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Depression and school performance in
middle adolescent boys and girls
Fröjd S, Nissinen E, Pelkonen M, Marttunen M, Koivisto A-M, Kaltiala-Heino R.
Abstract
The study aimed to investigate the associations between different levels of depression with different aspects
of school performance. The target population included 2516 7th–9th grade pupils (13–17 years) of whom
90% completed the questionnaire anonymously in the classroom. Of the girls 18.4% and of the boys 11.1%
were classified as being depressed (R-Beck Depression Inventory (BDI), the Finnish version of the 13-item
BDI). The lower the self-reported grade point average (GPA) or the more the GPA had declined from the
previous term, the more commonly the adolescents were depressed. Depression was associated with
difficulties in concentration, social relationships, self-reliant school performance and reading and writing as
well as perceiving schoolwork as highly loading. The school performance variables had similar associations
with depression among both sexes when a wide range of depression was studied but gender differences
appeared when studying the severe end of the depression scale. Our study indicates that pupils reporting
difficulties in academic performance should be screened for depression.
ARTICLE IN PRESS
Introduction
Adolescent depression has a significant negative impact on school performance and consequently produces
maladaptive outcomes in terms of subsequent education and occupational functioning. Several key
symptoms of depression, such as impaired ability to concentrate, loss of interest, poor initiative,
psychomotor retardation, low self-esteem, sense of worthlessness as well as social withdrawal may
significantly disturb cognitive performance and diminish initiative in learning (Beck, 1967; Hammen, 1998;
Kirkcaldy & Siefen, 1998; Kovacs & Goldston, 1991). Depression may impair cognitive functioning because
the depressed adolescent concentrates on depressive thoughts and interpretations instead of the actual
tasks, or because depression directly blocks cognitive resources, or due to both reasons (Hartlage, Alloy, Va´
zquez, & Dykman, 1993). The negative reactions of teachers and peers may also cause learning problems via
paying attention to the depressed adolescent’s behavior and emotional problems instead of learning (Adams,
1992). Failures and negative feedback are likely to further exacerbate the depressive
cognitive style typical of depression (Beck, 1967; Birmaher et al., 1996; Kendall & Lochman, 1994) or
strengthen depressive thought(s) promoting learned helplessness, passivity and avoidance (Seligman, 1975).
Previous studies have suggested an association between depression and poor school performance measured
by grade point average (GPA) or numerical evaluation of success on various courses (Economou &
Angelopoulos, 1989; Kaltiala-Heino, Rimpela¨ , & Rantanen, 1998; Kovacs & Goldston, 1991; Puig-Antich et
al., 1993; Reinherz, Frost, & Pakiz, 1991; Slotkin, Forehand, Fauber, McCombs, & Long, 1988). Major
depression was associated with poor school performance even long after symptomatic remission in some
studies (Kovacs & Goldston, 1991) but not in all (Lewinsohn, Gotlib, & Seeley, 1995). Reinherz et al. (1993)
found no association between major depressive illness and academic competence. However, self-reported
symptoms of depression have been suggested to associate with impaired academic performance (Reinherz
et al.,1991) and decreases in GPA in follow-up (Shahar et al., 2006). Dissatisfaction with grades has also been
suggested predictive of subsequent major depressive disorder (Lewinsohn et al., 1994). GPA can be
considered an objective measure of academic achievement in the sense that the GPA is independent of the
perceptions of the adolescent, but school performance can also be assessed with subjective measures, such
as perceived performance as compared to peers, or perceived difficulties in different skills. The hypothesis
then is that perceived inability to fulfill valued standards may give rise to depressive cognitions. Perceived
inefficacy to master various skills, to regulate learning and to form and maintain social relationships have all
been associated with both concurrent and subsequent depression (Alva & de Los Reyes, 1999; Bandura,
Pastorelli, Barbaranelli, & Caprara, 1999; Lewinsohn et al., 1994). Different components of academic self-
perception may, however, contribute differently to depressive feelings (Masi et al., 2001). Depression has
also been reported to associate more significantly with subjective measures of academic competence than
with actual academic performance (academic grades) both concurrently and in follow-up (Bandura et al.,
1999).
Some research has targeted the associations between depression and students’ motivational beliefs and
attitudes in coping with the responsibilities of school. Underestimation of academic competence and low
achievement expectations have been reported to be associated with adolescent depression (Cheung, 1995;
Cole, Martin, Peeke, Seroczynski, & Fier, 1999). Previous research has often focused on only one aspect of
school performance, perceived or objective. The issue of whether different areas of subjectively perceived
school performance are differently related to depression is also sparsely discussed. As to numerical marks as
the indicators of academic achievement, the recent changes in marks have been little considered, even if one
might expect that a change in academic achievement could be more relevant for depression than stable
marks, be they good or below average. More information is needed about the association of diverse aspects
of school performance in relation to depression in adolescence.
The aims of this study were to explore in a non-selected classroom survey sample among 13–17- year olds:
(1) the associations of objective school performance (as measured by GPA and change in GPA) and subjective
school performance (perceived loading of schoolwork and difficulties in different areas of schoolwork) with
self-reported depression among adolescents;
(2) which school-related variables are most strongly associated with self-reported depression when
evaluating both the subjective and objective aspects of school performance together.
We expected to find perceived difficulties in school performance more strongly associated with depression
than objective school performance, since there may be two intertwining effects of depression on perceived
difficulties. Depression may be associated with perceiving one’s performance inferior to that of peers, which
in turn may result in hopelessness and less effort and thus actual impairment in performance (which, again
may result in perceiving oneself as increasingly inferior).
Materials and methods
Participants
The material of the present study comprises the responses of the 7th–9th grade pupils (ages 13–17 years)
attending secondary school in Pori, a Finnish city of approximately 80,000 inhabitants, in spring 2000. Out of
a total of 2516 students enrolled in the study schools, all students present at school on the date of the survey
(n ¼ 2329) participated in the study and completed questionnaires anonymously in the classroom as a part
of a larger epidemiological study on adolescent mental health. Due to incomplete data 63 (3%)
questionnaires were excluded. Thus, the final analyzable sample totaled 2266 (90% of the target population,
97% of those present at school). The 50.8% of the subjects were girls. The mean age of the respondents was
15 years (s.d. ¼ 0.9), 71.5% were living with both parents and 62.2% reported stable employment of their
parents (none unemployed during the past 12 months). Of the subjects’ parents 16.5% of fathers and 16.7%
of mothers had completed a university degree. The study was approved by the
Ethics Committee of Tampere University Hospital.
Measures
Depression
Depression was measured using R-Beck Depression Inventory (BDI) (Raitasalo, 1995), the Finnish
modification of the 13-item version of BDI (Beck, 1972). The psychometric properties of the Finnish version
have been shown to be good among 14–16-year-old adolescents (Kaltiala- Heino, Rimpela¨ , Rantanen, &
Laippala, 1999). R-BDI comprises 13 statements showing increasing intensity of depressive emotions and
cognitions scoring 0–3 each, the theoretical range of the scale thus being 0–39. Overall scores of 0–4 indicate
no depression, 5–7 indicate mild, 8–15 indicate moderate and 16 and more indicate severe depression (Beck,
1972; Raitasalo, 1995). In the present study we use three dichotomized outcomes: depression (overall score
8 or more, yes/ no), moderate depression (overall score 8–15, yes/no) and severe depression (overall score
16 or more, yes/no).
Objective school performance
Objective school performance comprised of self-reported GPA (theoretical range 4.0–10.0), which was used
as a continuous variable, and change in GPA. Change in GPA from previous term was elicited by asking if GPA
had changed from end of fall term 1998 to the spring 1999 evaluation. The answering alternatives were:
improved 0.5 points or more, minor change (change in either direction is less than 0.5), declined 0.5–1.0
points, declined more than 1.0 point. Subjective school performance Subjective school performance
comprised of perceived loading of schoolwork and perceived difficulties in different areas of schoolwork.
Perceived loading of schoolwork was assessed by asking: How do you perceive the workload concerning your
schoolwork during this school year? The response alternatives were: continuously too high, quite often too
high, suitable, quite often too low, continuously too low. Due to low frequencies, the last two response
alternatives indicating low loading were combined for the logistic regression analyses. The final classes were
thus: continuously too high, quite often too high, suitable, and too low. Perceived difficulties in schoolwork
were elicited by asking: How is your school going? Do you have difficulties in the following areas of
schoolwork? Concentrating, paying attention to teaching, teamwork, getting along with peers, getting along
with teachers, doing homework, preparing for examinations, finding personal learning strategies, doing
activities requiring initiative, doing reading tasks and doing writing tasks. The response alternatives for each
area of schoolwork were: 0 ¼ not at all, 1 ¼ not so much, 2 ¼ quite much, 3 ¼ very much. For further analysis,
the 11 items eliciting perceived difficulties in schoolwork were combined to
four conceptual sum scales as follows (in parenthesis, range min–max of each scale):
(1) difficulties in concentration (difficulties in concentrating and difficulties in paying attention to teaching)
(0–6);
(2) difficulties in social relationships (difficulties in teamwork, difficulties with peers and difficulties in
relationships with teachers) (0–9);
(3) difficulties in self-reliant school performance (difficulties doing homework, difficulties preparing for
examinations, difficulties finding personal learning strategies and difficulties in activities requiring initiative)
(0–12);
(4) difficulties in reading and writing: (difficulties in reading tasks and difficulties in writing tasks) (0–6).
The internal consistency of the sum variables comprising different areas of difficulties in schoolwork was
good. The Cronbach’s alphas varied between 0.644 (difficulties in social relationships—scale for boys) and
0.782 (difficulties in concentration—scale for boys).
Statistical analysis
Associations between school performance and self-reported depression were first analyzed using the w2test for categorized variables and independent samples t-test for the continuous variables (GPA). A binary
logistic regression analysis applying enter procedure was then performed to find school performance
variables independently associated with depression. The dependent variables were s depression (yes/no),
moderate depression (yes/no) and severe depression (yes/no). The independent variables were age (in years)
and all the school performance variables: GPA, change in GPA, perceived loading of schoolwork and perceived
difficulties in schoolwork. All analyses were conducted separately for both genders. P-values smaller than
0.05 were considered as statistically significant (also when the confidence interval in logistic regression
included 1 in either end of the interval). Data were analyzed using SPSS for Windows (version 14.0) statistical
software.
