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HEALTH EDUCATION RESEARCH
Vol.24 no.3 2009
Pages 496–506
Advance Access publication 16 November 2008
Can social cognitive theory constructs explain
socio-economic variations in adolescent eating
behaviours? A mediation analysis
K. Ball*, A. MacFarlane, D. Crawford, G. Savige, N. Andrianopoulos and
A. Worsley
Abstract
Adolescents of low socio-economic position
(SEP) are less likely than those of higher SEP
to consume diets in line with current dietary recommendations. The reasons for these SEP variations remain poorly understood. We
investigated the mechanisms underlying socioeconomic variations in adolescents’ eating
behaviours using a theoretically derived explanatory model. Data were obtained from a community-based sample of 2529 adolescents aged
12–15 years, from 37 secondary schools in Victoria, Australia. Adolescents completed a webbased survey assessing their eating behaviours,
self-efficacy for healthy eating, perceived importance of nutrition and health, social modelling
and support and the availability of foods in the
home. Parents provided details of maternal education level, which was used as an indicator of
SEP. All social cognitive constructs assessed mediated socio-economic variations in at least one
indicator of adolescents’ diet. Cognitive factors
were the strongest mediator of socio-economic
variations in fruit intakes, while for energydense snack foods and fast foods, availability of
energy-dense snacks at home tended to be strong
mediators. Social cognitive theory provides a
useful framework for understanding socioeconomic variations in adolescent’s diet and
Centre for Physical Activity and Nutrition Research, School
of Exercise and Nutrition Sciences, Deakin University,
Burwood, VIC 3125, Australia
*Correspondence to: K. Ball. E-mail:
[email protected]
might guide public health programmes and
policies focusing on improving adolescent nutrition among those experiencing socio-economic
disadvantage.
Introduction
Nutrition during adolescence is critical for healthy
development [1], yet many adolescents have diets
that are not consistent with dietary guidelines for
health [2, 3]. Adolescents of low socio-economic
position (SEP) are at particular risk. For example, in
developed countries, adolescents of lower SEP tend
to consume fewer vegetables, fruits and high-fibre
foods, but more high-fat foods than their counterparts of higher SEP [4–7]. Irregular meal patterns
and snack consumption have also been associated
with low SEP [8]. These dietary variations have
been observed regardless of the indicator of SEP
used. Socio-economic variations in diet are of concern since they parallel socio-economic inequalities
in a range of chronic health conditions [9], and they
may represent a pathway by which socio-economic
disadvantage leads to poorer health. Despite this,
the mechanisms underlying socio-economic variations in adolescents’ diets remain unknown. There
is therefore little evidence upon which to base nutrition promotion interventions for this target group.
Potential explanations for the SEP variations in
adolescents’ diets can be inferred from the existing
literature on the determinants of dietary behaviour.
For example, there is evidence that cognitive factors such as nutrition knowledge and health considerations, influence dietary intake [10] and that these
influences are differentially distributed across SEP
Ó The Author 2008. Published by Oxford University Press. All rights reserved.
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doi:10.1093/her/cyn048
Socio-economic position and adolescent eating
groups [11, 12]. Attitudes and beliefs about healthy
lifestyle, eating and weight are also likely to influence dietary intake [13] and have been shown to
vary across SEP. For example, low SEP adults tend
to be less health conscious and have stronger beliefs
in the influence of chance on health [14] and less
positive attitudes towards healthy eating behaviour
[13] compared with high SEP adults. Among female adolescents, those of low SEP have less
awareness of the advantages of thinness, a higher
threshold for concern about fatness and greater use
of unhealthy weight control strategies [15].
There is also evidence that social and environmental factors influence dietary intake and that these
influences vary across SEP. For example, social support for healthy eating, particularly from a partner or
other family member, is a key influence on food
choice [16], and individuals of low SEP have
reported lower levels of support [17]. Home food
availability, a correlate of fruit and vegetable intake
in children and adolescents [18, 19], has also been
shown to vary by SEP. Adolescents of low SEP report poorer home availability of fruit and vegetables
[19, 20] and greater home availability of unhealthy
foods (e.g. soft drink, potato chips and confectionery
[20]) compared with their high SEP peers.
