1. family and parenting
2. children

Pediatric Exercise Science, 2008, 20, 342-356
© 2008 Human Kinetics, Inc.
Predicting Physical Activity Intention
and Behavior in School-Age Children
Louise Foley, Harry Prapavessis, Ralph Maddison,
Shauna Burke, Erin McGowan, and Lisa Gillanders
Two studies were conducted to predict physical activity in school-aged children.
Study 1 tested the utility of an integrated model in predicting physical activity (PA)
intention and behavior—the theory of planned behavior (TPB) and self-efficacy
theory. Six hundred and forty-five New Zealand children (aged 11–13 years) completed measures corresponding to the integrated model and a self-reported measure
of PA one week later. Perceived behavioral control (PBC) and subjective norm
were the two strongest predictors of intentions. Task efficacy and barrier efficacy
were the two strongest predictors of PA. A second study (Study 2) was conducted
to determine whether the self-efficacy measures could discriminate objectively
measured PA levels. Sixty-seven Canadian children (aged 11–13 years) completed
task and barrier self-efficacy measures. The following week, children classified
as ‘high’ (n = 11) and ‘lower’ (n = 7) for both task and barrier efficacy wore an
Actical® monitor for seven consecutive days to provide activity-related energy
expenditure (AEE) data. Results showed that children with high efficacy expended
significantly greater AEE than their lower efficacious counterparts. Findings from
these two studies provide support for the use of self-efficacy interventions as a
potentially useful means of increasing PA levels among school-aged children.
Participation in regular physical activity (PA) is associated with multiple
physical (24) and mental health (19) benefits and therefore has become an important public health objective. The global estimate for inactivity (doing no or very
little PA at work, at home, for transport or in discretionary time) is 17%, while the
global estimate for insufficient levels of activity (< 150 min moderate or < 60 min
of vigorous activity per week) is 40% (11). The U.S. Surgeon General’s report (45)
and recent review by Warburton, Nicol and Bredin (46) highlighted that substantial
health benefits can be gained through PA producing daily energy expenditure of
150 kcal · day–1 or 1000 kcal · week–1. Although the specific amount of energy
expenditure needed to obtain health benefits in children (e.g., decrease their risk of
cardiovascular disease) is not known, there is evidence that PA is inversely related
Foley, Prapavessis, Burke, and McGowan are with the School of Kinesiology, The University of Western Ontario London, Ontario, Canada N6A 3K7. Maddison is with the Clinical Trials Research Unit,
University of Auckland, Auckland, New Zealand. Gillanders is with the Dept. of Sport and Exercise
Science, University of Auckland, Auckland, New Zealand.
342
Predicting Physical Activity 343
to obesity (27), cardiovascular disease risk factors (18), and various physical and
psychological health complaints (26). It is recommended that children participate
in at least 60 min of moderate intensity PA most days of the week, preferably
daily (17).
Because of the potential of regular PA to improve health, considerable research
has focused on understanding the motivation and cognitive processes that underlie
the adoption and maintenance of the behavior. From a theoretical perspective,
several models cached within social cognitive theory have arisen to explain PA
behavior. Of these, the Theory of Planned Behavior (TPB)(1) has been widely used
and shown to be successful in predicting PA intentions and behavior in disparate
populations including adults, youth and those with heart disease (23). According to
the TPB, intention is proposed to be the most immediate determinant of behavior.
The constructs attitude, subjective norm, and Perceived Behavioral Control (PBC)
are proposed to influence intention. Because many behaviors pose difficulties of
execution that may limit volitional control, PBC is also posited to directly influence
behavior as shown in Figure 1.
Identifying the determinants of PA behavior in youth and adolescents may be
particularly important as the maintenance of regular PA from youth to adulthood
could result in the prevention of inactivity-related diseases (31). In youth-related
research to date, the TPB has been found to predict significant variance in exercise
intentions (14,20,36). Although these findings provide support for the utility of
TPB to predict PA intentions, none used a subjective or objective measure of PA
behavior. However, in adults, research evidence has generally supported a modest
positive relationship between intention and behavior (23).
A core argument for theory integration is that a greater understanding can be
gained through the joint use of theory than obtained through the use of a single
model approach (10,29). For instance, researchers have incorporated Self-Efficacy
Theory into the TPB to study PA intention and behavior in adolescent populations
(22). Self-efficacy is defined as “people’s belief about their capabilities to produce
performances that influence events affecting their lives” (6). More recently, Bandura
(7,8) has refined that definition of self-efficacy to encompass those beliefs regarding individuals’ capabilities to produce performances that will lead to anticipated
outcomes. Maddux (30) suggests that this definitional development has led to
the distinction between task efficacy (efficacy is assessed relative to capabilities
to successfully perform the targeted behavior) and self-regulation efficacy (e.g.,
barrier efficacy—overcoming impediments or challenges to successful behavioral
performance).
By definition there seems to be some degree of overlap between PBC and
self-efficacy. Both constructs, for instance, are concerned with control (32). One
way to distinguish between these two constructs is to highlight that control comes
in two forms: internal, based on factors within the individual, and external, based
on factors outside the individual. This argument has been developed by Terry and
O’Leary (40) who suggested that PBC is synonymous with external control factors, while self-efficacy is synonymous with internal control factors. Armitage and
Conner (5) also present a perspective on this issue that is in line with that adopted
by Terry and O’Leary (40). Hence, PBC and self-efficacy are thematically similar
constructs, but nevertheless conceptually different.