Results
Univariate associations between school performance and self-reported depression
Of the girls 18.4% (13% reporting moderate, 5% reporting severe symptoms) and of the boys 11.1% (8%
reporting moderate, 3% reporting severe symptoms) were classified as being depressed. During the last term
the mean GPA was 7.6 among the boys (s.d. ¼ 0.92) and 8.0 among the girls (s.d. ¼ 0.82). The mean GPA was
7.1 (s.d. ¼ 0.91) in depressed and 7.7 (s.d. ¼ 0.92) in non-depressed boys, while the respective figures in girls
were 7.6 (s.d. ¼ 0.79) and 8.0 (s.d. ¼ 0.90) (Po0.001). As compared with the adolescents with a minor change
in the GPA, the more the GPA had declined, the more common were all severity levels of depression among
both sexes, but especially moderate depression in boys was also associated with a change for the better.
Among those adolescents who perceived the loading of schoolwork to be continuously or quite often too
high, all severity levels of depression were more common compared to those who reported the loading to
be suitable.
Among boys all severity levels of depression were associated with perceiving the loading of schoolwork
continuously too low, whereas in girls only severe depression was associate with the perception of
continuously too low loading. Further, all severity levels of depression were more common among those who
had difficulties in concentrating, in paying attention to teaching, in teamwork, with peers, in relationships
with teachers, doing homework, preparing for examinations, finding personal learning strategies, in activities
requiring initiative, in reading tasks and in writing tasks. The associations between school performance and
all severity levels of depression were similar among the girls and the boys in almost all analyses. Among girls
moderate depression was not associated with difficulties in team work and severe depression was not
significantly associated with difficulties in writing tasks (Table 1).
Multivariate associations between school performance and self-reported depression
The school performance-related correlates of self-reported depression were the same among both sexes in
multivariate analysis concerning the widest range of depression (BDI scores 8 or more). Both measures of
objective school performance (GPA and a change in GPA) were associated with self-reported depression. The
association between a moderate (0.5–1.0 grades) decline in GPA with depression observed in univariate
analyses was sustained in the multivariate analysis, while the associations of a major decline in GPA and an
improvement of GPA were not. The subjective school performance variables that significantly predicted selfreported depression were high perceived loading of schoolwork and difficulties in social relationships and
self-reliant school performance (an increase in perceived difficulties increased the risk for depression) (Table
2).
Indices of objective school performance were not associated with moderate depression among either sex.
High GPA and an improvement in GPA were protective of severe depression in boys but not among girls.
Perceiving the loading of schoolwork continuously too high was the only subjective measure of school
performance associated with severe depression among boys whereas among girls almost all subjective
measures were associated with severe depression.
Discussion
The main finding was that aspects of both objectively measured and subjectively perceived poor performance
persisted as significantly associated with a wide range of depression in both sexes also when studied
simultaneously in a multivariate analysis. In more specific analyses concerning moderate depression only,
the indices of objective school performance did not prove significant in either sex. Finally studying the most
extreme cases of self-reported depression, gender differences appeared: objective school performance
seemed protective for severe depression in boys but not in girls, whereas subjective school performance was
much more strongly associated with severe depression in girls than in boys. In line with the majority of
previous studies (Economou & Angelopoulos, 1989; Kaltiala-Heino et al., 1998; Kovacs & Goldston, 1991;
Masi et al., 2000; Puig-Antich et al., 1993; Reinherz et al., 1991; Slotkin et al., 1988)—if not all (Lewinsohn et
al., 1995; Masi et al., 2001; Reinherz et al., 1991)—we found that self-reported depression was associated
with poor academic achievement in terms of low school marks. The lower the GPA, the more common was
depression. This is plausible, since depression could either result in lowered performance or be triggered by
current failure. The direction of causality may, however, differ between the sexes: baseline grades have been
suggested to predict female depression but not male depression in follow-up (Undheim & Sund, 2005). The
moderator between the association between depressive symptoms and decrease in GPA may be selfcriticism (Shahar et al., 2006). The contemporary society values academic achievement very high, and this
may place those less capable to that in a disadvantageous position not only related to career but also
regarding mental health. Perceived too heavy loading of schoolwork as well as perceived difficulties in many
areas in the school context were associated with self-reported depression, as reported also in several
previous studies (Alva & de Los Reyes, 1999; Bandura et al., 1999; Cole et al., 1999; Lewinsohn et al., 1994;
Masi et al., 2000, 2001; Puig-Antich et al., 1993; Seroczynski, Cole, & Maxwell, 1997).
All changes in the GPA were associated with depression in univariate analyses but nearly all associations with
different severity levels of depression disappeared when other school-related variables were controlled for.
In univariate analyses there was an interesting association of an improvement in GPA and depression showing
especially clearly in moderate depression among boys. While an improvement in the GPA per se might be
associated with depression because of a possible loss of popularity among peers or overwhelming stress and
tiredness resulting from the process of getting to that higher GPA; the association with depression was not
strong enough to persist in multivariate analysis.
When objective measures of school performance were controlled for, depression was associated with some,
but not all aspects of subjective school performance. Difficulties in concentration were associated only with
severe levels of depressive symptoms in girls. Difficulties in reading or writing did not seem to constitute a
risk for depression when other school performance variables were controlled for. On the other hand, school
achievement in specific skills such as reading has been suggested to be associated with externalizing
problems in adolescence (Richards, Symons, & Greene, 1995). Future studies should focus in more detail on
associations between aspects of schoolwork and different types of disorders, in order to find entries for
intervention for youth with both externalizing and internalizing disorders. Previous research has seldom
investigated simultaneously the associations between objective and subjective school performance and
depression. Recently, Undheim and Sund (2005) found in cross-sectional analyses that school stress and class
well-being were significantly associated with depressive symptoms in both sexes whereas grades were
associated with male depression only in a middle adolescent population. An association between poor
relationships with peers and/or teachers has been found with diverse study designs and samples (Masi et al.,
2000, 2001; Puig-Antich et al., 1993) but gender differences in these associations have seldom been
addressed. According to our findings gender differences in associations with
different kinds of indices of school performance may be especially evident concerning severe
symptomatology.
Due to the cross-sectional design of this study it remains unknown whether the adolescents’ school
performance was impaired due to depression, or whether negative life events, such as disappointments in
academic performance contributed to their depression (Birmaher et al., 1996; Lewinsohn et al., 1994). There
are many potential risk factors and diverse pathways to depression. However, cognitive aspects seem to be
of major importance in adolescent depression. From a theoretical point of view, an adolescent’s
conceptualization of decline in academic performance as a consequence of personal deficiencies seems to
contribute to depression via self-blame and negative attributions (Abramson, Alloy, & Metalsky, 1989).
Academic engagement and achievement are likely to be critical to continued patterns of personal adjustment
during adolescence, and poor achievement may constitute a risk factor for subsequent depression (Eccles,
Lord, & Roeser, 1996; Lewinsohn et al., 1994; Pelkonen, Marttunen, & Aro, 2003) and for low educational
level in adulthood (Koivusilta, Rimpela¨, & Vikat, 2003). However, the association may be complex, e.g. Cole
et al. (1999) reported that depression increased the likelihood of starting to underestimate one’s own
competence, but underestimating one’s own competence did not result in depression during follow-up. Early
detection of mental health problems among adolescents with poor academic performance seems to be
justified (DeSocio & Hootman, 2004).
Methodological considerations
The present population-based study provides a good opportunity to assess cross-sectionally the relationships
between different aspects of school performance and self-reported depression. The sample is large and
representative comprising all the secondary school students in an urban and sub-urban region. All shared the
same language and ethnic background. The results may not, however, be generalizable directly to
populations in rural regions and with different ethnic backgrounds. The questionnaires were completed in
classrooms in a controlled and motivated environment; which produced a high response rate of 90%.
Although depression is likely to be more prevalent among dropouts, the high response rate enhances the
generalizability of the results and it is unlikely that the associations between the phenomena studied would
be different among the drop outs.
A validated method of assessing self-reported depression among adolescents (R-BDI) was used. Using selfreport, no medical diagnoses of depressive disorders can be generated. To avoid bias due to normal mood
changes in adolescence, we set the cut-point to scores indicating at least moderate depression (KaltialaHeino et al., 1999). Prevalence of depression in the present sample fell within the range of estimates
suggested in previous studies reporting symptoms of selfreported depression (Pelkonen et al., 2003), or
depression as measured by standardized diagnostic interviews (Aalto-Setälä, Marttunen, Tuulio-Henriksson,
Poikolainen, & Lönnqvist, 2001; Wittchen, Nelson, & Lachner, 1998).
We were interested in the subjects’ own experiences regarding difficulties in the school context. Self-report
questionnaires offered valuable information on individual experiences in schooling during adolescence.
Estimating objectively measurable difficulties in reading, writing, attention or executive functions would
require specialized psychological examinations. Due to the gathering of delicate information our data are
anonymous. Hence it was impossible to check the individual GPAs from school records. Self-reported GPA of
university students has been suggested sufficiently adequate for research use (Cassady, 2001). Adolescents
with little psychological stress or no problem behavior may tend to overestimate their grades (Zimmerman,
Caldwell, & Bernat, 2002) and actual academic performance and cognitive skills may also mediate selfreported grade validity (Kuncel, Kuncel, Crede, & Thomas, 2005). Therefore, certain caution is needed in
interpreting the associations of depression with GPA.
Conclusions
Depressed young people had impaired abilities to cope with academic responsibilities. This emerges both in
external evaluation and in subjective experience. Perceiving difficulties in special areas of school work was
associated with moderate symptoms among boys and severe symptoms among girls. The proportion of
depressed individuals among adolescents perceiving very much difficulties in different areas of school work
was alarmingly high (30–64%). These findings seem to have implications for school and health care
professionals in early detection and intervention with depression-prone adolescents. Preventive efforts can
be selectively targeted at young people with academic problems. Mental health can be promoted in school
settings with support strategies such as enhancing self-esteem and promoting efficient learning strategies.
For youth with distressing depressive symptoms appropriate psychiatric assessment and treatment should
be offered to promote school performance, and through that, future academic aspirations.
Acknowledgements
The study was financially supported by the Yrjo¨ Jahnsson Foundation.
