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Adolescents‘ Self-Efficacy to Overcome Barriers to PhysicalActivity ScaleJohn J. M. Dwyer a , Tala Chulak a , Scott Maitland a , Kenneth R. Allison b , Daria C. Lysy c , Guy E. J.Faulkner c & Judy Sheeshka aa The Department of Family Relations and Applied Nutrition , University of Guelphb KR Allison Research Consulting , University of Torontoc The Physical Activity Research Program , University of TorontoPublished online: 26 Feb 2013.

To cite this article: John J. M. Dwyer , Tala Chulak , Scott Maitland , Kenneth R. Allison , Daria C. Lysy , Guy E. J. Faulkner & JudySheeshka (2012) Adolescents‘ Self-Efficacy to Overcome Barriers to Physical Activity Scale, Research Quarterly for Exercise and Sport,83:4, 513-521, DOI: 10.1080/02701367.2012.10599140

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RQES: December 2012 513

Dwyer, Chulak, Maitland, Allison, Lysy, Faulkner, and Sheeshka

Research Quarterly for Exercise and Sport©2012 by the American Alliance for Health,Physical Education, Recreation and DanceVol. 83, No. 4, pp. 513–521

Key words: factor analysis, validity, reliability

Strong et al. (2005) conducted an extensive review of physical activity interventions among youth, which

supported the recommendation that youth engage in at least 60 min or more of moderate-to-vigorous physical activity daily to achieve desired health and behavioral outcomes. However, some youth are not getting sufficient physical activity. For example, according to the 2005 Canadian Community Health Survey, 27% of Canadian youth ages 12–17 years were physically inactive as defined by self-reported participation in leisure-time activities ac-cumulatively equivalent to walking less than 30 min daily (Gilmour, 2007). Studies among U.S. and Canadian ado-lescents have indicated a steady decline in physical activity from ages 14 through 18 years (Allison, Adlaf, Dwyer, Lysy,

& Irving, 2007; Grunbaum et al., 2002). Physical inactivity during adolescence is a concern considering that physical activity levels during childhood and adolescence track into adulthood (Telama et al., 2005).

To address youth physical inactivity, it is important to understand the determinants of physical activity. An im-portant determinant is self-efficacy, a central component of the social cognitive theory that refers to belief in one’s ability to produce desired outcomes (Bandura, 1997). A resilient sense of self-efficacy allows an individual to productively use skills when faced with various personal, social, and situational impediments (Bandura, 1997). Self-efficacy to overcome barriers to physical activity has been recognized as a significant predictor of physical ac-tivity participation among adolescents (Allison, Dwyer, & Makin, 1999; Petosa, Hortz, Cardina, & Suminski, 2005). A recent review of physical activity correlates among adolescents reported that self-efficacy was significantly and positively associated with physical activity in 28 stud-ies (Van Der Horst, Paw, Twisk, & Van Mechelen, 2007).

To understand the construct of self-efficacy to over-come barriers, it is helpful to consider adolescents’ barri-ers to physical activity identified in previous focus group research. These barriers include lack of time and feeling tired (e.g., due to schoolwork, part-time jobs, and respon-sibilities at home; Allison et al., 2005; Dwyer et al., 2006), having other interests such as screen-time use (Dwyer et

Adolescents’ Self-Efficacy to Overcome Barriers to Physical Activity Scale

John J. M. Dwyer, Tala Chulak, Scott Maitland, Kenneth R. Allison, Daria C. Lysy, Guy E. J. Faulkner, and Judy Sheeshka

Submitted: June 25, 2009 Accepted: September 7, 2011 John J. M. Dwyer, Tala Chulak, Scott Maitland, and Judy Sheeshka are with the Department of Family Relations and Applied Nutrition at the University of Guelph. Kenneth R. Allison is with KR Allison Research Consulting and University of Toronto. Daria C. Lysy and Guy E. J. Faulkner are with the Physical Activity Research Program at the University of Toronto.

This paper describes a revised measure of self-efficacy to overcome barriers to moderate and vigorous physical activity in a sample of 484 high school students in Toronto, Ontario. The students had a mean age of 15.3 years. Principal axis factoring with oblique rotation yielded five factors: self-efficacy to overcome internal, harassment, physical environment, social environment, and responsi-bilities barriers. Two problematic items were removed, which resulted in a 22-item measure. Subsequent analyses were conducted on responses to this shortened measure. Confirmatory factor analysis supported the five-factor model and demonstrated age- and sex-invariance. The subscales had good internal consistency reliability. Structural regressions demonstrated a strong relationship between the resulting factors and a physical activity measure (energy expenditure), showing predictive validity.

