Changes in Intentions, Planning, and Self-efficacy Predict Changes in Behaviors: An Application of...

Changes inIntentions, Planning,and Self-efficacyPredict Changes inBehaviors

An Application of Latent TrueChange Modeling

TABEA REUTERUniversity of Konstanz, GermanyJOCHEN P. ZIEGELMANNFreie Universität Berlin, GermanyAMELIE U. WIEDEMANNCharité University Medicine Berlin, GermanyCHRISTIAN GEISERArizona State University, USASONIA LIPPKEFreie Universität Berlin, GermanyBENJAMIN SCHÜZGerman Centre of Gerontology, GermanyRALF SCHWARZERFreie Universität Berlin, Germany

Abstract

Can latent true changes in intention,planning, and self-efficacy account forlatent change in two health behaviors(physical activity as well as fruit andvegetable intake)? Baseline data onpredictors and behaviors andcorresponding follow-up data fourweeks later were collected from 853participants. Interindividualdifferences in change andchange–change associations wereanalyzed using structural equationmodeling. For both behaviors, similarprediction patterns were found:changes in intention and self-efficacypredicted changes in planning, whichin turn corresponded to changes inbehavior. This evidence confirms thatchange predicts change, which is aninherent precondition in behaviorchange theories.

A C K N O W L E D G E M E N T S . During the work on her dissertation, the firstauthor was a pre-doctoral fellow of the International Max Planck ResearchSchool ‘The Life Course: Evolutionary and Ontogenetic Dynamics’ (LIFE,www.imprs-life.mpg.de).

C O M P E T I N G I N T E R E S T S : None declared.

A D D R E S S . Correspondence should be directed to:TA B E A R E U T E R, University of Konstanz, Psychological Assessment &Health Psychology, PO Box 47, 78457 Konstanz, Germany.[Tel. +49 (0)7531 88 5321; Fax +49 (0)7531 885226;email: tabea.reuter@uni-konstanz.de]

Keywords

� behavior change� latent change modeling� planning� self-efficacy� self-regulation

MANY theories of behavior change assume thatbehavior change is dependent on social-cognitivefactors such as intentions (e.g. Ajzen, 1991), plans(e.g. Schwarzer, 2008), or perceived self-efficacy(e.g. Bandura, 1997). This idea includes the assump-tion that changes in these factors are associated withor produce actual behavior change. However, moststatistical approaches are of limited use to examinethese change hypotheses, because they mostly usestatic individual differences to predict behavior.Latent true change modeling, a relatively recentapproach to dynamic modeling of process datamight be a tool to examine such change–changeassociations. This article provides an example forthe usefulness of this methodology to answer ques-tions about change–change associations and sug-gests a method of modeling associations betweenchanges in social-cognitive predictors of healthbehavior and subsequent health behavior change.

Health behavior change

Most theories of health behavior change agree onthe idea that behavior change depends on changesin underlying cognitions. These cognitions mightvary according to how close an individual is tochanging behavior. It has thus been suggested thatthe process of self-regulated behavior change con-sists of at least two distinct phases: a motivationphase of goal setting which is followed by a volitionphase of goal pursuit, and it has been argued thatgoal setting is a necessary but not sufficient prereq-uisite for goal attainment (Heckhausen &Gollwitzer, 1987). When trying to translate inten-tions into behavior, individuals are faced withobstacles such as distractions, forgetting, or con-flicting bad habits. If not equipped with means tochange these obstacles, motivation alone does notsuffice to change behavior (Baumeister, Heatherton,& Tice, 1993). Thus, it has been proposed that, ifindividuals increase their levels of cognitions andskills such as self-efficacy beliefs and planning,they will be better suited to execute intended changesin behavior and subsequently maintain these changes(Gollwitzer & Sheeran, 2006; Schwarzer, 2008;Sniehotta, 2009).