References
182. Gibbs J, Young R, Smith G. Cholecystokinin decreases food intake in
rats. J Comp Physiol Psychol. 1973;84:488 – 495
183. Rohner-Jeanrenaud F, Walker C, Perotto R, Jeanrenaud B. Chronic intracerebroventricular administration of corticotropin releasing factor (CRF)
to genetically obese fa/fa rats arrests the further increase in their body
weight. In: Björntorp P, Rossner SJ, eds. Obesity in Europe ’88: Proceedings
of European Congress on Obesity. London, UK: John Libbey; 1988
184. Spina M, Merlo-Pich E, Chan R, et al. Appetite-suppressing effects of
urocortin, a CRF-related neuropeptide. Science. 1996;273:1561–1564
185. Geary N. Pancreatic glucagon signals postprandial satiety. Neurosci
Biobehav Rev. 1990;14:323–338
186. Turton M, O’Shea D, Gunn I, et al. A role for glucagon-like peptide-1
in the central regulation of feeding. Nature. 1996;379:69 –72
187. Kamatchi L, Veeraragavan K, Chandra D, Bapna J. Antagonism of
acute feeding response to 2-deoxyglucose and 5-thioglucose by GABA
antagonists: the relative role of ventromedial and lateral hypothalamus. Pharmacol Biochem Behav. 1986;25:59 – 62
188. Campfield L, Smith F. Functional coupling between transient declines
in blood glucose and feeding behavior: temporal relationships. Brain
Res Bull. 1986;17:427– 433
189. Oomura Y. Glucose as a regulator of neuronal activity. Adv Metab Dis.
1983;10:31– 65
190. VanItallie T. The glucostatic theory 1953–1988: roots and branches. Int
J Obes. 1990;14:1–10
Development of Eating Behaviors Among Children and Adolescents
Leann L. Birch, PhD, and Jennifer O. Fisher, PhD
ABSTRACT. The prevalence of obesity among children
is high and is increasing. We know that obesity runs in
families, with children of obese parents at greater risk of
developing obesity than children of thin parents. Research
on genetic factors in obesity has provided us with estimates
of the proportion of the variance in a population accounted
for by genetic factors. However, this research does not provide information regarding individual development. To design effective preventive interventions, research is needed
to delineate how genetics and environmental factors interact in the etiology of childhood obesity. Addressing this
question is especially challenging because parents provide
both genes and environment for children.
An enormous amount of learning about food and eating occurs during the transition from the exclusive milk
diet of infancy to the omnivore’s diet consumed by early
childhood. This early learning is constrained by children’s genetic predispositions, which include the unlearned preference for sweet tastes, salty tastes, and the
rejection of sour and bitter tastes. Children also are predisposed to reject new foods and to learn associations
between foods’ flavors and the postingestive consequences of eating. Evidence suggests that children can
respond to the energy density of the diet and that although intake at individual meals is erratic, 24-hour energy intake is relatively well regulated. There are individual differences in the regulation of energy intake as
early as the preschool period. These individual differences in self-regulation are associated with differences in
child-feeding practices and with children’s adiposity.
This suggests that child-feeding practices have the potential to affect children’s energy balance via altering
patterns of intake. Initial evidence indicates that imposition of stringent parental controls can potentiate preferences for high-fat, energy-dense foods, limit children’s
acceptance of a variety of foods, and disrupt children’s
regulation of energy intake by altering children’s responsiveness to internal cues of hunger and satiety. This can
occur when well-intended but concerned parents assume
that children need help in determining what, when, and
From the Department of Human Development and Family Studies and the
Graduate Program in Nutrition, Pennsylvania State University, University
Park, Pennsylvania.
Received for publication Oct 24, 1997; accepted Nov 6, 1997.
PEDIATRICS (ISSN 0031 4005). Copyright © 1998 by the American Academy of Pediatrics.
how much to eat and when parents impose child-feeding
practices that provide children with few opportunities
for self-control. Implications of these findings for preventive interventions are discussed. Pediatrics 1998;101:
539 –549; children, food, eating, obesity, dieting, parenting.
ABBREVIATION. BMI, body mass index.
T
his article addresses behavioral factors that influence food preferences, food intake, and energy regulation in children. Currently, the
prevalence of childhood overweight is high and has
increased dramatically since the 1970s.1,2 This increased prevalence is of concern because overweight
children are at increased risk for social stigmatization, adult obesity, and chronic disease. Obesity
shows familial aggregation; the risk of obesity
among children of two obese parents is much higher
than for children in families in which neither parent
is obese.3 Familial aggregation has focused research
attention on genetic factors in obesity,4 but the rapid
secular increase in the prevalence of obesity cannot
be attributable to genetic factors. The interaction of
genes and environment influences phenotypes for
intake and expenditure and suggests that a renewed
focus on the family environment may provide information about behavioral factors that contribute to
familial aggregation of adiposity.
Between 30% and 50% of the variance in adiposity
within a population is attributable to genetic differences.5 However, these heritability estimates describe populations, not individuals, and do not provide information about the ways genetics and
environment interact during development to produce childhood obesity. In the case of childhood
obesity, the question is especially complex because 1)
genes and environments also tend to be correlated—
parents typically provide children with both genetics
and environment; and 2) genetic factors can include
behavioral predispositions that affect food intake
and expenditure.4 A more complete understanding
of what may characterize obesigenic environments
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539
for children is needed, and this review of behavioral
factors that influence intake and expenditure indicates ways the family environment can interact with
genetic predispositions to produce patterns of food
preferences, food consumption, and physical activity
that can promote childhood obesity in susceptible
individuals.
Obesity results when intake exceeds expenditure,
and both sides of the energy-balance equation must
be considered in concert in research on the etiology
of childhood obesity. Unfortunately, scant research
has adopted a conjoint focus on intake and expenditure; thus, we have little knowledge about how children’s food intake and physical activity may interact
to influence energy balance. Our knowledge also is
limited by the lack of precision of the national databases on children’s energy intake and the absence of
a national database on children’s physical activity.
Finally, among adults and children, diets high in fat
and low in complex carbohydrates are associated
with greater adiposity. However, despite repeated
recommendations to reduce fat intake, adults’ and
children’s diets are still too high in fat and too low in
complex carbohydrates. Our analysis of behavioral
factors that influence children’s energy intake focuses on research on factors that influence the development of food preferences and the controls of food
intake in children. This research will reveal why it is
difficult to reduce the intake of energy-dense foods,
especially dietary fat intake, and increase complex
carbohydrate intake. These findings indicate that attempts to restrict and control children’s eating and
weight to prevent obesity may be iatrogenic, producing the very problem they are intended to avoid—
fostering the development of problems of energy
balance. This research also suggests some strategies
that can be incorporated into the design of preventive interventions.
DIET COMPOSITION: HIGH-FAT DIETS, COMPLEX
CARBOHYDRATES, AND OBESITY
Several investigators have recently reported links
among parental adiposity, parental fat intake, and
children’s adiposity and fat intake,6 – 8 which suggest
that familial patterns of adiposity may be partially
mediated by familial similarities in diet composition.
Factors that contribute to parent– child similarities in
diet composition, including availability, accessibility,
and exposure effects on children’s food preferences,
are discussed below.
In one of the few prospective longitudinal studies
of childhood obesity that included measures of physical activity, dietary intake, and familial predisposition for obesity in children, Klesges and colleagues9
obtained data over a 3-year period. They categorized
factors as modifiable and nonmodifiable and noted
that the largely nonmodifiable factors of initial body
mass index (BMI), sex, and number of obese parents
accounted for 9% of the variance in BMI change.
Dietary intake and physical activity accounted for an
additional 13%. High levels of dietary fat were associated with greater adiposity and greater gain. Although Klesges’ research is exemplary because it is
one of the few prospective studies to incorporate
540
both measures of energy intake and expenditure, his
parsing of factors into modifiable environmental factors (diet and physical activity) and nonmodifiable
genetic factors (number of obese parents) is conceptually problematic. As Leibel10 points out in his commentary on the article by Klesges and colleagues, the
genetic predispositions for obesity may well be heritable behavioral predispositions on both the intake
and expenditure sides, perhaps a predisposition for
high-fat diets and for low levels of physical activity.
Parents provide these genetic predispositions, but
they also provide the environment in which these
predispositions are expressed, and the evidence indicates that eating environments in families in which
parents are obese may differ in systematic ways from
families in which neither parent is obese. We return
to this point in subsequent sections.
Experimental work as well as epidemiologic
evidence in humans and rats has implicated the
percentage of energy from fat in the development
and maintenance of obesity.11,12 Because fat is very
energy-dense relative to fat and carbohydrate, diets
higher in fat typically are higher in total energy and
smaller in volume, an important satiety cue. In addition, high-fat foods are often very palatable, which
also leads to overconsumption. Several investigators
have also reported that fat is less satiating than protein or carbohydrate13–15 and that people are less
likely to adjust subsequent intake to compensate for
the energy in a high-fat meal,16 thereby leading to
consumption of excessive amounts of energy. Dietary fat also is stored more efficiently than ingested
carbohydrate or protein. Finally, perhaps because
people can store large amounts of fat, oxidation of
dietary fat does not increase as fat intake increases,
as is the case with protein and carbohydrate. The
control of fat intake appears to be critical in regulation of energy intake and body weight.17,18
In addition to its direct implication as a causal
agent for development of obesity, dietary fat intake
also may displace more micronutrient-dense, fibrous, carbohydrate-containing foods in the diet. In
the diets of adults and children in the United States,
intakes of fat and carbohydrate are negatively correlated; diets that are high in fruits and vegetables and
complex carbohydrates tend to be low in fat and vice
versa.19 This observed relationship has led to public
health messages that emphasize increasing the intake
of fruits, vegetables, whole grains, and low-fat dairy
products instead of focusing exclusively on messages
to decrease dietary fat intake. Because of the potential negative effects of restriction on children’s preferences and intake, these messages should continue
to be central in campaigns to prevent childhood obesity (eg, the “five-a-day” campaign), given that the
contemporary dietary patterns of children are most
similar to those of adults on high-fat diets. Findings
from the Bogalusa sample of 10-year-olds20 underscore the importance of emphasizing complex carbohydrates; children with the lowest fat intakes had
higher carbohydrate intakes than those with higher
fat intakes, but the lower fat intakes were associated
with higher intakes of simple sugars, not complex
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carbohydrates. Fruit and vegetable consumption did
not differ across the groups and was uniformly low.
