predicting health-check attendance among prior attenders and nonattenders: the role of prior...

Predicting Health-Check Attendance Among Prior

Behavior in the Theory of Planned Behavior Attenders and Nonattenders: The Role of Prior

PAUL NORM AN^ MARK CONNER Universiiy of Sheffield, U.K. University of Leeds, U.K.

A prospective study applying the theory of planned behavior (TPB) to the prediction of attendance at health checks is reported. Conducted in a single general practice, 307 patients completed questionnaires based on the TPB, and were invited to attend a health check. The role of prior behavior in the TPB was assessed in 2 ways. First, it was used to assess the sufficiency of the TPB. The addition of prior attendance behavior led to a significant increase in the prediction of current behavior. Second, its role as a moderator variable was assessed. While the TPB was unsuccessful in predicting attendance behavior among prior attenders, it was able to do so among prior nonattenders. The results are discussed in relation to recent work on decision-making processes.

Social psychological models have an important role in increasing our understanding of the factors that underlie health-related decisions and behaviors. The application of these models to the prediction of health-related behavior has two main benefits. First, the models provide a clear theoretical basis for research. They can be used to guide the selection of variables to measure, the procedure for developing reliable and valid measures, and the way in which these measures are combined to predict behavior. Second, to the extent that the models identify variables which are important in predicting health-related behavior, they further our understanding of health. Many models have been applied in this area, including the health belief model (Becker, 1974), protection motivation theory (Rogers, 1983), health locus of control (Wallston & Wallston, 198 I), and self-efficacy theory (Bandura, 1986). However, one model which has attracted growing interest in recent years is the theory of planned behavior (TPB; Ajzen, 1988, 1991), a recent extension of the widely used theory of reasoned action (TRA; Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975) which continues to dominate attitude-behavior research (Olson & Zanna, 1993). It is the application of the TPB to the prediction of attendance at health checks which forms the basis of this paper.

'Correspondence concerning this article should be addressed to Paul Norman, Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP, U.K.

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JoornalofApplied Social Psychology, 1996, 26, 11, pp. 1010-1026. Copyright 0 1996 by V. H. Winston & Son, Inc. All rights reserved.

PREDICTING ATTENDANCE AT HEALTH CHECKS 101 1

The provision of health checks has become a common feature of preventive care in general practice in the United Kingdom over the past few years (Fullard, Fowler, & Gray, 1987; Norman & Conner, 1992; Norman & Fitter, 199 1 ; Pill, French, Harding, & Stott, 1988). Health checks have been used to fulfill two main goals; first, the recording of risk factors, particularly for coronary heart disease; and second, the providing of health promotion advice based on this information (Department of Health and the Welsh Office, 1989). As with any screening service, if health checks are to be effective at a population level, it is imperative that high uptake rates are obtained. Unfortunately, several studies have reported disappointing uptake rates (Mann et al., 1988; Pill et al., 1988), and this has led to work aimed at identifying the factors which may encourage or inhibit attendance at health checks (Conner & Norman, 1994; Norman, 1993; Norman & Fitter, 1991; Pill et al., 1988). However, it is important to note that such preventive services are not designed as single-shot interventions. Rather, patients are encouraged to attend health checks at regular intervals. This is an issue which, to date, has received little attention in the literature (Fitzpatrick, 1991). There is, therefore, a need to identify factors which are associated with continued attendance or nonattendance at health checks. As indicated earlier, the TPB may be a useful framework for identifying these factors.

The TPB provides a theoretical account of the way in which a number of psychological variables combine to predict behavior. According to the TPB, the immediate determinant of a person’s behavior is behavioral intention- what the person intends to do. Intention itself is seen to be determined by three sets of factors. The first is the person’s attitude toward the behavior. The second is the subjective norm or perceived social pressure to perform the behavior. The third determinant of intention is the person’s perception of the amount of control he or she has over performance of the behavior (i.e., perceived behavioral control). The model without this last component is the widely used TRA. This component was added to the original TRA in order to extend it to the prediction of nonvolitional behavior. Ajzen (1988) argues that perceived behavioral control is important in predicting both behavioral intention and, when the individual is realistic in his or her perceptions of control, actual performance of the behavior. This additional component has been found to add to the prediction of behavioral intention and behavior in several studies (Ajzen & Driver, 1992; Beale & Manstead, 1991; Schlegel, d’ Averna, Zanna, DeCourville, & Manske, 1992).