Measurement and Evaluation

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514 RQES: December 2012


al., 2006; Hohepa, Schofield, & Kolt, 2006), having a busy social life (Allison et al., 2005), having friends and parents who do not support physical activity (Allison et al., 2005; Dwyer et al., 2006), and not having friends to do physi-cal activity with (Allison et al., 2005). Lacking motivation (Hohepa et al., 2006), being bored (Allison et al., 2005), the stress of competition (Dwyer et al., 2006), feeling intimidated by others when playing sports (Allison et al., 2005), insufficient physical skills (Hohepa et al., 2006), concern that peers would tease them for playing poorly (Allison et al., 2005), being self-conscious about their physical appearance and being overweight (Dwyer et al., 2006), and injury (Allison et al., 2005) are also barriers. Additional barriers include unavailable or inaccessible facilities and programs (e.g., costly programs, lack of transportation to facilities, and staying home because of the weather; Dwyer et al., 2006; Hohepa et al., 2006) and concern about unsafe facilities (e.g., facilities where gangs hang out; Dwyer et al., 2006; Hohepa et al., 2006).

Previous measures of self-efficacy to overcome bar-riers to physical activity participation had shortcomings, in particular poor content validity due to inadequately addressing some important barriers. The Self-Efficacy for Exercise Behaviors Scale (Sallis, Pinski, Grossman, Patterson, & Nader, 1988) has items referring to an ex-ercise program; thus, the content of the items reflects a narrower component of physical activity (i.e., structured physical activity to achieve a cardiovascular effect) rather than physical activity broadly. Also, factor analysis results were weak. The self-efficacy measure in the Reynolds et al. (1990) study has item content that reflects only exercise and has poor content validity. Also, factor analysis did not examine construct dimensionality. The Self-Efficacy Scale (Saunders et al., 1997) does not address important bar-riers, such as being teased and bullied by others, and the stress of competition and internal consistency reliabilities of the subscales are low. Ryan and Dzewaltowski (2002) did not examine the factorial validity of the Barriers Ef-ficacy Scale. Item response modeling, which focuses on item difficulty, was used to develop the Physical Activity Self-Efficacy Scale (Jago et al., 2009). It did not include various barriers, such as teasing and bullying by others, or facilities not being available or accessible. Also, there were insufficient easy and difficult items; thus, more psychomet-ric research on the measure is needed (Jago et al., 2009).

Dwyer, Allison, and Makin (1998) adapted measures used in previous research (Reynolds et al., 1990; Sallis et al., 1988) to develop their self-efficacy measure. Analysis yielded two factors: self-efficacy to overcome (a) external barriers and (b) internal barriers. As a cautionary note, this measure assesses self-efficacy to overcome barriers related to vigorous physical activity rather than both mod-erate and vigorous physical activity. Ryan and Dzewaltowski (2002) found that the type of physical activity referred to in self-efficacy measures (vigorous exercise vs. moderate

or vigorous physical activity) influenced the relationship between self-efficacy and physical activity levels among youth. Dwyer et al.’s measure addresses many barriers, but subsequent qualitative research among adolescents identified additional barriers the measure should address (e.g., being teased, bullied, or intimidated by others dur-ing physical activity; Allison et al., 2005; Dwyer et al., 2006).

The purpose of our study was to revise the Dwyer et al. (1998) self-efficacy measure by including additional barriers identified in previous qualitative research (Allison et al., 2005; Dwyer et al., 2006) and using moderate and vigorous physical activity (rather than exercise or vigorous physical activity) as a reference point, which is consistent with the recommended mode of physical activity (Centers for Disease Control and Prevention, 2008). The revised measure is the Self-Efficacy to Overcome Barriers to Physi-cal Activity Scale (SOBPAS). We examined the factorial validity, internal consistency reliability, and predictive validity of the SOBPAS. This measure could be used in research examining topics such as correlates of physical activity and physical activity intervention research.

