Biddle, S. J. H., Hagger, M. S., Chatzisarantis, N. L. D., & Lippke, S. (2007). Theoretical frameworks in
exercise psychology. In G. Tenenbaum & R. Eklund (Eds.), Handbook of sport psychology (3rd ed., pp.
537-559). Hoboken, NJ: Wiley.
CHAPTER 24
Theoretical Frameworks in Exercise Psychology
STUART J. H. BIDDLE, MARTIN S. HAGGER, NIKOS L. D. CHATZISARANTIS, and SONIA LIPPKE
five main phases. The first is to establish the link between
physical activity and health. The second phase is to develop methods for the accurate assessment of physical activity. The third phase is to identify factors that are
associated with different levels of physical activity. Given
the evidence supporting the beneficial effects of physical
activity on health, it is important to identify factors that
might be associated with the adoption and maintenance of
the behavior. This area is referred to as the study of correlates or determinants of physical activity. The fourth
phase is to evaluate interventions designed to promote
physical activity, and the fifth is to translate findings
from research into practice.
In this chapter, we focus on theoretical frameworks and
perspectives that assist in the understanding of determinants and correlates of physical activity, thus addressing
issues in phase 3 of the behavioral epidemiological framework. A theory is “a set of interrelated constructs (concepts), definitions, and propositions that present a
systematic view of phenomena by specifying relations
among variables, with the purpose of explaining and predicting phenomena” (Kerlinger, 1973, p. 9). Physical activity behavior researchers have adopted theories and models
from general, social, educational, and health psychology
(Sutton, 2004) and tested and applied them in the context
of physical activity. Given the emphasis of this book on
psychology, we, too, adopt an individual psychological
approach to understanding physical activity. However, we
recognize the importance of a wider set of influences,
including the broader social, environmental, and cultural
context of behaviors (Sallis & Owen, 1999).
To assist in the organization of diverse theories, we have
placed key theoretical frameworks into a classification
Reference to apparently unhealthy lifestyles in modern
society is now common in popular and academic media.
Contemporary diseases and conditions most often mentioned as having an association with physical inactivity
include obesity, diabetes, heart disease, and some cancers.
Indeed, obesity seems to be the health issue most worrying politicians and health professionals, and lack of physical activity is a key element of the energy imbalance that
is causing current obesity trends (Bouchard, 2000;
Bouchard & Blair, 1999). However, the beneficial effects
of physical activity go far beyond healthy weight management. The magic pill of physical activity is powerful, with
effects demonstrated on numerous health outcomes,
including positive mental health (Dishman, Washburn, &
Heath, 2004). Indeed, physical activity research pioneer
Professor Jeremy Morris (1994, p. 807) once referred to
physical activity as “ today’s best buy for public health,”
and we now have strong advocacy documents promoting
the importance of regular, moderate-intensity physical
activity on most days of the week (Department of Health,
2004; U.S. Department of Health and Human Services &
Centers for Disease Control and Prevention, 1996; Pate
et al., 1995). In addition, recommendations can be tailored
to individual needs, such as those of young people, adults,
older adults, and those with certain medical conditions
(Department of Health, 2004).
The five-phase behavioral epidemiology framework
advocated by Sallis and Owen (1999) is a useful way of
viewing various processes in the understanding of physical activity and health. Behavioral epidemiology considers the link between behaviors, health, and disease, such
as why some people are physically active and others are
not. In relation to physical activity, this framework has
537
538
Exercise and Health Psychology
Theories of
exercise behavior
Belief-attitude
theories
Competence-based
theories
Control-based
theories
Stage-based
theories
Hybrid
models
Example:
Theory of
planned behavior
Example:
Self-efficacy
theory
Example:
Self-determination
theory
Example:
Transtheoretical
model
Example:
HAPA
Figure 24.1 A framework for classifying theories of physical activity. Adapted from S. J. H. Biddle and C. R. Nigg, 2000, “ Theories of Exercise Behavior,” International Journal of Sport Psychology, 31, pp. 290–304. Adapted with permission.
system (Biddle & Nigg, 2000), as shown in Figure 24.1.
Such a system is a heuristic, and there will be overlap
between categories, as we discuss later concerning implementation intentions. Moreover, part of the classification
may reflect different types of theories, such as continuous
approaches like the theory of planned behavior, and stage
models that assume discontinuity between the different
stages, at least for some variables. In addition, Armitage
and Conner (2000) have made the distinction between
motivational and behavioral enaction models. The former,
such as the theory of planned behavior, tend to predict
behavior from a series of variables. The key variable is
intention or motivation that mostly mediates the influences
of predictors on behavior. Behavioral enaction models, on
the other hand, focus on the translation of intentions into
behavior by postintentional variables, such as implementation intentions (discussed later).
Throughout the chapter we refer to physical activity
in the context of sport, exercise, and physical activity
for health rather than high performance, some of the
models and frameworks may be appropriate beyond this
context.
BELIEF-ATTITUDE APPROACHES
Attitude has been one of the most influential and enduring
constructs in social psychology and has been incorporated
into many theoretical approaches adopted for the understanding of physical activity behavior (Hagger, Chatzisarantis, & Biddle, 2002b). The fascination of attitudes in
social psychology has been largely due to the premise that
attitudes predict behavior (Wicker, 1969). However, studies have often reported a considerable gap in the attitudebehavior relationship (Chatzisarantis, Hagger, Biddle, &
Smith, 2005; Sheeran, 2002). Social cognitive theories
developed in the past 2 decades or so have done much to
resolve this disparity, and contemporary theories incorporate attitude alongside measures of other fundamental
belief-based constructs in an attempt to understand the
mechanisms underlying social behavior such as physical
activity (Hagger & Chatzisarantis, 2005a). A brief
overview of attitudes as an important theoretical construct
in the context of physical activity is presented in this section, with a focus on the theories of reasoned action and
planned behavior (Ajzen, 1985, 1991; Ajzen & Fishbein,
1980) as an example of a popular social cognitive approach
that has adopted attitude and belief-based constructs to
study physical activity.
Attitude is commonly defined as an individual’s favorable or unfavorable evaluation of an attitude object or target behavior (Ajzen, 1991; Eagly & Chaiken, 1993).
Attitudes have typically been measured with semantic
differential scales using bipolar word adjectives (Ajzen,
2001). These scales have often been validated using
Theoretical Frameworks in Exercise Psychology
exploratory factor-analytic procedures to arrive at multiitem scales that capture the essence of the person’s beliefs
regarding the target behavior. In the domain of physical
activity, the adjectives that have been most frequently
used to measure attitudes reflect moral (e.g., good-bad),
instrumental (e.g., harmful-beneficial), and affective
(e.g., pleasant-unpleasant) beliefs about the target behavior (Ajzen & Driver, 1991). Such measures are known as
direct measures of attitudes because they focus on attitudes at the global level rather than the beliefs that underpin the construct.
The use of semantic differential attitude scales can be
problematic because their use may be susceptible to
method and context effects. Method effects describe
responses to attitude items that are triggered by similarities in the method used to measure attitudes rather than by
the content of the items (Mulaik & Millsap, 2000). Context
or order effects refer to biases in responses to attitude
items caused by responses to previously presented items
(Ajzen, 2002). For example, the presentation of an affective attitude item to which a person responds positively
may affect his or her response to an instrumental attitude
item presented subsequently. In fact, this often occurs in
attitude research in physical activity involving the administration of multi-item attitude inventories.
Problems associated with context and method effects
can be overcome by constructing a large set of belief statements that are relevant to physical activity and then asking
people to rate these statements on Likert-type scales
(Ajzen, 2002). Responses to these belief statements constitute an indirect or belief-based measure because attitudes
are inferred from the beliefs themselves (Fishbein &
Ajzen, 1975). Another popular conceptualization of attitudes that has been used in physical activity studies is the
expectancy-value model (Fishbein & Ajzen, 1975; Pender
& Pender, 1986; Riddle, 1980). This model also constitutes
an indirect measure of attitudes. It posits that a combination or multiplicative function of beliefs that behavior will
lead to certain consequences, known as behavioral beliefs,
and evaluations of these consequences, known as outcome
evaluations, are indicators of attitudes. Expectancy-value
measure of attitudes can be formulated by asking respondents to rate available behavioral beliefs on expectancy
scales (e.g., “Engaging in physical activities will make me
fitter ”) and then multiplying each of the likelihood ratings
with ratings of the desirability of behavioral beliefs (e.g.,
“Getting fitter is good/ bad”). Such indirect measures of
attitude are often used in social cognitive theories to
validate the direct measures by correlating the composite
539
expectancy-value items with the direct measures (Hagger,
Chatzisarantis, & Biddle, 2001).
The Theory of Reasoned Action
The theory of reasoned action (TRA; Ajzen & Fishbein,
1980) is a popular and widely cited social cognitive model
that incorporates attitudes in a belief-based framework
aimed at explaining behavioral intentions, the proximal
precursor of actual behavior. It preceded the more commonly studied model in contemporary literature, the theory of planned behavior. In the TRA, intention indicates the
degree of planning and effort people are willing to invest in
their performance of future behavior. It is therefore a construct that is motivational in nature and function. Intention
is the most immediate or proximal antecedent of behavior
and is a function of a set of personal and normative expectations regarding the performance of the behavior, termed
attitudes and subjective norms, respectively. Attitudes represent an overall positive or negative evaluation toward the
target behavior and have a dominant role in the formation
of intentions. Subjective norms are defined as the perceived influences that significant others may exert on the
execution of behavior. Generally speaking, the TRA predicts that the more favorable an individual’s attitude and
subjective norm, the stronger his or her intentions to perform the behavior. Finally, intentions are hypothesized to
lead directly to behavioral engagement and are proposed to
mediate the effects of attitudes and subjective norms on
behavior. This means that intentions explain the attitudebehavior and subjective norm-behavior relationships.
Intentions are therefore necessary to convert attitudes and
subjective norms into behavior.
Expectancy-value models of behavioral beliefs and outcome evaluations, identical to those outlined previously, are
thought to be the antecedents of attitudes (Ajzen & Fishbein, 1980). Similarly, the origins of subjective norms can
be traced to belief-based judgments that include normative
beliefs and motivation to comply. Normative beliefs refer to
behavioral expectations that important referent individuals
(or groups) approve or disapprove of performing the behavior. Motivation to comply is the actor’s general tendency to
go along with the wishes of the salient referents (Ajzen &
Fishbein, 1980). These constitute indirect measures of attitude and subjective norms, respectively, and are expected
to correlate with the direct measures of these constructs
(Hagger, Chatzisarantis, & Biddle, 2001). The TRA is
shown in Figure 24.2.
The major hypotheses of the TRA have been supported
in numerous studies across a number of different behaviors
540
Exercise and Health Psychology
Behavioral
beliefs x
values
Attitudes
Normative
beliefs x
compliance
Subjective
norms
Control
beliefs x
power
Perceived
behavioral
control
Intentions
Behavior
Figure 24.2 The theories of reasoned action and planned
behavior. Note: Constructs and relationships above the broken
line represent the theory of reasoned action, and constructs
above and below the broken line taken together represent the theory of planned behavior.
(Sheppard, Hartwick, & Warshaw, 1988), including exercise (Hagger et al., 2002b) and sports training (Theodorakis, Goudas, Bagiatis, & Doganis, 1993). In the exercise
domain, tests of the theory have provided strong evidence
for the overall predictive value of intentions and have
shown that attitudes have a pervasive effect on intentions.
A lesser role has been observed for subjective norms (Hagger et al., 2002b). In addition, panel studies have indicated
that the strong effects of attitudes on intentions remain stable over time (Chatzisarantis & Hagger, in press; Hagger,
Chatzisarantis, Biddle, & Orbell, 2001).
Applications of the theory have also revealed the
salient behavioral and normative beliefs related to physical activity. The beliefs are typically elicited from openended questionnaires administered to a pilot sample and
are used to develop belief-based measures of the attitude
and subjective norm constructs (Ajzen & Fishbein, 1980).
Behavioral beliefs identified in this research include
“good companionship,” “ weight control,” “ benefit my
overall health,” “ take too much time,” “ fun,” “get fit,”
“stay in shape,” “improve skills,” “get an injury,” and
“makes you hot and sweaty” (Hagger, Chatzisarantis,
Biddle, et al., 2001). Important referents for measures of
normative beliefs and motivation to comply tend to be
family members, such as parents, grandparents, and siblings, along with friends and schoolteachers (Hagger,
Chatzisarantis, Biddle, et al., 2001). However, these
beliefs have not been shown to unequivocally account for
unique variance in the directly measured attitude and subjective norm constructs, and alternative subsets of beliefs
may exist (Hagger, Chatzisarantis, Biddle, et al., 2001).
The TRA has not been without critics. It is a unidirectional model and so fails to offer the possibility that variables in the model can act in a reciprocal manner. In
addition, the model relies solely on cognitions and omits
other potentially important determinants of action, such as
environmental influences, and it usually predicts behavior
from measures of behavioral intention taken at one point in
time. Moreover, insufficient attention has been paid to the
measurement of behavior within the TRA. Without an
accurate measure of the behavior, the principle of correspondence cannot be applied. This casts some doubt on several studies, such as when assessment relies on unvalidated
self-reports or inappropriate “objective” measures (e.g.,
use of pedometers for people who are physically active predominantly through cycling or swimming). Finally, the
TRA allows the investigation of the interrelationships
among attitudes, subjective norms, intentions, and a single
behavior. It does not account for alternative behaviors. For
example, although many people intend to be more physically active, few see this through to action in a sustained way.
This could be due to physical activity being of lower priority than other behaviors, and so just does not get to the top
of the list of things to do.
In summary, the TRA has been at the forefront of
reestablishing attitude research as a powerful force in
social psychology, and both health and exercise psychology
have been quick to utilize such an approach. The TRA has
proved to be a viable unifying theoretical framework that
has been successful in furthering understanding of exercise
intentions and behaviors. It has also been instrumental in
moving research on physical activity correlates from being
largely atheoretical to theoretical.
The Theory of Planned Behavior
Although the TRA has been successful in predicting and
explaining participation in physical activities, a major limitation of the theory noted by Ajzen (1985) is that not all
behaviors are under volitional control. This led to Ajzen to
propose an alternative theory, the theory of planned
behavior (TPB), to resolve this limitation. As with the theory of reasoned action, it is proposed in the theory of
planned behavior that intention is a central determinant of
social behavior and a function of attitudes and subjective
norms with corresponding behavioral beliefs and normative beliefs, respectively. However, it is also proposed in
the TPB that when perceived control over behavior is problematic, an additional factor, termed perceived behavioral
control (PBC), can inf luence intention and behavior
(Ajzen, 1985).
Theoretical Frameworks in Exercise Psychology
For Ajzen (1991), the PBC construct refers to general
perceptions of control (see competence-based approaches in
the next section). He overtly compared it with Bandura’s
(1977) construct of self-efficacy that captures judgments of
how well one can execute volitional behaviors required to
produce important outcomes. The construct of PBC is also
underpinned by a set of control beliefs and the perceived
power of these beliefs (Ajzen & Fishbein, 1980). Control
beliefs refer to the perceived presence of factors that may
facilitate or impede performance of behavior, and perceived power refers to the perceived impact that facilitative
or inhibiting factors may have on performance of behavior
(Ajzen, 1991). In the same way that an expectancy-value
model is used to form indirect antecedents of attitudes and
subjective norm, an indirect measure of PBC can be formed
from the multiplicative composite of each control belief
multiplied by its corresponding perceived power rating
(Ajzen, 1991).
The inclusion of perceived behavioral control in the
TPB is important because it reveals the personal and environmental factors that affect behavior (Ajzen, 1985). To
the extent that PBC influences intentions and behavior, the
researcher can evaluate which behaviors are under the
volitional control of the individual and the degree to which
the behavior is impeded by personal and/or environmental
factors. Ajzen (1991) hypothesized that when control over
the behavior was problematic, PBC would exert two types
of effects. First, it would influence intentions alongside
attitudes and subjective norms; this additive effect
reflects the motivational influence of perceived control on
decisions to exercise. Second, PBC may predict behavior
directly, especially when perceptions of behavioral control
are realistic; this direct effect reflects the effect of actual,
real constraints or barriers to doing the behavior. In this
case, PBC is a proxy measure of actual control over the
behavior (Ajzen, 1991). These relationships are shown in
Figure 24.2.
A number of studies have shown the TPB to be superior
to the TRA in predicting and explaining volitional behavior
across many settings (Armitage & Conner, 2001), including physical activity behavior (Dzewaltowski, Noble, &
Shaw, 1990; Hagger et al., 2002b). In terms of the relative
contribution of the theory constructs, a number of studies
have shown that attitude and perceived behavioral control
predict intentions equally well (Hagger & Chatzisarantis,
2005a; Hagger, Chatzisarantis, & Biddle, 2002a). A number of control beliefs have also been identified, including
barriers and facilitating factors related to exercise, such
as “ bad weather,” “age,” “ heart pain,” “costs,” “ fatigue,”
541
and “no time” (Godin, Valois, Jobin, & Ross, 1991; Hagger,
Chatzisarantis, & Biddle, 2001). As with behavioral and
normative beliefs, control beliefs have been demonstrated
to vary considerably across different populations and
behaviors. For example, “age” and “ fear of having a heart
attack ” have been identified among the physical activity
control beliefs for older and clinical populations (Godin
et al., 1991), but these beliefs do not feature among the
control beliefs of younger populations (Hagger, Chatzisarantis, & Biddle, 2001).
We conducted a comprehensive synthesis of the extant
TPB research in the physical activity domain (Hagger
et al., 2002b). We meta-analyzed 72 studies that allowed
calculations of the relationships proposed in either the
TRA or TPB. In addition to reporting correlations between
variables, we did three things:
1. By using the correlation matrix, we tested the TRA and
TPB through path analysis.
2. We tested the additional variance accounted for by
adding variables to the TRA. This was done by first
adding PBC ( hence testing the TPB), then self-efficacy,
and finally past behavior.
3. We tested three moderator variables: age, attitudeintention strength, and the time between the assessment
of past behavior and (present) behavior.
Results supported the TPB. Intention was the only direct
predictor of behavior, intention was predicted more strongly by attitudes than by subjective norms (the latter showing
a small contribution), and PBC was associated with behavior through intention. Self-efficacy (which some might
argue is a more internal or “personal” aspect of PBC)
added to the prediction of both intentions and behavior, and
past behavior was associated with all TPB variables. Of
most importance was the finding that by adding past behavior to the model, other paths were reduced, suggesting that
studies that do not assess past behavior may be obtaining
artificially high correlations. Nevertheless, the relationship between attitude and intentions remained even when
past behavior was included. We concluded:
While past behavior had a significant and direct influence on
intention, attitude, PBC, and self-efficacy, these cognitions
are also necessary for translating past decisions about behavioral involvement into action. This is consistent with the
notion that involvement in volitional behaviors such as regular physical activity involves both conscious and automatic
influences. (Hagger et al., 2002b, p. 23)
542
Exercise and Health Psychology
One problem with the TPB, in addition to some of the
criticisms given for the TRA, is the lack of consistency in
defining and assessing perceived behavioral control. For
example, Ajzen (1988) originally said that perceived
behavioral control was “closely related to self-efficacy
beliefs” (p. 106) and that it “refers to the perceived ease
or difficulty of performing the behavior and it is assumed
to reflect past experience as well as anticipated impediments and obstacles” (p. 132). The similarity with selfefficacy has also been noted by Stroebe and Stroebe
(1995). Ajzen (1991), however, defines perceived behavioral control in terms of both perceived resources and
opportunities as well as perceived power to overcome
obstacles; thus, the construct represents both control
beliefs and perceived power.
It is often found in studies incorporating self-efficacy
and PBC that they make independent contributions to the
prediction of intentions or behavior. For example, Terry
and O’Leary (1995) found items reflecting self-efficacy
and PBC to be factorially distinct. Moreover, they found
that self-efficacy predicted intentions to be physically
active, but not activity itself, whereas PBC predicted physical activity but not intention.
Reasons for the success and popularity of the TPB can
be attributed to its efficacy in accounting for variance in
intention and behavior, its relative parsimony, and its flexibility. Furthermore, the original constructs of the TPB
have been shown to mediate the direct effect of other constructs on intentions and behavior, suggesting that the
belief systems that underpin the directly measured theory
constructs are able to account for the effects of other variables that have previously accounted for unique variance in
behavior (Conner & Abraham, 2001). However, researchers
have also indicated that the theory does not account for all
of the variance in intention and behavior, nor does it mediate the effects of certain “external variables” (Rhodes &
Courneya, 2003), personality and belief-based constructs
on intentions and behavior (Bagozzi & Kimmel, 1995; Conner & Abraham, 2001; Conner & Armitage, 1998; Rhodes,
Courneya, & Jones, 2002). Paradoxically, this weakness
has become the theory’s greatest strength. Ajzen (1991)
states that the theory should be viewed as a flexible framework into which other variables can be incorporated provided they make a meaningful and unique contribution to
the prediction of intentions and there is a theoretical precedence for the inclusion of such variables.
