BMI, body mass index; CVb, between-subject coefficient of variation; CVw, within-subject coefficient of variation; D,
number of days of dietary assessment; EFR, estimated food record; r, correlation coefficient; Sb2, between-subject variation of intake; SDw,
within-subject SD; SDb, between-subject SD; Sw2, within-person variation of intake; WFR, weighed food record; VR, ratio of the within- to
between-subject variation of intake.
⁎ Corresponding author. School of Public Health, University of São Paulo, Av Dr Arnaldo, 715 Cerqueira Cesar, 01246-90 São Paulo, SP, Brazil.
Tel.: +55 11 3061 7869; fax: +55 11 3061 7130.
E-mail addresses: [email protected] (M.A. Castro), [email protected] (E. Verly-Jr), [email protected] (M. Fisberg),
[email protected] (R.M. Fisberg).
0271-5317/$ – see front matter © 2014 Elsevier Inc. All rights reserved.
http://dx.doi.org/10.1016/j.nutres.2013.09.006
N U TR IT ION RE S EA R CH 3 4 ( 2 01 4 ) 7 4 –8 4
1.
Introduction
Dietary intake in infancy and childhood is of scientific interest
because of an increasing amount of evidence suggesting that
there is a relationship between early exposure to dietary
factors and the risk of developing noncommunicable
chronic diseases such as obesity, diabetes mellitus, and
cardiovascular diseases [1-3]. A major challenge in assessing dietary intake in infants and children is estimating
“true” food and nutrient intakes because the type and
amount of foods eaten change considerably during growth
and development, thus affecting the overall variability of
dietary intake.
Dietary variability mainly arises from changes in day-today food and nutrient intakes by an individual (withinsubject variation) as well as from differences in usual intake
among individuals (between-subject variation) [4]. From a
statistical standpoint, within-subject variation is an important source of error in the analysis and interpretation of data
[5] because it attenuates measures of association, such as
regression coefficients and relative risks, and introduces bias
in the proportion of individuals below or above the requirements of intake [6-9].
Within- and between-subject variation estimates can be
useful in identifying the number of days of dietary assessment (D) needed to evaluate energy and nutrient intakes in
groups of individuals at a given degree of precision [10-12].
For a particular dietary factor, the greater the variance ratio
(VR), that is, the ratio of the within-subject variation to the
between-subject variation, the greater D is needed to
evaluate the intake of the dietary factor with some degree
of precision [10,11].
Observational studies conducted in developed countries
have shown the influence of age, sex, culture, and socioeconomic status of the child on the magnitude of VR of
energy and nutrient intakes, as well as on D [9,12-15].
However, it remains unknown whether the body weight
status of a child may influence the VR and D of energy and
nutrient intakes. It has been suggested that overweight and
obese children have a lower ability to regulate the amount
of foods consumed during the day compared with normalweight children [16], which could affect the variation of
energy and nutrient intakes.
Considering this, our initial hypothesis is that the VR and
D of energy and nutrient intakes of Brazilian preschool
children would be different between overweight/obese
children and nonoverweight children, even after controlling
for age and sex effects. In addition, we also hypothesized
that older children (3-6 years) will exhibit greater VR and D
than younger ones (1-2 years). To investigate our hypotheses, we sought to estimate the within- and between-subject
variation of intakes through a multilevel model approach
and to calculate the VR and number of days of dietary
assessment of energy and nutrients, according to age group
and body weight status. With this study, we expect to
advance the current knowledge regarding factors related to
variability of intake in pediatric populations to help researchers obtain more precise estimates of dietary intake in
this group.
2.
Methods and materials
2.1.
Study population
75
Data were gathered from a large multicenter cross-sectional survey titled “Nutri-Brasil Infância” that was conducted
from February to December 2007. The survey was designed
to evaluate the nutritional risk by estimating the prevalence of inadequate nutrient intake among children from
daycare centers and preschools living in Brazil [17]. The
cities investigated were as follows: Manaus (northern
region), Recife and Natal (northeast region), Brasília and
Cuiabá (eastern-central region), Caxias do Sul (southern
region), and Belo Horizonte, Rio de Janeiro, and São Paulo
(southeast region).
For a total of 3150 children, 350 male and female children
between 1 and 6 years of age who attended public and private
daycare centers and preschools were invited from each of the
9 cities. Of the 350 children from each city, 250 were from
public institutions and 100 were from private ones, in
accordance with data from the National Scholar Census
conducted in 2005 by the Brazilian Ministry of Education.
The number of children invited in each city was based on an
estimated 65% prevalence of inadequate intake with a margin
of error of 5% and a confidence level of 95%. Owing to the lack
of national data regarding the prevalence of inadequate
nutrient intake in children and considering the great vulnerability of this life stage group to nutritional deficiencies, we
anticipated that 60% to 70% of the Brazilian children would
exhibit an intake inadequacy of at least 1 nutrient.
A total of 85 daycare centers and preschools were invited
and agreed to participate. Both public (n = 54) and private
schools (n = 31) were not randomly selected; hence, this study
used a nonrepresentative sample. The selection criteria for
schools were geographic location (ie, schools located in urban
areas), full-time attendance (7-8 h/weekday), and a conventional food service system (ie, the portioning of all foods and
beverages at school/center should be performed by the same
food service attendant who was trained to portion the same
amount of food). Of the 3150 children invited, 92 (3%) were not
evaluated because of the lack of parental consent for data
collection; hence 3058 children were evaluated overall.
This study was conducted according to the guidelines of
the Declaration of Helsinki, and all procedures involving
human subjects were approved by the Research Ethics
Committees at the Federal University of São Paulo and the
School of Public Health, University of São Paulo. Written,
informed consent was obtained from the parents or guardians
of the children evaluated.
2.2.
Data collection
Data collection occurred on weekdays (Monday to Friday)
and was performed by undergraduate nutrition students
who were trained by nutrition researchers from local
universities before the onset of the study. Structured forms
and manuals with instructions for data collection were
developed by these researchers, who also closely supervised
data collection.
76
2.2.1.
N U TR ITI O N RE S EA R CH 3 4 ( 2 01 4 ) 7 4 –8 4
Anthropometric measures
Each child's weight and height were measured in duplicate
using a calibrated digital scale [G-Tech (Zhongshan Camry
Electronic Co., Ltd., China), model Glass-6, accuracy: 100 g)]
and a portable wall-mounted stadiometer [Seca (Seca GmBh
&., Germany) model 206, accuracy: 1 mm]. The measurement
of weight (in kilograms) and height (in centimeters) was
performed with the child standing upright on a firm level
surface while wearing light clothes without shoes, and
internationally accepted techniques were followed [18]. For
children younger than 2 years, before calculating body mass
index (BMI), 0.7 cm was added to the measurement of height
to convert to the length as recommended by the World Health
Organization (WHO) [19]. Arithmetic means of weight and
height/length were used to calculate BMI values (kg/m2).