Much of the research on dietary determinants has
been based on the social cognitive theory (SCT) [10,
21], a theoretical framework that emphasizes the
importance of factors within the cognitive, socioenvironmental and behavioural domains and their
interactions. Few studies, however, have applied
SCT constructs to examine adolescents’ diets [19,
22, 23, 24], and to our knowledge none has used
SCT, or other theoretically derived models, to test
mechanisms underlying SEP variations in adolescents’ diets.
The aims of this study were 3-fold. Firstly, this study
aimed to examine SEP variations among adolescents
in three key indicators of dietary intake: fruit, energydense snack foods and fast foods. Secondly, this study
sought to examine whether key cognitive and social/
environmental determinants of behaviour posited by
SCT [i.e. self-efficacy; perceived importance of
healthy eating; social observation (modelling) and
healthy/unhealthy food availability in the home] are
also patterned by SEP among adolescents. Finally, we
aimed to determine whether socio-economic variations in these constructs explained socio-economic
variations in adolescents’ diets.
Methods
Study design, procedure and participants
As part of a study investigating changes in dietary
habits during adolescence, adolescents and their
parents/caregivers were administered self-completion
questionnaires between September 2004 and July
2005. This study was approved by the Deakin
University Ethics Committee, the Victorian
Department of Education and Training and the Catholic Education Office.
All co-educational state (government run) and
Catholic secondary schools, located in the southern
metropolitan region of Melbourne and the non-metropolitan region of Gippsland east of Melbourne,
Australia, that included years 7–12 and had >200
enrolments, were invited to participate. Of the 70
schools (47 metropolitan and 23 non-metropolitan)
that met these criteria, 37 schools (20 metropolitan
and 17 non-metropolitan) agreed to participate.
All students (n = 9842) from Year 7 (aged 12–13
years) to Year 9 (aged 14–15 years) were invited to
participate. Teachers distributed parental consent
forms to parents via students. In addition to requesting consent for their adolescent to participate in the
study, parents were asked to report sociodemographic information including their gender, relationship to the child and highest level of
schooling. They were also asked whether they
would be willing to complete a questionnaire about
their child’s eating habits. Parental consent was
obtained for 4502 (46%) of eligible students, but
due to absence from school on the day of testing,
teachers administered surveys to 3264 adolescents
(73% of eligible students with parental consent and
33% of all eligible students invited to participate).
A parental survey was also mailed to 2534 parents
who indicated that they would be willing to complete a questionnaire; of these, 1622 (64%) returned
a completed survey. In the present study, the
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K. Ball et al.
maternal education measure was the only data used
from the parental survey.
Teachers administered an online food habits survey to students during class time when they had
access to computers. The online survey was pretested with a small group of adolescents for clarity
and functionality. Teachers instructed the students
to type in the URL of the Youth Eating Patterns
survey, which was provided to teachers along with
additional information covering frequently asked
questions and procedures to recommence the survey at a later time if students were unable to complete on the day. Almost all schools indicated that
the major obstacle in obtaining parental consent
would be due to the apathy of adolescents in returning forms. To facilitate this process, the researchers
provided small gifts (movie and music vouchers) to
be offered to all adolescents returning consent
forms including forms denying consent. Further
details of the sample and data collection procedures
are described in a previous publication [25].
The present analyses are based on the subset of
2529 adolescents who had non-missing data for all
the variables examined in this study. This subset did
not differ from the full sample in terms of maternal
education or the outcome variables, with the exception of fast food intake, where those excluded due
to missing data had slightly higher average daily
fast food consumption frequencies than the sample
included in analyses (P < 0.05).
Measures
Outcome measures: food intake
Consistent with other large-scale studies of dietary
intake and eating behaviour of adolescents [19],
food intake was assessed in the survey using a food
frequency questionnaire (FFQ). This FFQ was
based on the 2001 National Food and Nutrition
Monitoring and Surveillance Project report [26],
which described a number of validated indices useful in describing the food intake patterns of groups.
