Journal of Consulting and Clinical Psychology

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7 Journal of Consulting and Clinical Psychology Copyright 2004 by the American Psychological Association 2004, Vol. 72, No. 3, X/04/$12.00 DOI: / X Stages of Change or Changes of Stage? Predicting Transitions in Transtheoretical Model Stages in Relation to Healthy Food Choice Christopher J. Armitage and Paschal Sheeran University of Sheffield Madelynne A. Arden Sheffield Hallam University Mark Conner University of Leeds Relatively little research has examined factors that account for transitions between transtheoretical model (TTM) stages of change. The present study (N 787) used sociodemographic, TTM, and theory of planned behavior (TPB) variables, as well as theory-driven interventions to predict changes in stage. Longitudinal analyses revealed that sociodemographic, TPB, and 1 of the interventions predicted transitions between most stages of change. In fact, only progression from the preparation stage was not predictable. However, given that this change of stage marks the transition between cognition and actual behavior, the identification of variables that bridge this gap is crucial for the development of interventions to promote stage transitions. Accumulated research findings show a direct link between excessive fat consumption and several leading causes of morbidity and mortality in industrialized nations. Predominant among these conditions are coronary heart disease (e.g., Martin, Hulley, Browner, Kuller, & Wentworth, 1986; Stamler, Wentworth, & Neaton, 1986) and cancer, at a number of sites, including the large bowel, lung, endometrium, and prostate (e.g., Shu et al., 1993; Wynder, Hebert, & Kabat, 1987). In response to these serious threats to health, governments have recommended that people limit their fat intake; the U.K. government, for example, recommends that people should derive no more than 35% of their total calorific intake from fat (Department of Health, 1992). However, recent figures from the National Food Survey Branch of the U.K. Ministry of Agriculture, Fisheries, and Food (2001; as cited in Conner & Armitage, 2002) suggest that, on average, U.K. citizens derive more than 38% of their food energy from fat. The importance of understanding food choice is underlined by evidence showing that a reduction of just one percentage point in the proportion of food energy derived from fat in the diet would result in 10,000 fewer deaths per year in the United States alone (Rose, 1985). There is a clear need to investigate the ways in which food choice behavior may be changed to enhance the effectiveness of interventions. One way of achieving this goal is to use psychological models of Christopher J. Armitage and Paschal Sheeran, Centre for Research in Social Attitudes, Department of Psychology, University of Sheffield, Sheffield, United Kingdom; Mark Conner, School of Psychology, University of Leeds, Leeds, United Kingdom; Madelynne A. Arden, Centre for Research in Human Behaviour, School of Social Science and Law, Sheffield Hallam University, Sheffield, United Kingdom. Correspondence concerning this article should be addressed to Christopher J. Armitage, Centre for Research in Social Attitudes, Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP, United Kingdom. c.j.armitage@sheffield.ac.uk behavior change to design and implement interventions that will be effective in promoting health. Prochaska and DiClemente s (e.g., 1983, 1984) transtheoretical model (TTM) provides a framework for understanding healthrelated behavior change. Central to the TTM is the idea that individuals pass through five stages in the process of changing their health behavior. The first stage, precontemplation, designates individuals who are not thinking about performing the health behavior in question and are not sufficiently aware of the health implications of their actions. The second stage is labeled contemplation, the stage at which persons start to think seriously about changing their behavior but have not yet acted. The third stage is called preparation and is characterized by people preparing themselves and their social world for a change in their behavior. Thus, people in the preparation stage are not actually performing the behavior in question, but they are orienting themselves toward it (e.g., by making an attempt at the target behavior). When individuals successfully and consistently perform the behavior in question, they are regarded as being in the action stage. Progression from the action stage to the maintenance stage occurs when the behavior in question has been performed for 6 months or more. The TTM is a model of health behavior change, because individuals can progress, regress, or remain static with respect to their initial TTM stage. The TTM proposes that decisional balance and self-efficacy are the key predictors of transitions between stages. Derived from work by Janis and Mann (1977), decisional balance concerns the pros and cons associated with performing a particular behavior; self-efficacy (from Bandura s, 1997, social cognitive theory) represents feelings of confidence in one s ability to perform that behavior. The TTM also includes processes of change, which are 10 strategies that people may use in efforts to progress to a subsequent stage of change or to prevent regression from their current stage of change. We refer to decisional balance and self- 1

