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4
The Use of ShortTerm Dietary
Intake Data to Estimate Usual
Dietary Intake
Dietary intake of an individual is not constant from
day to day but varies both in amount and in type of foods
eaten and, hence, in nutrient content ( intraindividual
variation). There are also variations between persons in
their usual nutrient intake averaged over time (interindi
vidual variation). For North American populations, the
intraindividual variation is usually as large as or greater
than interindividual variations and must be taken into
account in any approach to nutrient assessment.
RELATIONSHIP OF DAILY DIETARY INTAKE DATA TO USUAL INTAKE:
Many authors have compared the reliability of data from
1day dietary intake records and records for longer periods
(Garn et al., 1978; Marr, 1971; PeRkarinen, 1970; Young et
al., 1952a), and errors in usual intake estimation due to
intraindividual variation have attracted considerable inter
est. Initially, the interest of scientists was stimulated
by the desire to examine biological relationships in epide
miological data sets, for example, dietary intake and serum
lipid levels or energy intake and lipid levels (Beaton,
1982a: Beaton et al ., 1979: Jacobs et al ., 1979; Liu et
_., 1978; Stallones, 1982 ) . At about the same time, sev
eral researchers realized that when 1day dietary intake
data are used, intraindividual intake variation results in
a serious bias in regression and correlation analyses.
This bias can easily lead to false conclusions about the
underlying biological relationships (Beaton, 1982~; Beaton
_ al., 1979; Jacobs et al., 1979; Liu et al., 1978; Sempos
_ al., 1985; Stallones, 1982). The concept of measurement
error and the statistical approaches for dealing with it
are not new. It has been applied in other fields for many
17
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18
years. However, there is new appreciation of the applica
bility of these concepts to data on dietary intake.
As a result of the phenomenon of intraindividual varia
tion, when one uses a fixed cutoff point for an observed
intake distribution, the number of days on which intake
was recorded affects the apparent prevalence of inadequate
intake (Hegsted, 1972). Figure 41 illustrates the impact
of repeated observations on the apparent distribution of
intakes and on the apparent prevalence of intakes falling
below a fixed cutoff point.
30
25
J
320
>

Z As
LL
o
10
s
"usual intakes" observed
over several days
SD = 12.3 9
l
1
RDA]
/
\\ 1day observations
mean ~ \ SD = 19.0 9
66 9 \ \
\ \
\\`
l L_
0 20 40 60 80 100 120
PROTEIN g/day
FIGURE 4~. Effect of multiple days of observation on the
apparent distribution of nutrient intake.
From Hegsted, 1972. Reprinted by permission
of the author and publishers. Copyright
Gordon and Breach Science Publishers, Inc.
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19
Interest in the magnitude of this daytoday variation
in intake continued in recent years (Beaton et al., 1979,
1983; Hackett _ al., 1983; Mouser and Bebb, 1981; Hunt et_
al., 1983; Karvetti and Knuts, 1981; Liu et al., 1978;
McGee _ al., 1982; Rush and Kristal, 1982; Sempos et al.,
1985; Todd _ al., 1983).
Earlier reports addressed the
same issue with specific reference to estimating energy
intake or ways to derive information about energy and lipid
components (Balogh et al., 1971; Hankin et al., 1967; Kato
et al. , 1973 ; Keys, 1970 ; Marr, 1971; Morris et al. , 1963 ;
Tillotson et al., 1973). mese reports indicate that the
magnitude of the intraindividual component, relative to
the interindividual component, varies with nutrient; prob
ably with the age, sex, and sociocultural group; and with
dietary methodology. This is illustrated in Table 41,
which presents the ratio of intra to interindividual vari
ance for various studies by nutrient and sex. Several
authors noted that for nutrients with markedly skewed dis
tributions, the ratios for logarithmically transformed
intake distributions were lowered (Beaton et al., 1983;
Hunt _ al., 1983; Sempos et al., 1985).
me table shows
a consistency in the ratio of intra to interindividual
variation, in that most nutrients for all five studies
produced values greater than 1. However, the data of
Hackett _ al. (1983) for a group of children suggest
generally higher ratios for energy, protein, total fat,
and total carbohydrate. In addition, there seems to be
some difference between males and females and between vari
ous nutrients. The method of assessing dietary intake may
have affected the ratio of the variances, since true random
error would be included in the intraindividual component.
