Journal of Gerontology: PSYCHOLOGICAL SCIENCES
2006, Vol. 61B, No. 5, P270–P277
Copyright 2006 by The Gerontological Society of America
Physical Activity and Functional Limitations in
Older Women: Influence of Self-Efficacy
Edward McAuley, James F. Konopack, Katherine S. Morris, Robert W. Motl,
Liang Hu, Shawna E. Doerksen, and Karl Rosengren
Department of Kinesiology, University of Illinois at Urbana-Champaign.
This study examined the role of self-efficacy and physical function performance in the relationship between
physical activity and functional limitations. Older women (age, M = 68.2 years) completed measures of physical
activity, self-efficacy, physical function performance, and functional limitations at the baseline of an ongoing study.
Analyses indicated that physical activity was associated with self-efficacy for exercise, efficacy for gait and balance,
and physical function performance. Both measures of efficacy and physical functional performance were
associated with functional limitations. Demographic and health status variables did not differentially influence
these relationships. Although cross-sectional in nature, our findings suggest that physical activity, self-efficacy, and
functional performance may all play a role in reducing functional limitations. Of particular relevance is the fact
that both physical activity and self-efficacy represent important, modifiable factors that can enhance function.
I
T is well established that functional limitations in older
adults are important risk factors for subsequent disability
and institutionalization (Fried & Guralnik, 1997). Functional
limitations are typically manifest as self-reported frequency in
restrictions or difficulty in walking, lifting, or carrying, and
rates of limitation in function appear to be exacerbated by
sedentary behavior (Rejeski, Brawley, & Haskell, 2003). In
a recent review of the role played by physical activity in the
disablement process, Keysor (2003) concluded that physical
activity can have a protective effect on functional limitations
in the disablement process. For example, engaging in even a
relatively small amount of activity (e.g., walking 1 mile in
a week) has been shown to result in a significant slowing of the
functional limitation trajectory over a 6-year period (Miller,
Rejeski, Reboussin, Ten Have, & Ettinger, 2000). Keysor and
Jette (2001) report that there is evidence, if somewhat
inconsistent, that being involved in physical activity programs
also results in improvements in important physical function
performance behaviors, such as walking speed, rising and
transferring from a chair, and climbing stairs. Thus, physical
function performance involves a quantitative assessment of the
behavioral act (e.g., walking speed), whereas functional limitations are perceived restrictions in the frequency of being able
to carry out these activities.
In past research, it has been common to use either physical
function performance measures or self-reported restrictions of
activities as indicators of functional limitations. Stewart (2003),
however, has argued that such an approach to measurement and
definition is too broad to understand potentially subtle influences of physical activity on functional limitations. For example, Jylha, Guralnik, Balfour, and Fried (2001) noted that
self-reported restrictions in walking (functional limitations) and
objectively measured maximal walking speed (physical function performance) were independently associated with health
status in a sample of older women. Stewart suggested that it
is necessary for researchers to consider these related, but
independent, constructs separately in the disablement process
(Verbrugge & Jette, 1994). She has proposed that physical
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function performance precedes functional limitations in this
process. Thus, physical activity would influence functional
limitations indirectly through physical function performance.
In her review of physical activity and the disablement
process, Keysor (2003) noted that one of the key challenges in
this area is to identify the underlying mechanisms implicated in
this relationship. Although physical function performance is
likely to play a role, other psychosocial factors have also been
identified as potential mediators. One such factor is selfefficacy, an important social cognitive variable that has been
shown to influence a wide array of health behaviors (Bandura,
1997; McAuley & Blissmer, 2000). Self-efficacy expectations
are concerned with beliefs in capabilities to successfully carry
out courses of action (Bandura). Considerable evidence exists
to suggest that efficacy expectations are associated with physical function performance (McAuley & Blissmer). For example,
self-efficacy mediated the influence of an exercise intervention
on stair climbing in older adults with osteoarthritis of the knee
(Rejeski, Ettinger, Martin, & Morgan, 1998). Li and colleagues
(2001) provided further evidence for the self-efficacy and
physical function performance relationship in a randomized
controlled exercise trial. Moreover, Seeman and colleagues
(Seeman & Chen, 2002; Seeman, Unger, McAvay, & Mendes
de Leon, 1999) reported that self-efficacy expectations were
related to disability and functional declines and that selfefficacy influenced disability independent of physical abilities.