8 2 ARMITAGE, SHEERAN, CONNER, AND ARDEN F1 efficacy as predictors of change because the influence of the processes of change should be mediated through pros and cons and self-efficacy (see Prochaska, Redding, Harlow, Rossi, & Velicer, 1994; see Rosen, 2000, for a meta-analysis examining the direct relationship between the stages and processes of change). Decisional balance and self-efficacy have been used to test whether TTM stages can be discriminated from one another in several cross-sectional studies. For example, DiClemente et al. (1991) examined changes in decisional balance and self-efficacy across TTM stages with respect to quitting smoking. As predicted, the number of pros associated with smoking decreased in more advanced stages of change, whereas the number of cons increased; self-efficacy with respect to quitting smoking also increased across the stages of change. More recently, studies have tested the ability of self-efficacy and decisional balance to predict stage transitions, or changes of stage. For example, Herzog, Abrams, Emmons, Linnan, and Shadel (1999) compared cross-sectional analyses of stages of change with prospective assessments of stage transitions in the domain of smoking cessation. Consistent with most research in this area, Herzog et al. s cross-sectional analyses supported the TTM: There were linear increases in the cons associated with smoking and comparable decreases in the pros of smoking across stages (cf. DiClemente et al., 1991; Prochaska et al., 1994). In contrast, the longitudinal analyses indicate that... the pros and cons of smoking do not predict progressive movement out of the precontemplation, contemplation, or preparation stages (Herzog et al., 1999, p. 374). Mixed findings concerning the utility of decisional balance and self-efficacy for predicting stage transitions were also obtained in other studies of changes of stage (e.g., de Vries & Mudde, 1998; DiClemente, Prochaska, & Gibertini, 1985; Plotnikoff, Hotz, Birkett, & Courneya, 2001; Prochaska, DiClemente, Velicer, Ginpil, & Norcross, 1985; Velicer, Norman, Fava, & Prochaska, 1999). One possible explanation for this pattern of findings is that the focus on decisional balance and self-efficacy as predictors of change has excluded a range of alternative variables, many of which have proven predictive validity. For example, the behavioral beliefs that are held to underpin attitudes in the theory of planned behavior (TPB; see Ajzen, 1991) are formally equivalent to the pros and cons of performing a particular (health) behavior. Thus, a measure of attitude can be regarded as providing a summary of the processes associated with decisional balance (see Fishbein & Ajzen, 1975). Similarly, Ajzen (1998) argued that perceived behavioral control is synonymous with self-efficacy: I added the construct of self-efficacy or perceived behavioural control to the original theory of reasoned action when the work of Bandura and his associates made it clear that this construct was needed to deal with determinants of human behaviour that are not under complete volitional control. (p. 737) Attitude and perceived behavioral control therefore subsume decisional balance and self-efficacy from the TTM. It is important to note that Ajzen s (1991) TPB (see Figure 1) both mediates the effects of sociodemographic variables on health behavior (e.g., Blaxter, 1990) and incorporates two constructs that are not specified by the TTM, namely, subjective norm and behavioral intention. Subjective norm captures social influences on health decisions and refers to the social pressures experienced by Figure 1. The theory of planned behavior (Ajzen, 1991). The dashed AQ: 3 versus solid line denotes that perceived behavioral control does not always predict behavior. individuals with respect to engaging or not engaging in a particular behavior. Behavioral intentions, on the other hand, are indicators of how hard people are willing to try, of how much effort they are planning to exert, in order to perform the behavior (Ajzen, 1991, p. 181). The TPB has been widely used to predict health behavior, largely because of the substantial body of evidence that supports its predictive validity (e.g., Armitage & Conner, 2001). Moreover, several studies indicate that TPB variables provide very good discrimination between stages of change. For example, Armitage and Arden (2002) found stage-by-stage increases in TPB variables with respect to eating a low-fat diet: Individuals in the maintenance stage had more positive attitudes, perceived greater social pressure, reported more control, and were more likely to intend to eat a low-fat diet than those in the precontemplation stage. This pattern of findings has been replicated a number of times (e.g., Armitage, Povey, & Arden, 2003; Courneya, 1995). Moreover, Courneya, Plotnikoff, Hotz, and Birkett (2001) found that TPB variables were predictive of most transitions between the stages of change for exercise. Although problems with measurement and sampling (see Courneya et al., 2001, pp ) restricted the generalizability of their findings, Courneya et al. concluded that the TPB is a useful framework for predicting... stage transitions (p. 143). The present study therefore aims to extend the findings of Courneya et al. to the domain of healthy food choice. Derivation of the Present Study and Research Questions The literature reviewed above provides the following rationale for the present study. First, although the ability to predict changes of stage is important for the development of theory-based interventions, it is not yet clear what variables predict progressions between (or regressions from) TTM stages. Second, despite the TPB s success in cross-sectional studies, we were able to locate just one study that provided an adequate test of whether TPB variables can predict (longitudinal) changes of stage. Third, only two behaviors smoking and exercise have been studied longitudinally, so tests of other health behaviors are required to enhance generality. The present study tries to answer two questions: (a) To what extent does people s food choice remain static, progress, or regress across TTM stages over an 8-month time period? and (b) Can the TPB predict both stages of change and changes of stage?

