Journal of Physical Activity and Health, 2011, 8, 210 -219
© 2011 Human Kinetics, Inc.
Influences of Social Support, Perceived Barriers,
and Negative Meanings of Physical Activity
on Physical Activity in Middle School Students
Ya-Wen Hsu, Chih-Ping Chou, Selena T. Nguyen-Rodriguez, Arianna D. McClain,
Britni R. Belcher, and Donna Spruijt-Metz
Background: A profound decline in physical activity occurs in puberty. This phenomenon is not well understood. Therefore, the purpose of this study is to examine associations between family/friend social support
for physical activity, negative meanings of physical activity (NMPA), and internal /external barriers to physical activity with moderate to vigorous physical activity (MVPA), and sedentary and light behavior (SLB) in
youth. Methods: A total of 350 participants from 7 Los Angeles County middle schools participated in the
study (62% Latina, 79% females). Hypothesized pathways were examined using structural equation modeling.
Psychosocial variables and participation in MVPA and SLB were assessed by self-reported questionnaires.
Results: NMPA were related to lower levels of family/friend social support and greater internal/external barriers. Family social support was the only significant indicator of MVPA (β = 0.79). Low family social support
was related to higher SLB (β = –0.25). Conclusions: Family social support seems crucial to promote MVPA
and reduce SLB in adolescents and might be influenced by child’s feelings about physical activity. Future
research should consider the interrelationship between psychosocial correlates of physical activity.
Keywords: sedentary behavior, youth, social support, barrier
Pediatric obesity has reached epidemic proportions
across the globe. To reduce obesity, both diet and physical
activity must be modified. Although increases in moderate
to vigorous activity (MVPA) and decreases in sedentary
behavior have been shown to reduce obesity in youth,1,2
recent data shows that only 34.7% of youths in the United
States met physical activity recommendations, whereas
24.9% of youths used computers ≥3 hours and 35.4%
watched television ≥3 hours during school days.3 In
fact, there is compelling evidence that physical activity
declines and sedentary behavior increases as children
progress through adolescence.1 Therefore, it is important
to identify modifiable determinants of both physical
activity and sedentary behavior that can be targeted to
develop successful behavioral interventions to prevent or
treat obesity in youth.
Although engaging in physical activity and being
sedentary may seem like 2 sides of the same coin, there
are many reasons to consider them separately.4 The
determinants of physical activity in youth have been
widely studied.5,6 In a review of 108 studies on correlates
of physical activity in children and adolescents,5 Sallis
et al found a considerable degree of inconsistency in
The authors are with the Dept of Preventive Medicine, University of Southern California, Alhambra, CA.
210
the research findings. They concluded that additional
studies were needed to clarify relationships between
existing constructs and physical activity as well as to
identify potential new determinants that could be targeted
to effectively change activity levels in youth. Although
associations between sedentary and light behavior
(SLB) and weight status are more consistent than those
between physical activity and weight status, much less
is known about sedentary and light behavior (SLB). 7
SLB is tightly related not only to obesity,8 but also to
other adverse health outcomes, such as depression9 and
metabolic syndrome.10 This is an increasingly important
issue considering the fact that youth spend the majority
of their day in SLB.11 Accordingly, we studied 2 of the
most commonly assessed psychosocial correlates of
physical activity—social support and perceived barriers.5,12 The current study expanded on existing research
examining correlates associated with physical activity by
adding a potential new correlate—negative meanings of
physical activity (NMPA). Because there is a distinct lack
of research on correlates of SLB, we also explored the
relationships between the aforementioned 3 psychosocial
correlates and SLB.