Perceived self-efficacy describes optimisticbeliefs in one’s abilities to maintain a desired action(maintenance self-efficacy) and to recover fromlapses (recovery self-efficacy). It has been shownthat such specific self-efficacy beliefs are important

for adopting and maintaining a novel course ofaction (Luszczynska & Schwarzer, 2003; Scholz,Sniehotta, & Schwarzer, 2005). On the one hand,these effects can be direct—individuals high in self-efficacy are more likely to invest more stamina andenergy in pursuing goals. On the other hand, theeffects of self-efficacy on behavior can be mediatedby planning: someone who lacks confidence inbeing able to maintain a certain behavior is lesslikely to engage in planning exactly how to adopt anovel course of action (Lippke, Wiedemann,Ziegelmann, Reuter, & Schwarzer, 2009). Changein self-efficacy should thus result in changes in bothplanning and behavior.

Forming implementation intentions by planningwhen, where, and how to act can facilitate goalachievement (for an overview, see Gollwitzer &Sheeran, 2006). Here, the when and where of a sit-uation are linked to an action sequence (how to per-form the behavior). An extension of implementationintentions relates to planning in advance how toovercome possible internal or external barriers.This leads to a much higher likelihood of goal-directed action in critical situations than withoutsuch plans (coping planning; Sniehotta, Scholz, &Schwarzer, 2006; Ziegelmann, Lippke, & Schwarzer,2006). Again, the inherent change–change associa-tion, namely that those who increase planning aremore likely to change their behavior than thosewho do not increase their planning, has rarely beenexamined.

From static to dynamic modelingof behavior change

Much previous research on behavior change hasbeen dominated by a static, stability-orientedapproach (Nesselroade & Ghisletta, 2000). Cross-sectional research designs and oversimplifyingmethods of analysis have been used to draw infer-ences about change processes that occur withinindividuals over time (for a critique see Lippke &Ziegelmann, 2008; Sutton, 2002). Even with lon-gitudinal data, behavior change has often beenmodeled in a static fashion—individual differ-ences in predictor levels at baseline have beenused to explain individual differences in behaviorat follow-up measurements. Such approaches con-sequently fail to take into account interindividualvariability in cognitions over time and their relationsto behavior change.

JOURNAL OF HEALTH PSYCHOLOGY 15(6)

Yet, this change–change assumption is central forthe development of behavior change interventions:only if the relation between changes in predictorsand changes in behavior is established, interven-tions to change these predictors are justified. Thisprecondition however can only be tested withprospective research designs and analysis methodsthat allow modeling differences in change acrossmeasurement occasions.

Only a few studies so far have modeled intraindi-vidual behavior change processes. Using latent growthcurve models, Sniehotta, Nagy, Scholz, and Schwarzer(2006) found that level and change of self-regulation(action control) are distinct predictors of changes inintentions and physical exercise. Furthermore, Scholz,Nagy, Göhner, Luszczynska, and Kliegel (2009) haveshown that intraindividual changes in motivationaland volitional predictors predict changes in unhealthydietary and smoking behaviors and the respectiveintentions. These first results give promising insightsinto individual change–change associations, but needto be replicated in different behavioral contexts (e.g.increasing health behaviors instead of reducing riskbehaviors) and should take into account methodologythat allows for interindividual differences in intraindi-vidual change processes.

Research aims and hypotheses

This study aims at providing a deeper insight intothe assumed relation between changes in cognitionsand changes in behavior. We provide a pathway forprogressing from the static modeling of behaviorchange processes, which might oversimplify theintraindividual changes towards actually examiningthe dynamic attributes inherent in most theories ofhealth behavior change by applying latent truechange modeling (cf. Steyer, Eid, & Schwenkmezger,1997). We aim at predicting change in physicalactivity and fruit and vegetable intake from changesin intention, self-efficacy, and planning. Changes inself-efficacy and intention are expected to predictdifferential change in planning. Change in planning,in turn, is assumed to predict change in behavior.The effect of changes in intention and change inself-efficacy on change in behavior is assumed to bemediated by change in planning. Furthermore, thepredictive power of baseline differences in self-efficacy and intention on changes in planning andchanges in behavior as well as baseline differencesin planning on changes in behavior will be tested.