As a consequence, the theory has demonstrated considerable flexibility and has been adopted by researchers as a
general framework to investigate the effect of a number of
additional social cognitive constructs on intention and
behavior (Conner & Armitage, 1998). To the extent that
such constructs have a unique effect on intention or behavior and are not mediated by the core theory variables of
attitude, subjective norm, and PBC, the researcher has evidence to support the inclusion of that construct in the theory. A number of constructs have been found to have a
unique effect on intentions and/or behavior in this regard,
including anticipated affect and anticipated regret (Sheeran & Orbell, 1999a), self-schemas (Sheeran & Orbell,
2000), self-efficacy (Sparks, Guthrie, & Shepherd, 1997),
descriptive norms (Sheeran & Orbell, 1999a), desires
(Perugini & Bagozzi, 2001), and self-identity (Hagger &
Chatzisarantis, in press; Sparks & Guthrie, 1998).
In addition to the effects of other constructs, the influence of variations in the characteristics and nature of the
core theory of planned behavior constructs on intentions,
and of intention itself, on behavior have been investigated
(Sheeran, 2002). Examples include the stability of intentions (Sheeran, Orbell, & Trafimow, 1999), the accessibility of attitudes (Verplanken, Hofstee, & Janssen, 1998),
and hypothetical bias (Ajzen, Brown, & Carvahal, 2004).
In the same vein, researchers have also investigated the
extent to which individuals are oriented toward or base
their intentions on each of the core theory constructs
(Sheeran, Norman, & Orbell, 1999; Trafimow & Finlay,
1996). These modifications suggest that the antecedents of
volitional behaviors, such as physical activity, may be more
complex than originally conceived in the theory (Conner &
Armitage, 1998). Notwithstanding these modifications, the
theory still performs relatively well in terms of explaining
physical activity behavior, and in its most parsimonious
form can inform successful interventions to promote physical activity (Chatzisarantis & Hagger, in press; Hagger &
Chatzisarantis, 2005a).
Implementation Intentions
One reason the theories of reasoned action and planned
behavior do not fully explain the processes by which intentions are translated into action is that people often fail to
carry out their intentions (Gollwitzer, 1999; Orbell, 2000;
Orbell, Hodgkins, & Sheeran, 1997; Sheeran & Orbell,
1999b). Alternatively, individuals’ execution of their intentions may be interrupted because other, competing goaldirected behaviors gain priority over the original intended
behavior (Verplanken & Faes, 1999). Social cognitive theories like the theories of reasoned action and planned
Theoretical Frameworks in Exercise Psychology
behavior do not address these difficulties associated with
enactment of intentions, and as a result may not fully
explain the intention-behavior relationship.
One approach that has been put forward to resolve the
inadequacies of the intention-behavior relationship in the
TPB is implementation intentions (Gollwitzer, 1999).
These are self-regulatory strategies that involve the formation of specific plans that specify when, how, and
where performance of behavior will take place. Implementation intentions were developed from concerns about the
intention-behavior gap.
Experimental paradigms using implementation intention
strategies require research participants to specify explicitly when, where, and how they will engage in an intended
behavior to achieve their behavioral goals (Orbell, 2000).
According to Gollwitzer (1999), implementation intentions
help people move from a motivational phase to a volitional
phase, ensuring that intentions are converted into action.
Research has indicated that forming implementation intentions decreases the probability of people failing to initiate
their goal-directed intentions at the point of initiation
(Orbell, 2000; Orbell et al., 1997; Sheeran & Orbell,
1999b). This is because planning when and where to initiate a prospective action strengthens the mental association
between representations of situations and representations
of actions. Research has also shown that increased accessibility of situational representations in memory increases
the probability of action opportunities getting noticed and
of action initiation occurring, given that the mere perception of action opportunities can automatically trigger a
behavioral response (Orbell et al., 1997; Sheeran & Orbell,
1999b). Of particular importance, implementation intentions increase behavioral engagement through these postdecisional, automatic mechanisms, and not by concomitant
increases in motivation or intention (Orbell et al., 1997).
Recent research has evaluated the effectiveness of interventions that combine motivational techniques with volitional techniques, such as implementation intentions, in
influencing the performance of social and exercise behavior
(Koestner, Lekes, Powers, & Chicoine, 2002; Milne, Orbell,
& Sheeran, 2002; Prestwich, Lawton, & Conner, 2003;
Sheeran & Silverman, 2003). The rationale behind this
combined approach is that motivational strategies focus on
increasing intention levels but do not facilitate the enactment of intentions, and volitional strategies, such as implementation intentions, increase the probability that these
strong intentions will be converted into action without
changing intentions. Research has corroborated the utility
543
of these combined techniques in increasing exercise behavior. For example, Prestwich et al. demonstrated that an
intervention that had a combination of a rational decisionmaking strategy, or decisional balance sheet (weighing up
the pros and cons), and implementation intentions was
more effective in promoting physical activity behavior than
either of the strategies alone. These results support the
existence of two distinct phases of motivation: a motivational or predecisional phase, during which people decide
whether or not to perform a behavior, and a volitional, postdecisional, or implemental phase, during which people plan
when and where they will convert their intentions into
behavior (Gollwitzer, 1999). As a consequence, interventions that combine motivational and volitional techniques
are likely to be most effective in promoting physical activity behavior.
COMPETENCE-BASED APPROACHES
The social-cognitive perspectives currently favored when
studying individual motivation in the exercise psychology
literature have drawn extensively on self-efficacy theory.
This approach has had a large impact in both exercise
(McAuley & Blissmer, 2000; McAuley, Pena, & Jerome,
2001) and health (Stroebe & Stroebe, 1995) research.
Other confidence- and competence-related perspectives
are achievement motivation (Roberts, 2001) and selfpresentation (Leary, 1992). In addition, theories of
self-perceptions are relevant and will be discussed in
brief later.
Self-Efficacy Theory
Confidence has been identified at the anecdotal and empirical level as an important construct in exercise motivation.
Statements associated with self-perceptions of confidence
are commonplace in studies on exercise and sport and are
likely to be associated, in one way or another, with the initiation and maintenance of physical activity. Bandura
(1986, p. 391) defines perceived self-efficacy as
people’s judgments of their capabilities to organize and execute courses of action required to attain designated types of
performances. It is concerned not with the skills one possesses but rather, with judgments of what one can do with whatever skills one possesses.
Beliefs related to the ability to carry out a particular
behavior are self-ef ficacy expectations, whereas beliefs as
544
Exercise and Health Psychology
to whether the behavior will produce a particular result are
outcome expectations. People are likely to be concerned
about both types of expectancy, and both require study in
exercise psychology research. For example, it is important
to know whether efficacy expectations are influential in
the adoption of exercise programs, yet it is also likely that
outcome expectations will affect the maintenance of such
programs and the reinforcement necessary for continued
involvement.
It is thought that people gain or lose self-efficacy in four
main ways (Bandura, 1986, 1997):
1. Prior success and performance attainment
2. Imitation and modeling, particularly of people similar
to oneself
3. Verbal and social persuasion
4. Judgments of physiological states, such that states of relaxation can be achieved
C. Ewart (1989, p. 684) summarized the application of
these in the context of promoting exercise in a rehabilitation setting by saying that
the most effective way to encourage patients to adopt exercise activities for which they lack self-efficacy is to expose
them to the recommended activity in gradually increasing
doses [performance], arrange for them to see others similar
to themselves performing the activity [modeling], have
respected health care providers offer encouragement by providing reassurance and emphasizing the patient’s accomplishments [persuasion], and arrange the setting of the
activity so as to induce a relaxed but “ upbeat ” mood [arousal, physiological state].
Research Findings for Self-Efficacy in Exercise
Early work in this area tended to focus on patients, such as
those in cardiac rehabilitation. For example, C. E. Ewart,
Taylor, Reese, and DeBusk (1983) studied self-efficacy in
the context of treadmill running with post-myocardial
infarction patients. Before and after treadmill exercise,
assessment of self-efficacy to take part in walking, running, stair climbing, sexual intercourse, lifting, and general exertion was made. Results showed that positive changes
in self-efficacy took place following treadmill exercise,
and that this was greatest for running, suggesting that efficacy effects can generalize but appear to have stronger
effects on similar exercise modes. When counseling also
took place, it was found that efficacy perceptions for some
of the activities significantly increased above the level
attained after treadmill running. Studies on medical
patients in exercise rehabilitation have suggested that selfefficacy judgments can generalize but will be strongest for
activities similar to the activity experienced, that selfefficacy in “dissimilar ” activities can be enhanced through
counseling, and that self-efficacy better predicts changes
in exercise behavior than generalized expectancies of locus
of control (Biddle & Mutrie, 2001).
A number of researchers have now investigated nonpatient groups in physical activity. For example, self-efficacy
has been shown to predict walking in a large adult sample
contacted by mail (Hofstetter et al., 1991), has discriminated adherers from dropouts in an exercise weight loss
program (Rodgers & Brawley, 1993), and has predicted
positive affect after exercise (Bozoian, Rejeski, &
McAuley, 1994).
McAuley’s work on exercise self-efficacy has been
influential (e.g., McAuley, 1992; McAuley & Courneya,
1993; McAuley & Mihalko, 1998). In particular, McAuley
and colleagues have studied self-efficacy responses of
older adults, a population previously underrepresented in
the exercise psychology literature. Several studies by
McAuley and coworkers focus on a group of previously
sedentary 45- to 64-year-olds. These studies have shown
that for older adults, exercise self-efficacy:
• Can be increased through intervention
• Will predict participation, particularly in the early
stages of an exercise program
• Declines after a period of inactivity
• Is associated with positive exercise emotion
In summary, the studies investigating self-efficacy in
nonpatient exercise groups show a consistent relationship
between efficacy and exercise participation, as well as
relationships with other important factors, such as postexercise emotion.
Self-efficacy needs to be assessed in relation to specific behaviors if increased magnitude of behavioral prediction is required. Generalized perceptions of confidence are
not the same as perceptions of efficacy. Nevertheless, we
need more studies on the generalizability of self-efficacy
across different physical activity and exercise settings.
Similar to the attitude-behavior correspondence issue discussed earlier, the utility of self-efficacy is likely to be
greater when measures correspond closely to the behavior
in question, such as walking to work 4 days per week,
rather than using a general reference such as “exercise.”
Theoretical Frameworks in Exercise Psychology
Assessing self-efficacy in any meaningful way requires
the behavior to be associated with effort, potential barriers, and behavioral self-regulation. “Easy” habitual behaviors, such as tooth brushing, are likely to be unrelated to
feelings of efficacy, whereas physical exercise may be
highly associated with efficacy beliefs if exercise requires
planning, effort, and the overcoming of considerable barriers. This is probably why self-efficacy emerges as one of
the most consistent predictors of physical activity behaviors, particularly when physical activity includes elements
of vigorous exercise.
Physical Self-Perceptions and Competence
Dominant theories of competence-based motivation often
involve constructs related to self-perception, and these
have been a central feature in the exercise psychology literature. In contemporary self-esteem theory, it is proposed
that global self-esteem ( how we view ourselves overall) is
underpinned by perceptions of specific domains of our
lives, such as social, academic, and physical domains
(Shavelson, Hubner, & Stanton, 1976). Based on this
approach, Fox (1997; Fox & Corbin, 1989) has developed
an operational measure of physical self-perceptions whereby psychometrically sound scales assess the higher-order
construct of physical self-worth (PSW) and its selfperception subdomains of sport competence, perceived
strength, physical condition, and attractive body. It is proposed that everyday events are likely to affect more specific perceptions of self (e.g., the belief that one can walk
to work), which, if reinforced over time, may eventually
contribute to enhanced self-perceptions of physical condition or even PSW. As such, self-perceptions can be viewed
in terms of being more general in their orientation (i.e.,
domain-general) when they operate at the level of general
self-perceptions of competence and worth, such as PSW.
Self-perceptions can also be viewed in more specific
terms, such as specific competency perceptions like “Can
I finish this bike ride?” and “I have just walked to work for
the first time.”
At least two perspectives on competence perceptions
and motivation can be considered relative to Fox’s (1997)
model of physical self-worth. The self-enhancement model
of self-esteem is where positive self-perceptions play a
motivational role in behavior. For example, if I feel competent in physical activity, it is more likely that I will want to
demonstrate that competence, and hence be motivated to
be active. The reverse could also be true, whereby a lack of
motivation through perceptions of incompetence becomes
a determinant of sedentary habits. The personal develop-
545
ment model views self-esteem or physical self-perceptions
as outcomes of physical activity, such that positive outcomes will reinforce competence and boost selfperceptions, and negative outcomes will have the reverse
effect (Biddle & Mutrie, 2001).
CONTROL-BASED THEORIES
The research and lay literatures contain numerous references to the fact that changes in physical activity behaviors
are thought to be associated with the need for personal control of our lifestyles. The information that many of the
modern diseases linked with premature mortality are
lifestyle-related has the implicit message that we, as individuals, are at least partly responsible for our health and
well-being, thus implying the need for personal control and
change. However, it could be argued that a greater emphasis should be placed on social determinants of health.
Whatever the outcome of such a debate, it is clear that perceptions of control are important psychological correlates
of health-related behavior at the individual level.
Definitions of Control and Autonomy
The construct of control is one of the most popular and oftcited in the social psychology literature (Skinner, 1995,
1996). Its popularity stems primarily from the fact that
many theories of human motivation either make a reference to control or explicitly include a construct of control
in their proposed models (Biddle, 1999). Corresponding to
the popularity of the construct of control has been the proliferation of terms and confusion of theoretical definitions
used to describe this construct. According to Skinner
(1995) and Biddle (1999), the construct of control has been
used in the literature in three different ways.
First, the term control has been used to describe beliefs
that one has the capacity to control performance of the
means (i.e., behaviors) leading to outcomes. These control
beliefs, which are termed “capacity beliefs” (Skinner,
1995), are similar to Bandura’s (1997) self-efficacy
beliefs. Second, the construct of control has been used to
describe beliefs (strategy beliefs) concerning the necessary
availability of means to produce the desired outcome.
Third, the construct of control has been used to describe
beliefs (control beliefs) that an agent (person) has the
capacity to produce a desirable outcome. These control
beliefs are very similar to White’s (1963) and Deci and
Ryan’s (1985) definition of the need for competence,
which refers to the need that one has the capacity to produce outcomes and also understand the instrumentalities
546
Exercise and Health Psychology
leading to these outcomes. In fact, because Skinner’s and
White’s definitions of control beliefs and of the need for
competence are very similar, some researchers suggested
that the perceptions related to control are energized by the
need for competence (Hagger, Chatzisarantis, & Biddle,
2001; Hagger et al., 2002a).
There has also been some confusion about the construct
of autonomy. Autonomy and control are not the same (Deci
& Ryan, 1987). Autonomy describes the extent to which
performance of behavior is volitional and is endorsed with
a sense of choice and willingness. Autonomy does not mean
being able to control outcomes or have the capacity to perform behaviors leading to outcomes. The two constructs are
conceptually and empirically distinct: One can be compelled to engage in behaviors over which one has complete
control, and one can try to self-initiate a behavior over
which one has little control (i.e., people self-initiate challenging tasks; Deci & Ryan, 1985; Hagger et al., 2002a).
Applications of self-determination theory (SDT) are
reviewed in this section of the chapter because this is one
of the few theories in social psychology that explicitly recognizes the importance of the distinction between control
and autonomy in understanding social phenomena such as
physical activity.
Self-Determination Theory
Self-determination theory is a popular theory proposing
that human motivation and psychological well-being can be
explained on the basis of psychological needs for competence, self-determination (autonomy), and relatedness
(Deci & Ryan, 1985). Self-determination refers to the need
for experiencing oneself as an initiator and regulator of
one’s actions. Competence refers to the need for producing
behavioral outcomes and understanding the production of
these behavioral outcomes. Relatedness refers to the need
for experiencing satisfactory relationships with others and
with the social order in general (Deci & Ryan, 1991).
Psychological Needs and the Energization
of Behavior
The concept of psychological needs differs from the more
common usage that equates a need with any personal desire
or goal that people may try to achieve. According to Ryan
(1995), psychological needs are necessary for human development and growth. For example, it has been suggested that
as human beings require water and food to survive, in the
psychological domain human motivation requires experiences of autonomy, competence, and relatedness to sustain
and grow (Sheldon, Elliot, Kim, & Kasser, 2001). Individuals do not necessarily need, for example, fancy cars and
private jets to grow psychologically. These other needs are
not essential but substitute needs that are not universal but
learned through social processes (Deci & Ryan, 1980).
The concept of psychological needs is also important
because it makes motivation a dynamic concept (Ryan,
1995). By definition, a psychological need is an energizing
state that, if satisfied, enhances health and psychological
well-being; if not satisfied, then “ill-being” will result
(Ryan & Deci, 2000). Several studies in social and
sport /exercise psychology have consistently supported this
hypothesis (Gagne, Ryan, & Bargman, 2003; Hagger,
Chatzisarantis, & Harris, in press; Ntoumanis, 2001; Sarrazin, Vallerand, Guillet, Pelletier, & Cury, 2002; Wilson,
Rodgers, Blanchard, & Gessell, 2003). For example, in a
study of young gymnasts, Gagne et al. documented that
need satisfaction experienced during practice explained
changes in daily well-being. In addition, we have recently
documented that need satisfaction was associated with
positive physical activity attitudes, strong intentions to
exercise, and physical activity participation (Hagger et al.,
in press).
Interpersonal Context and the Fostering of Human
Motivation and Psychological Well-Being
In addition to addressing issues related to the energizing of
human motivation, SDT places importance on the environment and interpersonal contexts within which motivation
occurs. According to Ryan (1995), psychological needs and
the inputs that those psychological needs generate are
necessary but not sufficient for human development and
growth. For intrinsic motivational tendencies to sustain
and grow, psychological needs must be supported because
otherwise individuals will be alienated from these needs
(Deci & Ryan, 1980). It is evident, therefore, that SDT is a
dialectic theory that assigns to the environment the role of
a nurturer that actually does or makes efficient motivation
occur (Aristotle, 1993). The conceptual analogue of the
role of environment, as viewed by SDT, is that of a seed
that grows into a tree only if the greater environment (i.e.,
climatic conditions, soil) is conducive to its growth.
Self-determination theory differentiates among three
types of interpersonal contexts that either support or frustrate psychological needs. The general ambience of interpersonal context is said to be chaotic when the greater
environment is unstructured and significant others do not
provide feedback at all (Deci & Ryan, 1991). Chaotic envi-
Theoretical Frameworks in Exercise Psychology
ronments undermine competence and promote amotivation,
as demonstrated in physical education by Ntoumanis and
colleagues (Ntoumanis, Pensgaard, Martin, & Pipe, 2004).
The interpersonal context is said to be supportive of psychological needs (autonomy-supportive contexts) when significant others encourage choice and participation in
decision making, provide a meaningful rationale, use neutral language (e.g., “may,” “could” rather than “should,”
“must ”) during interpersonal communication, and
acknowledge people’s feelings and perspectives (Deci,
Koestner, & Ryan, 1999). For example, an instructor who
uses neutral language in conducting an exercise class and
explains reasons behind different exercise tasks is likely to
contribute to the development of an autonomy-supportive
climate. The interpersonal context is said to frustrate psychological needs (controlling contexts) when significant
others do not explain why performance of certain behaviors may be important, use pressuring language during
interpersonal communication (e.g., use of “should” and
“must ”), or do not acknowledge difficulties associated
with performance of a behavior. An exercise instructor
who makes people think, feel, or behave in particular ways
is likely to contribute to the development of a controlling
interpersonal context.
The importance of adopting autonomous-supportive
versus controlling interpersonal styles has been examined
in numerous studies and across different life domains.
Autonomy-supportive health care providers, parents,
coaches, physical educators, and peers have been found to
develop a better quality of motivation (Goudas, Biddle, &
Underwood, 1995; Hagger & Chatzisarantis, 2005b; Hagger, Chatzisarantis, Culverhouse, & Biddle, 2003;
Ntoumanis, 2001; Standage, Duda, & Ntoumanis, 2003;
Standage, Duda, & Ntoumanis, in press; G. C. Williams,
Gagné, Ryan, & Deci, 2002), promote adherence to sport
and physical activity (Chatzisarantis, Hagger, Smith, &
Sage, in press), and promote enhanced levels of satisfaction of psychological needs and psychological well-being
(Gagne et al., 2003; Levesque, Zuehlke, Stanek, & Ryan,
2004; Sarrazin et al., 2002). In contrast, young people and
adults develop less persistent forms of motivation
(Goudas, Biddle, & Fox, 1994; Goudas et al., 1995; Hagger
& Chatzisarantis, 2005b, in press; Vansteenkiste & Deci,
2003), tend to drop out from physical activity and sport
(Sarrazin et al., 2002; G. C. Williams & Deci, 1998), and
are left unsatisfied (Gagne et al., 2003) when significant
others pressure them or make them think, feel, and behave
in particular ways.
547
Forms and Quality of Human Motivation
Self-determination theory distinguishes not only between
autonomous and controlling interpersonal contexts but also
between autonomous and controlling motivational styles.
The conceptual analogue of motivational form is that of the
shape of materials and describes the structure by which
motivation is identified (Aristotle, 1993). Cognitive evaluation theory (a subtheory of SDT) initially distinguished
between two general forms of motivation. Intrinsic motivation refers to the doing of an activity for its inherent satisfactions rather than for some separable outcomes. Extrinsic
motivation refers to the doing of an activity for outcomes
that are separable from the activity itself (Ryan & Deci,
2000). However, subsequent studies have shown that extrinsic motivation can be further differentiated into external
regulation, introjection, identification, and integration.
