Stages of Change and Physical Activity Among Individuals With SevereMental Illness

Jill L. BezyakUniversity of Northern Colorado

Norman L. Berven and Fong ChanUniversity of Wisconsin–Madison

Objective: To apply the constructs of the transtheoretical model (TTM) of change to understandphysical activity among individuals with severe mental illness. Method: Predictions of stages ofchange of physical activity and self-reported physical activity were investigated among 92 adultswith severe mental illness, using cognitive and behavioral processes of change, self-efficacy, andperceived pros and cons of exercise as predictors. Results: Separate logistic regression analyses foradjacent pairs of stages indicated that 33% of the variance was accounted for when predictingpreparation versus action/maintenance stages, with behavioral processes making a significant uniquecontribution to prediction. A multiple-regression analysis was conducted to examine prediction ofself-reported physical activity on the basis of all of the TTM measures, and the full model accountedfor nearly 27% of the variance. Conclusion: Results suggest that TTM constructs hold promise inunderstanding physical activity of people with severe mental illness with significant implications forclinical practice and future research.

Keywords: physical activity, transtheoretical model of change, mental illness, stages of change

With over 49 million individuals in the United States with achronic disabling condition, it has become evident that healthpromotion is a crucial part of the rehabilitation process(Ravesloot, Seekins, & White, 2005; Waldrop & Stern, 2003).Barriers to health promotion include problems tailoring inter-ventions to the needs of people with disabilities, along withunclear practical guidelines for promoting these techniques(Harrison, 2006). The transtheoretical model (TTM) of changemay provide a foundation for identifying more effective inter-vention techniques, and research provides evidence to supportthe usefulness of the TTM in understanding physical activity(Gorczynski, Faulkner, Greening, & Cohn, 2010; Nigg et al.,2005).

Among people with disabilities, individuals with severe mentalillness have drawn increasing attention in the health promotionliterature. Severe mental illness is defined by an Axis I diagnosisaccording to the Diagnostic and Statistical Manual of MentalDisorders (4th ed., [DSM–IV]; American Psychiatric Association,1994), significant role impairments as a result of this diagnosis,and persistence of the disorder (Bond & Resnick, 2000). Approx-

imately 7.2% of adults, ages 18 and older, have a serious mentalillness during any 12-month period (Kessler et al., 2004), andindividuals with mental illness are at greater risk for a variety ofhealth problems and premature death when compared to the gen-eral population (Pelletier, Nguyen, Bradley, Johnsen, & McKay,2005; Sebastian & Beer, 2007). To be more specific, they havehigher rates of mortality from cardiovascular disease, cancer, andrespiratory disease, which are often influenced by high rates ofsmoking, poor nutrition, and deficiencies in medical care (Wallace& Tennant, 1998). Additional factors influencing mortality ratesinclude limited social support networks, limited housing options,and poverty (Byrne et al., 1999). It has been estimated that 50% ofindividuals with severe mental illness have at least one coexistingmedical problem, and 35% have medical problems that are undi-agnosed or untreated (Felker, Yazel, & Short, 1996). Individualswith severe mental illness are clearly at increased risk for healthproblems and, as a result, require increased attention to healthpromotion behavior.

Physical activity represents one important area of health pro-motion behavior. According to Farnam, Zipple, Tyrrell, and Chit-tinanda (1999), 60% of individuals with severe mental illnessengage in low levels of physical activity and are significantly lessactive than the general population (Daumit et al., 2005), and areview by Richardson et al. (2005) documented the empiricalevidence regarding this decreased level of activity. In a group of234 people with severe mental illness, only 12% of the individualssurveyed reported vigorous exercise in the previous week, com-pared to 35% of people in the general population (Davidson et al.,2001). However, it is encouraging that Faulkner, Taylor, Munro,Selby, and Gee (2007) found that a majority of their sample ofindividuals with severe mental illness indicated interest in becom-ing more physically active, suggesting some degree of readiness tochange.

This article was published Online First June 27, 2011.Jill L. Bezyak, Human Rehabilitative Services Program, University of

Northern Colorado; Norman L. Berven and Fong Chan, Department ofRehabilitation Psychology and Special Education, University of Wiscon-sin–Madison.

We thank Christine Ahrens, Jana Frey, and Suzanne Senn-Burke, alongwith the other staff members of the Program of Assertive CommunityTreatment in Madison, Wisconsin, for their assistance with this article.

