Colon cancer information as a source of exercise motivation

Psychology and Health December, 2006; 21(6): 739–755

Colon cancer information as a source of exercise motivation STACEY P. GRAHAM1, HARRY PRAPAVESSIS2, & LINDA D. CAMERON1 1

Department of Psychological Medicine, and Department of Psychology, The University of Auckland and 2School of Kinesiology, The University of Western Ontario (Received 17 March 2005; in final form 23 January 2006) Abstract Using a Protective Motivation Theory (PMT) framework, this study examined whether colon cancer is a meaningful source of exercise motivation. Participants were (N ¼ 173) teaching and school staff randomly assigned into one of three treatment conditions: PMT present, PMT absent (attention control) and no information (non-contact control). Two separate DVD videos were developed (one incorporating the four major components of PMT; perceived vulnerability (PV), perceived severity (PS), response efficacy (RE) and self-efficacy (SE) featured colon cancer and exercise information while the other DVDs featured cancer and nutritional information). Following treatment, participants completed questionnaires which assessed their beliefs towards colon cancer and exercise as well as their intentions to do more exercise. Two weeks later (T1), self-reported measures of exercise behaviour were assessed and then repeated at 1 month (T2). Only physically inactive participants were used in subsequent analyses (n ¼ 72). Results indicated that compared to the two control groups, the PMT present group scored significantly higher on RE and intention to engage in more exercise ( p-values ¼ 0.001). A trend effect in the expected direction also was noted for T1 exercise behaviour ( p ¼ 0.09). RE, SE and PV made significant and unique contributions to exercise intention scores, explaining 44% of the response variance. Intention was the only variable to show an association with T1 exercise behaviour, explaining 10% of the response variance. Overall, these findings suggest that a single exposure of media intervention grounded in theory can influence people’s beliefs, motivation and initial behaviour.

Keywords: Health promotion, disease, message tailoring, intentions, physical activity

Correspondence: Dr Harry Prapavessis, School of Kinesiology, Faculty of Health Science, The University of Western Ontario, London, Ontario, N6A 3K7 Canada. E-mail: [email protected] ISSN 0887-0446 print/ISSN 1476-8321 online ß 2006 Taylor & Francis DOI: 10.1080/14768320600603554

740 S. P. Graham et al. Introduction New Zealand has one of the highest rates of colon cancer in the world (American Cancer Society, 2003) with colon (bowel) cancer being the second most common cancer for both men and women (Ministry of Health, 2003). Evidence exists that physical activity is consistently related to lower risk of colon cancer (Everson, Stevens, Cai, Thomas, & Thomas, 2002; Lee, 2003; McTiernan, 2003). The median relative risk overall studies comparing the most active with their least active counterparts is 0.7 for males and 0.6 for females, indicating a 30–40% reduction in risk. This inverse relationship between exercise and risk of colon cancer holds after adjusting for potential confound factors such as diet, body mass index and smoking status. In a recent review article, Slattery and Potter (2002) observed a dose-response effect of exercise on colon cancer risk, when participation in activities was at least moderate in intensity (>4.5 estimated metabolic equivalent – MET) and activities were expressed as MET hours per week. Plausible mechanisms of protection include the positive effect of physical exertion on (a) insulin, prostaglandin and bile acid levels, all of which influence the growth and proliferation of colonic cells and (b) reducing bowel transit time and contact between faecal carcinogens and colonic mucosa (Batty, 2000). An important practical question is whether information about the protective benefits of exercise for colon cancer will have any impact on exercise motivation. To date, one study has shed light on this issue. Courneya and Hellsten (2001) examined whether cancer prevention is a meaningful source of exercise motivation using Protection Motivation Theory (PMT), a theory that has been successfully applied in many studies assessing determinants of exercise and other health behaviours (Floyd, Prentice-Dunn, & Rogers, 2000; Milne, Sheeran, & Orbell, 2000; Norman, Boer, & Seydel, 2005). Male and female undergraduate students (N ¼ 427) were randomly assigned to read persuasive communications that independently manipulated four cognitive beliefs that are central to PMT; perceived vulnerability (PV) towards the threat, perceived severity (PS) of the threat, response efficacy (RE) to effectively avert the threat and the ability to perform the coping response to avert the threat (i.e., self-efficacy – SE). Results showed that individuals who were led to believe that colon cancer was a severe disease, and also believed that exercise was effective in reducing the risk of colon cancer were more motivated to exercise than those who were led to believe that colon cancer was not a severe disease and exercise was only a minimally effective prevention. Despite these promising findings, they are not generalized beyond physically active young undergraduate students. In addition, exercise intention was the primary outcome measure and not actual exercise behaviour. The purpose of the proposed study is to extend the work of Courneya and Hellsten (2001) and examine the effectiveness of an intervention strategy grounded in PMT that seeks to modify exercise behaviour in physically inactive middle-aged adults. Inactive individuals were targeted because some

