RESEARCH
Ready-to-eat cereal consumption: Its relationship with
BMI and nutrient intake of children aged 4
to 12 years
ANN M. ALBERTSON, MS, RD; G. HARVEY ANDERSON, PhD; SUSAN J. CROCKETT, PhD, RD, FADA;
MICHAEL T. GOEBEL
ABSTRACT
Objective To examine the relationship between ready-to-eat
cereal consumption habits and body mass index of a sample
of children aged 4 to 12 years.
Design Fourteen-day self-reported food diary records were
obtained from a sample of 2,000 American households from
February 1998 through February 1999. Height and weight of
the family members were also self-reported.
Subjects/setting The sample population of 603 children,
aged 4 to 12 years, was broken into tertiles based on cereal
consumption over the 14 days: (three or fewer, four to seven,
or eight or more servings).
Statistical analysis Logistic regression and analysis of variance were used to determine associations between frequency
of ready-to-eat cereal consumption and body mass index or
nutrient intakes.
Results More than 90% of children aged 4 to 12 years consumed ready-to-eat cereal at least once in the two-week collection period. Within tertiles of consumption, children in the
upper tertile had lower mean body mass indexes than those
in the lowest tertile consistently across all age groups
(P⬍.01). Additionally, the proportion of children aged 4 to 12
years who were at risk for overweight/overweight was significantly lower in the upper tertile of cereal consumption
(P⬍.05). Children in the upper tertile also had lower fat intakes and higher intakes of many micronutrients.
Applications The consumption of ready-to-eat cereals at
breakfast should be encouraged as a component of an eating
pattern that promotes the maintenance of healthful body
weights and nutrient intakes in children. J Am Diet Assoc.
2003;103:1613-1619.
A
dramatic increase in the prevalence of obesity has occurred in the United States in recent years (1). Rapid
increases in childhood obesity rates have also been reported. Increases have been seen in both male and female populations, and across socioeconomic, racial, and ethnic
groups. In the United States, close to one in three children is
either at risk for overweight or is overweight (2). Overweight
children often become overweight adults (3). Prevention is
recognized to be the best solution to the problem, so the rising
prevalence of overweight in children is a major concern.
The etiology of obesity is undefined and no doubt of complex
origins (4). One major obvious environmental variable is diet.
To determine the dietary factors influencing body mass index
(BMI), eating patterns, food selection, and macronutrient composition of diets have been examined. However, the majority of
data available are based on survey intake information of one or
A. M. Albertson is a senior nutrition research scientist,
S. J. Crockett is director of nutrition, and M. T. Goebel is
a statistical programmer with The Bell Institute of Health
and Nutrition, General Mills, Inc, Minnapolis, MN. G. H.
Anderson is a professor of nutrition with the Department
of Nutrition Sciences, University of Toronto,Toronto, Ontario.
Address correspondence to: Ann M. Albertson, MS, RD,
Senior Nutrition Research Scientist, The Bell Institute of
Health and Nutrition, General Mills, Inc, 9000 Plymouth
Ave N, Minneapolis, MN 55427. E-mail:
[email protected]
Copyright © 2003 by the American Dietetic Association.
0002-8223/03/10312-0003$30.00/0
doi: 10.1016/j.jada.2003.09.020
Journal of THE AMERICAN DIETETIC ASSOCIATION / 1613
RESEARCH
two days. Hence, the survey data are not amenable to providing
an examination of longer-term dietary patterns.
The Bell Institute of Health and Nutrition Dietary Intake
Study contains data on food consumption patterns. It provides
the opportunity to determine the affect of food consumption
patterns on nutrient intake, but also because height and weight
are recorded, it also allows an examination of the relationship
between food consumption patterns and BMI.
One dietary pattern that is promoted as providing positive
nutrition benefits is the consumption of breakfast. Breakfast
eaters have higher nutrient intakes and lower-fat diets than
nonbreakfast eaters (5-8). In children, the consumption of
breakfast is also associated with better cognitive function in
school (9).
Ready-to-eat (RTE) cereal is a prevalent food in the diet of
American children. The majority of cereal is consumed at
breakfast and is a significant nutrient source in the diets of
American children (10). Information about the relationship of
breakfast eating and its composition to childhood obesity is
lacking, however. Therefore, this research investigated the relationship between RTE cereal consumption and BMI of
school-aged children (aged 4 to 12 years) using a 14-day food
intake methodology.