Correspondence concerning this article should be addressed to Jill L.Bezyak, PhD, CRC, Human Rehabilitative Services Program, CampusBox 132, University of Northern Colorado, Greeley, CO 80639. E-mail:jill.bezyak@unco.edu

Rehabilitation Psychology2011, Vol. 56, No. 3, 182–190

© 2011 American Psychological Association0090-5550/11/$12.00 DOI: 10.1037/a0024207

182

TTM of Change and Physical Activity

An examination of the factors that may predict physical activitybehavior among people with mental illness may provide importantinformation for the design of interventions to increase physicalactivity. One theoretical framework that has proven useful inidentifying predictive factors and structuring physical activity in-terventions is the TTM, which is primarily based on stages ofchange (Prochaska & DiClemente, 1983).

The stages of change are composed of five different stages,which represent different levels of motivation to change behavior(Prochaska, DiClemente, & Norcross, 1992). The first stage isprecontemplation, characterized by no intention to change behav-ior at present or in the foreseeable future. Contemplation is thesecond stage, in which there is awareness that a problem exists,together with thoughts about overcoming it, but with no commit-ment to action. The preparation stage combines intention andaction, in which there may be some attempts to change, with orwithout success. The fourth stage is action, in which major changeoccurs. Maintenance follows action and is the final stage, charac-terized by work to prevent relapse, to secure gains achieved in theaction stage, and to continue behavior change for at least 6 months.An additional important concept to the stages of change model, butnot a specific stage, is relapse, which is characterized by recyclingthrough certain stages as individuals attempt to modify behaviorsthroughout their lifetimes.

The TTM also incorporates three other important and relatedconcepts to provide an accurate description of behavior change.First, the processes of change are an integral part of the TTM,providing an explanation of the ways in which movement occursfrom one stage to the next (Prochaska et al., 1992). Ten processesof change have received the most empirical support, separated intofive cognitive processes of change (consciousness raising, dra-matic relief, environmental reevaluation, self-reevaluation, andsocial liberation) and five behavioral processes (self-liberation,helping relationships, counterconditioning, reinforcement manage-ment, and stimulus control). Second, the TTM incorporates self-efficacy as an important predictor of change. Bandura (1997)described self-efficacy as judgment regarding one’s ability to per-form a behavior that is intended to achieve a certain outcome. Mostresearch has suggested that self-efficacy is higher among individ-uals in the later stages of change, as compared to the earlier stages(Cardoso et al., 2009; Tung, Gillett, & Pattillo, 2005). Third,decisional balance is incorporated into the TTM, involving eval-uation of perceived pros (benefits) and cons (barriers) to engage ina behavior (Janis & Mann, 1977). Evidence suggests that people inearlier stages perceive greater barriers to change, whereas thosein later stages perceive greater benefits of change (Prochaska,Redding, & Evers, 2002).

Research has indicated that the processes of change (i.e., cog-nitive and behavioral processes), self-efficacy, and decisional bal-ance predicted the stages of change of physical activity. Forexample, Callaghan, Eves, Norman, Chang, and Lung (2002)found that individuals in the later stages of change placed a largeremphasis on the benefits of exercise and made greater use of thebehavioral processes of change; in addition, self-efficacy wasfound to increase across stages, but no differences were found inthe use of cognitive processes of change or in the emphasis placed

on the barriers to exercise. A similar study with a sample ofgovernment employees, investigating decisional balance and self-efficacy scores across the stages of change of exercise behavior,yielded higher ratings of the benefits of exercise (i.e., pros) andself-efficacy in action and maintenance stages; in addition, ratingsof negative aspects of exercise (i.e., cons) were significantly lowerin these later stages (Herrick, Stone, & Mettler, 1997). Evidencefrom these studies suggests that the TTM may be a useful tool inunderstanding physical activity.

Additional research has suggested that physical activity inter-ventions were not often based on any theoretical framework. Theapplication of theoretically based interventions may significantlyimpact the effort to increase physical activity across populations(Kahn et al., 2002). A recent study examined the usefulness of theTTM to structure physical activity programs for individuals withsevere mental illness. Results indicate self-efficacy and perceivedbenefits of physical activity significantly increased as individualsprogressed through the stages. Perceived barriers to physical ac-tivity also decreased through stage progression (Gorczynski et al.,2010).