Colon cancer information as a source of exercise motivation

741

theorists have advocated a stage-matched intervention approach using the Transtheoretical Model (TTM) to promote health behaviour change (Prochaska & Marcus, 1993). The TTM provides a framework for identifying the interventions that are most appropriate for a person at a particular stage of the targeted behaviour. The Cancer Prevention Research Consortium (1995) has exercise interventions for people in the various stages of the TTM. Research also has demonstrated the benefits of matching self-help manuals and other motivational materials to a person’s stage of change (e.g., Marcus et al., 1998a, 1998b).1 Hence, our research question was as follows: ‘Is colon cancer prevention information effective for motivating inactive people to seriously consider initiating exercise?’ Within this general research question, the following specific hypotheses were generated: (a) manipulation of the four PMT constructs (i.e., PV þ PS ¼ Threat, RE þ SE ¼ Coping Resources) using persuasive communications will influence people’s beliefs towards colon cancer and exercise. Specifically, those exposed to the persuasive communications will view colon cancer as more threatening as well as perceive they have greater coping resources to reduce the threat compared to those not receiving the persuasive communications; (b) those exposed to the persuasive communications will also show greater intention to perform more exercise as well as demonstrate higher levels of exercise behaviour compared to those not receiving persuasive communications and (c) beliefs towards colon cancer and exercise will be positively associated with concomitant increases exercise intention and exercise behaviour.

Method Participants A sample of 173 teaching and school staff (121 females and 52 males, whose ages ranged from 22.58 to 66.50 years – M ¼ 43.81, SD 11.50), who engage in varying levels of exercise were recruited from 13 different schools in the greater Auckland area. Relevant demographic characteristics are presented in Table I. Individuals of all exerciser levels were recruited to avoid singling out nonexercisers and making them feel uncomfortable about taking part in the study. Development of PMT and other material Two separate DVD videos were produced for use in this study. The first was designed to incorporate the four major components of PMT; PV, PS, RE and SE. An oncologist and a gastroenterologist sourced from Auckland Hospital were enlisted to feature on the intervention video. They presented factual information regarding the susceptibility (e.g., ‘‘There are 2,200 new colon cancer patients every year’’; ‘‘New Zealand has one of the highest rates of colon cancer in the western world’’) and severity (e.g., ‘‘Treating colon cancer involves a major operation which often is followed by chemotherapy, making one tired and weak for many weeks’’; ‘‘50% of those who contract colon cancer will die from the

742 S. P. Graham et al. Table I.

Demographic characteristics for the three treatment conditions.

Variable Age (years) Ethnicity European Other (Indian Maori, Pacific Islander) Gender Male Female BMI Hours worked per week Stage of Exercise Readinessa a

Experimental Attention control Non-contact (n ¼ 58) (n ¼ 51) control (n ¼ 64)

Statistic

p-level

44.84 (11.20)

41.60 (10.31)

44.65 (12.54)

F(2, 170) ¼ 1.35

0.26

82.8% 5.7%

86.3% 3.2%

93.8% 3.4%

2 (32) ¼ 10.48

0.11

25.9% 74.1% 25.27 (4.25) 47.82 (11.55)

23.5% 76.5% 24.93 (4.25) 48.35 (5.79)

31.9% 60.9% 26.66 (5.64) 47.29 (9.35)

2 (32) ¼ 3.99

0.14

F(2, 170) ¼ 1.99 F(2, 170) ¼ 0.179

0.14 0.84

4.05 (1.09)

3.90 (1.10)

3.71 (1.12)

F(2, 170) ¼ 1.34

0.26

1 ¼ Precontemplation; 2 ¼ Contemplation; 3 ¼ Preparation; 4 ¼ Action; 5 ¼ Maintenance.

disease’’) of colon cancer in New Zealand. A senior academic in Sports and Exercise Science from the University of Auckland was also enlisted to present information regarding evidence linking colon cancer and exercise (e.g., ‘‘Research suggests that physical activity and exercise reduce colon cancer risk by as much as 40%’’) and to give some common methods to help increase one’s SE for engaging in more exercise (e.g., ‘‘There are many ways for you to integrate more physical activity and exercise into your daily life to meet the recommended guidelines of 30 min a day of moderate to vigorous activity. For example, instead of taking the car to the dairy, walk or ride your bike . . . ’’; ‘‘Park your car further away from where you are going and walk . . . ’’; ‘‘Write these activity goals out and put reminders around the house . . . ’’). The viewing time for the intervention video was approximately 18 min. Video was chosen as the primary method of framing the PMT persuasive information because it is a popular form of media, targets people with a broad range of literacy skills, and ensured that the content was standardized for all participants (Meade, 1996). The second video was designed as an attention control condition. This design allowed the researchers to distinguish the specific effect of the intervention from the non-specific effect of receiving comparable attention. It featured a nutritionist giving information regarding the links between diet and cancer in general, and then went on to discuss more specifically the links between diet and colon cancer. The attention control video was approximately 15 min in length. Measures Stage of exercise readiness questionnaire (SERQ). The SERQ was adapted by Marcus, Rakowski and Rossi (1992) from smoking literature. Five statements are presented (one based on each stage of change) and the participants were asked to mark (tick) the statement that best describes their current level of exercise