Aspects of self-efficacy likely to be implicated in these relationships might encompass beliefs relative to exercise and gait
and balance capabilities.
In addition, the physical activity and self-efficacy relationship has been well documented in the literature, whereby selfefficacy is both a determinant and an outcome of physical
activity (McAuley & Blissmer, 2000). Therefore, it would appear that one possible way to understand the relationship between physical activity and functional limitations may be
through the pathways of self-efficacy and physical function
performance. That is, more active individuals could be argued
to be more efficacious, which would lead to better physical
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Figure 1. Alternative models of the hypothesized relationships among physical activity, self-efficacy, physical function performance, and
functional limitations: (a) self-efficacy influences functional limitations through functional performance; (b) self-efficacy has independent
associations with functional limitations and also operates through physical function performance.
function. Being able to function better would then be associated
with fewer functional restrictions or limitations. The proposed
relations are shown in Figure 1(a), where efficacy relative to
both balance and gait tasks and exercise are represented. An
alternative model, shown in Figure 1(b) and based on the
arguments of Keysor (2003) and Rejeski and colleagues (1998),
would suggest that self-efficacy also has independent associations with functional limitations in addition to operating
through physical function performance. In the present study, we
tested the proposed relationships in the context of baseline data
from a sample of older women at entry into an ongoing prospective study.
METHODS
Participants and Recruitment
Older (age, M ¼ 68.12 years; range ¼ 59–84 years) Black
(n ¼ 81) and White (n ¼ 168) women were recruited to
participate in a 24-month prospective study of women’s health
status. Inclusion criteria were sufficiently flexible as to permit
recruitment of a sample of older women closely representative
of the general population. Recruitment sources included local
media advertising, area health facilities, churches, and senior
centers. A total of 298 individuals were initially contacted following recruitment, with 49 being excluded as ineligible or
declining participation. Further details with respect to inclusion
criteria and recruitment methods have been reported elsewhere
(McAuley, Konopack, Motl, Rosengren, & Morris, 2005).
Measures
Demographic and health information. —Each participant
provided current demographic information and details of her
health history. As participants’ health status may have implications for physical function performance and functional limitations, we constructed a health status measure from the health
history questions. Specifically, participants indicated whether
or not they suffered from any of the following health conditions: cardiovascular disease, pulmonary disease, functional
impairment of the skeletomuscular system, hypertension, and
diabetes. We summed these responses to provide a health
status index that ranged from 0 to 5. Approximately 70% (n ¼
173) of the study participants reported having one or more of
these health conditions. Further information with respect to
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McAULEY ET AL.
baseline health status information has been reported elsewhere
(McAuley et al., 2005).
Functional limitations. —We used the abbreviated version
(McAuley et al., 2005) of the function component of the LateLife Function and Disability Instrument (LL-FDI; Haley et al.,
2002; Jette et al., 2002) to assess reported difficulty in executing discrete activities. This measure is composed of three
five-item subscales assessing advanced lower extremity function, basic lower extremity function, and upper extremity
function. Individuals are asked to rate the extent to which they
feel limited in performing certain activities by selecting a value
from 1 (cannot do) to 5 (none). We calculated a total score for
each subscale by summing ratings of all five items, resulting in
a possible range of 5–25, with higher scores reflecting less
difficulty in performing tasks. In the present analyses only the
measures of lower extremity function were used, as efficacy
scales pertained to lower body mobility and physical function
performance tasks were exclusive to the lower extremities.
Physical activity participation. —We assessed physical
activity with two measures, the Physical Activity Scale for
the Elderly (PASE; Washburn, Smith, Jette, & Janney, 1993)
and the Community Healthy Activities Model Program for
Seniors (CHAMPS; Stewart et al., 2001). The PASE is a
paper-and-pencil questionnaire whereas the CHAMPS is an
interviewer-administered measure of physical activity. Both
measures have been widely used to assess physical activity in
older adults.