METHODS
To determine the impact of food consumption patterns on nutrient intakes, a unique methodology utilizing a 14-day food
diary data was developed at the General Mills Bell Institute of
Health and Nutrition. This methodology combines 14-day food
diary data with portion size data from the US Department of
Agriculture’s Continuing Survey of Food Intakes by Individuals
and nutrient data from the University of Minnesota’s Nutrition
Data System for Research (NDS-R) (version 29, University of
Minnesota Nutrition Coordinating Center, 1998, Minneapolis,
MN). The resulting integrated database is housed and was analyzed using SAS (2000, SAS Institute, Cary, NC). The dynamic
software system allows users to categorize the population
based on “usual” consumption of food categories, specific
foods, and/or specific brands of foods and determines dietary
differences vs their “nonconsuming” counterparts.
Food Consumption Data
The food industry has traditionally used detailed food records
to track the consumption of specific branded food items, monitor the growth of food categories, and provide insight into
consumer purchasing and behavior. One supplier of this data is
The NPD Group, a marketing information company that has a
National Eating Trends service (NET). NET has been continuously tracking the eating habits of Americans since 1980. The
annual sample consists of 2,000 households representing approximately 5,000 persons.
This study utilized data collected by The NPD Group from
February 1998 through January 1999. The panel is demographically and geographically balanced to US Census Bureau statistics each year at the household level. The sample is divided into
52 subsamples and each week a group of nearly 60 households
begin recording all the foods and beverages consumed by all
household members. Reporting is distributed evenly throughout the year to be sensitive to seasonal eating habits. Each
household maintains a daily eating diary for two weeks. The
person most responsible for meal preparation is instructed to
record the name and brand of each food and beverage con1614 / December 2003 Volume 103 Number 12
sumed by any member of the household, including all additives,
ingredients, and cooking aids. The diary consists of separate
sections for each meal and snack situation, and collects food
names, flavor descriptors, brand names, package types, product forms, appliances used in preparation, and any special nutritional attributes, among other details. The same information
is collected on ingredient and additive items used to create
dishes or meals in the home. At the end of each day, the recorder is instructed to mail the daily diary to The NPD Group’s
offices. After all 14 daily diaries are received from a household
they are coded and made ready for data processing.
Portion-Size Data
NET panelists record the foods and beverages consumed by
household members but not the quantities of each food. This
procedure is standard for panel surveys to minimize recorder
burden and thus increase reliability. Portion-size data were
estimated from the Continuing Survey of Food Intakes by Individuals 1989-91 and 1994-96 (11,12), and were aggregated,
collapsed for like-foods to strengthen cell sizes, and smoothed
to eliminate outliers. Age- and gender-specific mean serving
weights were thereby determined for more than 800 food
types; these portions were subsequently assigned to each food
recorded and coded in the NET diary.
Nutrient Data
Nutrient intakes were estimated according to previously reported procedures (13-15). Briefly, the nutrient values for
foods recorded and coded in the NET diary were determined
using the recipe component of the NDS-R. This system is a
highly accurate and comprehensive nutrient calculation system that contains complete values for 113 nutrients for more
than 18,000 foods, including many brand-name products. Each
food or recipe was entered into NDS-R for 100 g of that food
and closely matched to the description provided in the NET
diary, including any special nutritional attributes (ie, low fat,
fat free, low cholesterol, low sodium, or reduced sodium). If
special attributes existed, special recipes were added to the
nutrient database to reflect these foods. Each food was assigned to one of more than 100 food groups, which makes analysis by specific food group possible. For this study, estimated
mean daily intake values for the following nutrients were reported: carbohydrate, sugar, fat, saturated fat, protein, cholesterol, sodium, dietary fiber, vitamin A, vitamin E, vitamin C,
thiamin, riboflavin, niacin, vitamin B-6, folate, calcium, magnesium, iron, zinc, and energy (kilocalories). Additionally, the
percentage of children below their Estimated Average Requirement (EAR) (16) was calculated for the total sample and for
children in each of the tertiles of cereal consumption.