9 STAGES OF CHANGE OR CHANGES OF STAGE? 3 Design and Setting Method The Leeds University Fat Assessment Trial was a large-scale study designed to test the effectiveness of two types of low-intensity intervention (belief-based change and tailored feedback) in reducing fat intake. The sample comprised nurses, nursing assistants, and administrative staff working at two hospitals in the U.K. Participants were randomized to a 3 (attitude change, perceived behavioral control change, information only control group) 2 (feedback versus no feedback) intervention design. The interventions were evaluated using measures taken pre- and postintervention. Participants and Procedure One thousand ninety-one hospital workers were sent a letter inviting them to participate in a study about their beliefs and attitudes toward eating a low-fat diet. Accompanying the letter was a brief questionnaire designed to measure key variables and to provide a baseline comparison (see the Measures section). Potential respondents were informed that participation was voluntary, and they were asked to complete the questionnaire and return it (via internal mail) to the Occupational Health Department if they wished to participate in the study. The data provided during this first wave of the study were used to design the interventions. The interventions were distributed to participants (in each of the six conditions) via the internal mail system 3 months postbaseline. Follow-up questionnaires were distributed 5 months postintervention (i.e., 8 months postbaseline). Interventions The interventions were administered using brief (four-page) leaflets that were designed specifically for the study. The information intervention included current U.K. government recommendations concerning fat intake as well as a list of foods that are high in fat. This information was provided to all participants. The belief-based interventions were informed by the work of Fishbein, Ajzen, and Bandura (e.g., Ajzen, 1991; Ajzen & Fishbein, 1980; Bandura, 1997; Fishbein & Ajzen, 1975).Fishbein and Ajzen (1975) argued that attitude change requires an understanding of the way in which attitudes are formed, and they therefore recommended targeting salient underlying beliefs. Thus, the attitude change intervention included a brief message designed to change the beliefs that discriminated individuals who were intending to eat a low-fat diet from those who were not intending to eat a low-fat diet (see Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975). Thus, messages designed to persuade people that eating a low-fat diet would maintain fitness, control weight, and enhance feelings of health were developed (see Armitage & Conner, 1999). Given that Ajzen (1991, 1998) regarded perceived behavioral control and self-efficacy as synonymous, the perceived behavioral control change intervention was based on Bandura (1997). Bandura (1997) argued that there are four ways in which self-efficacy may be enhanced: Personal mastery, persuasion, modeling, and relaxation techniques. In order to match the perceived behavioral control intervention with the attitude change intervention in terms of intensity, we focused on personal mastery and persuasion techniques. Personal mastery can be achieved by setting individuals achievable subtasks, and so we provided participants with low-fat options that would be simple to incorporate within their existing lifestyle, as well as recipes for low-fat meals that were cheap, easy to prepare, and not time consuming. We also incorporated a persuasive message based on the control beliefs that are held to underpin the perceived behavioral control construct (see Ajzen, 1991). This message attempted to persuade participants that eating a low-fat diet was not expensive, that information was freely available, and that it does not necessarily involve a great deal of will-power (see Armitage & Conner, 1999). All participants received a standard letter accompanying their intervention leaflet. The letters of individuals who were randomized to receive tailored feedback contained one additional sentence that informed them of their current dietary fat intake as assessed by a 63-item validated food frequency measure (Margetts, Cade, & Osmond, 1989). Accumulated evidence shows that even such a low-intensity technique can exert significant positive effects on people s behavior (Brug, Campbell, & van Assema, 1999). Measures Measures of age, gender, and socioeconomic status (derived from participants occupations) were taken at baseline. These and the following measures were included in the questionnaires at both baseline and follow-up. TPB. A measure of attitude was taken using a semantic differential scale. Respondents were presented with the stem: My eating a low-fat diet in the future is.... Eight pairs of adjectives were rated, each on a bipolar scale ( 3 to 3; e.g., bad good, unfavorable favorable, harmful beneficial). The mean of the eight items was used as the measure of attitude (Cronbach s s.87 and.90 for baseline and follow-up, respectively). Subjective norm was measured using three items (e.g., People who are important to me think I should not eat a low-fat diet should eat a low-fat diet ). All three items were measured on 7-point unipolar (1 to 7) scales. Cronbach s s were.80 (baseline) and.83 (follow-up). Perceived behavioral control was measured using four items (e.g., I believe I have the ability to eat a low-fat diet in the future definitely do not definitely do ). Cronbach s s indicated good internal reliability ( s.86 and.83 for baseline and follow-up, respectively). Behavioral intention with respect to eating a low-fat