Current dietary recommendations21–25 advocate reducing fat intake and increasing consumption of
complex carbohydrate and fiber-containing foods including vegetables and fruits. According to 1990 to
1991 data,24 only ;15% of 6- to 19-year-olds have
diets with the recommended levels of 30% of energy
from fat, consistent with the American Academy of
Pediatrics Guidelines.21 Approximately 15% of children have diets in which .40% of energy is from fat.
Additionally, children’s consumption of fruits and
vegetables is well below current recommendations.
In a study of 1797 second- and fifth-grade children,
40% of the children ate no vegetables on the days
studied, and 36% ate at least four different types of
snack foods.26 Continuing Survey of Food Intakes by
Individuals data from 1989 to 1991 revealed that only
one in five children and adolescents consumed five
or more servings of fruits and vegetables per day and
that 25% of the vegetables were French fries.27 Why
do children readily consume foods high in fat and
sugar and so few fruits and vegetables? A look at the
factors that influence children’s food preferences and
food intake may provide some answers.
FOOD PREFERENCES AS DETERMINANTS OF
CHILDREN’S INTAKE
Given children’s very low levels of fruit and vegetable intake and the negative association between
fruit and vegetable intake and dietary fat intake,
increasing fruit and vegetable consumption should
improve the quality of children’s diets. In recent
research, Baranowski and colleagues28,29 examined
the influence of a variety of psychological, social, and
demographic factors on children’s consumption of
fruits and vegetables. In these two studies of school
children, they noted that fruit and vegetable preferences were the only significant predictors of fruit and
vegetable consumption, and they concluded that interventions that alter children’s food preferences
may be more effective than other strategies pursued
to date. However, to do this we need information
about the factors that influence the formation of children’s food preferences. See reviews of this literature
by Birch and Fisher.30,31
As we examine the evidence on children’s food
preferences and dietary selections, we see that
achieving the objectives of dietary guidelines to reduce fat intake and increase complex carbohydrate
intake may constitute a formidable challenge: “The
most familiar and preferred foods in childhood tend
to combine these two principal ingredients: sugar
and fat.”32 Because children eat what they like and
leave the rest, food preferences are especially important determinants of the food intake of young children.6,28,33–35 The choices children make are important
in considering the overall nutritional quality of their
diets. In a recent study by Fisher and Birch,6 children’s 24-hour dietary intake was measured by using
weighed food intake data on six separate occasions.
Although the same diet consisting of ;33% of energy
from fat was served to all children, the food choices
the children made resulted in a wide range of ob-
served dietary fat intakes, ranging from 25% to 42%
of energy, from well below to well above the recommendations of the American Academy of Pediatrics.21 Offering an array of foods that constituted a
healthful diet (moderate in percentage of energy
from fat) was not sufficient to ensure intake of that
diet; children’s food preferences were determinants
of their consumption patterns. The children’s preferences for the high-fat foods predicted their fat consumption, and these preferences and consumption
patterns were related to their parents’ adiposity. Because children’s food preferences are important determinants of intake, understanding the factors that
shape food preferences in early development is critical in identifying those aspects of family environments that are potentially obesigenic for susceptible
individuals.
EARLY EXPERIENCE, LEARNING, AND
CHILDREN’S FOOD PREFERENCES
One of the first choices that parents make that
shapes a child’s experience with food and flavors is
the choice to breastfeed or formula-feed. The perception of flavors in milk also is one of the human
infant’s earliest sensory experiences, and there is
support for the idea that this early experience with
flavors has an effect on milk intake and on later food
acceptance.36 Mennella and Beauchamp37,38 have reported that flavors in breast milk influence infants’
consumption; breast milk flavored with garlic or vanilla increased the time attached to the maternal nipple
relative to breast milk produced on a bland diet. Although Mennella and Beauchamp’s research has not
focused on individual differences in infants’ responsiveness to flavors in milk, a potentially fruitful area for
research would explore whether infants who differ in
nutritive sucking style, which Agras and colleagues39
have related to infant adiposity, also differ in their
response to flavors in milk or in the extent to which
flavors modulate their ingestion of milk.
Galef and Henderson’s40,41 research with rat pups
has demonstrated that early experience with the flavors of the maternal diet can affect subsequent preference for and intake of solid foods. Because of repeated early experience with flavors of the maternal
diet present in the mothers’ milk, rat pups learn to
prefer their mothers’ diet. Certainly the early sensory
experience of breastfed infants is radically different
from that of the formula-fed infant, but we know
nothing about how this affects subsequent dietary
patterns. Formula-fed infants have experience with
only a single flavor, whereas breastfed infants are
exposed to a variety of flavors from the maternal diet
that are transmitted to the milk. The long-term effects of breastfeeding versus formula-feeding remain
unexplored, but very limited evidence suggests that
the varied flavor experience of breastfed infants can
facilitate acceptance of solid foods during the weanling period,36 with breastfed infants showing greater
initial acceptance of new foods than formula-fed infants. This finding is consistent with those showing
that early experience with a variety of flavors leads
to more ready acceptance of new foods later.42
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541
ity may differ in their early responsiveness to flavors
or in the transition to solid foods has not been explored, but such research could yield valuable information about the developmental history of gene–
environment interactions.
Thus, infant dietary experience is shaped by infant-feeding decisions and dietary patterns of the
mother, and it provides the basis for food acceptance
and patterns of intake in infancy. During the first
years of life, the infant transitions from an exclusive
milk diet to a variety of foods. This transition from
univore to omnivore is shaped by the infant’s innate
preference for sweet and salty tastes, by the rejection
of sour and bitter tastes,43 and by the omnivore’s
predisposition to associate food flavors with the contexts and consequences of eating.44 Infants and children also are predisposed to be neophobic and reject
new foods. With the exception of sweet and salty
foods, acceptance of new foods does not occur instantly; however, after repeated opportunities to consume new foods, liking for new food generally increases, producing increased intake, although 5 to 10
exposures often are required.36,45– 47 These findings
emphasize the central importance of early experience
with foods and food acceptance; children come to
like and eat what is familiar. What is familiar is what
is present in the environment. There are some data in
support of similar dietary patterns between parents
and children. Oliveria and colleagues48 reported that
parental eating habits had an effect on the nutrient
intakes of young children in that children whose
parents ate diets high in saturated fat also ate diets
high in saturated fats. Parents tend to have foods in
the home that they like and eat, and with repeated
opportunities to eat these foods, young children include many of them in their diets.
The food environment the parent provides shapes
children’s preferences and food acceptance patterns,
which in turn are linked to children’s adiposity;6,8,48
we argue that these effects are mediated primarily by
the patterns of preference that children have developed as a result of these exposure patterns. The early
exposure that children have to fruits and vegetables
and to foods high in energy, sugar, and fat may play
an important role in establishing a hierarchy of food
preferences and selection. The work of Baranowski’s
group49 also has confirmed that food availability and
accessibility was positively related to fruit and vegetable preferences and to their consumption by
school children. These authors noted that children
consumed more fruits and vegetables at schools
where more fruits and vegetables were served, and
they concluded that the extent to which fruits and
vegetables are made available and accessible to children may shape children’s liking for and consumption of those foods. However, although availability
and accessibility are necessary for acceptance of
many foods, the physiologic consequences of eating
as well as the social context also play a role.
Food Preferences: Predispositions to Prefer Energydense Foods
Although repeated exposure can enhance liking
for a wide variety of new foods, other learning mech542
anisms favor development of preferences for energydense foods. There is no evidence that children have
an innate, unlearned preference for high-fat or highenergy foods (see Birch30 for a discussion of this
point). Children are predisposed to learn to prefer
energy-dense foods over energy-dilute foods by
learning to associate the flavors of these foods with
the positive physiologic consequences that result
from eating energy-dense foods, especially when
they are hungry. Although limited, the findings from
research with young children50 –53 are consistent with
extensive data in the literature on conditioned preferences for energy-dense foods in animals.54
In particular, repeated experience with foods high
in energy can enhance children’s preferences for
those foods via associative conditioning. In this form
of learning, flavors in foods become associated with
satiation cues involved in digestion and absorption
of high-energy foods.52–54 These learned associations
can produce flavor preferences that may mediate the
relationship between children’s exposure to and liking of high-energy foods. In addition, the physiologic
consequences of consuming high-energy high-density foods may enhance the effects of repeated exposure on liking. The physiologic conditioning of flavor
preferences for foods high in energy density may
have the greatest effect on children’s liking of energy-dense foods among families in which those foods
are most available and accessible. To date, there has
been no research exploring whether children’s ability
to learn preferences for foods high in fat might differ
for children in obese and normal-weight families.
Genetic differences could shape phenotypic differences in how readily children learn preferences for
high-fat foods. Among adults, there are differences
between obese and normal-weight individuals in
their preferences for fat and for mixtures of sugar
and fat,55,56 and this suggests a mechanism that could
be involved in the etiology of these differences.
The Social Context of Eating: Effects on Children’s
Preferences
For children, eating typically is a social occasion,
and other eaters, including parents, other adults,
peers, and siblings, as well as children’s observations
of others’ eating behavior, influence the development of their own preferences and eating behaviors.
The social context in which children’s eating patterns
develop becomes important because the eating behavior of people in that environment serves as a
model for the developing child. Models can have
powerful effects on food selection, especially when
the model is similar to the observer57,58 or is seen as
particularly powerful,59 as in the case of older peers.
Findings suggest that day care could provide opportunities for expanding the availability and accessibility of foods and for fostering preferences for foods by
modeling effects. Birch57 found that when preschool
children were given opportunities during meals to
observe other children choosing and eating vegetables that the observing child did not like, preferences
for and intake of the disliked vegetables were increased.
Modeling appears to play an important role in
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establishing preferences for inherently unpalatable
substances. Rozin and Schiller60 demonstrated the
powerful role of social influences in the development
of preferences for chili peppers, a flavor that is aversive to animals and to many humans. Rozin and
Kennel61 reported that although a variety of established learning protocols failed to facilitate preference for chili peppers among nonhuman primates,
increased preference for chili-flavored crackers resulted after the monkeys observed their keepers eating the crackers. Rozin and colleagues60 reported a
similar phenomenon among humans in Mexican
families, in which the older members modeled eating
chili-flavored foods for young children, which facilitated children’s acceptance of hot foods.