344 Foley et al.
Although researchers (5,22,40) have argued that Azjen’s (1) PBC construct can
be separated into distinguishable internal and external control factors, it is problematic to accept the internal component of PBC as a true measure of self-efficacy
because of the manner in which it is assessed. PBC items are general in nature,
whereas items representing self-efficacy are more specific and precise. For instance,
it has been recommended that the optimal measurement of internal (i.e., task) selfefficacy should include an assessment of both the strength and the magnitude of
the efficacious belief (33). An example item is “How confident are you that you
can complete ten minutes of physical exercise at a light intensity level, three times
next week”. Traditional PBC items posited to measure internal control such as, “I
would find it easy to take part in regular physical activity next week” do neither of
these things. Furthermore, such an item makes it difficult to ascertain whether the
respondent is referring to internal control (i.e., my personal ability makes it easy)
or external control (i.e., things in my environment make it easy) factors. Because
of these conceptual and measurement differences, incorporating self-efficacy into
the TPB as an integrated model has the potential to provide a more comprehensive
understanding of PA intention and behavior.
Research in youth incorporating self-efficacy with the TPB has generally found
self-efficacy to explain approximately between 18% and 22% additional variance
in PA intentions over that of the TPB variables (22). Only a few studies exist that
have included a measure of PA (35,43). Of these, Trost et al. (43) found that intention explained 8% of the variance in objectively measured PA, which increased to
10% with the inclusion of PBC and self-efficacy. Another study of adolescent girls
found that self-efficacy and PBC had independent effects on change in subjectively
measured PA over the course of one year (35). Change in PBC predicted change in
levels of vigorous PA, whereas change in self-efficacy did not (35). Unfortunately,
all of the abovementioned studies assessed barrier efficacy and not task efficacy.
The current study seeks to extend previous research in this area by measuring (a)
the strength and magnitude of task efficacious beliefs, (b) both task and barrier
efficacious beliefs, and (c) PA behavior.
The aim of Study 1 was to integrate self-efficacy theory within the TPB framework to predict PA intentions and behavior in a large sample of youth (Figure 1).
Two hypotheses were generated for Study 1. First, intention would be predicted
by attitude, subjective norm, PBC, and also self-efficacy. Second, PA would be
predicted by intention, PBC, and the self-efficacy constructs.
Methods—Study 1
Participants
Participants were 645 intermediate school children (Years 7 and 8) recruited from
central Auckland, New Zealand. Participants were required to communicate in
English, and obtain parental consent. The sample consisted of both males (n = 348)
and females (n = 290) who ranged in age from 10 to 12 years (M = 11.59, SD =
.88) and represented various ethnic groups (NZ European 52%; Maori 14%; Pacific
11%; Asian 11%; South African 4%, Indian 4% and other 4%). The average BMI
for the sample was 20.26, SD = 5.05.
Predicting Physical Activity 345
Figure 1 — Predicting intention and behavior.
Measures Corresponding to the Integrated Model
Attitude. Attitude toward PA was assessed using the stem item “For me to take
part in regular physical activities during the next week is . . . ”. Six bipolar adjective scales were scored from 1 to 5. The scales included both experiential (e.g.,
enjoyable-not enjoyable, pleasant-unpleasant, fun-boring) and instrumental (e.g.,
useful-useless, harmful-beneficial) items (3). An alternative phrase or word was
used to describe the descriptors that anchored the attitude scale (e.g., another word
or phase for “harmful” is “dangerous” or “not good for you”) to assist the children
in identifying the most appropriate response. The instructions that preceded the
adjectives directed the participant to “Please put a tick on the line for the amount
that you agree or disagree with the statement”. To obtain an overall measure of a
participant’s attitude, each of the six items were summed. Possible scores for the
overall attitude measure could range from 6 to 30. The scale displayed an acceptable degree of internal consistency (α = .72).
Subjective Norm. Subjective norm was assessed using six questions which were
rated on a 5-point Likert scale using the following descriptors: 1 “strongly disagree
or completely false”, 2 “disagree or kind of false”, 3 “maybe”, 4 “agree or kind
of true”, 5 “strongly agree or completely true”. The items assessed both injunctive norms which evaluates whether important others approve/disapprove of the
desired behavior (e.g., “people who are important to me approve of me taking part
in regular physical activities over the next week”), as well as descriptive norms
which assess whether important others perform the behavior themselves (e.g., “my
mum/dad (guardian) participate in physical activities regularly”; 3). Possible scores
346 Foley et al.
for subjective norms could range from 6 to 30. The internal consistency for the six
item scale was acceptable (α = .73).
Perceived Behavioral Control (PBC). PBC was assessed using six items scored
on a 5-point Likert scale using the following descriptors: 1 “strongly disagree or
completely false”, 2 “disagree or kind of false”, 3 “maybe”, 4 “agree or kind of
true”, 5 “strongly agree or completely true” that captured both internal and external
control (3). Internal items reflected the perceived easy or difficulty of performing
PA (e.g., “I have the ability to exercise regularly in the next week”.). External items
reflected the participant’s belief that they have control over the behavior (e.g., “I
have control over whether I can take part in regular physical activities in the next
week”; 3). The internal consistency for this scale was good (α = .79).