743

(e.g., ‘‘I do not currently exercise and am not seriously thinking about changing in the next 6 months’’; precontemplation). The Kappa index of reliability for the SERQ, taken over a 2-week period was 0.78 (Marcus et al., 1992). All statements pertained to leisure-time physical activity outside of school. Leisure-time physical activity was defined as exercising 3 times per week for 30 min or more at a moderate intensity or higher (at least some light sweating, for example: fast walking, swimming, cycling, hockey, soccer and aerobics). Beliefs towards colon cancer and exercise. Four seven-point items commonly used in PMT literature and specifically used by Courneya and Hellsten (2001) assessed each of the components; PV, PS and RE. Each item was anchored by the descriptors strongly disagree (1) and strongly agree (7). As an example, sample items related to colon cancer are: ‘‘Personally, I feel vulnerable to developing colon cancer at some point in my life’’ (PV); ‘‘I feel colon cancer would be a very serious illness for me to develop’’ (PS); ‘‘I feel that increasing my current level of exercise would assist me personally to reduce the risk of developing colon cancer later on’’ (RE). In addition, SE was assessed by four seven-point items often used by Ajzen (1991) to assess perceived behavioural control, a construct conceptually similar to self-efficacy. A sample item is ‘‘If I wanted to, I could easily do the recommended exercise necessary to reduce the risk of developing colon cancer’’. Responses from participants were submitted to principal components analysis to identify sets of items likely to constitute internally consistent scales. Multiple criteria were examined to determine an appropriate oblique solution (Comrey, 1988). These selection criteria were as follows; (a) Factor eigen values greater than one, factor item loadings greater than 0.45 on the primary factor and (c) factor items loadings less than 0.20 on the other factors. Results showed that the 16 items grouped into four factors readily interpretable as PS (4 items), PV (4 items), RE (4 items) and SE (4 items). These four factors accounted for approximately 68% of the total response variance. Internal consistency Cronbach’s alpha values for all scales were good (PV, ¼ 0.75; PS, ¼ 0.91; RE, ¼ 0.88 and SE, ¼ 0.86). In addition to the four individual components of PMT, the PS and PV constructs were added to form a person’s threat appraisal and the RE and SE constructs were summed to produce a person’s coping appraisal. Exercise intentions. Three seven-point items drawn from the PMT literature and specifically used by Courneya and Hellsten (2001) assessed exercise intentions. A sample item is: ‘‘How likely is it that colon cancer prevention would motivate you to exercise?’’, with responses ranging between extremely unlikely (1) to extremely likely (7). The reliability coefficient value for the scale in the present study was 0.81. Exercise behaviour. Exercise behaviour was assessed using the Leisure Score Index (LSI) of the Godin and Shepard (1985) Leisure Time Exercise

744 S. P. Graham et al. Questionnaire. The LSI contains three questions covering the frequency of mild, moderate and strenuous exercise performed during free time for at least 30 min during a typical week. A total score can be derived by summing the reported weekly frequency ( f ) of participation at each of the three intensity levels multiplied by the corresponding estimated metabolic equivalents (METs) value (e.g., ( f )3 (mild) þ ( f )5 (moderate) þ ( f )9 (strenuous)). A MET is unit that represents the metabolic equivalent of an activity expressed in multiples of resting rate of oxygen consumption. Jacobs, Ainsworth, Hartman and Leon (1993) have shown the LSI to possess acceptable test–retest reliability and concurrent validity (correlates with objective indicators of exercise such as CALTRAC accelerometer and VO2 max). Design and procedure Ethical approval was obtained from the University of Auckland Ethics Committee. A three-group randomized controlled experimental design was used. Participants were recruited by canvassing of schools and referrals from principals whose staff had already participated in the study. The canvassing was conducted via letter, phone and visits in person by the lead investigator. First, a time was arranged to meet the principal. Once consent was secured, staff from each school was approached directly to participate in this study. Schools, once recruited, were randomly assigned to one of three treatment conditions: PMT present (experimental) – receive persuasive communications that focused on the PMT constructs threat (vulnerability and severity) and coping (response efficacy and self-efficacy); PMT absent (attention control) – receive non-PMT communication (diet and cancer) and no information (non-contact control). The distribution of participants to conditions were as follows; Experimental conditions consisted of five schools with a total of 58 participants, the attention control condition consisted also of five schools and totalled to 51 participants and the non-contact control group was derived from three schools supplying 64 participants to the study. Baseline demographic (i.e., age, gender, height, weight, education, stage of exercise readiness, ethnicity) data and self-reported measures of exercise behaviour (LSI) were obtained from each participant. The intervention (DVD viewing material) was presented to participants in groups that ranged in size from 4 to 26 participants. Immediately following treatment participants were then asked to complete a post intervention questionnaire which assessed their beliefs towards colon cancer and exercise. Finally, exercise intentions were assessed immediately after completing the beliefs questionnaire. The non-contact control condition participants completed beliefs and intention measures directly following the questionnaire on general demographic information. Two weeks later (T1), self-reported measures of exercise behaviour were assessed and then repeated at one month (T2). All participants were given the two follow-up exercise behaviour questionnaires (LIS) along with two stamped self-addressed envelopes. As part of the general demographic questionnaire,


745

participants were asked to supply an email address. This was used to remind participants to fill out and send away their follow-up LIS questionnaires the day they were due. Only physically inactive participants (those who reported being at either the precontemplation, contemplation or preparation stage of exercise readiness) were used in subsequent analyses (n ¼ 72) to test the three major hypotheses generated for the present study. The rationale for this was that there was no point in providing an intervention to influence exercise beliefs and intentions as well as change exercise behaviour in people who are already motivated and doing the targeted behaviour. The overall design of the study, along with the attrition rate for each group at each follow-up can be seen in Figure 1.