Social support examines the reciprocal influences
between social and environmental factors on health
behaviors.13 This is particularly important for adolescents
Physical Activity and Psychosocial Correlates 211
because they are at a transitional stage where both family
and peers have a strong influence on their health behaviors.5,14–16 Family social support has been consistently
found to be positively related to physical activity in youth,
however, the findings on peer social support for physical
activity have been inconsistent.5,17
Perceived barriers, a key component of the Health
Belief Model, are obstacles that people experience when
engaging in preventive health practices, and are specific
to that health practice.18 Perceived barriers to physical
activity have been the most consistent negative correlate
of physical activity in children; however in adolescents,
the relationship has been less consistently found.12 In adolescents, some studies have shown that perceived barriers
are inversely associated with physical activity levels19–22
while others have found no significant associations.23,24
Another problem is that very few studies distinguished
specific types of perceived barriers to physical activity. Thus, our study differentiated between internal and
external barriers to physical activity.25
NMPA, a construct derived from the Theory of
Meanings of Behavior (TMB), is a potential new psychosocial correlate of activity levels. The TMB was
developed to supplement existing cognitive behavioral
models by advancing understanding of how affect impacts
health related behavior in adolescents.26,27 According to
TMB, adolescents infuse health-related behaviors with
affective meanings which are related to personal feelings
and experiences.27,28 Because research suggested that
adolescents tend to be less cognitively and more emotionally driven,27,29 TMB posits that meanings of behavior
may bypass cognition to trigger behavior.26,27 NMPA
was developed to understand the negative personal and
primarily emotionally driven factors related to low levels
of physical activity in youth. Research has distinguished
NMPA,30 an affective construct, from perceived barriers
which are conceptualized as more cognitive in nature.
Meanings of behavior have been shown to be predictive of
smoking and eating behaviors.31,32 However, research on
the influence of the NMPA on physical activity is limited.
According to TMB, NMPA may be key to understanding
the personal and primarily emotionally-driven-reasons
that deter youth from engaging in physical activity.
To date, the majority of research in adolescents has
studied the direct effects of the psychosocial determinants
on physical activity and sedentary behaviors. Little is
known about how these determinants may interact to
influence activity levels. The synergy between these
determinants may have a different impact on physical
activity than any one of them alone. Therefore, the purpose of the current study is two-fold: 1) to explore the
interrelationships among theory-based psychological and
social factors that have been shown to be related to MVPA
and SLB, and 2) to examine their synergistic relationships
to MVPA and SLB in adolescents. We hypothesize that
higher levels of NMPA will be related to lower levels
of social support, greater perceived barriers. We also
hypothesize that higher levels of social support, lower
levels of perceived barriers, and lower levels of NMPA
will be related to more MVPA and less SLB.
Methods
School Selection Procedure
The cross-sectional data presented here comes from
baseline data from 666 students from 7 public and private
middle schools in Los Angeles County who participated
in a physical activity intervention.8 A questionnaire that
assessed psychological and psychosocial determinants of
physical activity was completed in class. The recruitment
rate was 85% (666/783) based on active or implied parental consent and assent provided by students. A detailed
description of school selection and recruitment has been
documented elsewhere.8,33 This study was approved
by the University of Southern California Institutional
Review Board.
Measures
Demographics. Age at the time of measurement in
whole years was obtained by self-report. Ethnic background was assessed using Phinney’s ethnic identity
scale.34 Participants were classified into 1 of 5 ethnic
groups: Latino, Asian, Multiethnic, White, and Other.
Height, Weight, and BMI Percentile. Body weight and
height were measured with the Tanita TBF 300/A analyzer and with a Seca Mobile Height Rod, respectively.
Body mass index (BMI) and age- and gender-specific
BMI percentile were calculated based on the Centers for
Disease Control’s SPSS syntax.35 Categories for BMI
percentiles in children are <5th percentile (underweight),
5th through 84th percentile (normal weight), 85th through
94th percentile (overweight), and 95th percentile and
above (obese).
Social Support for Physical Activity. Ten items from
the scale developed by Sallis et al36 were used to measure
perceived social support specific to physical activity.
These 10 items asked participants to specify how often
friends or family exhibited support for exercise during
the past 3 months. Support from family and friends were
assessed separately. Responses were coded on a 5-point
Likert scale ranging from 1 (none) to 5 (very often) with
higher scores indicating greater social support for physical activity. Internal consistency for the social support
by family subscale (α = 0.90) and the social support by
friend subscale (α = 0.89) were acceptable.
Perceived Barriers to Physical Activity. Perceived
barriers to physical activity were assessed by a 16-item
scale.25 Participants scored the degree to which the 16
listed barriers interfered with physical activity along
a 5-point Likert scale, ranging from 1 (not at all) to 5
212 Hsu et al
(a great deal). Two factors were obtained: internal barriers
and external barriers. Internal consistency was adequate
(α = 0.81 for perceived internal barriers, and α = 0.73
for perceived external barriers).