Children’s food preferences may be shaped by
observing food selection patterns and eating behavior of their parents. For instance, Harper and Sanders62 observed that toddlers put foods in their
mouths more readily when they were following the
example of their mothers relative to the same modeling behavior by a stranger. Especially in families in
which obesity, dieting, and weight control are salient
issues, children’s eating may be influenced by parents’ eating, dietary restraint, and disinhibition.63
Models also may play a role in the emergence of
dieting behaviors in childhood and adolescence. Pike
and Rodin64 reported that dieting daughters are
likely to have dieting mothers and that parents who
report dietary disinhibition and problems in controlling their own eating are likely to have daughters
who show similar patterns,65 which suggests a role
for modeling in these familial patterns.
Effects of Television on Children’s Food Preferences
and Food Selection
Television is a pervasive purveyor of culture, providing children with a wide array of models and
messages about eating that can influence children’s
food preferences and food selection as well as their
activity patterns. However, despite the central role of
television as a purveyor of American culture, surprisingly few studies have investigated the effect of
television on children’s food preferences, intake, and
adiposity. Although there do not appear to be dramatic changes in watching television by children
during the period of increased prevalence of childhood obesity, the largest share of advertisements
during children’s programming is for food products.66 In 1987, in a content analysis of food advertisements that were on television during a 12-hour
period, Cotugna67 observed that 80% of advertisements showed foods with low nutritional value, including breakfast cereals high in simple sugars and
snack foods high in sugar, fat, and salt. A number of
investigations have revealed that children’s requests
for foods were related to the frequency with which
children saw the foods advertised on television.68,69
Goldberg et al70 observed that children who were
exposed to advertisements selected more sugared
foods than children who had not viewed any advertisements. Thus, repeated exposure to food advertisements for particular types of foods may foster
children’s preferences for energy-dense, nutrient-
poor foods. We know even less about how the depiction of eating, dieting, and exercise patterns may
influence children’s food intake and activity patterns.
Child-feeding Practices, Children’s Food Preferences,
and Food Selection
Parents shape their children’s eating environments
in a variety of ways: through the choice of an infant
feeding method, by the foods they make available
and accessible, by direct modeling influences, by the
extent of media exposure in the home, and by way
they interact with children in the eating context.
Parents believe that their feeding practices can exert
a major influence on children’s food preferences and
on developing control of children’s food intake,71
although recent research indicates that the influence
is not necessarily in the ways that parents intend.72
The pervasive messages directed at ways that nutrition can improve health and appearance have created an increasingly complex eating environment in
which parents attempt to foster healthy eating behaviors in their children. For example, the messages
of the dietary guidelines convey the importance of
consuming certain types of foods and limiting the
consumption of others.23 The means by which parents attempt to shape children’s eating toward nutritionally desirable dietary outcomes can have unintended consequences for children’s eating behavior.
Parents’ practices may be especially controlling and
may have particularly negative effects on children
when there is heightened concern that the child may
be at risk for obesity.73
Although current guidelines attempt to convey the
importance of variety and moderation, these nutritional guidelines are cognitively complex. Rozin et
al,74 in a study of adults’ understanding of nutritional
concepts, concluded that even well-educated adults
engaged in categorical thinking, ie, grouping foods
as either “good” or “bad,” and a monotonic mind
belief that something that is harmful in large quantities (such as dietary fat) is also harmful at low
levels. Nutritional messages interpreted with such
categorical thinking may result in parental attempts
to restrict children’s intake of “bad” foods and encourage the intake of “good” foods.
Parental Control to Encourage or Limit Children’s
Eating
Child-feeding practices that control what and how
much children eat also can affect their food preferences. Child-feeding strategies that encourage children to consume a particular food increase children’s
dislike for that food.75–77 Many of the foods that parents encourage children to consume are the fruits
and vegetables they would like to see consumed
with greater frequency and in greater quantities.
Hertzler78 noted that parents’ feedback to children
about eating vegetables was associated with children’s preferences for fewer vegetables.
In carrying out what one clinician described as
“good food intentions,” parents frequently may limit
their children’s consumption of “bad” but palatable
foods by withholding these foods as punishment.79,80
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In a recent study assessing parental opinions on the
efficacy of using various practices to modify their
children’s food preferences, 40% of parents spontaneously reported the belief that restricting or forbidding the consumption of a particular food would
decrease their child’s preference for that food,81 a
prediction opposite research findings. Contrary to
these parental beliefs, restricting children’s access to
foods does not produce food dislikes for the restricted food; instead, such practices enhance liking
and can increase intake. Birch et al82 found that children’s preferences for foods increased after the foods
were used as rewards for performing a nonfoodrelated task; limiting the availability of the reward
food promoted children’s liking of those foods.
Again, these restricted foods are typically “bad”
foods, those palatable foods that are high in sugar,
fat, and energy and that we would like to see consumed in smaller amounts and on fewer occasions.
Restricting children’s access to foods actually may
promote their overconsumption of those foods.
Fisher and Birch6 found that maternal restriction of
children’s access to snack foods was related to girls’
(but not to boys’) consumption of those same foods
in an unrestricted setting.
Creating dietary habits that include moderation
and limited consumption of dietary fat and sugar
constitutes a desirable objective of child-feeding.
However, the limited evidence on the effects of these
practices on children’s preferences suggests that restricting children’s consumption of “bad” foods and
encouraging consumption of “good” foods does not
provide a means of achieving these dietary goals.
From a developmental perspective, this type of feeding practice may send mixed messages to children,
because these same forbidden foods are offered in
positive social contexts such as parties, dinners out,
and holiday celebrations, and are restricted in others.
Finally, restricting access to some foods and encouraging consumption of others may well foster children’s categorical thinking about “good” and “bad”
foods. In a study that involved focus groups of kindergarten children, Murphy et al83 concluded that
children tended to indicate preference for fatty and
sugary foods but that they also identified those foods
as being high in fat and “not good for you.” However, just to show that the implications for intervention are not simple, the work by Smith and Epstein84
confirmed that limiting the availability of a preferred
food also may enhance children’s desire to obtain a
less preferred food when both types are present.
Parental Control and Children’s Self-regulation of Food
Intake
In addition to influencing which foods children
prefer and select, controlling child-feeding practices
may affect children’s ability to regulate energy intake
and the amount of food consumed. As indicated
above, a very early decision parents make is choosing whether to formula-feed or breastfeed their infant. In the United States, formula-fed infants show
more rapid growth than breastfed infants, and these
differences are of sufficient magnitude that some
experts have suggested that different growth norms
544
should be used for breastfed and bottle-fed infants.
Fomon85 speculates that the differences in infant
growth may be attributable to differences in intake,
with the greater intakes by formula-fed infants resulting from overfeeding as a result of heightened
maternal control over the infant’s intake. In the case
of the formula-fed infant, the mother can see how
much formula remains in the bottle and she may be
inclined to take control of how much the infant eats,
encouraging the infant to finish the bottle. In contrast, the breastfed infant may have more control
over the size of the feed because this feedback is not
available to the mother.
Clearly, maternal feeding practices can influence
infant intake. In a series of experiments to investigate
whether the energy density of formula influenced
infants’ intake, Fomon and colleagues varied the energy density of formulas infants were consuming
and demonstrated that in the absence of maternal
control, infants 6 weeks old adjusted their volume of
formula intake, consuming more of energy-dilute
than of concentrated formulas, so that total energy
intake did not differ across the conditions.85 Fomon’s
research suggests that when given the opportunity,
infants are capable of being responsive to the energy
density of formula and adjusting intake accordingly;
however, maternal control can override the infant’s
regulatory ability. There is some evidence for a parallel relationship between parental control and children’s ability to regulate intake during early childhood. Although many children continue to be
sensitive to internal signals arising from the energy
density of the diet in controlling their food intake,
child-feeding practices that encourage or restrict children’s consumption of foods may decrease the extent
to which children use internal signals of hunger and
satiety as a basis for adjusting energy intake. To
investigate children’s responsiveness to energy density, we have conducted research examining children’s intake within individual meals by using twocourse preloading protocols as well as by examining
children’s intake across meals by using 24-hour selfselected energy intakes.
To determine whether they could adjust intake in
response to the energy density of foods within meals,
we had children consume a fixed amount of a first
course that varied in energy density, by manipulating fat or carbohydrate content, and looked at effects
on children’s self-selected food intake in a second
course. We predicted that if children were responsive
to the energy density of the first course, they would
eat less in the self-selected second course after the
high-energy than after the low-energy first course.
Across a series of experiments, findings confirmed
this prediction,50 –53,86,87 providing evidence that children can regulate their intake based on feedback
arising from the energy content of foods just consumed. Subsequent research extended our findings
to reveal that children’s responsiveness to energy
density can affect their food intake not only within a
meal but also across a series of meals, influencing
their 24-hour energy intake. Children showed adjustments in energy intake across successive meals in a
manner similar to the adjustments in energy intake
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observed within meals. These adjustments resulted
in relatively consistent coefficients of variation for
24-hour energy intake, which averaged ;10%, compared with the much higher coefficients of variation
of ;40% for energy intake at individual meals.88,89
Children also adjusted their intake to compensate for
reduced energy density of meals produced when we
substituted olestra for dietary fat, indicating that at
least when energy density is reduced, children are
responsive to and adjust intake for the energy dilution. Whether children’s ability to adjust their food
intake in response to energy density is impaired
when diets are very high in fat remains an unanswered question.
With respect to whether child-feeding practices
can alter children’s responsiveness to energy density,
our initial findings revealed that children’s responsiveness to energy density was diminished when
adults used control strategies that focused children
on external cues to encourage consumption. We
explored children’s ability to adjust food intake in
response to differences in energy density in two different child-feeding contexts. In one condition, childfeeding practices focused on internal cues of hunger
and satiety as controls of food intake. In this condition, children showed clear evidence of adjusting
their intake in response to the energy-density differences in the first course. In the other condition, childfeeding practices included rewarding children for
cleaning their plates and a focus on external cues to
control eating, such as the amount of food remaining
on the plate. In the latter condition, all evidence of
responsiveness to the energy content of the foods
disappeared, and children’s intake was significantly
increased by rewarding them for eating.86 These findings suggested a powerful role for child-feeding
practices in shaping how much children eat and the
extent to which children are responsive to the energy
density of the diet in controlling their food intake.