diet was assessed using three items, each on a bipolar ( 3 to 3) scale (e.g., I intend to eat a low-fat diet in the future definitely do not definitely do ). The mean of these items produced a scale. Cronbach s for the baseline measure was.78; for the follow-up, it was.84. The psychometric properties of each of these measures have been tested in earlier work (Armitage & Conner, 1999). TTM. The measure of transtheoretical stage was adapted from Courneya (1995; Courneya et al., 2001; see also Armitage & Arden, 2002). Respondents were asked to indicate which of five statements best described their current dietary behavior: I currently do not eat a low-fat diet and I am not thinking about starting (precontemplation); I currently do not eat a low-fat diet but I am thinking about starting (contemplation); I currently eat a low-fat diet but not on a regular basis (preparation); I currently eat a low-fat diet but I have only begun to do so in the last 6 months (action); and I currently eat a low-fat diet and I have done so for longer than 6 months (maintenance). These statements map closely onto accepted definitions of the TTM stages and provide a parsimonious method of classifying individuals into stages. In the present study, the test retest correlation across 8 months for the stages-of-change measure was strong (r.71, p.01). Construct validity was confirmed by using the data from Margetts et al. s (1989) food frequency measure to examine differences in the proportion of calories derived from fat across the stages of change. As expected, fat intake declined across the stages of change at both baseline (precontemplation M 40.33; maintenance M 32.16) and follow-up (precontemplation M 40.79; maintenance M 32.19). Analyses of variance confirmed that these decreases in fat intake were significant, F(4, 786) 44.33, p.01, at baseline, and F(4, 786) 44.33, p.01, at follow-up. As recommended by Sutton (2000), these effects were further tested using polynomial-based contrast analyses (with an adjustment for unequal sample sizes); these analyses tested linear, quadratic, and cubic relationships between the stages of change and fat intake. For the baseline data, there were significant linear, F(1, 786) , p.01; quadratic, F(1, 786) 7.85, p.01; and

10 4 ARMITAGE, SHEERAN, CONNER, AND ARDEN cubic, F(1, 786) 4.56, p.05, relationships between the stages of change and fat intake, although the linear relationship had by far the largest effect size (linear r.42; quadratic r.10; cubic r.08). For the follow-up data, there was a significant linear relationship between the stages of change and fat intake, F(1, 498) 86.65, p.01, but nonsignificant quadratic and cubic relationships, Fs(1, 498) 2.65 and 0.16, ns, respectively. These data confirm the construct validity of the stages-ofchange measure and demonstrate that transitions between stages are related to changes in actual fat intake. Representativeness Check Results Of the 1,091 people who were contacted initially, 787 (72% response rate) returned completed questionnaires. Analyses indicated that nonrespondents were no different to respondents in terms of age, z 1.38, ns, or gender, 2 (1, N 1,091) 3.33, ns. Follow-up measures were taken 5 months postintervention. Of the 787 individuals who responded at baseline, we successfully contacted 499 at follow-up (63% response rate). Analysis of people who responded at follow-up versus those who did not revealed significant differences on some of the measures: Stage of change, F(1, 735) 17.16, p.05; perceived behavioral control, F(1, 735) 6.04, p.05; and age, F(1, 735) 4.28, p.05. Those who responded were in more advanced stages of change, reported greater perceived behavioral control, and were older than those who did not respond. We return to the significance of these findings in the Discussion section. Randomization Check Multivariate analysis of variance (MANOVA) indicated that randomization of participants to the intervention conditions was successful: There were no significant differences between any of the six conditions on any of the measured variables at baseline, F(18, 1448) 0.85, ns. Univariate tests were all nonsignificant. Table 1 Cross-Sectional Analysis of Stage-of-Change Data: Baseline (N 787) TPB variables Precontemplation (n 81) Contemplation (n 117) Cross-Sectional Analyses: Predicting Stages of Change Preliminary analysis of the data indicated that participants were spread across the stages at both time points (see Tables 1 and 2). At baseline, 10% were in precontemplation, 15% were in contemplation, 40% were in preparation, 10% were in the action stage, and 25% reported that they were in the maintenance stage (see Table 1). At follow-up, 9% were in precontemplation, 8% were in contemplation, 42% were in preparation, 9% were in the action stage, and 33% reported that they were in the maintenance stage (see Table 2). Tables 1 and 2 also presents the means and standard deviations for TPB variables across each of the stages for baseline (Table 1) and follow-up (Table 2). These data were analyzed using discriminant function analysis. Discriminant function analysis is mathematically identical to MANOVA but emphasizes the prediction of group membership on the basis of a range of predictors (in this case, predicting TTM stage from TPB and demographic variables) rather than according to whether group membership produces reliable differences on a combination of DVs [dependent variables] (Tabachnick & Fidell, 1989, p. 505). Thus, the baseline and follow-up data were analyzed using separate discriminant function analyses, with stage of change as the grouping variable and demographic (age, gender, socioeconomic status) and TPB variables as the independent variables. It was necessary to dummy code the TPB-based interventions because intervention constitutes a categorical variable with three levels (information, attitude change, perceived behavioral control change) and, therefore, cannot be used as an independent variable in discriminant function analysis. The three new variables were information-only intervention (0 did not receive,1 did receive), attitude change intervention (0 did not receive, 1 did receive) and perceived behavioral control change intervention (0 did not receive, 1 did receive). These dummy-coded intervention variables (as well as the presence or absence of personalized feedback, coded 1 and 0, respectively) were included in all subsequent multivariate analyses. Preparation (n 311) Action (n 79) Maintenance (n 199) M SD N M SD N M SD N M SD N M SD N Attitude 0.57 a b b c c 0.74 Subjective norm 4.07 a b,d b c c,d 1.24 Perceived behavioral 4.05 a b c d e 0.83 Control (PBC) Behavioral intention 0.37 a b b c c 0.94 Age Women Men Socioeconomic status Attitude change condition PBC change condition Control condition Received feedback Did not receive feedback T1-2 Note. Values with different subscripts within a row indicate significant ( p.05) differences between stages, on the basis of Newman Keuls post hoc tests. TPB theory of planned behavior; PBC perceived behavioral control.

11 STAGES OF CHANGE OR CHANGES OF STAGE? 5 Table 2 Cross-Sectional Analysis of Stage-of-Change Data: Follow-Up (N 499) Precontemplation (n 42) Contemplation (n 37) Preparation (n 210) Action (n 44) Maintenance (n 166) TPB variables M SD N M SD N M SD N M SD N M SD N AQ: T1 Attitude 0.77 a b c a e 0.66 Subjective norm 4.09 a a b,c c 1.19 Perceived behavioral 4.38 a b c Control (PBC) Behavioral intention 0.67 a b b c c 0.99 Age Women Men Socioeconomic status Attitude change condition PBC change condition Control condition Received feedback Did not receive feedback Note. All multivariate and univariate Fs associated with TPB variables are significant ( p.01) at both baseline (dfs 16, 2331) and follow-up (dfs 16, 1485); Fs range from to 95.54). Values with different subscripts within a row indicate significant ( p.05) differences between stages, on the basis of Newman Keuls post hoc tests. TPB theory of planned behavior; PBC perceived behavioral control. Analysis of the interventions produced few significant effects in the sample as a whole. In spite of this, it was deemed important to control for any potential influence of the interventions on transitions between the stages of change. It is also worth noting that two-way interactions between TPB and feedback interventions were tested in each of the analyses reported below. However, because these interactions were all nonsignificant, findings are not presented to aid clarity of presentation. Baseline. At baseline, the analyses revealed three significant discriminant functions, with a combined 2 (12, N 755) , p.01, that accounted for 91.39%, 7.54%, and 1.07% of the explained variance, respectively, and the first function had the largest group centroid compared with the other functions for four out of the five stages of change. Behavioral intention (r.92), perceived behavioral control (r.79), attitude (r.62), and subjective norm (r.32) were all significantly correlated with the first function. Perceived behavioral control and gender were most closely related to the second derived function (rs.61 and.39, respectively), and behavioral intention (r.32) and gender (r.89) were most strongly related to the third derived function. The predictors correctly classified 55.66% of the participants, which is 27.29% better than would be anticipated by chance alone (see Tabachnick & Fidell, 1989, p. 544). Discriminant function analysis also allows the researcher to test for significant differences between each of the groups across all of the predictor variables using pairwise Fs. In terms of the present study, the pairwise Fs take each stage in turn and contrast it with subsequent stages (e.g., contemplation vs. preparation, action, and maintenance). Thus, precontemplation was discriminated from contemplation, preparation, action, and maintenance, Fs(3, 730) , ps.01; contemplation was distinct from preparation, action, and maintenance, Fs(3, 730) , ps.01; preparation was distinguished from action and maintenance, Fs(3, 730) and 51.65, ps.01, respectively; and action was discriminated from maintenance, F(3, 730) 2.97, p.01. Findings indicate that participants in later stages of change generally scored higher on the measures of attitude, subjective norm, perceived behavioral control, and behavioral intention compared with participants in earlier stages of change. However, the discriminant function analysis indicates that behavioral intention and perceived behavioral control on their own provide the best combination of variables for distinguishing between stages of change. Follow-up. Discriminant function analysis of the follow-up data included the dummy-coded intervention variables in addition to the demographic and TPB variables. Two significant functions were derived, with a combined 2 (8, N 485) , p.01. The first derived function accounted for 95.78% of the explained variance, and the second derived function accounted for 4.22% of the explained variance, 2 (3, N 485) 13.84, p.01. Behavioral intention (r.99), attitude (r.58), subjective norm (r.37), and perceived behavioral control (r.69) were significantly correlated with the first derived function; perceived behavioral control (r.73) was most strongly correlated with the second derived