BMI
Individual, self-reported heights and weights were recorded in
the diary by each respondent and used to calculate BMI using
the formula: BMI⫽weight (lb)⫼height (in)2⫻704.5. The BMI
was compared against 2000 Centers for Disease Control and
Prevention age- and sex-specific growth charts to determine if
the child is at risk of being overweight. The statistical definition
of the at-risk of overweight population is at or above the 85th
percentile, but less than the 95th percentile of BMI for age from
these charts. Overweight is defined as at or above the 95th
percentile of BMI from these charts (17). Children who did not
RESEARCH
have a recorded height and weight were excluded from the
analysis (n⫽170).
Data Tabulation
To be included in the study, a minimum of seven days of food
collection was required, however no child had less than that
number. Ninety-two percent of children had complete diaries
with food intake data for all 14 days. The number of times RTE
cereal was consumed in 14 days was recorded for each of the
603 children. The children were categorized into tertiles based
on consumption during their 14-day data collection period. For
subjects with incomplete diaries, cereal consumption was normalized to 14 days by multiplying the rate of RTE consumption
per day by 14.
STATISTICAL ANALYSIS
Analysis of variance was used to determine if BMI differed
among the cereal consumption tertiles in each of three age
groups, 4 to 6 years, 7 to 9 years, and 10 to 12 years, as well as
all ages (4 to 12 years) combined. Pairwise post hoc t tests were
performed where differences were found among the tertiles.
Logistic regression was used to analyze the association between cereal consumption pattern and risk for overweight in
each of the three age groups and for all ages. The contrasts
were examined between the possible pairs of cereal tertiles
using the Wald ␹2 test. Comparisons were made using analysis
of variance on intakes of 21 key nutrients among cereal consumption tertiles with post hoc comparisons. An ␣ level of 0.01
was used for analysis of variance analyses except where otherwise noted. All analyses were performed using the Statistical
Analysis System (version 8.0, 2000, SAS Institute, Cary, NC).
RESULTS
The sample of 2,000 households (approximately 5,000 subjects, including 603 children aged 4 to 12 years) collected from
February 1998 to February 1999 that was used in this study
closely approximates the US census data for age and race (Table 1).
The 603 children were categorized according to the child’s
age and cereal consumption pattern (Table 2). Intake ranged
from zero to more than 15 servings in 14 days (Figure). There
was a statistically significant inverse relationship between BMI
and frequency of RTE cereal consumption (P⬍.01) within each
age group as well as for the total sample (Table 2). Children
aged 4 to 12 years who consumed eight or more servings of RTE
cereal in two weeks had significantly lower BMI compared to
the children who consumed two or fewer servings during a
two-week period (P⬍.0001) (Table 2).
A significant inverse relationship also exists between the
population at risk for being overweight and frequency of cereal
consumption (P⬍.01) (Table 3). The proportion of children
aged 4 to 12 years at risk for overweight or overweight according to Centers for Disease Control and Prevention standards
(17) is 33.67% or roughly one in three. When children aged four
to 12 ate RTE cereal eight or more times in two weeks that risk
lowers to 21.3% or nearly one in five. Conversely, when children ate RTE cereal zero to three times in two weeks their risk
for overweight increases to 47.4%, nearly one in two. This inverse trend is consistent across each of the age groups (4 to 6
years, 7 to 9 years, and 10 to 12 years). Frequent cereal eaters
(eight or more servings during two weeks) in the 7- to 9-year-
