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What Predicts Intention-Behavior Discordance? A Review of the Action Control Framework

Rhodes, Ryan E.1; de Bruijn, Gert-Jan2

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Exercise and Sport Sciences Reviews: October 2013 - Volume 41 - Issue 4 - p 201-207
doi: 10.1097/JES.0b013e3182a4e6ed
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INTRODUCTION

Numerous studies demonstrate that regular physical activity has extensive benefits through improved health and reduced chances of chronic disease that accumulate to considerable reductions in all-cause mortality (35). To specify, there is irrefutable evidence that regular physical activity contributes to the primary and secondary prevention of several chronic diseases, including cardiovascular disease, diabetes, cancer, obesity, and osteoporosis (35). In addition to the direct physical health benefits of physical activity, the documented mental health benefits include the improvement of well-being, the reduction of depression and anxiety, and the enhancement of cognitive functioning (35). Furthermore, physical activity has been shown to enhance the experience of well-being and increase physical functioning in those with poor health, thereby improving overall quality of life (35).

Despite the overwhelming evidence that supports the beneficial effects of physical activity on overall health, less than 20% of North American adults (1,33) meet the public health guidelines of at least 150 min of moderate to vigorous physical activity per week. Furthermore, positive physical activity changes from contemporary intervention efforts have been extremely modest (2). Clearly, continued physical activity promotion is an essential target for preventive medicine.

In an attempt to improve our intervention efforts, it would seem important to ensure that we have a solid understanding of the proximal antecedents of physical activity. Behavioral scientists have turned to theories from other disciplines, primarily social psychology, to understand physical activity and inform intervention efforts (25). Almost all of these theories include intention, the motivational decision to act, as the proximal antecedent to behavioral performance. The supposition is that if we can promote positive physical activity intentions, it will result in subsequent behavioral enactment. Overall, this construct has been validated in correlational tests of the theory of planned behavior (32), protection motivation theory (17), and variants of social cognitive theory (29) and self-determination theory (9). Intention also is the pivotal construct in stage-based algorithms such as the stages of change used in the transtheoretical model (14). Meta-analyses have placed the point estimate of the intention-behavior relationship as r = 0.50 (32), which is larger than any other known correlate of physical activity.

This evidence clearly speaks to the importance of intention and its relationship to physical activity. Nevertheless, some problems with the absolute predictive value of the intention-behavior relationship have generated the term “intention-behavior gap” in the scientific community. First, experimental evidence for the translation of intention change into behavior change has shown extremely diminished returns (21). For example, a recent meta-analytic examination of experimental changes in intention-behavior relationships showed that medium-sized changes in intention resulted in trivial-sized changes in behavior (r = 0.06). Second, passive prospective designs that separate the intention-behavior relationship into quadrants also demonstrate the discordance of intention and behavior. Indeed, our recent meta-analysis of those with positive physical activity intentions and subsequent failure to enact those intentions was 48% (19). Finally, the intention-behavior gap clearly is evident in physical activity intervention trials, where participants report to the trial with high intentions at baseline (i.e., often the driving reason for study participation), yet low physical activity. This phenomenon poses a challenge to intention-based theories because those high intentions are considered the proximal variable to behavioral enactment — it would seem that having an intention to be active is not always enough to change behavior.

THE ACTION CONTROL FRAMEWORK

Intention-behavior discordance has sparked researchers to attention. Some researchers have produced and subsequently have validated models that postulate post–motivational planning and self-regulation constructs meant to bridge the intention-behavior relationship (e.g., de Vries et al. (6), Heckhausen and Gollwitzer (10), Schwarzer (30)). Other researchers have shown that measurement effects (scale correspondence, duration between intention-behavior assessments, better assessment of intention strength) can attenuate the relationship (22). Furthermore, dual-process models suggest and provide evidence that automaticity/habit may account for some of this discordance (22).