In a recent study by Sempos et al. (1985), dietary data
were collected for a sample of 15 women who completed two
randomly selected 1day records per month for a total of
29 records during a period of 2 years. m e data were then
analyzed for each year (Table 41). The various ratios
were very similar in each year, suggesting that there were
stable characteristics within the population. These
authors demonstrated that the variance ratio does reflect
the true usual intake and daytoday variation and vali
dates other studies of shorter duration.
Both Hunt _ al. (1983) and Sempos et al. (1985) noted_ _
that use of nutrient supplements altered the variance
ratios. Sempos _ al. (1985) found ratios less than 1 and
usually less than 0.5 for iron, thiamin, riboflavin, niacin,
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20
TABLE 41. Observed Ratiosa of Intraindividual and Interindividual
Variancesb
. . _
24Hour 3Day 24Hour
Recall by Record by 1Day Recall 7Day Recall by
Young c Older d by Women e Record Pregnant
Nutrient Adults Adults Year 1 Year 2 tar Menf Wameng
Males:
Energy 1.1 1.0 0.8
Protein 1 . 5 1.2 1.4
Carbohydrate 1.6 2.1 0.6
Fat 1.2 1.2 1.3
SFAh I.1 2.2 I.4
PUFA1 2.8 3.S 1.9
Cholesterol 3.4 5.6 1.6
Vi tamin A ~ 1.6
Vitamin C 3. 5 2.3
miamin 2.5 0.9
Ribof lavin 2.4 0.9
Niacin 1.6 2.2
Calcium 2.2 1.1
Iron 1.7 1.8
Females:
.
Energy 1.4 0.8 1.6 1.6 1.1
Protein 1.S 1.3 2.1 2.1 1.4
Carbohydrate 1 . 4 1.2 NRk NE 1.2
Fat 1.6 0.9 NR NR 1.2
SFAh 1.4 1.7 NR NR NE
PUFAi 4.0 2.2 NR NR NR
Cholesterol 4.3 4.2 NO NR NR
Vitamin A 24.3 2.5 7.7 10.9 NR
Vitamin C 2.0 2.8 2.3 2.5 NR
Thiami n 4.4 1.6 3.3 3.9 NR
Ribof lapin 2 . 2 1.8 3 . 0 3. 3 NR
Niacin
equivalent 4.0 2.5 NR NR NR
Calcium 0. 9 1. 7 1.1 1.2 1.0
Iron 2.5 1.5 2.7 2.5 NR
aA ratio of 1. 0 indicates that the ir~traindividual and interindd vidual
variances are equal. A ratio greater than one indicates that
intraindividual variance is greater than interindividual variance.
bThe original papers contain additional data. Only those nutrient
variables examined in two or more papers are included here.
CFrae Beaton et al., 1979, 1983.
dFrma }tunt et al . 1983 .
~ _ _ ,
"Fran Scopes et al ., 1985 .
fFrae McGee et al., 1982.
gFrom Rush et al. 1982.
~ _ ,
'saturated fatty acids.
polyunsaturated fatty acids.
scone of the variance could be assigned to subjects.
kNR=Not reported.
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21
and vitamin C ( as well as for some other nutrients not
included in Table 41) when food intakes with supplements
were analyzed.
PROCEDURE FOR ADJUSTING INTAKE DATA
When the intraindividual variation of dietary intake and
the nether of days of observation are known, it is possible
to determine reliability of dietary intake data for each
particular person. The usual intake for each person lies
within the bounds described by the following equation 95%
of the time if the daytoday variations are normally
distributed:
+ 2 x SD (intra)/ By,
where SD (intra) is the measure of intraindividual varia
tion and n is the number of observations for the individ
ual person. The following discussion assumes normally dis
tributed (Gaussian) data, even though this is rarely the
case with food intake data. The appropriate transformations
to obtain Gaussian distributions are discussed later.
When the number of days of observation increases from 1
to 4, the confidence limits are reduced by onehalf, and the
reliability of the estimate of usual dietary intake is
improved accordingly. If one considers the actual magnitude
of intraindividual variation, however, the results are some
what disheartening. For example, the intraindividual coef
ficient of variation for energy in adults is approximately
25% of the mean (Beaton et al., 1979). This suggests that
about 3 weeks of intake data are needed to estimate usual
energy intake with confidence limits of approximately +10%.
