Effects of Exercise Interventions on Body Image: A Meta-analysis

Abstract

Our meta-analysis examined the impact of exercise interventions on body image; and participant, intervention, and design features associated with larger effects. We identified 57 interventions (with pre-and post-data for the exercise and control groups) examining the effects of exercise on body image. A small random effect indicated that exercise intervention conditions had improved body image compared to control conditions; and that participant (age), design (year of publication), and intervention (exercise frequency and specificity) features moderated the effect size. Research examining the mechanisms and the exercise dose-response required for body image change is needed.

Full text

780
Effects of Exercise
Interventions on
Body Image
A Meta-analysis
ANNA CAMPBELL &
HEATHER A. HAUSENBLAS
University of Florida, USA Abstract
Our meta-analysis examined the
impact of exercise interventions on
body image; and participant,
intervention, and design features
associated with larger effects. We
identified 57 interventions (with pre-
and post-data for the exercise and
control groups) examining the effects
of exercise on body image. A small
random effect indicated that exercise
intervention conditions had improved
body image compared to control
conditions; and that participant (age),
design (year of publication), and
intervention (exercise frequency and
specificity) features moderated the
effect size. Research examining the
mechanisms and the exercise dose-
response required for body image
change is needed.
Journal of Health Psychology
Copyright © 2009 SAGE Publications
Los Angeles, London, New Delhi, Singapore
and Washington DC
www.sagepublications.com
Vol 14(6) 780–793
DOI: 10.1177/1359105309338977
COMPETING INTERESTS: None declared.
ADDRESS. Correspondence should be directed to:
HEATHERA. HAUSENBLAS, Department of Applied Physiology and
Kinesiology, University of Florida, Gainesville, FL32611, USA.
[email: heatherh@hhp.ufl.edu]
Keywords
■body dissatisfaction
■body image
■meta-analysis
■physical activity

‘BODY IMAGE’ is defined as the internal representation
of a person’s outer appearance (Thompson, Heinberg,
Altabe, & Tantleff-Dunn, 1999). For both genders,
negative body image is common and it has detri-
mental physical, psychological, and economic
consequences. More specifically, negative body
image is related to emotional distress (Johnson &
Wardle, 2005), smoking (Croghan et al., 2006),
dramatic measures to alter appearance, for exam-
ple, steroid use (Raevuori et al., 2006), social anx-
iety (Cash & Fleming, 2002), impaired sexual
functioning (Wiederman, 2002), depression (Stice
& Bearman, 2001), and eating disorders (Stice,
Presnell, & Spangler, 2002). Widespread body-image
disturbance is associated with US consumers spend-
ing billions of dollars annually for products aimed at
changing their body size and shape such as diet pills,
unnecessary cosmetic surgery, beauty products, and
fitness products. Because of the detrimental out-
comes associated with negative body image and its
malleable and subjective nature, society could bene-
fit from a better understanding of the efficacy of
interventions aimed at improving body image.
Body-image interventions typically consist of
psychoeducational, cognitive-behavioral, or drug
therapies (e.g. weight loss pills/programs; Gollings &
Paxton, 2006). Because many of these interventions
are expensive, in short supply and often not suitable
for young populations other more practical strategies
should be examined and promoted. Furthermore,
although effective treatments exist, only a small pro-
portion of those with body image problems access
treatment, and thus, evaluated treatments are under-
used. There are numerous explanations for this. Some
of these explanations relate to the practicalities of
treatment delivery such as geographic distance,
cost, and lack of availability. Others relate to the
nature of body-image problems, such as patient
shame and ambivalence about change (Banasiak,
Paxton, & Hay, 1998). One promising alternative
mode of intervention for negative body image is
exercise (i.e. exercise training).
Because of the high prevalence of negative body
image and its negative consequences a comprehen-
sive meta-analytic review of the exercise intervention
and body image literature is timely. The few available
reviews are narrow in scope with selection bias
(Bane & McAuley, 1998; Fox, 2000; Greenleaf
et al., 2007; Hausenblas & Fallon, 2006; Martin &
Lichtenberger, 2002). Two meta-analyses have been
published (Greenleaf et al., 2007; Hausenblas &
Fallon, 2006), but they pooled data from studies
that included correlational, quasi-experimental, and
intervention designs that rendered it impossible to
examine moderators of the exercise intervention in
detail. Although both statistical reviews found that
exercise positively influenced body image, differences
in the magnitude of the effect, moderators, design
(e.g. correlation, quasi-experimental) and number
of studies reviewed were evidenced. For example,
Hausenblas and Fallon (2006) found small effect
sizes based on 121 studies; in comparison Greenleaf
et al. (2007) found moderate effect sizes based on
35 studies. Because these reviews did not converge
on similar findings, there remains a need to exam-
ine the effects of exercise on body image more
closely. It would be informative also to control for
pre-intervention scores, and examine the impact of
moderator variables and publication bias. Thus, the
overarching goal of our study was to address this
gap in the literature. The first purpose of our review
was to statistically summarize exercise intervention
programs and their effects on body image. The second
purpose was to examine participant, intervention, and
design features that are associated with larger inter-
vention effects. Given the heterogeneity in the effects
from these interventions, it is important to system-
atically consider the moderators associated with inter-
ventions that produced the largest effects.