Clearly, many variables have been posited to account for intention-behavior discordance. It seems helpful to collect and appraise the evidence for which variables may have the most utility in predicting the intention-behavior gap within a simple framework for the purpose of theory development and improvement of our interventions. Our proposed action control framework, based on the initial work of Orbell and Sheeran (16), may assist in the process (Fig. 1). The term “action control” has been used in several contexts across health and social behavior research. Action control in this context is used to describe intention-behavior discordance and not a specific dispositional construct or a volitional phase of planning enactment. In the action control framework, intention and subsequent behavior are divided into quadrants by the criterion of physical activity at public health guidelines. This creates four possible quadrants for the intention-behavior relationship, including two concordant (nonintenders who subsequently are not active; successful intenders who subsequently are active) and two discordant (nonintenders who subsequently are active; unsuccessful intenders who failed to enact their positive intentions) profiles (also see Orbell and Sheeran (16) and Godin et al. (8) for alternative labels). The action control framework provides a template to understand the intention-behavior gap around clinical guideline levels of physical activity performance. The model places public health guidelines at the forefront of its consideration, which should help translate directly to public health intervention aims and outcomes.

Figure 1
Figure 1:
The action control framework. Note: Percentages come from the meta-analysis by Rhodes and de Bruijn (19) and do not necessarily add to 100% because they are weighted point estimates.

Our recent meta-analysis of the action control framework (N = 3899 participants) showed that nonintenders who subsequently did not engage in physical activity represented 21% of participants, whereas nonintenders who subsequently performed physical activity comprised only 2% of the samples (19). This clearly speaks to intention as a pivotal construct in physical activity enactment. Nevertheless, intenders who were not successful at following through with physical activity represented 36% of the samples, which suggests that intention is necessary but not sufficient for many people to achieve regular physical activity. Contemporary research that is validating and exploring additional constructs (e.g., self-regulation, automaticity) that augment intention seems warranted.

In this article, we review the evidence for predictors of physical activity intention-behavior discordance using our concept of the action control framework (3,4,18,27,28). We hypothesize that intention-behavior discordance is from a combination of motivational, self-regulatory, and habituated processes. The results of this overview should help provide insight into the action control framework as a method for understanding intention-behavior discordance and highlight the contemporary evidence for which constructs predict this discordance.

PREDICTORS OF THE ACTION CONTROL FRAMEWORK

Review Eligibility and Search Strategy

The studies included in this review were obtained through a systematic search of the published literature until July 2012 using Academic Search Complete, ERIC, Medline, PsychINFO, and SPORTDiscus. The full details can be found in a recent systematic review (22) and meta-analysis of the action control framework (19). This article, however, reviews these articles for predictors of physical activity intention-behavior discordance using the action control framework. Briefly, a study was included for this review if it (a) used the action control framework in some capacity by dividing intention (high/low) and behavior (enacted/not enacted) into quadrants and (b) included variables to predict group membership in these intention-behavior profiles. Excluded studies were those that were written in any language other than English.

A combination of keywords were used, including “intention,” “action control,” “inclined actor,” “inclined abstainer,” “disinclined actor,” “disinclined abstainer,” “goal,” “physical activity,” “exercise,” “walking,” “running,” “cycling,” “active transport,” “theory of planned behavior,” “theory of reasoned action,” “health belief model,” “protection motivation theory,” and “social cognitive theory.” Manual cross-referencing of bibliographies also was completed. Finally, authors of articles found in the search were contacted and asked to provide any additional published work on the topic that might have been missed by the search.

Review Results

The previous review (22) showed that 2865 records and 248 relevant abstracts subsequently were obtained and assessed. From these, five papers met inclusion criteria (4,8,18,27,28). The new literature search yielded 48 potentially relevant records, and two additional studies were identified (3,20). Authors from these studies were contacted subsequently to inquire about any additional research, and this yielded three additional studies (5,7,24). Thus, 10 studies describing unique samples were included (see Rhodes and de Bruijn (19) for flow diagram).

The full details of these articles are given in Rhodes and de Bruijn (19). The settings of the studies included universities (k = 6), community (k = 2), and the workplace (k = 2). All samples included adults within working years (i.e., 19 to 64 yr). Sample size ranged from n = 153 to n = 1192, and participants were of mixed sexes. Physical activity most commonly was assessed using the Godin Leisure Time Exercise Questionnaire (k = 5) or International Physical Activity Questionnaire (k = 3). All of the studies were prospective in design, ranging from 2 wk (k = 6) to 6 months (k = 2) between intention and physical activity assessments. Most of the studies were conducted in Canada (k = 7), with the remaining collected in the Netherlands (k = 3).

The Table details the aggregate results for predictors of the intention-behavior profiles (3–5,7,8,18,24,27,28). These predictors were tested using a multivariate approach, so the findings can be considered independent contributions to the prediction of the profiles.

TABLE
TABLE:
Prediction of action control by construct.