Research evidence also provided support to the ability of theTTM constructs to predict stages of change of physical activity,while also predicting actual physical activity behavior. Kosma,Cardinal, and McCubbin (2004) investigated the ability of pro-cesses of change (cognitive and behavioral processes), self-effi-cacy, and decisional balance to differentiate stages of change ofphysical activity among a sample of individuals with physicaldisabilities. A discriminant analysis revealed that behavioral pro-cesses, cognitive processes, self-efficacy, and decisional balanceprovided discrimination between precontemplation and the otherfour stages. The cognitive processes of change were found todistinguish those in the contemplation stage from those in prepa-ration, action, and maintenance stages. Kosma, Gardner, Cardinal,Bauer, and McCubbin (2006) followed up this investigation with acloser look at the ability of the TTM constructs to predict stages ofchange, using hierarchical discriminant function analysis to deter-mine the most important stage-of-change predictors. Behavioralprocesses and cognitive processes were found to be the mostimportant predictors, and these processes differentiated the pre-contemplation stage from preparation and precontemplation/con-templation from action/maintenance. Hierarchical multiple regres-sion indicated that the most important predictors of actual physicalactivity in descending order were behavioral processes of changeand cognitive processes of change/self-efficacy. In summary, TTMconstructs appear to predict actual physical activity and stages ofchange of physical activity among individuals with disabilities.

Purpose

As a result of preliminary empirical evidence supporting thepredictive ability of the TTM constructs and the usefulness of thistheoretical model when developing physical activity interventionsfor individuals with severe mental illness (Gorczynski et al., 2010;Kosma et al., 2004, 2006), the TTM served as the theoreticalfoundation for the current study. The specific purpose of the studywas to investigate the ability of TTM constructs to predict stagesof change of physical activity and actual physical activity behaviorwith a sample of individuals with severe mental illness. To bemore specific, the following research questions were addressed:

183SPECIAL SECTION: PHYSICAL ACTIVITY AND MENTAL ILLNESS

RQ1: Do processes of change (behavioral and cognitive),self-efficacy, and decisional balance (perceived pros andcons) predict stages of change of physical activity behavior?

RQ2: Do processes of change (behavioral and cognitive),self-efficacy, and decisional balance (perceived pros andcons) predict physical activity behavior?

Method

Participants

The sample for the current study included 92 individuals withmental illness, all participating in an assertive community treat-ment (ACT) program in a Midwestern city, offering comprehen-sive, long-term support and outpatient treatment to individualswith severe and persistent mental illness. The sample was com-prised of all program clients who gave their consent to participateand had information available in the agency client database formeasures used in the study. The sample consisted of 71 (74.7%)men and 24 (25.3%) women with ages ranging from 18 to 70years. Primary psychiatric diagnoses were schizophrenia (55.8%),schizoaffective disorder (33.7%), bipolar disorder (6.3%), othermood disorders (3.2%), and other psychotic disorders (1.1%).Available data also included information regarding physicalhealth, specifically body mass index (BMI) and weight. Currentweights ranged from 128 to 449 pounds, with BMI estimatesranging from 19 to 54, and 35 (37.2%) individuals met the widelyused definition of obesity, with a BMI greater than 30. Participa-tion in the study was not dependent on any additional screeningcriteria.

Procedure

The ACT program used the measures incorporated into thisstudy as a part of their biannual assessment protocol for all clients,as physical activity and wellness had been established as treatmentpriorities, and case file information was maintained in a computerdatabase. The most recent biannual assessment information wasused in this study, and a data file was made available to theresearchers by the ACT program, which was de-identified accord-ing to criteria specified in the Health Insurance Portability andAccountability Act (HIPAA) of 1996. Informed consent of partic-ipants was sought by the researchers to obtain the de-identifieddata. A follow-up assessment was conducted approximately one totwo weeks after the initial data collection for 17 participants toobtain test–retest reliability estimates for each of the measuresused.

Instrumentation

Stage of Exercise Behavior Change (SEBC). A scale de-veloped by Marcus, Selby, Niaura, and Rossi (1992) was used toassess stages of change of exercise behavior. The scale is amodified version of a similar measure developed for smokingcessation (Prochaska & DiClemente, 1983) and was constructed tospecifically describe exercise behavior. The scale has one itemwith five response choices corresponding to each of the five stages

of change: precontemplation (I currently do not exercise and I donot intend to start exercising in the next six months); contempla-tion (I currently do not exercise, but I am thinking about startingto exercise in the next six months); preparation (I currently exer-cise some but not regularly); action (I currently exercise regularly,but I have only begun doing so in the last six months); maintenance(I currently exercise regularly and have done so for longer than sixmonths). Respondents select the option that best describes theircurrent exercise behavior, and that category is then identified astheir stage of change. Marcus and Simkin (1993) reported supportfor the validity of the SEBC by showing significant differencesbetween individuals in different stages in total minutes of vigorousand moderate activity, with physical activity higher at the prepa-ration, action, and maintenance stages than at the precontemplationand contemplation stages. Additional evidence provided by Mar-cus et al. (1992) reported a test–retest reliability estimate over a2-week period, using a � coefficient of .78. The test–retest estimatein the current study, also using a � coefficient, was .25, indicatinglimited consistency in self-report of stage of change from the firstto the second occasion; however, 13 of the 17 participants in thetest–retest reliability subsample reported the stage of change onboth occasions at either the same or immediately adjacent stage-of-change category.