Method

Participants and Procedure

Five schools were conveniently selected in Toronto, Ontario, and students in grades 9, 10, and 11 were invited to participate in the study. University and school board committees provided research ethics approval for the research protocol. An information letter and parental con-sent form (active consent) were sent home with students. During February to May 2005, research staff administered the self-report measures in the schools to students who returned a signed consent form. Five hundred eighteen students completed the measure. We removed 34 par-ticipants with outliers in the Self-Administered Physical Activity Checklist (SAPAC) data (more than 3 hr/day on a specific physical activity). This resulted in a sample of 484 participants consisting of 177 students in grade 9, 149 in grade 10, and 158 in grade 11. There were 14- (n = 117), 15- (n = 162), 16- (n = 147), 17- (n = 55), and 18-year-olds (n = 3) in the sample; M age = 15.3 years, SD = 1.0. There were 194 boys (40%) and 290 girls (60%). This sample and the outlier group did not significantly differ in score distribution for age (χ2 = 3.48, p ≥ .01) and gender (χ2 = 4.62, p ≥ .01).

Measures

We pilot tested the measures among 90 students in one high school (45 students in grade 9, 28 students in grade 10, and 17 students in grade 11) to determine whether they had difficulty answering specific questions and to improve measures as necessary. The SOBPAS is a

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revision of a 20-item measure of self-efficacy to overcome barriers to vigorous physical activity among high school students (Dwyer et al., 1998). The Dwyer et al. measure, which consisted of an external barriers subscale (e.g., lack of support from friends) and an internal barriers subscale (e.g., not having fun), had good internal consistency reli-ability and did not correlate significantly with social desir-ability. We made several revisions to the measure based on the findings of qualitative studies on barriers to physical activity among adolescents. We removed “feel stressed,” because the Dwyer et al. measure already included items reflecting sources of stress (e.g., too much schoolwork, having a job, and family responsibilities). We replaced “lack willpower” and “do not feel in the mood” with “not motivated” (Hohepa et al., 2006) and replaced “long-term illness, disability, or injury” with the more general “being sick or injured.” We added six additional barriers based on qualitative studies: “weather” (Allison et al., 2005), “not having a safe place to do physical activity” (Dwyer et al., 2006), “being bullied or intimidated by others” and “being teased by friends during physical activity or sports” (Allison et al., 2005; Hohepa at al., 2006), “not having transportation to facilities” (Allison et al., 2005), and “too much competition” (Allison et al., 2005; Dwyer et al., 2006). The SOBPAS question stem states: “How confident are you that you can overcome this thing and still do moderate or vigorous physical activities?” It provided examples of moderate and vigorous physical activities. The question stem identifies the type of physical activity but does not specify frequency, duration, and time frame, which is consistent with an approach used previ-ously (Dwyer et al., 1998; Saunders et al., 1997). Thus, the SOBPAS measures self-efficacy to overcome barriers to moderate and vigorous physical activity in general rather than barriers related to a specific type of physical activity, which was inconsistent in previous research. For example, Ryan and Dzewaltowski (2002) specified at least 60 min of moderate or vigorous physical activity on most days of the week, Jago et al. (2009) used 30 min or more of physical activity either “one day” or at least 4 days/week, and Sallis et al. (1988) referred to exercise during at least the next 6 months. Twenty-four barriers are listed, and response options are 1 = not at all confident, 2 = not very confident, 3 = somewhat confident, 4 = confident, and 5 = very confident. Also, participants reported their age, grade level, and sex and completed other measures not directly related to the focus of this article.

We used a modified version of the SAPAC to assess participation in physical activity. Originally, the SAPAC was developed as a 1-day recall of physical activity participa-tion and selected sedentary activities (Sallis et al., 1996). The physical activity component was shown to have good reliability and validity (Sallis et al., 1996). The SAPAC has been adapted to a 7-day recall of these activities (Marshall, Biddle, Sallis, McKenzie, & Conway, 2002). We used this

recall form in the current study to assess physical activity participation. We used the modified SAPAC to determine metabolic equivalent (MET) hours/week (energy ex-penditure). Participants received a list of 25 activities, such as soccer and dance, and five spaces to list other activities. They reported the number of days they performed the activities in the previous 7 days and the average number of minutes they did each activity. The frequency (days), duration (hours), and MET values representing the inten-sity of each activity for youth (Ridley, Ainsworth, & Olds, 2008) were multiplied and summed to determine MET hours/week. Other researchers have used this calcula-tion procedure (Imperatore, Cheng, Williams, Fulton, & Gregg, 2006). We calculated only activities with an MET value of 3.0 or higher (i.e., moderate to vigorous intensity) to be consistent with the SOBPAS focusing on self-efficacy to overcome barriers related to moderate or vigorous physical activity. We considered participants reporting more than 3 hr/day on a specific physical activity to be outliers and removed them. MET hours/week were posi-tively skewed, so a square root transformation was done to yield a normal distribution.