Intention. Intention to perform PA over the course of the next week was assessed
in this study using items taken from Ajzen (3). Participants responded to four items
scored on a 5-point Likert scale using the following descriptors: 1 “strongly disagree
or completely false”, 2 “disagree or kind of false”, 3 “maybe”, 4 “agree or kind of
true”, 5 “strongly agree or completely true”. A sample item was, “I plan to take
part in regular physical activity next week”. The four-item scale displayed a high
degree of internal consistency (α = .84).
Self-Efficacy. Both task and barrier efficacy were assessed. Task efficacy was
assessed using an adapted version of the Self-Efficacy Scale (33). Participants
rated their confidence to complete regular PA for increasing time periods (10,
30, and 60 min) at various intensities (light, moderate and hard) on a color-coded
scale ranging from 0% (no confidence at all) to 100% (completely confident). To
calculate an overall task efficacy value, scores for each item were then summed and
divided by the number of items. Higher task efficacy scores on the scale equate to
greater efficacy to participate in PA for longer periods of time at a greater intensity level. The task efficacy scale also demonstrated an excellent level of internal
consistency (α = .95). Barrier efficacy was assessed using a modified version of
the Barrier Efficacy Scale (33). Six salient items derived from a previous pilot
study were rated on a color-coded scale from 0% (no confidence at all) to 100%
(completely confident). Participants rated their confidence to perform regular PA
in the presence of six common barriers (e.g., “the weather is very bad”, “I have a
lot of school work to do”) that prevent children and adolescents from participating
in PA. Scores were summed and divided by the number of items to derive barrier
efficacy. Internal consistency was high (α = .86).
Physical Activity. The Physical Activity Questionnaire for Children (PAQ-C) was
used to assess PA behavior in the previous week (15). This is a self-administered
seven day recall of PA designed for elementary school children who are currently
in the school system and have recess as a regular part of their school week. The
PAQ-C is a 9-item measure scored on a 5-point Likert scale from 1 to 5, with
higher values indicating greater levels of PA. A sample item is: “In the last 7 days,
what did you do most of the time at recess?”. Overall PA is provided by deriving
the mean of all 9 items. Evidence exists confirming the reliability and validity of
the PAQ–C (15).
Predicting Physical Activity 347
Procedure
Several steps were taken to ensure children completely understood the questions
related to both the TPB and self-efficacy constructs. First, wording and presentation
changes were made based on consultation with other Year 7 and 8 students and
teachers from a pilot study. From this consultation the following steps were taken
to ensure clarity and comprehension. First, a clear and simple operational definition
of organized and nonorganized PA was provided before completing the TPB scales.
Second, a clear and simple operational definition of mild, moderate, and hard PA
was provided along with accompanying pictures before completing the self-efficacy
scales. Third, trained research assistants administered the questionnaire package
visually using overhead projection. This ensured that each question was properly
explained, read and completed before students moved on to the next question.
All study procedures and related documents were approved by the regional
ethics committee. Contact was initiated with the school principal to discuss the
study purpose and procedures and to obtain consent to approach the students. Once
permission was obtained, researchers made contact with the school children via the
respective class teachers. Participant information sheet and consent forms were then
administered for parental consent. Children completed assent forms. Once parental
consent was obtained, the researchers returned to the school and administered the
social cognitive measures at time 1 (T1) and then returned one week later (T2) to
administer the PAQ-C questionnaire. The response rate was high at 90%.
Results
Descriptive Statistics
Descriptive statistics and bivariate correlations for the variables of interest are
presented in Table 1.
Table 1 Descriptive Statistics
Variable
Mean
SD
2
3
4
5
6
7
1. Subjective norm
2. Attitude
3. PBC
4. Intention
5. Task efficacy
6. Barrier efficacy
7. Physical activity: PAQ-C
4.15
4.46
4.34
4.41
81.31
67.42
3.21
.63
.69
.63
.68
19.31
22.30
.81
.42**
—
.59**
.50**
—
.63**
.54**
.68**
—
.42**
.57**
.50**
.52**
—
.40**
.49**
.39**
.43**
.66**
—
.35**
.40**
.32**
.37**
.46**
.51**
—
Notes: PBC = perceived behavioral control; PAQ-C = Physical Activity Questionnaire for Children. Subjective
norm, attitude, perceived behavioral control, and goal intention were scored on a scale of 1–5. Task efficacy and
barriers efficacy were rated on a confidence scale ranging from 0 (not at all confident) to 100 (completely confident)
with higher values indicating greater perceptions of efficacy for PA. Subjective PA behavior was assessed using a
5-point scale with higher values representing greater levels of PA during the previous week.
Because of missing data for some variables, N varied from 636 to 642.