Method

ParticipantsAn online study on physical activity and dietarybehavior was launched using the software dynQuest(Rademacher & Lippke, 2007). Participants wererecruited by a university press release and subsequentcoverage (magazine report and university press) aswell as by an advertisement on the university website.A sample of 1948 participants completed the ques-tionnaire and provided their e-mail addresses in orderto receive an invitation for a follow-up assessment.The time lag between Time 1 and follow-up measure-ment was four weeks (Time 2, T2).

MeasuresTime 1 and Time 2 questionnaires were identicaland contained validated psychometric scales(Schwarzer, 2008; Schwarzer, Luszczynska,Ziegelmann, Scholz, & Lippke, 2008). If not indi-cated otherwise, the response format for all scaleswas a four-point Likert scale ranging from 1 (disagree)to 4 (agree). Intention, self-efficacy, planning, andself-reported behavior were assessed regardingtwo behaviors: physical activity and fruit and veg-etable intake. Item examples below are translatedfrom German.

The self-efficacy scale comprised two items foreach behavior, such as ‘I am confident that I canmaintain eating five servings of fruit and vegetablesa day even if it is difficult’ (maintenance self-effi-cacy for fruit and vegetable intake) or ‘I am confi-dent that I can restart being physically active even ifI was not for several days’ (recovery self-efficacyfor physical activity).

Planning was measured with two items for eachbehavior, for example, ‘I have already preciselyplanned when, where, and how to eat five servingsof fruit or vegetables throughout the day’ (actionplanning for fruit and vegetable consumption) or ‘Ihave already precisely planned how I can bephysically active even in the face of difficulties andbarriers’ (coping planning for physical activity).

Behavioral intentions were measured by a singleitem for each behavior: ‘I intend to performstrenuous physical activity (i.e. with an increasedheart rate and sweating) in my leisure-time’ and ‘Iintend to eat at least five servings of fruit andvegetables a day’.

Fruit and vegetable intake was assessed by twoitems: ‘How often did you eat at least five servingsof fruit and vegetables a day during the last four

REUTER ET AL.: CHANGE PREDICTS CHANGE

weeks?’ The response format was a six-point Likertscale ranging from 1 (never) to 6 (always). Thesecond item ‘Regarding the last four weeks: Howmany portions of fruit and vegetables did you eat onan average day?’ was answered in an open-endedformat. Participants received detailed informationabout portion sizes. Similar single-item measures offruit and vegetable intake have been validated suc-cessfully against dietary biomarkers (Steptoe et al.,2003).

Physical activity was assessed with two items:‘During the last four weeks, how often did youmanage to be physically active for three days perweek for at least 30 minutes in your leisure-timewith an increased heart rate and sweating?’Responses were scored on a six-point Likert scaleranging from 1 (never) to 6 (always). The seconditem was worded ‘I have performed physical activ-ity and sports for at least 30 minutes per week in myleisure time (e.g. gym, playing soccer) with anincreased heart rate and sweating’. Responses werescored on a four-point scale with 1 (less than once),2 (at least once), 3 (at least three times), and 4 (atleast five times) per week regarding the last fourweeks. The external validity of a similar scale wasdemonstrated by Siconolfi, Lasater, Snow, andCarleton (1985).

Latent change analysesLatent true change (LTC) modeling has been intro-duced by Steyer et al. (1997; Steyer, Partchev, &Shanahan, 2000). It is a structural equation approachto examine interindividual differences in intraindi-vidual change that allows for interindividual differ-ences in change. The starting point for a latentchange analysis using LTC models is the basic lon-gitudinal measurement model of confirmatory fac-tor analysis (CFA). This basic CFA model is shownin Fig. 1A for the two repeatedly administered indi-cators of the construct self-efficacy.