Table 1
Sample demographics compared with 1998 US census data
Demographic
All persons
Age (years)
0-12
4-6
7-9
10-12
13-17
18-34
35-44
45-54
55-64
65⫹
Families
Income
⬍$12,500
$12,500-$19,999
$20,000-$29,999
$30,000-$39,999
$40,000⫹
Household size
2 Members
3-4 Members
5⫹ Members
Age of female head of household (years)
⬍35
35-44
45-54
55⫹
Female employment
Employed
Not Employed
Race
White
Nonwhite
Nonfamilies
Income
⬍$7,500
$7,500-$14,999
$15,000-$24,999
$25,000⫹
Sex
Female
Male
Age of female head of household (years)
⬍35
35-54
55⫹
Age of male head of household (years)
⬍35
35-54
55⫹
Census
(%)
Sample
(%)
20
4.4
4.5
4.3
8
24
17
13
8
11
20
5.8
5.0
4.3
7
19
17
14
10
12
9
9
13
12
57
10
9
15
13
53
42
44
14
43
43
14
26
28
20
26
25
27
22
26
59
41
56
44
84
16
87
13
14
22
20
45
14
23
20
44
55
45
54
46
18
24
58
20
33
47
33
40
27
23
37
40
Journal of THE AMERICAN DIETETIC ASSOCIATION / 1615
RESEARCH
Table 2
Mean body mass index (BMI) by cereal consumption tertiles (N⫽603)
Age group
Cereal consumption
≤3 Servings BMI
4-6 years
7-9 years
10-12 years
4-12 years
≥8 Servings BMI
4-7 Servings BMI
Total BMI
P
MeanⴞSD
n
MeanⴞSD
n
MeanⴞSD
n
MeanⴞSD
n
18.2⫾5.0x
18.7⫾5.9x
21.0⫾4.3x
19.3⫾5.2x
59
56
58
173
16.8⫾3.6xy
17.6⫾3.6xy
19.4⫾4.8xy
17.9⫾4.2y
72
60
59
191
15.9⫾2.8y
16.1⫾2.7y
18.1⫾3.5y
16.7⫾3.1z
90
74
75
239
16.8⫾3.8
17.3⫾4.3
19.4⫾4.4
17.8⫾4.3
221
190
192
603
.0019
.0027
.0004
⬍.0001
Means within the same row with the same letter are not significantly different (P⬍.01).
Table 3
Proportion of children aged 4 to 12 years at risk for overweighta by cereal consumption tertiles (N⫽603)
Age group
Cereal consumption
≤3 Servings
4-6 years
7-9 years
10-12 years
4-12 years
≥8 Servings
4-7 Servings
Total
P
% at risk for
Overweight
n
% at risk for
Overweight
n
% at risk for
Overweight
n
% at risk for
Overweight
n
47.5x
50.0x
44.8x
47.4x
59
56
58
173
34.7xy
38.3xy
37.3xy
36.7y
72
60
59
191
25.6y
16.2y
21.3y
21.3y
90
74
75
239
34.4
33.2
33.3
33.7
221
190
192
603
SD⫽standard deviation.
Proportions within the same row with the same letter are not statistically significantly different (P⬍.05).
Based on 2000 Centers for Disease Control and Prevention definitions (17).
a
FIG. The distribution of ready-to-eat (RTE) cereal consumption over a 14-day collection period.
1616 / December 2003 Volume 103 Number 12
.02
⬍.001
.011
⬍.001
RESEARCH
Table 4
Mean daily nutrient intake of children aged 4 to 12 years by cereal consumption tertiles (N⫽603)
Nutrient
Cereal consumption
≤3 servings
(nⴝ173)
≥8 servings
(nⴝ239)
4-7 servings
(nⴝ191)
P
4™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™ Mean⫾standard deviation ™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™3
1726⫾454
1693⫾437
1,681⫾414
.57
219.4⫾59.1
218.5⫾60.8
227.4⫾61.1
.24
108.5⫾37.0
105.6⫾38.3
113.0⫾38.9
.13
x
xy
y
65.9 ⫾20.0
61.7 ⫾17.1
⬍.001
68.7 ⫾20.9
24.0⫾7.4
23.4⫾7.7
22.5⫾7.1
.11
62.2⫾17.7
61.2⫾15.7
69.9⫾14.1
.33
x
x
y
197⫾78
170 ⫾61
⬍.001
216 ⫾105
2966⫾818
2929⫾745
2854⫾720
.31
11.2⫾3.5
11.2⫾3.8
11.6⫾3.5
.35
x
y
z
631.7 ⫾211.1
738.3 ⫾208.0
⬍.001
559.5 ⫾238.3
7.4⫾2.5
7.0⫾2.2
7.0⫾2.5
.21
83.9⫾44.2
81.3⫾39.7
93.5⫾47.4
.011
x
y
z
1.5 ⫾0.4
1.7 ⫾0.4
⬍.001
1.4 ⫾0.4
1.8y⫾0.5
2.0z⫾0.5
⬍.001
1.6x⫾0.5
x
y
z
18.1 ⫾4.2
19.6 ⫾4.0
⬍.001
16.7 ⫾4.6
x
y
z
1.5 ⫾0.3
1.7 ⫾0.4
⬍.001
1.3 ⫾0.4
x
y
z
212.3 ⫾59.1
265.5 ⫾68.4
⬍.001
188.9 ⫾68.2
806.6xy⫾275.4
877.3y⫾296.4
⬍.001
784.4x⫾314.4
200.9⫾57.3
200.9⫾53.8
213.9⫾53.5
.021
12.1y⫾2.9
13.9z⫾3.4
⬍.001
10.6x⫾2.9
x
y
z
8.9 ⫾2.3
10.3 ⫾2.5
⬍.001
8.2 ⫾2.4
Energy (kcal)
Carbohydrate (g)
Total sugar (g)
Fat (g)
Saturated fat (g)
Protein (g)
Cholesterol (mg)
Sodium (mg)
Dietary fiber (g)
Vitamin A (mcg rae)
Vitamin E (mg ␣-tocopherol)
Vitamin C (mg)
Thiamin (mg)
Riboflavin (mg)
Niacin (mg)
Vitamin B-6 (mg)
Folate (mcg)
Calcium (mg)
Magnesium (mg)
Iron (mg)
Zinc (mg)
Means within the same row with the same letter are not significantly different (P⬍.01).