348 Foley et al.
Predicting Physical Activity Intention
A hierarchical regression was conducted where intention served as the dependent
measure. The three constructs of the TPB (i.e., subjective norm, attitude, and PBC)
were entered simultaneously into Step 1 of the regression equation and were found
to significantly predict intention [F(3,635) = 284.17, p < .001], accounting for 56%
of the response variance. Task and barrier efficacy were entered into Step 2 and
were also found to contribute significantly to the prediction [R2∆ = .01, F(2,633)
= 8.55, p < .001], accounting for 1% of the response variance. When all variables
were entered together, PBC (β = .37, t = 10.61, p < .001) and social norms (β = .29,
t = 8.72, p < .001) emerged as the two strongest predictors of intention, followed
by attitude (β = .15, t = 4.44, p < .001) and task efficacy (β = .12, t = 3.26, p <
.001). Barrier efficacy (β = .02, t = .57, p > .05) did not make a significant unique
contribution to intention
Predicting Physical Activity Behavior
A second hierarchical multiple regression analysis was conducted whereby selfreported PA behavior represented the dependent variable1. Intention was the first
variable entered in the regression and was found to be a significant predictor
[F(1,630) = 97.95, p < .001], accounting for 13% of the response variance. PBC
was entered in Step 2 and also contributed significantly to the prediction [R2∆ =
.01, F(1,629) = 6.79, p < .01], accounting for 1% of the variance. Finally, the inclusion of task and barrier efficacy in Step 3 resulted in a further increase of 16% in
the amount of explained variance [R2∆ = .16, F(2,627) = 70.70, p < .001]. When
all variables were considered together in the model, barrier (β = .35, t = 7.73, p <
.001) and task efficacy (β = .16, t = 3.26, p < .001) emerged as the two strongest
predictors of PA behavior, followed by intention (β = .12, t = 2.42, p < .01). PBC
(β = .03, t = .53, p > .05) did not make a significant unique contribution to PA.
Discussion
The purpose of this prospective study was to integrate Self-Efficacy Theory within
the TPB framework to predict PA intentions and behavior in a large sample of
youth. Overall, results generally supported the hypotheses. For the prediction of
PA intention, the TPB variables of attitude, subjective norm, and PBC were found
to explain 56% of the variance in intention. The addition of the two self-efficacy
variables increased the explained variance by a modest 1%. In the final model,
PBC was found to be the strongest individual predictor of intention followed by
subjective norm and attitude.
The amount of variance in intention explained by the TPB variables was
higher than other studies, which have typically explained between 30 and 47% of
variance in youth populations (14,20,36). One reason for these different findings
is the substantial contribution subjective norms made to the prediction of intentions in our study. Subjective norms were assessed using both injunctive norms
(what participants felt important others expected them to do) and descriptive norms
(whether important others actually perform the behavior), which is consistent with
the recommendations of Ajzen (2). Furthermore, the assessment of injunctive norms
Predicting Physical Activity 349
included reference to the specific time frame (i.e., one week) that corresponded
with the time frame of the intention measure. Typically the assessment of norms
has only included the injunctive component with no time reference. Hence, these
two methodological improvements most likely contributed to the increased prediction of intentions, as is evident in other studies that have used both types of
norm (38). Sampling might be another reason to explain these different findings.
Our sample consisted of both boys and girls aged between ten and twelve years,
which differs from other research studies that have used younger (36) and older
(43) aged participants.
The addition of the self-efficacy constructs resulted in a modest but statistically
significant increase in the amount of variance explained in intention. This finding is
lower than other studies which have used self-efficacy measures that are conceptually similar to PBC (22,43). Furthermore, in the final model, task efficacy was the
weakest significant independent contributor to intention, whereas barrier efficacy
essentially made no contribution at all. These results suggest that compared with
PBC, neither task nor barrier efficacy were salient constructs for the formation of
PA intentions in this sample.
For the prediction of PA behavior, intention explained 13% of the variance in
behavior. The addition of PBC resulted in a modest (1%) increase of the explained
variance; however the addition of the two self-efficacy constructs increased the
explained variance by 16%. The final model explained 30% of PA behavior, with
the strongest independent predictors being barrier and task efficacy, followed by
intention. These results suggest that efficacious beliefs to perform activities and
overcome obstacles to regular PA participation are important in the prediction of
self-reported PA behavior in school-aged children. Compared with the findings
of Trost et al. (43) and Motl et al. (35), our findings provided larger amounts of
explained variance in PA behavior. These disparate findings may be related to sampling differences or to the different self-efficacy and behavioral measures used.
From our data, there was a differential effect of self-efficacy and PBC in
explaining PA intentions and behavior. Self-efficacy was not a strong predictor of
intentions, but was a strong predictor of PA behavior. Conversely, PBC was the
strongest individual predictor of intention but the weakest predictor of behavior.
There are several potential explanations for these findings. The first is related to
scale correspondence between the self-efficacy, PBC, intention and PA measures
(13). The PBC items reflected participant’s control over regular PA participation
and general perceived ease or difficulty to be physically active over the following
week, whereas (a) task efficacy assessed participant’s confidence to be active at
specific levels of intensity and duration and (b) barrier efficacy assessed participant’s confidence to overcome common obstacles that interfere with PA. The PBC
scale corresponds better to the general measurement of intention (“I plan to do
regular physical activity in the next week”) than to the specific measurement of PA
(structured activities that the person actually participates in over the past week).
Conversely the task efficacy and barrier efficacy scales correspond better with specific PA than with general intentions. Furthermore, the PAQ-C primarily focuses on
structured and organized PA whereas the PBC is congruent with volitional behavior
and hence, PBC may have little bearing on the PA measure used.
Another plausible explanation for the lack of effect of PBC to behavior may
be related to the age of the children and the nature of volitional PA. Children in this
350 Foley et al.
study were young (11–13 years) and still very much subject to parental and school
influence and control. Hence, although a child may indicate they have greater control
to perform regular PA, the opportunity for volitional PA is limited.