External regulation refers to a behavior that is performed to obtain a reward or approval from a significant
other (Ryan & Connell, 1989). For example, people who
exercise because their spouse pressures them to do so are
externally regulated. Introjection lies next to external regulation; it refers to a behavior that is performed to avoid a
pressuring emotion of guilt or shame. A person who exercises for reasons of weight management and feels guilty
when missing some exercise sessions is said to be introjected. External regulation and introjection describe controlling forms of motivation because they describe
behaviors that are performed under some form of internal
(e.g., introjection) or external (e.g., external regulation)
pressure. A less controlling and more self-determined form
of motivation is identification. This refers to a behavior
that is performed because the individual values it. During
identification, individuals accept and endorse the value of
physical activity, and for this reason, identified behavior
represents a more self-determined form of motivation. The
most autonomous and least controlling form of behavior is
integrated regulation. This refers to identifications that are
brought into congruence with other behaviors and roles that
are enacted in life. This definition presupposes that identification is a less autonomous form of behavioral regulation
than integration because regulation through identification
may conflict with preexisting values and behaviors.
In the domain of sport and physical activity, many
instruments measure the motivational styles proposed by
SDT. The Sport Motivation Scale, developed by Pelletier
and colleagues (1995), assesses amotivation, external regulation, introjection, identification, and three types of
intrinsic motivation: intrinsic motivation to know (e.g.,
548
Exercise and Health Psychology
“ for the pleasure it gives me to know more about the sport
that I practice”), to accomplish (e.g., “ because I feel lot of
personal satisfaction while mastering certain difficult
movements”), and to experience stimulation (e.g., “ for the
intense emotions”).
In the context of physical activity, there is the Behavioral Regulation in Exercise Questionnaire (Markland &
Tobin, 2004; Mullan, Markland, & Ingledew, 1997) and the
Exercise Motivation Scale (EMS; Li, 1999). Both of these
instruments measure amotivation, external regulation,
introjection, identification, and intrinsic motivation. However, the EMS measures integrated regulation as well.
Finally, Goudas et al. (1994) adapted Ryan and Connell’s
(1989) self-regulation questionnaire and Vallerand et al.’s
(1992) academic motivation scale in the physical education
context. Goudas et al.’s scale measures external regulation,
introjection, identification, and intrinsic motivation but not
integrated regulation.
The importance of adopting an autonomy-supportive
versus controlling motivational style has been examined
in numerous studies across different domains. Supporting
assumptions underling SDT, we have documented that
motivational styles form a motivational continuum
(Chatzisarantis, Hagger, Biddle, Smith, & Wang, 2003).
In this continuum, which is often described as a developmental continuum of self-determination (Deci & Ryan,
1991), external regulation and intrinsic motivation are
located at the opposite ends, and introjection and identification lie in between external regulation and intrinsic
motivation. In addition, several prospective studies have
shown that autonomous motivational styles are more
strongly associated with intentions to exercise, prolonged
participation in sport, adherence to physical activity, and
psychological well-being than controlling motivational
styles (Chatzisarantis & Biddle, 1998; Chatzisarantis,
Biddle, & Meek, 1997; Chatzisarantis, Hagger, Biddle, &
Karageorghis, 2002; Gagne et al., 2003; Hagger et al.,
2002a; Matsumoto & Takenaka, 2004; Vansteenkiste &
Deci, 2003). In addition, empirical evidence supports
relationships between autonomy-supportive interpersonal
contexts and autonomous motivational styles on the one
hand (identification and intrinsic motivation), and
between controlling interpersonal contexts and controlling motivational styles on the other (Hagger & Chatzisarantis, 2005b, in press; Hagger et al., 2003; Ntoumanis,
2001; Standage et al., 2003, in press).
Research in sport and physical activity has also examined relationships between motivational styles and other
psychological variables not necessarily included in SDT.
For example, in the context of physical education, Goudas
et al. (1994) found relationships between motivational
styles and task and ego achievement goal orientations, perceived competence, and intentions. Wilson, Rodgers, Blanchard, et al. (2003) documented that motivational styles
are related to different imagery techniques used by exercisers. Appearance imagery was associated with introjection, and identification and intrinsic motivation were
associated with imagery techniques reflecting technique
and energy.
In summary, SDT has emerged as an important theoretical approach in exercise psychology (Biddle, Chatzisarantis, & Hagger, 2001; Chatzisarantis et al., 2003). Building
on prior work concerning intrinsic and extrinsic motivation, SDT now embraces a wider view of human motivation, including differentiated types of extrinsic motivation,
and the role of need satisfaction (Deci & Ryan, 2002). The
challenge for practitioners remains how to create an autonomy-supportive climate whereby self-determined forms of
motivation dominate in the physical activity context.
STAGE-BASED MODELS
The theoretical approaches discussed so far tend to be continuous models; that is, constructs are reflected as continuous variables rather than discrete stages. The most
well-known stage conceptualization is that of the transtheoretical model (TTM), developed by Prochaska and
DiClemente (1984, 1986; Prochaska, Norcross, &
DiClemente, 1994; Prochaska & Velicer, 1997). The TTM
emerged from an analysis of change systems used in psychotherapy to treat addictive behaviors such as smoking
and drug use. More recently, it has been offered as a coherent framework to help understand readiness to begin physical activity. Literature on the TTM in physical activity is
now diverse, including descriptive studies (Marcus, Rossi,
Selby, Niaura, & Abrams, 1992; Mullan & Markland,
1997), interventions (Mutrie et al., 2002), narrative
overviews (Prochaska & Marcus, 1994), a meta-analysis
(Marshall & Biddle, 2001), and practical guidelines (Marcus & Forsyth, 2003). The TTM treats behavior change as
a dynamic process rather than an all-or-nothing phenomenon. Evidence suggests that individuals attempting to
change their physical activity behavior move through a
series of stages. The stages are characterized by a temporal dimension of readiness to change. Five stages have been
proposed that differ according to an individual’s intention
and behavior, the latter being defined in relation to a criterion level. Whether people move into a more advanced
Theoretical Frameworks in Exercise Psychology
stage will partly be determined by how the level of the
behavioral criterion is defined. The stages have been
labeled precontemplation, contemplation, preparation,
action, and maintenance (see Table 24.1).
Original formulations of the model proposed that individuals moved through the stages in a linear fashion. It is
now recognized that stage progression is more likely to follow a cyclical pattern whereby individuals progress and
regress through the stages in an effort to create lasting
change. This is analogous to the board game “Snakes and
Ladders,” where you make progress up the ladders but
sometimes regress down the snakes before climbing another ladder.
Four factors are hypothesized to mediate the change
process: (1) an individual’s self-efficacy for change, (2) the
weighing up of perceived advantages (pros) and (3) disadvantages (cons) of change (decisional balance), and (4) the
strategies and techniques individuals use to modify their
thoughts, feelings, and behavior (referred to as the processes of change). As such, it is possible to identify how people
change through the stages and why people change through
the mediators. Changes in the mediators should result in
behavior change.
The importance of self-efficacy for initiating and maintaining a pattern of regular physical activity derives from
social cognitive theories of behavior (Bandura, 1986) and
was discussed earlier in the chapter. Numerous studies
have revealed a consistent positive relationship between
exercise self-efficacy and stage of change (Marcus, Eaton,
Rossi, & Harlow, 1994). Narrative reviews of the literature
are unequivocal that higher efficacy is associated with
advancing stage, with many concluding the relationship to
be linear (Prochaska & Marcus, 1994). Self-efficacy also
appears to successfully differentiate between individuals
at most stages, as shown in our meta-analysis (Marshall &
Biddle, 2001).
Behavior change is assumed to involve a systematic
evaluation of the potential gains (pros) and losses (cons)
associated with adopting the new behavior. Our metaTable 24.1
549
analysis showed that pros increase for every forward stage
transition, with the largest change being from precontemplation to contemplation, whereas the change from contemplation to preparation was virtually zero. All other
stage transitions had small effects. For the cons of behavior change, we showed that these generally decreased
across stages, as predicted, and that the largest change was
from precontemplation to contemplation, whereas the
smallest was from action to maintenance (Marshall & Biddle, 2001). Our results, therefore, suggest that interventions might usefully focus on increasing pros and reducing
cons, particularly at the important transition from precontemplation to contemplation. For example, a decisional
balance exercise could be adopted in self-help change
leaflets for community-level interventions or could be
used in individual exercise counseling (Biddle, 2004;
Breckon, 2002; Loughlan & Mutrie, 1997).
Ten basic processes of change have been proposed that
describe the techniques and strategies individuals use to
modify their thoughts, feelings, and behavior (Prochaska &
DiClemente, 1983). These processes have been organized
into two higher-order constructs: cognitive processes
(thinking or experiential processes) and behavioral (doing)
processes (Prochaska, Velicer, DiClemente, & Fava, 1988).
Narrative reviews in the physical activity domain have also
concluded that a two-factor model is appropriate, and
stage-specific trends exist for these higher-order constructs (Prochaska & Marcus, 1994). The general consensus is that cognitive processes are more important during
the early stages, with behavioral processes important at
later stages, but our meta-analysis was not able to support
this (Marshall & Biddle, 2001). Indeed, the majority of
conclusions in previous reviews are based on a single primary study that found the correlation between cognitive
and behavioral processes to be .91 (Marcus et al., 1992), a
finding that actually argues against a two-factor model.
Few studies are available that make process-specific
predictions at each stage of change. It has been suggested
that consciousness raising is particularly important when
Defining Stages of the Transtheoretical Model
Stage
Meeting Criterion Level
of Physical Activity?
Precontemplation
No
Little or no physical activity
No
Contemplation
No
Little or no physical activity
Yes
Preparation
No
Small changes in physical activity
Yes
Action
Yes
Physically active for less than 6 months
Yes
Maintenance
Yes
Physically active for more than 6 months
Yes
Current Behavior
Intention to Meet Criterion
Level of Physical Activity?
550
Exercise and Health Psychology
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Effect size
Effect size
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moving from precontemplation to contemplation (Reed,
1999), and the findings from our meta-analysis were in
support of this. However, the greatest effect size from precontemplation to contemplation was for self-liberation.
This is the belief that change is possible and that responsibility for change lies within the individual. Items measuring self-liberation appear theoretically consistent with
concepts of autonomy, as discussed earlier in the section on
self-determination theory, and this has predicted interest
in and adherence to physical activity. Based on our metaanalysis, we believe that processes of change for physical
activity require further investigation before definitive
statements about their use can be made for interventions.
The vast majority of studies investigating the TTM in
physical activity (Marshall & Biddle, 2001) and other
health behavior contexts (Sutton, 2000) are cross-sectional.
This presents difficulties in establishing causal relationships between constructs and stages. Moreover, many
studies using such a design provide support for what Weinstein and colleagues (Weinstein, Rothman, & Sutton,
1998) have called “pseudo-stage” models, where there is a
linear pattern of change (actually “difference” in a crosssectional design) between variables rather than having an
a priori assumption of discontinuity whereby a variable is
predicted to act differently at different stages (Sutton,
2000). Data from our meta-analysis are more supportive of
a pseudo-stage model, as shown in Figures 24.3 and 24.4.
Figure 24.4 Differences in effect sizes in self-efficacy across
stages for physical activity studies. Source: “ The Transtheoretical Model of Behavior Change: A Meta-Analysis of Applications
to Physical Activity and Exercise,” by S. J. Marshall and S. J. H.
Biddle, 2001, Annals of Behavioral Medicine, 23, pp. 229–246.
Reprinted with permission.
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In Figure 24.3, physical activity differences between
stages follow essentially a linear pattern, with only a hint
of discontinuity through a flat S-shaped curve. In Figure
24.4, self-efficacy is largely linear across the stages. Similarly, in a study we conducted on the extent and determinants of promoting physical activity for patients by mental
health professionals (Faulkner & Biddle, 2001), we
assessed mean score differences in variables from the theory of planned behavior among three stages: no promotion
of physical activity, irregular promotion, and regular promotion. Again, results supported a pseudo-stage model
rather than a true stage model because the differences
across the three groups for each variable were essentially
linear (see Figure 24.5). Future studies on the TTM and
physical activity need to test for the discontinuity of
variables across stages and establish whether the variable
is an antecedent or consequence of stage transition (Sutton, 2000).
Figure 24.3 Differences in effect sizes in physical activity
across stages for physical activity studies. Source: “ The Transtheoretical Model of Behavior Change: A Meta-Analysis of
Applications to Physical Activity and Exercise,” by S. J. Marshall and S. J. H. Biddle, 2001, Annals of Behavioral Medicine,
23, pp. 229–246. Reprinted with permission.
THEORETICAL INTEGRATION
Recent investigations have combined stage models with linear, continuous models. One approach is to investigate the
means in variables derived from continuous theories, such
as the TPB, across the different stage groups. Another
strategy might be to test the architecture of a linear model
within each stage group separately.
Theoretical Frameworks in Exercise Psychology
6
ability. Individuals in action are more realistic, and those
in maintenance are actually reducing their vulnerability
because of their behavior. However, such a pattern is not
expected in variables such as self-efficacy due to its core
importance in all stages (Bandura, 1986, 1997). Therefore,
it is important to employ the appropriate test variables for
investigating discontinuity patterns (Lippke & Plotnikoff,
2005; Lippke, Sniehotta, & Luszczynska, 2005).
Intention
Attitude
Subjective norm
PBC
5
4
Mean
551
3
Longitudinal Patterns
2
1
0
No
Irregular
Regular
Promotion of physical activity
Figure 24.5 Mean scores for variables from the theory of
planned behavior across three stage groups based on the extent
of physical activity promotion by mental health professionals.
Note: PBC = Perceived behavioral control. Source: “Predicting
Physical Activity Promotion in Health Care Settings,” by G.
Faulkner and S. Biddle, 2001, American Journal of Health Promotion, 16(2), pp. 98–106. Reprinted with permission.
Cross-Sectional Patterns
As described previously, the TPB variables often show linear patterns (Faulkner & Biddle, 2001). However, for variables that might operate in a more stage-specific way, we
have found nonlinear patterns and support for stage
assumptions (Lippke & Plotnikoff, 2005). Strongest support for discontinuity patterns were revealed for perceptions of vulnerability (subjective chances of contracting a
disease if one is not physically active). Individuals in the
precontemplation stage felt least vulnerable, those in contemplation and action reported the highest vulnerability,
and individuals in preparation and maintenance had
reduced vulnerability. The higher level of vulnerability in
the contemplation stage, in comparison to precontemplation, is in accordance with the stage definition. Individuals
in precontemplation are either unaware of the risk behavior
(such as not being physically active enough) or subjectively
reduce their vulnerability due to an incorrect optimistic
mind-set. In contemplation, persons become aware of their
risk. However, if they plan to start performing the behavior
in question in the near future, or if they are already performing some behavior, their vulnerability estimation
becomes relevant and they may express feelings of vulner-
Another strategy is to combine a linear model with a stage
model. In some studies, the stage membership has been
predicted additionally or alternatively to intention and
behavior in these linear estimations, as, for example, in the
TPB (Courneya, Nigg, & Estabrooks, 1998). Because stage
is conceptualized as analogous to intention and behavior
probability, the advantage of including the stage variable
alongside intention and behavior additionally or alternatively is rather small. In contrast, to investigate stage as a
moderator and to examine stage-dependent processes has
been shown to be fruitful (Lippke, Nigg, & Maddock,
2004). These processes are analogous to the assumption of
most social cognitive models, intention formation, action
planning, and behavior change. With this strategy one
might also test discontinuity patterns by testing whether,
depending on the stage, different social cognitive variables
are more or less influential (Weinstein et al., 1998).
By testing the TPB over 1 year, we found that attitude
and intention were highly associated in all stages. Perceived behavioral control, subjective norm, and intention
were significantly and positively related. In all other stage
groups, the subjective norm and intention relations were
not significant. Perceived behavioral control was not related to intention or behavior except in the maintenance stage.
If individuals who performed physical activity over a
longer period of time perceived more control, they also had
a higher intention and performed more behavior. Intention
and physical activity were correlated in precontemplation,
preparation, action, and maintenance, but not in contemplation (Lippke, Nigg, et al., 2004).
A Hybrid Model: The Health Action
Process Approach
The health action process approach (HAPA; Schwarzer,
1992, 2001) is a model that explicitly integrates continuous
and stage assumptions and is thereby a hybrid model. At the
same time, the HAPA integrates motivational (prediction
of intention) and behavior-enabling models (inclusion of
postdecisional facets such as implementation intentions).
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The HAPA makes a distinction between a motivation
phase and a volition /postdecision phase of health behavior
change. The basic idea is that individuals experience a shift
of mind-set when moving from the first phase (motivational) to the second (volitional). The moment people commit
themselves to an intention to exercise, for example, they
enter the volitional phase. In this phase, a division into two
subphases appears to be meaningful, wherein people can be
labeled as either intenders or actors. First, they intend to
act but they remain inactive. Second, they initiate the
intended action. Thus, three phases or stages can be distinguished, as shown in Figure 24.6. In the (a) nonintentional
stage, a behavioral intention is being developed, which is
similar to the contemplation stage in the TTM. Afterward,
individuals enter ( b) the intentional stage, where they have
already formed an intention but still remain inactive (or at
least not active at the recommended level), while the exercise behavior is being planned and prepared. If these plans
are translated into action, individuals reside in (c) the
action stage. They are then physically active at the recommended goal behavior level.
In the nonintentional stage, an intention has to be developed. In this phase, risk perception is a distal antecedent
within the motivational phase. Risk perception is sufficient
to enable the undecided person to form an intention. Furthermore, it is a prerequisite for a contemplation process
SE
Pros
Intention
Plan
Behavior
Nonintentional
Stage
Intentional
Stage
Action
Stage
Cons
Risk
Figure 24.6 The health action process approach, with its
architecture and stages adapted from Lippke, Ziegelmann, and
Schwarzer (2005). Note: SE = Self-efficacy; Risk = Risk perception. Dashed arrows indicate stage-specific effects and
mechanisms; solid arrows show generic effects. Source: “SelfEfficacy in the Adoption and Maintenance of Health Behaviors:
Theoretical Approaches and a New Model” (pp. 217–243), by R.
Schwarzer, in R. Schwarzer (Ed.), Self-Ef ficacy: Thought Control
of Action, 1992, Bristol, PA: Taylor & Francis. Reprinted with
permission.
and further elaboration of thoughts about consequences and
capacities. Risk perception operates at a stage-specific
level, and therefore its effect on intention is represented by
a dashed line in Figure 24.6; in the intentional stage, risk
perception has no effect (Lippke, Ziegelmann, & Schwarzer, 2005). The belief in one’s capabilities to perform a
desired action (self-efficacy) is necessary for goal pursuit.
That is, perceived self-efficacy promotes intention formation and behavior implementation in all stage groups (Lippke, Ziegelmann, et al., 2005); the arrow is therefore drawn
as a solid line, not dashed, in Figure 24.6.
After a decision has been made, the intentional stage is
entered: The individual has a high intention but is not performing the behavior. The intention has to be transformed
into detailed plans on how to perform the behavior.
Instructions on the goal pursuit may contain assisting
intentions and precise implementation intentions or plans.
These plans involve when, where, and how the goal behavior will be initiated (Lippke, Ziegelmann, & Schwarzer,
2004) whereby cognitive links between concrete opportunities and the intended behavior will be built. Social cognitive variables change in dominance and interplay. Risk
perception has no further influence, but outcome expectancies remain important. Self-efficacy is also important in
the planning and initiation process, especially if barriers
occur or no enabling situation arises. Self-efficacy keeps
the intention high and the plans flexible to compensate for
setbacks and stay on track to initiation.
If the goal behavior has been initiated, the individual
enters the action stage. The behavior has to be controlled by
cognitions in order to be maintained. Self-regulatory skills
are substantial for the maintenance process. Effort has to be
invested, useful situations for implementation of the new
behavior have to be detected, and distractions have to be
resisted. The behavior will mainly be directed by selfefficacy (Schwarzer, 2001) because it regulates how effort is
invested and persistence is managed if barriers and setbacks
occur. The performed behavior has to be maintained, and
relapses have to be managed by action control strategies.
Due to individuals having to first set a goal that then may
be translated into plans and behavior, this process is stagespecific; only persons in intentional and action stages are
more likely to make plans and subsequently perform the
goal behavior (dashed lines in Figure 24.6; Lippke, Ziegelmann, et al., 2005). Also, the influence of self-efficacy on
postdecisional processes, such as planning and behavior
performance, depends on whether one has decided to
change ( here it is crucial to believe in one’s own competencies) or not ( here only intention formation can be support-
Theoretical Frameworks in Exercise Psychology
ed by self-efficacy). The HAPA also includes aspects such
as situational barriers and resources (Schwarzer, 1992,
2001), but not much work has been done on these to date.
CONCLUSION
In this chapter, we have summarized the most popular theories in exercise psychology. One objective was to provide a
framework or heuristic to differentiate the various theories.