Results Group equivalency Chi-square and one-way ANOVA procedures were used to test for group equivalency between the three treatment groups on demographic characteristics and other factors that could influence beliefs about exercise and colon cancer, exercise intentions and subsequent exercise behaviour. As can be seen in Table I there was group equivalency between groups across all these variables.2 Due to equivalency between groups, it was deemed unnecessary to use demographic variables as covariates in the subsequent group analyses. Correlation and ANOVA procedures also were conducted to determine relationships among the demographic variables (i.e., age, BMI, gender, ethnicity, stage of exercise readiness) and the PMT variables, exercise intentions and exercise behaviour. Results showed that age was moderately related to exercise intentions (r ¼ 0.31, p < 0.001). Results also showed that stage of exercise readiness was positively related to all three exercise behaviour end point scores (r values ranged between 0.36 and 0.50) and SE scores (r ¼ 0.41, p < 0.001). ANOVAs showed a trend effect for gender at baseline, F(1, 171) ¼ 3.63, p < 0.06, and T2, F(1, 171) ¼ 3.20, p < 0.08, exercise behaviour (male LSI scores were higher than female at both time points).3 An ANOVA also was made to determine whether there were true differences between the ‘‘non-exercisers’’ (M ¼ 13.56, SD ¼ 11.42) and ‘‘exercisers’’ (M ¼ 35.71, SD ¼ 20.54) on baseline exercise behaviour (LIS) scores and the result was significant, F(1, 173) ¼ 67.99, p < 0.001. Beliefs towards colon cancer and exercise One-way ANOVA was used to test for differences between treatment groups on their beliefs towards colon cancer and exercise questionnaire scores (see Table II). ANOVAs were followed by planned comparisons to determine if the PMT present group differed from the other two control groups. Results show that the PMT present group was significantly higher on coping appraisal than the other two control groups, which was carried through to RE but not SE. For threat

746 S. P. Graham et al. 173 participants obtained through sampling process and randomized by school

Experimental condition (nonexercisers) n = 22

Attention control condition (nonexercisers) n = 20

Participants complete demographic and baseline measures


Participants view educational program based on PMT, colon cancer and exercise

Participants view educational program based on diet and cancer

Participants complete post intervention beliefs questionnaire



Participants complete first follow-up questionnaire (T1) n = 20 91%



Participants complete second follow-up questionnaire (T2) n = 15 68%



Figure 1.

Flow diagram of design and overall procedure.

Non contact control condition (nonexercisers) n = 30


5.99 (0.69)

5.02 (1.17)

RE

SE

4.29 (1.22)

5.05 (1.01)

6.23 (1.11)

3.86 (1.29)

9.34 (1.60)

10.09 (1.93)

4.48 (1.26)

3.77 (1.14)

5.84 (0.94)

3.53 (1.45)

8.24 (1.90)

9.37 (1.92)

Non-contact control (NC) n ¼ 30

2.13

33.90

1.57

2.33

18.36

2.86

F (df ¼ 2, 69)

0.13

0.001

0.22

0.10

0.001

0.06

Significance

0.05

0.49

0.04

0.06

0.34

0.08

Effect size (2)

Expt > AC & NC ( p ¼ 0.08) Expt > AC & NC ( p < 0.001) Expt > AC & NC ( p ¼ 0.07) Expt ¼ AC & NC ( p ¼ 0.41) Expt > AC & NC ( p < 0.001) Expt > AC & NC ( p < 0.05)

Planned comparisons

Notes: Expt ¼ PMT group (information on colon cancer and its relationship with exercise); AC ¼ Attention control group (information on cancer and diet); NC ¼ Non-contact control group. Bonferroni adjustment to alpha for planned comparisons is 0.05/7 ¼ 0.007.

6.23 (0.67)

Severity

Coping

4.32 (1.10)

11.01 (1.26)

Threat

Vulnerability

10.55 (1.44)

Variables

Attention control (AC) n ¼ 20

Beliefs towards colon cancer and exercise data between treatment conditions for PMT constructs.

Experimental (Expt) n ¼ 22

Table II.