Respondents indicated how frequently they had
consumed 37 food items during the previous
month. Seven response categories were ‘never or
not in the last month’, ‘several times per month’,
498
‘once a week’, ‘a few times a week’, ‘most days’,
‘once a day’ and ‘several times a day’.
The present analyses are based on a subset of 10
food items from the FFQ, which were categorized
into three food groups: fruit, energy-dense snacks
and fast foods. These indicators were selected due
to their importance in contributing to the healthfulness of overall diet. Fruit consumption is linked
with decreased risk of a range of chronic diseases
[27]. Frequent fast food consumption is associated
with poorer dietary profiles, such as higher total
energy, percent energy from fat and soft drink intake and lower consumption of fruit and vegetables,
and also with excess weight gain, among adolescents [28, 29]. Studies have also shown that the
consumption of energy-dense snack foods is associated with poorer nutrient profiles among children
[30]. Initially, vegetable consumption was also
examined, but the socio-economic variations in this
outcome were minimal in this sample (P = 0.34).
The frequency of consumption for each of the 10
food items comprising these outcomes in the past
month was converted into a daily equivalent, which
is an established method [31] that has been used in
other dietary studies [32, 33]. Daily equivalent
scores were calculated as follows: not in the last
month (0.00 per day), several times per month
(0.11 per day), once a week (0.14 per day), a few
times a week (0.36 per day), most days (0.71 per
day), once per day (1.00 per day) and several times
per day (2.50 per day). The daily intake for each of
the three food groups was calculated by summing
the daily equivalence for food items in each food
group. The estimated daily intake of the energydense snack group included the summed equivalence of four items (confectionary, cakes, sweet
biscuits and potato crisps). The fast food group included the summed equivalence of five items [food
from fast food chains, fish and chips, hot chips,
pizza and pastry goods (savoury) like meat pies].
The daily intake of fruit included fruit as one item
(fresh, canned, frozen or dried).
Predictor measure
Maternal education was used as the indicator of
SEP for two reasons. Firstly, of the three commonly
Socio-economic position and adolescent eating
used indicators of SEP, education, income and occupation, it has been suggested that education is the
strongest and most consistent in terms of predicting
health behaviours [34]. Secondly, maternal education is an important determinant of dietary intake of
children and adolescents and consequently is probably the most commonly used indicator of SEP in
studies of childhood and adolescent eating behaviours [35–37]. Maternal education data were
obtained from the parental consent form (if completed by the mother/female carer) or otherwise
from the parent questionnaire (which assessed both
parents’ education level). Response categories were
categorized into three groups: ‘low’, having completed <12 years of secondary school; ‘medium’,
having completed 12 years of secondary school
and/or a technical or trade certificate/apprenticeship
and ‘high’, having a University or tertiary (other
formal, non-compulsory, education that follows
secondary education) qualification.
Mediators
Cognitive, social and environmental constructs
from SCT were included as mediators. Self-efficacy
was assessed using items adapted from Project Eating Among Teens (EAT) [19]. Adolescents were
asked three questions about their confidence in cutting down on ‘junk food’ (i.e. food that is low in
nutritional value and typically high in energy): ‘If
you wanted to, about how confident (sure) are you
that you could cut down on junk food when you’re
hanging out with friends’, ‘. at school’ and ‘. at
home’ and three questions on confidence in eating
more fruit in the same situations. Response options
were given on a four-point Likert scale, ranging
from 1: not at all confident through to 4: very confident. Responses were summed to provide two
self-efficacy scores, one for cutting down on
junk food (Cronbach’s alpha = 0.80) and one for
increasing
fruit
consumption
(Cronbach’s
alpha = 0.84).
Four questions, developed specifically for this
study, assessed the perceived importance to the adolescent of health behaviours: eating healthy food,
limiting junk food exercising and staying fit and
limiting TV/DVD viewing. Responses were
marked on a four-point Likert scale, from 1: not
at all important through to 4: very important and
summed (Cronbach’s alpha = 0.74). The last two
questions were included since previous studies
demonstrate that dietary intake is closely linked to
physical activity [38, 39] and TV/DVD viewing
[40, 41].