This is challenging due to the multiple overlaps of different
theories and constructs (Armitage & Conner, 2000). The
correspondence of the TRA and the TPB is obvious. However, there also are similarities in other theories, as, for
example, the TPB and self-efficacy theory due to relationships between perceived behavior control and self-efficacy
(Bandura, 2004). Perceived behavioral control and control
variables might not be clearly separable (Schwarzer, 2001).
Another example of overlap is attitudes in the TPB, outcome
expectancies in self-efficacy theory, and the decisional balance (pros and cons) in the TTM (D. M. Williams, Anderson, & Winett, 2005). In the future, investigators need to
state which theory is the most appropriate for a particular
research question or intervention strategy. With the current
overlap in constructs, however, it might be difficult to compare the theories because one might not easily test the discriminant validity of the constructs. One solution would be
to assume that the constructs match each other and thus can
be used for comparing the theories, as Garcia and Mann
(2003) have done. They identified the HAPA as being superior to other models, such as the TPB, by investigating
explained variances. With this method, we can compare
how well the different theories explain the variation in
behavior and which theoretical constructs might be most
valuable. Other methods to assess the appropriateness of a
theory could be to evaluate (a) whether the theory provides
statistically significant outcomes (e.g., tested in an experimental design); ( b) its effect size and whether it is clinical
meaningful; (c) the public health impact if the theory is
tested in a public health intervention; and (d) how generalizable the theory is to different populations, cultural subgroups, and circumstances (Nigg & Jordan, 2005).
One other approach in the future is not necessarily comparing and separating the theories but, instead, combining
them to explain more variance. However, researchers and
pragmatists have to avoid overloading theories and intervention plans. Theories have to be complete but also parsimonious—in other words, clear and simple (Michie et al.,
2005). One model that aims to achieve this end is the
HAPA due to its integration of motivational and volitional
553
stages and generic factors of self-efficacy and stage-specific features, such as translating intentions into behavior
via implementation intentions. With this model, we have
learned, depending on the stage of the target group, that
different mechanisms are important. For example, for those
described as “intentional individuals,” implementation
intentions or plans are most crucial to translate their intentions into successful behavior.
Most of the theories in this chapter focus on the individual, but environmental factors are also important (Biddle & Mutrie, 2001). Some theories include external
factors such as the TPB with subjective norms and SDT
with its external forms of motivation. In these models,
external factors are included as a subjective representation
in the individual. However, numerous studies have also
shown that objective features of the environment can
explain why individuals are likely to be physically active or
not. For instance, if there are more parks and pavement and
sidewalks in the neighborhood, people walk more (Suminski, Poston, Petosa, Stevens, & Katzenmoyer, 2005). In the
future, individual approaches should be tested under different environmental circumstances. This could test, for
example, whether more variance is accounted for using
TPB in a positive physical activity environment (e.g.,
where people perceive that they just have to change their
own behavior to be active) than in unattractive environments (where individuals feel they cannot change their
behavior just by individual decisions and become subject to
learned helplessness in terms of their exercise behavior).
These thoughts require further testing.
Empirically supported models are useful for creating
interventions by identifying and altering particular factors
(e.g., derived from continuous social cognitive models) that
help people move from one stage to the next. Stage models
offer the possibility of designing programs and treatments
that will be more effective and efficient than one-size-fitsall interventions (Weinstein et al., 1998). Therefore, translating research into practice is important, and theory-based
interventions are imperative for successful exercise and
health-enhancing physical activity promotion.
REFERENCES
Ajzen, I. (1985). From intentions to actions: A theory of
planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action
control: From cognition to behavior (pp. 11–39). New York:
Springer-Verlag.
Ajzen, I. (1988). Attitudes, personality and behavior. Milton
Keynes, England: Open University Press.
554
Exercise and Health Psychology
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
state of research] (pp. 19–55). Paris: Presse Universitaires
de France.
Ajzen, I. (2001). Nature and operation of attitudes. Annual
Review of Psychology, 52, 27–58.
Biddle, S. J. H., & Mutrie, N. (2001). Psychology of physical
activity: Determinants, well-being and interventions. London:
Routledge.
Ajzen, I. (2002). Residual effects of past on later behavior:
Habituation and reasoned action perspectives. Personality
and Social Psychology Review, 6, 107–122.
Ajzen, I., Brown, T. C., & Carvahal, F. (2004). Explaining the
discrepancy between intentions and actions: The case of
hypothetical bias in contingent valuation. Personality and
Social Psychology Bulletin, 30, 1108–1121.
Ajzen, I., & Driver, B. (1991). Prediction of leisure participation
from behavioral, normative and control beliefs: An application of the theory of planned behavior. Leisure Sciences, 13,
185–204.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
Aristotle, N. (1993). Nichomachian ethics. Athens: Cactus.
Armitage, C. J., & Conner, M. (2000). Social cognition models
and health behavior: A structured review. Psychology and
Health, 15, 173–189.
Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of
planned behavior: A meta-analytic review. British Journal of
Social Psychology, 40, 471–499.
Bagozzi, R. P., & Kimmel, S. K. (1995). A comparison of leading theories for the prediction of goal-directed behaviors.
British Journal of Social Psychology, 34, 437–461.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of
behavioral change. Psychological Review, 84, 191–215.
Bandura, A. (1986). Social foundations of thought and action: A
social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.
Bandura, A. (1997). Self-ef ficacy: The exercise of control. New
York: Freeman.
Biddle, S. J. H., & Nigg, C. R. (2000). Theories of exercise behavior. International Journal of Sport Psychology, 31, 290–304.
Bouchard, C. (2000). Introduction. In C. Bouchard (Ed.), Physical activity and obesity (pp. 3–19). Champaign, IL: Human
Kinetics.
Bouchard, C., & Blair, S. N. (1999). Introductory comments for
the consensus on physical activity and obesity. Medicine and
Science in Sports and Exercise, 31(11, Suppl.), S498–S501.
Bozoian, S., Rejeski, W. J., & McAuley, E. (1994). Self-efficacy
influences feeling states associated with acute exercise. Journal of Sport and Exercise Psychology, 16, 326–333.
Breckon, J. (2002). Motivational interviewing and exercise
preparation. In D. Lavallee & I. Cockerill (Eds.), Counseling
in sport and exercise contexts ( pp. 48– 60). Leicester,
England: British Psychological Society, Sport and Exercise
Psychology.
Chatzisarantis, N. L. D., & Biddle, S. J. H. (1998). Functional
significance of psychological variables that are included in
the theory of planned behavior: A self-determination theory
approach to the study of attitudes, subjective norms, perceptions of control, and intentions. European Journal of Social
Psychology, 28, 303–322.
Chatzisarantis, N. L. D., Biddle, S. J. H., & Meek, G. A. (1997).
A self-determination theory approach to the study of intentions and the intention-behavior relationship in children’s
physical activity. British Journal of Health Psychology, 2,
343–360.
Bandura, A. (2004). Health promotion by social cognitive
means. Health Education and Behavior, 31(2), 143–164.
Chatzisarantis, N. L. D., & Hagger, M. S. (in press). Effects of a
brief intervention based on the theory of planned behavior on
leisure time physical activity participation. Journal of Sport
and Exercise Psychology.
Biddle, S. J. H. (1999). Motivation and perceptions of control:
Tracing its development and plotting its future in exercise and
sport psychology. Journal of Sport and Exercise Psychology,
21, 1–23.
Chatzisarantis, N. L. D., Hagger, M. S., Biddle, S. J. H., & Karageorghis, C. (2002). The cognitive processes by which perceived locus of causality predicts participation in physical
activity. Journal of Health Psychology, 7, 685–699.
Biddle, S. J. H. (2004). Individual and small group interventions.
In P. Oja & J. Borms (Eds.), Perspectives—The multidisciplinary series of physical education and sport science: Vol. 6.
Health enhancing physical activity (pp. 343–368). Oxford:
Meyer & Meyer.
Chatzisarantis, N. L. D., Hagger, M. S., Biddle, S. J. H., &
Smith, B. (2005). The stability of the attitude-intention relationship in the context of physical activity. Journal of Sports
Sciences, 23, 49–61.
Biddle, S. J. H., Chatzisarantis, N. L. D., & Hagger, M. S.
(2001). Theorie de l’autodetermination dans le domaine du
sport et de l’exercice physique [Self-determination theory in
sport and exercise]. In F. Cury & P. Sarrazin (Eds.), Theories de la motivation et pratiques sportives: Etats des
recherches [Theories of motivation and sport practice: The
Chatzisarantis, N. L. D., Hagger, M. S., Biddle, S. J. H., Smith, B.,
& Wang, C. K. J. (2003). A meta-analysis of perceived locus of
causality in exercise, sport, and physical education contexts.
Journal of Sport and Exercise Psychology, 25, 284–306.
Chatzisarantis, N. L. D., Hagger, M. S., Smith, B., & Sage, L. (in
press). The influences of intrinsic motivation within the theory of planned behavior. European Journal of Social Psychology.
Theoretical Frameworks in Exercise Psychology
Conner, M., & Abraham, C. (2001). Conscientiousness and the
theory of planned behavior: Toward a more complete model
of the antecedents of intentions and behavior. Personality and
Social Psychology Bulletin, 27, 1547–1561.
Conner, M., & Armitage, C. J. (1998). Extending the theory of
planned behavior: A review and avenues for further research.
Journal of Applied Social Psychology, 28(15), 1429–1464.
Courneya, K. S., Nigg, C. R., & Estabrooks, P. A. (1998). Relationships among the theory of planned behavior, stages of
change, and exercise behavior in older persons over a three
year period. Psychology and Health, 13, 355–367.
Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic
rewards on intrinsic motivation. Psychological Bulletin, 125,
627–668.
Deci, E. L., & Ryan, R. M. (1980). Self determination theory:
When mind mediates behavior. Journal of Mind and Behavior,
1, 33–43.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and selfdetermination in human behavior. New York: Plenum Press.
Deci, E. L., & Ryan, R. M. (1987). The support of autonomy and
the control of behavior. Journal of Personality and Social Psychology, 53, 1024–1037.
Deci, E. L., & Ryan, R. M. (1991). A motivational approach to
self: Integration in personality. In R. A. Dienstbier (Ed.),
Nebraska Symposium on Motivation: Vol. 38. Perspectives on
motivation (pp. 237–288). Lincoln: University of Nebraska
Press.
Deci, E. L., & Ryan, R. M. (2002). Self-determination
research: Reflections and future directions. In E. L. Deci &
R. M. Ryan (Eds.), Handbook of self-determination research
(pp. 431–441). Rochester, NY: University of Rochester
Press.
Department of Health. (2004). At least five a week: Evidence on
the impact of physical activity and its relationship to health—
A report from the chief medical of ficer. London: Author.
Dishman, R. K., Washburn, R. A., & Heath, G. W. (2004). Physical activity epidemiology. Champaign, IL: Human Kinetics.
Dzewaltowski, D. A., Noble, J. M., & Shaw, J. M. (1990). Physical activity participation: Social cognitive theory versus the
theories of reasoned action and planned behavior. Journal of
Sport and Exercise Psychology, 12, 388–405.
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes.
Fort Worth, TX: Harcourt Brace Jovanovich.
Ewart, C. (1989). Psychological effects of resistive weight training: Implications for cardiac patients. Medicine and Science
in Sports and Exercise, 21, 683–688.
Ewart, C. E., Taylor, C. B., Reese, L. B., & DeBusk, R. F.
(1983). Effects of early post myocardial infarction exercise
testing on self perception and subsequent physical activity.
American Journal of Cardiology, 51, 1076–1080.
555
Faulkner, G., & Biddle, S. J. H. (2001). Predicting physical
activity promotion in health care settings. American Journal
of Health Promotion, 16(2), 98–106.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and
behavior: An introduction to theory and research. Reading,
MA: Addison-Wesley.
Fox, K. R. (1997). The physical self and processes in self-esteem
development. In K. R. Fox (Ed.), The physical self: From
motivation to well-being (pp. 111–139). Champaign, IL:
Human Kinetics.
Fox, K. R., & Corbin, C. B. (1989). The Physical Self Perception
Profile: Development and preliminary validation. Journal of
Sport and Exercise Psychology, 11, 408–430.
Gagne, M., Ryan, R. M., & Bargman, K. (2003). Autonomy support and need satisfaction in the motivation and well being of
gymnasts. Journal of Applied Sport Psychology, 15, 372–390.
Garcia, K., & Mann, T. (2003). From “I wish” to “I will”:
Social-cognitive predictors of behavioral intentions. Journal
of Health Psychology, 8(3), 347–360.
Godin, G., Valois, P., Jobin, J., & Ross, A. (1991). Prediction of
intention to exercise of individuals who have suffered from
coronary heart disease. Journal of Clinical Psychology, 47,
762–772.
Gollwitzer, P. M. (1999). Implementation intentions: Strong
effects of simple plans. American Psychologist, 54, 493–503.
Goudas, M., Biddle, S. J. H., & Fox, K. (1994). Perceived locus
of causality, goal orientations, and perceived competence in
school physical education classes. British Journal of Educational Psychology, 64, 453–463.
Goudas, M., Biddle, S. J. H., & Underwood, M. (1995). A
prospective study of the relationships between motivational
orientations and perceived competence with intrinsic motivation and achievement in a teacher education course. Educational Psychology, 15, 89–96.
Hagger, M. S., & Chatzisarantis, N. L. D. (2005a). First- and
higher-order models of attitudes, normative influence, and
perceived behavioral control in the theory of planned behavior. British Journal of Social Psychology, 44, 513–535.
Hagger, M. S., & Chatzisarantis, N. L. D. (2005b). The social
psychology of exercise and sport. Buckingham, UK: Open
University Press.
Hagger, M. S., & Chatzisarantis, N. L. D. (in press). The
transcontextual model in four nations. Journal of Educational
Psychology.
Hagger, M. S., Chatzisarantis, N. L. D., Barkoukis, V., Wang,
C. K. J., & Baranowski, J. (2005). Perceived autonomy support in physical education and leisure-time physical activity:
A cross-cultural evaluation of the trans-contextual model.
Journal of Educational Psychology, 97, 376–390.
Hagger, M. S., Chatzisarantis, N., & Biddle, S. J. H. (2001). The
influence of self-efficacy and past behavior on the physical
556
Exercise and Health Psychology
activity intentions of young people. Journal of Sports Sciences, 19, 711–725.
Hagger, M. S., Chatzisarantis, N. L. D., & Biddle, S. J. H.
(2002a ). The inf luence of autonomous and controlling
motives on physical activity intentions within the theory of
planned behavior. British Journal of Health Psychology, 7,
283–297.
Hagger, M. S., Chatzisarantis, N. L. D., & Biddle, S. J. H.
(2002b). A meta-analytic review of the theories of reasoned
action and planned behavior in physical activity: Predictive
validity and the contribution of additional variables. Journal
of Sport and Exercise Psychology, 24, 3–32.
Hagger, M. S., Chatzisarantis, N., Biddle, S. J. H., & Orbell, S.
(2001). Antecedents of children’s physical activity intentions
and behavior: Predictive validity and longitudinal effects.
Psychology and Health, 16, 391–407.
Hagger, M. S., Chatzisarantis, N. L. D., Culverhouse, T., & Biddle, S. J. H. (2003). The processes by which perceived autonomy support in physical education promotes leisure-time
physical activity intentions and behavior: A trans-contextual
model. Journal of Educational Psychology, 95, 784–795.
Lippke, S., & Plotnikoff, R. C. (2005). Stages of change in physical exercise: A test of stage discrimination and nonlinearity. American Journal of Health Behavior, 30(3), 290–301.
Lippke, S., Sniehotta, F. F., & Luszczynska, A. (2005). Social
cognitions across the stages of behavior change: A comparison of two stage models. Polish Psychological Bulletin, 36(1),
43–50.
Lippke, S., Ziegelmann, J. P., & Schwarzer, R. (2004). Initiation
and maintenance of physical exercise: Stage-specific effects
of a planning intervention. Research in Sports Medicine, 12,
221–240.
Lippke, S., Ziegelmann, J. P., & Schwarzer, R. (2005). Stage-specific adoption and maintenance of physical activity: Testing
a three-stage model. Psychology of Sport and Exercise, 6,
585–603.
Loughlan, C., & Mutrie, N. (1997). A comparison of three interventions to promote physical activity: Fitness assessment,
exercise counseling, and information provision. Health Education Journal, 56, 154–165.
Marcus, B. H., Eaton, C. A., Rossi, J. S., & Harlow, L. L. (1994).
Self-efficacy, decision-making and stages of change: An integrative model of physical exercise. Journal of Applied Social
Psychology, 24, 489–508.
Hagger, M. S., Chatzisarantis, N. L. D., & Harris, J. (2006).
From psychological need satisfaction to intentional behavior:
Testing a motivational sequence in two behavioral contexts.
Personality and Social Psychology Bulletin, 32, 131–138.
Marcus, B. H., & Forsyth, L. H. (2003). Motivating people to be
physically active. Champaign, IL: Human Kinetics.
Hofstetter, C. R., Hovell, M. F., Macera, C., Sallis, J. F., Spry,
V., Barrington, E., et al. (1991). Illness, injury and correlates
of aerobic exercise and walking: A community study.
Research Quarterly for Exercise and Sport, 62, 1–9.
Marcus, B. H., Rossi, J. S., Selby, V. C., Niaura, R. S., &
Abrams, D. B. (1992). The stages and processes of exercise
adoption and maintenance in a worksite sample. Health Psychology, 11, 386–395.
Kerlinger, F. (1973). Foundations of behavioral research. New
York: Holt, Rinehart and Winston.
Markland, D., & Tobin, V. (2004). A modification to the Behavioral Regulation in Exercise Questionnaire to include an
assessment of amotivation. Journal of Sport and Exercise Psychology, 26, 191–196.
Koestner, R., Lekes, N., Powers, T. A., & Chicoine, E. (2002).
Attaining personal goals: Self-concordance plus implementation intentions equals success. Journal of Personality and
Social Psychology, 83, 231–244.
Leary, M. R. (1992). Self presentational processes in exercise
and sport. Journal of Sport and Exercise Psychology, 14,
339–351.
Levesque, C., Zuehlke, N., Stanek, L., & Ryan, R. M. (2004).
Autonomy and competence in German and U.S. university
students: A comparative study based on self-determination
theory. Journal of Educational Psychology, 96, 68–84.
Li, F. (1999). The Exercise Motivation Scale: Its multifaceted
structure and construct validity. Journal of Applied Sport Psychology, 11, 97–115.
Lippke, S., Nigg, C. R., & Maddock, J. E. (2004). Testing stagespecific predictions of the ToPB in the stages of the TTM for
physical activity. In S. Keller & W. F. Velicer (Eds.),
Research on the transtheoretical model: Where are we now,
where are we going? (pp. 76–78). Lengerich, Germany: Pabst
Science.
Marshall, S. J., & Biddle, S. J. H. (2001). The transtheoretical
model of behavior change: A meta-analysis of applications to
physical activity and exercise. Annals of Behavioral Medicine,
23, 229–246.
Matsumoto, H., & Takenaka, K. (2004). Motivational profiles
and stages of exercise behavior change. International Journal
of Sport and Exercise Science, 2, 89–96.
McAuley, E. (1992). Understanding exercise behavior: A selfefficacy perspective. In G. C. Roberts (Ed.), Motivation in
sport and exercise (pp. 107–127). Champaign, IL: Human
Kinetics.
McAuley, E., & Blissmer, B. (2000). Self-efficacy determinants
and consequences of physical activity. Exercise and Sport Sciences Reviews, 28, 85–88.
McAuley, E., & Courneya, K. S. (1993). Adherence to exercise
and physical activity as health-promoting behaviors: Attitudinal and self-efficacy influences. Applied and Preventive
Psychology, 2, 65–77.
Theoretical Frameworks in Exercise Psychology
McAuley, E., & Mihalko, S. L. (1998). Measuring exercise-related self-efficacy. In J. L. Duda (Ed.), Advances in sport and
exercise psychology measurement (pp. 371–390). Morgantown, WV: Fitness Information Technology.
McAuley, E., Pena, M. M., & Jerome, G. J. (2001). Self-efficacy as
determinant and an outcome of exercise. In G. C. Roberts (Ed.),
Advances in motivation in sport and exercise (pp. 235–261).
Champaign, IL: Human Kinetics.
Michie, S., Johnston, M., Abraham, C., Lawton, R., Parker, D.,
& Walker, A. (2005). Making psychological theory useful for
implementing evidence based practice: A consensus
approach. Quality and Safety in Health Care, 14, 26–33.
Milne, S. E., Orbell, S., & Sheeran, P. (2002). Combining motivational and volitional interventions to promote exercise
participation: Protection motivation theory and implementation intentions. British Journal of Health Psychology, 7,
163–184.
Morris, J. N. (1994). Exercise in the prevention of coronary heart
disease: Today’s best buy in public health. Medicine and Science in Sports and Exercise, 26, 807–814.
Mulaik, S. A., & Millsap, R. E. (2000). Doing the four-step
right. Structural Equation Modeling, 7, 36–73.
Mullan, E., & Markland, D. (1997 ). Variations in selfdetermination across the stages of change for exercise in
adults. Motivation and Emotion, 21, 349–362.
Mullan, E., Markland, D., & Ingledew, D. (1997). A graded conceptualization of self-determination in the regulation of exercise behavior: Development of a measure using confirmatory
factor analytic procedures. Personality and Individual Dif ferences, 23, 745–752.