Physical function performance. —Gait speed was assessed by
using normal walking speed over a 7-m pathway and timed
ascent and descent of a flight of 15 stairs. The final performance
measure was the timed 8-Foot Up-and-Go (Rikli & Jones,
1999), which assessed participants’ ability to rise from a seated
position, walk a distance of 8 ft (2.43 m) as quickly as possible,
and return to a seated position. Thus, all lower scores (faster
times) in all physical function performance measures represent
better performance.
Self-efficacy. —We assessed self-efficacy by using measures
of beliefs in capabilities relative to maintaining one’s balance,
ambulating over objects, walking, and general exercise. We
used the Activity-Specific Balance Confidence Scale (ABC;
Powell & Myers, 1995) to assess individuals’ confidence in
their ability to perform various basic activities of daily living
without compromising their balance. The six-item Gait Efficacy
Scale (GES; McAuley, Mihalko, & Rosengren, 1997) was used
to assess individuals’ beliefs in their capability to negotiate
stairs and objects in their path. We also used the Exercise
Self-Efficacy Scale (EXSE; McAuley, 1993), which measured
individuals’ beliefs in their ability to accumulate 30 min or
more of physical activity per day on 5 or more days per week in
the future. Finally, we used the Self-Efficacy for Walking Scale
(SEW; McAuley, Courneya, & Lettunich, 1991) to assess beliefs in capabilities to successfully walk for a specified duration
of time, ranging from 5- to 40-min bouts, at a moderately fast
pace without stopping. For each measure, we calculated an efficacy score by summing all given response scores and dividing
by the total number of items, resulting in a possible range of
0–100. In subsequent analyses, these variables were used to
represent a latent exercise efficacy factor (SEW and EXSE) and
a latent gait and balance efficacy factor (GES and ABC). All
efficacy measures had excellent internal consistencies in the
current study (a . 0.90).
Procedures
Participants completed all measures at the baseline of an
ongoing 24-month prospective study of physical activity and
health status in older women. Upon entry into the study and
completion of approved Institutional Review Board informed
consent, participants completed questionnaires assessing basic
demographic information, physical activity (PASE), selfefficacy, and general health information. Interviewers collected
the LL-FDI and CHAMPS data.
Two trained testers assessed physical function performance
measures in a laboratory. They assessed time taken to walk
down and up a flight of 15 stairs as participants entered and left
the laboratory, respectively. They assessed participants’ height
and weight upon their entry into the laboratory; participants
then completed the walking task at their normal walking pace
and the 8-Foot Up-and-Go as quickly as possible. For all timed
tasks, two experimenters used handheld stopwatches to time the
participants. Each condition (with the exception of the stairs
ascent and descent) was repeated twice, and the best times were
used in subsequent analyses.
Data Analysis
We analyzed the data using covariance modeling with the
full-information maximum likelihood (FIML) estimator in
Mplus 3.11 (Muthén & Muthén, 1998–2004). FIML is an
optimal method for the treatment of missing data in structural
equation modeling that has yielded accurate parameter estimates and fit indices with simulated missing data (Arbuckle,
1996; Enders, 2001; Enders & Bandalos, 2001). Missing data
composed 3.2% of the PASE data (n ¼ 8), 1.2% of the Stairs–
up data (n ¼ 3), and 0.5% of Stairs–down data (n ¼ 1). Reasons
for missing data included failure or refusal to return the PASE
questionnaire and inability to walk up or down stairs as the
result of a disability. There were no missing data for any of the
other variables.
Model testing. —As we had multiple indicators of each
construct, we used a latent variable framework to examine the
proposed relationships among constructs. We analyzed the data
by using a two-step procedure (Anderson & Gerbing, 1988).
The first step involved a confirmatory factor analysis that tested
the fit of an overall measurement model. The overall measurement model is presented in Figure 2 and consisted of five
correlated latent variables. These latent constructs comprised
physical activity (CHAMPS and PASE), exercise self-efficacy
(EXSE and SEW), gait and balance self-efficacy (ABC and
GES), physical function performance (Stairs–up and Stairs–
down, 8-Foot Up-and-Go, 7-m walking speed), and functional
limitations (LL-FDI lower body function scales). Because of
the similarity of assessment methods for the stair-climbing
tasks, we allowed a correlated uniqueness between the Stairs–
up and Stairs–down indicators of the functional performance
latent variable.