Statistical Analyses

We used SPSS to perform statistical analyses. Descrip-tive statistics for demographics were calculated, and Prin-cipal Axis Factoring (PAF, which focuses on identifying a theoretical factor solution) (Tabachnick & Fidell, 2007) was performed on the SOBPAS items. Pairwise deletion was used to deal with missing data.

We extended our analyses to incorporate confirma-tory factor analysis (CFA) to determine the strength of the SOBPAS model. Models were tested using Amos 17 (SPSS, 2009). We analyzed covariance matrices with results reported as standardized estimates for ease of in-terpretation. For factor scaling, we set one factor loading to 1.0 for each factor. The chi-square difference test (Δχ2; Joreskog & Sorbom, 1987) was used to compare nested models and evaluate whether model modifications pro-vided significant improvement at each step. Model fit was evaluated by examining the following fit indexes: model χ2 and the ratio of χ2/df (Bollen, 1989), goodness of fit index (GFI; Joreskog & Sorbom, 1987), non-normed fit index (NNFI; Bentler & Bonnett, 1980), comparative fit index (CFI; Bentler, 1990), and root mean square error of approximation (RMSEA; Steiger, 1990). The model fit well if a consensus of measures met or exceeded gener-ally accepted levels. It is widely accepted that model χ2 should be nonsignificant if the model fits well, although this measure is sensitive to large sample sizes. Therefore, we consulted GFI, NNFI, and CFI values, and all should exceed .90 to indicate acceptable fit. Additionally, RMSEA, which demonstrates the amount of error variance per degree of freedom, should result in values less than .05.

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Beyond simply ensuring that a measurement model fits the data well, the extension to multigroup factor models tests whether measures mark the same constructs across comparison groups (Maitland, Nyberg, Bäckman, Nilsson, & Adolfsson, 2009). We conducted tests of mea-surement equivalence or invariance (ME/I; Vandenberg & Lance, 2000) for age and gender groups (n 14–15 = 240, n 16–18 = 162; n boys = 167, n girls = 235). To address whether the factor solution was the same for boys and girls, we tested ME/I of the best fitting model. First, the configural model assessed whether the factor structure was the same for boys and girls. Once equivalence was established, we tested three further hypotheses: equivalent factor loadings, equivalent relationships between factors, and the hypothesis of equivalent measurement residuals. We followed similar procedures to test for invariance between the two age groups. We conducted analyses on all participants with complete data, thus allowing the GFI and modification indexes to be calculated.

We analyzed internal consistency reliability on the subscales. Finally, structural models regressed the physical activity measure (MET hours/week) onto the resulting SOBPAS subscales to further assess our model validity. We performed a square root transformation on MET hours/week to normalize the distribution. Evidence of gender and age invariance controlled for differences accountable to those personal characteristics.

Results

Exploratory Factor Analysis (EFA)

We examined several assumptions of factor analysis (Tabachnick & Fidell, 2007). The sample size of 484 was above the recommended 300 for a reliable factor analy-sis. Z scores and squared multiple correlations (SMCs) showed no univariate and multivariate outliers, respec-tively (Tabachnick & Fidell, 2007). Skewness and kurtosis values, histograms, and normal Q-Q plots showed nor-mally distributed data (Field, 2009). Scatter plots of items showed the assumption of linearity was met (Tabachnick & Fidell, 2007). Multicollinearity was not present in the matrix, as correlations were below the .80 cut-off value and SMCs were not close to 1.0 (Tabachnick & Fidell, 2007).

Next, we assessed the factorability of the correlation matrix. There was a relationship between the items in that Bartlett’s test of sphericity was significant (χ2 = 5,799.12, p < .001), and the Kaiser-Meyer-Olkin value exceeded Kaiser’s (1974) recommendation of > .90 (Tabachnick & Fidell, 2007). Also, measures of sampling adequacy for each item were above .93, larger than the recommended value of > .70 (Kaiser, 1974). Examination of the correlation matrix indicated that all items correlated significantly (p < .05) and positively with each other, ranging from .21 to .73.