PASIPD. The Physical Activity Scale for Individuals withPhysical Disabilities (PASIPD) was developed by Washburn, Zhu,McAuley, Frogley, and Figoni (2002) and is a modification of thePhysical Activity Scale for the Elderly developed by Rockhill et al.(1999). Slight modifications were made for administration in thecurrent study to direct items to individuals with mental illnessrather than people with physical disabilities. The scale contains 13items, with only the last 12 scored, focusing on leisure, household,and occupational physical activities. By measuring additionaltypes of physical activity (i.e., household and occupational activ-ities), this instrument captures the full extent of an individual’sphysical activity behavior, which is often not provided in tradi-tional measures of physical activity. Respondents are initiallyasked to indicate the frequency of each activity (e.g., “During thepast seven days, how many days did you walk outside your homeother than specifically for exercise?, e.g., going to work, walkingthe dog, shopping, etc.”), with four response choices from never to5–7 days. Unless respondents indicated never, they were thenasked to indicate the duration of their engagement in the activity(e.g., “On average how many hours per day did you spend walkingoutside your home?”), with four response choices from less thanone hour to more than 4 hours. Scores are computed by multiply-ing the average hours per day of an activity by a MET value, whichis also referred to as a metabolic equivalent. The MET valuerepresents the intensity and energy expenditure of physical activ-ities, which are comparable for individuals of different ages andweights. The scores are summed across all items, and the maxi-mum possible score for the instrument is 199.5 MET hours per day(hr/day). As discussed by Bollen and Lennox (1991), the physicalactivity items would be considered “causal” rather than “latent”indicators of the MET hours per day scores. Thus, test–retestestimates would provide more appropriate indicators of reliabilitythan internal consistency estimates. In previous research, a test–retest estimate over a 1-week interval of .77 was reported by vander Ploeg et al. (2007), and Kosma et al. (2006) found thatparticipants who rated themselves as physically active and in

184 BEZYAK, BERVEN, AND CHAN

excellent health had higher scores on the PASIPD. In the presentstudy, a test–retest reliability estimate of .65 was found.

Processes of Exercise Behavior Change. The Processes ofExercise Behavior Change scale, developed by Nigg, Norman,Rossi, and Benisovich (1999), was used to measure both cognitiveand behavioral processes of change. The scale is comprised of 28items and generates two scores, with 14 items measuring cognitiveprocesses of change (e.g., “I think that regular exercise plays a rolein reducing health care costs”) and 14 items measuring behavioralprocesses of change (e.g., “My friends encourage me to exercise”).For all items response options, indicating how frequently an eventoccurred in the past month, ranged from 1 (never) to 5 (repeat-edly), and both the cognitive and behavioral scores were used inthe present study. Research findings indicate support for the two-factor model of the processes of change outlined by the TTM, asindicated by indexes of fit, and internal consistency estimates fromprevious research have ranged from .64 to .88 for the cognitiveprocesses and from .74 to .87 for the behavioral processes scores(Kosma et al., 2006; Nigg et al., 1999). Estimates of internalconsistency found for the sample in the present study, usingCronbach’s alpha, were .89 and .92 for the cognitive and behav-ioral processes of change scores, respectively, along with currenttest–retest reliability estimates of .83 and .59.

MSEQ. Benisovich, Rossi, Norman, and Nigg (1998) devel-oped the Multidimensional Self-Efficacy Questionnaire (MSEQ)for exercise behavior, based on an exercise self-efficacy measurecreated by Marcus et al. (1992). The instrument consists of 18items measuring an individual’s confidence to exercise (e.g., “I amconfident to exercise when I am under a lot of stress”), withresponse choices ranging from 1 (not at all confident) to 5 (com-pletely confident), and the total self-efficacy score was used in thepresent study. In support of validity, the total MSEQ self-efficacyscore was found to differentiate individuals at different stages ofchange; in addition, Benisovich et al. (1998) estimated internalconsistency of the total self-efficacy score at .78, and a test–retestreliability estimate over a 2-week interval was .90. Using thesample in the present study, a Cronbach’s alpha internal consis-tency estimate of .97 and a test–retest estimate of .77 were found.