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Figure 2. Overall measurement model illustrating the relationships among the five latent variables tested by use of confirmatory factor analysis
(PASE ¼ Physical Activity Scale for the Elderly; CHAMPS ¼ Community Healthy Activities Model Program for Seniors; EXSE ¼ Exercise SelfEfficacy Scale; SEW ¼ Self-Efficacy for Walking Scale; ABC ¼ Activity-Specific Balance Confidence Scale; GES ¼ Gait Efficacy Scale; ALEF ¼
Function and Disability Instrument’s advanced lower extremity function; BLEF ¼ Function and Disability Instrument’s basic lower extremity
function).
The second step involved testing the proposed structural
models shown in Figure 1 on the basis of the hypothesized
relationships among the latent variables. Model 1a specified (a)
direct effects of physical activity on exercise self-efficacy and
gait and balance self-efficacy; (b) direct effects of physical
activity, exercise self-efficacy, and gait and balance selfefficacy on physical function performance; (c) a direct effect of
physical function performance on functional limitations; and
(d) a single correlation between disturbance terms for the exercise and gait and balance self-efficacy latent variables (not
depicted in Figure 1). As the measured constructs of the two
latent efficacy factors reflect related aspects of physical activity,
there is both conceptual and theoretical sense for allowing this
covariation. Next, we compared the fit of this model with the
alternative model shown in Figure 1(b). This involved testing
the same paths proposed in Model 1a but with additional paths
from the two self-efficacy factors to functional limitations.
Covariates. —We conducted a final analysis in which we
used age, race, and health status as covariates in the best-fitting
model to determine whether the fit of the model and the
proposed relationships were differentially influenced by these
important health and demographic variables.
Model fit. —We assessed the fit of the model to the data by
using the chi-square statistic, root mean square error of approximation (RMSEA), standardized root mean square residual
(SRMR), and the Comparative Fit Index (CFI). The chi-square
statistic assessed absolute fit of the model to the data (Jöreskog
& Sörbom, 1996). The SRMR is the average standardized
residual value derived between the variance–covariance matrix
for the hypothesized model and the variance–covariance matrix
of the sample data (Bollen, 1989). The value of the SRMR
should approximate or be smaller than .08 for a good-fitting
model (Hu & Bentler, 1999). The RMSEA represents closeness
of fit, and values approximating .06 are indicative of a close fit
(Browne & Cudeck, 1993). The CFI tests the proportionate
improvement in fit by comparing the hypothesized model with
the independence model (Bentler, 1990). The value of the CFI
should approximate 0.95 or greater for a good-fitting model (Hu
& Bentler, 1999).
RESULTS
Descriptive Statistic
Descriptive statistics and correlations among all measures
included in the data analysis are presented in Table 1.
Step 1: Confirmatory Factor Analyses
The five-factor measurement model depicted in Figure 2
represented a good fit for the data (v2 ¼ 120.71, df ¼ 43,
SRMR ¼ 0.04, RMSEA ¼ 0.08, CFI ¼ 0.96). Although the
value of the chi-square was statistically significant ( p , .001),
the RMSEA, SRMR, and CFI approximate criteria for a good
model–data fit. The factor loadings for the indicators on each of
the latent variables were all statistically significant ( p , .05),
suggesting that the measures were acceptable indicators of the
latent variables. The correlations among latent variables are
presented in Table 2 and were all statistically significant and
moderate to large in magnitude. Recall that a higher score
reflects fewer functional limitations and that better physical
function performance is represented by lower scores (i.e., faster
times). Specifically, women who were more active had higher
gait and balance efficacy and exercise self-efficacy, better
physical function, and reported fewer functional limitations.
Being more efficacious was associated with better physical
functioning and fewer functional limitations. Finally, poorer
functional performance was associated with greater frequency
of functional limitations.
McAULEY ET AL.