function. The predictors correctly classified 54.31% of participants, which is 23.67% better than would be expected by chance alone. Consistent with the analysis of the baseline data, there were significant differences in pairwise Fs between most of the stages. More specifically, precontemplation was discriminated from contemplation, preparation, action, and maintenance, Fs(2, 479) , ps.01; contemplation was distinct from action and maintenance, Fs(2, 479) and 29.80, ps.01, respectively; preparation was also distinguished from action and maintenance, Fs(2, 479) and 46.79, ps.01, respectively. In contrast to the baseline analysis, the contemplation and preparation stages were not discriminated, F(2, 479) 2.59, p.10, neither were the action and maintenance stages, F(2, 479) 2.05, p.13. However, these latter findings only narrowly missed conventional statistical significance, and probably reflect the loss in power associated with the smaller sample size at follow-up.

12 6 ARMITAGE, SHEERAN, CONNER, AND ARDEN T3 AQ: 1 T4 Longitudinal Analyses: Predicting Changes of Stage In order to assess stage transitions, the baseline stage of change measure was subtracted from the follow-up stage measure. Positive values indicated progression (i.e., those that moved to a later stage), zero indicated no movement ( static ), and negative values signified regression (i.e., participants who moved to an earlier stage). Table 3 presents the frequency counts for individuals divided into the stages of change and the three change of stage categories (i.e., regress, static, progress). Overall, 61.52% of individuals remained in the same stage at both time points, 24.25% progressed and 14.23% regressed. However, these figures hide considerable variability between different stages. Chi-square tests comparing static versus regress and static versus progress confirmed this view, 2 (3, N 378) 75.30, p.05, and 2 (3, N 428) 50.56, p.05, respectively. Closer examination of Table 3 reveals that, in general, lower proportions of people regressed than either remained static or progressed. One exception was the action stage: 41.07% regressed, whereas 19.64% remained static and 39.29% progressed, suggesting that this stage is relatively volatile. Contemplators were particularly likely to progress from their stage: 66.67% of them did so. In contrast, more individuals remained static than progressed in both the precontemplation and preparation stages. We focused on predicting adjacent changes of stage: For each stage of change, we contrasted remaining in the same stage (static) with regression from that stage, and static versus progression from that stage using two methods. First, we conducted discriminant function analyses for transitions relating to each of the five stages of change and computed pairwise Fs to determine whether regression and progression could be distinguished from static using TPB, demographic, and (dummy coded) intervention variables as predictors. Second, we conducted planned comparisons (MANOVAs and Newman Keuls post hoc tests) on the variables that were most strongly associated with the discriminant functions (according to F to remove analyses, see Tabachnick & Fidell, 1989) in order to determine the best predictors of stage transitions. For each change of stage, the predictors correctly classified participants in excess of 10% better than would be expected by chance (range 10.76% 17.48% improvement in classification). The pairwise F analyses in Table 4 show that regression from the preparation, action, and maintenance stages were predicted from the demographic, TPB, and intervention variables (all ps.05). Progression from the precontemplation, contemplation, and action stages was also predicted (all ps.05). The findings indicate that of the seven possible stage transitions examined here, only progression from the preparation stage was not reliably predicted, F(1, 186) 3.45, ns. Planned comparisons presented in Table 5 confirm the results of the pairwise F analyses: Progression from the preparation stage was the only change of stage that was not reliably predicted. Looking at the predictors of transitions for each stage in turn indicates that having been the recipient of the attitude change intervention was the most important predictor of progress from the precontemplation stage. Analysis of the contemplation stage revealed that older participants and participants with greater perceived behavioral control were likely to progress. Behavioral intention differed significantly between individuals who regressed, remained static, and progressed from the preparation stage ( p.05). Newman Keuls post hoc tests revealed that behavioral intention was predictive of regression, but not progression, from the preparation stage (Ms 0.89, 1.44, and 1.80 for regress, static, and progress, respectively). Age and perceived behavioral control were associated with transitions in relation to the action stage: Younger individuals were more likely to remain static (M 30.18) than regress (M 42.56) or progress (M 40.91) from this stage whereas greater perceived behavioral control was predictive of progression but not regression from the action stage. Finally, regression from the maintenance stage was predicted by weak behavioral intentions to eat a low-fat diet. In summary, the findings show that the attitude change intervention (but not the other interventions), behavioral intentions, and perceived behavioral control from the TPB, and age reliably predicted regression from all stages and predicted progression from Table 3 Number of Participants Regressing, Remaining Static, or Progressing From Each Stage Stage Table 4 Pairwise Fs Testing Differences Between Static and Regress Progress Stage of change Regress Static Progress Total N % N % N % N % dfs Regress vs. static Static vs. progress Precontemplation (1, 40) 5.84* Contemplation a (1, 51) 10.08** Preparation (1,186) 4.20* 3.45 Action (2, 49) 5.07** 8.40** Maintenance (1,136) 5.84* a This category was removed from the Regress vs. static analyses due to the small cell size (n 4). * p.05. ** p.01. AQ: T2 AQ: T3 T5 Precontemplation Contemplation a Preparation Action Maintenance Total a This category was removed from subsequent Regress analyses due to the small cell size.