Based on z-score values of BMI-for-age and cutoffs proposed
by WHO [19] (<−2 SD, = −2 SD and ≤+1 SD, >+1 SD and ≤+2 SD,
and >+2 SD, respectively), children were categorized as
underweight, normal weight, overweight, or obese.
2.2.2.
Dietary intake assessment
Weighed food records (WFRs) were used to measure children's
dietary intake at school mealtime. For this process, 3 samples
of each food and beverage offered at the school meal were
portioned by the food service attendant and weighed by the
undergraduate student on a calibrated electronic scale [Plenna
(Plebal Plenna Balanças Comércio Importação e Exportação
LTDA., Brazil), model MEA06030, 3 kilogram maximum
capacity, accurate to 1 g]. This procedure was performed to
standardize the portion size of the foods and beverages
portioned by the attendant and to minimize the influence of
the attendant's variability in portioning the foods offered at
the meal. Arithmetic means of the 3 samples were calculated
and considered as the amount of foods and beverages offered
to all children. After all foods and beverages were sampled
and weighed, the same attendant portioned the foods and
beverages and offered them to each child. If a child received a
second portion of any food or beverage, this was added to the
averaged portion offered.
All leftovers/spillages from each plate were collected in
individual bags and weighed. The beverages were weighed
separately. A proportional estimate of the contribution of
individual foods to the total plate waste was calculated for
each child. The amounts of foods and beverages eaten at school
mealtime were estimated by the difference between the
portion offered and the individual plate waste of each child.
Dietary intake away from school was evaluated using the
estimated food record (EFR) method that was completed by 1
parent, mainly the mother, on the same day that WFR was
performed. If the mother was illiterate, the child's father or
another adult family member completed the EFR. Instructions
for recording in real time were given to the parents. Detailed
descriptions of all foods and beverages, such as recipes, cooking
methods, household measures, condiments, and brand names,
were collected. Quality control of the EFR was conducted during
data collection to identify and correct reporting errors.
All household measures were quantified in grams or
milliliters based on the standardized methods of Pinheiro et
al [20] and Fisberg and Villar [21]. Daily energy and nutrient
intakes were calculated using the Nutrition Data System for
Research software (database version 2007; Nutrition Coordinating Center, University of Minnesota, MN, USA), whose main
database is the food composition table that was developed by
the US Department of Agriculture. Regional food preparations
not included in the database software, such as mixed dishes,
were obtained from national publications [20,21] and entered
into the program as standard recipes. Nutritional values of
regional foods were obtained from the national food composition table (Tabela Brasileira de Composição de Alimentos).
A second dietary measurement was performed in about
25% of the sample (n = 788) to allow the estimation of the
within-subject variation of nutrient intakes. The second
dietary measurement occurred with at least 1 day apart
from the first measurement (ie, nonconsecutive days), to
obtain more reliable estimates of the within-subject variation
[4]. In each city, a maximum of 87 children were randomly
selected for reevaluation. Dietary data collection was the
same as the first data collection. Therefore, the sample
comprised 2270 children with 1 day of dietary intake data
and 788 children with 2 days of dietary intake data.
Owing to incomplete dietary data (dietary data provided
only by the WFR), 77 children (2.5%) were excluded from the
analysis. For the present analysis, 2981 children between 1
and 6 years of age were included (1525 boys and 1456 girls),
with 2231 having 1 day of dietary measurement and 750
having 2 nonconsecutive days of dietary measurement.
2.3.
Statistical analyses
Descriptive analysis of means and SD of energy and nutrient
intakes were estimated for each age evaluated. To estimate
the within- and between-subject variation of intake as well as
the number of days of dietary assessment, a Box-Cox power
transformation was applied for each nutrient to correct the
skewness of the data. After transformation, cholesterol and
vitamins A, C, D, E, and K, as well as copper and selenium, did
not follow a symmetrical distribution (normal distribution)
and could not be further analyzed.
The within- and between-subject variation of energy and
nutrients was estimated by a multilevel regression model.
This model was chosen because it is suitable for nested or
grouped data, thus allowing for correlation between units or
observations at the same level, that is, for nonindependent
data [22]. In this study, dietary data (level 1) were assessed in
children (level 2) “nested” within schools (level 3), which were
nested within cities (level 4).
For each nutrient, the model included the age of the child,
as a fixed and a random effect and adjusted for the fixed effect
of sex [14]. The multilevel model equation used was developed
based on the notation of Goldstein [22]:
γijkl ¼ β 0ijkl þ β 1 x jkl þ β 2jkl
β 0ijkl ¼ β 0 þ f 0l þ v 0kl þ u 0jkl þ e0ijkl
ð1Þ
ð2Þ
where yijkl is the nutrient intake estimated by the ith dietary
measurement of the jth child belonging to the kth daycare/
preschool from the lth city of the study and β0ijkl is the random
intercept. The fixed part of the model is represented by β1, the
regression coefficient of the variable sex (x), and β2, the regression
coefficient of the variable age (z), in years. The terms f0l, ν0kl, u0jkl,
N U TR IT ION RE S EA R CH 3 4 ( 2 01 4 ) 7 4 –8 4
and e0ijkl are the random effects of the intercept at each
hierarchical level: level 4 (fl), cities; level 3 (νkl), daycare/preschools; level 2 (ujkl), children; and level 1 (eijkl), dietary
measurement. All random effects were assumed to follow a
normal distribution with a mean equal to 0 and variances
represented by σ2f0l, σ2v0kl, σ2u0jkl, and σ2e0ijkl, respectively.
In this model, the between-subject variation is the variance
at level 2 (Var(ujkl)) and the within-subject variation is the
variance at level 1 (Var(eijkl)). Both variances were estimated
for each child as a function of the age:
Var u jkl ¼ σ2 u0 þ 2z ijkl σu02
ð3Þ
Var eijkl ¼ σ2 e0 þ 2z ijkl σe02
ð4Þ
where σ2u0 is the variance of the intercept at level 2, z is the age
of the child, and σu02 is the covariance between intercept (β0)
and slope (β2; ie, regression coefficient of the variable age) at
level 2. The term σ2e0 is the variance of the intercept at level 1,
and σe02 is the covariance between intercept and slope at this
level. Positive values of covariance suggest an increase in
within- or between-subject variation of intake with the age of
the child, whereas negative values suggest a decrease. The
multilevel models used the restricted iterative generalized
least squares parameter estimation, which is suitable for
unbalanced data sets, that is, for data sets with a variable
number of units or observations across levels (eg, number of
days of dietary measurements per children). The restricted
iterative generalized least squares also accounts for the loss of
degrees of freedom in fixed effects estimation and provides
unbiased estimates of the random effects [23].