Decisional Balance for Exercise Adoption. Nigg, Rossi,Norman, and Benisovich (1998). developed a 10-item decisionalbalance scale, with five items to measure perceived pros of exer-cise (e.g., “I would feel less stressed if I exercised regularly”) andfive to measure perceived cons (e.g., I would feel embarrassed ifpeople saw me exercising”). Respondents were asked to rate eachstatement in terms of its importance in their decisions as toexercise, using a scale ranging from 5 (not important) to 1 (ex-tremely important), and both pros and cons scores were used in thepresent study. Nigg et al. (1998) found empirical support for thetwo-factor model of decisional balance on the basis of factoranalysis conducted with the scale, and Kosma et al. (2004) re-ported internal consistency estimates of .82 and .57 for the pro andcon scores, respectively. In the present study, internal consistencyreliability estimates were .87 and .75 for the pros and cons scores,respectively, with test–retest reliability estimates of .38 and .40.

Exploratory questions. In addition to the instruments de-scribed above, participants were asked three additional questionsfor exploratory purposes, which were primarily focused on addi-tional factors that may influence physical activity. The first ques-tion assessed the motivation for physical activity (for those who

chose to be physically active). The second question assessedwhether participants’ relationships with ACT program staff influ-enced their decision to be physically active. The third questionassessed whether participants had physical health risk factors, andhow participants worked with ACT staff to decrease this risk.Analysis of the exploratory questions involved measures of fre-quency where applicable, along with use of the constant compar-ative method to analyze additional qualitative information.

Data Analysis

As previously described, the purpose of the current study is toinvestigate the predictive ability of the TTM variables with spe-cific attention to each adjacent pair of stages in the stages ofchange hierarchy. As a result, the outcome variable is dichoto-mous. Therefore, logistic regression analysis was used to examinethe association between processes of change, self-efficacy, anddecisional balance and stages of change of physical activity be-havior for the initial research question (Hosmer & Lemeshow,2000). Multiple-regression analysis was used to examine the rela-tionship between processes of change, self-efficacy, and decisionalbalance and actual physical activity behavior for the second re-search question.

Results

Prior to conducting the analyses, the data were screened formultivariate outliers using the Mahalanobis distance measure, andthree participants were dropped from the sample. For remainingparticipants, missing data were handled by prorating total scoreson the basis of items with valid responses, but scores were com-puted for a measure only if a participant provided responses to atleast half of the items, resulting in varying n’s for differentmeasures.

Means, standard deviations, and intercorrelations are presentedfor all continuous measures in Table 1. For the Physical ActivityScale, the total sample M � 13.76 MET hr/day (SD � 12.12),which compares to the M � 20.2 MET hr/day (SD � 14.5) for thesample of adults with physical disabilities used by Washburn et al.(2002) to develop and field test the scale, suggesting a lower levelof physical activity for the sample of adults with mental illness inthe present study. In addition, on the categorical SEBC scale,29.2% (n � 26) of the present sample were categorized in theprecontemplation or contemplation stages, with 23.9% (n � 22) inthe preparation stage and 36.9% (n � 34) in the action or main-tenance stages. With regard to intercorrelations among measures,with the exception of those involving decisional balance-consscores, all correlations were significant, ranging from r � .24 to.75 and, also with the exception of the correlation with decisionalbalance-cons, those between physical activity behavior and theremaining predictors ranged from r � .24 (with decisional bal-ance-pros) to .43 (with processes of change-behavioral), and threeof the five were significant at p � .01.

Prediction of the stages of change of physical activity behav-ior. To examine the prediction of stages of change of exercisebehavior on the basis of processes of change, self-efficacy, anddecisional balance, logistic regression was used. For purposes ofthese analyses, the five categories of stages of change were re-

185SPECIAL SECTION: PHYSICAL ACTIVITY AND MENTAL ILLNESS

duced to three: a combined precontemplation/contemplation cate-gory, the preparation category, and a combined action/mainte-nance category. The rationale for combining stages was the smallsample size relative to the number of stages; in addition, precon-templation and contemplation share similarities (e.g., feelings ofambivalence and little or no change in behavior), as do action andmaintenance (e.g., having initiated and establishing behaviorchange). This approach has been used by researchers in otherstudies investigating physical activity among individuals withmental illness (e.g., Gorczynski et al., 2010).

Three separate logistic regression analyses were conducted, onefor each adjacent pair of stages of change. The first logisticregression examined processes of change (cognitive and behav-ioral), self-efficacy, and decisional balance (pros and cons) aspredictors of precontemplation/contemplation versus preparationstages of change. Results of the omnibus test for the logisticregression model were not significant, �2(5, N � 58) � 9.40, p �.094, indicating that there is no relationship between the predictorsand the outcome variable. Individuals in the precontemplation/contemplation and the preparation stage could not be differentiatedby processes of change, self-efficacy, and decisional balances.Thus, no further analyses were conducted for this model.