Social observation of the eating behaviour of two
key persons (best friend and mother) was assessed
with items developed specifically for this study. For
each, the adolescent provided a rating of their
agreement (disagree, coded 1; not sure, coded 2
or agree, coded 3) with four separate statements:
my best friend/mother eats healthy food, limits junk
foods, eats vegetables most days and eats fruit most
days. Two variables were calculated by summing
responses to the four items (Cronbach’s
alpha = 0.77 for best friend and 0.71 for mother).
In addition, two social support variables were
derived by summing responses (1: never/rarely, 2:
sometimes 3: often) to four items assessing support
separately from friends and family: whether friends/
family make you feel good about what you eat, eat
healthy food with you, discourage you from eating
junk food and encourage you to eat healthy food
(Cronbach’s alpha = 0.76 for friend’s support and
0.72 for family support). These items were adapted
from Sallis et al. [42].
The availability of different foods within the
home environment (environmental predictor) was
assessed with items adapted from Project EAT
[19]. Respondents were asked how frequently (1:
never/rarely, 2: sometimes, 3: usually, 4: always)
the following items were available within the home:
fruit, vegetables, cakes or sweet biscuits, potato
crisps or salty snacks, chocolate or lollies and soft
drink. The frequency of availability of fruit and
vegetable items was summed (Cronbach’s
alpha = 0.73), as were the frequencies of the energy-dense snack items (Cronbach’s alpha = 0.80).
Data analysis
Descriptive statistics were used to summarize the
sociodemographic and dietary characteristics of the
sample, as well as the cognitive, social and environmental mediators. Since distributions on the
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K. Ball et al.
outcome variables energy-dense snack and fast
food intakes were skewed, a square root transformation was applied to these variables to achieve
more normal distributions. Differences by maternal
education in the three dietary indicators and the
mediating variables were examined using analyses
of variance (ANOVAs). Where ANOVAs revealed
significant differences, these were investigated further using post hoc tests with a Bonferroni adjustment for multiple comparisons. The mediating
effect of each cognitive, social and environmental
variable was investigated using the Freedman–
Schatzkin test of mediation [43]. The Freedman–
Schatzkin test is based on the difference in the
unstandardized regression coefficient for the association between an independent and dependent variable, unadjusted (s) and adjusted (s#) for the
proposed mediator. The significance of the mediating effect is computed by dividing this difference in
coefficients by its standard error and comparing the
obtained value to a t-distribution with n 2 degrees
of freedom. This approach represents a more powerful test of mediation effects than the commonly
used Baron and Kenny [44] causal step approach,
which has relatively low statistical power [45].
Such a powerful approach allows detection of mediating effects that may potentially be small due to
the inclusion of confounding variables. A further
advantage of this approach is its ability to test for
mediating effects even when the main effects are
not highly significant. Mackinnon et al. [46]
provide a further discussion of cases in which the
overall relationship of predictor to outcome is nonsignificant, yet mediation still exists. All regression
models adjusted for sex, age (year level) and region
of residence (metropolitan/non-metropolitan).
Results
The mean age of the sample on which these analyses are based was 13.5 years (SD = 1.3). More participants were female (54%) than male (46%), more
resided in the metropolitan (66%) than in the nonmetropolitan (34%) region of Melbourne and more
had mothers with a low (47%) than with a medium
(29%) or high (24%) level of education.
500
Table I shows the distributions of dietary outcomes according to maternal education level. Compared with adolescents of higher SEP, those of
lower SEP consumed fruits less frequently, but
ate energy-dense snacks (borderline significant,
P = 0.096) and fast foods more frequently.
The distributions of social cognitive constructs
by maternal education are also presented in Table
I. With only two exceptions (social observation of
best friend; social support for healthy eating from
friends, both non-significantly associated with
maternal education), all the constructs showed educational gradients in the expected directions. That
is, compared with those whose mothers were highly
educated, adolescents whose mothers had low education reported lower levels of self-efficacy for increasing fruit intake; lower levels of self-efficacy
for reducing junk food; lower perceived importance
of healthy eating; less healthy eating behaviours of
their mothers; less familial support for healthy eating and less healthy and greater unhealthy food
availability in the home.