PAF relates to theory development or identifying underlying constructs (Tabachnick & Fidell, 2007); thus, we used this factor extraction technique. It is recom-mended that researchers explore different numbers of factors to identify the best factor solution (Tabachnick & Fidell, 2007). We used several methods (eigenvalues > 1, Kaiser, 1960; scree plot, Cattell, 1966; percent of variance explained in extracted factors, the residuals, and factor interpretability) to identify the number of factors. Only items with factor loadings of .32 or larger, which represents 10% variance overlap between an item and factor, were interpreted (Tabachnick & Fidell, 2007). PAF with oblique (direct oblimin) rotation, using Kaiser’s criterion, initially yielded a three-factor solution. We used oblique rotation because previous research showed moderate to large cor-relations among self-efficacy subscales (Dwyer et al., 1998; Saunders et al., 1997). In contrast to Kaiser’s criterion re-sults, the scree plot to identify distinct changes in the slope for plotted eigenvalues (Cattell, 1966) indicated it was best to retain five factors. The three-factor solution was unclear, as items on various factors did not fit together conceptually, and some loaded significantly on more than one factor. Kaiser’s criterion may over- or underestimate the number of factors (Tabachnick & Fidell, 2007). Thus, we examined two-, four-, five-, and six-factor solutions, with direct obli-min rotation. The two-, four-, and six-factor solutions were difficult to interpret. Again, the solutions were unclear, as factor items did not conceptually fit together and some had significant loadings on more than one factor.

The five-factor solution was stable (except for the un-supportive parent item, there were no double loadings of items on factors), had fewer residuals with absolute values greater than .05 (3%), and had interpretable factors (fac-tor items fit conceptually to reflect specific constructs); therefore, we deemed it the best solution. Table 1 shows factor loadings in the factor pattern matrix, the commu-nalities, and the variance explained by the solution. The communalities indicated that the five factors accounted for 29–74% of the variance in each item, and the solution accounted for 54% of the variance. In all factor solutions, the barrier “weather” had lower factor loadings and less fit within a factor. For exploratory purposes, this item was removed and all factor solutions were reanalyzed. Remov-ing the item produced factor solutions that were less in-terpretable; thus, the item was retained in the five-factor solution in EFA. The factors were labeled internal barriers, harassment barriers, physical environment barriers, social environment barriers, and responsibilities barriers.

CFA and Gender and Age Invariance

The five-factor model was specified in Amos; the initial model fit was reasonable but required some modifi-cations. The weather and unsupportive parent items were removed, and data for the 22-item SOBPAS were used in

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the remaining analyses. Analyzing the model resulted in: χ2 Baseline = 611.81, df = 199, χ2/df = 3.07, p < .001, GFI =.87, NNFI = .90, CFI = .91, RMSEA = .07. Results were mixed, as the NNFI and CFI were good, whereas other fit indexes suggested model improvement was warranted. Modification indexes suggested correlating residuals for: (a) how confident are you that you can overcome feeling tired and low on energy, and (b) how confident are you that you can overcome not feeling motivated. It was also apparent that residuals should be allowed to correlate

for: (a) how confident are you that you can overcome community programs and facilities not being available, and (b) how confident are you that you can overcome school programs not being available. Reanalyzing the model with these modifications resulted in improved fit: χ2 M1 = 548.00, df = 197, χ2/df = 2.78, p < .001, GFI =.89, NNFI = .92, CFI = .93, RMSEA = .07. The NNFI, CFI, and GFI values improved. The GFI value did not exceed .90 but improved nonetheless. Although the modifications did not improve the RMSEA value, they did not make this index worse. One would not necessarily expect every fit index to improve when making modifications that are not extremely substantial. Our global assessment showed an overall pattern indicating the modifications improved the model. Comparing the two models showed a signifi-cant improvement: Δχ2 Baseline–M1 (2) = 63.81, p < .001; therefore, we accepted the second model as the best fit. All factor loadings were statistically significant at p < .001 and ranged from .55 to .85. The SMCs ranged from .35 to .72 indicating that 35–72% of the resulting factors ac-counted for item variance.