The second analysis examined processes of change, self-effi-cacy, and decisional balance as predictors of preparation versusaction/maintenance stages of change. The omnibus test for thelogistic regression model was found to be statistically significant,�2(5, N � 56) � 15.06, p � .01 indicating that there is a significantrelationship between the predictors and the outcome variable. TheHosmer Lemeshow goodness-of-fit test was not significant, �2(8,N � 56) � 13.19, n’s, suggesting that the model adequatelydescribes or fits the data. The Negelkerke R2 � .33 indicated that33% of the variance could be explained by the predictor variables.Thus, results indicated that behavioral processes of change pre-dicted preparation versus action-maintenance stages of change. Forpeople with mental illness, using behavioral processes of changewas associated with statistically significant action/maintenancephysical activity behaviors (odds ratio [OR] � 3.92, 95% confi-dence interval [CI], [1.01, 15.06]).

Last, processes of change, self-efficacy, and decisional balancewere examined as predictors of precontemplation/contemplationversus action/maintenance stages of change. As expected, behav-ioral processes also predicted precontemplation/contemplationversus action/maintenance stages of change (OR � 5.22, 95% CI,[1.31, 20.85]). The omnibus test for the logistic regression model

was found to be statistically significant, �2(5, N � 61) � 24.61,p � .01 indicating that there is a significant relationship betweenthe predictors and the outcome variable. The Hosmer Lemeshowgoodness-of-fit test was not significant, �2(8, N � 61) � 9.67, n’s,suggesting that the model adequately describes or fits the data. TheNegelkerke R2 � .44 indicated that 44% of the variance could beexplained by the predictor variables.

Prediction of Physical Activity Behavior

To examine the prediction of self-reported actual physical ac-tivity behavior on the basis of processes of change, self-efficacy,and decisional balance, multiple regression was used, and theresults are summarized in Table 2. The full model for predictingphysical activity accounted for 27% of the variance in physicalactivity behavior, R2 � .27, F(5, 77) � 5.81, p � .001. However,when looking at the predictive ability of the individual predictors,none were found to make a unique and significant contribution inthe presence of the other predictors. For exploratory purpose,stepwise regression based on forward selection was used to selecta reduced model that only contains the explanatory variables,which provides important information about the outcome variable.The reduced model with cognitive processes, behavioral processes,and self-efficacy as predictor variables also accounted for 27% ofthe variance in physical activity behavior, R2 � .27, F(3, 79) �9.90, p � .001. Examining the standardized partial regressioncoefficients, behavioral processes significantly contributed to thevariance in physical activity scores, with � � 0.39, t(81) � 2.18,p � .01, The result is consistent with the previously mentionedfinding that behavioral processes of change is associated with theaction/maintenance stage of change.

Table 1Means, Standard Deviations, and Intercorrelations for Total Scores on Physical Activity and TTM Predictors

Measures n M SD

Intercorrelations

1 2 3 4 5 6

1. Physical activity behavior 92 13.76 12.12 —2. Processes of change–Cognitive 89 2.78 0.82 .38�� —3. Processes of change–Behavioral 89 2.58 1.00 .50�� .75�� —4. Self-efficacy 84 2.44 1.15 .44�� .47�� .68�� —5. Decisional balance–Pros 87 3.49 1.23 .30�� .56�� .51�� .33�� —6. Decisional balance–Cons 87 1.80 0.89 –.14 –.02 –.20 –.13 .19 —

Note. TTM � transtheoretical model.� p � .05. �� p � .01.

Table 2Regression Analysis Summary for Variables PredictingPhysical Activity

Variable B SE B �

Processes of change–Cognitive 0.71 2.53 .05Processes of change–Behavioral 3.36 2.45 .27Self-efficacy 1.67 1.48 .16Decisional balance–Pros 0.42 1.27 .04Decisional balance–Cons �1.72 1.60 �.12

Note. N � 83.