Mutrie, N., Carney, C., Blamey, A., Crawford, F., Aitchison, T.,
& Whitelaw, A. (2002). “Walk in to work out ”: A randomized
controlled trial of self help intervention to promote active
commuting. Journal of Epidemiology and Community Health,
56, 407–412.
Nigg, C. R., & Jordan, P. J. (2005). Commentary: It’s a difference of opinion that makes a horserace. Health Education
Research, 20(3), 291–293.
Ntoumanis, N. (2001). A self-determination approach to the
understanding of motivation in physical education. British
Journal of Educational Psychology, 71, 225–242.
Ntoumanis, N., Pensgaard, A. M., Martin, C., & Pipe, K. (2004).
An idiographic analysis of amotivation in compulsory physical education. Journal of Sport and Exercise Psychology, 26,
197–214.
Orbell, S. (2000). Motivational and volitional components in
action initiation: A field study of the role of implementation
intentions. Journal of Applied Social Psychology, 30, 780–797.
Orbell, S., Hodgkins, S., & Sheeran, P. (1997). Implementation
intentions and the theory of planned behavior. Personality
and Social Psychology Bulletin, 23, 945–954.
557
Pate, R. R., Pratt, M., Blair, S. N., Haskell, W. L., Macera, C. A.,
Bouchard, C., et al. (1995). Physical activity and public
health: A recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. Journal of the American Medical Association, 273,
402–407.
Pelletier, L. G., Fortier, M. S., Vallerand, R. J., Tuson, K. M.,
Briere, N. M., & Blais, M. R. (1995). Toward a new measure
of intrinsic motivation, extrinsic motivation, and amotivation
in sports: The Sport Motivation Scale (SMS). Journal of Sport
and Exercise Psychology, 17, 35–53.
Pender, N. J., & Pender, A. R. (1986). Attitudes, subjective
norms and intentions to engage in health behaviors. Nursing
Research, 35, 15–18.
Perugini, M., & Bagozzi, R. P. (2001). The role of desires and
anticipated emotions in goal-directed behaviors: Broadening
and deepening the theory of planned behavior. British Journal
of Social Psychology, 40, 79–98.
Prestwich, A., Lawton, R., & Conner, M. (2003). The use of
implementation intentions and the decision balance sheet in
promoting exercise behavior. Psychology and Health, 18,
707–721.
Prochaska, J., & DiClemente, C. C. (1983). Stages and processes of self-change of smoking: Toward an integrative model of
change. Journal of Consulting and Clinical Psychology, 51,
390–395.
Prochaska, J., & DiClemente, C. C. (1984). The stages of change.
In J. O. Prochaska (Ed.), The transtheoretical approach:
Crossing traditional boundaries of therapy (pp. 21–32).
Homewood, IL: Dow Jones-Irwin.
Prochaska, J., & DiClemente, C. C. (1986). Toward a comprehensive model of change. In W. R. Miller & N. Heather
(Eds.), Treating addictive behaviors (pp. 3–27). New York:
Plenum Press.
Prochaska, J., & Marcus, B. H. (1994). The transtheoretical
model: Application to exercise. In R. K. Dishman (Ed.),
Advances in exercise adherence (pp. 161–180). Champaign,
IL: Human Kinetics.
Prochaska, J., Norcross, J. C., & DiClemente, C. C. (1994).
Changing for good. New York: Avon.
Prochaska, J., & Velicer, W. (1997). The transtheoretical model
of health behavior change. American Journal of Health Promotion, 12, 38–48.
Prochaska, J., Velicer, W. F., DiClemente, C. C., & Fava, J. L.
(1988). Measuring processes of change: Application to the
cessation of smoking. Journal of Consulting and Clinical Psychology, 56, 520–528.
Reed, G. R. (1999). Adherence to exercise and the transtheoretical model of behavior change. In S. J. Bull (Ed.), Adherence
issues in sport and exercise (pp. 19–46). Chichester, England:
Wiley.
558
Exercise and Health Psychology
Rhodes, R. E., & Courneya, K. S. (2003). Relationships between
personality, an extended theory of planned behavior model
and exercise behavior. British Journal of Health Psychology,
8, 19–36.
Rhodes, R. E., Courneya, K. S., & Jones, L. W. (2002). Personality, the theory of planned behavior, and exercise: A unique
role for extroversion’s activity facet. Journal of Applied
Social Psychology, 32, 1721–1736.
Riddle, P. K. (1980). Attitudes, beliefs, behavioral intentions
and behaviors of women and men toward regular jogging.
Research Quarterly for Exercise and Sport, 51, 663–674.
Roberts, G. C. (Ed.). (2001). Advances in motivation in sport and
exercise. Champaign, IL: Human Kinetics.
Rodgers, W. M., & Brawley, L. R. (1993). Using both the selfefficacy theory and the theory of planned behavior to discriminate adherers and dropouts from structured programs.
Journal of Applied Sport Psychology, 5, 195–206.
Ryan, R. M. (1995). Psychological needs and the facilitation of
integrative processes. Journal of Personality, 63, 397–427.
Ryan, R. M., & Connell, J. (1989). Perceived locus of causality
and internalization: Examining reasons for acting in two
domains. Journal of Personality and Social Psychology, 57,
749–761.
Sheeran, P., & Orbell, S. (1999a). Augmenting the theory of
planned behavior: Roles for anticipated regret and descriptive
norms. Journal of Applied Social Psychology, 29, 2107–2142.
Sheeran, P., & Orbell, S. (1999b). Implementation intentions and
repeated behavior: Arguing the predictive validity of the theory of planned behavior. European Journal of Social Psychology, 29, 349–369.
Sheeran, P., & Orbell, S. (2000). Self schemas and the theory of
planned behavior. European Journal of Social Psychology, 30,
533–550.
Sheeran, P., Orbell, S., & Trafimow, D. (1999). Does the temporal stability of behavioral intentions moderate intentionbehavior and past behavior-future behavior relations?
Personality and Social Psychology Bulletin, 25, 721–730.
Sheeran, P., & Silverman, M. (2003). Evaluation of three interventions to promote workplace health and safety: Evidence
for the utility of implementation intentions. Social Science
and Medicine, 56, 2153–2163.
Sheldon, K. M., Elliot, A. J., Kim, Y., & Kasser, T. (2001). What
is satisfying about satisfying events? Testing 10 candidate
psychological needs. Journal of Personality and Social Psychology, 80, 325–339.
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory
and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78.
Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research with
recommendations for modifications and future research.
Journal of Consumer Research, 15, 325–343.
Sallis, J. F., & Owen, N. (1999). Physical activity and behavioral
medicine. Thousand Oaks, CA: Sage.
Skinner, E. (1995). Perceived control, motivation, and coping.
Thousand Oaks, CA: Sage.
Sarrazin, P., Vallerand, R., Guillet, E., Pelletier, L., & Cury, F.
(2002). Motivation and dropout in female handballers: A 21month prospective study. European Journal of Social Psychology, 32, 395–418.
Skinner, E. (1996). A guide to constructs of control. Journal of
Personality and Social Psychology, 71, 549–570.
Schwarzer, R. (1992). Self-efficacy in the adoption and maintenance of health behaviors: Theoretical approaches and a new
model. In R. Schwarzer (Ed.), Self-ef ficacy: Thought control
of action (pp. 217–243). Bristol, PA: Taylor & Francis.
Schwarzer, R. (2001). Social-cognitive factors in changing
health-related behaviors. Current Directions in Psychological
Science, 10(2), 47–51.
Shavelson, R. J., Hubner, J. J., & Stanton, G. C. (1976). Selfconcept: Validation of construct interpretations. Review of
Educational Research, 46, 407–441.
Sheeran, P. (2002). Intention-behavior relations: A conceptual
and empirical review. In W. Stroebe & M. Hewstone (Eds.),
European review of social psychology (pp. 1–36). London:
Wiley.
Sheeran, P., Norman, P., & Orbell, S. (1999). Evidence that intentions based on attitudes better predict behavior than intentions based on subjective norms. European Journal of Social
Psychology, 29, 403–406.
Sparks, P., & Guthrie, C. (1998). Self-identity and the theory of
planned behavior: A useful addition or an unhelpful artifice?
Journal of Applied Social Psychology, 28(15), 1393–1410.
Sparks, P., Guthrie, C. A., & Shepherd, R. (1997). The dimensional structure of the “perceived behavioral control” construct. Journal of Applied Social Psychology, 27, 418–438.
Standage, M., Duda, J., & Ntoumanis, N. (2003). A model of
contextual motivation in physical education: Using constructs
from self-determination and achievement goal theories to
predict physical activity intentions. Journal of Educational
Psychology, 95, 97–110.
Standage, M., Duda, J. L., & Ntoumanis, N. (in press). A self
determination theory in school physical education. Journal of
Educational Psychology.
Stroebe, W., & Stroebe, M. S. (1995). Social psychology and
health. Buckingham, England: Open University Press.
Suminski, R. R., Poston, W. S. C., Petosa, R. L., Stevens, E., &
Katzenmoyer, L. M. (2005). Features of the neighborhood
environment and walking by U.S. adults. American Journal of
Preventive Medicine, 28(2), 149–155.
Theoretical Frameworks in Exercise Psychology
Sutton, S. R. (2000). Interpreting cross-sectional data on stages
of change. Psychology and Health, 15, 163–171.
Sutton, S. R. (2004). Determinants of health-related behaviors:
Theoretical and methodological issues. In S. Sutton, A.
Baum, & M. Johnston (Eds.), The Sage handbook of health
psychology (pp. 94–126). London: Sage.
Terry, D. J., & O’Leary, J. E. (1995). The theory of planned
behavior: The effects of perceived behavioral control and
self-efficacy. British Journal of Social Psychology, 34,
199–220.
Theodorakis, Y., Goudas, M., Bagiatis, K., & Doganis, G.
(1993). Reasoned action theory and the prediction of training
participation in young swimmers. British Journal of Physical
Education Research Supplement, 13, 10–12.
Trafimow, D., & Finlay, K. A. (1996). The importance of subjective norms for a minority of people: Between-subjects and
within-subjects effects. Personality and Social Psychology
Bulletin, 22, 820–828.
U.S. Department of Health and Human Services & Centers for
Disease Control and Prevention. (1996). Physical activity and
health: A report of the surgeon general. Atlanta, GA: Author.
Vallerand, R. J., Pelletier, L. G., Blais, M., Briere, N. M.,
Senecal, C., & Vallieres, E. F. (1992). The Academic Motivation Scale: A measure of intrinsic, extrinsic, and amotivation
in education. Educational and Psychological Measurement,
52, 1003–1017.
Vansteenkiste, M., & Deci, E. L. (2003). Competitively contingent rewards and intrinsic motivation: Can losers remain
motivated? Motivation and Emotion, 27, 273–299.
559
Verplanken, B., & Faes, S. (1999). Good intentions, bad habits,
and effects of forming implementation intentions on healthy
eating. European Journal of Social Psychology, 29, 591–604.
Verplanken, B., Hofstee, G., & Janssen, H. (1998). Accessibility
of affective versus cognitive components of attitudes. European Journal of Social Psychology, 28, 23–35.
Weinstein, N. D., Rothman, A. J., & Sutton, S. R. (1998). Stage
theories of health behavior: Conceptual and methodological
issues. Health Psychology, 17, 290–299.
White, R. W. (1963). Ego and reality in psychoanalytic theory.
New York: International University Press.
Wicker, A. W. (1969). Attitudes versus actions: The relationship
of verbal and overt behavioral responses to attitude objects.
Journal of Social Issues, 25, 41–78.
Williams, D. M., Anderson, E. S., & Winett, R. A. (2005). A
review of the outcome expectancy construct in physical activity research. Annals of Behavioral Medicine, 29(1), 70–79.
Williams, G. C., & Deci, E. L. (1998). The importance of supporting autonomy in medical education. Annals of Internal
Medicine, 129, 303–308.
Williams, G. C., Gagné, M., Ryan, R. M., & Deci, E. L. (2002).
Facilitating autonomous motivation for smoking cessation.
Health Psychology, 21, 40–50.
Wilson, P. M., Rodgers, W. M., Blanchard, C. M., & Gessell, J.
(2003). The relationship between psychological needs, self
determined motivation, exercise attitudes and physical fitness. Journal of Applied Social Psychology, 33, 2373–2392.
CHAPTER 25
A Social-Cognitive Perspective of Perceived and
Sustained Effort
GERSHON TENENBAUM and JASMIN C. HUTCHINSON
Most of the physiological processes that are associated with
the perception of effort occur more or less unconsciously, including heart-rate, oxygen consumption, blood pressure, and
even lactate production. As exercise intensity grows, so does
the possibility that sensation will receive more conscious attention, especially those readily available to consciousness,
such as pulmonary ventilation and regionalized pain. (Noble
& Robertson, 1996, p. 207)
(i.e., how sensations of effort are perceived as a function
of gradual physiological increase). At the same time, various RPE scales were developed for various tasks (see
Noble & Noble, 1998). Perhaps the most frequently used
measure of perceived effort is Borg’s 15 graded category
scale (Borg, 1971). This scale was designed so that it
directly paralleled the heart rate (HR) range of a normal,
healthy male. According to the theory, if the scale ratings
were multiplied by 10, HR could be calculated: HR = RPE
× 10 (Borg, 1961). Borg’s RPE scale has been well validated, represents a reliable measure of perceptual intensity, and has proved robust in its usefulness (Noble &
Noble, 1998). However, a number of researchers have
questioned the efficacy of a single-item measure of effort
(e.g., Hardy & Rejeski, 1989; Hutchinson & Tenenbaum,
in press-a; McAuley & Courneya, 1994; Parfitt, Markland, & Holmes, 1994; Tenenbaum, 2005). It is our belief
that a single-item measure of effort, such as Borg’s RPE
scale, is insufficient to capture the whole range of perceptual sensations that people experience when exercising
or being physically active. We concur with Noble and
Noble that “emphasis should be placed on understanding
perception, not on studying the results of the Borg scale.
Until that is done, the study of perceptual response during
physical activity will reflect only what the Borg scale
measures” (p. 356).
As early as 1973 it was suggested that physiological
responses account for approximately two-thirds of the variance in perceived exertion, and that different psychological
factors might explain the remaining one-third of the variance (Morgan, 1973; Noble, Metz, Pandolf, & Cafarelli,
1973). Despite this early insight, scant research attention
has been paid to the effect of psychological factors on per-
Perceived exertion* has been defined as one’s subjective
rating of the intensity of work being performed (Morgan,
1973), and later as the act of detecting and interpreting
sensations arising from the body during physical exercise
(Noble & Robertson, 1996). Perceived effort represents an
important complement to behavioral and physiological
measurements of physical performance and work capacity.
Ratings of perceived exertion (RPE) scales are widely used
in an array of situations, including monitoring individuals
during graded exercise testing and prescribing and monitoring exercise intensity. Less attention has been given to
effort tolerance (i.e., the ability to sustain and cope with
feelings of effort for a period of time).
Interest in perceived effort was initiated with the work
of Gunnar Borg during the early 1960s. In 1962, Borg
viewed “perceived force” as one’s perception of effort in
short-time exercise and “perceived fatigue/exertion” as
representing exertion during aerobic activities (Noble &
Robertson, 1996). In the 1950s and 1960s, perceived exertion was studied using a psychophysiological perspective
* We prefer the term perceived ef fort to define the concept. However, where researchers have used the term exertion in other publications, we use this term to maintain the original terminology.
560
A Social-Cognitive Perspective of Perceived and Sustained Ef fort
ceptions of effort. According to Noble and Robertson
(1996), of the 450 published articles on perceived exertion,
only 39 (8.6%) examined psychological factors. The main
areas of research were descriptive studies on how perceived effort related to physiological factors and conditions, clinical applications, methodological issues, exercise
and perception, and environmental factors. The main purpose of this chapter is to describe the psychological variables that affect perceived effort and effort tolerance, from
both theoretical and scientific perspectives.
A GUIDING CONCEPTUAL MODEL
Perceived effort and effort tolerance are two psychological states that are determined by the interaction of sever-
al variables. Perceived effort and effort tolerance can be
regarded as complex phenomena in which the performer
makes attempts to adapt to the social and physical
demands imposed on him or her while engaged in exercise. Perceptions of effort are determined by individual
disposition, demographic characteristics, the task
(whether aerobic, anaerobic, or both), the intensity level,
the conditions under which the task is performed (e.g.,
temperature, humidity, time of day), and the coping
strategies used when experiencing these feelings (see Figure 25.1). The conceptual framework displayed in Figure
25.1 assumes a mutual relationship between perceived
effort and effort tolerance. More specifically, when perceived effort is reported to be low, under any task and
environmental condition, the exerciser can adhere to and
Coping strategies
Individual
Active
Task
familiarity
Dispositions
561
Affect
Passive
Demographic
characteristics
Internal
External
Environmental conditions
Perceived effort
Social
Physical
Effort tolerance
Task
Aerobic metabolism
Anaerobic metabolism
Aerobic/anaerobic metabolism
Figure 25.1 A model that postulates the effect of an individual’s characteristics, environmental conditions, task characteristics, and
coping strategies on both perceived effort and effort tolerance. Perceived effort and effort tolerance are believed to be independent
variables, though strongly linked.
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cope with effort longer than when perceived effort is
reported to be high.
Dispositional Characteristics
Noble and Robertson (1996) summarized the limited
research linking personal dispositions to perceived effort.
They found that (a) the more people desire to impress others, the lower they tend to report their perceived exertion
(Boutcher, Fleischer-Curtian, & Gines, 1988); ( b) augmenters (i.e., people who exaggerate the importance of
events in their life) report greater perceived exertion than do
reducers (i.e., people who underestimate the importance of
events in their life; Robertson, Gillespie, Hiatt, & Rose,
1977); (c) internal and external locus of control fails to
determine perceived exertion (Kohl & Shea, 1988); (d) feminine sex-typed women report greater perceived exertion
than do masculine or androgynous women (Hochstetler,
Rejeski, & Best, 1985); (e) extraverts suppress painful stimuli and rate their perceived exertion lower than introverts
(Morgan, 1973); and (f ) self-efficacy is negatively related to
perceived exertion (McAuley & Courneya, 1992). A clear
relationship between Type A / B personality and perceived
exertion has not been established (De Meersman, 1988;
Hardy, McMurray, & Roberts, 1989; Rejeski, Morley, &
Miller, 1983).
Recent research by Hall, Ekkekakis, and Petrozzello
(2005) indicates that relationship of RPE to dispositional
psychological factors may be intensity-dependent. Hall
et al. found that extraversion and behavioral activation
showed significant negative correlations with RPE at lower
but not higher intensities, whereas neuroticism was unrelated to RPE, and behavioral inhibition was positively
related across all three levels of intensity.
In general, the relationships between dispositional variables and perceived effort are limited. The association
found between the desire to impress others, the over/underestimation of life events, femininity/masculinity type, extraversion/introversion, and perceived effort can be regarded
as the effect of social desirability on reported effort. The
failure to find any consistent link between locus of control
and Type A / B personality with perceived effort appears to
be a consequence of a lack of sound theory and measurement problems. For example, the most frequently employed
measure of Type A and B personality styles, the Jenkins
Activity Survey (Jenkins, Zyzanski, & Rosenman, 1979),
is known to be no better than chance in its ability to discriminate Type A from Type B personalities (Noble &
Robertson, 1996). The result has been a limited selection
of psychological constructs with an appropriate method-
ological plan aimed at verification or modification of these
relations. More specifically, the link between motivational
components that are responsible for energizing and inhibiting human actions and effort components has not been sufficiently postulated and, therefore, not studied. An
exception is McAuley and Courneya’s (1992) study that
linked perceived effort to task-specific self-efficacy. In line
with this study, possible links between motivational variables such as goal orientation, self-efficacy, task-specific
commitment and determination, effort tolerance, effort
investment with perceived effort, and maintaining an
exertive state are discussed in this chapter.
Task Familiarity
The importance of previous athletic experience was
attested to by Rejeski (1981, p. 313), who wrote, “RPE for
a given task is, at least in part, a function of past experience.” Previous data on arm and leg training (Burkhardt,
Wilkinson, Butts, Kirkendall, & Seery, 1982) and running
and cycling training (Hassmén, 1990) suggest that RPE
scores during a given mode of exercise will be lower for
individuals for whom it is the primary mode of training.
In addition, Hassmén showed that active individuals
reported lower RPE scores than sedentary subjects at the
same absolute power outputs.
Janot, Steffen, Maher, Zedaker, and Porcari (1998)
reported that novice sport climbers experienced higher perceptions of effort than more experienced climbers during
two indoor climbing bouts. However, novice climbers also
experienced higher heart rates during the climbs, so it is
difficult to conclude with any certainty that the differences
in RPE were not simply attributable to more efficient
climbing technique in the experienced climbers. Lagally,
McCaw, Young, Medema, and Thomas (2004) found no difference in RPE between novice and recreationally trained
lifters during resistance exercise.