Method
Sample of studies
We retrieved research related to body image and
exercise by June 2008 by using the following four
procedures to avoid bias retrieval of searching only
major journals and to obtain fugitive studies
(Rosenthal, 1995). First, we conducted computer-
based searches in the following databases that were
available at our institution: PubMed, PsychINFO and
Dissertation Abstracts International using relevant
key words (i.e. body image, exercise, physical activity,
body dissatisfaction, body satisfaction). Second,
ancestry searches were conducted using the refer-
ences lists of all retrieved intervention studies. We
also manually searched all available issues of perti-
nent journals in the field. Third, we contacted active
researchers in the field to retrieve either current or
unpublished research. Of the 29 authors contacted,
nine responded resulting in the retention of seven
studies and exclusion of two. Fourth, computerized
searches were conducted on all authors of retrieved
studies meeting the inclusion criteria. Finally, often
because of the magnitude of controlled trials, multiple
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JOURNAL OF HEALTH PSYCHOLOGY 14(6)
782
publications occurred. In an attempt to effectively
code the studies, we also retrieved other publications
related to the trial in an attempt to comprehensively
record the moderator variables (e.g. Sallis et al.,
1999). When several publications provided informa-
tion about a single study, study characteristics were
coded based on the multiple reports, and effect sizes
were calculated for the pre–post-assessment only.
Selection criteria
Criteria for inclusion were that the dependent vari-
able was a body image measure that was assessed
before and after an experimental exercise interven-
tion. We excluded studies that were cross-sectional,
correlational or did not have a control group
because it is impossible to differentiate between
spontaneous changes in body image over time as
opposed to the effects of exercise. Studies of behav-
ioral interventions which offered either education
and/or advice on increasing exercise, or structured
supervised/unsupervised exercise programs were
considered for inclusion. If a study had pre-, mid-
and post-intervention data, we used the pre- and
post-intervention data only to compute effect sizes.
Thus, we focused exclusively on studies that tested
whether the change in the outcomes over time was
greater in the intervention group versus the control
group. It is necessary to control for initial levels of
the outcome variable because otherwise the analy-
ses are not providing a test of differential change
over time across conditions (Stice, Shaw, & Marti,
2006). Characteristics of the individual studies
included in our meta-analysis are available from the
first author.
Coding the studies
We developed a coding frame, which we pilot tested,
revised and applied to the primary studies. Coding
was performed by both authors independently.
Disagreements were resolved by discussion and
by further examining the studies until 100 percent
agreement was reached (Orwin, 1994). All the
coded characteristics were used as descriptions of
the studies retrieved and as potential moderator
variables (Rosenthal, 1995), in addition to the main
moderators examined. We coded certain moderators
two ways in an effort to ensure that we were not
missing the effects of a moderator, because we did
not operationalize it optimally. A priori lists of out-
come measures to select when multiple measures
were present in a study were developed to minimize
the impact of coder bias on the selection process.
Participant features We coded the following
participant features: age; gender; ethnicity; body
composition; and pre-intervention fitness level. In
addition to coding the average age of participants at
baseline, we also coded for age category of elemen-
tary school, middle school, high school, university,
adults, and older adults. For ethnicity, we coded
both the percentage of participants who were
White (continuous variable), because this group is
at high risk for negative body image compared to non-
Whites, and the dominant ethnic group repre-
sented in the sample (nominal variable).
Exercise intervention features We coded the
exercise type (i.e. aerobic, resistance training, both),
duration (i.e. minutes per session), length (i.e. length
in weeks), frequency (i.e. number of sessions per
week) and intensity (i.e. strenuous, moderate, mild).
The intervention specificity variable was coded into
two categories: exercise only and exercise in addition
to another treatment. Finally, we coded whether
the experimental and control participants showed
improvements on physical fitness and body compo-
sition from pre- to post-intervention.
Design features We coded for the type of con-
trol group, recruitment method, publication year (as
a measure of recency), random assignment, type of
exercise intervention (i.e. exercise or lecture-based),
body image measure, and publication status (i.e. pub-
lished or unpublished). Studies that did not report
randomization or group assignment methodology
were assumed to have used nonrandomized protocol.