186 BEZYAK, BERVEN, AND CHAN

Exploratory Questions

As previously indicated, study participants were asked to answerthree exploratory questions in addition to the measures used for theprimary analyses. In the first question, individuals were asked toindicate their reasons for choosing to be physically active, ifphysical activity was important to them. Participants provided awide variety of responses including the benefit of improved phys-ical and/or mental health, improved body image, increased social-ization, and the simple enjoyment of physical activity. The secondexploratory question asked whether their relationships with ACTprogram staff members encouraged them to be physically active.Of the 74 participants who responded to this item, 59.5% indicatedthat relationships with staff members did encourage them to bemore active, whereas 33.8% reported that relationships with staffdid not provide encouragement, and 6.8% indicated that relation-ships were partially encouraging. Finally, participants were askedwhether they had any physical health risk factors, including highblood pressure, high cholesterol, and high blood sugar, whichmight be influenced by physical activity. Seventy individuals re-sponded to this item, and 48.6% indicated that physical health riskfactors were present, whereas 51.4% indicated no physical healthrisk factors. These exploratory questions may provide guidance tofuture research exploring the rationale for physical activity and therole of social support. However, it is important to note that theself-perceptions expressed regarding health risk factors may ormay not be consistent with medical records indicating actual healthrisks.

Discussion

Individuals with severe mental illness have been found to besignificantly less active than the general population (e.g., Daumitet al., 2005), and the limited physical activity can contribute togreater risk for health problems and premature death for manyindividuals with mental illness (Pelletier et al., 2005). However,limited research has been conducted to examine factors that maypromote physical activity among individuals with mental illness(Richardson et al., 2005). The primary purpose of the current studywas to explore the usefulness of TTM constructs in predicting bothstages of change and actual self-reported physical activity. De-scriptive data regarding the sample suggest a need for physicalactivity, with more than one third of the participants meeting thecommonly accepted BMI criterion for obesity, in addition toself-reported physical activity that appeared to be substantially lessthan the standardization sample of adults with physical disabilitieson the physical activity measure reported by Washburn et al.(2002).

Prediction of Stages of Change of PhysicalActivity Behavior

The first logistic regression analysis provided no evidence thatthe five TTM variables, including processes of change (cognitiveand behavioral), self-efficacy, and decisional balance (pros andcons), predicted the precontemplation/contemplation versus prep-aration stages of change of physical activity behavior. However,the TTM variables were found to predict preparation versus action/

maintenance and precontemplation/contemplation versus action/maintenance stages of change, with behavioral processes of changeidentified as the one significant unique contributor to prediction.Those participants having higher behavioral processes of changescores were nearly four times as likely to be in the action/main-tenance stages versus the preparation stage and nearly five times aslikely to be in the action/maintenance stages versus precontempla-tion/contemplation. This finding is consistent with TTM theoryand research suggesting that behavioral processes are of greaterimportance in moving between later stages of change, while cog-nitive processes are hypothesized to be more important in movingbetween earlier stages (Callaghan et al., 2002; Prochaska & Mar-cus, 1994; Rosen, 2000).

Prediction of Physical Activity Behavior

With the exception of perceived cons, correlations between eachof the other four TTM predictors and self-reported physical activ-ity behavior were found to be significant. Similar significant rela-tionships have been reported in the literature for behavioral andcognitive processes of change (e.g., Kosma et al., 2006) andself-efficacy (Oliver & Cronan, 2005). Similar to the results of thepresent study, Kosma et al. (2006) reported a positive correlationfor perceived pros but no significant correlation for perceived cons.A recent investigation by Gorczynski et al. (2010) also substanti-ated the results presented in this study indicating that self-efficacy,perceived pros, and perceived cons all differed across stages ofphysical activity in the direction predicted by the TTM.

Multiple-regression analysis indicated a significant relationshipbetween the five TTM predictors and physical activity behavior,with the predictors explaining 27% of the variance in actualself-reported activity. This result is consistent with other researchusing multiple regression that has documented relationships be-tween TTM predictors and physical activity with groups of peoplewith disabilities (Kosma et al., 2006). However, although correla-tions were significant between physical activity and each of theindividual TTM variables, except for perceived cons, the multipleregression indicated no unique significant contributions from in-dividual predictors. An additional analysis using stepwise regres-sion indicated the behavioral processes did significantly contributeto the variance in physical activity. As expected, the five TTMvariables, as a group, did predict physical activity.

The results of the current study do lend support to the use of theTTM when designing and implementing physical activity inter-ventions among individuals with severe mental illness. As indi-cated by Kahn et al. (2002), a large majority of physical activityinterventions are atheoretical in nature, which may contribute tothe modest impact these interventions typically have on behavior.Not only does the present study concur with previous investiga-tions suggesting the TTM’s usefulness in designing physical ac-tivity interventions (Gorczynski et al., 2010), but the investigationof the TTM variables among individuals with severe mental illnessserves as a more rigorous test of the theory, which promotesincreased use of the TTM in clinical practice and research.