The TPB has been successfully applied to a range of health-related behaviors (Ajzen & Timko, 1986), including dietary behavior (Sparks, Hedderley, & Shepherd, 1992) and exercise behavior (Godin, Vezina, & Leclerc, 1989). However, the application of the TPB to the prediction of

1012 NORMAN AND CONNER

attendance at health checks has been very limited. Conner and Norman (1 994) found the intention, attitude, and perceived behavioral control components to correlate with attendance, although in a regression analysis, the TPB could only account for 4% of the variance in attendance behavior. To date, the TPB has not been applied to the prediction of repeated attendance or nonattendance at health checks.

This application is a particularly interesting one for the TPB as it allows for an assessment of the role of prior behavior. This role can be considered in one of two ways. First, prior behavior can be considered as a predictor variable (Bentler & Speckart, 1979), inasmuch as it may have a direct influence on future behavior. However, Ajzen (1988) has argued that the components of the TPB should mediate any influence of prior behavior. It is therefore possible to argue that in the present context, patients may have different views about attending a health check depending on whether they have previously attended or failed to attend and that it is these differences which are important in predicting subsequent behavior. Thus, prior behavior is only important to the extent that it influences the components of the TPB, which in turn determine behavior. If this is the case, prior behavior should not have a direct effect on future behavior and the TPB can be said to be sufficient. However, a number of recent studies have revealed that the addition of prior behavior to the TPB leads to a small but significant increase in the predictability of behavior (Ajzen, 1991; Beck & Ajzen, 1991; van Ryn & Vinokur, 1990). These and earlier results from the TRA have led some researchers to suggest that prior behavior should be included as an independent variable in the model predicting behavior (Bentler & Speckart, 1979; Fredricks & Dossett, 1983). The present paper addresses this issue through the application of the TPB to the prediction of attendance at health checks where a measure of previous attendance or nonattendance is available.

A second approach to the role of prior behavior is to consider prior behavior as a moderator variable. Clearly, attending a health check in response to an invitation when one has previously attended is likely to be quite a different behavior than attending after having failed to attend previously. This is in line with Marteau et al.’s (1992) claim that attendance at a screening should be viewed not as a homogeneous behavior, but rather as a group of behaviors, each with its own set of predictor variables. If this proposition is correct, the predictors of attendance behavior should be different for prior attenders and nonattenders, and prior behavior can be said to moderate the relationship between the components of the TPB and behavior. Some evidence in support of this position has been provided by Jepson and Rimer (1993), who examined the determinants of mammography intentions among women who had previously been screened or not been screened. They were able to explain 46% of

PREDICTING ATTENDANCE AT HEALTH CHECKS 1013

the variance in mammography intentions among prior nonscreenees, but only 14% of the variance among prior screenees, with the psychological variables they measured having a stronger role in determining the intentions of prior nonscreenees. Prior behavior could therefore be seen to moderate relationships with mammography intentions. However, Jepson and Rimer’s (1 993) study was unable to assess the role of prior behavior in the prediction of future behavior. This issue is addressed in the present study.

In order to assess the role of prior behavior in the TPB, a prospective study was conducted applying the TPB to the prediction of attendance behavior at a health check. On the basis of previous research, two main predictions were made. First, that prior behavior would show a direct relationship with attendance behavior at the health check, and second, that the predictors of attendance behavior would vary according to patients’ prior attendance behavior.

Method

Participants

The study was conducted in a single rural general practice in Norfolk, England. The practice consisted of four general practitioners with a combined list size of approximately 6,500 patients. The practice had decided to offer health checks to all its adult patients in 10-year age bands over a number of years. In the first year, all patients aged between 30 and 40 were identified to be invited to attend a health check at the practice. In order to evaluate the effectiveness of advice given at this first health check, the practice decided to invite those patients who had attended a health check for a follow-up health check 1 year later. In addition, patients who failed to attend their first health check were re-invited 1 year later. Thus, 749 patients aged between 3 1 and 42 who had been invited to attend a health check 1 year previously were invited a second time to attend a health check. Of these patients, 427 had attended a health check and 322 had failed to attend a health check in response to an invitation 1 year previously. The patient sample included roughly equal num- bers of males and females, and a full range of socioeconomic groups.

Questionnaire

Questionnaires were sent to patients before they received their invitation letters to attend a second health check. The measures were based on those used in an earlier study looking at the predictors of first-time attendance at a health check (Norman & Conner, 1993) and were designed to cover the main


components of the TPB. Unless otherwise indicated, all items were rated on 7-point response scales and were scored from -3 to +3.