Maternal education explained 3% of the variation
in fruit and fast food intakes and 0.2% in energydense snack intakes. Tables II–IV show the results
of mediation analyses procedures applied to the prediction of fruit, energy-dense snacks and fast food
intakes, respectively. Prediction of fruit intake by
maternal education level declined significantly after
adjusting for all predictor variables except for
friend’s support, suggesting that socio-economic
variations in fruit intake were mediated by these
constructs (Table II). Based on the magnitude of
t-values, cognitive factors, in particular the perceived importance of healthy behaviours, played
the greatest mediating role (t(2527) = 11.9,
P < 0.001).
Table III shows the mediating effects of SCT
constructs on the association between SEP and energy-dense snack food consumption. The strongest
mediator of this association was the availability of
energy-dense snack foods in the home (t(2527) =
17.8, P < 0.001). Friend’s support and the availability of fruit and vegetables in the home were the
only two variables that did not mediate socioeconomic variations in energy-dense snack
Socio-economic position and adolescent eating
Table I. Means (SDs) or frequencies of demographics, dietary outcomes and social cognitive constructs by maternal education
among adolescents (n = 2529) in Australia
Maternal education
Low (n = 1192)
Sex (n, %)
Male (1158, 46%)
Female (1371, 54%)
Dietary indicators (mean, SD)
Fruit
Energy-dense snack foods
Fast foods
Cognitive mediators (mean, SD)
Self-efficacy increasing fruit (range 3–12)
Self-efficacy reducing ‘junk’ (range 3–12)
Perceived importance health behaviours (range 4–16)
Social mediators
Social observation (mean, SD; range 4–12)
Best friend
Mother
Social support for healthy eating (mean, SD; range 4–12)
Friends
Family
Environmental mediators (mean, SD)
Fruit and vegetable availability in home (range 2–8)
Energy-dense snack food availability in home (range 4–16)
Mid (n = 737)
High (n = 600)
P-value*
545 (46)
647 (54)
323 (44)
414 (56)
290 (48)
310 (52)
0.83 (0.80)a
1.42 (1.43)
0.89 (1.14)a
0.90 (0.82)a
1.40 (1.37)
0.81 (1.09)a,b
1.19 (0.93)b
1.28 (1.28)
0.73 (1.04)b
<0.001
0.096
0.010
9.13 (2.51)a
8.56 (2.39)a
11.42 (2.56)a
9.23 (2.39)a,b
8.65 (2.24)a
11.53 (2.46)a,b
9.50 (2.45)b
9.11 (2.23)b
11.85 (2.43)b
0.012
<0.001
0.002
9.10 (2.18)
10.89 (1.57)a
9.12 (2.17)
11.11 (1.38)b
9.27 (2.14)
11.37 (1.28)c
0.286
<0.001
6.77 (2.15)
9.52 (2.02)b
0.641
<0.001
7.60 (0.92)b
9.61 (2.52)b
<0.001
<0.001
6.87 (2.23)
9.08 (2.10)a
7.40 (1.10)a
10.25 (2.52)a
6.85 (2.16)
9.28 (2.02)a,b
7.56 (0.93)b
10.20 (2.62)a
0.258
*
Significance in chi-square test (sex) or univariate ANOVAs (dietary and social cognitive constructs).
Different superscripts within a row indicate significant difference between means according to pairwise Bonferroni post hoc tests. SD,
standard deviation.
consumption. The inclusion of friend’s support in
the regression model actually increased the magnitude of the association between SEP and energydense snack intake, suggesting a masking effect of
friend’s support. That is, adolescents of higher SEP
had slightly (though not statistically significantly)
lower levels of friend’s support, and lower friend’s
support predicted higher energy-dense snack intake. However, adolescents of higher SEP had
lower energy-dense snack intakes, despite their
lower levels of friends’ support. Friend’s support
therefore masked some of the association of SEP
with energy-dense snack intake.