Negative Meanings of Physical Activity (NMPA). NMPA
were measured by an 8-item scale.30 Psychometric properties and factor structure of the NMPA scale were validated across 2 samples of minority girls (N = 299 (39%
Hispanic) and N = 361 (70% Hispanic), respectively)
aged 11 to 15. Three factors emerged (social, personal,
and functional) with acceptable internal consistency
(none lower than α = 0.83). These 3 factors, social,
personal, and functional meanings, were significantly
related to low activity and sedentary behavior (P < .01).
This instrument employed a 4-point Likert scale response
format ranging from 0 (Never) to 4 (Always). Respondents to the scale indicated the extent of their agreement
with each item. Higher scores suggested greater NMPA.
Internal consistency was acceptable in our study sample
(α = 0.83).
Physical Activity. A modified previous day physical
activity recall (PDPAR) was used to assess physical
activity and sedentary behavior.37,38 Students identified
different activities (from a list of 55 activities provided)
to describe their activity in half-hour intervals during a
day from 7:00 AM to 12:00 AM, and rated how much
effort (intensity level) they put into each activity (light,
moderate, hard or very hard). Activity types were converted into half-hour blocks of either light, moderate, or
vigorous physical activity using a combination of the
intensity ratings provided by the participants and the
compendium of physical activities.39 Based on Metabolic Equivalent (MET) levels obtained, each 30-minute
block was assigned a rate of relative energy expenditure
according to the equation provided by Weston et al.37
The cut-offs for light, moderate, and vigorous physical
activities were <3 METs, 3 to 6 METs, and >6 METs,
respectively. Half-hour blocks spent watching TV, playing
video games, surfing the internet, and watching movies
were coded separately as sedentary behavior.
Statistical Analysis
For structural equation modeling (SEM), there is a tradeoff between model size (refers to number of the parameters of the proposed model) and the power of the results
(which is dependent on the sample size).40,41 Bentler and
Chou42 have suggested the ratio of minimum sample size
to estimated parameters to be 5:1. Therefore, to obtain
sufficient power given our sample size, we used factor
analyses to identify the most parsimonious measures
for social support scales and perceived barrier scales.
Exploratory factor analyses (principle component) were
first used to identify the more reliable items with the
highest loadings. Based on the original scale constructions,25,36 we constrained each scale to 1 factor, thus no
rotations were performed. Items with factor loadings <0.6
were excluded.43 These analyses elicited 4 items for each
of the social support scales, and 3 items for each of the
perceived barriers scales. The sample size to estimated
parameter ratio in our SEM model meets the minimum
suggested ratio (5:1) and thereby provides sufficient
confidence in the results. Detailed descriptions of the
included items for each measure are listed in Table 1.
These identified items served as the indicator variables
for each original scale (proposed latent factor) in the following: t tests, confirmatory factor analysis (CFA) and
SEM. Independent-samples t tests were used to examine
gender differences in physical activity level and psychosocial variables. Analyses were conducted using SAS 9.1
software (Cary, NC).
For the CFA and SEM presented here, moderate
physical activity and vigorous physical activity were
combined to obtain the MVPA latent factor. Sedentary
behavior and light intensity were combined to obtain the
SLB latent factor. CFA was performed separately for
MVPA and SLB, to confirm the adequacy of indicator
variables used to represent the proposed latent factors
(family/friend social support, internal/external barriers,
NMPA, MVPA, and SLB). Once the CFA verified the
presence of 7 distinct constructs of the hypothesized
model, causal pathways among physical activity-related
psychosocial variables and their effects on MVPA and
SLB were derived simultaneously through SEM analyses, using maximum likelihood estimators. One distinct
advantage of using the latent factor to represent each
original scale is that it takes into account measurement
errors, thus, the parameter estimates using these factors
will often be more accurate and stronger than models
using only observed variables.44
SEM was then applied to clarify the associations
among physical activity-related psychosocial variables,
MVPA and SLB. Hypothesized pathways were first evaluated for MVPA. The proposed SEM model for MVPA
was later tested to examine whether the same model
could be applied to SLB. Indicators for MVPA and SLB
were log transformed to achieve normal distributions.