Table 5
Percent of children aged 4 to 12 years not meeting their Estimated Average Requirement (EAR) by cereal consumption tertiles (N⫽603)
Nutrient
Vitamin A (mcg rae)
Vitamin E (mg ␣-tocopherol)
Vitamin C (mg)
Thiamin (mg)
Riboflavin (mg)
Niacin (mg)
Vitamin B-6 (mg)
Folate (mcg)
Magnesium (mg)
Iron (mg)
Zinc (mg)
Cereal consumption
≤3 Servings
(nⴝ173)
4-7 Servings
(nⴝ191)
≥8 Servings
(nⴝ239)
Total
(nⴝ603)
P
14.4x
54.9
7.5
1.2
0.6
1.7
2.9
59.0x
19.1
0.6
9.8x
3.7y
58.6
4.2
0
0
0
0
42.4y
14.1
0
2.1B
0.4y
57.7
2.1
0
0
0
0
8.8z
9.2
0
0.8y
5.5
57.2
4.3
0.3
0.2
0.5
0.8
33.8
13.6
0.2
3.8
⬍.0001
.77
.03
NA
NA
NA
NA
⬍.001
.02
NA
⬍.001
Means within the same row with the same letter are not significantly different (P⬍.01).
Journal of THE AMERICAN DIETETIC ASSOCIATION / 1617
RESEARCH
old age group had a one in six risk for being overweight vs a one
in two risk in the infrequent cereal-eaters group.
Although energy intake was not statistically significantly different across cereal consumption tertiles, there were differences in intakes of fat and cholesterol as well as vitamin A,
vitamin B-6, thiamin, riboflavin, niacin, folate, calcium, iron,
and zinc (Table 4). As seen with BMI, there was a significant
inverse relationship with frequency of RTE cereal consumption
and daily fat intake (P⬍.01) and daily cholesterol intake
(P⬍.01). Intakes of vitamin A, vitamin B-6, thiamin, riboflavin,
niacin, and folate intakes increased from the low cereal tertile
to the upper tertile. Calcium, iron, and zinc intake also increased from the low cereal tertile to the upper tertile.
The percent of the population consuming below 100% of
their EARs (18) was also examined for the children in the three
tertiles of cereal consumption. Sizable proportions of children
did not meet their EARs for vitamin E (57%) and folate (34%);
however, folate intakes were not measured on dietary folate
equivalents because data was not available in the nutrient database. Proportions of children not meeting their EARs for vitamin A, folate, and zinc were highest in the low tertile of cereal
consumption (P⬍.01) (Table 5).
DISCUSSION
Children who consumed RTE cereal most frequently had the
most appropriate age-related BMI, were least likely to be at risk
for overweight, and had the most positive nutrient intake profiles. The positive relationship between RTE cereal consumption and nutrient intakes can be explained, at least in point, by
the nutrient fortification and low fat content of the cereals. The
relationship with BMI, however, is more difficult to explain.