In a study designed to explicitly examine the influence
of athletic experience on perceived effort and effort tolerance, Tenenbaum et al. (2001) compared the perceived and
sustained effort of individual-sport athletes (swimmers,
cyclists, triathletes, and long-distance runners), team sport
athletes ( basketball, hockey, and soccer players), and
untrained participants on two fatiguing tasks: an isometric
handgrip task and a treadmill running task. Results indicated that individual-sport athletes rated perceived effort
consistently lower than did their team sport and untrained
counterparts throughout both tasks. The three groups were
also significantly different on effort tolerance. On average,
individual-sport, team sport, and untrained participants
A Social-Cognitive Perspective of Perceived and Sustained Ef fort
tolerated effort for 161, 150, and 74 s, respectively, during
the handgrip task, and for 475, 313, and 279 s, respectively, during the running task. Given that individual-event athletes must cope with effort and physical discomfort on a
regular basis during training, Tenenbaum et al. proposed
that they were relatively accustomed to it, would be motivated to endure it, and would likely have developed the
appropriate strategies to deal with it. In contrast, team
sport athletes would not be as familiar with prolonged
effort and discomfort given that they generally perform
relatively brief bouts of intense physical activity yet rarely
must endure intense effort for sustained periods. Interestingly, greater differences existed between individual-sport
and team sport participants on the running task than the
handgrip task—a clear indication that coping with and sustaining effort is task-specific.
Techniques for Coping with Stress
Strategies for coping with physical effort can take two
forms: active and passive (Morgan & Pollack, 1977). As
illustrated in Figure 25.1, active strategies are classified as
either internal or external to the performer. External (dissociative) strategies are those in which the performer
shifts attention to external events to reduce perceptions of
neural exertion signals coming from the muscles and joints
and the cardiopulmonary systems. Internal (associative)
strategies are aimed at coping directly with feelings of
overuse and effort through “ fighting” against them or
other negative events. A passive form of coping with effort
is considered when people do not attempt to do anything
that will enable them to better tolerate the sensory signals
of fatigue, discomfort, exertion, and pain.
Morgan and Pollock (1977) studied the strategies used
by elite marathoners and recreational long-distance runners
in coping with perceptions of exertion. Marathon runners
reported using an association strategy whereby attention
was given to internal sensory cues. In comparison, recreational runners used a dissociation strategy in which they
deflected internal bodily signals with various forms of distractive thinking. This finding was expanded by Schomer
(1986), who claimed that increases in task intensity resulted in a shift from dissociative to associative thinking.
A number of studies have been concerned with the
effect of coping strategies on perceived effort. For example, Pennebaker and Lightner (1980) manipulated external
attentional focus by activating external street sounds;
internal attentional focus was manipulated by asking participants to attend to their own breathing while walking on
a treadmill. Greater fatigue was experienced when attend-
563
ing to one’s own breathing compared to attending to street
sounds. Fillingham and Fine (1986) asked participants
while running to either count the word “dog” (external
attention) or to focus on their breathing and heartbeat
(internal attention). Fewer exercise symptoms were reported under the external attention compared to the internal
attention condition. This finding has been supported by a
number of subsequent studies using a variety of distraction
methods, such as solving math problems (Johnson & Siegel,
1987) and attending to music (Potteiger, Schroder, & Goff,
2000), and across a variety of exercise modes, such as stationary cycling (Potteiger et al., 2000), treadmill running
(Stones, 1980), repetitive leg-lift tasks (Gill & Strom,
1985), and isometric leg-extension tasks (Weinberg, 1985).
Boutcher and Trenske (1990) compared a sensorydeprivation (internal focus) condition to a music (external
focus) condition and a control condition at three different
levels of intensity using untrained subjects on a cycle
ergometer. Ratings of perceived exertion responses in the
music condition were significantly lower than responses
in the deprivation condition at low (60% HR max) exercise intensity, but no differences in RPE were found at
moderate (75% HR max) or high (85% HR max) exercise
intensities. These findings led the authors to conclude
that the influence of music on RPE was “load-dependent.”
In addition to perceptions of effort, the effect of different attentional strategies on effort tolerance has been examined. Morgan, Horstman, Cymerman, and Stokes (1983), for
example, found that a dissociative cognitive strategy resulted in 32% longer endurance on a treadmill compared to a
control condition. In a subsequent study, Weinberg, Smith,
and Jackson (1984) compared associative, dissociative, and
positive self-talk strategies in the performance of an
endurance task. Participants were asked to employ one of
these strategies throughout the duration of a leg-extension
endurance task. Results indicated that the dissociation and
positive self-talk conditions produced significantly greater
tolerance than the association or control conditions. Rejeski and Kenney (1987) varied the complexity of a dissociative coping task to verify if this affected effort tolerance.
The time that a participant could maintain an isometric contraction of 40% on a handgrip dynamometer was used as a
dependent variable. No differences were found between the
simple and complex dissociation groups, with both tolerating fatigue better than controls.
Another common technique for coping with aversive
stimuli is imagery. Explanations for the effectiveness of
imagery in coping with exertive stimuli are based on the
premise that a close link exists among emotions, images,
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and sensations. In the same way that emotions are accompanied by physical sensations, images may evoke emotions.
Visualization is believed to affect feelings and physical
sensations by altering images.
Various relaxation techniques, such as meditation,
exercising, rhythmic breathing, and attending to music,
are often used to decrease stress symptoms. Progressive
relaxation, which consists of active contraction and passive relaxation of gross muscle groups, is a technique frequently used with guided imagery (Edgar &
Smith-Hanrahan, 1992). Guided imagery involves the
development of mental representations of reality or fantasy. Guided imagery is aimed at reducing the pain and
autonomic reactivity. The principle is to hold the image
(e.g., a peaceful, pain-free scene) during a painful experience (James, 1992; Taylor, 1995). Imagery can also help
to transform pain into numbness or an irrelevant sensation. It may divert attention from internal and external
events. In addition, pain can be controlled through somatization (i.e., the focus of attention on the painful area but
in a detached manner; Melzack & Wall, 1989).
Images used to cope with aversive stimuli vary. Murphy,
Woolfolk, and Budney (1988) manipulated emotive images
by instructing participants to develop imagery-arousing
specific feelings while performing a strength task with a
handgrip dynamometer. Participants were instructed to
imagine a scene in which they felt either angry, afraid, or
relaxed. They were then asked to visualize the scene until
the feeling was evoked. When feeling fully involved in the
scene, they were asked to squeeze the dynamometer as hard
as possible. It was found that anger and fear images
increased arousal level, but not strength. Relaxation images
resulted in decreased strength. Relaxation imagery is more
frequently used than emotive imagery in controlling pain
and uncomfortable feelings (Taylor, 1995). Both techniques induce a mood state (relaxation or excitement) that
may aid in tolerating pain or discomfort. Whereas relaxation imagery improves pain tolerance through physiologically calming the body, emotive imagery increases mental
arousal and enhances the body’s coping mechanisms to better tolerate exertive experiences. For example, motivational general-mastery imagery incorporates images of coping
in difficult conditions, staying focused throughout a workout, maintaining effort even when tired, and being confident or mentally tough (Giacobbi, Hausenblas, Fallon, &
Hall, 2003).
Coote and Tenenbaum (1998) randomly divided 48
female university students into 3 groups. Two groups were
taught relaxation or aggressive imagery techniques, and the
third (control) group spent an equivalent amount of time
discussing various irrelevant topics. Participants completed
two sessions involving a 50% max handgrip squeeze to
fatigue: one prior to learning the imagery techniques, and
the other after learning the imagery techniques. Ratings of
perceived exertion were measured at regular (15 s) intervals during both tasks. Analyses indicated that from the
first to second trial, control group performance declined by
3.7%. In contrast, the aggressive imagery group tolerated
effort at the second trial 30.5% longer than the first
attempt, and the relaxation imagery group improved by
28%. The three groups were similar in RPE throughout the
entire exertive experience.
Environmental Conditions
The physical aspects of the environment that can affect RPE
include altitude, ambient temperature, music and noise, and
air conditions such as wind velocity, humidity, and airborne
pollutants (Borg, 1998). In addition, the social context in
which exercise is performed may significantly influence
RPE. According to Hardy, Hall, and Prestholdt (1986), perceived effort and effort tolerance are directly influenced by
the salience of social cues present in the environment. For
example, Hardy et al. had participants cycle alone, and in
the presence of a coactor performing at the same exercise
intensities (25%, 50%, and 75% of VO2 max). They found
that participants reported lower RPE when cycling at 25%
and 50%, but not 75% of VO2 max in the presence of a coactor. Perceived effort was also lower when instructors were
of the opposite sex, but this was less salient for highly
trained athletes (Sylva, Boyd, & Magnum, 1990).
The main environmental factors that evoke feelings of
effort are the intensity and duration of a task. “Perceptual
responses are an expression of the sensory link between
external stimuli arising from physical work and internal
responses reflecting physiological function” (Noble, 1977,
as cited in Noble & Robertson, 1996, p. 93). Kinsman and
Weiser (1976), Weiser and Stamper (1977), and Pandolf
(1982) developed a model that describes the relationship
between physiological symptoms occurring during exercise
and how they are perceived by the exerciser. There are four
levels of subjective reporting of sensory experiences during an ongoing physical exercise, each associated with
physiological processes that induce fatigue. The first level,
discrete symptoms, is associated with symptoms such as
sweating, perspiring, panting, heart pounding, leg aches
and cramps, muscle tremors, leg twitching, heavy and
shaky legs, tiredness, drive, vigorous mood, and determination. The second level, the subordinate, is associated
with cardiopulmonary, leg, and general fatigue. The third
level, ordinate, is linked to task aversion and the motiva-
A Social-Cognitive Perspective of Perceived and Sustained Ef fort
tion to adhere in the task. The fourth level, superordinate,
is associated with extreme fatigue and/or physical exhaustion. At this stage, one cannot identify specific sensations
(i.e., muscle aches, breathing, leg cramps), but only
extreme general fatigue and exhaustion (see Noble &
Robertson, 1996, chap. 4, for a detailed description).
As discussed earlier, the subjective-objective link to
effort is strongly related to the attentional mode of the exerciser. Noble and Robertson (1996) concluded that exertion
feelings intensify with an increase in physical load, and consequently attention shifts from an external-dissociative mode
to an internal-associative mode. Under low effort conditions,
perceived effort can be manipulated by attending to external
cues such as music (passive) or problem solving (active).
However, diverting attention is much harder to implement
when the exerciser is in the superordinate level. At this stage,
an exerciser needs a high level of determination and effort
tolerance to persist in the task. The subjective-objective link
with respect to attentional mode and perceived effort manipulation is illustrated in Figure 25.2.
Several recent studies provide validation for the model
presented in Figure 25.2. Tenenbaum et al. (2004) conducted three studies that examined the effect of music on sensations and thoughts experienced by runners under high
physical load. In the first two studies, 15 male participants
who were not regular runners participated in a 90% VO2
max treadmill run to fatigue under four conditions: silence,
rock music, inspirational music, and dance music. The
order of the four running conditions was counterbalanced
to mask any order effect. Ratings of perceived exertion
(Borg, 1982) and HR were monitored every 30 seconds
during the run. At the termination of each run, participants
completed the Running Discomfort Scale. (Tenenbaum
et al., 1999) and answered open-ended interview questions
on motivation, perception, and attention focus they experienced during the run. The third study was a field study in
which 25 male physical education students ran a hilly 2.2
km course as fast as possible under different conditions
(same music conditions as in the previous two studies). The
same questionnaire and open interview administered in the
two laboratory studies were used in the field study, with
the instructions adjusted to the context of a field run. The
overall findings of these three studies were that music
failed to influence HR, sensations of exertion (RPE), or
effort tolerance, although about 30% of the participants
indicated that the music helped them at the beginning of
the run. Tenenbaum et al. (2004) concluded that the
exertive symptoms experienced by the participants at the
90% VO2 max level were beyond the distraction capabilities of the external stimulus (music). The practical application of this is that when one is engaged in strenuous
running and attends to one’s preferred music, it may result
in better feelings at the beginning of the run but not during
the latter stages, when the physical effort is very high.
Levels of
Subjective Report
Focal
(internal
associative)
(RPE) “hard” to
be manipulated
Superordinate
(undifferentiated
fatigue)
Attention
focus
Ordinate (task aversion,
running/cycling fatigue,
motivation)
Subordinate (fatigue;
legs, general,
cardiopulmonary)
Distributed
(external
dissociative)
(RPE) “easy” to
be manipulated
Low
Moderate
Discrete symptoms
(sweating, breathing,
leg aches, etc.)
High
Exercise Intensity
Figure 25.2
565
Perceived effort as a function of exercise intensity and the physiological substrata.
566
Exercise and Health Psychology
Hutchinson and Tenenbaum (in press-a) carried out two
studies to examine individuals’ attentional strategies during
engagement in two enduring physical tasks and the effect of
workload on attention focus. In the first study, 35 moderately active male and female participants completed a sustained
handgrip-squeezing task at 25% max grip strength to fatigue.
In the second study, 13 moderately active male and female
participants completed a stationary cycling task for 5 minutes
at 50% VO2 max, for a further 5 minutes at 70% VO2 max,
and then to volitional fatigue at 90% VO2 max. During both
tasks participants were instructed to vocally express their
current thoughts—in sentences, phrases, or words—continuously during the testing procedure. Participants’ statements
were written down by the examiner and later classified
according to Schomer’s (1986) thought classification system
to reveal patterns of associative and dissociative attention
focus. Results revealed that the frequency counts of classified
thoughts differed significantly across the beginning, middle,
and end time phases of both exertive tasks. In the handgrip
task, frequency of associative thoughts was significantly
greater during the middle and end stages of exercise, accounting for 64% and 94% of total reported thoughts, respectively,
during these stages. In contrast, dissociative thoughts were
more prevalent at the beginning of the task, accounting for
71% of total reported thoughts at this stage. Figure 25.3 displays the observed relationship between attention focus and
task intensity during the handgrip task.
The same pattern was evident in the cycle task, where
associative thoughts accounted for 91% of all thoughts
reported during the final exercise stage and 61% of all
thoughts reported during the middle exercise stage. Dissociative thoughts were more prevalent at the beginning of the
task, accounting for 78% of total reported thoughts at this
time. Figure 25.4 displays the observed relationship between
attention focus and task intensity during the cycle task.
The results of these studies indicate that attention
focus during sustained effort is largely dependent on
stimulus intensity. Specifically, with increasing task
intensity and feelings of great effort, attention shifts from
an external-dissociative mode to an internal-associative
mode. Thus, dissociative coping strategies can be influential on perceived effort and effort tolerance at low to
moderate levels of workload, but they are not likely to be
effective at higher levels of exercise intensity, when attention is focused on overwhelming physiological sensations
that dominate focal awareness.
120
Association
Dissociation
90
80
Association
Dissociation
100
70
80
Frequency
Frequency
60
50
60
40
40
30
20
20
10
0
50%
0
Beginning
Middle
End
70%
90%
VO2 Max
Exercise Phase
Figure 25.3 Categories of thoughts for beginning, middle, and
end time phases of the handgrip task. Source: “Attention Focus
during Physical Effort: The Mediating Role of Task Intensity,”
by J. C. Hutchinson and G. Tenenbaum, in press-a, Psychology in
Sport and Exercise. Reprinted with permission.
Figure 25.4 Categories of thoughts for beginning (50% VO2
max), middle (70% VO2 max), and end (90% VO2 max) time
phases of exercise. Source: “Attention Focus during Physical
Effort: The Mediating Role of Task Intensity,” by J. C. Hutchinson and G. Tenenbaum, in press-a, Psychology in Sport and Exercise. Reprinted with permission.
A Social-Cognitive Perspective of Perceived and Sustained Ef fort
THE SOCIAL-COGNITIVE PERSPECTIVE
In this section, the primary variables studied using the
social-cognitive approach in psychology are reviewed. The
social-cognitive approach, in particular, self-efficacy theory (Bandura, 1977, 1982, 1986, 1997), when applied to
exercise behavior, emphasizes the role that task-specific
states have on perceiving and coping with exercise stimuli
(e.g., physical effort).
Goal Orientation
Goal orientations reflect individual differences in assigning subjective meaning to outcomes (Ames, 1984; Maehr &
Braskamp, 1986). The subjective meaning given to success
and failure is linked to either a differentiated or an undifferentiated concept of ability. A differentiated concept of
ability is determined by comparing one’s performance and
outcome to others’. Ability can be evidenced by performing better than others, by surpassing normative-based standards, or by achieving success with little effort (Covington,
1984). This goal orientation is termed ego orientation. An
undifferentiated concept of ability is utilized when subjective achievements are compared to self-referenced standards. This orientation is termed task orientation and is
evoked by the need to meet and improve personal standards
(Nicholls, 1984, 1989). Task goal orientation is associated
with behaviors such as skill improvement, task mastery,
working hard, and persistence. In contrast, ego goal orientation is associated with maladaptive or inhibitive behaviors when social comparisons are avoided; as a
consequence, effort and confidence decrease (Jagacinski &
Nicholls, 1990).
Task and ego goal orientations are independent of each
other and important determinants of motivation (see Duda
& Hall, 2001, for a review). It is assumed that
the choice to invest in any activity, the amount of effort
expended on a task, the level of persistence shown toward a
challenge, and the cognitive and affective responses associated with the resulting behaviors emanate from the meaning
that is attached to one’s achievement striving. (p. 417)
To date, few studies have examined the effects of goal
orientations on perceived effort and effort tolerance.
Duda, Sedlock, Noble, Cohen, and Chi (1990) examined
the effects of goals on RPE and affective responses while
performing a cycle ergometer task at 70% VO2 max. They
found that a combination of high task and low ego orientation led to lower perceptions of effort and more positive
affect associated with exercise than a combination of
567
high ego and low task orientation. Stephens, Janz, and
Mahoney (2000) examined the relationship between
adolescents’ goal orientation in sport and RPE during
a graded exercise test and concluded that a task
orientation was negatively related to RPE in girls, but not
in boys.
Tenenbaum et al. (2001) sought to extend the work of
Duda et al. (1990) and Stephens et al. (2000) by examining the predictive capabilities of goal orientations in both
a local muscular endurance task and a treadmill running
task. In two separate studies, participant’s goal orientations were assessed using the Task and Ego Orientation in
Sport Questionnaire (Duda & Nicholls, 1992). Participants in the first study were then asked to tolerate a sustained handgrip squeeze at 50% of their maximum
capacity for as long as they could maintain it. Sustained
effort (determined as time elapsed from reporting RPE
“ hard” until ceasing the task) was used as the dependent
variable. Participants in the second study completed a
submaximal (90% VO2 max) running task on a treadmill.
Again, sustained effort was used as the dependent variable. Tenenbaum et al. reported that task and ego goal orientations accounted for 21% of the variance of sustained
effort in a muscular endurance task and 20% of accounted variance in a treadmill running task. Though both ego
and task orientation accounted for substantial variance in
the prediction of effort, each of them separately did not
correlate with the amount of time participants spent in
sustained effort.
Goal orientation describes an individual’s disposition to
be ego- or task-oriented, and goal involvement describes
situationally emphasized goal perspectives, or different
motivation states. According to Nicholls (1989), task and
ego goals are determined by both dispositional and situational factors. However, research has mainly addressed
goal orientations, and goal involvement states have
received little direct attention (Gernigon, d’ArripeLongueville, Delignières, & Ninot, 2004). Furthermore,
research to date has primarily examined dispositional goal
orientations and goal involvement states as separate constructs. An interactionist approach that looks to integrate
dispositions and the motivational climate promises a more
complete understanding of achievement behaviors in sport
and exercise (Roberts, 2001). This is especially important
for a measurement standpoint. Tenenbaum et al. (2001,
p. 1620) concluded:
If the meaning of achievement is a psychological state (Maehr
& Braskamp, 1986), then it is necessary to examine the meaning of achievement in the specific context of interest. . . . One
568
Exercise and Health Psychology
cannot simply assume that by measuring participants’ goal
orientations in sport, they will be reflective of the meaning of
achievement on a contrived motor task.
Perceived Competence and Self-Efficacy
Competence is a multidimensional construct that is produced by mastery attempts in various tasks, and consequently leads to the development of behaviors and
perceptions of control (S. E. Harter, 1978). People with
similar goal orientations differ from each other in various
tasks performed under similar conditions due to their different levels of self-perceived competence. In educational
settings, learners with low perceived competence or ability,
accompanied by ego goal orientation, were found to reduce
their learning effort (Jagacinski & Nicholls, 1990). In football, players with high ego goal orientation and low perceived competence exhibited higher competitive anxiety
before competitions than did players with greater perceived
competence (Boyd, Callaghan, & Yin, 1991). Conversely,
Ommundsen and Pedersen (1999) reported that high task
goal orientation and high perceived competence predicted a
reduced tendency to report cognitive anxiety during competition in young tennis players. Thus, the greater an individual’s perceived competence in a specific task or activity,
the better the ability to cope with its physical demands.
Perceived self-efficacy “refers to beliefs in one’s power
to produce a given level of attainment ” (Bandura, 1997,
p. 382). It is a cognitive state that has a direct impact on how
well actions are performed. Beliefs of self-efficacy constitute the key factor of human agency. People who lack selfefficacy believe they also lack the power to perform the
task. Bandura states that a sense of personal efficacy is represented by prepositional beliefs embedded in a network of
functional relationships. An example is coping with aversive
experiences in which exertion and discomfort are present.