Colon cancer information as a source of exercise motivation 747

748 S. P. Graham et al. Table III. Descriptive statistics for exercise intentions and exercise behaviour. Experimental

Intention Exercise behaviour (Baseline) Exercise behaviour (Time 1) Exercise behaviour (Time 2)

Attention control

Control

Mean

SE

Mean

SE

Mean

SE

5.51 11.68

0.20 2.24

4.90 14.85

0.19 2.79

4.35 14.30

0.16 2.10

24.20

5.80

17.89

5.96

9.42

2.28

15.26

4.21

15.35

4.40

12.83

2.55

Exercise behavior – total weekly energy expenditure. SE – Standard error. Notes: Experimental (PMT information); Attention control (non-PMT information); Control (non-contact control).

appraisal, a trend effect in the expected direction was found which was not carried through to PV or PS (see Table II). Exercise intentions A one-way ANOVA was used to test for differences between treatment groups on their exercise intention scores (see Table III). The ANOVA was significant, F(2, 69) ¼ 11.80, p < 0.001, 2 ¼ 0.26. Planned comparisons test showed that the intention to engage in more exercise was significantly greater in the PMT present group when compared with the other two control groups, t(1, 69) ¼ 4.59, p < 0.001. Exercise behaviour Exercise behaviour (LIS) scores between treatment groups across time are presented in Table III and illustrated in Figure 2. Our analysis plan involved conducting a treatment condition ANCOVA on each follow-up exercise behaviour time point. Pre intervention exercise behaviour served as a covariate to remove the variance in post intervention exercise behaviour that is due to pre intervention exercise behaviour thereby increasing the power and sensitivity of the F-test (Stevens, 1996). Prior to conducting these analyses, the assumptions underlying the use of ANCOVA (e.g., linearity, homogeneity of regression) were tested and met (Tabachnick & Fidell, 2001). Results showed that after controlling for baseline exercise behaviour scores, a condition trend effect was found in exercise behaviour at T1 (2 weeks post intervention), F(1, 60) ¼ 2.51, p ¼ 0.09, 2 ¼ 0.08, but not at T2 (4 weeks post intervention), F(1, 50) ¼ 0.113, p ¼ 0.893, 2 ¼ 0.005. Planned comparisons test showed a trend effect, t(1, 57) ¼ 1.76, p ¼ 0.08. Specifically, those in the PMT group reported higher levels of exercise behaviour at T1 compared to their control counterparts (see Figure 2).

749


Total weekly energy expenditure (METS)

35

30

25

20

Experimental Attention control Control

15

10

5

0 Baseline

Time 1

Time 2

Figure 2. Mean and SE scores in exercise behaviour (LIS) between treatment groups across time. Notes: Experiment (PMI information); Attention control (non-PMT information); Control (non-contact control).

Table IV.

Inter-correlations of all PMT variables, exercise intentions and exercise behaviour.

Variable 1. 2. 3. 4. 5. 6. 7. 8.

Coping Threat Vulnerability Severity RE SE Exercise intention Exercise behaviour (Baseline) 9. Exercise behaviour T1 10. Exercise behaviour T2

1

2

3

4

5

6

0.18 0.19 0.09 0.79** 0.74** 0.88** 0.73** 0.30** 0.04 0.23 0.57** 0.02 0.27** 0.03 0.18

7 0.61** 0.15 0.04 0.27** 0.57** 0.36**

8

9

10

0.07 0.17 0.19 0.07 0.08 0.04 0.05 0.05 0.10 0.05 0.02 0.00 0.20 0.17 0.16 0.11 0.09 0.13 0.04 0.31* 0.26 0.27* 0.56** 0.40**

*p < 0.05; **p < 0.01 (2-tailed).

Relationships between PMT constructs, exercise intentions and exercise behaviour The correlations between the PMT variables, exercise intentions and exercise behaviour are presented in Table IV. If bivariate relations were found between the predictor variables and the criterion variable of interest then they were put into a regression analysis to determine their uncorrelated contribution. Intention to engage in more exercise was significantly related to all PMT constructs expect PV. Standard multiple regression analysis revealed that the remaining three constructs explained 44% of the variance in exercise intentions, F(4, 67) ¼ 13.02, p < 0.001, with all the three constructs making significant