Effect size calculation and analytic
strategy
Effect size calculations Using random effects
modeling procedures, we calculated effect sizes by
subtracting the mean change for a control group
from the mean change for an experimental group
and divided this difference by the pooled standard
deviation of the pre-test score (Morris, 2008). We
adjusted all effect sizes using Hedges and Olkin’s
(1985) small size bias correction before entering
them into the analysis. When the Nat the pre-test
differed from the Nat the post-test, the smaller Nwas
used. If the results were available for subgroups (e.g.
men and women; high school and middle school),
effects were computed for the subgroups. This pro-
cedure enabled a more comprehensive examination
of the moderator variables of interest.
When precise mean and standard deviation data
were not reported, effect sizes were estimated from

CAMPBELL & HAUSENBLAS: EFFECTS OF EXERCISE INTERVENTIONS ON BODY IMAGE
783
Ftests, ttests, p-values, or figures. For studies in
which precise standard deviations were not reported,
the standard deviation was drawn from published
norms. We also estimated effect sizes when a report
contained inexactly described p-values—such as
when the authors indicated that a given finding was
not significant at .05. Along with the weighted aver-
age effect sizes, we computed the 95 percent confi-
dence intervals. If the confidence interval did not
include zero, then the mean effect size was statisti-
cally significant at p< .05. We also graphed a forest
plot (available upon request from the first author),
which is a graph of each study as a point estimate
bounded by its confidence intervals.
To determine heterogeneity of the effect sizes, we
computed the dispersion on the forest plot and cal-
culated both the Q-statistic, which tests the hypoth-
esis that the observed variance in effect sizes is no
greater than that expected by sampling error alone,
and I2, the variance (between-studies)/variance
(total), to quantify the heterogeneity (Higgins &
Thompson, 2002; Higgins, Thompson, Deeks, &
Altman, 2003). Under the null hypothesis that all
studies derive from the same population, Q follows
a chi-square distribution for d.f. = k, where k is the
number of outcomes minus one. The absence of a
significant p-value cannot be taken by itself as
evidence of homogeneity as it could reflect a low
power rather than actual consistency (Borenstein &
Rothstein, 1999). The I2quantifies the dispersion
and I2values of 25, 50 and 75 are considered low,
moderate and high, respectively. Thus, an I2in the
low range suggests that the effect sizes are homo-
geneous relative to the precision of the individual
studies (Higgins et al., 2003). For moderator analyses,
we used QB, with a mixed effects analysis, to explore
the impact of categorical variables on the effect size;
and we used meta-regression to explore the impact
of continuous variables on the effect size. Data were
analyzed using SPSS-15 and Comprehensive Meta-
analysis-2 (BioStat, Englewood, New Jersey).
Dependence A fundamental assumption of
most analyses used in meta-analysis is the indepen-
dence of effects. When multiple outcomes are mea-
sured for the same individuals, or when the same
outcome is measured at several time points for the
same individuals, effect sizes computed for those
multiple outcomes or time points will not be indepen-
dent.A number of ways exist to deal with dependence
(Becker, 2000). The primary approach used in our
synthesis was to separate effects into groups that
included primarily independent effect. For example,
some studies had two control groups (i.e. waitlist
control and placebo control). In these instances we
coded for both of the control groups. It was com-
mon for studies to include multiple assessments of
body image. In these instances, each independent
group was limited to one body image effect size.
The removal order was first unstandardized ques-
tionnaires. In studies with two or more effect sizes
still remaining, the rule was to give priority to body
dissatisfaction measures (Groesz, Levine, & Murnen,
2002). Body dissatisfaction is one of the most con-
sistent and robust risk and maintenance factors for
eating pathology; thus, it was selected over other
body image measures. If, at this point, there were
still two or more remaining body dissatisfaction
standardized measures, random removal was con-
ducted until one effect remained per independent
group. If we did not know what the body image
measure assessed in particular, we retrieved the article
that contained the scale and examined the individual
items to determine what type of body image the scale
assessed. If studies reported on mid-experiment
data or follow-up data, we recorded the immediately
pre- and post-intervention scores only.
Publication/dissemination bias Publication
bias occurs whenever the research that appears in the
published literature is systematically unrepresentative
of the population of completed results (Rothstein,
Sutton, & Borenstein, 2005). To assess publication
bias, we undertook the following graphical and sta-
tistical methods: forest plots, funnel plots, Duval and
Tweedie’s Trim and Fill,Egger’s Test, and Fail
Safe N(Nfs) or file drawer analysis using both
Orwin’s (1983) and Rosenthal’s (1979) procedures.