The number of days of dietary assessment (D) to correctly
rank individuals into tertiles, quartiles, or quintiles of the
distribution of the nutrient intake was calculated for each
nutrient using the equation proposed by Black et al [11], which
is based on the ratio of the within-subject variation (Sw2) to
between-subject variation (Sb2), as well as on the hypothetical
correlation coefficient (r) between observed and true intakes:
D¼
r2
Sw2
1−r 2 Sb2
ð4Þ
The correlation coefficient can be interpreted as a measure of
how correctly individuals can be ranked, such as into tertiles,
quartiles, and quintiles of nutrient intakes [12]. In this study,
the ratio of the within- to between-subject variation (ie, the
VR) was calculated according to age group (1-2 years; 3-6
years) and body weight status (nonoverweight/obese; overweight/obese). The age group was defined according to the
Brazilian Ministry of Health, which proposes specific food
guidelines for children up to 2 years of age. The arithmetic
means of Sw2 and Sb2 were used to calculate VR, and r = 0.8
and r = 0.9 were considered in our calculations. These values
of correlation were chosen because they correctly classify a
high proportion of children into tertiles of the distributions
(72% and 80%, respectively) and grossly misclassify a small
proportion of them (3% and <1%, respectively) [12].
In this article, the VR, the within-subject coefficient of
variation (CVw), and the between-subject coefficient of
variation (CVb) were presented. Coefficients of variation
were calculated as: CVw = (SDw/mean) × 100; CVb = (SDb/
77
mean) × 100, where SDw is the within-subject SD and SDb is
the between-subject SD. In addition, 4 sets of graphs were
constructed plotting the within- and between-subject variation of energy and macronutrients (carbohydrates, protein,
and total fat), according to the age of the child.
Descriptive analyses were performed using Stata (Statistics/Data Analysis, version 10.0; Stata, College Station, TX,
USA), and the multilevel models were fitted by MLwiN (Centre
for Multilevel Modeling, version 2.16; University of Bristol,
Bristol, United Kingdom).
3.
Results
The distribution of children according to sociodemographic and
anthropometric variables is presented in Table 1. On average
(SD), the children were 3.9 (1.0) years of age, and had BMI values
of 16.1 (1.7) kg/m2. Boys and girls were almost equally
represented within age groups. A total of 68.4% of the children
were normal weight, 29.5% were overweight/obese, and 2.0%
were underweight. In addition, 74% of the children belonged to
low-income families (ie, families with <R$1000.00 Brazilian
reals per month; equivalent to 500 US dollars per month).
Small differences were observed in the average intake of
energy and most nutrients, among children aged 1 to 6 years
(Table 2). The average intake of energy increased only about
10% in children aged 1 to 6 years (from 1528 to 1683 kcal/d),
whereas the average intake of carbohydrates, protein, and
total fat increased about 6% (from 228.60 to 243.90 g/d), 9%
(from 55.71 to 60.82 g/d), and 18% (from 45.28 to 53.73 g/d),
respectively. The largest increases were observed for copper
(40%; from 1.05 to 1.47 mg/d), vitamin B12 (36%; from 4.18 to
5.67 mg/d), and folate (33%; from 414.54 to 550.39 μg/d).
Conversely, the SD of energy and nutrients increased
substantially in children aged 1 to 6 years, with the exception
of carbohydrates and vitamin C.
In the youngest group (1–2 years), CVw was quite large
compared with CVb, especially for total fat, total fiber, and
sodium. Regarding the VR, the values ranged widely from 1.17
for calcium to 8.70 for total fat. Riboflavin had the second and
pantothenic acid the third lowest VR (1.73 and 1.83, respectively), whereas zinc exhibited the second and total fiber the
third highest VR (7.36 and 6.60, respectively). Overall, 7 days of
dietary assessment would be sufficient to achieve r = 0.8 and
16 days to achieve r = 0.9 in the assessment of all but 5
nutrients (total fat, fiber, thiamine, sodium, and zinc), in this
age group (Table 3).
In the oldest group (3–6 years), CVw was larger than CVb for
all nutrients, notably for total fat, total fiber, and zinc. The VR
ranged from 1.47 for calcium to 8.95 for total fat. Pantothenic
acid exhibited the second and phosphorus the third lowest VR
(1.92 and 2.15, respectively), whereas total fiber had the
second and zinc the third highest VR (8.41 and 5.52,
respectively). A total of 7 days of dietary assessment would
be sufficient to achieve r = 0.8 and 18 days to achieve r = 0.9 in
the assessment of all but 4 of the nutrients evaluated (total fat,
fiber, folate, and zinc; Table 3).
Compared with the youngest group, the oldest one showed
higher CVw in 72% of the nutrients evaluated. Differences in
78
N U TR ITI O N RE S EA R CH 3 4 ( 2 01 4 ) 7 4 –8 4
Table 1 – Sociodemographic and anthropometric characteristics of children from daycare centers and preschools according
to age group
Age group
a
1-2 y (n = 715)
Age (y), mean (SD)
Weight (kg), mean (SD)
Height (cm), mean (SD)
BMI (kg/m2), mean (SD)
Sex, n (%)
Male
Female
Body weight status, n (%) c
Underweight
Normal weight
Overweight
Obese
Family Income (reals/mo), n (%) d
<1000.00
≥1000.00
Dietary assessment, n (%)
1d
2d
a
b
c
d
2.5
13.7
90.9
16.5
Total
b
3-6 y (n = 2266)
(0.3)
(1.8)
(5.4)
(1.6)
4.3
17.2
103.1
16.1
(0.8)
(3.4)
(7.4)
(1.8)
3.9
16.3
100.2
16.1
(1.0)
(3.4)
(8.6)
(1.7)
367 (51.3)
348 (48.7)
1158 (51.1)
1108 (48.9)
1525 (51.2)
1456 (48.8)
16
470
155
74
44
1570
471
181
60
2040
626
255
(2.2)
(65.7)
(21.7)
(10.4)
(1.9)
(69.3)
(20.8)
(8.0)
(2.0)
(68.4)
(21.0)
(8.6)
501 (70.1)
214 (29.9)
1702 (75.1)
564 (24.9)
2203 (73.9)
778 (26.1)
552 (77.2)
163 (22.8)
1679 (74.1)
587 (25.9)
2231 (74.8)
750 (25.2)
One year old (n = 58); 2 years old (n = 657).