Finally, all variables except for friend’s support
and social observation of best friend significantly
mediated socio-economic variations in fast food intake (Table IV), with energy-dense snack
food availability again the strongest mediator
(t(2527) = 16.2, P < 0.001).
Discussion
This study confirmed previous reports that adolescents of low SEP generally have diets less consistent with current recommendations, based on
a range of dietary indicators, than their peers of
higher SEP [4–7]. The present study advances previous findings to demonstrate that most constructs
of SCT also varied according to adolescents’ SEP.
Compared with those whose mothers were highly
educated, adolescents whose mothers had low levels of education scored relatively poorly on cognitive, social and environmental constructs posited by
SCT to be supportive of healthy eating. The two
exceptions were the constructs social observation
of best friend and social support for healthy eating
from friends, both of which were not significantly
associated with maternal education in this sample.
Possibly, adolescents may attend school and
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K. Ball et al.
Table II. Effects of adjustment for potential mediators in the association between maternal education and fruit intake among
adolescents (n = 2529) in Australia
Potential mediators
s (mediator-unadjusted coefficient regressing
fruit intake on maternal education)
= 0.169 (SE = 0.021)
Cognitive mediators
Self-efficacy fruit
Self-efficacy junk
Importance of behaviours
Social mediators
Social observation
Best friend
Mother
Social support
Friends
Family
Environmental mediators
Fruit and vegetable availability
Energy-dense snack food availability
s#
SE
s
0.148
0.149
0.149
0.019
0.020
0.020
0.165
0.151
s#
SE
t
P-value
0.021
0.020
0.020
0.002
0.002
0.002
9.064
9.567
11.863
<0.001
<0.001
<0.001
0.020
0.020
0.004
0.018
0.001
0.003
3.447
6.263
<0.001
<0.001
0.170
0.151
0.020
0.020
0.001
0.018
0.001
0.002
0.939
8.958
0.348 (ns)
<0.001
0.151
0.157
0.020
0.020
0.018
0.012
0.002
0.002
8.944
5.606
<0.001
<0.001
s, unstandardized regression coefficient for association of fruit consumption with maternal education, adjusting for confounders (sex,
year level and region of residence), before adjustment for mediator. In this model, predicting fruit consumption, s = 0.169, SE = 0.021;
s#, unstandardized regression coefficient for association of fruit consumption with maternal education, adjusting for confounders and
mediator; s s#, difference between the two regression coefficients, which, when divided by its standard error, can be compared
against a t-distribution with n 2 degrees of freedom; SE, standard error; ns, not significant.
socialize with friends from a range of socio-economic backgrounds, including those different to
their own, so their friends’ eating behaviours and
attitudes may also vary, which would explain the
non-significant associations with adolescents’ own
SEP.
The present findings also demonstrate that variations in SCT constructs play an important role in
mediating SEP variations in adolescents’ intakes of
fruits, energy-dense snack foods and fast foods.
This is a novel finding, since few previous studies
have investigated socio-economic variations in
such constructs, and to our knowledge this is the
first study to explicitly test a theoretically based
explanatory model of socio-economic variations
in adolescents’ diets. There were several differences
in the mediators that were most important for the
three dietary outcomes examined. It is interesting
that cognitive factors, particularly self-efficacy and
the perceived importance of healthy behaviour,
were important mediators of socio-economic variations in fruit consumption, while food availability
502
was a more important mediator for both consumption of energy-dense snacks and fast food. This
finding is consistent with those of other studies that
have also found that home availability may be a less
important correlate of fruit intake than intakes of
other foods in young people [47]. While we do
not know the reasons for these differences, possibly, consumption of foods considered healthy may
involve conscious decision making, while consumption of energy-dense snacks may be more
impulse- and exposure-driven. However, there
were also similarities in findings for the three dietary outcomes. For example, cognitive factors
were found to be strong mediators across all three
dietary indices. This suggests that, while increasing
policy attention is paid to modifying structural environmental factors in an effort to improve dietary
behaviours in socio-economically disadvantaged
individuals [48–50], there remains a need for health
promotion efforts to focus on cognitive factors
such as self-efficacy and the value attached to
health-promoting behaviours.