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Table 1. Correlations and Descriptive Statistics for All Measures
Variable
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1. Age (years)
68.1 (6.1)
2. Health statusa
0.11
1.1 (1.0)
3. FDI: ALEF
0.24** 0.41** 17.9 (4.8)
4. FDI: BLEF
0.14* 0.35**
0.64** 23.6 (2.3)
5. CHAMPSb,c
0.04
0.15*
0.33**
0.19** 20.2 (10.2)
6. PASEb
0.20** 0.28*
0.40**
0.20**
0.36** 156.4 (70.9)
7. 7-m walk (s)
0.27**
0.33** 0.50** 0.38** 0.19**
0.24**
6.7 (1.7)
8. Stair ascent (s) 0.18**
0.32** 0.61** 0.43** 0.27**
0.35**
0.66**
9.8 (3.4)
9. Stair descent (s) 0.26**
0.39** 0.58** 0.51** 0.31**
0.38**
0.63**
0.85**
9.7 (4.0)
10. 8-Foot
Up-and-Go (s) 0.35**
0.27** 0.54** 0.52** 0.16*
0.24**
0.71**
0.69**
0.65**
7.0 (1.7)
11. ABC
0.13* 0.39**
0.58**
0.53**
0.22**
0.31** 0.35** 0.46** 0.53** 0.41** 90.9 (13.2)
12. GES
0.10
0.43**
0.67**
0.57**
0.20**
0.31** 0.44** 0.56** 0.54** 0.49** 0.85** 88.2 (18.4)
13. SEWb
0.23** 0.40**
0.65**
0.38**
0.34**
0.34** 0.42** 0.46** 0.45** 0.42** 0.50**
0.53** 71.7 (31.8)
b
14. EXSE
0.15* 0.28**
0.49**
0.35**
0.39**
0.36** 0.37** 0.40** 0.40** 0.34** 0.47**
0.48**
0.68** 80.9 (28.1)
Notes: *p , .05; **p , .01; FDI ¼ Function and Disability Inventory, ALEF ¼ Advanced Lower Extremity Function, BLEF ¼ Basic Lower Extremity Function, CHAMPS ¼ Community Healthy Activity Model Program for Seniors, PASE ¼ Physical Activity Scale for the Elderly, ABC ¼ Activity-Specific Balance
Scale, GES ¼ Gait Efficacy Scale, SEW ¼ Self-Efficacy for Walking Scale, EXSE ¼ Exercise Self-Efficacy Scale.
a
Health status data reflect prevalence of chronic conditions.
b
These descriptive data were previously published by McAuley and colleagues (2005).
c
CHAMPS data reflect frequency of activities of all intensities.
Step 2: Structural Models
The structural model hypothesized in Figure 1(a) provided
a relatively poor fit to the data (v2 ¼ 196.36, df ¼ 46, SRMR ¼
0.06, RMSEA ¼ 0.11, CFI ¼ 0.92) and fit worse than the
baseline measurement model. Subsequently, we tested the
structural model hypothesized in Figure 1(b). This model
involved estimating two additional paths from exercise selfefficacy and gait and balance efficacy to functional limitations.
This model provided a good fit to the data (v2 ¼ 122.47, df ¼
44, SRMR ¼ 0.04, RMSEA ¼ 0.08, CFI ¼ 0.96). The fit of this
model represented a substantial improvement over the model in
Figure 1(a), v2diff ¼ 73.89, df ¼ 2, p , .001, and did not differ
significantly from the measurement model, v2diff ¼ 1.75, df ¼ 1,
p . .10. Therefore, the model in Figure 1(b) supports the
hypothesized pattern of the relationships among the latent
constructs of physical activity, self-efficacy, physical function
performance, and functional limitations.
Statistically significant path coefficients are shown in Figure
3. As one can see, physical activity was statistically related to
both exercise self-efficacy and gait and balance self-efficacy
( ps , .01). In addition, physical activity was significantly
associated with physical function performance ( p , .05), as
was gait and balance self-efficacy ( p , .01). Physical function
performance, in turn, had a statistically significant direct effect
Table 2. Correlations Among the Six Latent Variables in the
Initial Confirmatory Factor Analysis
Latent Variable
1.
2.
3.
4.
5.
Physical activity
Exercise self-efficacy
Balance self-efficacy
Functional limitations
Functional performance
1
2
—
0.69
0.44
0.63
0.51
—
0.62
0.75
0.58
3
—
0.76
0.62
4
—
0.75
5
—
Note: All correlations are statistically significant ( p , .05). Higher scores
on functional limitations represent fewer limitations, and lower scores on
functional performance indicates better (faster) performance.
on functional limitations ( p , .01), as did exercise self-efficacy
and gait and balance self-efficacy ( p , .01). The correlation
between exercise and gait and balance self-efficacy was statistically significant ( p , .01). Overall, physical activity and
the self-efficacy variables accounted for 47% of the variation
in physical function performance, whereas self-efficacy and
physical function performance accounted for 78% of the
variance in functional limitations.