Three years old (n = 974); 4 years old (n = 836); 5 years old (n = 398), and 6 years old (n = 58).
According to the BMI cutoff points for age and sex proposed by WHO [19].
US $1 ≈ 2.5 reals.
VR were found between age groups for all nutrients. Total fat,
total fiber, riboflavin, folate, calcium, phosphorus, and iron
exhibited greater VR and D in the oldest group. Conversely,
energy, carbohydrates, protein, thiamine, magnesium, sodium, potassium, and zinc showed greater VR and D in the
youngest group. Pantothenic acid, vitamin B6, and niacin
showed slight differences in VR but not in the number of days
of dietary assessment between age groups.
Changes in within- and between-subject variation of
energy, carbohydrates, protein, and total fat, according to
the age of the child, are illustrated in Fig. For energy,
carbohydrates, and protein, both within- and betweensubject variation increased with increasing age. The greatest
relative increase was observed for the between-subject
variation of energy (58%), carbohydrates (29%), and protein
(29%). For total fat, however, the within-subject variation
increased from 1 to 6 years, whereas the between-subject
variation decreased until around 3.5 years and then increased from this age onward.
Higher CVw and lower CVb were observed in overweight/
obese children for around 61% of the nutrients investigated
(Table 4). Lower VR was observed among overweight/obese
children for energy, carbohydrates, protein, total fat, thiamine, niacin, magnesium, sodium, potassium, and zinc.
Calcium had the lowest VR in both body weight status
groups, whereas total fat had the highest VR in nonoverweight/obese children and total fiber had the highest in
overweight/obese children. In nonoverweight/obese children, all but 5 nutrients (total fat, total fiber, folate, sodium,
and zinc) required a total of 7 days of dietary assessment to
achieve r = 0.8 and 17 days to achieve r = 0.9. In overweight/
obese children, all but 4 nutrients (total fat, total fiber, folate,
and zinc) required 7 days to ensure r = 0.8 and 16 days to
ensure r = 0.9 between observed and true intakes.
4.
Discussion
This was the first study to investigate the influence of age and
body weight status on VR of energy and nutrient intakes as
well as D among children from daycare centers and preschools
living in Brazil.
For all nutrients, the VRs were larger than 1.0, indicating
that the within-subject variation was higher than the
between-subject variation of intake. In fact, the VR was
larger for total fat, total fiber, and zinc and smaller for
calcium, phosphorus, riboflavin, and pantothenic acid.
These findings indicate that Brazilian children consume,
on a daily basis, less regular amounts of the main dietary
sources of fat (oils and fatty foods), fiber (leafy vegetables
and wholegrain cereals), and zinc (meat products and
seafood) than the main dietary sources of calcium, phosphorus, riboflavin, and pantothenic acid (namely, milk and
dairy products).
For most nutrients, the average of intake increased less
from 1 to 6 years than the SD of intake. This suggests that the
effects of increasing age were less expressive in increasing
food consumption than in increasing the variation of intake. It
is possible that young children have a low ability to selfregulate food intake, especially when they are fed by
caregivers and that this effect progressively declines as
children get older and more autonomous in controlling their
own intakes. Caregivers' attitudes that encourage children to
clean their plate or stimulate food consumption through
79
N U TR IT ION RE S EA R CH 3 4 ( 2 01 4 ) 7 4 –8 4
Table 2 – Energy and nutrient intakes according to children's age
Nutrients
Age (y)
1 (n = 58)
Mean
SD
2 (n = 657)
Mean
SD
3 (n = 974)
Mean
SD
4 (n = 836)
Mean
SD
5 (n = 398)
Mean
SD
6 (n = 58)
Mean
SD
Energy (kcal/d)
1528
456
1507
461
1588
479
1614
473
1635
490
1683
498
Carbohydrates (g/d)
228.60
81.21 219.34
74.46 230.79
79.25 232.49
71.33 240.53 76.97 243.09
69.92
Protein (g/d)
55.71
16.28
56.93
20.37
58.50
20.12
60.19
21.97
58.69 20.97
60.82
22.04
Total fat (g/d)
45.28
14.79
46.79
18.99
49.93
19.32
51.35
19.75
51.01 19.86
53.73
21.19
Total fiber (g/d)
16.05
7.50
16.30
7.47
17.19
7.59
18.51
8.05
18.81
8.13
19.50
7.80
Cholesterol (mg/d)
154.63
76.90 168.17 116.93 168.06 102.42 172.31 108.31 165.10 105.10 172.55 101.72
Thiamine (mg/d)
1.16
0.44
1.22
0.60
1.28
0.70
1.29
0.56
1.28
0.64
1.37
0.48
Riboflavin (mg/d)
1.48
0.51
1.71
0.88
1.66
0.85
1.64
0.70
1.58
0.64
1.68
0.69
Panthotenic acid (mg/d)
4.22
1.23
4.69
2.26
4.58
3.17
4.45
2.09
4.25
1.77
4.23
1.65
Vitamin B6 (mg/d)
1.20
0.35
1.30
0.52
1.34
0.74
1.35
0.60
1.35
0.61
1.32
0.55
Vitamin B12 (mg/d)
4.18
3.74
6.90
13.57
5.68
8.85
5.38
8.12
4.63
6.81
5.67
8.98
Vitamin C (mg/d)
374.33 1008.79 238.99 708.91 296.52 898.11 333.95 901.82 284.09 783.49 321.61 709.51
665.84 577.00 973.03 1836.44 791.82 1199.02 741.10 1114.95 645.03 943.67 704.57 1256.47
Vitamin A a (μg/d)
4.52
2.56
5.24
4.26
5.51
5.87
5.38
6.58
4.93
5.51
5.02
7.71
Vitamin D b (μg/d)
Vitamin E c (mg/d)
4.16
1.55
4.76
2.77
4.89
4.31
4.81
2.81
4.66
2.45
4.53
1.90
46.46
24.92
54.30
49.74
58.18
55.55
64.60
71.27
63.76 72.92
56.32
41.98
Vitamin K d (μg/d)
Niacin equivalents (mg/d)
26.57
7.40
27.15
10.24
27.83
11.07
28.22
10.66
27.55
9.71
28.10
9.36
Dietary folate equivalents (μg/d) 414.54 157.96 444.88 189.84 466.20 270.88 486.92 189.71 503.17 186.47 550.39 186.81
Calcium (mg/d)
692.42 322.64 766.36 362.04 747.38 357.02 720.06 358.47 700.13 338.62 723.27 334.53
Phosphorus (mg/d)
893.74 273.02 962.88 344.60 959.15 328.45 961.21 338.05 939.87 321.98 969.76 328.30
Magnesium (mg/d)
206.57
58.27 222.08
81.95 223.74
77.01 226.54
77.95 224.86 75.33 215.75
62.70
Iron (mg/d)
10.18
3.54
11.02
4.95
11.36
4.44
11.73
4.61
11.85
4.37
12.35
4.27
Sodium (mg/d)
2057.17 602.55 2062.45 785.83 2190.23 751.64 2317.42 875.44 2333.56 888.48 2530.07 897.38
Potassium (mg/d)
2110.14 592.60 2212.48 714.41 2235.38 736.22 2261.33 737.64 2271.60 746.09 2211.03 715.83
Zinc (mg/d)
8.21
2.98
8.64
3.56
8.81
3.46
9.05
3.97
8.69
3.77
9.05
3.93
Copper (mg/d)
1.05
0.77
1.59
2.68
1.38
1.80
1.36
1.65
1.26
1.41
1.47
1.87
Selenium (μg/d)
75.00
23.30
78.59
29.49
80.89
29.71
84.57
31.65
83.32 30.62
93.49
37.03
The total number of subjects was 2981.