In this basic model in Panel A, there is a time-specific latent factor that represents the construct ateach time point. These time-specific factors can bereferred to as latent state factors1 as they representthe latent state of individuals with respect to a con-struct on a specific measurement occasion (Steyer,Ferring, & Schmitt, 1992). Figure 1A implies thefollowing measurement model for the observedvariables:

Yijk = αij + λij*Sjk + εijk, (1.0)

where Yijk refers to an observed variable (indicator;e.g. an item) i measuring construct j (e.g. self-effi-cacy) on time point k. The constants αij and λij referto the measurement intercept and factor loading.Note that these coefficients carry no occasion indexk, because we assume them to be time-invariant.Time-invariance of intercepts and loadings impliesstrong measurement invariance (Meredith, 1993).This assumption is commonly made in longitudinalCFA and is required for a meaningful interpretationof the latent change factor introduced later (Steyeret al., 1997, 2000). Sjk is the time-specific latent fac-tor (latent state factor, e.g. error-free self-efficacy atT1 and T2 in Figure 1A). εijk indicates the measure-ment error variable.

The basic idea of latent change modeling (Steyeret al., 1997, 2000) is that the latent state factor forTime 2 (T2) can be decomposed into the initial statefactor at Time 1 (T1) and a latent change factor—representing latent change (or growth/decline) fromT1 to T2.

S2 = S1 + (S2 – S1) (2.0)

Equation 2.0 is a simple restatement that justadds S1 and subtracts it at the same time. This mayseem trivial, but it allows us to include change as alatent factor (represented by the variable (S2 – S1))in the model and to connect this change factor toother factors. If we consider the measurement equa-tion for an indicator measured at T2,

Y112 = α11 + λ11*S2 + ε112 (3.0)

and replace according to equation 2.0, equation 3.0can be rewritten as follows:

Y112 = α11 + λ11*[S1 + (S2 – S1)] + ε112

= α11 + λ11* S1 + λ11*(S2 – S1) + ε112 (3.1)

This reformulation of the basic longitudinal CFAmodel is easily implemented as shown in Fig. 1B.To summarize, the reformulation in equation 2.0makes use of the (trivial) decomposition of the T2state factor into T1 state factor plus latent changescore T2–T1. Change factors can be treated like anyother factor in a structural equation model. Theycan, for example, be correlated with or regressed onother variables to connect change to other variables.

An issue often encountered in longitudinalresearch is the problem of indicator-specific effects.Indicator-specificity is shown by higher correlationsof the same indicator with itself over time than withother indicators measuring the same construct.Indicator-specific effects can for example be due to a

unique item wording or method effects. Failure toaccount for indicator-specificity can lead to modelmisspecification and biased parameter estimates (e.g.an overestimation of the stability of constructs).

In the present study, indicator-specific effectswere modeled by introducing indicator-specific(method) factors for the second indicator of eachconstruct.2 According to Eid, Schneider, and

Schwenkmezger (1999), it is sufficient to use oneindicator-specific factor less than the number ofindicators per construct. The loadings on the indica-tor-specific factors were also assumed to be timeinvariant by fixing them to 1 at each time point andthe indicator-specific factors were uncorrelatedwith the latent state factors that pertained to thesame construct.

Figure 1. Longitudinal confirmatory factor analysis (latent state) model. Yijk denotes the ith observed variablemeasuring construct j on occasion k. A: Latent state model with correlated state factors. B: Reformulation of the latentstate model as latent change model. The T2 state factor is perfectly determined by the T1 state factor and change fromT1 to T2 and thus has no residual term. The latent change model is just a reparametrization of the latent state model inPanel A. The factor loadings λij have no occasion index k since invariance of factor loadings over time is assumed forall indicators. The models are identified by fixing the loading of the first indicator to one for each factor.

Figure 2 shows the complete latent change modelestimated in the present study. For reasons of clarity,the structural model (Panel A) is shown separatelyfrom the measurement models with the indicator-specific factors (Panel B). However, it should be notedthat the measurement models and the structural modelwere estimated simultaneously in the present study.

We specified latent change variables of self-efficacyas well as intention and baseline measures at T1 of

self-efficacy and intention to be predictors of thelatent change variables of planning and behavior. Inaddition, latent change variables of planning andbaseline planning were specified as predictors ofbehavior change. Intention was measured with asingle item, thus, state intention1 and differenceintention2–intention1 are observed variables. All T1measures were allowed to correlate with each otheras well as with all change scores.