Gender Invariance. We extended the model to a mul-tigroup framework to examine invariance between male and female students. The unconstrained or configural model resulted in a fit decrement compared to the model described but was deemed acceptable: χ2 M2 = 848.37, df = 394, χ2/df = 2.13, p < .001, GFI = .84, NNFI = .89, CFI = .91, RMSEA = .05. The next model tested whether the factor loadings could be constrained to be equal between male and female students, and this was also acceptable: χ2 M3 = 852.00, df = 411, χ2/df = 2.07, p < .001, GFI = .84, NNFI = .90, CFI = .91, RMSEA = .05. Comparing the two models resulted in accepting the hypothesis of equivalent factor loadings: Δχ2 M3–M2 (17) = 13.63, p = .693. The next comparison examined whether the factor variances and covariances could be constrained between genders: χ2 M4 = 871.71, df = 426, χ2/df = 2.05, p < .001, GFI = .84, NNFI = .90, CFI = .91, RMSEA = .05. Comparison of the measurement weights model with the more restrictive model constraining variances/covariances demonstrated invariance: Δχ2 M4–M3 (15) = 19.71, p = .183. The final test of invariance constrained the measurement residuals to be equal between boys and girls and compared this result against the previously accepted invariant variance/covari-ance model. This result was also tenable: χ2 M5 = 893.76, df = 450, χ2/df = 1.99, p < .001, GFI = .83, NNFI = .91, CFI = .91, RMSEA = .05, Δχ2 M5–M4 (24) = 22.05, p = .576.

Age Invariance. Next, we examined age invariance between two age groups: 14–15-year-olds compared to 16–18-year-olds. The unconstrained or configural model resulted in a decrement of fit but was viewed as accept-able: χ2 M6 = 846.54, df = 394, χ2/df = 2.15, p < .001, GFI =.84, NNFI = .89, CFI = .91, RMSEA = .05. The next model tested whether factor loadings could be constrained to be equivalent between the two age groups, and this was

Table 1. Principal axis factoring with direct oblimin rotation on SOBPAS items

Self-efficacy item (a–x) Factor Com. 1 2 3 4 5

v. Embarrassed about others watching .60 .61x. Not motivated .54 .65l. Too much competition .52 .63q. Concerned about weight .44 .46p. Other interests (e.g., internet, TV or videos, and computer games) .41 .39i. Not enough skills .38 .55m. Teased by friends .67 .74g. Bullied or intimidated by others .64 .67f. Not having a safe place to do physical activity .40 .56r. Community programs are not available .89 .74t. School programs are not available .76 .66s. Parent is not supportive .35 .38 .56o. Cost of doing physical activity .36 .47k. Not having transport to facilities .33 .42c. Not having someone to do physical activity with .69 .61d. Not having fun .67 .55e. Friends are not supportive .55 .52b. Busy social life .44 .46a. Weather .38 .29j. Too much school work .55 .45u. Feeling tired .47 .56n. Sick or injured .45 .43w. Family responsibilities .39 .46h. Having a job .38 .40Eigenvalue 10.8 1.5 1.1 1.0 0.8Percent variance accounted for 43.0 4.3 3.0 2.2 1.4

Note. SOBPAS = Self-Efficacy to Overcome Barriers to Physical Activity Scale; Com. = communality; loadings less than .32 are not shown; 1 = internal barriers; 2 = harassment barriers; 3 = physical environment barriers; 4 = social environment barriers; 5 = responsibilities barriers.

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also accepted: χ2 M7 = 865.89, df = 411, χ2/df = 2.11, p < .001, GFI = .84, NNFI = .90, CFI = .91, RMSEA = .05. Comparison of the models supported the hypothesis of equivalent factor loadings: Δχ2 M7–M6 (17) = 19.36, p = .308. The next comparison examined whether the factor variances and covariances could be constrained between age groups: χ2 M8 = 883.61, df = 426, χ2/df = 2.07, p < .001, GFI = .83, NNFI = .90, CFI = .91, RMSEA = .05. Comparing the measurement weights model with the more restric-tive model constraining factor variances/covariances demonstrated acceptable invariance: Δχ2 M8–M7 (15) = 17.72, p = .278. The final test of invariance constrained the measurement residuals to be equal between the two age groups and compared this model against the previously

accepted model of invariance of factor variances/covari-ances. This result was also accepted as plausible at p < .01: χ2 M9 = 925.23, df = 450, χ2/df = 2.06, p < .001, GFI = .82, NNFI = .90, CFI = .90, RMSEA = .05, Δχ2 M9–M8 (24) = 41.64, p = .014. The standardized solution demonstrating invariance of all coefficients including residuals is shown in Figure 1.