Self-efficacy expectations differ on three dimensions:
magnitude, generality, and strength. The magnitude of
self-efficacy refers to the level of task difficulty that a person believes he or she is capable of executing. Generality
refers to efficacy expectations that may be specific to a
task, or a more generalizable sense of efficacy (i.e., related to several tasks). Finally, the strength of expectations
refers to the degree to which one exhibits perseverance
when facing aversive or frustrating situations that evoke
physical exertion and discomfort.
Bandura, O’Leary, Barr Taylor, Gauthier, and Gossard
(1987) maintained that judgments of self-efficacy determine the effort people invest while performing a task and
their perseverance in the face of either aversive experiences
or taxing environmental demands. Stemming from the area
of pain research, there is considerable evidence to support
the notion that efficacy cognitions play an important role in
influencing an individual’s ability to sustain and cope with
symptoms of discomfort. It is reasonable to assume that
such cognitions might also play a role in the ability to cope
with exertive discomforts associated with exercise.
Turk, Michenbaum, and Genest (1983) studied participants who utilized self-efficacy boosting strategies to tolerate a noxious stimulus (e.g., cold pressure task). They
found that participants who could apply efficient coping
strategies tolerated the task longer than did those who
were unable to apply efficient coping strategies. Litt
(1988) investigated discomfort tolerance caused by a cold
pressure task and found that self-efficacy predicted persistence in the task, and efficacy expectations strongly
determined performance duration. Participants with
higher degrees of both self-efficacy and perceived control
were able to tolerate the cold pressure task longer. Discomfort tolerance was longest when both of these factors
were high. Similarly, Baker and Kirsch (1991) found selfefficacy to be a strong predictor of discomfort tolerance
in a cold water task. In this study, participants who used
strategies to boost self-efficacy while immersing their
hand in cold water for as long as possible showed
increased discomfort tolerance, but did not report a
decrease in perceived discomfort.
The effects of both self-efficacy and drugs on tolerating
the discomfort of a cold pressure task were investigated by
Bandura et al. (1987). Participants were given either selfefficacy-related cognitive methods to cope with discomfort
tolerance, a placebo, or no intervention control. To test
whether changes in discomfort tolerance were mediated by
activation of the endorphin system, half the participants in
each condition received 10 mg of naloxone, a drug that
inhibits the effect of opiates and, therefore, increases the
sensation of pain. The other half received 10 mg of a saline
solution. Results suggested that those who received cognitive training strengthened their self-efficacy to withstand
and reduce pain. The cognitive training and naloxone
group, when compared to the group receiving cognitive
training and saline, was less able to tolerate discomfort.
However, the cognitive group that received the naloxone
was still able to increase discomfort tolerance to some
degree. This suggests a nonopioid component in cognitive
pain control (Bandura et al., 1987).
In conclusion, self-efficacy can be altered, but its effectiveness is accurately assessed only under task-specific
conditions.
A Social-Cognitive Perspective of Perceived and Sustained Ef fort
Given the wealth of studies pertaining to the effect of
self-efficacy on pain tolerance, it is surprising to observe
the lack of studies examining self-efficacy and effort tolerance. In his extensive literature review, Bandura (1997)
failed to locate any studies that examined perceived selfefficacy and beliefs of control with effort tolerance. However, he described the role of cognitive activities in
displacing sensations from consciousness and altering their
aversiveness:
If aversive sensations are supplanted in consciousness or are
construed benignly . . . they become less noticeable and less
distressingly intrusive. Research . . . shows that belief that
pain is controllable to some extent makes it easier to manage. . . . The ameliorative effects of such pain control techniques operate partly through changes in self-efficacy.
. . . The stronger the instated perceived coping efficacy, the
higher the pain tolerance and the less dysfunction pain produces. (pp. 393–394)
In a study designed to test this hypothesis, Tenenbaum
et al. (2001) confirmed that perceived dispositional and
task-specific self-efficacy can determine how long one can
tolerate effort and discomfort on both a running and a
strength-endurance task (see Tenenbaum, 2001, for details).
This finding was supported in an unpublished study by
Tenenbaum and Hutchinson (2004), who observed that taskspecific self-efficacy and task-specific perceived ability
accounted for a substantial variance of effort tolerance in a
handgrip task, and that physical self-efficacy contributed
significantly to variance of effort tolerance in a cycle task.
Further research examining this effect is warranted.
Research pertaining to self-efficacy and perceived
effort has been more forthcoming. McAuley and Courneya
(1992) took 88 middle-aged sedentary participants and
measured perceptions of their ability to ride a cycle
ergometer at 70% of age-predicted HR max for gradually
increasing periods of time. Results indicated that a strong
sense of self-efficacy resulted in participants perceiving
themselves to have exerted less effort than those subjects
with lower sense of self-efficacy. After controlling for fitness, body fat, age, gender, and affect, preexercise selfefficacy accounted for 3.1% (p <.05) of the variance in
RPE at the conclusion of the protocol. Rudolph and
McAuley (1996) reported similar findings in a sample of
50 young men who ran on a treadmill at 60% VO2 max for
30 min. After controlling for VO2 max, preexercise selfefficacy accounted for 14% (p < .001) of RPE in the final
minute of the protocol. This finding was recently replicated with teenage girls by Pender, Bar-Or, Wilk, and
569
Mitchell (2002), who reported that preexercise selfefficacy accounted for 14% of the variance in average RPE
collected at 4-min intervals during a 20-min bout of cycle
ergometry at 60% VO2 peak.
Recent research by Hall et al. (2005) indicates that the
relationship between RPE and self-efficacy may be intensity-dependent. Self-efficacy was measured on a 100-point
scale at regular intervals during three 15-min treadmill
runs, one 20% below, one at, and one 10% above the ventilatory threshold (VT). Results indicated that self-efficacy
produced consistently negative correlations with RPE
below and at the VT, but no significant correlations were
observed at intensities above the VT.
Future research ought to focus on how self-efficacy
influences perceived effort and effort tolerance. Several
explanations have been put forward. Bandura et al. (1987)
reported that an actual decrease in the appraisal of aversive
stimuli might be responsible for lower perceptions of effort
and increased effort tolerance. According to Bandura
(1995, p. 359), “Self-percepts foster actions that generate
information as well as serve as a filtering mechanism for
self-referent information in the self-maintaining process.”
Studies reporting a negative relationship between selfefficacy and RPE (Hall et al., 2005; Pender et al., 2002;
Rudolph & McAuley, 1996) support this contention. An
alternative explanation offered by Hardy and Rejeski
(1989) is that efficacy cognitions determine affective reactions to tasks that challenge personal skills or capabilities.
For example, engaging in exercise produces demands on the
system that can result in considerable in-task affect, which,
if positive, might lead to continued participation and, if
negative, to ultimate disengagement from the activity. This
assumption was confirmed by McAuley and Blissmer
(2000), who successfully manipulated self-efficacy via
false performance feedback in a group of young, low active
women and concluded that the self-efficacy manipulation
differentially influenced feeling state responses. Specifically, high-efficacy participants reported significantly
greater positive well-being and less psychological distress
and fatigue than low-efficacy participants exercising at the
same intensity. Finally, S. Harter (1990) suggested that
self-efficacy plays an influential role in moderating effort
tolerance because it represents a critical aspect of selfworth. Therefore, self-judgments about one’s competence
on meaningful tasks moderate the motivational effects of
aversive feedback or physical exertion on persistence and
performance. Future investigations are called for that
attempt to elucidate the mechanisms by which self-efficacy
positively impacts effort tolerance.
570
Exercise and Health Psychology
Task-Specific Commitment, Determination,
and Effort
Task-specific commitment /determination and the effort
one is ready to invest in and tolerate while exercising may
affect coping and persisting behaviors. The concept of
commitment has been conceptualized as a psychological
state related to extended engagement in activity over a
given period of time that leads to persistence in the face of
difficulties or setbacks (Scanlan, Carpenter, Schmidt,
Simons, & Keeler, 1993). Commitment is related to the
determination, dedication, and effort needed to persist in a
particular activity. Scanlan and Simons (1992) further conceptualized commitment as a multidimensional concept,
consisting of five main components: enjoyment, personal
investment, social constraints, involvement alternatives,
and involvement opportunities. Of these five dimensions,
personal investment (i.e., how much effort one is ready to
invest in the activity) is most clearly relevant to effort tolerance. Commitment can be considered a causing variable
rather than an outcome behavior. In other words, a person
with more commitment and determination coupled with
readiness to invest effort and tolerate exertion will adhere
longer to aversive stimuli.
Tenenbaum et al. (2001) reported that task-specific
determination, commitment, and effort investment
accounted for 32% of sustained effort variance in a handgrip task. Similarly, the same constructs added 11% to the
accounted sustained effort variance in a running task (see
Tenenbaum, 2001, for details). These results can be viewed
as strong evidence for the role that task-specific variables
play in effort tolerance.
NEW DIRECTIONS: MULTIDIMENSIONALITY
OF EFFORT PERCEPTIONS
Recent research has supported the conceptualization of
perceived effort as a multidimensional construct. Borg’s
concept of perceived exertion was introduced in the 1950s
as a holistic concept that incorporated perceived exertion,
local fatigue, and breathlessness. Currently, Borg (1998)
conceptualizes exertion within a gestalt framework, that is,
a configuration of sensations such as strain, aches, and
fatigue that stem from the peripheral muscles, pulmonary
system, somatosensory receptors, cardiovascular system,
and other sensory organs and cues. In the gestalt conceptualization of perceived exertion, motivation and emotions
are psychological variants that are viewed as an integral
part of the experience of exertion. In this respect, Borg
views exertion as a latent variable that incorporates many
other symptoms and yet uses measures to estimate exertion
level that fail to account for the many symptoms that constitute perceived exertion.
Recently, it has been proposed that differentiated exertional signals provide a more precise definition of the physiological and psychological processes that shape the
perceptual context during exercise (Noble & Robertson,
1996). Differentiated ratings of perceived exertion have
been used to examine in greater detail the central and local
factors contributing to an individual’s RPE (Demura &
Nagasawa, 2003; Gearhart et al., 2001, 2002; Lagally et al.,
2002; Marsh & Martin, 1998; Pincivero & Gear, 2000; Pincivero, Gear, Moyna, & Robertson, 1999; Robertson et al.,
2000). In these studies, RPE scores were assigned to central (cardiopulmonary) and peripheral (muscles and/or
joint) sources, alone or in conjunction with an overall RPE
score (Marsh & Martin, 1998). The intensity of the various
differentiated perceptual signals usually differs from that
of the undifferentiated signal at a given time point during
submaximal exercise (Noble & Robertson, 1996).
Certain types of exertional symptoms are not specifically related to physiological processes (Noble & Robertson, 1996). These nonspecific symptoms ref lect
psychological factors and represent distinct inputs in the
perceptual report. Thus, conceptually, exercise-related
effort must take into account the different psychological
components that reflect signals of motivation and affect, in
addition to physical components (Hardy & Rejeski, 1989;
Parfitt, Markland, & Holmes, 1994). A series of studies
incorporating aerobic and anaerobic tasks are presented
that shed light on physiological and psychological factors in
perceived effort.
Hutchinson and Tenenbaum (in press-b) studied the
effort perceptions of volunteer male and female participants who were exposed to the sensation of physical effort
via two exhaustive tasks: a handgrip squeezing task and a
stationary cycling task. The handgrip task involved a sustained isometric contraction at 25% maximum grip
strength to fatigue using a calibrated handgrip dynamometer. The cycle task involved pedaling on a stationary cycle
ergometer at 50%, 70%, and 90% of previously established
VO2 max to fatigue. Three dimensions of perceived effort
(sensory-discriminative, motivational-affective, and cognitive-evaluative sensations) were measured, via selfreport, at regular intervals for the duration of the two
tasks. Participants were asked to rate their current perceptions of each sensation on a 0 to 10 scale. The sensorydiscriminative dimension comprised muscle aches, pain,
A Social-Cognitive Perspective of Perceived and Sustained Ef fort
9
8
Perceived sensations
7
6
5
4
Sensory-discriminative
3
Motivational-affective
Cognitive-evaluative
2
1
0
15
30
45
60
75
90
105
120
Time (sec)
Figure 25.5 Mean ratings for each dimension within 15 sec
intervals during 120 sec duration for the handgrip task. Source:
“Attention Focus during Physical Effort: The Mediating Role of
Task Intensity,” by J. C. Hutchinson and G. Tenenbaum, in pressa, Psychology in Sport and Exercise. Reprinted with permission.
10
9
8
Perceived sensations
and fatigue; the motivational-affective dimension comprised concentration, determination, and mental toughness; and the cognitive-evaluative dimension comprised
effort, exertion, and task aversion. Results indicated that
the three dimensions were perceived distinctly and operated differently in the duration of the two physical tasks.
In the handgrip task, analysis revealed significant effects
for effort sensation, task endurance, and an effort sensationby-task endurance interaction effect. On average, motivational-affective sensations were rated 17% higher than
sensory-discriminative sensations (effect size [ES] = 0.78)
and 11% higher than cognitive-evaluative sensations (ES =
0.30). Cognitive-evaluative sensations were rated 7% higher
than sensory-discriminative sensations (ES = 0.43). Over
the time course of the task both sensory-discriminative and
cognitive-evaluative sensations increased by 68% and 53%,
respectively. The cognitive-evaluative sensations were rated
higher during the initial 60 seconds of the task, but the two
sensations were rated similarly during the last 60 seconds.
In contrast, motivational affect remained more stable over
time, showing a slight increase of 27% from the outset. A
graphic representation of this effect is shown in Figure 25.5.
In the cycle task, findings revealed significant effects for
effort sensation, task endurance, and an effort sensation-bytask endurance interaction effect. On average, motivational-
571
7
6
5
4
Sensory-discriminative
3
Motivational-affective
2
Cognitive-evaluative
1
0
30
90
150 210 270 330 390 450 510 570 630 690 750 810 870
Time (sec)
Figure 25.6 Mean ratings for each sensation dimension in 30
sec intervals during 900 sec of the cycle task. Source: “Attention
Focus during Physical Effort: The Mediating Role of Task Intensity,” by J. C. Hutchinson and G. Tenenbaum, in press-a, Psychology in Sport and Exercise. Reprinted with permission.
affective sensations were rated 37% higher than sensorydiscriminative sensations (ES = 0.64) and 29% higher than
cognitive-evaluative sensations (ES = 0.82). Cognitiveevaluative sensations were rated 11% higher than physical
sensations (ES = 0.08). Similarly to the handgrip task, the
three sensations were rated differently each from the other.
The effort sensation-by-task endurance effect is represented in Figure 25.6. The graphic presentation indicates
that the three effort sensations resulted in different patterns over the time course. Both the sensory-discriminative
and cognitive-evaluative sensations increased monotonically over time, by 76% and 68%, respectively. Cognitiveevaluative sensations were rated higher than the
sensory-discriminative sensations throughout the task.
Motivational-affective sensations, in contrast, remained
relatively stable over time, with a slight (3%) average
decrease at the end of the task.
Similar findings have been reported by Ekkekakis, Hall,
and Petruzzello (2004). Perceived activation and perceived
exertion rose continuously over the duration of an exertive
task, whereas perceived affect (pleasure-displeasure) did
not. Ekkekakis et al. subjected two groups of young,
healthy volunteers to incremental treadmill tests until volitional exhaustion. During testing, perceived exertion was
assessed via Borg’s (1998) RPE scale, perceived activation
was assessed by the Felt Arousal Scale (FAS; Svebak &
Murgatroyd, 1985), and affect was assessed by the Feeling
572
Exercise and Health Psychology
Scale (FS; Hardy & Rejeski, 1989). Participants gave selfratings on the RPE scale, FS, and FAS (in that order) every
minute from the beginning of the incremental phase of
exercise to the point of volitional exhaustion. Results
demonstrated a significant main effect of task endurance
for all variables. Trend analyses showed that linear trends
were significant for all variables, but quadratic trends were
significant for only FS. Specifically, affective valance
showed a pattern of quadratic decline, initiated once the
ventilatory threshold was exceeded.
Evidence for a differentiated postexercise affective
response comes from the recent work of Arent and colleagues. Arent, Landers, Matt, and Etnier (2004) examined the dose-response gradient of exercise-induced
affective change using a resistance training protocol.
Male and female participants completed three resistance
training protocols (40%, 70%, and 100% of 10-repetition
max) and a no-treatment control condition. Affective
responses were assessed immediately before and at 0 to 5,
15, 30, 45, and 60 minutes postexercise. Salivary cortisol
and heart rate responses were also assessed during each
condition. Ratings of perceived exertion were assessed
using Borg’s (1998) scale, and affective variables encompassing state anxiety, arousal /activation, and positive and
negative affect were assessed using the State-Trait Anxiety Inventory (Spielberger, Gorsuch, Lushene, Vagg, &
Jacobs, 1983), the Activation-Deactivation Adjective
Check List (Thayer, 1989), and the Positive and Negative
Affect Schedule (Watson, Clark, & Tellegen, 1988),
respectively. Results revealed a significant linear relationship between exercise intensity and RPE. Significant
curvilinear trends for intensity were found for all affective variables. Moderate-intensity strength training was
found to produce the greatest improvements in postexercise anxiety, positive affect (PA), negative affect (NA),
and arousal. High-intensity training resulted in increased
anxiety, NA, and tense arousal. Low-intensity exercise
was generally ineffective in producing beneficial changes
in affect and typically was no different from the control
condition. These findings led the authors to conclude that
“affective change following resistance training occurs at
a dimensional level (i.e., the dimension of PA and NA) and
categorical level (i.e., anxiety) level” (Arent et al., 2004,
p. 104). In addition, Arent et al. concluded that HR and
cortisol responses were significant predictors of changes
in negative affective states but did not predict changes in
positive affective states. This suggests that changes in
negative affect are more heavily influenced by interoceptive cues associated with the physiological demands of
exercise, whereas changes in positive affect are brought
about by cognitive appraisals based on exteroceptive cues
associated with the exercise bout (Arent et al., 2004).
Together these studies demonstrate that perceived effort
comprises several distinct inputs that are perceived differently across the duration of a demanding physical task.
This conclusion lends support to the assertion that exertion
is only one of many sensations that are felt during exercise
engagement (Hardy & Rejeski, 1989; McAuley & Courneya, 1994; Parfitt et al., 1994; Tenenbaum, 2005) and
questions the efficacy of a one-item measure of effort via
the term exertion.
CONCLUSION
In this chapter, psychological components that affect and
mediate perceived effort and effort tolerance have been
examined. The literature offers evidence for several conclusions. First, effort develops in stages with load increase.
It starts with discrete symptoms such as sweating, breathing, and leg aches, and ends with an undifferentiated
extreme. Second, attention to the exertive symptoms narrows with increase in physical load (i.e., from a distributed
mode to a symptom-focused mode). Third, perceptions of
effort are influenced by both physical and social environmental conditions. And finally, exercise participants who
are relatively accustomed to enduring feelings of physical
discomfort appear more motivated to tolerate and sustain
effort, although this effect appears to be task-specific. The
dispositional characteristics associated with perceived
effort and effort tolerance were found to be inconsistent,
partially because of measurement problems and lack of
sound theory to support such relationships.
Strategies for coping with physical effort can take two
forms: active and passive. Active coping strategies classified as internal (associative) or external (dissociative) to
the performer were reviewed. The available research suggests that dissociative strategies of coping with effort are
more salient under low physical load. In contrast, under
heavy and continuous loads, associative strategies are more
common and perhaps even unavoidable. Another technique
often used for coping with aversive stimuli is guided
imagery. Explanations for the effectiveness of imagery in
coping with exertive stimuli are based on the premise that
a close link exists among emotions, images, and sensations.
Visualization is believed to affect feelings and physical
sensations by altering images. Research evidence supports
the contention that both relaxation and aggressive imagery
may aid in tolerating pain or discomfort.
A Social-Cognitive Perspective of Perceived and Sustained Ef fort
Social-cognitive theory seems to have much potential
in accounting for perceived effort and effort tolerance.
Recent studies indicate that task-specific variables such
as commitment /determination and the effort one is ready
to invest in and tolerate while experiencing exertion
account for substantial amounts of the variance in effort
tolerance. Physical self-efficacy and perceived competence in tolerating effort and discomfort appear to be
strong predictors of discomfort tolerance. However, no
intervention studies have yet been conducted to examine
this contention in an exercise setting. The relationship
between self-efficacy and perceived effort and the impact
of these perceptions on task persistence is a fruitful
avenue for future research. Studies that go beyond the correlational nature of the efficacy-perceived effort relationship and directly manipulate self-efficacy are warranted.
Such studies will advance our knowledge of how we might
structure interventions to maximize efficacy, and in turn
influence both psychosocial and behavioral outcomes
associated with exercise (McAuley & Blissmer, 2000).
As stated previously, few studies have attempted to
examine the effects of goal orientations on perceived effort
and effort tolerance. Findings from the limited number of
studies available to date indicate that task orientation is
associated with a superior level of coping with exertive
experiences compared to ego goal orientation. However,
further research is needed to confirm this assertion.
New research directions pertaining to the influence of
psychological factors in determining perceived effort and
effort tolerance have adopted a multidimensional
approach. Recently, Hutchinson and Tenenbaum (in pressb), and Ekkekakis et al. (2004) have demonstrated that different dimensions of effort are perceived distinctly during
exercise and operate differently in the duration of an
exertive task. Arent et al. (2004) observed similar trends
postexercise.