Other evidence on this issue comes from observations of family meal times by Klesges et al.90 They
found that parental prompts to eat were positively
associated with time spent eating and degree of overweight in children. Interestingly, in an examination
of the sequencing of parental prompts to eat, Klesges
and colleagues91 observed that prompts to eat were
preceded by food refusals by the child and followed
by the child eating. These findings suggest that parental prompts to eat may act to oppose children’s
own attempts to control the amount consumed and
promote consumption within a meal.
Factors Influencing Child-feeding Practices: Gender,
Adiposity, and Eating Style
Although the potential of nutritional messages to
promote restrictive child-feeding practices exists on a
societal level, eating-related issues within families
also determine parental use of controlling feeding
practices. In discussing children’s obesity proneness,
Costanzo and Woody73 assert that parents impose
behavioral control in domains of their children’s development when 1) the parents have problems regulating their own behavior, 2) the child is perceived
to be at risk for developing problematic behavior,
and 3) the child demonstrates a lack of self-regulatory behavior. These authors use obesity proneness
as an example and contend that the use of controlling
parenting styles impedes children’s ability to develop self-regulatory behavior, thereby promoting
the problems they attempt to avoid.
Subsequent research by Johnson and Birch72 confirmed the links among child-feeding practices and
children’s responsiveness to energy density. Children’s compensation for energy density in our standard two-course-meal protocol was used as a measure of individual differences in regulation of energy
intake. Parents who reported using a high degree of
control over what and how much their children ate
had children who showed relatively little evidence of
energy regulation; a high degree of parental control
was associated with low self-control in children. For
girls, energy regulation was related to their adiposity, with thinner girls regulating energy intake more
precisely than heavier ones. In addition, for girls, but
not for boys, parental control was linked to the girls’
adiposity, with parents using more control with
heavier girls. Parental control also was linked to the
parents’ dieting and weight history. Mothers who
were more restrained used more control and had
daughters (but not so for mothers with sons) who
showed little evidence of energy regulation. These
early gender differences may be precursors of later
gender differences in problems of energy balance, in
which the prevalence of eating problems is much
higher among women. These findings are limited to
middle-class white families; we have no data on
whether such relationships exist among other racial,
ethnic, or socioeconomic groups. One area for future
research is to determine whether racial and socioeconomic differences in the prevalence of obesity in
children (reported by Troiano and Flegal)1 may be
mediated in part by racial and socioeconomic differences in parenting practices.
Fisher and Birch65 found that young children’s
weight for height predicted the degree to which
mothers reported restricting their child’s intake of
snack foods. For girls, the parents’ dieting and restrictive eating predicted the level of maternal restriction. The cross-sectional nature of the data cannot address the causal direction of the relationships
observed, but the results suggest that controlling
child-feeding practices adversely affects children’s
ability to self-regulate food intake and hence their
adiposity, although it is possible that parents of
heavier children are more controlling. We are obtaining longitudinal data on this question.
The child’s eating style also may elicit parental
concern and control and influence the extent to
which the parent sees the child as at risk for obesity
and eating problems. Stunkard and Kaplan92 reported that the obese ate at a faster rate than normal
individuals. Comparisons of obese and normal children have yielded similar results. Drabman et al93
reported that obese preschoolers ate at a faster rate,
taking more bites, and chewing each bite fewer
times. Marston and colleagues94 reported similar patterns of results for school children. Recently, Barkeling et al95 compared eating behaviors of obese and
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545
normal-weight 11-year-olds. Obese children ate
faster and failed to show the normal pattern of slowing down the rate of eating toward the end of the
meal; the authors suggest that this pattern could
reflect an impaired satiety signal or an impaired
response to such signals. Evidence for links between
eating style and adiposity appears even during infancy. Agras et al39 found that infants with more
rapid suckling during feedings at 2 and 4 weeks had
greater intakes and that this sucking pattern was
associated with greater skinfolds and BMI at 1 and 2
years of age. Stunkard and colleagues’ recent research (unpublished observations) on genetic and
environmental factors predicting infant weight gain
also has shown sucking rate to be a predictor of later
weight. One potentially productive area of research
would investigate the extent to which children’s different eating behaviors elicit different child-feeding
practices.
In summary, controlling child-feeding practices
can have negative and unintended effects on children’s food preferences and the developing controls
of food intake. It is likely that such practices foster
rather than prevent the development of childhood
obesity and eating problems, although additional research is needed to confirm this point. Satter96,97 has
discussed an alternative to such controlling childfeeding practices based on her extensive clinical
work. She suggests a division of responsibility between parent and child; it is the parents’ responsibility to supply the child with a healthful array of foods
and a supportive eating context, and it is the child’s
responsibility to decide when and how much to eat.
Emergence of Dieting to Control Weight
By middle childhood, even before the fat deposition that occurs in girls at puberty, gender differences in weight concerns and dieting prevalence apparent in adulthood already are emerging.98 Societal
values equating physical attractiveness and thinness
with femininity foster the pervasive trend of dieting
among most young women, many of whom do not
need to lose weight. By the age of 7, children identify
overweight silhouette drawings as being less attractive, having fewer friends, and being less smart than
their thin counterparts. Even before puberty, girls
report a heightened sense of body dissatisfaction and
a desire to be thinner, with dieting behaviors reported among children as young as 9 years of age.99
Serdula et al100 reported that 44% of high school girls
were dieting. Although the nature of dieting behaviors in young girls is not well characterized, the
potential adverse consequences of dieting on physical growth and well-being must be addressed.
Early dieting itself may constitute a risk behavior
for the development of obesity. Dietary restriction
involving cognitive restriction of food intake involves explicit denial of hunger cues and includes
stopping eating while still hungry and skipping
meals. Dietary restriction has been associated with
overeating in adolescents101 and adults.102 Westenhoefer and colleagues102 have suggested that restrained eating may give rise to eating binges
through the weakening of satiety cues and the
546
heightened attractiveness of food in restrained eaters. Dieting may be iatrogenic, producing the very
outcome it is used to avoid. Dieting, with its selfimposed restriction, has certain parallels to controlling child-feeding practices restricting children’s intake. Dietary restraint, or the intent to restrict food
intake cognitively, has been associated with a number of adverse psychosocial outcomes. In a study of
adolescent girls, Killen103 found that restrained eaters
exhibited high levels of worthlessness, body dissatisfaction, fear of weight gain, and disaffect, and they
were heavier and were more physically developed
than unrestrained girls. Similarly, Rosen et al104
found that adolescents’ restraint scores were positively associated with depression, body dissatisfaction, social anxiety, and weight status. This all suggests that attempts designed to reduce the
prevalence of childhood obesity must consider the
potential costs of approaches that may increase the
prevalence of early dietary restriction.
CONCLUSIONS
Most experts would agree that obesity results
when susceptible individuals are placed in adverse
environments. To date, there have been few prospective studies of childhood obesity, and these have not
tended to focus on the role of environmental factors
and how they interact with genetic predispositions
that affect intake and expenditure. There is extensive
evidence that children’s food intake is shaped by
early experience with food and eating, and these
findings suggest ways in which parenting practices
and the family environment may be promoting obesity.
Children’s eating is modified by exposure and accessibility of foods; by modeling behavior of peers,
siblings, and parents; by the physiologic consequences of ingestion; and by child-feeding practices.
In particular, children’s liking for and consumption
of foods high in energy, sugar, and fat may be enhanced by environments where those foods are
present, consumed by peers or family members, and
made unavailable periodically. Parental directives
intended to encourage or restrict children’s consumption of various foods may have adverse consequences for the development of children’s food preferences and regulation of energy intake. These
parental directives may even be linked to subsequent
development of dieting. In particular, directives in
child-feeding may discourage children’s choices and
focus children’s attention cues other than feelings of
hunger and satiety. Because parents tend to encourage children’s consumption of fruits and vegetables
and to limit foods high in energy, sugar, and fat,
directive styles of child-feeding may negatively affect children’s liking of these foods by teaching them
to dislike the very foods we want them to consume
and to prefer those that should be consumed in relatively limited quantities.
The findings reveal ways children’s environments
can be obesigenic for susceptible individuals. However, the database is very limited; the research reviewed above has focused primarily on white middle-class children of normal weight. There is little
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evidence about how factors influencing children’s
food preferences and intake, especially child-feeding
practices, may differ systematically across socioeconomic and racial groups. In addition, little research
has been conducted with infants and toddlers, in
whom dramatic dietary change is occurring, or with
adolescents, in whom most research has focused on
eating disorders and, to some extent, on dieting behavior. Both infancy and adolescence are critical periods in the development of obesity,105 yet we know
very little about the development of the controls of
food intake during these periods.
The research on the developing controls of food
intake in children of normal weight allows us to
generate hypotheses about ways the child’s genetic
predispositions may interact with environmental factors to produce childhood obesity. What remains on
the research agenda are tests of these hypotheses in
prospective, longitudinal studies of childhood obesity, which should investigate the possibility that
genetic differences contributing to obesity are manifest in individual differences in feeding and exercise
predispositions and should include a joint focus on
intake and expenditure (see Kohl and Hobbs).106 Future research should include children and their families identified as differing in familial risk for obesity,
in whom definitions of risk that include behavioral
factors such as parenting practices, parental adiposity, and dieting history should be explored. The etiology of childhood obesity is multifaceted and requires a multidisciplinary approach, including
expertise on genetics, environmental and behavioral
factors, and on both energy intake and the components of energy expenditure. In cases for which double-labeled water is used to measure energy expenditure and anthropometric measures are taken over
time, the validity of dietary intake measures can be
addressed. The conjoint focus on children’s intake
and physical activity will have the added benefit of
allowing us to address issues of measurement error,
which is essential to advancing our knowledge base.