750 S. P. Graham et al. unique contributions: RE ( ¼ 0.52, t ¼ 5.28, p < 0.001); SE ( ¼ 0.29, t ¼ 3.04, p < 0.01); PS ( ¼ 019, t ¼ 1.93, p < 0.05). Intention to engage in more exercise was the only variable related to exercise behaviour (see Table IV). Regression analysis showed that intention accounted for 10% of the variance in exercise behaviour at T1, ( ¼ 0.31, t ¼ 2.46, p < 0.01). In light of intention’s contribution to exercise behaviour at T1, a hierarchical regression was conducted entering baseline exercise behaviour at step 1 followed by intentions at step 2. Results showed that after controlling for baseline exercise behaviour, R2 ¼ 0.06, F(1, 58 ¼ 4.07, p < 0.05, intentions continued to make a significant contribution to exercise behaviour, R2 change ¼ 0.09, F change (1, 57) ¼ 6.46, p < 0.01. Discussion Our data supports the notion that colon cancer prevention is a meaningful source of exercise motivation. In general, the persuasive message framing developed for the present study was effective in manipulating participants’ coping appraisal (RE), which in turn influenced their intentions to perform more exercise, which in turn influenced their behaviour to do more initial exercise. The failure to strongly manipulate participants’ SE and threat appraisal (PS and PV) was unfortunate because both the SE and PS component made a significant and unique contributions to exercise intention scores. Response bias may be one reason for which SE was manipulated to a lesser extent than RE. For instance, the SE intervention was designed to encourage participants to get better organized and start planning for ways to incorporate more physical activity into their daily lives. Based on the post intervention belief items scores, it seems participants in the two control groups felt just as confident in achieving this goal (all three groups showed moderate levels of confidence – see Table II). Perhaps participants in the control groups did not want to convey lower levels of confidence in order to protect their self-worth and self-esteem. An equally plausible reason, however, is that the intervention material was not strong enough to positively influence SE beliefs. There are a number of plausible reasons as to why the threat appraisal components were not manipulated as successfully as the coping appraisal components. First, our persuasive material was based on factual information unlike the design of Courneya and Hellsten (2001), which focused on using bogus written information to manipulate the four PMT constructs into high versus low levels. For example, in their study, PS of colon cancer was characterized by either limited treatment problems and an 80% five-year relative survival rate (low PS), or by major treatment problems and a 20% five-year relative survival rate (high PS). These design differences, in part, likely contributed to why the Courneya and Hellsten study successfully manipulated PS and we did not. Our failure to manipulate PS is not surprising. One would expect middle-aged people to be aware of the seriousness of cancer in general, and perhaps colon cancer specifically. This is reflected in the high mean score


751

of PS between all treatment conditions. Clearly a ceiling effect was operating which restricted the potential impact of PS information. Second, of the four PMT constructs, PV scores were the lowest irrespective of treatment condition (overall mean was 3.7 on the seven-point scale). This may be due to defensive denial where participants discount themselves from the threat in order to blunt its psychological impact (cf Wiebe & Korbel, 2003). On the one hand, these moderate vulnerability rates may reflect rational appraisal of personal risk. The average individual has a low-to-moderate risk of colon cancer and so high vulnerability ratings would not be accurate. On the other hand, high rating for colon cancer severity, SE and RE would be reasonable and desirable from a health promotion perspective. The main reason Courneya and Hellsten (2001) offered for their inability to successfully manipulate PV was that their sample population was made up of university undergraduate students with an average age of 19.7 years and that an optimistic bias is naturally held by young individuals when it comes to diseases such as cancer. Our findings taken together with those of Courneya and Hellsten (2001) suggest that altering perceptions of vulnerability to colon cancer remains a major challenged for cancer prevention health professionals. As predicted, significant differences in intention to do more exercise were found between treatment conditions. Specifically, those in the PMT group scored higher on this construct than their two control counterparts (see Table III). A trend effect also was noted between treatment conditions in post intervention exercise behaviour at T1 (2 weeks) but not T2 (4 weeks). Those in the PMT group reported higher exercise behaviour scores at T1 compared to those the two control groups. The eta-squared statistic (0.08) indicated a medium effect size (Cohen, 1992) and that a significant finding would likely have occurred with a larger sample and its accompanying smaller variability (standard error) in exercise behaviour LIS scores (see Table III and Figure 2). Perhaps we might have found a stronger effect if RE had shown an association with exercise behaviour or intention’s association with exercise behaviour was more robust. The large effect observed in both RE and intention suggested that these two variables had the greatest potential to produce an exercise behaviour effect. It is noteworthy that of the three treatment conditions, the PMT group was the only one to show an increase in early exercise behaviour across time. Also as predicted, three of the four PMT variables (PS, RE, SE) were significantly related with intentions to engage in exercise (see Table IV). Standard regression analysis showed that all three variables made unique contributions to intention scores (i.e., explained 44% of the response variance). Of the three PMT variables, RE had the strongest relationship with exercise intentions. These findings are in line with the Milne et al. (2000) meta-analysis on PMT research. They reported that overall, coping variables were more strongly and consistently associated with intention than threat appraisal variables. It appears that documenting reductions in colon cancer risk from exercise is associated with concomitant increases in intention to engage in more exercise. Intention was the only construct to show an association with exercise behaviour.