Outliers Potential outliers were identified by
examining the effect sizes graphically (i.e. forest
plot) and then omitting one case at a time and
checking for large externally standardized residuals
or substantially reduced measures of heterogeneity
(Hedges & Olkin, 1985; a table containing this
information is available from the first author). The
identified outliers were examined to determine if
characteristics unique to the study may have pro-
duced the extreme scores. The effect of outliers is
often a notable increase in observed variance and a
distortion of the mean. When sample sizes are small
to moderate (the usual case), extreme values can
occur because of large sampling errors. Such values
are not true outliers and should not be eliminated

JOURNAL OF HEALTH PSYCHOLOGY 14(6)
784
from the data. Because of this, we did not remove any
outliers in our analyses (Hunter & Schmidt, 2004).
Results
Description of studies
The overall Mrandom effect size (ES) of 0.29
(SE = .04, CI = +.07; Q(97) = 207.03; I2= 52.66)
was small and revealed that exercise resulted in
improved body image from pre- to post-intervention
for the intervention group compared to a control
group. A total of 57 publications (Mstudy year =
1997, range = 1972–2007) with 98 separate com-
parisons were included in our meta-analysis (a list
of excluded studies is available from the first
author). Reasons for more than one effect size per
study were that studies reported results separate by
age (n= 6) and/or gender (n= 2), and studies with
either multiple control groups (n= 7) or exercise
groups (n= 15).
The average participant age was 30.04 (SD =
15.35, range = 10.02 to 63.40), with nine studies not
reporting the participant age. For age group cate-
gory, most participants were university students
(35.7%), followed by adults (26.5%), older adults
(12.2%), elementary (7.1%), middle (8.2%) and
high school (2.0%); with three studies not reporting
age group information. Most studies included
female populations (n= 31), followed by both gen-
ders (n= 22) and male populations (n= 4); one
study did not report gender information. Most stud-
ies were conducted in the United States (n= 38),
followed by United Kingdom (n= 4), Canada (n= 3),
Turkey (n= 3), Germany (n= 3), Australia (n= 2),
Norway (n= 2), Sweden (n= 1), and Switzerland
(n= 1). Most studies were disseminated as journal
articles (n= 49); the remainder were dissertations/
theses (n= 8). Only 36 studies described the ran-
domization process. Attrition was described in 31
studies, with a Mattrition of 22.69 percent (SD =
16.84, range = 0 to 67%). Only 18 studies reported
ethnicity, with White participants being the domi-
nant ethnic group in 15 studies, followed by African
American (n= 2) and Hispanic (n= 1). Participants’
body composition was described in 32 studies; with
18 studies consisting of normal weight participants
and 14 studies consisting of overweight/obese par-
ticipants. Finally, 28 studies described pre-interven-
tion fitness level, with 27 studies reporting sedentary/
low activity participants, followed by active partici-
pants (n= 1).
Publication bias
Rosenthal’s Nfs was 2981 indicating that we would
need to locate 2981 null studies for the combined
p-value to exceed .05. There would need to be 30
missing studies for every observed study for the
effect to be nullified. In comparison, Orwin’s Nfs was
131, revealing that we would need to locate 131
studies with a mean standard difference in Ms of 0
to bring the combined standard difference in Ms under
0.1. This large number of unpublished null trials led
us to conclude that our findings were unlikely to be
biased by the file-drawer problem.
Examination of the funnel plot (with the ES on
the X axis and the standard error on the Y axis)
revealed that the distribution of the ES showed a
pattern suggestive of publication bias. More specif-
ically, the bottom of the funnel plot showed a higher
concentration of studies to the right of the Mreflect-
ing that smaller studies were more likely to be pub-
lished if they had larger than average effects; which
makes them more likely to meet the criterion for
statistical significance. A statistical test of sym-
metry in the plot indicated significant asymmetry
(intercept = 1.55, + = 0.71, t(96) = 4.36, p< .001;
Egger, Smith, Schneider, & Minder, 1997). The Duval
and Tweedie’s Trim and Fill procedure pointed
to the possibility of some publication bias. More
specifically, this method suggested 30 studies were
missing and that the point estimate and 95 percent
confidence interval using the Trim and Fill imputed
point estimate was 0.12 (+.08). In summary, although
these tests provide some evidence of potential pub-
lication bias in the data, there are other sources of
asymmetry in funnels plots. Among those is true
heterogeneity in the effects (e.g. caused by gender
and/or age differences across studies). Thus, further
analyses investigating the sources of heterogeneity
via moderator analyses were warranted.
Moderator analyses
Participant features Although a larger ES was
evidenced for female populations (M ES = 0.32)
compared to male populations (MES= 0.19), the
ES difference was not statistically significant, QB(1)
= 1.11, p= .29 (see Table 1). Mean age moderated
the size of the effect, with larger ESs evidenced
for older participants (z= 3.36, p< .001). For age
category, because of the small number of studies
examining elementary (n= 3 studies representing
7ES), middle (n= 5 studies representing 8 ES) and
high school (n= 2 studies representing 2 ES), we com-
bined these groups for the age category comparison.