REFERENCES
1. Troiano RP, Flegal KM. Overweight children and adolescents: description, epidemiology, and demographics. Pediatrics. 1998;101:497–504
2. Troiano RP, Flegal KM, Kuczmarski RJ, Campbell SM, Johnson CL.
Overweight prevalence and trends for children and adolescents: the
National Health and Nutrition Surveys, 1963 to 1991. Arch Pediatr
Adolesc Med. 1995;149:1085–1091
3. Garn SM, Clark DC. Trends in fatness and origins of obesity. Pediatrics.
1976;57:443– 456
4. Rosenbaum M, Leibel RL. The physiology of body weight regulation:
relevance to the etiology of obesity in children. Pediatrics. 1998;101:
525–539
5. Bouchard C. Genetics of body fat content. In: Angel A, Anderson H,
Bouchard C, Lau D, Leiter L, Mendelson R, eds. Progress in Obesity
Research. Vol 7. London, UK: John Libbey; 1996:33– 41
6. Fisher JA, Birch LL. 3–5 Year-old children’s fat preferences and fat
consumption are related to parental adiposity. J Am Diet Assoc. 1995;
95:759 –764
7. Gazzaniga JM, Burns TL. Relationship between diet composition and
body fatness, with adjustment for resting expenditure and physical
activity, in preadolescent children. Am J Clin Nutr. 1993;58:21–28
8. Nguyen VT, Larson DE, Johnson RK, Goran MI. Fat intake and adiposity in children of lean and obese parents. Am J Clin Nutr. 1996;63:
507–513
9. Klesges RC, Klesges LM, Eck LH, Shelton ML. A longitudinal analysis of
accelerated weight gain in preschool children. Pediatrics. 1995;95:126–132
10. Leibel RL. Obesity: a game of inches. Pediatrics. 1995;95:131–132
11. Boozer CN, Schoenbach G, Atkinson RL. Dietary fat and adiposity: a
dose–response relationship in adult male rats fed isocalorically. Am J
Physiol. 1995;268:E546 –E550
12. Salmon DM, Flatt JP. Effect of dietary fat content on the incidence of
obesity in ad libitum fed mice. Int J Obes. 1988;9:443– 449
13. Blundell JE. Regulation of energy intake: appetite control and the
potential for weight gain. In: Angel A, Anderson C, Bouchard D, Leiter
L, Mendelson R, eds. Progress in Obesity Research. Vol 7. London, UK:
John Libbey; 1996:215–222
14. Rolls BJ, Kim-Harris S, Fischman MW, Foltin RW, Moran TH, Stoner SA.
Satiety after preloads with different amounts of fat and carbohydrate:
implications for obesity. Am J Clin Nutr. 1994;60:476 – 487
15. Stubbs RJ, van Wyk MCW, Johnstone AM, Harbron CG. Breakfasts
high in protein, fat or carbohydrate: effect on within-day appetite and
energy balance. Eur J Clin Nutr. 1996;50:409 – 417
16. Blundell JE, Burley VJ, Cotton JR, Lawton CL. Dietary fat and the
control of energy intake: evaluating the effects of fat on meal size and
postmeal satiety. Am J Clin Nutr. 1993;57:772S–778S
17. Poppitt SD. Energy density of diets and obesity. Int J Obes. 1995;19:
S20 –S26
18. Prentice AM. Food and nutrient intake and obesity. In: Angel A,
Anderson H, Bouchard C, Lau D, Leiter L, Mendelson R, eds. Progress
in Obesity Research. Vol 7. London, UK: John Libbey; 1996:451– 457
19. Subar AF, Ziegler RG, Patterson BH, Ursin G, Graubard B. US dietary
patterns associated with fat intake: the 1987 national health interview
survey. Am J Public Health. 1994;84:359 –366
20. Nicklas TA, Webber LS, Koschak M, Berenson GS. Nutrient adequacy
of low fat intakes for children: the Bogalusa heart study. Pediatrics.
1992;89:221–228
21. American Academy of Pediatrics, Committee on Nutrition. Statement
on cholesterol. Pediatrics. 1992;90:469 – 473
22. Bayerl C, Dodd J, Finelli E, et al. ADA supports USDA School Meals
Initiative for Healthy Children but recommends more improvements
for child nutrition. J Am Diet Assoc. 1994;94:841– 842
23. US Department of Agriculture, US Department of Health and Human
Services. Nutrition and Your Health: Dietary Guidelines for Americans. 3rd
ed. Home and Garden Bulletin 232. Washington, DC: US Government
Printing Office; 1990
24. US Department of Agriculture, Federation of American Societies for
Experimental Biology, Life Sciences Research Office. Prepared for the
Interagency Board for Nutrition Monitoring and Related Research.
Third Report on Nutrition Monitoring in the United States. Vol 1. Washington, DC: US Government Printing Office; 1995
25. US Department of Health and Human Services. Healthy People 2000:
National Health Promotion and Disease Prevention Objectives. DHFHS
Publication No PHS-91-50212. Washington, DC: US Government Printing Office; 1990
26. Wolfe WS, Campbell CC. Food pattern, diet quality, and related characteristics of school children in New York State. J Am Diet Assoc.
1993;93:1280 –1284
27. Krebs-Smith SM, Cook A, Subar AF, Cleveland L, Friday J, Kahle LL.
Fruit and vegetable intakes of children and adolescents in the United
States. Arch Pediatr Adolescent Med. 1996;150:81– 86
28. Domel SB, Thomson WO, Davis HC, Baranowski T, Leonard SB,
Baranowski J. Psychosocial predictors of fruit and vegetable consumption among elementary school children. Health Educ Res. 1996;11:
299 –308
29. Resnicow K, Hearn M, Smith M, et al. Social cognitive predictors of
children’s fruit and vegetable intake. Health Psychol. 16:272–276
30. Birch LL. Children’s preferences for high-fat foods. Nutr Rev. 1992;50:
249 –255
31. Birch LL, Fisher JA. Appetite and eating behavior in children. In: Gaull
GE, ed. The Pediatric Clinics of North America: Pediatric Nutrition. Philadelphia, PA: WB Saunders; 1995:931–953
32. Drewnowski A. Sensory preferences for fat and sugar in adolescence
and adult life. Ann NY Acad Sci. 1989;561:243–250
33. Birch LL. Dimensions of preschool children’s food preferences. J Nutr
Educ. 1979;11:77– 80
34. Birch LL. Preschool children’s food preferences and consumption patterns. J Nutr Educ. 1979;11:189 –192
35. Domel SB, Baranowski T, Davis H, Leonard SB, Riley P, Baranowski J.
Measuring fruit and vegetable preferences among 4th and 5th grade
students. Prev Med. 1993;22:866 – 879
36. Sullivan SA, Birch LL. Infant dietary experience and acceptance of
solid foods. Pediatrics. 1994;93:271–277
37. Mennella JA, Beauchamp G. The effects of repeated exposure to garlic-
SUPPLEMENT
Downloaded from http://pediatrics.aappublications.org/ by guest on March 25, 2018
547
flavored milk on the nursling’s behavior. Pediatr Res. 1993;34:805– 808
38. Mennella JA, Beauchamp GK. The human infants’ response to vanilla
flavors in mother’s milk and formula. Infant Behav Dev. 1996;19:13–19
39. Agras WS, Kraemer HC, Berkowitz RI, Korner AF, Hammer LD. Does
a vigorous feeding style influence early development of adiposity.
J Pediatr. 1987;110:799 – 804
40. Galef BG. Studies of social learning in Norway rats: a brief review. Dev
Psychobiol. 1982;15:279 –295
41. Galef BG, Henderson PW. Mother’s milk: a determinant of the feeding
preferences of weaning rat pups. J Comp Physiol Psychol. 1972;78:
213–219
42. Capretta PI, Petersik IT, Steward DI. Acceptance of novel flavours is
increased after early experience of diverse tastes. Nature. 1975;254:
689 – 691
43. Cowart B. Development of taste perception in humans: sensitivity and
preference throughout the life span. Psychol Bull. 1981;90:43–73
44. Birch LL, Fisher JA. The role of experience in the development of
children’s eating behavior. In: Capaldi ED, ed. Why We Eat What We
Eat: The Psychology of Eating. Washington, DC: American Psychological
Association; 1996:113–141
45. Birch LL, Marlin DW. I don’t like it; I never tried it: effects of exposure
to food on two-year-old children’s food preferences. Appetite. 1982;4:
353–360
46. Birch LL, McPhee L, Shoba BC, Pirok E, Steinberg L. What kind of
exposure reduces children’s food neophobia? Appetite. 1987;9:171–178
47. Sullivan S, Birch L. Pass the sugar; pass the salt: experience dictates
preference. Dev Psychol. 1990;26:546 –551
48. Oliveria SA, Ellison RC, Moore LL, Gillman MW, Garrahie EJ, Singer
MR. Parent– child relationships in nutrient intake: the Framingham
children’s study. Am J Clin Nutr. 1992;56:593–598
49. Hearn MD, Baranowski T, Baranowski J, et al. Environmental influences on dietary behavior among children: availability and accessibility of fruits and vegetables enable consumption. J Health Educ. 1998; In
press
50. Birch LL, Deysher M. Conditioned and unconditioned caloric
compensation: evidence for self-regulation of food intake by young
children. Learn Motiv. 1985;16:341–355
51. Birch LL, Deysher M. Caloric compensation and sensory specific
satiety: evidence for self regulation of food intake by young children.
Appetite. 1986;7:323–331
52. Johnson SL, McPhee L, Birch LL. Conditioned preferences: young
children prefer flavors associated with high dietary fat. Physiol Behav.
1991;50:1245–1251
53. Kern DL, McPhee L, Fisher J, Johnson S, Birch LL. The postingestive
consequences of fat condition preferences for flavors associated with
high dietary fat. Physiol Behav. 1993;54:71–76
54. Sclafani A. How food preferences are learned: laboratory animal models. Proc Nutr Soc. 1995;54:419 – 427
55. Drewnowski A, Kurth C, Holden-Wiltse J, Saari J. Food preferences in
human obesity: carbohydrates versus fats. Appetite. 1992;18:207–221
56. Drewnowski A, Kurth CL, Rahaim JE. Taste preferences in human
obesity: environmental and familial factors. Am J Clin Nutr. 1991;54:
635– 641
57. Birch LL. Effects of peer models’ food choices and eating behaviors on
preschoolers’ food preferences. Child Dev. 1980;51:489 – 496
58. Birch LL. The relationship between children’s food preferences and
those of their parents. J Nutr Educ. 1980;12:14 –18
59. Duncker K. Experimental modification of children’s food preferences
through social suggestion. J Abnorm Soc Psychol. 1938;33:490 –507
60. Rozin P, Schiller D. The nature of a preference for chili pepper by
humans. Motiv Emotion. 1980;4:77–101
61. Rozin P, Kennel K. Acquired preferences for piquant foods by chimpanzees. J Intake Res. 1983;4:69 –77
62. Harper LV, Sanders KM. The effect of adults’ eating on young children’s acceptance of unfamiliar foods. J Exp Child Psychol. 1975;20:
206 –214
63. Cutting TM, Grimm-Thomas K, Birch LL. Is maternal disinhibition
associated with children’s overeating? FASEB J. 1997;11:A174
64. Pike KM, Rodin J. Mothers, daughters, and disordered eating. J Abnorm Psychol. 1991;100:198 –204
65. Fisher JO, Birch LL. Maternal restriction of young girls’ food access is
related to intake of those foods in an unrestricted setting. FASEB J.