Socio-economic position and adolescent eating
Table III. Effects of adjustment for potential mediators in the association between maternal education and energy-dense snack intake
among adolescents (n = 2529) in Australia
Potential mediators
s#
s (mediator-unadjusted coefficient regressing
energy-dense snack intake on maternal education)
= 0.023 (SE = 0.012)
Cognitive mediators
Self-efficacy fruit
Self-efficacy junk
Importance of behaviours
Social mediators
Social observation
Best friend
Mother
Social support
Friends
Family
Environmental mediators
Fruit and vegetable availability
Energy-dense snack food availability
SE
s
s#
SE
t
P-value
0.016
0.007
0.014
0.012
0.011
0.012
0.007
0.016
0.009
0.001
0.001
0.001
9.961
11.150
11.324
<0.001
<0.001
<0.001
0.022
0.020
0.012
0.012
0.001
0.003
0.000
0.002
2.902
1.901
<0.01
<0.05
0.024
0.020
0.012
0.012
0.001
0.003
0.000
0.001
4.669
2.939
<0.001a
<0.01
0.022
0.003
0.012
0.011
0.001
0.026
0.001
0.001
0.978
17.832
0.328 (ns)
<0.001
s, unstandardized regression coefficient for association of energy-dense snack consumption with maternal education, adjusting for
confounders (sex, year level and region of residence), before adjustment for mediator. In this model, predicting energy-dense snack
consumption, s = 0.023, SE = 0.012; s#, unstandardized regression coefficient for association of energy-dense snack consumption with
maternal education, adjusting for confounders and mediator; s s#, difference between the two regression coefficients, which, when
divided by its standard error, can be compared against a t-distribution with n 2 degrees of freedom; SE, standard error; ns, not significant.
a
Adjustment for support from friends was not a significant mediator, but rather increased the magnitude of the association between
maternal education and energy-dense snack intake, suggesting that friend’s support had a masking effect on the association.
Social factors appear to be less consistent mediators of socio-economic variation in dietary intakes
among adolescents, and their importance seems to
vary according to the dietary indicator under study
and the source of support (familial versus friends).
Overall, support for healthy eating from friends
(and social observation of friends related to fast
food intake) was less important than support and
observation of family. This finding is consistent
with that of a previous study [35], which also demonstrated that family circumstance was more
strongly associated with adolescent’s dietary habits
than were factors related to social groups at school.
Social support from family may be more important
because adolescents share more meals and eating
occasions with family than with friends.
Several limitations of this study should be acknowledged. All data were collected by self-report
and are subject to socially desirable response bias
(which may have affected responses non-randomly
across SEP groups) or other misreporting. Perceptions of the terms junk food or ‘healthy food’ may
differ among adolescents or across sex, age or SEP
groups. The response rate of 33% was low, and it is
unknown how representative it is of the school population as we could not collect data from those not
consenting. The low response could potentially bias
our sample towards socio-economically advantaged
groups since previous studies show that nonrespondents tend to be persons from socio-economically disadvantaged backgrounds [51]. In our sample, however, a higher proportion of participants
had mothers with low levels of education (47%)
than had mothers with medium (29%) or high
(24%) levels of education. The study was crosssectional, and hence strong conclusions about
causal mediating pathways are not possible. We
cannot assume that our constructs for SCT (e.g.