752 S. P. Graham et al. These findings are again in accordance with the Milne et al. (2000) meta-analysis on PMT research. They found from reviewing twelve studies that intention had the strongest, most robust and most consistent association with concurrent behaviour. As mentioned earlier, some theorists have suggested a stage-matched intervention approach to health behaviour change (Prochaska & Marcus, 1993). In the present study the majority of participants (n ¼ 101) reported being in the action/maintenance stage of exercise readiness and hence were not included in subsequent analyses. This raises the question: ‘what would happen to our findings if we included the full sample?’ To shed light on this issue we re-analyzed our data using the entire sample (N ¼ 173). The following notable differences were found: (a) the effect found for exercise intention between groups was substantially smaller (2 ¼ 0.11); (b) the PMT variables explained much less variance in exercise intention scores (25%); (c) no trend effect in the expected direction was found for exercise behaviour (LIS scores) at T1 and (d) intention was no longer a significant predictor of exercise behaviour. These post hoc findings, together with the findings reported for the ‘‘non-exercise’’ sub sample, allow the following conclusions to be made. Intention to do more exercise can result by framing video information that influences people’s coping appraisal (i.e., RE – documenting the colon cancer protective benefits of engaging in regular exercise). This video information can also improve early exercise behaviour for those not currently engaged in exercise. For this targeted subgroup, exercise intention is the most salient factor influencing exercise behaviour. Overall, our findings support the potential effectiveness of health interventions that focus on self-regulation processes (i.e., coping resource appraisal, goal intentions) in enhancing coping appraisals, intentions and behaviour. Although the present findings are promising, there are a number of limitations in this study that should be acknowledged. First, the failure to successfully manipulate SE, PV and PS is problematic. To adequately test PMT in facilitating exercise through colon cancer prevention, all components of the model need to be manipulated. However, as previously mentioned, this presents a challenge (especially framing vulnerability messages) for health professionals. Second, the absence of a pre-test belief assessment period prevented conclusions to be drawn about actual change in the PMT constructs. Third, the measure of exercise behaviour was exclusively self-report, which depends on an individual’s accurate recall of physical activity. The use of more objective measures of exercise behaviour (e.g., accelerometer, heart rate, monitors) are needed to strengthen conclusions about PMT constructs and exercise behaviour. Fourth, the study would have been strengthened with a larger sample of non-exercisers as it would have increased our power to detect small to medium effects. A larger sample also would have allowed us to examine our data across gender. Lastly, the sample used was teaching and administration staff employed at primary, intermediate and secondary schools, and hence


753

our findings may not be generalized beyond this generally well-educated population. There are a number of fruitful research avenues stemming from the findings of the present study. For instance, self-regulation strategies such as planning or what Gollwitzer (1999) referred to as ‘‘implementation intentions’’ may help bridge the intention–behaviour (i.e., exercise) gap. It has been suggested that intention goals do not trigger behaviour directly, but they likely lead to specific intention plans which in turn trigger behaviour (Taylor, Pham, Rivkin, & Armor, 1998). For example, Sniehaotta, Scholz and Schwarzer (2005) found that a measure of planning mediated the goal intention–exercise behaviour relationship. Furthermore, Milne, Orbell and Sheeran (2002) found that a PMT intervention was only successful in changing exercise behaviour when combined with an implementation intention intervention. This issue deserves further research attention. The adoption of regular exercise requires that individuals’ progress through three critical phases: (a) sufficient motivation and intention to exercise; (b) successful initiation of exercise and (c) successful maintenance of the exercise over time (Estabrooks & Gyurcsik, 2003). The present study was designed to only address motivation, intention and initiation of exercise, hence future intervention work is needed to determine whether cancer prevention can be used as source of exercise adoption and maintenance. From other interventions such as smoking cessation, it is known that the rate of recidivism is high and it is unlikely that a relatively short period of activity would substantially decrease the risk of colon cancer. Empirical based evidence showing that exercise can be increased and maintained is essential before longitudinal prospective studies can be conducted that evaluate the colon cancer protective benefits of exercise and physical activity. A final recommendation is to consider message tailoring that corresponds with an individual’s style of processing health-relevant information (cf Williams-Piehota et al., 2004; Salovey & Williams-Piehota, 2004). The premise here is that matched messages will be more effective in promoting behaviour change (i.e., exercise and physical activity) than mismatched messages. It is likely that the messages used in the present experiment were not matched to all participants’ processing styles.

Acknowledgements The authors would like to acknowledge the valuable contribution of Drs Bryan and Suzan Perry for their expert commentary on (a) the severity and vulnerability of colon cancer and (b) the benefits of exercise in reducing the risk of colon cancer. The authors would also like to acknowledge Ms Jenny Pearce for her expert commentary on diet and its relationship to cancer in general. Finally, the authors would like to thank Neil Morris and Richard Smith from the Education

754 S. P. Graham et al. Media centre for their assistance with filming and editing the intervention material. All participants were provided the results from the study and those in the attention and non-contact control groups were provided the opportunity to view the PMT intervention.

Notes [1]

[2] [3]

A recent systematic review (Bridle et al., 2005) of the effectiveness of health behaviour interventions (including physical activity) based on the transtheoretical model (TTM) showed limited evidence for the utility of stage-based interventions as a basis for behaviour change or facilitating stage progression. The authors do acknowledge, however, that lack of evidence may be due in part to poor model specification, and the inappropriate manner in which interventions have been developed and delivered. Group equivalency between the three treatment conditions on the demographic factors also was found for the ‘‘non-exercise’’ (n ¼ 72) subgroup. For the ‘‘non-exercise’’ subgroup, relations found among the demographic variables, PMT variables, exercise intentions, and exercise behaviour parallel those reported for the entire sample (N ¼ 173).