Testing Alternative Models
To determine whether adding a direct path from physical
activity to functional limitations changed the parameter estimates or added additional variance, we tested an additional
model. This path was nonsignificant (b ¼ .13, p . .05), and the
pattern of relationships, fit of the model, and variance
accounted for remained essentially unchanged (v2diff ¼ 1.76,
df ¼ 1, p ..10).
One final possible consideration of the relationships among
the constructs of interests might be to determine whether the fit
of the model changes when one considers the relationships
between self-efficacy and functional limitations independent of
any influence of physical function performance. Consequently,
we retested the model proposed in Figure 3 but did not specify
a path from physical function performance to functional
limitations. This model proved to be a poorer fit to the data
(v2 ¼ 148.30, df ¼ 45, SRMR ¼ 0.05, RMSEA ¼ 0.10, CFI ¼
0.94) and represented a significantly worse fit than the model in
Figure 3 (v2diff ¼ 25.83, df ¼ 1, p , .001).
Effects of Covariates on Hypothesized Relationships
We next tested the best-fitting model (Figure 3) while
statistically controlling for race, age, and health status. Neither
the fit of the model, the magnitude of the hypothesized relationships, nor their statistical significance was affected by the
inclusion of the covariates in the model. There were, however,
some interesting relationships among the covariates and the
model components. For example, race exhibited a statistically
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significant, although modest, association with physical activity
(b ¼ .18, p , .05), functional performance (b ¼.14, p , .05),
and functional limitation (b ¼ .14, p , .05). White women
reported higher levels of physical activity and performed better
on the battery of functional tests than did Black women, but
they reported more frequent functional limitations. Older
women reported lower levels of physical function performance
(b ¼ .23, p , .05). With respect to health status, individuals
with more chronic conditions had lower self-efficacy for gait
and balance (b ¼.32, p , .05) and exercise (b ¼ .20, p , .05).
DISCUSSION
In the present study we compared two structural models of
the relationship between physical activity and functional
limitations in older women at the baseline of an ongoing,
24-month prospective trial. These models were based in part on
social cognitive theory (Bandura, 1986) and on Stewart’s
(2003) perspective that performance-based measures of function should occupy a distinct step in the disablement process.
Initially, we tested the proposition that physical activity had,
in part, an indirect effect on physical function performance
through self-efficacy as well as a direct effect on physical
function performance, which was, in turn, associated with
functional limitations. In addition, as self-efficacy has been
reported to have an influence on functional limitations that is
independent of functional abilities (Seeman et al., 1999), we
tested an alternative model that also included direct associations
between self-efficacy and functional limitations. Our analyses
found this latter model to fit the data best.
We believe these findings to be important from several
perspectives. First, although physical activity participation has
frequently been cited as having a protective effect on functional limitations in older adults (Keysor, 2003; Miller et al.,
2000), it would appear that this relationship may operate
through physical activity’s association with physical function
performance and self-efficacy. Our findings support those of
Rejeski and colleagues (1998) in this respect but with one
important difference. That is, the association between efficacy
and functional limitations in this study was both direct and
indirect through functional performance, findings that support
work by Seeman and colleagues (Seeman & Chen, 2002;
Seeman et al., 1999). Thus, older adults who are more
physically active and have stronger beliefs relative to their
general physical capabilities appear to display better physical
function performance and, in turn, fewer restrictions in daily
tasks involving the lower body. Thus, there is support for the
position of Keysor and Rejeski and colleagues that self-efficacy
is implicated in the relationship between physical activity and
functional limitations. Moreover, such findings are in line with
reported effects of self-efficacy on other health behaviors
(Bandura, 1997).