a
As retinol equivalents activity.
b
As cholecalciferol.
c
As α-tocopherol.
d
As phylloquinone.
rewards have been considered external cues that contribute to
lack of self-regulation of intake in children [24].
In disagreement with our initial hypothesis, a noticeable
finding was that the VRs were higher in the 3- to 6-year-old
group for some but not all nutrients evaluated. Based on
previous studies about nutrient intake variability among
preschool children [9,15], we hypothesized that VR of energy
and nutrient intakes would be consistently higher in the older
age group in comparison with the younger age group. Instead,
the influence of age on the VR among Brazilian children varied
according to the nutrient under investigation. Hence, it seems
that for energy, carbohydrates, protein, thiamine, magnesium, sodium, potassium, and zinc, the VR tended to decrease
with age; whereas for total fat, total fiber, riboflavin, folate,
calcium, phosphorus, and iron, the VR tended to increase with
age. A more consistent pattern of increase in the VR, according
to the age of the child, was observed by Huybrechts et al [9] in
Belgian preschoolers aged 2.5 to 6.5 years and by Erkkola et al
[15] in Finnish children aged 1, 3, and 6 years.
The VR decrease for energy, carbohydrates, and protein with
age may be explained by the larger increase in the betweensubject variation than in the within-subject variation of intake.
However, for total fat, the VR increase may be explained by 2
different aspects: the increase in the within-subject variation in
parallel with the reduction in the between-subject variation
until around 3.5 years and the larger increase in the withincompared with the between-subject variation from 3.5 years
on. These changes in within- and between-subject variation of
energy and macronutrient intakes, especially of fat, occurred
during a period of change in growth rate as well as in the body
composition of the child [25]. During the first and the second
years of life, children exhibit a rapid reduction in their weight
and height growth rate, whereas from 3 to 6 years of age, their
growth rate tends to stabilize [25]. Along this period of growth
rate stabilization, the body fat mass increases as a physical
adaptation to the pubertal growth spurt (adiposity rebound)
[26-29]. The relation of growth and body composition with
changes in the within- and between-subject variation of energy
and macronutrients, however, could not be addressed in this
study and should be further investigated.
Another noticeable finding was that overweight/obese
children showed lower VR of energy, carbohydrates, protein,
and total fat than did their nonoverweight counterparts,
which was in agreement with our initial hypothesis. Examining the within- and between-subject variation of intake,
overweight/obese children presented lower within-subject
80
N U TR ITI O N RE S EA R CH 3 4 ( 2 01 4 ) 7 4 –8 4
Table 3 – Nutrients intake, variability estimates, and number of days of dietary assessment according to age group
Nutrients
Mean
Energy (kcal/d)
1-2 y
1507
3-6 y
1606
Carbohydrates (g/d)
1-2 y
219.75
3-6 y
233.41
Protein (g/d)
1-2 y
56.87
3-6 y
59.2
Total fat (g/d)
1-2 y
42.81
3-6 y
50.72
Total fiber (g/d)
1-2 y
16.29
3-6 y
18.01
Thiamine (mg/d)
1-2 y
1.22
3-6 y
1.28
Riboflavin (mg/d)
1-2 y
1.70
3-6 y
1.74
Pantothenic acid (mg/d)
1-2 y
4.67
3-6 y
4.77
Vitamin B6 (mg/d)
1-2 y
1.29
3-6 y
1.34
Niacin equivalents (mg/d)
1-2 y
27.12
3-6 y
27.93
Dietary folate equivalents (μg/d)
1-2 y
443.53
3-6 y
482.11
Calcium (mg/d)
1-2 y
763.08
3-6 y
728.43
Phosphorus (mg/d)
1-2 y
959.81
3-6 y
956.68
Magnesium (mg/d)
1-2 y
221.39
3-6 y
224.81
Iron (mg/d)
1-2 y
10.98
3-6 y
11.60
Sodium (mg/d)
1-2 y
2062.21
3-6 y
2269.38
Potassium (mg/d)
1-2 y
2207.93
3-6 y
2250.87
Zinc (mg/d)
1-2 y
8.62
3-6 y
8.88
CVw a
CVb b
VR
Dc
Dd
23.0
24.7
13.4
11.9
3.43
3.09
6
6
15
13
74.74
75.89
23.9
26.0
13.9
11.9
3.41
3.17
6
6
15
14
20.2
21.0
28.5
29.6
16.8
13.6
3.73
3.56
7
6
16
15
21.59
19.61
36.9
33.2
12.0
11.7
8.7
8.95
15
16
37
38
7.46
7.89
37.3
35.6
8.3
9.5
6.60
8.41
12
15
28
36
0.59
0.64
34.4
37.6
30.8
28.0
4.22
4.01
8
7
18
17
0.87
0.76
30.8
32.6
29.3
23.3
1.73
2.16
3
4
7
9
2.22
2.57
31.5
37.7
26.5
36.4
1.81
1.92
3
3
8
8
0.51
0.67
29.7
39.3
21.9
25.6
2.54
2.69
5
5
11
11
10.13
10.65
28.4
31.1
18.1
16.3
3.27
3.18
6
6
14
14
188.6
228.6
30.7
40.4
20.9
21.2
3.74
4.81
7
9
16
21
360.5
354.2
36.0
34.0
28.1
29.6
1.17
1.47
2
3
5
6
341.9
330.8
26.5
27.1
20.0
17.2
1.83
2.15
3
4
8
9
81.09
76.78
29.7
26.4
16.6
16.5
2.72
2.26
5
4
12
10
4.89
4.49
31.6
32.8
24.7
15.0
2.51
3.11
4
6
11
13
778.3
829.9
33.4
30.7
9.5
13.7
4.95
4.20
9
7
21
18
709.5
737.9
24.8
26.6
17.4
13.6
3.00
2.59
5
5
13
11
32.7
36.5
19.0
13.1
7.36
5.52
13
10
31
24
SD
460
479
3.54
3.72
The total number of subjects was 2981.