Relationships between variables were measured using
regression coefficients (β), adjusted for age, gender, and
weight status. Model parameters were standardized. The
significance of all study findings was evaluated at the P <
.05 level. CFA and SEM analyses were conducted using
EQS 6.1 software.45
Model Fit. The adequacy of the different competing
models was assessed using multiple indices. We relied
on Chi-square statistic, Chi-square statistic to degrees
of freedom ratio (χ2/df), Comparative Fit Index (CFI),
Root Mean Square Error of Approximation (RMSEA),
and Goodness of Fit Index (GFI) to evaluate the modeldata fit. We interpreted χ2/df ratio < 2 as a good fit.26
The overall fit of the SEM model was assessed using the
CFI and GFI.46 A CFI and GFI value of 0.90 and 0.95
indicated minimally acceptable and good fit and very
good fit of the model, respectively.47,48 A RMSEA value
< 0.05 suggested a close fit of the model.49
Model Specification. In the basic model, the intercorrelations among 5 psychosocial latent factors and
pathways between factors were specified. Correlated
Physical Activity and Psychosocial Correlates 213
Table 1 Items Assessing Psychosocial Variables, Moderate to Vigorous Physical Activity,
and Sedentary and Light Behavior
No. of
variables
Latent factor
Family social support for PA
4
Description
Change schedule so we could do physical activity together (schedule)
Give rewards for being active (reward)
Helped plan events around my physical activity (events)
Asked me ideas for being more active (ideas)
Friend social support for PA
4
Offer to do physical activity with me (do PA)
Give me encouragement to stick with activity program (encourage)
Change schedule so we could do physical activity together (schedule)
Discuss physical activity with me (discuss PA)
Internal barriers to PA
3
Lack self-discipline or will power (lack self-discipline/will power)
Long-term illness, disability, or injury (illness/disability/injury)
Feeling stressed (stressed)
External barriers to PA
3
Lack of support from family (lack family SS)
Lack of support from friends (lack friend SS)
Lack of time due to family responsibility (family responsibility)
Negative meaning of PA
3
Social meaning of physical activity (social)
Personal meaning of physical activity (personal)
Functional meaning of physical activity (functional)
Moderate to vigorous PA
2
Minutes of vigorous physical activity (vigorous PA) a
Sedentary and light behavior
2
Minutes of moderate physical activity (moderate PA) a
Minutes of light physical activity (light PA) a
Minutes of sedentary behaviors (SB) a
Abbreviations: SS = social support, PA = Physical Activity, SB = sedentary behavior, SLB = sedentary and light behavior.
a Log-transformed values were used.
error terms were found between family social support
and friend social support, and between external barriers
and internal barriers.
Results
Sample Characteristics
Among the 666 seventh- and eighth-grade students who
responded to the questionnaires, complete data were
available for 350 students (mean age: 12.55 ± 0.65,
61.71% Latina, 78.86% female). There were no statistical differences between participants with and without
complete data on demographic characteristics, including ethnic distribution and BMI. However, there were
significant differences between groups in age (P = .029)
and gender (P < .001). Compared with those without
complete data, those with complete data were more likely
to be older and female (data not shown). A summary of
descriptive statistics of demographic characteristics is
shown in Table 2.
Table 3 shows descriptive statistics of psychosocial
variables and physical activity levels. Adolescents in our
study spent 86.2% and 10.7% of their time engaging in
light physical activity and in sedentary behavior, while
they only spent 4% and 9.8% of their time in vigorous and
214 Hsu et al
in moderate physical activity. Girls reported significantly
higher perceived internal barriers (P = .001), personal
NMPA (P < .001), and functional NMPA (P = .014) than
boys. Compared with boys, girls reported significantly
less total minutes spent in MVPA (P = .027). Interestingly, girls reported significantly fewer total minutes
spent in sedentary behavior than boys (P = .005). There
are no significant differences in physical activity level and
psychosocial variables between overweight adolescents
and nonoverweight adolescents when further stratified
by gender (data not shown).