Respondents’ intentions to attend the second health check were measured using three items (“If you had the opportunity, how likely is it that you would attend a health check at your doctor’s surgery?”; “I intend to attend a health check if offered the opportunity”; “If I was offered a health check, I would try to attend.”). Cronbach’s (1951) alpha for the intention scale was .92. A direct measure of attitude toward attending a health check was obtained through the use of nine semantic differential scales (“Attending a health check would be . . . worryingheassuring, bad/good, harmfuUbeneficial, unpleasanVpleasant, unsatisfactoiyhatisfactory, dijficult/easy, negative/ positive, punishinghewarding, foolish/wise”). Coefficient alpha for the nine- item scale was .9 1. A belief-based measure of subjective norm was constructed by asking respondents “What do you think the following people or groups would advise you to do if you were invited to a health check?” with the referents being, “your spouse, other members of your family, your closest friend, other friends, health experts, your doctor, the government, the media.” In addition, respondents were asked to indicate “In health matters, how much do you want to do what the following groups think you should do?” on 7-point response scales scored from 1 to 7. Responses to matching items were multi- plied together and averaged. Coefficient alpha for the eight-item scale was .93. Perceived behavioral control was measured using two items (“I could easily attend a health check if I wanted to”; “How much control have you over whether you attend a health check?”; a = .54).

Attendance Behavior

Both current and prior attendance behavior were dummy coded as dichotomous variables of attendance or nonattendance. This information was recorded and provided by the practice.

Procedure

Patients who one year previously had been invited to attend a health check were identified to be invited to a second health check. These patients included both previous attenders and nonattenders. Questionnaires were sent to these patients by a University Department complete with prepaid return envelopes. One to 3 months after the sending of the questionnaires, patients were sent an appointment to attend a health check by the practice. The patients then either attended or failed to attend their appointment, and this was recorded by the practice.

PREDICTING ATTENDANCE AT HEALTH CHECKS 101 5

At the health check, patients were seen individually by a practice nurse for approximately 30 min. Details about a range of preventive health behaviors (e.g., smoking status, alcohol consumption, exercise levels, diet) were taken along with some simple clinical measures (e.g., blood pressure, weight). On the basis of this information, the practice nurse gave health promotion advice to the patient. In some cases, patients were invited to attend more specialized health promotion clinics in the practice (e.g., quit smoking, healthy eating).

Data Analysis

In order to test the sufficiency of the TPB, hierarchical regressions were used in which the TPB components were entered at the first step and prior behavior was entered at the second. If the TPB is sufficient, then the addition of prior behavior at the second step should not lead to a significant increase in the amount of variance accounted for.

The role of prior behavior as a moderator variable was tested through conducting the regression analyses separately for prior attenders and nonattenders. This is in line with the view of Baron and Kenny (1986), who recommend that when the moderator is a dichotomy (i.e., prior attendance behavior) and the independent variable is a continuous variable (i.e., TPB), separate regressions should be run and the (unstandardized) regression coefficients compared (see Edwards, 1984, for tests of the difference between regression coefficients). If the regression coefficients are significantly different, then this indicates that prior attendance behavior acts as a moderator variable.2

Results

Attendance Rates

Of the 749 patients invited, 367 subsequently attended a health check; an attendance rate of 49.0%. More detailed analysis revealed the attendance rate among patients who had attended a year previously to be significantly higher (60.4%) than that obtained for previous nonattenders (33.9%), x2(df= 1) = 5 1 . 8 6 , ~ < .001 (Table 1).

21t should be noted that the data were analyzed using linear regressions. While this is appropriate for the prediction of intention, it is more appropriate to use logistic regression analyses when predicting attendance behavior, as this is a dichotomous variable. In the present study, the predictors of attendance behavior were considered in this way. However, the results of the logistic regression analyses were not different from the linear regression analyses. For ease of presentation, only the results of the linear regressions are reported.


Table 1

Attendance Rates for Previous Attenders and Nonattenders

Previous behavior Invited Attended Attendance rate

Attenders 427 258 60.4% Nonattenders 322 109 33.9% Overall 749 367 49.0%

Questionnaire Data

Before invitation letters were sent out to the patients in the study, questionnaires based on the TPB were sent to the 749 patients. After two mailings, completed questionnaires had been returned by 307 patients (41 .O% response rate). These responders included 193 patients who subsequently attended a health check and 114 who subsequently failed to attend a health check. Ques- tionnaire data were available for more of the attenders (52.6%) than the nonattenders (29.8%), x2(df= 1) = 40.03, p < .001.