CVw = (SDw/mean) × 100; estimated in a Box-Cox scale through a multilevel model.
b
CVb = (SDb/mean) × 100; estimated in a Box-Cox scale through a multilevel model.
c
D—considering r = 0.8.
d
D—considering r = 0.9.
a
variation and larger between-subject variation for these
nutrients than nonoverweight/obese children. Considering
the impaired control of food consumption as one of the main
factors affecting the development and maintenance of obesity
in childhood [30] and the influence of body weight status on
children's ability to self-regulate energy intake [24], it is
possible that overweight/obese children fail in compensating
energy and macronutrient intakes between days, as do the
81
N U TR IT ION RE S EA R CH 3 4 ( 2 01 4 ) 7 4 –8 4
Table 4 – Nutrients intake, variability estimates, and number of days of dietary assessment according to body weight status
Nutrients
Energy (kcal/d)
Nonoverweight/Obese
Overweight/Obese
Carbohydrates (g/d)
Nonoverweight/Obese
Overweight/Obese
Protein (g/d)
Nonoverweight/Obese
Overweight/Obese
Total fat (g/d)
Nonoverweight/Obese
Overweight/Obese
Total fiber (g/d)
Nonoverweight/Obese
Overweight/Obese
Thiamine (mg/d)
Nonoverweight/Obese
Overweight/Obese
Riboflavin (mg/d)
Nonoverweight/Obese
Overweight/Obese
Pantothenic acid (mg/d)
Nonoverweight/Obese
Overweight/Obese
Vitamin B6 (mg/d)
Nonoverweight/Obese
Overweight/Obese
Niacin equivalents (mg/d)
Nonoverweight/Obese
Overweight/Obese
Dietary folate equivalents (μg/d)
Nonoverweight/Obese
Overweight/Obese
Calcium (mg/d)
Nonoverweight/Obese
Overweight/Obese
Phosphorus (mg/d)
Nonoverweight/Obese
Overweight/Obese
Magnesium (mg/d)
Nonoverweight/Obese
Overweight/Obese
Iron (mg/d)
Nonoverweight/Obese
Overweight/Obese
Sodium (mg/d)
Nonoverweight/Obese
Overweight/Obese
Potassium (mg/d)
Nonoverweight/Obese
Overweight/Obese
Zinc (mg/d)
Nonoverweight/Obese
Overweight/Obese
Mean
1524
1647
SD
459
464
CVw a
CVb b
VR
Dc
Dd
23.8
25.4
14.4
12.1
3.18
3.10
6
6
14
13
220.32
234.22
73.24
75.36
24.9
26.2
14.2
11.9
3.39
3.22
6
6
14
14
55.39
59.88
19.80
21.22
28.1
31.9
15.9
13.8
3.58
3.49
6
6
15
15
47.14
51.04
18.90
19.74
34.5
33.8
11.3
11.7
9.32
7.77
17
14
40
33
16.46
17.48
7.15
8.96
34.6
37.9
11.0
9.1
7.59
8.79
13
16
32
37
1.21
1.30
0.65
0.56
36.3
35.4
32.6
23.9
3.97
3.90
7
7
17
17
1.60
1.69
0.8
0.75
31.8
36.7
29.9
16.2
1.82
2.28
3
4
8
10
4.43
4.62
2.66
2.00
38.6
34.5
40.1
19.9
1.90
1.99
3
4
8
8
1.29
1.39
0.65
0.59
37.2
34.9
26.9
24.6
2.59
2.83
5
5
11
12
26.45
28.98
10.54
10.52
29.6
32.9
17.3
14.8
3.34
3.27
6
6
14
14
470.81
479.92
228.17
195.11
38.7
32.6
24.2
16.5
4.37
5.11
8
9
19
22
725.83
759.43
350.62
369.87
32.8
35.3
28.1
26.7
1.29
1.66
2
3
5
7
946.08
981.19
328.05
345.19
25.8
28.9
17.2
16.9
2.22
2.43
4
4
9
10
220.66
230.89
75.02
81.98
28.9
26.4
14.1
18.3
2.36
2.17
4
4
10
9
11.01
12.92
4.77
4.99
32.5
32
15.4
18.7
2.79
3.39
5
6
12
14
2147.29
2344.56
807.14
857.33
29.6
34
14.7
13.2
4.39
4.21
8
7
19
18
2169.35
2374.72
717.96
759.22
25.2
26.4
14.9
16.5
2.80
2.59
5
5
12
11
8.68
8.99
3.59
3.73
36.4
36.8
15.1
12.3
6.15
5.42
11
10
26
23
The total number of subjects was 2981.
CVw = (SDw/mean) × 100; estimated in a Box-Cox scale through a multilevel model.
b
CVb = (SDb/mean) × 100; estimated in a Box-Cox scale through a multilevel model.
c
D—considering r = 0.8.
d
D—considering r = 0.9.
a
nonoverweight ones. Thus, this may contribute to lower
within-subject variation of intake. In the study of Jansen
et al [31], overweight children aged 8 to 12 years failed to regulate
food intake after eating a small portion (preload) of appetizing
food, whereas normal-weight children down-regulated their
intake because of greater satiety responsiveness.
82
N U TR ITI O N RE S EA R CH 3 4 ( 2 01 4 ) 7 4 –8 4
Fig. – Within- and between-subject variation of energy, carbohydrates, protein, and total fat intakes estimated through a
multilevel model according to the age of the child (n = 2981), Brazil, 2007.