The results of Study 1 highlight the importance of theory integration in predicting PA in a youth population. As both task and barrier self-efficacy were found to
be particularly important for predicting subjective PA behavior, further research is
required to test the utility of these constructs in predicting objectively measured PA
behavior—this was the aim of Study 2. The hypothesis was that children who were
more highly efficacious toward regular PA would perform greater amounts of PA and
expend more energy in a 7-day period than their less efficacious counterparts.
Methods—Study 2
Participants
Participants were 67 primary school children (Grades 7 and 8) recruited from
Southwestern Ontario, Canada. Participants were required to communicate in
English, and obtain parental consent. The sample consisted of both male (n = 25)
and female (n = 42) participants who ranged in age from 10 to 13 years (M = 11.59,
SD = .88), and represented a variety of ethnic groups (North American 55%; Arab
10%; British 9%; European 9%; Asian 6%; other 5%; African 3%; Aboriginal 2%;
and Caribbean 2%).
Measures
Physical Activity Behavior. Objective PA was assessed using the Actical® (MiniMitter, Oregon), a small (approximately 2.8 × 2.7 × 1.0cm3), lightweight (17g),
and water resistant omnidirectional accelerometer. The device is sensitive to low
frequency movements in the range of 0.5–3.2 Hz, which is the common range for
human movement (25). The Actical® has been shown to be a valid and reliable
predictor of energy expenditure in youth (25). Participants were instructed to wear
the device on the right hip during the waking hours of a 7-day period. Data were
collected at 15-s epochs, and were converted to 1-min epochs for data analysis
(16). For complete data, participants were required to provide a minimum of ten
hours per day for at least five days (including weekend; 42). The Actical® sensors
were programmed with the participant’s personal information (e.g., age, weight,
and height) to provide an estimate of activity-related energy expenditure (AEE—
the number of kilocalories expended per minute per kilogram of subject weight).
Adopting a pragmatic approach, AEE per day were summed and divided by the
days worn to provide mean daily AEE.
In addition, the average time per day in minutes within predetermined intensity
cut-points (i.e., sedentary, light, moderate, and vigorous) was compared between
efficacious groups. These cut-off points were in line with those defined by Trost et
al. (44). Finally, as with Study 1, the PAQ-C (15) was used to assess participant’s
self-reported PA behavior over the course of the previous 7 days.
Predicting Physical Activity 351
Self-Efficacy. As with Study 1, both task and barrier efficacy were assessed. Inter-
nal consistency was high for both task (α = .93) and barrier efficacy (α = .92).
Procedures
Identical ethical and documentation procedures to Study 1 were followed. Once
school and parental consent was obtained, the researchers returned to the school
and administered a demographic form (e.g., sex and ethnicity) and self-efficacy
questionnaire (task and barrier), which were completed during school time. Participants (n = 21) who scored in the upper and lower quartiles of task (upper M =
98.08, SD = 1.12 and lower M = 63.33, SD = 10.92) and barrier efficacy (upper
M = 93.03, SD = 3.23 and lower M = 46.78, SD = 11.48) were identified and contacted the following week for the next phase of the study. They were all informed
that they had been randomly selected to take part in a PA monitoring study. At the
beginning of the school week, the targeted sample of children was asked to wear
the Actical® monitor for seven consecutive days. The researchers returned one week
later to collect the devices and administer the PAQ-C.
Treatment of the Data
Separate ANOVAs were used to determine whether differences in AEE and PAQ-C
scores would exist between the high and lower efficacious groups. Before analysis,
assumptions for ANOVA (i.e., normality, outliers, linearity, missing data) were
checked (39). Normality and linearity were satisfied but there were two cases of
missing data. These participants provided less than five days of data making it
difficult to reliably estimate weekly habitual PA (42). In addition, one case was
an extreme outlier as inspection of the Boxplot procedure showed that this case
extended more than three box-lengths from the edge of the box. These three cases
were eliminated from subsequent analysis.
Results
Descriptive Statistics
Descriptive statistics for the overall sample and the subgroups of ‘high’ and ‘lower’
self-efficacious children are provided in Table 2.
Objective Physical Activity. Significant differences between groups for AEE
(F(1,16) = 7.23, p < .05; partial η2 = .31) were found. Children who reported high
levels of task and barrier efficacy expended more energy through PA compared with
their lower efficacious counterparts2. Results also showed a significant trend effect
for the moderate and vigorous cut-off points. That is the high efficacy group (M =
164.02, SD = 40.24) spent more time in moderate physical activity (F = 3.65, p <
.08, 2 = .19) compared with the lower efficacy group (M = 131.54, SD = 24.49).