Prediction of Intentions

The first step in the analysis of the questionnaire data was to consider the predictors of intention to attend a health check. Hierarchical regression analysis was conducted on the data in which the variables were entered in two steps in order to predict intention: (a) attitude, subjective norm, and perceived behavioral control, and (b) prior behavior. Thus, it was possible to assess the sufficiency of the TPB in accounting for the effects of prior behavior on intentions to attend.

As can be seen from Step 1 in Table 2, the TPB was able to explain 50% of the variance in intention to attend, F(3, 259) = 88.04, p < .001 (R2 = .SO), with all three components of the TPB emerging as significant independent predictors. The addition of prior behavior at Step 2 led to a small but significant improvement in the prediction of intention, F(4, 258) = 15.85, p < .001

The first step of the above regression analysis was repeated separately for prior attenders and prior nonattenders (Table 2). For prior attenders, the TPB was able to explain 55.0% of the variance in intention to attend, F(3, 186) = 7 7 . 1 3 , ~ < .001 (R2 = .55), with all three components emerging as significant

( R ~ A = .03).


Table 2

Hierarchical Regression Analyses for Intention

Prior Prior Overalla attendersb nonattendersC

r P R2 r P R2 r P R2

Step 1 ATT .64*** .42*** .62*** .33*** .65*** .55*** SN .56*** .26*** .64*** ,33*** .45*** .18 PBC .48 * * * . 1 8 * ** S O .56* * * .24* * * .5 5 .27* .05 .44

Step 2 ATT .64*** .40*** SN .56*** .27*** PBC .48*** .16*** Bo .29*** .17*** .53

Note. ATT = attitude. SN = subjective norm. PBC =perceived behavioral control. Bo =

prior behavior. an = 262-270. = 189-192. = 73-77. *p < .05. ***p < .001.

independent predictors. For prior nonattenders, the TPB again explained a large amount of the variance in intention, F(3,69) = 1 8 . 4 0 , ~ < .001 (R2 = .44), although only the attitude component emerged as a significant independent predictor. Comparison of the unstandardized regression coefficients for the two groups revealed no significant differences between the regression coefficients for the attitude ( t = 1.95, ns), subjective norm (t = 0.28, ns), and perceived behavioral control (t = 1 .O 1, ns) components.

Prediction of Behavior

The next step in the analysis of the data was to identify the predictors of attendance behavior. As with the predictors of intention, the analyses were performed for the sample as a whole, and then separately for patients who either attended or failed to attend one year previously. Considering the patient sample as a whole, hierarchical regression analysis was conducted on the data

101 8 NORMAN AND CONNER

Table 3

Hierarchical Regression Analyses for Behavior

Prior Prior Overalla attendersb nonattendersC

r P R 2 r P R 2 r P R2

Step 1 BI .16** .06 .04 .06 .20 .13 PBC .13* .13 .03 .01 .05 .OO .29** .23* .09

Step 2 BI .16** .05 PBC .13* .08 Bo .16** .13* .07

Note. BI = behavioral intention. PBC = perceived behavioral control. Bo = prior behavior. an = 268-284. bn = 191-192. *p < .05. **p < .01.

= 77-79.

in which the variables were entered in two steps in order to predict attendance behavior: (a) intention and perceived behavioral control, and (b) prior behavior. Thus it was possible to assess the sufficiency of the TPB in accounting for the effects of prior behavior on future (i.e., attendance) behavior.

As Table 3 shows, the TPB (Step 1) was able to explain only 3% of the variance in attendance behavior, F(2, 265) = 3.85, p < .05 (R2 = .03), with neither of the TPB’s components emerging as significant predictors. The addition of prior behavior at Step 2 led to a significant improvement in the prediction of behavior, F(3,264) = 1 0 . 6 8 , ~ < .001 (R2A = .04).