Table 2 Characteristics of the Sample (N = 350)
Variable
Frequency (%)
Mean age (year)
12.55 (SD = 0.65)
Gender
Female
276 (78.86%)
Male
74 (21.14%)
Ethnicity
Asian/Pacific Islander
61 (17.43%)
Latino
216 (61.71%)
Multiethnic
36 (10.29%)
Other
25 (7.14%)
White
12 (3.43%)
School grade
7th grade
168 (48.00%)
8th grade
182 (52.00%)
Weight status
Underweight
13 (3.71%)
Healthy weight
196 (56.00%)
Overweight
61 (17.43%)
Obese
80 (22.86%)
Table 3 Descriptive Statistics of Psychosocial Variables and Physical Activity (N = 350)
Variable
Mean (SD)
Range
T value for gender difference
(boys as reference group)
Family social support for physical activitya
8.93 (4.21)
1–20
0.89
activityc
11.37(4.54)
1–20
–0.94
5.33 (2.06)
1–15
2.76†
7.08 (2.63)
1–15
0.85
4.58 (1.53)
1–12
0.75
Personal meanings
5.03 (1.58)
1–12
3.31†
Functional meanings
3.58 (1.38)
1–8
2.48*
40.97 (63.57)
1–1020
–2.24*
Friend social support for physical
Internal barriers to physical activityc
External barriers to physical
activityd
Negative meanings of physical activity
Social meanings
Physical activity (minutes)
Vigorous
Moderate
99.69 (100.98)
1–1020
0.03
Light
879.34 (120.44)
1–1020
1.44
Sedentary behaviors
109.29 (91.69)
1–1020
–2.88†
*P < .05, † P < .01, ‡ P < .001 indicate the significance of gender difference.
a Sum score of the 4 selected variables from Table 1.
Sum score of the 4 selected variables from Table 1.
Sum score of the 3 selected variables from Table 1.
d Sum score of the 3 selected variables from Table 1.
b
c
Physical Activity and Psychosocial Correlates 215
Confirmatory Factor Analyses
Estimates of factor loadings indicated that all indicators
loaded significantly onto their respective latent factors.
This provides support for the adequacy of indicator variables used to represent the proposed latent factors (family/
friend social support, perceived internal/external barriers,
social/personal/functional NMPA, MVPA, and SLB). Fit
indices indicate that the proposed model fits the data well
and thus verified the presence of these distinct constructs
(CFI = 0.944 to 0.951, RMSEA = 0.040 to 0.043, GFI
= 0.937 to 0.941, χ2/df =1.57 to 1.65, χ2 = 214.494 to
225.93, df = 137, P < .001).
Structural Equation Modeling Analyses
Model Specification. To improve model fit, free
parameters were added following practical measurement considerations and the Lagrange Multiplier (LM)
test procedure of the EQS program.50 The final models
are presented in Figure 1 and Figure 2 for MVPA and
SLB, respectively. Three relationships not shown in the
figure were added in our final model to improve model
fit. These included: the paths from the “NMPA” factor
to the “friends offer to do physical activity with me”
indicator; from the “family social support” factor to the
“friends change schedule so we could do physical activity together” indicator; and from the “external barriers”
factor to the “lack self-discipline or will power” indicator.
Moderate to Vigorous Physical Activity. Although the
p value for the Chi-square statistic was significant (χ2
= 254.963, df = 178, P = .001), other model fit indices
suggest that the proposed model yielded a good modeldata fit (CFI = 0.952, GFI = 0.939, χ2/df =1.432, RMSEA
= 0.035, RMSEA 90% CI= 0.025 to 0.044). Figure 1
displays the direction and magnitude of the associations
in the final tested MVPA model for adolescents. NMPA
had a significant effect on the following: a negative effect
on family social support (β = –0.250), a negative effect
on friend social support (β = –0.186), a positive effect
on internal barriers (β = 0.580), and a positive effect on
external barriers (β = 0.295). Social support by family
was the only factor that was significantly related to participation in MVPA (β = 0.792). Family social support
was significantly and positively correlated to friend social
Figure 1 — Proposed model for moderate to vigorous physical activity in adolescents. Note: Figures represent the standardized
parameter estimates. Each parameter estimate has been adjusted for age, gender, and weight status. Solid lines represent significant
paths (P < .05); dashed lines represent nonsignificant paths. Abbreviations: SS = social support, PA = Physical Activity, MVPA =
moderate to vigorous physical activity.