Several explanations can be proposed for the association
between cereal consumption and body weight. First, RTE cereal consumption may be a marker for other healthful lifestyle
factors practiced by the children and possibly the other household members or caregivers. Similar to these results, adults
(men and women aged 35 to 64 years) who consumed RTE
cereal at least every other day had more healthful BMIs and
were less likely to be overweight or obese (19,20).
Second, calcium intakes were higher for the high cereal consumers who also had the more appropriate body weight (Table
4). It has been demonstrated that increased consumption of
dairy calcium is related to lower BMI in adults (21), and a
similar effect has recently been demonstrated in children
(22,23). Because RTE cereal is most frequently consumed with
milk, which is a good source of calcium, it is possible that the
proposed mechanism (24) is a factor in contributing to the
better intake regulation of frequent cereal eaters. At the time of
collection, breakfast cereals were not routinely fortified with
calcium. Calcium intake of frequent cereal eaters would be
expected to increase with current calcium fortification levels of
many RTE cereals.
Third, the children who ate cereal most frequently were children who most often ate breakfast. Thus, the association may
also reflect on eating patterns that are more favorable for the
regulation of body weight. For example, more frequent breakfast eating has been associated with lower BMI in adults (25,26)
and lower fat intakes (27).
Because RTE cereals are traditionally lower-fat foods than
other breakfast alternatives, fat intakes have been shown to be
lower for both adults and children consuming RTE cereal most
frequently (28,29). High-fat diets have been associated with
1618 / December 2003 Volume 103 Number 12
higher BMI in adults (30), suggesting that lower fat intake in
the daily diet may also make a contribution to more favorable
energy balance and hence more favorable BMI.
Consumption of RTE cereal has been shown to improve not
only macronutrient intake but also micronutrient intakes and
dietary fiber (28,31,32). Similarly, the frequent cereal consumers in this study had significantly higher intakes of the B vitamins, iron, zinc, and calcium and were most likely to meet their
recommended levels of these nutrients. Higher intakes of these
nutrients in particular are characteristic of a breakfast including nutrient-fortified RTE cereals eaten with milk. The improved nutrient intake profile appears to be largely related to
the consumption of RTE cereal, the foods it could be replacing
at breakfast, and a pattern for healthful eating throughout the
day (27).
In our study, energy intake was not correlated with BMI, but
this is not surprising given the small sample size and assumptions required in making estimates of intake. Often with survey
data, the instrument is not sensitive enough to detect small
energy intake differences. Similarly, it may be argued that the
nutrient intake data do not describe precisely the actual intakes of the children and therefore the adequacy or inadequacy
of their diets.
This study has certain inherent limitations that need to be
recognized. The food records were self-reported. However,
The NPD Group trains panelists to fully describe food intakes
on a daily basis. Food dairies are returned each day and NPD
Group conducts follow-up to help ensure complete records.
Height and weight were also self-reported, but this method is
known to be accurate even when used to calculate BMI (33).
Nutrient intake data were derived from records of foods consumed combined with estimates of serving size taken from survey data. The latter assumes that an average serving size applies to all persons of same age and gender in the sample, which
clearly results in errors of the estimates for individual persons.
However, when applied to the total sample, it can be expected
that mean intakes approximate estimates of intake provided by
dietary survey data. Although no direct comparison has been
made in this sample population, the average intakes in these
children (Table 4) are similar to that reported in other large,
population-based surveys (12,34). Although this lack of proof
of validity can be seen as a weakness it should be noted that
differences in the mean comparisons across tertiles of RTE
cereal consumption cannot be explained by an error in accuracy of the estimate of total intake, an error that would be
present across all tertiles. Thus, the conclusion remains that
the most frequent consumers of RTE cereals have better nutrient intakes than those who consumed RTE cereals least frequently. A strong element of this survey design is the recording
of 14 days of foods consumed. Thus, it is possible to determine
additional associations between other food patterns and categories with the BMI of children, as well as adults, and this will
lead to continued research in this area.
APPLICATIONS
Although regular cereal consumption in itself may not ensure
healthful BMI, it may be an indication of a pattern of healthful
eating that includes regular breakfast consumption and contributes to age-appropriate energy intakes. Thus, the consumption of RTE cereals at breakfast should be encouraged as a
■
RESEARCH
component of an eating pattern that promotes maintenance of
healthful body weights and nutrient intakes by children.
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obesity in the United States: prevalence and trends, 1960-94. Int J Obes.
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