Moreover, Brazilian preschool children required more days
of dietary assessment than did Finnish [15] and Belgian [9]
preschoolers to ensure the same level of accuracy in ranking
energy and nutrient intakes. In fact, Finnish preschoolers aged
1, 3, and 6 years evaluated in the Type 1 Diabetes Prediction and
Prevention Project study [15] required a maximum of 8 days to
achieve r = 0.8 and 18 days to achieve r = 0.9 in the simultaneous
assessment of all nutrients evaluated in the present study.
Likewise, Belgian preschoolers [9] aged between 2.5 and 6.5
years showed a lower number of days (considering r = 0.9),
mainly for total fat (10–13 days), total fiber (6 days), and zinc
(1–13 days) but a similar number of days for calcium (3–4 days).
Differences in age group definitions, in dietary intake assessment methods, and in statistical procedures to estimate the
within- and between-subject variation might have contributed
to divergent results between studies.
The number of days of dietary assessment was also higher
than those obtained by Salles-Costa et al [32], who evaluated
Brazilian infants aged 6 to 30 months in the metropolitan
region of Rio de Janeiro. The authors found that a maximum of
6 days of dietary assessment was needed to achieve r = 0.9 for
energy, carbohydrates, protein, total fat, calcium, iron, and
ascorbic acid. It is possible that the inclusion of children not
attending daycare or preschools and the high proportion of
children living in households with food insecurity (77%)
contributed to the differences in D between studies.
In this study, 7 days of dietary assessment was sufficient to
achieve r = 0.8 between observed and true intakes for most
nutrients reported, irrespective of children's age and body
weight status. This implies that around 72% of children will be
correctly classified into tertiles of the distribution, whereas
only 3% of them will be grossly misclassified. If a correlation
coefficient of 0.9 were used, D would increase to 16 or more
and the proportion of children correctly classified would
increase only up to 80%. The advantage of increasing the
correlation coefficient to 0.9 is mainly to reduce the proportion
of children grossly misclassified (to <1%) rather than increase
the proportion of children correctly classified.
Our findings about D have important implications for
planning studies and analyzing data about this population
[33]. Too few days of dietary assessment can result in less
precise estimates at individual levels [34]. However, the poor
compliance of subjects and the high cost of studies that use
multiple days of dietary assessment make it difficult to obtain
reliable and precise estimates. To overcome this, many
researchers advocate collection on at least 2 nonconsecutive
days of short-term dietary measurement and apply statistical
methods such as the National Cancer Institute Method
(developed at National Cancer Institute) and the Web-based
statistical technique Multiple Source Method (developed at
German Institute of Human Nutrition Potsdam-Rehbrücke) to
adjust data for the within-subject variation [33-37]. Another
N U TR IT ION RE S EA R CH 3 4 ( 2 01 4 ) 7 4 –8 4
approach is to use external variance estimates (ie, VR) from
similar populations when only 1 day of short-term dietary
measurement is available [7,9,34,38-40].
The present study does have some limitations. First, our
results may not be generalized to children who do not attend
daycare or preschool because the repertoire of foods offered to
the child may vary depending on the responsible caregiver for
the child's diet, that is, family members or school staff.
Second, it is conceivable that children from the same school
had exhibited a low variation of the foods eaten, which could
reduce the between-subject variation of intake and, consequently, increase the VR. However, the large number of
schools evaluated, the large sample size, and the multilevel
analysis contributed to mitigate this bias. Third, the study
used 2 different methods to evaluate food intake: WFR and
EFR. Errors commonly observed in the EFR collection, such as
those in the recording process or in portion size quantification
[41], can overestimate the within-subject variation of energy
and nutrient intakes. We aimed to alleviate this by identifying
and correcting reporting errors, using standardized data
collection procedures, and issuing careful instructions to the
parents to complete the EFR. Fourth, the second dietary
measurement was collected in only a subsample of children
from daycare centers and preschools (25% of the sample). It
was demonstrated that the percentage of subjects with more
than 1 dietary measurement in the sample (ie, the replication
rate) may affect the precision of the usual intake estimates of
episodically consumed foods by widening, in different extents, the confidence intervals of estimates [42]. However, the
effects of different replication rates on the nutrient intake
estimates remain unclear.
In summary, the influence of age and body weight status
on the VR among Brazilian children from daycare centers and
preschools varied according to the nutrient under investigation. The elevated VR reinforces the need for about 7 days of
dietary assessment to achieve a correlation coefficient equal
to 0.8 between observed and true intakes among Brazilian
preschool children. Considering that the age and body weight
status of each child were 2 factors that affected the withinand between-subject variation and, consequently, the VR of
energy and nutrient intakes, our study suggests that researchers should take these factors into account during
studies focusing on childhood nutrition.
Acknowledgment
The authors acknowledge the institutions that collaborated
with study design and data collection as well as Fundação de
Amparo à Pesquisa do Estado de São Paulo (procedural no.
2008/07549-0) that granted the scholarship to the first author.
The authors declare that they have no conflict of interest.
REFERENCES
[1] Power C, Lake JK, Cole TJ. Measurement and long term health
risks of child and adolescent fatness. Int J Obes Relat Metab
Disord 1997;21:507–26.
83
[2] Berenson GS, Srinivasan SR, Bao W, et al. Association
between multiple cardiovascular risk factors and atherosclerosis in children and young adults. N Engl J Med
1998;338:1650–6.
[3] Freedman DS, Dietz WH, Sathanur R, et al. Risk factors and
adult body mass index among overweight children: the
Bogalusa Heart Study. Pediatrics 2009;123:750–7.
[4] Tarasuk V, Beaton G. Statistical estimation of dietary
parameters: implications of patterns in within-subject variation—a case study of sampling strategies. Am J Clin Nutr
1992;55:22–7.
[5] Verly Jr E, Fisberg RM, César CLG, et al. Sources of variation of
energy and nutrient intake among adolescents in São Paulo,
Brazil. Cad Saúde Públ 2010;26:2129–37.
[6] Kipnis V, Midthune D, Freedman L, et al. Bias in dietary-report
instruments and its implications for nutrition epidemiology.
Pub Health Nutr 2002;5:915–23.
[7] Jahns L, Arab L, Carriquiry A, et al. The use of external withinperson variance estimates to adjust nutrient intake distributions over time and across populations. Pub Health Nutr
2005;8:69–76.
[8] Rutishauser IHE. Dietary intake measurements. Pub Health
Nutr 2005;8:1100–7.