216 Hsu et al
Figure 2 — Proposed model for sedentary and light behavior in adolescents. Note: Figures represent the standardized parameter
estimates. Each parameter estimate has been adjusted for age, gender, and weight status. Solid lines represent significant paths (P
< .05); dashed lines represent nonsignificant paths. Abbreviations: SS = social support, PA = Physical Activity, SB = sedentary
behavior, SLB = sedentary and light behavior.
support (γ = 0.536). Perceived internal barriers were significantly and positively correlated to perceived external
barriers (γ = 0.529).
Sedentary and Light Behavior. The model for MVPA
in adolescents was then tested with SLB. The model fit
for the SEM model for SLB was good. Although the
p-value for the Chi-square statistic was significant (χ2
= 240.536, df = 178, P = .001), other fit indices indicated
that the proposed model fit the data well (CFI = 0.961,
RMSEA = 0.032, GFI = 0.944, χ2/df =1.351, and RMSEA
90% CI= 0.020 to 0.041).
The direction and magnitude of the associations in
the final model for SLB are displayed in Figure 2. As
found in the SEM model for MVPA, NMPA had a negative small-to-moderate effect on family social support
(β = –0.252), a negative small effect on friend social
support (β = –0.185), a positive large effect on internal
barriers (β = 0.581), and a positive moderate effect on
external barriers (β = 0.292). In regards to the influences
of psychosocial variables on SLB, the only significant
association was found between family social support
and SLB (β = –0.248). Again, family social support
and friend social support were correlated (γ = 0.537), as
were perceived internal barriers and perceived external
barriers (γ = 0.527).
Discussion
Consistent with previous studies, we found that girls
spent less time engaging in MVPA than did boys.51,52
This gender difference might be due to differences in
motor skills, freedom to engage in activities independently outside the home, or changes in body composition
during puberty.53 In our sample, girls reported higher
levels of perceived internal barriers to physical activity
and more NMPA than boys. Because social values are
Physical Activity and Psychosocial Correlates 217
crucial determinants of girls’ physical activity levels,
greater perceived barriers and higher levels of NMPA
might explain why the girls in our sample had lower
levels of physical activity. However inconsistent with
prior research,54 we found that boys spent more time in
sedentary behavior than girls. This inconsistency might
be explained by cultural differences since our sample
was predominantly Latina and Asian girls, whose cultures emphasize the traditional expectation that females
share household responsibilities.55,56 Perhaps helping
with housework during leisure time minimizes girls’
time to engage in sedentary behavior. Furthermore, our
definition of sedentary behavior (watching TV, playing
video games, surfing the internet, and watching movies)
excluded certain sedentary behaviors that are common to
girls. Although girls and boys spend similar amounts of
time watching TV,51 boys tend to engage more often in
electronic recreations for leisure time activity54 while girls
tend to hang out with their friends in their bedrooms57
or talk on the phone. Exclusion of some major sedentary
activities that girls enjoy as part of the sedentary behavior construct might help explain why girls reported less
time in sedentary behavior. More research is needed to
explore gender differences in sedentary behavior among
minority adolescents.
This is the first study to describe the interrelationships among psychosocial factors (NMPA, social support,
and perceived barriers) of MVPA and SLB in adolescents.
The directions and magnitude of the associations from
NMPA to social support and perceived barriers were
approximately the same for MVPA and SLB. We found
that participants who reported more NMPA experienced
lower levels of both family and friend social support.
One possible explanation is that expressions of negative
feelings about physical activity may serve as a disincentive for family and friends to provide social support. We
also found that participants who reported more NMPA
experienced higher levels of perceived internal and
external barriers to physical activity. Negative affect
toward physical activity thus seems to go hand in hand
with negative cognitions about physical activity, implying additional obstacles to physical activity involvement.
The associations between NMPA, perceived barriers and
the receipt of social support provide important insights
that may inform future physical activity interventions.
Classroom-based interventions have been successfully
used to modify meanings of eating and nutritional practices from previous research.27 Although social support
and perceived barriers did not mediate the relationship
between NMPA and physical activity, our results suggest
that the affective pathway from NMPA to social support
and perceived barriers may influence physical activity/
inactivity among adolescents. Therefore, minimizing
NMPA may reduce perceived barriers to being active
and increase social support, which in turn could increase
participation in physical activity and reduce time spend
in SLB. However, due to the cross-sectional design of
the current study, longitudinal studies are needed to
determine causality.