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785
Thus, we compared youth (elementary, middle and
high school), with university, adult and older adults
(i.e. age 50 and older). We found a significant age
category difference, QB(3) = 9.37, p= .02. Examination
of the M ES scores indicated that the largest effects
were evidenced for adults (M ES = 0.44) and older
adults (M ES = 0.33) compared to university (M ES =
0.22) and high school/middle/elementary school
students (M ES = 0.16). Only 18 studies (ES = 29)
reported the participants’ethnicity. We found that the
percentage of White participants did not moderate
the size of the effect: z= –0.17, p= .86. For body
composition, we found a nonsignificant larger effect
for overweight/obese participants (ES M = 0.34)
compared to normal weight participants (M ES = 0.18),
QB(1) = 3.24, p= .07.
Design features We found a negative relation-
ship between publication year and the size of the
effect (z= –3.65, p< .001). A significantly larger
effect was found for exercise-based interventions
compared to lecture-based or combined lecture- and
Table 1. Descriptive information for the moderator analyses
Moderator M ES SE
±
95% CI N ES p-value
Age
Elementary 0.006 0.07 0.14 7 .93
Middle 0.24 0.16 0.32 8 .15
High school 0.49 0.28 0.55 2 .09
University 0.22 0.06 0.13 35 .001
Adults 0.44 0.06 0.21 27 *
Older Adults 0.33 0.10 0.19 12 0.001
Combined elementary, middle 0.16 0.08 0.11 17 0.06
and high school
Gender
Male 0.19 0.09 0.17 12 .04
Female 0.32 0.05 0.10 56 *
Body composition
Normal 0.18 0.05 0.10 30 .001
Overweight 0.34 0.07 0.13 24 *
Mode
Aerobic 0.30 0.06 0.11 52 *
Resistance 0.37 0.06 0.13 18 *
Both 0.26 0.07 0.14 22 *
Improved in fitness
Yes 0.34 0.06 0.12 38 *
No 0.34 0.05 0.10 14 *
Specificity of exercise
Specific 0.36 0.05 0.10 62 *
Nonspecific 0.14 0.05 0.10 33 .007
Met exercise guidelines
Yes 0.30 0.11 0.22 14 .008
No 0.36 0.04 0.09 57 *
Type of control group
No treatment 0.37 0.05 0.10 50 *
Placebo 0.23 0.06 0.08 14 *
Publication status
Published 0.29 0.04 0.09 85 *
Unpublished 0.34 0.08 0.15 14 *
Exercise intervention
Exercise-based 0.37 0.05 0.09 70 *
Lecture-based 0.12 0.09 0.18 6 0.17
Both 0.12 0.06 0.12 23 0.06
*p< .001

JOURNAL OF HEALTH PSYCHOLOGY 14(6)
786
exercise-based interventions: QB(2) = 12.12, p= .002.
The type of control group (i.e. no treatment or placebo
control) did not moderate the size of the effect:
QB(1) = 2.89, p= .08. No difference in the size of the
effect was evidenced for those who improved on either
fitness or body composition from pre- to post-inter-
vention compared to those who did not improve on
fitness (QB(1) = 0.001, p= .97) or body composition
(QB(1) = 0.12, p= .73) from pre- to post-intervention.
Finally, no significant ES difference was evidenced
for published versus unpublished research: QB(1) =
0.25, p= .61. Most of the studies used a validated
body image measure (n= 54), the most common body
image measure being the Body Dissatisfaction sub-
scale of the Eating Disorder Inventory (n= 10), fol-
lowed by the Body Cathexis Scale (n= 9). Because
most studies used a validated body image measure,
we were unable to assess the moderating effect of
standardized versus unstandardized measures.
Exercise intervention features No significant
difference in the size of the effect was found for
exercise interventions that met exercise guidelines
versus interventions that did not meet the guidelines:
QB(1) = 0.23, p= .63. With regard to the dose-
response for exercise, we found that exercise duration
(Mduration = 49.09 min, range = 20 to 75 min; z= .75,
p= .45), length of intervention in weeks (Mlength
= 12.69, range = 4 to 52; z= –0.004, p= .99), and
mode (i.e. aerobic, resistance or both) did not mod-
erate the size of the effect: QB(2) = 1.47, p= .47.
Frequency per week of exercise (Mfrequency = 2.81,
range = 1 to 5), however, did moderate the size of
the effect, with larger effects evidenced for interven-
tions of higher frequency per week (z= 2.50, p= .01).