It is important to note that all of the relationships discussed
herein were not affected by the inclusion of age, race, and health
status as covariates. However, these covariates were associated
with variables in the model. For example, as one would expect,
older women had poorer physical function performance. Women
with more chronic health conditions were less efficacious
relative to their gait and balance and capability to be physically
active. Finally, White women were more active and had better
Figure 3. Best-fitting structural model, with age, health status, and
race controlled for. (Note that having better physical function
performance is indicated by lower scores and having fewer functional
limitations is indicated by higher scores. Unless otherwise indicated,
all standardized path coefficients are statistically significant, with p ,
.05. To improve the clarity of the figure, we did not include the items
and uniquenesses; D1–D4 are disturbances, i.e., residual terms.)
functional performance but reported more functional limitations.
Age and health status are clearly important correlates of efficacy,
physical function, and limitations (Bandura, 1997); however, it
is equally important that the theoretical relationships proposed in
our model did not change in magnitude or significance when
these covariates were controlled. Such finding support the work
of Seeman and Chen (2002), who reported that functional
declines in older adults with chronic disease conditions cannot
be solely attributed to health status, as their data suggest that
patterns of functional decline may also be inversely related to
physical activity participation. Subsequent tests of the proposed
associations should continue to consider the roles played by race,
age, and health status in the relationship between physical
activity and functional limitations.
We believe the present study to have a number of strengths.
For example, we adopted a social cognitive perspective
(Bandura, 1986, 1997) to better understand the relationships
among physical activity, functional performance, and functional limitations in a relatively large sample of older women.
Furthermore, we assessed each of the constructs of interest by
using multiple indicators of each construct rather than relying
on single measures. One advantage of using multiple indicators
is that these observed variables can be modeled to assess
underlying latent constructs. Moreover, such an approach
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McAULEY ET AL.
allows for a more powerful and accurate test of structural
relations among theoretically related constructs. This increase
in power and accuracy is largely attributable to estimating
structural relationships by using latent variables that allow for
the partitioning of true, common, and error score variance.
Thus, we believe that our current results demonstrate some
initial support for the roles played by self-efficacy and functional performance in the physical activity and functional
limitations relationship.
However, it is important to acknowledge that the models
tested are cross-sectional and restricted to a sample of older
women. Thus, causal direction cannot be determined, and
whether the findings hold in men awaits empirical examination.
It could be argued that having functional limitations makes
one less active and that relationships in our model could be
operating in the reverse direction. Of course, testing such
a model would not change the relationships. A social cognitive
perspective would argue for reciprocally determining relationships among model constructs, and such relationships can be
tested with longitudinal data. Our model testing was driven by
both theoretical predictions and empirical research (e.g.,
Rejeski et al., 1998). Indeed, the fit of the structural model
did not differ significantly from that of the measurement model,
suggesting that the hypothesized model is one possible
explanation for the bivariate relationships among the latent
constructs. Thus, tests of models other than those presented are
unlikely to demonstrate a more parsimonious fit. Certainly, this
does not preclude the possibility of testing alternative models of
change among construct relationships over time. Furthermore,
it may be that participating in a study of women’s health may
have resulted in a self-selected sample biased toward being
more active and healthier than the normal population. However,
as we noted earlier, approximately 70% of our sample had one
or more chronic health conditions, and our sampling frame was
relatively flexible to allow for a broad representation of women.
Thus, we believe that our findings lay the foundation for
subsequent testing of the roles played by self-efficacy and
physical function performance in mediating effects of physical
activity behavior on functional limitations and, ultimately,
disability in older women.
In conclusion, our results support a growing body of research
showing that lifestyle factors such as physical activity and
psychosocial factors such as self-efficacy appear to be implicated in functional limitations in older adults (Keysor, 2003;
Rejeski et al., 1998; Seeman & Chen, 2002; Seeman et al.,
1999). This is heartening from a prevention perspective, because both of these factors are modifiable. Therefore, steps can
be taken to further promote physical activity participation for
older women and to create environments that maximize selfefficacy through the provision of performance feedback and
social modeling and persuasion (Bandura, 1986).
ACKNOWLEDGMENTS
This research was funded by the National Institute on Aging (Grant
RO1 AG20118). We are grateful to April Bell, MS, for her contributions to
this study.
Address correspondence to Edward McAuley, Department of Kinesiology and Community Health, University of Illinois, 906 South Goodwin
Avenue, Urbana, IL 61801. E-mail: [email protected]
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Received August 8, 2005
Accepted February 3, 2006
Decision Editor: Karen Hooker, PhD