13 STAGES OF CHANGE OR CHANGES OF STAGE? 7 Table 5 Means and F Ratios for Variables That Predict Stage Transitions AQ: T4-T5 Predictor of stage transitions dfs Regress Static Progress Univariate Fs AQ: t6 Precontemplation stage (1, 45) ACI * Contemplation stage (1, 54) Age ** Perceived behavioral control * Preparation stage (2,191) Behavioral intention 0.89 a 1.44 b 1.80 b 4.66* Action stage (2, 53) Age a b a 5.37** Perceived behavioral control 5.70 a 5.43 a 6.41 b 5.71** Maintenance stage (1,142) Behavioral intention * Note. Variables were derived from analysis of Wilk s lambda coefficients. Values with different subscripts within a row indicate significant ( p.05) differences between groups, on the basis of Newman Keuls post hoc tests. ACI the attitude change intervention (dummy coded: 0 did not receive; 1 did receive). * p.05. ** p.01. the majority of the stages. The one exception to this pattern of findings was the failure to predict progression from the preparation stage. Discussion The present findings corroborate a considerable body of research that has examined differences in social cognitive variables across the stages of change (e.g., DiClemente et al., 1991; Herzog et al., 1999). Consistent with previous studies, the values of TPB variables increased in a linear fashion in successive stages of change (cf. Armitage & Arden, 2002; Courneya, 1995). Thus, participants had more positive evaluations of eating a low-fat diet, perceived greater social pressure to do so, were more confident in their dieting ability, and had stronger intentions in later, compared with earlier, stages of change. However, because cross-sectional analyses can provide only limited support for the TTM, longitudinal analyses were used to examine transitions between stages. Consistent with Courneya et al. (2001), the present study demonstrated that transitions between the stages of change could be predicted. More specifically, the attitude change intervention successfully advanced individuals from the precontemplation stage, and age and perceived behavioral control predicted progression from both the contemplation and action stages. Behavioral intention predicted regression from the preparation and maintenance stages, and age predicted regression from the action stage. Thus, two variables derived from the TPB, namely, behavioral intention and perceived behavioral control, were predictive of a number of stage transitions. Behavioral intention predicted regression from the preparation and maintenance stages, suggesting that interventions designed to bolster existing intentions might be effective in preventing regression from these stages of change. Perceived behavioral control was predictive of progression from the contemplation and action stages: Enhancing perceived behavioral control might therefore increase the likelihood that individuals would progress from these stages (cf. Bandura, 1997). Contrary to predictions, age was also a predictor of progression from the contemplation stage and of regression and progression from the action stage; in both cases, younger people remained static. This finding was not anticipated because the TPB is regarded as a complete model of social behavior, meaning that the effects of age on stage transitions should be mediated through TPB variables (cf. Ajzen, 1991). The implication is that the TPB may not be a sufficient model of dietary change and that additional variables are needed to explain the relationship between age and change of stage. It is particularly curious that age was positively associated with both progression and regression from the action stage. One explanation of this finding is that older people ( 30 years old) are more likely to change their diets, especially in response to medical information (see Haslam et al., 2000). Possibly, therefore, a measure of the number of previous failed attempts to change diet could have mediated the effects of age on transitions relating to the action stage. Future research should examine this possibility. Although TPB variables were generally effective at predicting both regression and progression, the model did not account for all stage transitions, and so further work is required to identify which variables are predictive. Of particular note is the fact that progression from the preparation stage was not predicted. Progression from the preparation stage is crucial because it involves the transition to actual performance of the behavior unlike most other stage transitions which involve changes in mental readiness for action. Individuals in the preparation stage are characterized by vague attempts to perform the behavior in question (e.g., purchasing foods that are lower in fat), in contrast with actors and maintainers, who have been performing the behavior for some time. Thus, the transition from preparation to action maintenance marks the translation of cognition into action. The implication is that the TPB cannot fully account for this gap between cognition ( preparation ) and action. In fact, several commentators have identified the failure to address processes that might explain the intention behavior gap as a significant limitation of the TPB (e.g., Sheeran, 2002). For example, Gollwitzer (1993) argued that implementation intentions are important in ensuring that cognitions are translated into action. Implementation intentions are plans that specify the conditions under which target behaviors will be performed and help to ensure that the decision is acted upon. Thus,