The first step of the above regression analysis was repeated separately for prior attenders and prior nonattenders (Table 3). For prior attenders, the TPB was unable to explain any of the variance in attendance behavior, F(2, 188) = 0.20, ns (R2 = .003). For prior nonattenders, the TPB was able to explain 9% of the variance in attendance behavior, F(2, 74) = 3.52, p < .05 (R2 = .09), with perceived behavioral control emerging as a significant independent predictor of behavior. However, comparison of the unstandardized regression coefficients revealed no significant differences between the regression coefficients


Table 4

Comparison of Prior Attenders and Nonattenders on the TPB Variables (t Tests)

Variable Attendersa Nonattendersb df t value

BI 2.45 1.55 268 5.01*** ATT 1.99 1.57 268 3.05* SN 1.07 0.95 267 1.75 PBC 2.29 1.71 268 3.50**

Note. BI = behavioral intention. ATT = attitude. SN = subjective norm. PBC = perceived behavioral control. High scores indicate strong agreement. an = 191-192. bn = 77-79. *p < .05. **p < .01. ***p < .001.

for the behavioral intention ( t = 0.10, ns) and perceived behavioral control ( t = 0.65, ns) components.

Comparison of Prior Attenders and Nonattenders

In order to test the hypothesis that prior attendance behavior may influence patients’ views about attending a health check, the responses of prior attenders and nonattenders on the TPB measures were compared. As Table 4 shows, prior attenders were found to have stronger intentions to attend a health check, to have a more positive attitude toward attending health checks, and to perceive greater control over attending.

Discussion

The present study sought to apply the theory of planned behavior (TPB) to the prediction of repeated attendance and nonattendance at health checks. In doing so, it was possible to assess the role of prior behavior in the TPB. The results of the study showed the TPB to be a poor predictor of attendance behavior, accounting for a mere 3.0% of the variance in behavior. When prior behavior was included in the regression equation, the amount of variance explained rose to a still modest 7.0%, with prior behavior being the only significant predictor of current behavior. This result coincides with other studies which have assessed the role of prior behavior in the TPB and have found its inclusion to lead to a small, but significant increase in the amount of


variance explained (Ajzen, 1991; Beck & Ajzen, 1991; van Ryn & Vinokur, 1990). Some researchers have therefore called for prior behavior to be included as an independent variable in the model (e.g., Bentler & Speckart, 1979). However, Ajzen (1987) has argued that prior behavior should not be considered in this way as it has no independent explanatory value. Arguing from a trait perspective, he states that when “the ‘trait’ used to predict a behavior is the tendency to perform that very behavior, its explanatory power is completely lost” (p. 41). For example, in the present context, patients do not attend a health check because they have attended one in the past. As a result, prior behavior may be better used to assess the sufficiency of the TPB (Ajzen, 1991).

Although it might be inappropriate to consider prior behavior as a predictor of current behavior, it is possible to consider it as a moderator inasmuch as one’s prior behavior may influence both the strength of and the relationship between the components of the TPB and behavior (Beale & Manstead, 1991; Manstead, Profitt, & Smart, 1983). Consistent with the first part of this argument, prior attenders held stronger intentions to attend a health check, had more positive attitudes toward attending, and were more likely to perceive that attending would be under their own volitional control. Thus, prior behavior can be seen to influence the strength of the components of the TPB. Similar results have been reported by Jepson and Rimer (1 993), who found that prior mammography screening status influenced intentions, perceived efficacy, and nor- mative beliefs.

Considering the second part of this argument, separate regressions were conducted for prior attenders and nonattenders. In this way, it was possible to test whether prior behavior acts as a moderator variable. The results of the regression analyses revealed that the TPB was unable to account for any of the variance in current behavior among prior attenders. In contrast, the TPB explained 9.0% of the variance in current behavior among prior nonattenders, with the perceived behavioral control component being the sole significant predictor. That the predictors of current attendance should vary as a function of past behavior is in line with Marteau et al.’s (1992) claim that attendance at a screening should be seen as a group of behaviors, each with its own set of predictor variables. The present results suggest that not only do prior nonattenders believe that attending a health check is less controllable, but also that perceptions of control have greater importance in determining future behavior. However, more detailed analysis revealed no significant differences between the strength of the regression coefficients for the TPB components in the regression equations for prior attenders and nonattenders. Nevertheless, the TPB was found to explain a much larger proportion of the variance in behavior among prior nonattenders. Jepson and Rimer (1993) likewise found that they could explain more of the variance in mammography intentions among prior


nonscreenees. However, this difference in the prediction of intention was not found in the present study. Overall, the present results suggest that there may be some merit in considering prior behavior as a moderator variable when assess- ing the social psychological determinants of the uptake of screening services.