Figure 2. Latent change model. A: Structural model. B: Measurement models with indicator-specific factors. The firstindicators were selected as ‘reference indicators’ for which no indicator-specific factors were specified. The modelsare identified by fixing the loading of the first indicator to one for each factor. State Intention at T1 and differenceIntention T2–Intention T1 are observed variables.

Mplus Version 3 (Muthén & Muthén, 2005) withfull information maximum likelihood estimation(FIML) was used for model estimation. FIMLmakes use of all available data and is preferred tolistwise deletion or ad hoc strategies for handlingmissing data (Schafer & Graham, 2002).

Indirect (mediator) effects were tested according tothe method recommended by MacKinnon, Lockwood,and Williams (2004). In this method, a bias-correctedbootstrap procedure is employed to estimate appro-priate confidence intervals (CIs) for indirect effects.These bootstrap CIs are then used to determine thestatistical significance of each indirect effect. If zerois not included in a 95% CI, the indirect effect issignificant at the .05 level. MacKinnon et al. (2004)have shown that conventional methods of signifi-cance testing for indirect effects are less appropriate.

Results

The final longitudinal sample consisted of 853 par-ticipants, 77.5 percent of which were women.Participants had a mean age of 37.4 years (SD = 12.62ranging from 18 to 78 years), 53.5 percent of thissample were unmarried, and 75.7 percent had asenior high school degree. No significant mean differ-ences (p > .05) between the initial sample and thosewho completed both measurement points in the pre-dictors of health behavior (intention, self-efficacy,and planning) were found. Small, albeit statisticallysignificant mean differences in baseline behaviorindicate that those retained reported more physicalactivity (effect size d = .17) and fruit and vegetableintake (d = .13) than those lost to follow-up. Men,older participants and persons without a high schooldegree were more likely to drop out.

Goodness of fitModel fit was assessed by examining the χ² test, thecomparative fit index (CFI), and the root-mean-square error of approximation (RMSEA). A satisfac-tory model fit is indicated by a low χ² value relativeto its degrees of freedom (d.f.), a high CFI (> .95),and a low RMSEA value < .05 (Schermelleh-Engel,Moosbrugger, & Müller, 2003).

The latent change model for physical activityyielded a good fit: χ²(38, N = 1948) = 71.6; p < .001;CFI = .99; RMSEA = .021; 90% CIRMSEA = .014, .029.The model fit for the latent change model for fruit andvegetable intake was also adequate: χ²(38, N = 1948)= 128.4; p < .001; CFI = .99; RMSEA = .035; 90%

CIRMSEA = .028, .042. Although the χ²-test was sig-nificant in both cases, the other fit indices indicateda very good approximate fit of the models.

Measurement modelsTable 1 shows the unstandardized intercepts, factorloadings, and variance components. The standard-ized state factor loadings (i.e. the correlationsbetween each indicator and its latent state factor) arerelatively high, whereas the standardized loadingson the indicator-specific factors are rather small.This indicates that the indicators are homogeneous(i.e. there is not much indicator-specific variance).

The squared standardized loadings represent thepercentage of variance accounted for by the stateand indicator-specific factors. The sum of bothtypes of squared loadings gives the total proportionof variance explained by systematic factors (thereliability coefficients). The reliability coefficientsare high for all indicators. Only 5–19 percent of theobserved variable variance is due to reliable indica-tor-specific effects, that is, the indicators measure toa large degree the same latent dimension.

Latent change model for physicalactivityAs shown in Fig. 3, interindividual differences inintraindividual change in planning was significantlypredicted by change in self-efficacy and change inintention. Change in physical activity could beexplained by change in self-efficacy and change inplanning. The effects of change in intention on phys-ical activity was mediated by change in planning(indirect effect: β = .06; bias-corrected bootstrapped95% CI = .02, .13). Change in planning also medi-ated between change in self-efficacy and change inphysical activity (β = .18; 95% CI = .07, .44).

Baseline levels of intention and self-efficacywere neither predictive of change in planning nor ofchange in physical activity. Also, baseline levels ofplanning did not predict changes in physical activ-ity. Overall, 32 percent of the variance in change inplanning and 13 percent of the variance in change inbehavior was explained by baseline differences andchanges in intention, self-efficacy, and planning.