1996;10:A225
66. Williams JO, Achterberg C, Sylvester GP. Targeting marketing of food
products to ethnic minority youths. In: Williams CL, Kimm SY, eds.
Prevention and Treatment of Childhood Obesity. Ann NY Acad Sci. 1995;
699:107–114
67. Cotugna N. TV ads on Saturday morning children’s programming:
548
what’s new? J Nutr Educ. 1988;20:125–127
68. Galst JP, White MA. The unhealthy persuader: the reinforcing value of
television and children’s purchase-influencing attempts at the supermarket. Child Dev. 1976;47:1089 –1096
69. Taras HL, Sallis JF, Patterson TL, Nader PR, Nelson JA. Television’s
influence on children’s diet and physical activity. Dev Behav Pediatr.
1989;10:176 –180
70. Goldberg ME, Gorn GJ, Gibson W. TV messages for snack and breakfast foods: do they influence children’s preferences? J Consumer Res.
1978;5:73– 81
71. Burroughs M, Terry RD. Parents’ perspectives toward their children’s
eating behavior. Top Clin Nutr. 1992;8:45–52
72. Johnson SL, Birch LL. Parents’ and children’s adiposity and eating
style. Pediatrics. 1994;94:653– 661
73. Costanzo PR, Woody EZ. Domain-specific parenting styles and their
impact on the child’s development of particular deviance: the example
of obesity proneness. J Soc Clin Psychol. 1985;3:425– 445
74. Rozin P, Ashmore M, Markwith M. Lay American conceptions of
nutrition: dose insensitivity, categorical thinking, contagion, and the
monotonic mind. Health Psychol. 1996;6:438 – 447
75. Birch LL, Birch D, Marlin D, Kramer L. Effects of instrumental eating
on children’s food preferences. Appetite. 1982;3:125–134
76. Birch LL, Marlin DW, Rotter J. Eating as the “means” activity in a
contingency: effects on young children’s food preference. Child Dev.
1984;55:432– 439
77. Newman J, Taylor A. Effect of a means: end contingency on young
children’s food preferences. J Exp Child Psychol. 1992;64:200 –216
78. Hertzler AA. Children’s food patterns—a review. II. Family and group
behavior. J Am Diet Assoc. 1983;83:555–560
79. Eppright ES, Fox HM, Fryer BA, Lamkin GH, Vivian VM, Fuller ES.
Nutrition of infants and preschool children in the North Central Region of
the United States of America. World Rev Nutr Diet. 1972;14:269–332
80. Stanek K, Abbott D, Cramer S. Diet quality and the eating environment
of preschool children. J Am Diet Assoc. 1990;90:1582–1586
81. Casey R, Rozin P. Changing children’s food preferences: parent opinions. Appetite. 1989;12:171–182
82. Birch LL, Zimmerman S, Hind H. The influences of social-affective
context on preschool children’s food preferences. Child Dev. 1980;51:
856 – 861
83. Murphy AS, Youatt JP, Hoerr SL, Sawyer CA, Andrews SL. Kindergarten students’ food preferences are not consistent with their knowledge of the dietary guidelines. J Am Diet Assoc. 1995;95:219 –223
84. Smith JA, Epstein LH. Behavioral economic analysis of food choice in
obese children. Appetite. 1991;17:91–95
85. Fomon SJ. Nutrition of Normal Infants. St Louis, MO: Mosby-Yearbook;
1993
86. Birch LL, McPhee L, Shoba BC, Steinberg L, Krehbiel R. “Clean up
your plate”: effects of child feeding practices on the conditioning of
meal size. Learn Motiv. 1987;18:301–317
87. Birch LL, McPhee L, Steinberg L, Sullivan S. Conditioned flavor preferences in young children. Physiol Behav. 1990;47:501–505
88. Birch LL, Johnson SL, Andresen G, Peters JC, Schulte MC. The variability
of young children’s energy intake. N Engl J Med. 1991;324:232–235
89. Birch LL, Johnson SL, Jones MB, Peters JC. Effects of a non-energy fat
substitute on children’s energy and macronutrient intake. Am J Clin
Nutr. 1993;58:326 –333
90. Klesges RC, Malott JM, Boschee PF, Weber JM. The effects of parental
influences on children’s food intake, physical activity, and relative
weight. Int J Eat Disord. 1986;5:335–346
91. Klesges RC, Coates TJ, Brown G, et al. Parental influences on children’s
eating behavior and relative weight. J Appl Behav Anal. 1983;16:371–378
92. Stunkard A, Kaplan D. Eating in public places: a review of reports of
the direct observation of eating behavior. Int J Obes. 1977;1:89 –101
93. Drabman RS, Cordua GD, Hammer D, Jarvie GJ, Horton W. Developmental trends in eating rates of normal and overweight preschool
children. Child Dev. 1979;50:211–216
94. Marston AR, London P, Cooper LM. A note on the eating behavior of
children varying in weight. J Child Psychol Psychiatry. 1976;17:221–224
95. Barkeling B, Ekman S, Rössner S. Eating behaviour in obese and
normal weight 11-year-old children. Int J Obes. 1992;16:355–360
96. Satter EM. The feeding relationship. J Am Diet Assoc. 1986;86:352–356
97. Satter EM. Internal regulation and the evolution of normal growth as
the basis for prevention of obesity in children. J Am Diet Assoc. 1996;
96:860 – 864
98. Hill AJ, Oliver S, Rogers PJ. Eating in the adult world: the rise of
dieting in childhood and adolescence. Br J Clin Psychol. 1992;31:95–105
99. Gustafson-Larson AM, Terry RD. Weight-related behaviors and concerns of fourth-grade children. J Am Diet Assoc. 1992;92:818 – 822
SUPPLEMENT
Downloaded from http://pediatrics.aappublications.org/ by guest on March 25, 2018
100. Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers
T. Do obese children become obese adults? A review of the literature.
Prev Med. 1993;22:167–177
101. Huon G. Dieting, binge eating, and some of their correlates among
secondary school girls. Int J Eat Disord. 1994;151:159 –164
102. Westenhoefer J, Pudel V. Failed and successful dieting: risks of restrained eating and chances of cognitive control. In: Angel A, Anderson H, Bouchard C, Lau D, Leiter L, Mendelson R, eds. Progress in
Obesity Research. Vol 7. London, UK: John Libbey; 1996:481– 487
103. Killen JD, Taylor CB, Hayward C, et al. Pursuit of thinness and
onset of eating disorder symptoms in a community sample of 16
adolescent girls: a three-year prospective analysis. Int J Eat Disord.
1994;16:227–238
104. Rosen JC, Gross J, Vara L. Psychological adjustment of adolescents
attempting to lose or gain weight. J Consult Clin Psychol. 1987;55:
742–747. Special issue: eating disorders
105. Dietz WH Jr. Health consequences of obesity in youth: childhood
predictors of adult disease. Pediatrics. 1998;101:518 –525
106. Kohl HW III, Hobbs KE. Development of physical activity behaviors
among children and adolescents. Pediatrics. 1998;101:549 –554
Development of Physical Activity Behaviors Among Children and
Adolescents
Harold W. Kohl III, PhD*, and Karen E. Hobbs, MPH‡
ABSTRACT. Physical activity is a key component of
energy balance and is promoted in children and adolescents as a lifelong positive health behavior. Understanding the potential behavioral determinants necessitates
understanding influences from three fundamental areas:
1) physiologic and developmental factors, 2) environmental factors, and 3) psychological, social, and demographic factors. The literature to date has generally investigated potential predictors of physical activity in
children and adolescents in each of these three general
areas, although existing data rely largely on cross-sectional studies in which it is difficult to distinguish a
determinant from a correlate. In all likelihood, aspects of
each of these three areas interact in a multidimensional
way to influence physical activity in youth. This article
reviews evidence of potential determinants of physical
activity in children and adolescents and provides
recommendations for future work. Pediatrics 1998;101:
549 –554; youth, exercise, tracking, determinants.
ABBREVIATION. NCYFS, National Children and Youth Fitness
Study.
A
s indicated elsewhere in this article,1 childhood obesity and overweight is of substantial
clinical and public health concern. Physical
activity is a key component of the expenditure aspect
of energy balance, providing a major outlet for daily
caloric usage. Cross-sectional observational and experimental intervention data suggest a significant
short-term influence of exercise training on weight
loss in children and adolescents,2 although prospective observational studies designed to determine the
role of physical activity in the prevention of weight
gain are lacking. It logically follows that an understanding of the determinants of physical activity behavior in children and adolescents will lead to future
opportunities for intervention and prevention of obesity and overweight.
From the *Baylor Sports Medicine Institute, Baylor College of Medicine,
Houston, Texas, and the ‡University of Texas-Houston, Health Science
Center, School of Public Health, Houston, Texas.
Received for publication Oct 24, 1997; accepted Nov 6, 1997.
PEDIATRICS (ISSN 0031 4005). Copyright © 1998 by the American Academy of Pediatrics.
Physical activity is to be encouraged among children and adolescents based largely on the assumption that the behavior will become part of the person’s life and carry into adulthood, where it will help
lower the risk of several chronic diseases as well as of
premature mortality.3 The underlying assumption is
that there will be a positive experience in childhood
or adolescence and the behavior then will track into
adulthood, when it is more likely to provide physiologic benefits. Although several lines of investigation point to evidence of tracking of other cardiovascular disease risk factors, such as serum total
cholesterol and systolic blood pressure,4 data supporting the tracking of physical activity behaviors
into adulthood are scarce. Given current difficulties
in accurate assessment of physical activity among
children and adolescents,5 the lack of evidence for
tracking physical activity may be a problem of assessment as much as it is one of tracking. Moreover,
physical inactivity may be a better indicator of longterm behavior. Of additional and substant...