When determining usual intakes in populations, there is a
need not for reliable estimates of the dietary intake for
each person but, rather, for reliable estimates of the dis
tribution of usual intakes for the population (Hegsted,
1972). Unlike individual intakes, the distribution of usual
dietary intakes for the population can be approximated from
a modest number of repetitions of the 1day intake data;
however, seasonal variations in intake and variation between
weekdays and weekend days must also be taken into account in
the data collection.
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22
The overall variability in a distribution of dietary
intake can be described in the following terms:
V(total) = V(inter) + V(intra)/n,
where V(total) = total variance of data (square of observed
SD), V(inter) = betweensubject variance, V(intra) = within
subject variance or residual error teem in an analysis of
variance, and n = number of days of intake data. In this
equation, the interindividual variation represents the dis
tribution of usual intake referred to previously.
Statistical theory allows us to derive an estimate of the
distribution of usual intakes, given the observed mean, the
total variance, and an estimate of the intraindividual vari
ance. Replicated observations of 1day dietary intake are
needed to obtain an estimate of intraindividual variation.
In theory, the replicated observations should be independent
of one another in tome rather than on consecutive days,
although it is not yet known whether this is important in
practice.
The magnitude of the intraindiv~dual variation can be
estimated by analysis of variance (ANOVA). If the original
distribution is not normal, the distribution must be trans
formed into a more normal form before the ANOVA procedure is
applied. (See Appendix A for an example of a logarithmic
transformation for this purpose.) To adjust the intake
distribution, the deviation of each point from the popula
tion mean is multiplied by the ratio of the interindividual
standard deviation to observed standard deviation. With
this nonpar~etric procedure, it is not necessary to assume
a perfect fit of the normal distribution, and the distinc
tive shape of the original distribution is preserved. The
adjusted data are then transformed back to the original
scale for subsequent analyses. Figures 42 and 43 depict
two distributions derived from Nationwide Food Consumption
Survey (NFCS) data that have been adjusted to estimate the
distribution of usual dietary intake of protein for males
and iron for females.
Appendix A provides full details of the approach used in
generating distributions for this report. Although ANOVA is
not recommended as the only approach to estimating inter
individual variation and eliminating the effects of dayto
day variability, it can serve as an example of possible
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23
14.8
11.9
of
o
~ C 8.9
o
~ 5.9
at
3.0
o
A
,W,
0 44
/ \ USUAL INTAKE
ken\ \
\~ 1DAY INTAKE

88 132 176 220
PROTEI N INTAKE, g/day
FIGURE 42. Comparison of 1day and adjusted distributions
for protein intake by male adults. Derived
from the 19771978 NFCS data analysis described
in Appendix A.
Q Q

7.1
o
~ 5.3
J
2 3.5
~9
t.8
o
O
 ~ USUAL INTAKE
ted/) \
'1
Am\ 1 DAY I NTAKE
g  __
6.3 12.6 ~ 8.9 25.2 31.5
I RON I NTAK E, mg/day
FIGURE 43. Comparison of 1day and adjusted distributions
for iron intake by female adults. Derived from
the 19771978 NFCS data analysis described in
Appendix A.
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24
approaches (Trumpler and Weaver, 1953). The method of
normalizing the original distribution should be appropriate
to the data set under study (Box and Cox, 1964).
The current NFCS is designed to collect information for 3
consecutive days for all subj eats . There is a possibility
that dietary intake on consecutive days is correlated within
the individual. If this is true, the statistical power of
the estimates is reduced. On the other hand, the subcom
mittee believes that 3 days of observation may be more than
is required for the derivation of the distribution of usual
intakes. For purposes other than the analysis of dietary
adequacy, such as using dietary intake data for multivariate
analysis, data for additional days would probably be re
quired. All goals of the survey must be considered when the
final decision Is made.
In summary, the Impact of daytoday variation in intake
contributes to errors in estimations of usual intake. If
the survey data include an adequate number of independent
replicate observations of intake measurements, methods can
be used to adjust the observed intake distribution to gen
erate a good estimate of the distribution of usual intakes.
In a large survey, such as the NFCS, this approach is feast
ble and may even permit collection of fewer data than are
now collected, given an appropriate sampling design. The
feasibility of this reduction depends, of course, on other
uses of the data. For example, if providing descriptive
data on patterns of food use other than intake is a purpose
of the survey, more replications may be needed. There is no
need to collect more days of dietary data per individual
than in the recent NFCS to implement the analytical approach
described in this chapter for adjustment of the intake
distributions. The only additional cost involves the
statistical resources needed to design and analyze the data.