Only four studies examined mild exercise. Therefore,
we did not include this category in the moderator
analyses. Thus, we compared moderate intensity to
strenuous intensity (excluding studies that combined
moderate and strenuous exercise). We found the exer-
cise intensity (moderate vs strenuous) did not mod-
erate the size of the effect: QB(1) = 0.41, p= .41.
Only 12 studies based the exercise intervention on
a theory, with the most common theories being the
Transtheoretical Model and the Exercise and Self-
Esteem Model. Exercise specificity was significant,
with larger effects for exercise-only interventions
compared to exercise-in-addition-to-another-treatment:
QB(1) = 9.55, p= .002. Finally, 36 studies had a mea-
sure of exercise/fitness, with 24 studies using objec-
tive measures, eight studies using self-report and four
studies using both objective and self-report measures.
Discussion
The main purpose of our review was to provide a
statistical summary of exercise intervention programs
and their effects on body image. We found a small
effect size indicating that exercise interventions
resulted in improvements in body image compared
to a control group. Given the heterogeneity in the
effects from these interventions, we examined partic-
ipant, design, and exercise intervention features that
may account for the heterogeneity.
First, with regard to participant moderators,
although we did not find significant gender differ-
ences, it is important to note that larger effects were
evidenced for female (M ES = 0.32) compared to
male (M ES = 0.19) populations. The nonsignificant
findings may be due to the large confidence intervals
and the small number of studies exclusively exam-
ining male populations. Across the lifespan, female
populations are at higher risk for negative body image
than male populations (Elgin & Pritchard, 2006;
Feingold & Mazzella, 1998). Thus, it is not surprising
that most studies included in our review focused on
female populations. Most females with body image
concerns are dissatisfied because they feel overweight
(Thompson et al., 1999). In contrast, the reasons
men give for body image concerns are more hetero-
geneous, with nearly half indicating they are weight
dissatisfied and want to gain weight (e.g. muscle
mass; McCabe & Ricciardelli, 2004). With the rise
in male body-image concerns, and the pressure for
men to achieve a ‘fit’ physique, further research is
needed examining the gender effects of exercise
interventions on body image, in particular with a focus
on resistance exercise.
With regard to age, we found larger effects for
older compared to younger populations. This age
effect may be due to adults reporting higher body
image concerns than younger populations. For
example, about 40 percent of elementary girls and
25 percent of elementary boys are dissatisfied with
their body (McCabe & Ricciardelli, 2004; Smolak,
2002). Body dissatisfaction continually increases
across adolescents for female and male populations,
with girls continuing to report higher negative body
image than boys (Eisenberg, Neumark-Sztainer, &
Paxton, 2006; Muth & Cash, 1997; Wang, Byrne,
Kenardy, & Hills, 2005). Body dissatisfaction, how-
ever, levels off and remains stable across the adult
life span for men and women (Kemmler, Whitworth,
& Biebl, 2006; McLaren & Kuh, 2004; Tiggemann,
2004), with over 60 percent of female adults and

older adults (aged 60–70 years) reporting body
dissatisfaction (Garner, 1997; Mangweth-Matzek
et al., 2006). Further research is needed examining
the effects of exercise on body image in youth con-
sidering the high percent that report body image
concerns coupled with the increase in obesity.
For body composition, we found a nonsignificant
larger effect for participants who were overweight/
obese at the beginning of the intervention compared
to normal weight participants. Negative body image
arises primarily from sociocultural pressures to be
thin and physical deviation for the current thin-ideal
espoused for women and the lean and muscular-
ideal espoused for men (Thompson et al., 1999).
Elevated adiposity is theorized to promote body
dissatisfaction because the current ideal physique
for men and women is lean. Thus, the greater the
degree of deviation from the current ideal physique,
the greater the ensuing body dissatisfaction (Stice
& Shaw, 2002). Of significance, overweight/obese
populations are more likely to have higher body-
image disturbance and benefit from weight reduction
during an exercise intervention compared to normal
weight populations (Franklin, Denyer, Steinbeck,
Caterson, & Hill, 2006; Hrabosky et al., 2006).
We found that ethnicity did not moderate the size
of the effect. Meta-analyses reveal that non-White
populations (particularly African Americans) have
more favorable body image compared to Whites
(Grabe & Hyde, 2006; Roberts, Cash, Feingold, &
Johnson, 2006), suggesting that programs targeting
Whites might be more effective because there is
greater opportunity for intervention effects.