Social cognitive predictors of action control

Social cognitive predictors using the theory of planned behavior constructs ranged from 8 to 10 tests as predictors of these profiles, depending on which constructs were used in each specific study. These tests supported the prediction of nonintenders from intenders for affective attitude (i.e., enjoyment and pleasure expected from physical activity), perceived behavioral control (i.e., control over performing physical activity, confidence to perform physical activity), and instrumental attitude (utility of performing a behavior). By contrast, subjective norm (perceived social pressure to perform the behavior) showed equivocal findings with only half of its eight tests supportive of discriminating the difference between intenders and nonintenders. Although no specific falsifiability hypothesis testing of social cognitive variables with intention has been specified in the theory of planned behavior (15), these results show support for attitude and perceived behavioral control, but limited utility for subjective norm. No particular demographic, design, or sample characteristics could explain the mixed findings when observing the results across studies.

In terms of discriminating successful intenders who followed through with physical activity from unsuccessful intenders who failed to enact physical activity, both affective attitude and perceived behavioral control showed consistent evidence for their predictive capability in seven of eight and eight of nine tests, respectively. However, instrumental attitude failed to predict these intender profiles in 9 of 10 samples. Similarly, subjective norm did not discriminate successful intenders from unsuccessful intenders in any of its eight tests.

Self-regulatory constructs as predictors of action control

Self-regulatory constructs (i.e., strategies used to maintain goal pursuit, management practices of behavioral enactment) were assessed in four tests to predict intention-behavior profiles. Two of these tests used the experiential and behavioral processes of change (i.e., cognitive and behavioral self-regulation strategies) (27,28), whereas other studies used an assessment of regulating motivation for other leisure-time behaviors (24) and planning for physical activity (3). Intenders were predicted from nonintenders in both tests using the processes of change and the planning construct. For the prediction of successful versus unsuccessful intenders, however, only three of the four studies showed significant prediction of the profiles. Specifically, the behavioral processes of change (27,28) (i.e., strategies such as rewards, self-monitoring, enlisting support, and creating stimulus control) and regulation over other leisure behaviors (24) were significant predictors.

Automaticity as a predictor of action control

Two studies used a habit construct (i.e., enacting physical activity from external cues, starting physical activity without deliberation) to assess the intention-behavior quadrants (20,24). Both of these tests used the self-reported habit strength index or a variant of this instrument. The habit measure was able to predict intenders from nonintenders in one of the two samples, but successful intenders from unsuccessful intenders were predicted in both samples.

Personality as a predictor of action control

Finally, two studies used personality trait measures of extraversion (i.e., sociability, positive affect, assertiveness, preference for lively activity) and conscientiousness (i.e., industriousness, orderliness, self-discipline) to predict the action control framework (4,18). Results of the tests were mixed with significant prediction of successful intenders from unsuccessful intenders for both personality traits in one of the two possible tests.

Extensions of the Action Control Framework

Four studies have extended the four-quadrant action control framework with additional variables (3,5,27,28). For example, Rhodes and colleagues (27,28) extended the framework to include past physical activity behavior to examine whether action control differed by adoption or maintenance of physical activity. The results of these two studies showed that two thirds of unsuccessful intenders are people trying to adopt physical activity, and two thirds of the successful intenders are those people who are maintaining physical activity. The division also showed that only five of eight possible quadrants are relevant to physical activity: nonintenders with minimal physical activity history who subsequently do not engage in physical activity, unsuccessful adopters, successful adopters, unsuccessful maintainers, and successful maintainers. Interestingly, the only variable that distinguished action control adoption from maintenance, in terms of being important for one group but not the other, was the behavioral processes of change. Adopters who used these self-regulatory strategies showed successful translation of their intentions into behavior, but they did not discriminate action control for maintainers. Higher levels of perceived behavioral control/self-efficacy, however, distinguished all profiles.

Taking a deeper conceptual approach to the adoption and maintenance process, de Bruijn (3) extended the action control framework with habit and self-identity. These constructs attempt to underlay the potential processes from adoption to maintenance rather than using a simple assessment of past behavior. The results generally echoed the previous work with past behavior and showed that most of the people who report low habit strength but intend to be active are unsuccessful adopters (i.e., there was not a meaningful grouping of successful intenders with low habit strength), whereas half of the people who report high habit strength comprise the successful intenders profile. None of the predictor variables in this study (theory of planned behavior and planning) could reliably distinguish the action control groupings. The same approach also was evaluated recently with exercise identity (5). Specifically, those with low exercise identity comprised nonintenders and those with high exercise identity comprised almost all of the intenders (i.e., both unsuccessful and successful in following through with behavior). Affective attitude and perceived behavioral control distinguished these profiles, similar to the standard action control framework. It was interesting to note that the sample was not composed of meaningful numbers of people with low exercise identity who were intending to be active — a group that conceivably would represent the adoption process.