In considering the role of attitudinal variables in the prediction of exercise behavior, Dishman (1 982) made the distinction between the initiation of a behavior and its maintenance and noted that while the former is reliably predicted by attitudinal variables, the latter is not. It may be possible to put forward a similar argument in relation to the present results. Thus, attending when one has previously failed to attend may require the mediation of cognitive variables as found in the TPB; it may require a good deal of cognitive effort to attend a health check for the first time. In contrast, attending when one has previously attended may not require the mediation of cognitive variables; it may be an easier behavior to perform, as suggested by the finding that previous attenders were more likely to believe that attending a health check would be under their own volitional control. As a result, when previous attenders receive an invitation to attend another health check, a simpler decision-making process may operate.

Though speculative, this position coincides with recent work on attitude change which has focused on the depth of information processing (Chaiken, Lieberman, & Eagly, 1989). It is argued that only under certain conditions may the processing of information be similar to that outlined in the TPB (Conner, 1993); that is, thoughtful, effortful processing of information. For many behavioral decisions, simplified decision-making rules may be employed instead (Norman & Conner, 1993). Thus it is possible to make the distinction between systematic and heuristic processing of persuasive messages (Chaiken et al., 1989), and there is a clear need for researchers to identify the conditions under which behavior is based on the systematic processing of information. Ronis, Yates, and Kirscht (1989) suggest that conscious decision making is likely to occur in novel situations or in old situations when new problems arise. Simi- larly, Petty and Cacioppo (1986) have argued that the depth of processing is determined by the person’s motivation and ability to systematically process persuasive messages.

To date, these ideas have not been applied to attitude-behavior models such as the TPB, although Fazio’s (1990) MODE model makes a distinction between the deliberate and automatic effects of attitudes on behavior. Only in situations where individuals have the opportunity and motivation to think deliberately will attitudes predict behavior in the way outlined in models such as the TPB (Sanbonmatsu & Fazio, 1990); in other conditions, behavior is determined by highly accessible attitudes (Fazio, Powell, & Williams, 1989). Future applica- tions of the TPB may benefit from incorporating ideas from work on decision- making processes in order to identify the conditions under which it may be


successfully used to predict behavior. This may be particularly relevant to the application of the TPB in the health area, where people are often presented with threats to their health. Such conditions may increase people’s motivation, but reduce their ability, to think deliberately or to systematically process the total set of information available to them. In this way it may be possible to incorpo- rate often neglected emotional arousal variables (Oliver & Berger, 1979) into work using the TPB to predict a range of health-related behaviors.

While the present results suggest that it may be appropriate to take a more differentiated view of attendance behavior at health checks, they are still disappointing inasmuch as the amounts of variance explained by the model were small. This is in line with other studies which have used other social cognitive models to predict attendance at screening (Norman, 1991; Norman & Fitter, 1991) and health-related behavior more generally (Conner & Norman, 1995). Such results have led some researchers to question the predictive utility of these models in relation to health-related behavior (Hunt, 1988). There are a number of plausi- ble reasons for this poor performance in addition to those outlined above.

First, there may frequently be a problem with missing data, and this may undermine the predictive power of these models. In the present study, only a 41% response rate was obtained which, although typical of many postal sur- veys (Kerlinger, 1973), means that any conclusions must be made with some caution and the potential generalizability of the results may be questioned. The problem of missing data is particularly relevant in relation to nonattenders, who were less likely to have returned their questionnaires. One solution to this problem may be to attempt to follow up nonresponders after they have failed to attend their health check. However, such an approach is clearly limited to the extent that patients may merely offer post-hoc rationalizations for their behavior (Norman & Fitter, 1989; Pill et al., 1988).

Second, there may be a problem with the fidelity of behavior measures (Conner & Norman, 1994). To some extent, this can be seen to be a problem in the present context in which attendance behavior was measured on a single occasion: Patients either attended or failed to attend their appointment in response to a single invitation. It is unlikely that attitudes will be strong predictors of specific behaviors performed on specific occasions (Ajzen & Fishbein, 1977), due to the fact that such behaviors are likely to be open to a whole range of idiosyncratic influences. Hence, the uptake of a single offer of a health check may be influenced by a series of unexpected events such as missing the bus, having an ill child, or simply forgetting. The occurrence of such events will serve to attenuate any relationship between the TPB and behavior. As such, the present data may underestimate the potential of the TPB in predicting attendance at health checks. The use of multi-act criteria to measure health behavior is an issue which is worthy of further investigation (Epstein, 1979).