Finally, for participant moderators, although a
finer-grained examination of some participant fea-
tures (e.g. risk status, fitness level) would have been
desirable, the available data were not amenable to
such analyses. Our review presents descriptive data
for these variables because many studies did not
report this information, and there were too few stud-
ies in the literature for a powerful test of numerous
moderators (Hedges & Pigott, 2004). For example,
effect sizes from studies in which the target popula-
tion was representative of a particular clinical popu-
lation were coded as clinical, and others were coded
as nonclinical. The relatively small number of stud-
ies examining clinical populations did not allow a
careful examination of each subgroup, so the clinical
group was far from a homogeneous category.
Second, for design moderators, exercise specificity
moderated the size of the effect, with larger effects
evidenced for specific compared to non-specific
interventions. Interventions are specific when
exercise is the sole intervention and they are non-
specific when another therapy (e.g. drug therapy,
cognitive-behavioral therapy) is added to the exer-
cise training. The independent effect of exercise on
body image is unknown when another therapy is
added. When an exercise intervention was completed
in conjunction with a second therapy, only a few
investigators included an exercise-only comparison
condition. Consequently, it is unclear whether there
is an additive effect on body image when a second
therapy is added to an exercise intervention. Puetz,
O’Connor and Dishman (2006) in their meta-analysis
of the effects of exercise interventions on feelings
of energy and fatigue found that the effect varied
according to the presence or absence of a placebo
control or whether exercise was completed alone or
in combination with another therapy. However, they
did not report the effects independently for inter-
vention specificity.
In contrast to the extant literature, we found
that publication status (i.e. unpublished versus pub-
lished) was not related to the intervention effect size
(Hopewell, McDonald, Clarke, & Egger, 2007).
Including unpublished studies (also known as gray
literature) allowed us to include a richer variety of
studies (Conn, Valentine, Cooper, & Rantz, 2003).
Typically, meta-analyses that only include published
studies are more likely to overestimate the magnitude
of the true population effect, because the biggest dif-
ference between published and unpublished research
is the statistical significance of the results (Hopewell
et al., 2007). Publication is an inadequate proxy for
study quality, in part because authors often do not
submit studies unless they have statistically signif-
icant treatment effects (Conn & Rantz, 2003).
The type of control group did not moderate the
size of the effect. Variations in control conditions
have made it difficult to interpret the intervention
literature on exercise and body image. Controls have
ranged from no-treatment controls (e.g. waitlist con-
trols, assessment only controls) to placebo control
conditions that involved participants in physical
or cognitive activities. Placebo controls (e.g. health
education classes) usually are structurally matched
to the intervention in terms of contact hours, and they
often contain features included in exercise programs
that may be therapeutic, such as opportunities for
social interaction. Theoretically, the no-treatment
controls will produce larger effect sizes than the
placebo controls when they are compared with
the intervention because the placebo control groups
CAMPBELL & HAUSENBLAS: EFFECTS OF EXERCISE INTERVENTIONS ON BODY IMAGE
787

more effectively control for demand characteristics,
participant expectancies and other nonspecific fac-
tors that contribute to intervention effects (Stice &
Shaw, 2004). Because there is no consensus as to the
most appropriate control conditions for interpreting
data from exercise interventions, there is a need to
determine whether body image is influenced by
the type of control conditions selected (Puetz et al.,
2006). Although we found the control group did not
moderate the size of the effect, no-treatment controls
had larger effect sizes than placebo controls.
We were unable to examine the moderating effect
of body image measure because most of the studies
used a validated body image measure. Interventions
that use psychometrically sound measures should
be better positioned to detect intervention effects
that do occur. In support of this, larger effects were
found for validated than unvalidated measures in
eating disorder prevention trials (Stice & Shaw, 2004).
Third, for exercise intervention moderators, we
found that fitness and body composition level
improvements did not moderate the size of the
effect. That is, the size of the effect did not differ for
participants whose fitness level and body composi-
tion improved from pre- to post-intervention versus
participants whose fitness level and body composi-
tion did not improve from pre- to post-intervention.
To draw conclusions, the effects of systematic exer-
cise interventions on both fitness and body image
need to be examined. Furthermore, general findings
across the exercise and psychological well-being
literature indicate that improvements in physical
status—such as cardiovascular fitness, strength and
weight or fat loss—are not consistently related to
changes in psychological well-being. There is evi-
dence from several studies that fitness change is not
necessary for enhanced body image (Fox, 2000;
Martin & Lichtenberger, 2002). This parallels the
obesity treatment literature where amount of weight
lost is not consistently reflected in the psychological
benefits. Perceptions of health, physical competences,
fitness and body image may arise because there is a
feeling that the body is improving through exercise.