CONCLUSIONS

The intention–physical activity gap is a topic of considerable contemporary research, given that most of our models used to understand physical activity suggest that intention is the proximal antecedent of behavioral enactment. Our review of the potential antecedents of intention-behavior discordance yielded helpful information on what constructs may be useful in intention translation models. We have represented these findings in an action control framework schematic (Fig. 2) to guide future research and intervention efforts. These results are complementary to the work of several researchers who also have proposed approaches to understanding the intention-behavior gap (e.g., de Vries et al. (6), Heckhausen and Gollwitzer (10), Schwarzer (30)).

Figure 2
Figure 2:
Schematic diagram of the antecedents of action control from initial motivation to behavioral habituation.

The sizable variance of participants who fall into nonintenders (who do not act), unsuccessful intenders (who do not act), and successful intenders (who act) is recognized via the dependent variables of intention formation (intenders vs nonintenders) and action control adoption (unsuccessful vs successful intenders who previously have not been active) and maintenance (unsuccessful vs successful intenders who previously have been active). Traditional models of intention would consider the intention formation variable as the critical dependent variable for behavioral enactment, yet the action control framework suggest that 48% of participants who intend to be active will fail to enact physical activity. Thus, intention formation is considered the penultimate dependent variable in Figure 2, whereas the action control variables are considered the critical dependent variables along a behavior change continuum from the beginning of motivation initiation to the potential habituation of regular physical activity.

The role of social cognition in physical activity intention formation has been well-established in previous work (32). Thus, interventions to increase intention should target physical activity attitudes and perceived control. The novelty in Figure 2 clearly lies in its action control variables. The discordance between intention and behavior has prompted scientists to create contemporary models that place high importance on a volitional phase of postmotivational constructs that are self-regulatory in nature (e.g., Heckhausen and Gollwitzer (10), Schwarzer (30)). The results of this review, and the subsequent inclusion of a behavioral-regulation variable in Figure 2, show support for this line of inquiry. For example, the behavioral processes of change, a collection of self-regulatory strategies, showed reliable evidence for their utility in predicting successful intenders from unsuccessful intenders. Cross-behavioral regulation (i.e., regulating motivation for other activities) also predicted the successful translation of intentions. Simple planning, however, was not useful, which mimics previous reviews on the topic (11). The results suggest that behavioral regulation that is more complex than making simple plans may be required to turn physical activity intentions into behavior. Similar commentary and evidence have been found in previous research, where coping plans (i.e., planning around barriers) were found more predictive of physical activity than action plans (e.g., planning where, when to do the activity) (31). Some evidence outside the exercise domain also has stressed the relevance of preparatory actions to facilitate intention translation (34). For physical activity behavior, this may entail such preparatory actions as buying relevant exercise equipment. This suggests some possibility of phased behavioral regulation during the adoption process (preparatory regulation to monitoring and coping). Collectively, these behavioral regulation strategies are proposed to facilitate action control adoption. Interestingly, there is a paucity of self-regulation constructs in many of the theories applied to physical activity, despite their continued evidence as mediators of behavior change (26) and their utility in interventions (2). These findings, using the action control framework, help bridge the results from early intention theories and intervention studies. From a practical standpoint, the results also support that participants who come to physical activity interventions with high intentions may still require regulatory strategies for behavioral enactment. Overall, Figure 2 suggests that positive intentions will need to be supplemented by behavioral regulation strategies that help people maintain their physical activity intentions and keep these goals at the forefront of competing intentions for other behaviors.

It is important to note that our review also identified some social cognitive constructs as predictors of intention-behavior discordance. There was very reliable evidence that successful intenders had higher affective attitude and perceived behavioral control/self-efficacy compared with unsuccessful inteders. By contrast, subjective norm and instrumental attitude had no evidence for their utility in understanding intention-behavior discordance. The potential role of perceived behavioral control/self-efficacy as a facilitating or inhibiting factor in intention translation (or goal pursuit) is within the theoretical tenets of both the theory of planned behavior and self-efficacy. Affective attitude, on the other hand, has had negligible attention in social cognitive models until recently (13,23), yet its prominence suggests that the affective domain should bear considerable import in future model development and interventions.