The high efficacy group (M = 5.80, SD =5.56) also spent more time in vigorous
activity (F = 3.84, p < .07, 2 = .19) compared with the lower efficacy group (M =
1.52, SD = 1.71). No significant differences or significant trend effects were found
352 Foley et al.
Table 2 Descriptive Statistics of the Variables of Interest
Variable
N
Mean
SD
Overall task efficacy
Overall barrier efficacy
Objective PA (AEE)
Lower self-efficacy group
Higher self-efficacy group
Subjective PA (PAQ-C)
Lower self-efficacy group
Higher self-efficacy group
BMI
Lower self-efficacy group
Higher self-efficacy group
67
67
18
7
11
18
7
11
18
7
11
85.82
74.49
517.87
428.36
574.83
3.38
3.01
3.61
17.89
17.69
18.01
13.27
18.51
131.68
108.39
115.12
.67
.71
.55
3.39
3.98
3.16
Notes. Task efficacy and barrier efficacy were rated on a confidence scale ranging from 0
(not at all confident) to 100 (completely confident) with higher values indicating greater
perceptions of efficacy for PA. Objective PA behavior was measured in terms of Activity
Energy Expenditure (AEE); the number of kilocalories expended per day. Subjective
PA behavior was assessed with the PAQ-C and uses a 5-point scale with higher values
indicating greater levels of PA during the previous week. BMI is body mass index.
Pearson correlation between AEE and PAQ-C, r = .36 (p = .14)
for sedentary (high efficacy group M = 388.25, SD = 45.46; lower efficacy group
M = 416.99, SD = 30.16) or light activity (high efficacy group M = 254.14, SD =
29.34; lower efficacy group M = 270.27, SD = 55.85).
Subjective Physical Activity. Children who reported high levels of task and
barrier efficacy were more physically active (M = 3.61, SD = .55) than their lower
efficacious counterparts (M = 3.01, SD = .71; F(1,16) = 4.12, p = .06; partial η2
= .21)2.
Discussion
This second study sought to determine whether task and barrier efficacy could differentiate levels of objectively measured PA. Our hypothesis was supported; children
who had high efficacious beliefs were significantly more active and expended more
energy than their less efficacious counterparts. The observed effect size was large
η2=0.31. Values over 0.14 are considered a large effect (12). Self-efficacy scores
for participants in the lower self-efficacy group ranged between 47% and 63%.
Therefore, we propose the effects found would be greater in a sample that included
truly low efficacious children, who are likely to be minimally active or sedentary.
As the children’s efficacious profile was based on their task and barrier efficacy
scores, future research should delineate which one is more important in explaining
objectively measured PA.
Predicting Physical Activity 353
The correlation between subjective and objective measures of PA was of a
moderate magnitude (r = .36), which is consistent with previous research reported
in children (37). Our findings suggest that although these two PA constructs share
common variance they are essentially measuring different types of PA. This is not
surprising given the fact that the PAQ-C is designed primarily to assess structured
leisure time PA whereas the Actical® is designed to assess any free living ambulatory PA. The fact that self-efficacy was found to be highly related to both PA
measures further strengthens the importance of self-efficacy in understanding PA
behavior in this population.
Using essentially two different types of PA also highlights the possibility that
the TPB variables intention and PBC might have performed better in an integrative
model predicting objectively measured PA. The fact that this integrative model
was not tested using objectively measured PA is a limitation of this study. Costs
associated with the Actical® however, made it impossible to replicate Study 1
using this device.
A final observation warrants discussion regarding AEE in this sample population. The mean AEE (517.87 kcal/day) found in this study is comparable to studies
of other youth populations using the current ‘gold standard’ technique of doubly
labeled water. Johnson, Russ, and Goran (28) reported values of 598 kcal/day and
313 kcal/day for boys and girls respectively. If converted to 2.16 MJ/day (1 MJ =
239 kcal), our findings are also similar to those of Montgomery et al. (34), who
found AEE values of 2.7 MJ/day and 1.8MJ/day for boys and girls respectively.
When the average time per day in minutes within predetermined intensity
cut-points (i.e., sedentary, light, moderate, and vigorous) was compared between
efficacious groups, notable differences were seen at both moderate and vigorous
PA. The high efficacious group spent 170 min per day engaging in moderate to
vigorous PA, whereas their lower efficacy group spent 133 min per day. Once again,
we propose the differences found would be greater in a sample that included truly
low efficacious children. These data are in line with other children studies using
similar accelerometry cut-off points. For instance, Guinhouya et al. (21) found
that 8–11 year old French children spent 141 min per day in moderate to vigorous
PA and Trayers et al. (41) found 8–12 year old inner city British children spent
approximately 145 min per day in moderate to vigorous PA. As mentioned previously, it is recommended that children and adolescents participate in at least 60 min
of moderate intensity PA most days of the week, preferably daily (17). However,
others have argued that 90 min or more of moderate to vigorous activity is necessary
to reduce cardiovascular risk among children (4). At this stage, whether children
are meeting the recommended daily PA seems to be a function of which intensity
cut-off points are being used as well as which recommendations are being followed.
Future work is needed to clarify this important issue.
In summary, the results of Study 1 suggest that integration of the TPB and
Self-Efficacy Theory allows for a more complete understanding of PA intentions
and behavior in school-age children. In particular, it seems that PBC is an important
predictor of PA intentions, whereas task and barrier efficacy are important predictors of subjectively measured PA. Furthermore, these two efficacious constructs are
354 Foley et al.
important predictors of objectively measured PA (Study 2). Self-efficacy Theory
based interventions targeted toward increasing PA in children are warranted.