The present results have a number of practical implications for those who wish to encourage regular attendance at health checks. First, the results suggest that there may be a strong compliance element in continued attendance at health checks: Almost two thirds of previous attenders reattended. Second, the results suggest that the perception of control is an important variable in determining attendance behavior for prior nonattenders. Thus, those who offer health checks should ensure that as few barriers as possible prevent patients from attending. This may involve having a wide range of available appointment times so that patients may choose appointment times which best suit their lifestyles. In conclusion, though, it is important to note that the present study highlights the potential of using social psychological models to increase our understanding of health-related decisions and behaviors.

References

Ajzen, I. (1987). Attitudes, traits and actions: Dispositional prediction of behavior in personality and social psychology. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 20, pp. 1-64). New York, NY: Academic.

Ajzen, I. (1988). Attitudes, personali& and behavior. Milton Keynes, England: Open University Press.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Making Processes, 50, 179-2 1 1 .

Ajzen, I., & Driver, B. L. (1992). Application of the theory of planned behavior to leisure choice. Journal of Leisure Research, 24,207-224.

Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84, 888- 918.

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall.

Ajzen, I., & Timko, C. (1986). Correspondence between health attitudes and behavior. Journal of Basic and Applied Social Psychology, 7,259-276.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51,

Beale, D. A., & Manstead, A. S. R. (1991). Predicting mothers’ intentions to limit frequency of infants’ sugar intake. Journal ofApplied Social Psychol-

1173-1 182.

ogy, 21,409-43 1 .


Beck, L., & Ajzen, I. (1991). Predicting dishonest actions using the theory of planned behavior. Journal of Research in Personality, 25,285-301.

Becker, M. H. (Ed.). (1974). The health belief model and personal health behavior. Health Education Monographs, 2,324-508.

Bentler, P., & Speckart, G. (1979). Models of attitude-behavior relations. Psychological Review, 86,452-464.

Chaiken, S., Lieberman, A., & Eagly, A. H. (1989). Heuristic and systematic information processing within and beyond the persuasion context. In J. S. Uleman & J. A. Bargh (Eds.), Unintended thought: Limits of awareness, intention and control (pp. 215-252). New York, NY: Guilford.

Conner, M. (1993). Pros and cons of social cognition models in health psychology. Health Psychology Update, 14,24-30.

Conner, M., & Norman, P. (1994). Comparing the health belief model and the theory of planned behaviour in health screening. In D. R. Rutter & L. Quine (Eds.), The social psychology of health and safety: European per- spectives (pp. 1-24). Aldershot, England: Avebury.

Conner, M., & Norman, P. (1995). Predicting health behaviour: Research and practice with social cognition models. Buckingham, England: Open University Press.

Cronbach, L. J. (1 95 1). Coefficient alpha and the internal structure of tests. Psychometrika, 16,297-334.

Department of Health and the Welsh Office. (1 989). General practice in the national health service: A new contract. London, England: H M S O .

Dishman, R. K. (1 982). Compliance/adherence in health-related exercise. Health Psychology, 1,237-267.

Edwards, A. C. (1984). An introduction to linear regression and correlation (2nd ed.). New York, NY: Freeman.

Epstein, S. (1979). The stability of behavior: On predicting most of the people most of the time. Journal of Personality and Social Psychology, 37,

Fazio, R. H. (1990). Multiple processes by which attitudes guide behavior: The MODE model as an integrative framework. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 23, pp. 75-109). San Diego, CA: Academic.

Fazio, R. H., Powell, M. C., & Williams, C. J. (1989). The role of attitude accessibility in the attitude-to-behavior process. Journal of Consumer Research, 16,280-288.

Fishbein, M., & Ajzen, I . (1975). BelieJ attitude, intention and behavior. New York, NY: John Wiley & Sons.

Fitzpatrick, R. (1991, September). Factors affecting women’s response to an invitation to attend a second breast cancer screening examination. Paper

1097-1 126.


presented at the B.P.S. Health Psychology Section Conference, University of Nottingham, England.

Fredricks, A. J., & Dossett, D. L. (1983). Attitude-behavior relations: A comparison of the Fishbein-Ajzen and the Bentler-Speckart models. Journal of Personality and Social Psychology, 45,501-512.

Fullard, E., Fowler, G., & Gray, M. (1987). Promoting prevention in primary care: Controlled trial of low technology, low cost approach. British Medi- cal Journal, 294, 1080-1082.

Godin, G., Vezina, L., & Leclerc, 0. (1989). Factors influencing intentions of pregnant women to exercise after giving birth. Public Health Reports, 104,

Hunt, S. M. (1988). Theoretical aspects of health-related behaviour change. Research unit in health and behaviour change review document. Edinburgh, Scotland: RUHBC.