14 8 ARMITAGE, SHEERAN, CONNER, AND ARDEN asking individuals in the preparation stage to form an implementation intention of the form I intend to perform behavior x in situation y could be effective in increasing the likelihood of progression to the action stage. In fact, Armitage (2004) recently demonstrated that an implementation intention that specified that people should plan to eat a low-fat diet paying particular attention to the situations in which they would implement those plans enabled participants to eat more healthily even after attitude, perceived behavioral control, and behavioral intention had been taken into account. There were a number of limitations of the present study that should be addressed. First, the theory-driven interventions exerted only limited impact on changes of stage. This may have been due to the fact that the interventions were relatively low intensity: For example, because the persuasive communications were distributed through the internal mail system, we had no control over the extent to which participants attended to, or elaborated, the arguments contained therein, which may have undermined their impact (cf. Petty & Cacioppo, 1986). Further work is required to examine the effective implementation of theory-driven interventions. That said, it is worthwhile noting that progression from the precontemplation stage was predicted by the attitude change intervention. This change of stage is important because participants in the precontemplation stage consumed at least 2% more fat (as a proportion of total energy intake) than participants in any other stage of change. A second limitation of the present study is that the predictive validity of the processes of change was not examined. Previous research has demonstrated consistent and robust relations among the processes and the stages of change (Rosen, 2000) and the inclusion of the processes in the present study may have substantially increased the predictive power, particularly in predicting progression from the preparation stage. A third limitation was that the sample comprised hospital employees, who may be more receptive to health information compared with other samples. However, this consideration does not seriously undermine the validity of our findings because (a) participants represented a range of occupations and (b) more than half of the sample were not in the action or maintenance stage at either time point, despite their potential receptiveness to health information. Similarly, although the level of attrition between baseline and follow-up poses another potential threat to the generalizability of the study, the attrition rate was similar to that reported in comparable studies (e.g., Courneya et al., 2001; Herzog et al., 1999; Velicer et al., 1999). Moreover, attrition in the present study is understandable, given that nonrespondents were more likely to be precontemplators and to have lower perceived behavioral control than respondents. In sum, although we do not believe that there are serious grounds for suggesting that the present sample is unrepresentative, caution is probably warranted before making generalizations to other samples. In conclusion, the present study provides the first test of TTM stage transitions in the domain of health-related food choice. The distribution of participants across stages at each time point and the extent of participants movement between stages over time were comparable to that found in previous research. Also consistent with previous research was the finding that TPB variables discriminated between the stages of change (at both time points). However, a unique feature of the present study was that we identified variables that were predictive of transitions between the stages of change, for the first time, variables that predict changes of state were determined. Participants who received an attitude change intervention progressed from the precontemplation stage; age and perceived behavioral control predicted progression from the contemplation stage; behavioral intention predicted regression from the preparation stage; age predicted action stage transitions, and perceived behavioral control predicted progression from the action stage; and behavioral intention predicted regression from the maintenance stage. The present research also identified a serious limitation of the TPB (and, by extension, of decisional balance and self-efficacy), namely, that it was not possible to predict progression from preparation to action. Future studies should investigate the utility of Gollwitzer s (1993) concept of implementation intentions in helping people make this crucial transition from cognition to action. References Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, Ajzen, I. (1998). Models of human social behavior and their application to health psychology. Psychology and Health, 13, Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall. Armitage, C. J. (2004). Evidence that implementation intentions reduce dietary fat intake: A randomized trial. Health Psychology, 23, XXX XXX. Armitage, C. J., & Arden, M. A. (2002). 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