Notes
1. Determining whether intention serves as mediator would provide a stronger theoretical test of
the proposed integrated model. Barron and Kenny (9) have pointed out that a variable functions
as a mediator when it meets the following conditions: (a) the independent variable (PBC, task
efficacy, barrier efficacy), the mediator variable (intention) and the dependent variable (PA) must
affect one another (b) the effect of the mediator must stay statistically significant when regressed
with the independent variable and (c) the effect of the independent variable on the dependent
variable must be statistically nonsignificant when regressed with the mediator. All conditions for
mediation were satisfied with the exception of one—the independent variable (i.e., PBC-beta =
.13, task efficacy-beta = .37 and barrier efficacy-beta = .43) stayed statistically significant (all p
values < .009) when regressed with the mediator (intention). The failure to show this reduction
suggests that intention is not a potent mediator.
2. There were more males (n = 7) than females (n = 4) in the high self-efficacy groups. Conversely there were more females (n = 5) than males (n = 2) in the lower self-efficacy groups.
Research has shown that male children are typically more active than female children (e.g.,
Johnson, Russ, & Goran (28)). Hence, separate ANCOVAs were conducted were gender served
as the covariate and AEE (objective measure of PA) and PAQ-C (subjective measure of PA)
scores served as the dependent variables. Before conducting these analyses, the assumptions
underlying the use of ANCOVA were tested and met (39). Results parallel those reported in the
manuscript suggesting that the differences found in objective and subjective PA are because of
self-efficacy and not gender.
References
1. Ajzen, I. From intention to actions: a theory of planned behavior. In: Action-control:
from cognition to behavior, J. Kuhl and J. Beckmann (Eds.). Heidelberg: Springer,
1985, pp. 11–39.
2. Ajzen, I. http://www.unix.oit.umass.edu/~aizen/index.html. 2002.
3. Ajzen, I. Perceived behavioral control, self-efficacy, locus of control, and the theory
of planned behavior. J. Appl. Soc. Psychol. 32:665–683, 2002.
4. Andersen, L.B., M. Harro, L.B. Sardinha, et al. Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study).
Lancet. 368:299–304, 2006.
5. Armitage, C.J., and M. Conner. Distingusihing perceptions of control from self-efficacy:
Predicting consumptioin on a low-fat diet using the theory of planned behavior. J. Appl.
Soc. Psychol. 29:72–90, 1999.
6. Bandura, A. Human agency in social cognitive theory. Am. Psychol. 44:1175–1184,
1989.
7. Bandura, A. On rectifying conceptual ecumenism. In: Self-efficacy, adaptation and
adjustment: Theory, research, and application, J.E. Maddux (Ed.). New York: Plenum
Press, 1995, pp. 347–375.
8. Bandura, A. Self-efficacy: The exercise of control. New York: W.H. Freeman/Times
Books/ Henry Holt, 1997.
9. Baron, R.M., and D.A. Kenny. The moderator-mediator variable distinction in social
psychological research: Conceptual, strategic, and statistical considerations. J. Pers.
Soc. Psychol. 51:1173–1182, 1986.
Predicting Physical Activity 355
10. Brawley, L.R. The practicality of using social psychological theories for exercise and
health research and intervention. J. Appl. Sport Psychol. 5:99–115, 1993.
11. Bull, F.C., et al. Physical inactivity. In: Comparative Quantification of Health Risks.
Global and Regional Burden of Disease Attributable to Selected Major Risk Factors,
M. Essati (Ed.). Geneva: World Health Organization, 2004, pp. 729–881.
12. Cohen, J.W. Statistical power analysis for the behavioral sciences, 2nd ed. Hillsdale,
NJ: Lawrence Erlbaum Associates, 1988.
13. Courneya, K.S., and E. McAuley. Predicting physical activity from intention: Conceptual
and methodological issues. J Sport Exer Psychol. 15:50–62, 1993.
14. Craig, S., J. Goldberg, and W.H. Dietz. Psychosocial correlates of physical activity
among fifth and eighth graders. Prev. Med. 25:506–513, 1996.
15. Crocker, P.R., D.A. Bailey, R.A. Faulkner, K.C. Kowalski, and R. McGrath. Measuring general levels of physical activity: preliminary evidence for the Physical Activity
Questionnaire for Older Children. Med. Sci. Sports Exerc. 29:1344–1349, 1997.
16. Crouter, S.E., J.R. Churilla, and D.R. Basset. Estimating energy expenditure using
accelerometers. Eur. J. Appl. Physiol. 98:601–612, 2006.
17. Department of Health and Human Services; Centers for Disease Control and Prevention. Physical activity for everyone. Retrieved from: http://www.cdc.gov/nccdphp/dnpa/
physical/recommendations/young.htm. 2007.
18. Eisenmann, J.C. Physical activity and cardiovascular disease risk factors in children
and adolescents: an overview. Can. J. Cardiol. 20:295–301, 2004.
19. Faulkner, G.E.J., and A.H. Taylor. Exercise, health and mental health: emerging relationships. Oxon: Routledge, 2005.
20. Godin, G., and R.J. Shephard. Psychosocial factors influencing intentions to exercise
of young students from grades 7 to 9. Res. Q. Exerc. Sport. 57:41–52, 1986.
21. Guinhouya, C.B., H. Hubert, S. Soubrier, C. Vilhelm, M. Lemdani, and A. Durocher.
Moderate-to-vigorous physical activity among children: Descrepancies in accelerometry-based cut-off points. Obesity (Silver Spring). 14:774–777, 2006.