self-efficacy for eating more fruit) actually cause
adolescents to change their behaviour (e.g. eat more
503
K. Ball et al.
Table IV. Effects of adjustment for potential mediators in the association between maternal education and fruit intake fast food intake
among adolescents (n = 2529) in Australia
Potential mediators
s (mediator-unadjusted coefficient regressing
fast food intake on maternal education)
= 0.042 (SE = 0.010)
Cognitive mediators
Self-efficacy fruit
Self-efficacy junk
Importance of behaviours
Social mediators
Social observation
Best friend
Mother
Social support
Friends
Family
Environmental mediators
Fruit and vegetable availability
Energy-dense snack food availability
s#
SE
s
s#
SE
t
P-value
<0.001
<0.001
<0.001
0.037
0.031
0.036
0.010
0.010
0.010
0.005
0.011
0.006
0.001
0.001
0.001
8.538
12.279
9.059
0.042
0.035
0.010
0.010
0.000
0.007
0.000
0.001
0.000
5.324
0.999 (ns)
<0.001
0.042
0.039
0.010
0.010
0.000
0.003
0.000
0.001
0.000
3.527
0.999 (ns)
<0.001
0.036
0.027
0.010
0.010
0.006
0.015
0.001
0.001
7.041
16.242
<0.001
<0.001
s, unstandardized regression coefficient for association of fast food consumption with maternal education, adjusting for confounders
(sex, year level, region of residence), before adjustment for mediator. In this model, predicting fast food consumption, s = 0.042,
SE = 0.010; s#, unstandardized regression coefficient for association of fast food consumption with maternal education, adjusting for
confounders and mediator; s s#, Difference between the two regression coefficients, which, when divided by its standard error, can be
compared against a t-distribution with n 2 degrees of freedom; SE, standard error; ns, not significant.
fruit). A large body of research has established that
behaviour affects perceptions in many ways, particularly when these behaviours are repeated or ongoing, like daily food intake [52]. For example, people
who already eat lots of fruit are more confident
about their ability to eat more fruit than people
who only eat fruit occasionally. Despite this, it is
plausible that maternal education would have been
in most cases established temporally prior to the
development of the adolescents’ food-related cognitions and the social and environmental factors
reported here.
Further limitations of this study include the use
of only one indicator of SEP (maternal education),
although this is not atypical for studies in this field.
Nonetheless, findings may have differed were occupation, income or family SEP considered. The
dietary measures were based on frequencies rather
than amounts, but we still found SEP variations in
the expected directions. This study only assessed
SEP variations in three dietary outcome measures:
fruit, energy-dense snacks and fast food. Different
504
mediators may have been important for other dietary outcomes, such as vegetable intakes. Further,
these three indicators alone should not be taken to
represent overall dietary quality. Strengths of the
study include the large, regionally diverse sample;
the application of a well-established theoretical
framework and the use of powerful statistical mediation techniques, which allowed the testing of
mediating pathways even where main effects were
not highly statistically significant (i.e. for energydense snack consumption).
Acknowledging these limitations, the present
results are important since little is known about
the mechanisms underlying SEP variations in diet.
The findings suggest that public health programmes
and policies aimed at promoting healthy eating
among socio-economically disadvantaged adolescents might focus not only on promoting the importance of healthy behaviour and self-efficacy for
healthy eating but also on decreasing the availability of unhealthy foods in the home. This may require targeting both adolescents and their parents,
Socio-economic position and adolescent eating
who not only support and model healthy eating
behaviours but also most likely control the availability of foods within the home environment. Increasing awareness of health and access to healthy
foods could be achieved by overcoming barriers
that prevent or hinder acquisition of healthy foods
in homes, in low SEP areas particularly, such as
advocating for local markets or home delivery. It
is also noteworthy that the SEP variations in intakes
of fruits, energy-dense snacks and fast foods were
relatively small and that intakes of these foods were
also not ideal in terms of health recommendations
among adolescents of high SEP (for instance, high
SEP adolescents consumed fruit on average just
over once per day). Nutrition initiatives targeting
all adolescents remain warranted, but this is particularly the case among those experiencing socioeconomic disadvantage. Further research is required
to confirm these results longitudinally and also to
examine why these SCT constructs vary by SEP. A
better understanding of the mechanisms underlying
socio-economic variations in adolescents’ diets and
dietary determinants is essential for promoting
healthy eating and reducing socio-economic variations in diets at this important life stage.
Funding
Australia Research Council (DP0452044); William
Buckland Foundation; National Health and Medical
Research Council Senior Research Fellowship
(479513 to K.B.); VicHealth Senior Public Health
Research Fellowship to D.C.
Conflict of interest statement
None declared.
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Received on January 29, 2008; accepted on September 5, 2008