References American Cancer Society (2003). Cancer facts and figures-2003, www.cancer.org Ministry of Health. Health Statistical Services (2003). Mortality and demographic data 1999. New Zealand: Wellington. Ajzen, I. (1991). The theory of planned behaviour. Organizational Behavior and Human Decision Processes, 50, 179–211. Batty, D. (2000). Does physical activity prevent cancer? British Medical Journal, 321, 1424–1425. Bridle, C., Riemsma, R. P., Pattenden, J., Sowden, A. J., Mather, L.Watt, I. S., et al. (2005). Systematic review of the effectiveness of health behaviour interventions based on the transtheoretical model. Psychology and Health, 20, 283–301. Cancer Prevention Research Consortium (1995). Pathways to health. Kingston, RI: University of Rhode Island. Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. Comrey, A. L. (1988). Factor-analytic methods of scale development in personality and clinical psychology. Journal of Consulting and Clinical Psychology, 56, 754–761. Courneya, K. S., & Hellsten, L. A. (2001). Cancer prevention as a source of exercise motivation: An experimental test using protection motivation theory. Psychology, Health & Medicine, 6, 59–64. Estabrooks, P. A., & Gyurcsik, N. C. (2003). Evaluating the impact of behavioural interventions that target physical activity; issues of generalizability and public health. Psychology of Sport and Exercise, 4, 41–55. Everson, K. R., Stevens, J., Cai, J., Thomas, R., & Thomas, O. (October 2002). The effects of cardiorespiratory fitness and obesity on cancer mortality in men and women. Medicine and Science in Sports and Exercise, 35, 270–277. Floyd, D. L., Prentice-Dunn, S., & Rogers, R. W. (2000). A meta-analysis of research on protection motivation theory. Journal of Applied Social Psychology, 30, 407–429. Godin, G., & Shephard, R. J. (1985). A simple method to assess exercise behavior in the community. Journal of Applied Sport Science, 10, 141–146.


755

Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54, 493–503. Jacobs, D. R., Ainsworth, B.E., Hartman, T. J., & Leon, A. S. (1993). A simultaneous evaluation of ten commonly used physical activity questionnaires. Medicine and Science in Sports and Exercise, 25, 81–91. Lee, I. M. (2003). Physical activity and cancer prevention – Data from epidemiological studies. Medicine and Science in Sports and Exercise, 35, 1823–1827. Marcus, B., Rakowski, W., & Rossi, J. S. (1992). Assessing motivational readiness and decision making for exercise. Health Psychology, 11, 257–261. Marcus, B. H., Bock, B. C., Pinto, B. M., Forsyth, L. H., Roberts, M. B., & Traficante, R. M. (1998a). Efficacy of an individualized, motivationally tailored physical activity intervention. Annals of Behavioral Medicine, 20, 174–180. Marcus, B. H., Emmons, K. M., Simkin-Silverman, L., Linnan, L. A., Taylor, E. R.Bock, B. C., et al. (1998b). Evaluation of motivationally tailored versus self-help physical activity interventions at the workplace. American Journal of Health Promotion, 12, 138–146. McTiernan, A. (2003). Intervention studies in exercise and cancer prevention. Medicine & Science in Sports & Exercise, 35, 1841–1845. Meade, C. D. (1996). Producing videotapes for cancer education: Methods and examples. Oncology Nursing Forum, 23, 837–846. Milne, S., Sheeran, P., & Orbell, S. (2000). Prediction and intervention in health-related behavior: A meta-analytic review of protection motivation theory. Journal of Applied Social Psychology, 30, 106–143. Milne, S., 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. Norman, P., Boer, H., & Seydel, E. R. (2005). Protection motivation theory. In M. Conner & P. Norman (Eds), Predicting health behavior: Research and practice with social cognition models (2nd Edn, pp. 81–126). Buckingham: Open University Press. Prochaska, J., & Marcus, B. H. (1993). The transtheoretical model: Applications to exercise. In R. K. Dishman (Ed.), Advances in exercise adherence (pp. 161–168). Champaign, IL: Human Kinetics. Salovey, P., & Williams Piehota, P. (2004). Field experiments in social psychology: Message framing and the promotion of health protective behaviors. American Behavioral Scientists, 47, 488–505. Slattery, M. L., & Potter, J. D. (2002). Physical activity and colon cancer: Confounding or interaction? Medicine and Science in Sports and Exercise, 34, 913–919. Sniehotta, F. F., Scholz, U., & Schwarzer, R. (2005). Bridging the intention-behaviour gap: Planning, self-efficacy, and action control in the adoption and maintenance of physical activity. Psychology and Health, 20, 143–160. Stevens, J. (1996). Applied multivariate statistics for the social sciences (3rd Edn). Mahway, NJ: Lawrence Erlbaum. Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (2nd Edn). New York: Allyn and Bacon. Taylor, S. E., Pham, L. B., Rivkin, I. D., & Armor, D. A. (1998). Harnessing the imagination: mental stimulation, self-regulation, and coping. American Psychologist, 53, 429–439. Wiebe, D. J., & Korbel, C. (2003). Defensive denial, affect, and the self-regulation of health threats. In L. D. Cameron & H. Leventhal (Eds), The self-regulation of health and illness behaviour (pp. 184–203). New York: Routledge. Williams-Piehota, P., Schneider, T. R., Pizarro, J., Mowad, L., & Salovey, P. (2004). Matching health messages to health locus of control beliefs for promoting mammography utilization. Psychology and Health, 19, 407–423.