Although the exercise dose-response is well
established for the physical health benefits of phys-
ical activity (ACSM, 2000); the dose-response of
exercise needed to obtain the psychological benefits
of exercise is controversial. We did not find a mod-
erating effect for exercise duration, intensity, mode,
or length. We did, however, find that exercise fre-
quency moderated the size of the effect, with greater
exercise frequency per week resulting in larger effect
sizes. Also, we found that exercise interventions that
met theACSM exercise guidelines did not differ from
exercise interventions that failed to meet the guide-
lines. This finding is in contrast to our expectations
that exercise interventions that meet the physical
activity guidelines would result in larger effects than
interventions that did not meet the guidelines. Further
research into this issue is much needed, and should
be systematically examined in future intervention
studies. Finally, because only a few studies have
conducted follow-ups, it is uncertain whether fitness
interventions produce lasting body image change.
One important next step is the need to better
understand which biological, psychological, and
social aspects of exercise contribute to improved
body image. For example, exercise interventions
often involve substantial social interaction. Thus, if
the effect of exercise on body image is to be under-
stood, it is critical to investigate or control for vari-
ables that are independent of exercise itself, such as
social interaction (Puetz et al., 2006). If the factors
that cause body image to change can be identified,
then exercise interventions can be designed to be
more effective by targeting these mechanisms. It
is most likely that objective physical changes (e.g.
weight loss) are just one set of variables that can
influence body image. Indeed, Cash’s (2002) cog-
nitive-behavioral model of body image identifies
physical characteristics as one of four developmen-
tal influences on body image (in addition to cultural
socialization, interpersonal experiences, and per-
sonality variables). Of relevance to understanding
the effects of exercise, Cash noted that changes in
body weight, muscularity, and physical competence
can all influence body image. Another set of vari-
ables that might explain the effects of exercise on
body image are changes in people’s perceptions of
their physical characteristics. For example, strength
training can make exercisers feel stronger, thinner,
and more toned. These perceived physical changes
may elicit improvements in body image indepen-
dent of objective physical changes (Martin &
Lichenberger, 2002). In support of this, Martin
Ginis, Eng, Arbour, Hartman, and Phillips (2005)
found that body image improvements were related
to subjective physical changes in participants of a 12-
week strength-training program. In short, although
there is sound evidence that exercise produces posi-
tive changes in well-being through improved phys-
ical self-perceptions, the question still remains as
to the main mechanisms underpinning such change.
For the fine-tuning of intervention design, it is
JOURNAL OF HEALTH PSYCHOLOGY 14(6)
788

important not only for mechanisms to be deter-
mined but also for the conditions under which
they optimally function to be identified.
Limitations of the extant literature include the
fact that many studies did not adequately prescribe,
monitor, and control the frequency, duration and
intensity of the exercise to ensure fitness gains.
Despite this limitation it is still possible to examine
the psychological effects of participation in an exer-
cise program independent of changes in fitness.
However, it does not permit the examination of the
psychological effects of increased fitness. Similarly,
researchers have not adequately described the phys-
iological measures used to assess change in fitness
and often assumed that participation in an exercise
program was synonymous with increased fitness.
Finally, while many studies demonstrated the effects
of exercise on body image over limited time periods,
few studies have been designed to examine the long-
term intervention phase of a study in a nonclinical
population. The question as to whether the effects of
exercise are transitory or long term remains virtually
unexplored.
Although we attempted to exhaustively identify
eligible studies through our search strategies, limi-
tations do exist. For example, our computer-based
searches were limited to PubMED, PsychINFO and
Dissertation Abstracts International. Because none of
the electronic databases include all published studies,
searching multiple databases is recommended. For
example, searching EMBASE can possibly add up
to 30 percent more references, mainly from European
journals that are not indexed on PubMED (Khoshdel,
Attia, & Carney, 2006). However, omission of
EMBASE electronic database references does not
appear to bias the results of meta-analysis, but only
reduces precision (Suarez-Almazor, Belseck, Homik,
Dorgan, & Ramos-Remus, 2000).
In summary, exercise represents a practical and
widely accessible intervention for negative body
image. A meta-analysis of stand-alone cognitive-
behavioral therapy for body image revealed large
effect sizes (Jarry & Ip, 2005). Although, we found
small effect sizes for the effects of exercise on body
image, exercise has advantages over other types of
therapy, such as cognitive-behavioral therapy. For
example, exercise has the ability to reach and benefit
large audiences. Other practical advantages of exer-
cise are that, compared to other interventions, exercise
has a low cost, negligible negative side-effects, and
is a socially acceptable behavior; which may result
in greater treatment acceptance. Finally, exercise is
self-sustaining because it can be maintained once
the basic skills are learnt. Further research is needed
comparing exercise to other body image interventions
in randomized controlled trials.
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ANNA CAMPBELL is a Master’s student at the
University of Florida.
HEATHER HAUSENBLAS is an Associate Professor
and Director of the Exercise Psychology Lab at the
University of Florida.
Author biographies