Internal Consistency Reliability

Cronbach’s coefficient alpha for the SOBPAS sub-scales were: self-efficacy to overcome (a) internal barriers, .86, (b) harassment barriers, .81, (c) physical environment barriers, .82, (d) social environment barriers, .80, and (e)

Figure 1. Standardized solution for final Self-Efficacy to Overcome Barriers to Physical Activity Scale (SOBPAS) model. The values next to each observed measure are squared multiple correlations, which indicate how much variance in each observed measure is accounted for by the factors.

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responsibilities barriers, .79. These values were above the suggested .70 value (Nunnally, 1978), indicating good internal consistency reliability.

Predictive Validity of SOBPAS

We used structural regressions to examine the rela-tionship between the five SOBPAS latent factors and the physical activity measure (MET hours/week). The model fit declined from the measurement model described: χ2

M10 = 1341.38, df = 220, χ2/df = 6.09, p < .001, GFI = .82, NNFI = .74, CFI = .77, RMSEA = .11. The structural regres-sions between the SOBPAS latent factors and physical activity level were all statistically significant and ranged from β = .09 (responsibilities barriers) to β = .25 (internal barriers). Overall, the five SOBPAS factors accounted for 44% of the variance in physical activity level (see Table 2).

Discussion

In this study, we revised a previous measure of self-efficacy to overcome barriers to vigorous physical activity among high school students (Dwyer et al., 1998) by modi-fying the question stem to assess self-efficacy to overcome barriers to both moderate and vigorous physical activity, removing redundant barriers, and adding barriers based on qualitative research among adolescents (Allison et al., 2005; Dwyer et al., 2006). Factor analysis and internal consistency reliability analysis of the SOBPAS, which ad-dresses a more comprehensive barrier list than previous measures (Dwyer et al., 1998; Reynolds et al., 1990; Ryan & Dzewaltowski, 2002; Sallis et al., 1988; Saunders et al., 1997), yielded five theoretically meaningful interpretable factors. The results suggest that self-efficacy to overcome impediments to moderate and vigorous physical activity among adolescents is multidimensional. This is consis-tent with previous research that used a multidimensional measure of self-efficacy for physical activity (Dwyer et al.,

1998; Saunders et al., 1997). However, there are mixed views about the dimensionality of self-efficacy for physi-cal activity. For example, using CFA, Motl et al. (2000) suggested that self-efficacy to overcome physical activity barriers is unidimensional. However, they examined the factorial validity among adolescent girls only, and their results cannot be generalized to adolescent boys.

In our study, the five factors logically describe the factor content. The first subscale addressed self-efficacy to overcome internal barriers such as embarrassment about others watching, concern about weight, insufficient skills, not feeling motivated, and having other interests. The sec-ond subscale dealt with being teased and bullied by others and was named self-efficacy to overcome harassment barri-ers. Participants in qualitative research expressed concerns about safety by referring to gangs hanging out at physical activity facilities and crime at facilities (Dwyer et al., 2006; Ries et al., 2008); therefore, lack of safety seemed to fit in this subscale. The third subscale focused on unavailable and inaccessible community resources and was named self-efficacy to overcome physical environment barriers.

The fourth subscale addressed self-efficacy to over-come social environment barriers such as not having someone to do physical activity with, unsupportive friends, and a busy social life. Not having fun fit in this subscale in that some adolescents enjoy the social aspect of physical activity and may have more fun doing physical activity with others. For example, previous research found that adolescents enjoyed physical activity as an opportunity to hang out with friends (Hohepa et al., 2006). Nonetheless, further research is needed to better understand how and why adolescents have fun doing physical activity regardless of whether it involves socializing with friends. The fifth subscale focused on self-efficacy to overcome barriers such as school work, family responsibilities, and a job. Feeling tired fit in this subscale because it may occur with addi-tional responsibilities. For example, Noland, Price, Dake, and Telljohann (2009) found that many adolescents did not get enough sleep because of too much homework (46%), home chores (23%), and a part-time job (21%).

CFA provided further evidence that the factor struc-ture showed stable measurement properties. The results were impressive, as we demonstrated invariance of factor loadings, often considered the minimum equivalence level to appropriately compare solutions across groups (see Vandenberg & Lance (2000) for further explanation) and found ME/I for the factor variances and covariances as well as measurement residuals. This level of invariance is rarely demonstrated.