Taken together, these constructs also denote the importance of motivation still present in the physical activity intention-behavior gap. The results suggest that not all postintentional variance may be volitional matters such as self-regulation, but instead, our intentions do not take accurate portrayals of control and affect fully into account. This finding also has evidence from experimental designs, where motivation-based interventions still have some effect on behavior despite high initial intentions (26). These results suggest that affective appraisals of physical activity and perceived behavioral control probably need to be much higher to enact physical activity than to form the intention (Fig. 2). The use of the action control framework helps identify that affective attitude and perceived behavioral control likely are better definitive markers of behavioral enactment than intention. Overall, the results of Figure 2 illustrate that action control adoption has some linear transition from strong intentions and motivation but also represents an area for additional intervention via building strong behavioral regulation skills. Interventions need to support high levels of control and affective attitude in addition to self-regulation strategies for successful behavioral enactment, but instrumental attitude becomes less relevant.

Extensions of the action control framework with past physical activity behavior, habit/automaticity, and exercise identity yielded evidence that intention-behavior discordance may differ for adopters compared with maintainers. First, these studies showed that most of the successful intenders are those individuals who are maintaining the behavior (i.e., high past physical activity, high habit strength, high identity with physical activity), whereas many of those who are unsuccessful intenders are attempting to adopt the behavior (i.e., low previous activity, low habit strength). These studies also showed some collective evidence that successful adoption is reliant on self-regulation, whereas successful maintenance may be somewhat reliant on habituation and identity. The findings help suggest a process of physical activity action control, which starts through high motivation and self-regulation during adoption but shifts into motivation and habituation for maintenance. The phased (left-to-right) inclusion of behavioral regulation and automaticity as predictors of action control adoption and subsequent maintenance in Figure 2 is an attempt to highlight this potential transition. The automaticity construct still requires considerable research, but early evidence suggests that habituated behavior is founded on highly rewarding (high affective attitude), easily executable (high perceived control) behavior, with consistency and similar cues (fostered through initial regulation efforts) (12).

Similar to the division between intention formation and action control adoption, action control maintenance and physical activity habit development are conceived as nonlinear, where habits substantively vary between persons because of context-relevant factors such as affect, control, and within-person cues and physical activity practices. Thus, action control maintenance is founded on strong intention and self-regulation, but it also provides an opportunity for additional intervention in the form of individual-level monitoring of physical activity practices that emphasize consistency and environmental cues to form habits. By combining self-regulation strategies, increases to physical activity affect and control, and an individual-level habit development monitoring process, it is expected that the framework outlined in Figure 2 should be helpful for both physical activity theory and interventions.

The findings of this review need to be considered within the context of its limitations and possibilities for future research. Six of the 10 studies included undergraduate samples so a wider range of sampling may be prudent. We saw no difference between community samples and undergraduate samples in terms of these results, but young people, older adults, and special populations are not represented. Second, the findings generally use the theory of planned behavior or related constructs so other models of physical activity may be useful to understand action control in the future. The theory of planned behavior shares considerable redundancy with most social cognitive models, but ecological models (i.e., models that include policy, environment, and social situation, as well as individual-level variables) may be interesting to evaluate within this frame. Third, an assessment of prediction time frame across the action control framework is needed. The studies reviewed ranged from 2-wk to 6-month prospective, and although the current sample is too small to examine any moderation by time frame, it may be an important future research question. Fourth, the use of this framework has been limited to regions of Canada and the Netherlands. Replication at other regions would help establish the generalizability of these results.

Acknowledgments

R.E.R. is supported by a Senior Scientist Award from the Canadian Cancer Society and the Give to Live organization as well as through funds from the Social Sciences and Humanities Research Council of Canada, the Canadian Cancer Society, and the Canadian Institutes for Health Research. The authors also thank Holly Murray for her literature review work during the initial search stage of this article. Please note that the authors also recognize the work of other researchers on these topics, which could not be cited because of the reference limitations and the ESSR focus on researcher-based work. The authors report no professional conflict of interest for this article.

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Keywords:

theory of planned behavior; self-regulation; volitional planning; habit; automaticity; maintenance; adoption

© 2013 American College of Sports Medicine