Limitations

There are potential limitations that should be considered wheninterpreting the results of this study. The sample was drawn from

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one ACT program in one community, and results may not gener-alize to other individuals with mental illness who are receivingtreatment from other programs in other communities, or to thosewho are not receiving any formal treatment or support. In addition,the sample was predominantly male (74.7%), European American(81.1%), and single (91.6%) and may differ in other characteristicsfrom other groups of persons with mental illness, also limitinggeneralizability of results.

Another limitation concerns the measurement of variables. In-ternal consistency estimates for measures did not generally appearto be problematic, but test–retest reliability estimates were not aspositive, indicating limited consistency of participant responsesfrom one time to another for several of the measures. Limitedconsistency over time could have been influenced by inconsisten-cies in administration, as instruments were sometimes adminis-tered by different staff at the two points in time. Another contrib-uting factor could be difficulties that participants may have had inproviding reliable self-reports, which has been noted in otherstudies of physical activity and health behaviors with samples ofpersons with mental illness (e.g., Durante & Ainsworth, 1996;Farnam et al., 1999; Sallis & Saelens, 2000; Soundy, Taylor,Faulkner, & Rowlands, 2007). Another limitation related to mea-surement is the possibility of self-report error and bias, which isalways a potential concern in with self-report measures, and itwould seem possible that participants in the present study mayhave overestimated their physical activity.

Finally, the design for this study was descriptive and correla-tional, so causal relationships cannot be inferred. In addition, thesmall sample size, combined with the limited reliability of somemeasures, may have obscured the detection of some predictiverelationships.

Implications

Clinical implications. The results may suggest interventionsto facilitate movement across the stages of change, leading to anincrease in physical activity behavior. To be more specific, resultssuggest that behavioral processes of change may contribute moresubstantially than other variables studies in differentiating betweenthe stages of change. Both behavioral processes of change andself-efficacy tend to increase across stages. As a result, interven-tions that encourage the use of behavioral processes of change andenhance self-efficacy may improve movement into later stages ofchange and to increasing physical activity. With additional re-search uncovering the influence of other TTM variables, specificintervention techniques, such as the one previously described,directed toward participants in different stages of change can bedeveloped.

Future research implications. The findings from this studysuggest implications for future research. First of all, reliabilityestimates indicate that more attention must be paid to measurementinstruments and test–retest reliability estimates when instrumentsare used with individuals with mental illness. Future researchshould include multiple administrations of measures, perhaps oneven more than two occasions. Considering self-report at multiplepoints in time may reduce the impact of specific daily life stressors(e.g., increased psychiatric symptoms) on results and provide moreaccurate estimates of physical activity. It may also be possible tofurther reduce the impact of poor test–retest reliability estimates by

going beyond the use of traditional paper self-report measures.Information could be collected from members of the individual’ssocial support network or health professionals (e.g., case manag-ers) that work very closely with individuals with mental illness,who may be able to provide helpful information regarding suchbehaviors as physical activity. Additional attention to the use ofbehavioral measures of physical activity would also reduce prob-lems associated with low test-retest estimates of self-report instru-ments. For example, Jerome et al. (2009) monitored physicalactivity of participants in psychiatric rehabilitation programs usinga triaxial accelerometer, which captures all physical movementsand allows for determination of intensity of all physical activitiesover specific time periods. By conducting future investigationswith alternative measures of physical activity, the previously dis-cussed limitations may be significantly reduced, and researchersmay find stronger evidence regarding the predictive power of TTMand other variables on physical activity.

Finally, the demographic characteristics of participants and theexploratory questions included in the present study suggest addi-tional areas for future research. Although over half of respondentsindicated that the relationship with staff members did encouragethem to be more physically active, nearly one third indicated therelationship was not an encouraging factor. The importance ofsupport and encouragement both inside and outside of the mentalhealth community has been identified as a key factor in thepromotion of physical activity among individuals with severemental illness (Van Citters et al., 2010). As a result, the relation-ships between people with mental illness and their case managersand other mental health professionals may be a significantly influ-ential factor warranting additional investigation. This investigationmay be extended by examining the relationship of other sources ofsocial support and physical activity or the role of perceived coer-cion by mental health professionals on physical activity By clari-fying motivational factors such as these, increasingly effectiveinterventions may be designed leading to more substantial in-creases in physical activity and related health benefits amongindividuals with severe mental illness. Future research incorporat-ing measures of physical health (e.g., weight, BMI, presence ofphysical health problems) and mental health symptomatology mayalso prove helpful. Clarifying the impact of physical activity onvariables such as these may increase awareness of the importanceof physical activity among individuals with mental illness.

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Received September 11, 2010Revision received February 14, 2011

Accepted March 4, 2011 �

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