[9] Huybrechts I, Bacquer DD, Cox B, et al. Variation in energy
and nutrient intakes among preschool children: implications
for study design. Eur J Public Health 2008;18:1–8.
[10] Beaton GH. Approaches to analysis of dietary data: relationship between planned analyses and choice of methodology.
Am J Clin Nutr 1994;59:253S–61S.
[11] Black AE, Cole TJ, Wiles SJ, et al. Daily variation in food intake
of infants from 2 to 18 months. Hum Nutr Appl Nutr
1983;37A:448–58.
[12] Nelson M, Black AE, Morris JA, et al. Between- and within
subject variation in nutrient intake from infancy to old age:
estimating the number of days required to rank dietary
intakes with desired precision. Am J Clin Nutr 1989;50:155–67.
[13] Lanigan JA, Wells JCK, Lawson MS, et al. Number of days
needed to assess energy and nutrient intake in infants and
young children between 6 months and 2 years of age. Eur J
Clin Nutr 2004;58:745–50.
[14] Jahns L, Carriquiry A, Arab L, et al. Within- and betweenperson variation in nutrient intakes of Russian and U.S
children differs by sex and age. J Nutr 2004;134:3114–20.
[15] Erkkola M, Kyttäla P, Takkinen HM, et al. Nutrient intake
variability and number of days of dietary needed to assess
intake in preschool children. Brit J Nutr 2011;106:130–40.
[16] Barkeling B, Ekman S, Rössner S. Eating behaviour in obese
and normal weight 11-year-old children. Int J Obes
1992;16:355–60.
[17] Bueno MB, Fisberg M, Maximino P, et al. Nutritional risk
among Brazilian children aged two to six years old: a
multicenter study. Nutr 2013;29:405–10.
[18] Gordon CC, Chumlea WC, Roche AF. Stature, recumbent
lenght, and weight. In: Lohman TG, Roche AF, Martorell R,
editors. Antropometric standardization reference manual.
Champaign: Human Kinetics; 1988. p. 3–8.
[19] WHO Multicentre Growth Reference Study Group. WHO Child
Growth Standards: length/height-for-age, weight-for-age,
weight-for-length, weight-for- height and body mass indexfor-age: methods and development. Geneva: WHO; 2006.
[20] Pinheiro AVB, Lacerda EMA, Benzecry EH, et al. Tabela para
avaliação do consumo alimentar em medidas caseiras. 4th.
ed. São Paulo: Atheneu; 2000.
[21] Fisberg RM, Villar BS. Manual de receitas e medidas caseiras
para cálculo de inquéritos alimentares: manual elaborado
para auxiliar o processamento de inquéritos alimentares. São
Paulo: Signus; 2002.
[22] Goldstein H. Multilevel statistical models. 2nd ed. London:
Hodder Arnold; 2002.
84
N U TR ITI O N RE S EA R CH 3 4 ( 2 01 4 ) 7 4 –8 4
[23] Searle SR, Casella G, McCulloch C. Variance components. New
York: John Wiley; 1992.
[24] Johnson SL. Improving preschoolers' self-regulation of energy
intake. Pediatrics 2000;106(6):1429–35.
[25] Chumlea W, Guo S. Physical growth and development. In:
Samour PQ, Helm KK, Lang CE, editors. Handbook of
pediatric nutrition. 2nd ed. Maryland: Aspen Publication;
1999. p. 3–15.
[26] Rolland-Cachera MF, Deheeger M, Belislle F, et al. Adiposity
rebound in children: a simple indicator for predicting obesity.
Am J Clin Nutr 1984;39:129–35.
[27] Williams S, Davie G, Lam F. Predicting BMI in young adults
from childhood data using two approaches to modeling
adiposity rebound. Int J Obes Relat Metab Disord
1999;23:348–54.
[28] Cole TJ. Children growth and horses race: is the adiposity
rebound a critical period for later obesity? BMC Pediatr
2004;12(4):6.
[29] Kroke A, Hahns S, Buyken A, et al. A comparative evaluation
of two different approaches to estimating age at adiposity
rebound. Int J Obes 2006;30:261–6.
[30] Maffeis C, Schultz Y, Zaffanello M, et al. Elevated energy
expenditure ad reduced energy intake in obese prepubertal
children: paradox of poor dietary reliability in obesity? J Ped
1994;124:348–54.
[31] Jansen A, Theunissen N, Slechten K, et al. Overweight
children overeat after exposure to food cues. Eat Behav
2003;4:197–209.
[32] Salles-Costa R, Barroso GS, De Mello MA, et al. Sources of
variation in energy and nutrient intakes among children from
six to thirty months old in a population-based study. Cad
Saúde Públ 2010;26:1175–86.
[33] Livingstone MBE, Robson PJ. Measurement of dietary intake in
children. Proc Nutr Soc 2000;59:279–93.
[34] Hoffman K, Boing H, Dufour A, et al. Estimating the
distribution of usual dietary intake by short-term measurements. Eur J Clin Nutr 2002;56:53S–62S.
[35] Hartig U, Haubrock J, Knüppel S, et al. The MSM program:
Web-based statistics package for estimating usual dietary
intake using the Multiple Source Method. Eur J Clin Nutr
2011;65:S87–91.
[36] Haubrock J, Nöthlings U, Volatier JL, et al. Estimating usual
food intake distributions by using the Multiple Source Method
in the EPIC-Postdam Calibration Study. J Nutr 2011;141:914–20.
[37] Tooze JA, Midthune D, Dodd KW, et al. A new statistical
method for estimating the usual intake of episodically
consumed foods with application to their distribution. J Am
Diet Assoc 2006;106:1575–87.
[38] National Research Council. Nutrient Adequacy. Assessment
Using Food Consumption Surveys. Washington, DC: National
Academy Press; 1968.
[39] Nusser SM, Carriquiry AL, Dodd KW, et al. A semiparametric
transformation approach to estimating usual daily intake
distributions. J Am Stat Assoc 1996;91:1440–9.
[40] Guenther PM, Kott PS, Carriquiry AL. Development of an
approach for estimating usual nutrient intake distributions at
the population level. J Nutr 1997;127:1106–12.
[41] Tucker KL. Assessment of usual dietary intake in population
studies of gene-diet interaction. Nutr Metab Cardiovasc Dis
2007;17:74–81.
[42] Verly Jr E, Castro MA, Fisberg RM, Marchioni DML. Precision of
usual food intake estimates according to the percentage of
individuals with a second dietary measurement. J Acad Nutr
Diet 2012;112(7):1015–20.