Together, these findings imply that feelings of effort are
a consequence of several physiological and psychological
determinants. To study the dependence of perceived effort
on one physiological index is an oversimplification of the
psychophysiological construct. Accordingly, a single-item
measure of effort, such as Borg’s (1998) RPE scale, is
insufficient to capture the whole range of sensations
that people experience when exercising or when being
physically active (Hutchinson & Tenenbaum, in press-b).
An adequate theory of effort perception ought to sufficiently account for the distinct inputs that shape the perceptual milieu during sustained physical activity. Future
studies of perceived effort and effort tolerance using mul-
573
tidimensional measures will provide additional insights
into the various psychophysiological determinants of perceived effort.
The social-cognitive theory introduced in this chapter in
relation to perceived effort and effort tolerance reveals
new horizons in the study of the psychological states and
mechanisms that affect the complex psychophysiological
construct of effort. Innovative methodological designs and
paradigms should be initiated to shed new light on this
interesting area of study.
REFERENCES
Ames, C. (1984). Competitive, co-operative, and individualistic
goal structure: A motivational analysis. In R. Ames & C.
Ames (Eds.), Research on motivation in education: Student
motivation (pp. 177–207). New York: Academic Press.
Arent, S. M., Landers, D. M., Matt, K. S., & Etnier, J. L. (2004).
Dose-response and mechanistic issues in the resistance training and affect relationship. Journal of Sport and Exercise Psychology, 27, 92–110.
Baker, S. L., & Kirsch, I. (1991). Cognitive mediators of pain
perception and tolerance. Journal of Personality and Social
Psychology, 61, 504–510.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of
behavior change. Psychological Review, 84, 191–215.
Bandura, A. (1982). Self efficacy mechanisms in human agency.
American Psychologist, 37, 122–147.
Bandura, A. (1986). Social foundations of thought and action.
Englewood Cliffs, NJ: Prentice-Hall.
Bandura, A. (1995). Self-ef ficacy in changing societies. New
York: Cambridge University Press.
Bandura, A. (1997). Self-ef ficacy: The exercise of control. New
York: Freeman.
Bandura, A., O’Leary, A., Barr Taylor, C., Gauthier, J., & Gossard, D. (1987). Perceived self-efficacy and pain control:
Opioid and nonopioid mechanisms. Journal of Personality and
Social Psychology, 53, 563–571.
Borg, G. (1961). Perceived exertion in relation to physical workload and pulse rate. Kungliga Fysiografiska Sallskapetes I
Lund Forhandlingar, 31, 105–115.
Borg, G. (1962). Physical performance and perceived exertion.
Studia Psychologia et Paedagogica, 11, 1–35.
Borg, G. (1971). The perception of physical performance. In
R. J. Shephard (Ed.), Frontiers of fitness (pp. 280–294).
Springfield, IL: Charles C. Thomas.
Borg, G. (1982). Psychophysiological bases of perceived exertion. Medicine and Science in Sports and Exercise, 14,
377–381.
574
Exercise and Health Psychology
Borg, G. (1998). Borg’s perceived exertion and pain scales.
Champaign, IL: Human Kinetics.
Boutcher, S. H., Fleischer-Curtian, L. A., & Gines, S. D. (1988).
The effects of self-presentation on perceived exertion. Journal of Sport and Exercise Psychology, 10, 270–280.
Boutcher, S. H., & Trenske, M. (1990). The effects of sensory
deprivation and music on perceived exertion and affect during exercise. Journal of Sport and Exercise Psychology, 12,
167–176.
Fillingham, R. B., & Fine, M. A. (1986). The effects of internal
versus external information processing on symptom perception in an exercise setting. Health Psychology, 5, 115–123.
Gearhart, J. R., Goss, F. L., Lagally, K. M., Jakicic, J. M., Gallagher, J., Gallagher, K. I., et al. (2001). Standardized scaling
procedures for rating of perceived exertion during resistance
exercise. Journal of Strength and Conditioning Research, 15,
320–325.
Boyd, M., Callaghan, J., & Yin, Z. (1991, June). Ego involvement
and low competence in sport as a source of competitive trait
anxiety. Paper presented at the North American Society for
the Psychology of Sport and Physical Activity, Asilomar, CA.
Gearhart, J. R., Goss, F. L., Lagally, K. M., Jakicic, J. M., Gallagher, J., Gallagher, K. I., et al. (2002). Ratings of perceived
exertion in active muscle during high-intensity and lowintensity resistance exercise. Journal of Strength and Conditioning Research, 16, 87–91.
Burkhardt, J., Wilkinson, C., Butts, N. K., Kirkendall, D., &
Seery, A. (1982). Effects of arm / leg training on ratings of
perceived exertion. Medicine and Science in Sports and Exercise, 14, 158.
Gernigon, C., d’Arripe-Longueville, F., Delignières, D., &
Ninot, G. (2004). A dynamical systems perspective on goal
involvement states in sport. Journal of Sport and Exercise Psychology, 26(4), 572–596.
Coote, D., & Tenenbaum, G. (1998). Can emotive imagery aid in
tolerating exertion efficiently? Journal of Sports Medicine
and Physical Fitness, 38, 344–354.
Giacobbi, P. R., Hausenblas, H. A., Fallon, E. A., & Hall, C. R.
(2003). Even more about exercise imagery: An in-depth interview study of exercise imagery. Journal of Applied Sport Psychology, 15, 160–175.
Covington, M. V. (1984). The self-worth theory of achievement
motivation: Findings and implications. Elementary School
Journal, 85, 5–20.
De Meersman, R. E. (1988). Personality, effort perception and
cardiovascular reactivity. Neuropsychobiology, 19, 192–194.
Demura, S., & Nagasawa, Y. (2003). Relations between perceptual and physiological response during incremental exercise
followed by an extended bout of submaximal exercise on a
cycle ergometer. Perceptual and Motor Skills, 96, 653–663.
Duda, J. L., & Hall, H. (2001). Achievement goal theory in sport:
Recent extensions and future directions. In R. N. Singer,
H. A. Hausenblas, & C. M. Janelle (Eds.), Handbook of
research on sport psychology (pp. 417–443). New York:
Wiley.
Gill, D. L., & Strom, E. H. (1985). The effect of attentional
focus on the performance of an endurance task. International Journal of Sport Psychology, 16, 217–223.
Hall, E. E., Ekkekakis, P., & Petruzzello, S. J. (2005). Is the relationship of RPE to psychological factors intensity-dependent?
Medicine and Science in Sport and Exercise, 37(8),
1365–1373.
Hardy, C. J., Hall, E. G., & Prestholdt, P. H. (1986). The mediational role of social influence in the perception of exertion.
Journal of Sport and Exercise Psychology, 8, 88–104.
Hardy, C. J., McMurray, R. G., & Roberts, S. (1989). A / B types
and psychophysiological responses to exercise stress. Journal
of Sport and Exercise Psychology, 11, 141–151.
Duda, J. L., & Nicholls, J. G. (1992). Dimensions of achievement
motivation in schoolwork and sport. Journal of Educational
Psychology, 84, 1–10.
Hardy, C. J., & Rejeski, W. J. (1989). Not what, but how one
feels: The measurement of affect during exercise. Journal of
Sport and Exercise Psychology, 11, 304–317.
Duda, J. L., Sedlock, D. A., Noble, B., Cohen, B., & Chi, L.
(1990, April). The inf luence of goal perspective on perceived
exertion and af fect ratings during sub-maximal exercise.
Paper presented at the annual meeting of the American
Alliance for Health, Physical Education, Recreation, and
Dance, New Orleans, LA.
Harter, S. (1990). Causes, correlates, and the functional role of
global self-worth: A life span perspective. In J. Kolligan & R.
Sternberg (Eds.), Perceptions of competence and incompetence across the life-span (pp. 67–97). New Haven, CT: Yale
University Press.
Edgar, L., & Smith-Hanrahan, C. M. (1992). Nonpharmacological pain management. In J. H. Watt-Watson & M. I. Donovan
(Eds.), Pain management: Nursing perspective (pp. 162–199).
Sydney, Australia: Mosby.
Ekkekakis, P., Hall, E. E., & Petruzzello, S. J. (2004). Practical
markers of the transition from aerobic to anaerobic metabolism during exercise: Rationale and a case for affect-based
exercise prescription. Preventive Medicine, 38, 149–159.
Harter, S. E. (1978). Effectance motivation reconsidered:
Toward a developmental model. Human Development, 21,
34–64.
Hassmén, P. (1990). Perceptual and physiological responses to
cycling and running in groups of trained and untrained subjects. European Journal of Applied Physiology, 60, 445–451.
Hochstetler, S. A., Rejeski, W. J., & Best, D. L. (1985). The
influence of sex-role orientation on ratings of perceived exertion. Sex Roles, 12, 825–835.
A Social-Cognitive Perspective of Perceived and Sustained Ef fort
575
Hutchinson, J. C., & Tenenbaum, G. (in press-a). Attention focus
during physical effort: The mediating role of task intensity.
Psychology in Sport and Exercise.
McAuley, E., & Blissmer, B. (2000). Self-efficacy determinants
and consequences of physical activity. Exercise and Sport Science Reviews, 28(2), 85–88.
Hutchinson, J. C., & Tenenbaum, G. (in press-b). Perceived
effort: Can it be considered gestalt? Psychology in Sport and
Exercise.
McAuley, E., & Courneya, K. S. (1992). Self-efficacy relationships with affective and exertion responses to exercise. Journal of Applied Social Psychology, 22, 312–326.
Jagacinski, C. W., & Nicholls, J. G. (1990). Reducing effort to
protect perceived ability: “ They’d do it but I wouldn’t.” Journal of Educational Psychology, 82, 15–21.
McAuley, E., & Courneya, K. S. (1994). The Subjective Exercise
Experiences Scale (SEES): Development and preliminary validation. Journal of Sport and Exercise Psychology, 16, 163–177.
James, P. T. (1992). Cognitive therapies. In S. Tyrer (Ed.), Psychology, psychiatry, and chronic pain (pp. 137–147). Sydney,
Australia: Butterworth-Heinemann.
Melzack, R., & Wall, P. D. (1989). The challenge of pain (2nd
ed.). Harmondsworth, England: Penguin.
Janot, J. M., Steffen, J., Maher, P., Zedaker, J. M., & Porcari,
J. P. (1998). Heart rate and perceived exertion responses during indoor climbing in beginner and recreational sport
climbers. Medicine and Science in Sport and Exercise,
30(Suppl. 5), 240.
Jenkins, C. D., Zyzanski, S. J., & Rosenman, R. H. (1979). Jenkins Activity Survey—Form C: Instructions for hand scoring.
New York: Psychological Corporation.
Johnson, J., & Siegel, D. (1987). Active versus passive attentional manipulation and multidimensional perceptions of
exercise intensity. Canadian Journal of Sport Sciences, 12,
41–45.
Kinsman, R. A., & Weiser, P. C. (1976). Subjective symptomatology during work and fatigue. In E. Simonson & P. C. Weiser (Eds.), Psychological aspects and physiological correlates
of work and fatigue (pp. 336–405). Springfield, IL: Charles
C. Thomas.
Kohl, R. M., & Shea, C. H. (1988). Perceived exertion: Influences of locus of control and expected work intensity
and duration. Journal of Human Movement Studies, 15,
225–272.
Lagally, K. M., McCaw, S. T., Young, G. T., Medema, H. C., &
Thomas, D. Q. (2004). Ratings of perceived exertion and
muscle activity during the bench press exercise in recreational and novice lifters. Journal of Strength and Conditioning
Research, 18(2), 359–365.
Lagally, K. M., Robertson, R. J., Gallagher, K. I., Goss, F. L.,
Jakicic, J. M., Lephart, S., et al. (2002). Perceived exertion,
electromyography, and blood lactate during acute bouts of
resistance exercise. Medicine and Science in Sports and Exercise, 34(3), 552–559.
Litt, M. D. (1988). Self efficacy and perceived control: Cognitive mediators of pain tolerance. Journal of Personality and
Social Psychology, 54, 149–160.
Morgan, W. P. (1973). Psychological factors influencing perceived exertion. Medicine and Science in Sports, 5, 97–103.
Morgan, W. P., Horstman, D. H., Cymerman, A., & Stokes, J.
(1983). Facilitation of physical performance by means of a
cognitive strategy. Cognitive Therapy Research, 7, 252–264.
Morgan, W. P., & Pollock, M. L. (1977). Psychological characteristics of elite runners. Annals of the New York Academy of
Science, 301, 382–403.
Murphy, S. M., Woolfolk, R. L., & Budney, A. J. (1988). The
effect of emotive imagery on strength performance. Journal
of Sport and Exercise Psychology, 10, 334–345.
Nicholls, J. G. (1984). Achievement motivation: Conceptions of
ability, subjective experience, task choice, and performance.
Psychological Review, 91, 328–346.
Nicholls, J. G. (1989). The competitive ethos and democratic education. Cambridge, MA: Harvard University Press.
Noble, B. J., Metz, K. F., Pandolf, K. B., & Cafarelli, E. (1973).
Perceptual responses to exercise: A multiple regression
study. Medicine and Science in Sports, 5, 104–109.
Noble, B. J., & Noble, J. M. (1998). Perceived exertion: The measurement. In J. L. Duda (Ed.), Advances in sport and exercise
psychology measurement (pp. 351–360). Morgantown, WV:
Fitness Information Technology.
Noble, B. J., & Robertson, R. J. (1996). Perceived exertion.
Champaign, IL: Human Kinetics.
Ommundsen, Y., & Pedersen, B. H. (1999). The role of achievement goal orientations and perceived ability upon somatic
and cognitive indices of sport competition trait anxiety: A
study of young athletes. Scandinavian Journal of Medicine
and Science in Sports, 9(6), 333–343.
Pandolf, K. B. (1982). Differentiated ratings of perceived exertion during physical exercise. Medicine and Science in Sports
Exercise, 14, 397–405.
Maehr, M. L., & Braskamp, L. (1986). The motivation factor: A
theory of personal investment. Lexington, MA: Heath.
Parfitt, G., Markland, D., & Holmes, C. (1994). Response to
physical exertion in active and inactive males and females.
Journal of Sport and Exercise Psychology, 16, 178–186.
Marsh, A. P., & Martin, P. E. (1998). Perceived exertion and the
preferred cycling cadence. Medicine and Science in Sport and
Exercise, 30(6), 942–948.
Pender, N. J., Bar-Or, O., Wilk, B., & Mitchell, S. (2002). Selfefficacy and perceived exertion of girls during exercise.
Nursing Research, 51, 86–91.
576
Exercise and Health Psychology
Pennebaker, J. W., & Lightner, J. M. (1980). Competition of
internal and external information in an exercise setting. Journal of Personality and Social Psychology, 39, 165–174.
Stephens, D., Janz, K., & Mahoney, L. (2000). Goal orientation
and ratings of perceived exertion in graded exercise testing of
adolescents. Perceptual and Motor Skills, 90, 813–822.
Pincivero, D. M., & Gear, W. S. (2000). Neuromuscular activation and perceived exertion during a high intensity, steadystate contraction to failure. Muscle Nerve, 23, 514–520.
Stones, M. J. (1980). Running under conditions of visual input
attenuation. International Journal of Sport Psychology, 11,
172–179.
Pincivero, D. M., Gear, W. S., Moyna, N. M., & Robertson, R. J.
(1999). The effects of rest interval on quadriceps torque and
perceived exertion in healthy males. Journal of Sports Medicine and Physical Fitness, 39, 294–299.
Svebak, S., & Murgatroyd, S. (1985). Metamotivational dominance: A multimethod validation of reversal theory constructs. Journal of Personality and Social Psychology, 48,
107–116.
Potteiger, J. A., Schroeder, J. A., & Goff, K. L. (2000). Influence
of music on ratings of perceived exertion during 20 minutes
of moderate intensity exercise. Perceptual and Motor Skills,
91(3), 848–854.
Sylva, M., Boyd, R., & Mangum, M. (1990). Effects of
social influence and sex on rating of perceived exertion in
exercising elite athletes. Perceptual and Motor Skills, 70,
591–594.
Rejeski, W. J. (1981). The perception of exertion: A social psychophysiological integration. Journal of Sport Psychology, 4,
305–320.
Taylor, S. E. (1995). Health psychology. New York: McGrawHill.
Rejeski, W. J., & Kenney, E. (1987). Distracting attentional
focus from fatigue: Does task complexity make a difference?
Journal of Sport Psychology, 9, 66–71.
Rejeski, W. J., Morley, D., & Miller, H. (1983). Cardiac rehabilitation: Coronary-prone behavior as a moderator of graded
exercise. Journal of Cardiac Rehabilitation, 3, 339–346.
Roberts, G. C. (2001). Understanding the dynamics of motivation in physical activity: The influence of achievement goals
on motivational processes. In G. C. Roberts (Ed.), Advances
in motivation in sport and exercise (pp. 1–50). Champaign,
IL: Human Kinetics.
Robertson, R. J., Gillespie, R. L., Hiatt, E., & Rose, K. D.
(1977). Perceived exertion and stimulus intensity modulation. Perceptual and Motor Skills, 45, 211–218.
Robertson, R. J., Moyna, N. M., Sward, K. L., Millich, N. B.,
Goss, F. L., & Thompson, P. D. (2000). Gender comparison
of RPE at absolute and relative physiological criteria.
Medicine and Science in Sports and Exercise, 32(12),
2120–2129.
Rudolph, D. L., & McAuley, E. (1996). Self-efficacy and perceptions of effort: A reciprocal relationship. Journal of Sport
and Exercise Psychology, 18, 216–223.
Scanlan, T. K., Carpenter, P. J., Schmidt, G. W., Simons, J. P., &
Keeler, B. (1993). An introduction to the sport commitment
model. Journal of Sport and Exercise Psychology, 15, 1–5.
Scanlan, T. K., & Simons, J. P. (1992). The construct of sport
enjoyment. In G. Roberts (Ed.), Motivation in sport and exercise (pp. 192–215). Champaign, IL: Human Kinetics.
Schomer, H. (1986). Mental strategies and the perception of
effort of marathon runners. International Journal of Sport
Psychology, 17, 41–59.
Spielberger, C. D., Gorsuch, R. L., Lushene, R., Vagg, P. R., &
Jacobs, G. A. (1983). Manual for the State-Trait Anxiety
Inventory. Palo Alto, CA: Consulting Psychologists.
Tenenbaum, G. (2001). A social-cognitive perspective of perceived exertion and exertion tolerance. In R. N. Singer, H. A.
Hausenblas, & C. M. Janelle (Eds.), Handbook of sport psychology (2nd. ed., pp. 810–820). New York: Wiley.
Tenenbaum, G. (2005). The study of perceived and sustained
effort: Concepts, research findings and new directions. In
D. Hackfort, J. S. Duda, & R. Lidor (Eds.), Handbook of
research in applied sport psychology: International perspectives (pp. 335–350). Morgantown, WV: Fitness Information
Technology.
Tenenbaum, G., Fogarty, G., Stewart, E., Calcagnini, N., Kirker,
B., Thorne, G., et al. (1999). Perceived discomfort in running:
Scale development and theoretical considerations. Journal of
Sport Sciences, 17, 183–196.
Tenenbaum, G., Hall, H. K., Calcagnini, V., Lange, R., Freeman,
G., & Lloyd, M. (2001). Coping with physical exertion and
frustration experiences under competitive and self-standard
conditions. Journal of Applied Social Psychology, 31(8),
1582–1626.
Tenenbaum, G., & Hutchinson, J. C. (2004, October). Perceived
and sustained ef fort: Multidimensionality and applications.
Colloquia presented at the 2004 annual conference of the
Association for the Advancement of Applied Sport Psychology, Minneapolis, MN.
Tenenbaum, G., Lidor, R., Lavyan, N., Morrow, K., Tonnel, S.,
Gershgoren, A., et al. (2004). The effect of music type on
running perseverance and coping with effort sensations. Psychology of Sport and Exercise, 5(2), 89–109.
Thayer, R. E. (1989). The biopsychology of mood and arousal.
New York: Oxford University Press.
Turk, D. C., Michenbaum, D., & Genest, M. (1983). Pain and
behavioral medicine: A cognitive-behavioral approach. Hillsdale, NJ: Erlbaum.
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development
and validation of brief measures of positive and negative
A Social-Cognitive Perspective of Perceived and Sustained Ef fort
577
affect: The PANAS scales. Journal of Personality and Social
Psychology, 54, 1063–1070.
endurance performance. Canadian Journal of Applied Sport
Science, 9(1), 25–32.
Weinberg, R. S. (1985). Relationship between self-efficacy and
cognitive strategies in enhancing performance. International
Journal of Sport Psychology, 17, 280–292.
Weiser, P. C., & Stamper, D. A. (1977). Psychophysiological
interactions leading to increased effort, leg fatigue, and respiratory distress during prolonged, strenuous bicycle riding.
In G. Borg (Ed.), Physical work and ef fort (pp. 401–416).
New York: Pergamon Press.
Weinberg, R. S., Smith, J., & Jackson, A. (1984). Effect of association, dissociation and positive self-talk strategies on