These results differ from the internal structure of the Dwyer et al. (1998) measure. The SOBPAS comprises five subscales, whereas the Dwyer et al. measure consists of two. The Dwyer et al. measure did not address harassment bar-riers. The differences in factor structure for the SOBPAS and previous measures may be due to a number of issues.

Table 2. Structural regression predicting metabolic equivalent hours/week (square root)

SOBPAS factor St. β SE β R2

Predicting MET hours/week .44Internal barriers .25* .04Harassment barriers .12* .04Physical environment barriers .13* .04Social environment barriers .12* .04Responsibilities barriers .09* .04

Note. St. = standardized; SE = standard error; SOBPAS = Self-Efficacy to Overcome Barriers to Physical Activity Scale; MET = metabolic equivalent.*p < .001.

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First, the SOBPAS measures self-efficacy to overcome bar-riers to physical activity, whereas some others focus on self-efficacy to overcome impediments to exercise (Reynolds et al., 1990; Sallis et al., 1988). Exercise is a structured physical activity that is done for fitness reasons; thus, self-efficacy to overcome barriers to exercise and physical activity in general may be different. Second, the SOBPAS measures self-efficacy to overcome barriers to both moderate and vigorous physical activity, whereas the Dwyer et al. measure deals with self-efficacy to overcome barriers to vigorous physical activity only. Some people may be less confident that they can handle impediments, such as not having fun and not feeling motivated, in more strenuous physical activities. Third, the number of items in various measures likely influenced the factor structure; including more items allows various dimensions to be examined more compre-hensively. For example, the SOBPAS assesses self-efficacy to overcome 22 barriers to physical activity, whereas other measures focus on fewer barriers (e.g., Sallis et al., 1988, 12 obstacles; Dwyer et al., 1998, 20 obstacles).

The regression results are consistent with findings from previous research showing that self-efficacy to overcome barriers to physical activity predicts adolescent physical activity. For example, researchers have found that self-efficacy to overcome barriers predicted moder-ate and vigorous physical activity among youth (Ryan & Dzewaltowski, 2002), vigorous physical activity among high school students (Allison et al., 1999), and physical activity participation among both rural and urban adoles-cents (Loucaides, Plotnikoff, & Bercovitz, 2007). In those studies, self-efficacy accounted for one third or less of the variance in physical activity (3–8%, Allison et al., 1999; 14–18%, Loucaides et al., 2007; 26–37%, Ryan & Dzewal-towski, 2002). The SOBPAS factors demonstrated a strong ability to predict physical activity level, accounting for a bit less than half the variance in energy expenditure, clearly demonstrating the value of self-efficacy to overcome bar-riers in explaining this relationship.

A limitation of this study is that the number of nonre-spondents was not tracked; thus, the response rate could not be calculated to examine sample representativeness. To further assess the SOBPAS validity, future research could examine the relationship between the SOBPAS and a measure of general self-efficacy, its ability to predict physical activity assessed by a more objective measure such as an accelerometer, and whether it predicts a change in physical activity levels over time. Test-retest reliability could also be examined. Although the SOBPAS provides ex-amples of moderate and vigorous physical activity, adding a definition (e.g., an activity that causes some increase in breathing, heart rate, or sweating) to the measure would be a useful frame of reference for respondents.

In conclusion, the SOBPAS is a revision of the Dwyer et al. (1998) self-efficacy measure. It includes additional barriers to physical activity identified in previous qualita-

tive research (Allison et al., 2005; Dwyer et al., 2006), and unlike other instruments it measures self-efficacy to overcome barriers to both moderate and vigorous physical activity. Analyses of SOBPAS data from a sample of male and female high school students in different grades in-dicated the SOBPAS is a multidimensional measure of self-efficacy to overcome barriers and has good construct validity, internal consistency reliability, and predictive validity. Results suggest the SOBPAS shows promise in research on the correlates of physical activity and physi-cal activity intervention research targeting self-efficacy to overcome barriers as a determinant of behavior change.

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Authors’ Notes

This research was funded by the Heart and Stroke Founda-tion of Ontario (HBR4860). Please address correspond-ence concerning this article and requests for a copy of the SOBPAS to John Dwyer, Department of Family Relations and Applied Nutrition, College of Social and Applied Hu-man Sciences, University of Guelph, Macdonald Institute Building, 50 Stone Road East, Guelph, Ontario, Canada, N1G 2W1.

E-mail: [email protected]

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