22. Hagger, M.S., N. Chatzisarantis, and S.J.H. Biddle. The influence of self-efficacy
and past behaviour on the physical activity intentions of young people. J Sport Sci.
19:711–725, 2001.
23. Hagger, M.S., N. Chatzisarantis, and S.J.H. Biddle. A meta-analytic review of the
Theories of Reasoned Action and Planned Behavior in physical activity: predictive
validity and the contribution of additional variables. J Sport Exer Psychol. 24:3–32,
2002.
24. Hardman, A.E., and D.J. Stensel. Physical activity and health: the evidence explained.
London: Routledge, 2003.
25. Heil, D.P. Predicting activity energy expenditure using the Actical® activity monitor.
Res. Q. Exerc. Sport. 77:64–80, 2006.
26. Janssen, I., P.T. Katzmarzyk, W.F. Boyce, and W. Pickett. The independent influence
of physical inactivity and obesity on health complaints in 6th to 10th grade Canadian
youth. J Phys Act Heal. 1:331–343, 2004.
27. Janssen, I., P.T Katzmarzyk, and W.F Boyce et al. Comparison of overweight and
obesity prevalence in school-aged youth from 34 countries and their relationships with
physical activity and dietary patterns. Obes. Rev. 6:123–132, 2005.
28. Johnson, R.K., J. Russ, and M.I. Goran. Physical activity related energy expenditure
in children by doubly labeled water as compared with the Caltrac accelerometer. Int.
J. Obes. 22:1046–1052, 1998.
29. Maddux, J.E. Social cognitive models of health and exercise behaviour: An introduction
and review of conceptual issues. J. Appl. Sport Psychol. 5:116–140, 1993.
30. Maddux, J.E. Looking for common ground: A comment on Bandura and Kirsch. In:
Self-efficacy, adaptation, and adjustment: Theory, research, and application, J.E.
Maddux (Ed.). New York: Plenum Press, 1995, pp. 377–385.
356 Foley et al.
31. Malina, R.M. Physical activity and fitness: pathways from childhood to adulthood. Am.
J. Hum. Biol. 13:162–172, 2001.
32. Manstead, A.S.R., and S.A.M. van Eekelen. Distinguishing between perceived behavioral control and self-efficacy in the domain of academic achievement intentions and
behaviors. J. Appl. Soc. Psychol. 28:1375–1392, 1998.
33. McAuley, E., and S.L. Mihalko. Measuring exercise-related self-efficacy. In: Advances
in sport and exercise psychology measurement, J.L. Duda (Ed.). Morgantown, WV:
Fitness Information Technology, 1998, pp. 371–390.
34. Montgomery, C., J.J. Reilly, D.M. Jackson, et al. Relation between physical activity
and energy expenditure in a representative sample of young children. Am. J. Clin. Nutr.
80:591–596, 2004.
35. Motl, R.W., R.K. Dishman, D.S. Ward, et al. Comparison of barriers self-efficacy
and perceived behavioral control for explaining physical activity across 1 year among
adolescent girls. Health Psychol. 24:106–111, 2005.
36. Mummery, W.K., J.C. Spence, and J.C. Hudec. Understanding physical activity intention in Canadian school children and youth: an application of the theory of planned
behavior. Res. Q. Exerc. Sport. 71:116–124, 2000.
37. Pate, R.R. Physical activity assessment in children and adolescents. Crit. Rev. Food
Sci. Nutr. 33:321–326, 1993.
38. Rhodes, R.E., H.M. Macdonald, and H.A. McKay. Predicting physical activity intention
and behaviour among children in a longitudinal sample. Soc. Sci. Med. 62:3146–3156,
2006.
39. Tabachnick, B.G., and L.S. Fidell. Using multivariate statistics, 4th ed. Boston, MA:
Allyn and Bacon, 2001.
40. Terry, D.J., and J.E. O’Leary. The theory of planned behaviour: the effects of perceived
behavioural control and self-efficacy. Br. J. Soc. Psychol. 34:199–220, 1995.
41. Trayers, T., A.R. Cooper, C.J. Riddoch, et al. Do children from an inner city British
school meet the recommended levels of physical activity? Results from a cross sectional
survey using objective measurement of physical activity. Arch. Dis. Child. 91:175–176,
2006.
42. Trost, S.G., K.L. McIver, and R.R. Pate. Conducting acceleromter-based activity assessments in field-based research. Med. Sci. Sports Exerc. 37:S531–S543, 2005.
43. Trost, S.G., R.R. Pate, M. Dowda, D.S. Ward, G. Felton, and R. Saunders. Psychosocial
correlates of physical activity in white and African-American girls. J. Adolesc. Health.
31:226–233, 2002.
44. Trost, S.G., R.R. Pate, and J.F. Sallis. Age and gender differences in objectively measured physical activity in youth. Med. Sci. Sports Exerc. 34:350–355, 2002.
45. U.S. Department of Health & Human Services. Physical activity and health: A report
of the surgeon general. Atlanta, Georgia: U.S Department of Health & Human Services, Centres of Disease Control and Prevention, National Center for Chronic Disease
Prevention and Health Promotion, 1996.
46. Warburton, D.E.R., C.W. Nicol, and S.S.D. Bredin. Health benefits of physical activity:
the evidence. Can. Med. Assoc. J. 174:801–809, 2006.