Latent change model for fruit andvegetable intakeA similar prediction pattern emerged for fruit andvegetable intake. Change in planning was predictedby changes in self-efficacy and changes in intention.Change in planning, in turn, predicted changes in

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behavior. Change in self-efficacy exerted a directeffect on change in behavior as well as an indirecteffect via change in planning (indirect effect:β = .10; 95% CI = .04, .18). Change in intentiondirectly predicted change in behavior and wasmediated by change in planning (indirect effect:β = .07; 95% CI = .03, .13).

Baseline levels of intention predicted changes inbehavior, but not changes in planning. Baselinelevels of self-efficacy predted neither changes inbehavior nor changes in planning. Likewise, base-line levels of planning did not predict changes inbehavior. Overall, 27 percent of the variance inplanning change and 40 percent of the variance inchange in fruit and vegetable intake was explainedby baseline differences and changes in intention,self-efficacy, and planning.

Discussion

This study aimed at examining the processesinvolved in self-regulated behavior change. A latenttrue change model was examined, in which interindi-vidual differences in true intraindividual change(i.e. free of measurement error) in behavior arepredicted from baseline values and interindividual

differences in change in social-cognitive predictors.Our study is one of the first to explicitly address thechange–change-associations (changes in cognitionscause changes in behavior) inherent in most theo-ries of behavior change.

Our results suggest that the true changes inpredictors, namely intention, planning, and self-efficacy account for variance in behavior changeover and above the baseline of these predictors.This underlines that the common practice of pre-dicting interindividual differences in behaviors withinterindividual differences in antecedents at base-line underestimates the effect of cognition changes,because such modeling of behavior change over-simplifies the complex interdependent changes incognitions and behaviors.

In terms of theoretical concepts, our results sug-gest that changes in self-efficacy and changes inplanning explain how intentions are translated intobehavior. This corroborates theoretical claims thatholding an intention is not sufficient for successfulgoal pursuit (Schwarzer, 2008). For both behaviorsunder study (physical activity and fruit and veg-etable intake), changes in intention and self-efficacyare positively associated with change in planning.Change in planning, in turn, is predictive of change

Figure 3. Structural model with standardized path coefficients for physical activity and fruit and vegetable intake. The uppercoefficients (in bold) relate to the model for physical activity. The lower coefficients relate to fruit and vegetable intake.*p < .05; ***p < .001

in behavior. Change in intention has an indirecteffect on change in behavior through change inplanning. This finding is in line with the assumptionthat planning acts as a mediator of the intention–behavior relation (e.g. Schwarzer, 2008). For fruitand vegetable intake, we found an additional directeffect of change in intention on change in behaviorthat remained significant and was not mediated bychanges in planning. The partial mediation effect ofplanning in the intention–behavior relation con-cerning fruit and vegetable intake suggests addi-tional mediators of the intention–behavior relation.For instance, the dependence on action control forbehavioral performance, which refers to cognitivebehavior regulation while acting (cf. Schüz,Sniehotta, & Schwarzer, 2007) might be higher forfruit and vegetable intake than for physical activity.

Changes in self-efficacy, the perceived capabilityto maintain one’s behavior change and to recoverfrom relapses, are an important predictor for changein planning and change in behavior over and abovechange in intention. This result supports the con-tention that self-efficacious persons respond to bar-riers to action with better strategies, more effort,and persistence when changing their health behav-ior. In previous studies on physical exercise anddietary behavior high self-efficacious people weremore likely to make a plan (i.e. perceived self-efficacy moderates the intention-planning link)and were more likely to translate these plans inbehavior (i.e. self-efficacy moderates the planning–behavior link; Gutiérrez-Doña, Lippke, Renner, Kwon,& Schwarzer, 2009, Study 1; Lippke et al., 2009).

Overall, our findings supported the proposedrelations among the predictors of behavior change.Almost identical change association patterns werereplicated for two different behaviors. Our resultsunderpin the idea of targeting self-efficacy beliefsand planning skills in interventions—not necessar-ily a novel conclusion, but our results are among thefirst to support these intervention contents bydemonstrating that changes in these cognitions arein fact related to changes in behavior.