Because previous research has often studied MVPA
and SLB as 2 separate constructs, we discuss them
separately here. In the MVPA model, family social
support was the only significant predictive indicator for
participation in MVPA, after adjusting for age, gender,
weight status, and all other psychosocial correlates. It has
been proposed that influences of peers increase during
adolescence,58 but few prior studies have compared the
roles of social support from both peers and family in
adolescent physical activity. The 2 studies14,17 examining
both sources of support behavior for youth in predominantly non-Hispanic White adolescents found that friend
support was an important predictor of physical activity,
while the effect of family support for physical activity was
not significant. The differences between our findings and
previous research may be due to the fact that our study
population consisted largely of Latino and Asian adolescents, and the nature and traditions of both cultures place
great emphasis on family functions and relationships.
Moreover, middle school students may not be old enough
to independently make decisions regarding their activity participation, as their involvement in activities still
largely relies on their parents’ approval and provision,
including the cost of, and transportation to sporting events
or team practices. It is reasonable to expect that family
social support would outweigh peer social support in our
study. Perceived barriers have previously been shown to
be important indicators for physical activity participation in youth,59,60 but the strength of this association has
varied across studies.5 In our study, perceived internal
barriers were related to fewer minutes of MVPA (data
not shown). However, this association was not significant
after controlling for other correlates. Further, we did not
find a significant association between perceived external
barriers and MVPA. Nonsignificant findings on perceived
barriers to physical activity in the current study might
also be explained by different cultural characteristics of
the study sample, since perceived barriers to physical
activity may differ depending on the population studied.61
This study is among the very few that assess the
associations between psychosocial correlates and sedentary behavior in addition to physical activity. We found
that higher levels of family social support were related
to lower levels of SLB while friend social support was
not related to SLB. These findings were consistent with
1 prior study,62 Springer et al found that neither a friend’s
encouragement nor his/her participation in physical
activity buffered against SLB while family participation
in physical activity did. Our finding that only family
social support was related to SLB suggests that support
from family may be more important for reducing SLB
in minority youth.
Limitations
Limitations to this study include reliance on self-report
measures. Future research should include objective measures of physical activity. The cross-sectional nature of
this study limits inferences regarding causality. Future
218 Hsu et al
longitudinal studies are needed to clarify associations
found here. In addition, this sample consisted of primarily
urban Latino and Asian youth, which might limit the generalizability of the findings. Finally, the PDPAR assessed
only 1 day of behavior, and the type of day (ie, weekday
or weekend) was unknown. In addition, PDPAR data
only measures the primary activity for each 30-minute
block, which might result in missing intermittent activity.54 This is important for adolescents, who tend to do
multiple things simultaneously,1 especially while using
media such as watching television or surfing the internet. As more interactive video games that encourage the
expenditure of energy are being marketed, the behaviors
that are described as physically inactive are also changing.
Future research needs to be more specific in assessments
of sedentary behavior.
Conclusion
The current study provides 3 important insights that may
inform future physical activity and sedentary behavior
interventions for adolescents. First, our findings support
the important role that psychosocial determinants play in
physical activity and inactivity independent of the individual’s weight status. Results indicated that interpersonal
influences (such as social support) on MVPA and SLB
were stronger than cognitive and affective factors (eg,
perceived barriers and NMPA). Family social support
was more important for both MVPA and SLB than friend
social support in this sample of primarily minority urban
youth. Second, we observed that NMPA was associated
with lower family/friend social support, and positively
associated with internal/external barriers. Identification
of the indirect effect from NMPA to MVPA and SLB via
social support suggested that a stronger emphasis could
be placed on minimizing youth’s negative and primarily
emotionally-driven determinants of physical activity.
Third, aside from the direct influences of psychosocial
correlates on activity levels, future research should also
consider the interrelationships between psychosocial correlates of physical activity among adolescents.
Acknowledgments
We would like to thank the project manager Dolly Yang and the
Get Moving team. We are most grateful to our study participants
and funders, without whom this research would not have been
possible. This research was funded by the National Institutes
for Health, National Institution of Diabetes and Digestive and
Kidney Diseases (NIDDK, Grant# KO1, DK 59293-01). This
work was also supported by the University of Southern California Center for Transdisciplinary Research on Energetics and
Cancer (NCI U54 CA 116848).
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