Jepson, C., & Rimer, B. K. (1993). Determinants of mammography intentions among prior screenees and nonscreenees. Journal of Applied Social Psy-

Kerlinger, F. N. (1973). Foundations of behavioural research (2nd ed.). London, England: Holt, Rinehart, & Winston.

Mann, J. I., Lewis, B., Shepherd, J., Winder, A. F., Fenster, S., Rose, L., & Morgan, B. (1988). Blood lipid concentrations and other cardiovascular risk factors: Distributions, prevalence, and detection in Britain. British Medical Journal, 296, 1702-1 706.

Manstead, A. S. R., Profitt, C., & Smart, J. L. (1983). Predicting and understanding mothers’ infant-feeding intentions and behavior: Testing the theory of reasoned action. Journal of Personality and Social Psychology, 44, 657-67 l .

Marteau, T. M., Johnston, M., Kidd, J., Michie, S., Slack, J., Shaw, R. W., & Cook, R. (1992). Psychological models in predicting uptake of prenatal screening. Psychology and Health, 6, 13-22.

Norman, P. (1 99 1). Social learning theory and the prediction of attendance at screening. Psychology and Health, 5,23 1-239.

Norman, P. (1993). Predicting the uptake of health checks in general practice: Invitation methods and patients’ health beliefs. Social Science and Medicine, 37, 53-59.

Norman, P., & Conner, M. (1992). Health checks in general practice: The patient’s response. Family Practice, 9,48 1-487.

Norman, P., & Conner, M. (1 993). The role of social cognition models in predicting attendance at health checks. Psychology and Health, 8, 447-462.

Norman, P., & Fitter, M. (1989). Intention to attend a health screening appointment: Some implications for general practice. Counseling Psychology Quarterly, 2,26 1-272.

188-195.

chology, 23,40-5 1.


Norman, P., & Fitter, M. (1991). Predicting attendance at health screening: Organizational factors and patients’ health beliefs. Counseling Psychology Quarterly, 4, 143-155.

Oliver, R. L., & Berger, P. K. (1979). A path analysis of preventive health care decision models. Journal of Consumer Research, 6 , 113-122.

Olson, J. M., & Zanna, M. P. (1993). Attitudes and attitude change. Annual Review of Psychology, 44, 117-154.

Petty, R. E., & Cacioppo, J. T. (1986). Communication and persuasion: Cen- tral andperipheral routes to attitude change. New York, NY: Springer.

Pill, R., French, J., Harding, K., & Stott, N. C. H. (1988). Invitation to attend a health check in a general practice setting: Comparison of attenders and nonattenders. Journal of the Royal College of General Practitioners, 38,

Rogers, R. W. (1 983). Cognitive and physiological processes in fear appeals and attitude change: A revised theory of protection motivation. In J. T. Cacioppo & R. E. Petty (Eds.), Social psychophysiology: A source book (pp. 153-176). New York, NY: Guilford.

Ronis, D. L., Yates, J. F., & Kirscht, J. P. (1989). Attitudes, decisions, and habits as determinants of repeated behavior. In A. R. Pratkanis, S. J. Breckler, & A. G. Greenwald (Eds.), Attitude structure and function (pp. 2 13-239). Hillsdale, NJ: Lawrence Erlbaum.

Sanbonmatsu, D. M., & Fazio, R. H. (1990). The role of attitudes in memory- based decision making. Journal of Personality and Social Psychology, 59,

Schlegel, R. P., d’Averna, J. R., Zanna, M. P., DeCourville, N. H., & Manske, S. R. (1992). Problem drinking: A problem for the theory of reasoned action? Journal of Applied Social Psychology, 22, 358-385.

Sparks, P., Hedderley, D., & Shepherd, R. (1992). An investigation into the relationship between control, attitude variability, and the consumption of two common foods. European Journal of Social Psychology, 22,55-7 1.

Van Ryn, M., & Vinokur, A. D. (1990). The role of experimentally manipulated self-eficacy in determining job search behavior among the unemployed. Unpublished manuscript, Institute for Social Research, University of Michigan at Ann Arbor.

Wallston, K. A., & Wallston, B. S. (1981). Health locus of control scales. In H. Lefcourt (Ed.), Research with the locus of control construct (Vol. 1, pp. 189-243). New York, NY: Academic.

53-56.

614-622.

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