There are limitations concerning the presentstudy. In this study, behavioral intentions were mea-sured by using a single item only. However, this isin line with most of the research in this area(Courneya, 1994). For studying the latent change inintention, multiple indicators for intentions shouldbe assessed in future studies. Data on behavioraloutcomes were assessed with self-reports and thus

might be biased. However, some studies suggestthat self-reported measures are sufficiently valid(e.g. Steptoe et al., 2003 for fruit and vegetableintake; e.g. Bernstein et al., 1998 for physical activ-ity). The present study examines rather short-termchanges in social-cognitive factors and behaviorover a time interval of four weeks. Inferences onlong-term relations between changes in thesefactors can only be drawn when investigating long-term changes (e.g. Reuter, Ziegelmann, Lippke, &Schwarzer, 2009). The empirical relationshipsfound here can only be interpreted cautiously ascauses and effects. Based on our theoretical consid-erations, we assumed an influence of changes involitional factors on behavior change. Nevertheless,a behavior such as regular physical activity ordietary behavior consists of complex actionsequences. Experience with the actual behavior willinfluence behavioral intention, self-efficacy, andplanning in turn (cf. Ziegelmann, Luszczynska,Lippke, & Schwarzer, 2007). It has to be consideredthat intention, perceived self-efficacy, planning, andbehavior are likely to influence each other in a rec-iprocal manner. Thus, the present analyses consti-tute only one possibility of modeling the relationsbetween the variables in the study. Experimentaldesigns are needed to draw causal inferences.Especially for mediation analyses, experimental-causal-chain designs are a promising alternative tostatistical mediation analysis in examining media-tors of the intention–behavior relation (Reuter,Ziegelmann, Wiedemann, & Lippke, 2008). Finally,it has to be mentioned that there was a selectivedropout in the study which may limit the generaliz-ability of this study (e.g. persons without a highschool degree were more likely to drop out).However, no significant mean differences werefound in the predictors of health behavior betweenthe initial sample and those who completed bothmeasurement points in time.

Conclusion

This study examined latent change in behavior andits social-cognitive determinants. It demonstrated thatchanges in intention, self-efficacy, and planning canaccount for change in behavior. Drawing on LTCmodels that disentangle true-score components andmeasurement-error components of intraindividualchange (Steyer et al., 1997, 2000), we investigatedchange–change associations rather than using only

initial levels of social-cognitive variables for predict-ing change in behavior. This study adds to previousevidence in that it examines whether the inherentprecondition in behavior change theories, namelythat change predicts change holds true and can beapplied for the development of interventions tochange health behaviors.

1. Note that in Latent-State-Trait-Theory (LST-Theory; e.g. Steyer et al., 1992), these statefactors are further decomposed into a latenttrait (stable component) and a latent occasion-specific residual factor (situation-specificcomponent). However, in the present case, ourgoal is not to separate trait and state components,but rather to model change over time as alatent variable. Therefore, we applied LTC ratherthan LST models.

2. We decided not to model indicator-specific effectsby means of correlated errors in order to separateindicator-specific variance from error variance,which is not possible with correlated errors.

References

Ajzen, I. (1991). The theory of planned behavior.Organizational Behavior and Human Decision Processes,50, 179–211.

Bandura, A. (1997). Self-efficacy: The exercise of control.New York: Freeman.

Baumeister, R. F., Heatherton, T. F., & Tice, D. M. (1993).When ego threats lead to self-regulation failure:Negative consequences of high self-esteem. Journal ofPersonality and Social Psychology, 64, 141–156.

Bernstein, M., Sloutskis, D., Kumanyika, S., Sparti,A., Schutz, Y., & Morabia, A. (1998). Data-basedapproach for developing a Physical Activity FrequencyQuestionnaire. American Journal of Epidemiology,147, 147–156.

Courneya, K. S. (1994). Predicting repeated behaviorfrom intention: The issue of scale correspondence.Journal of Applied Social Psychology, 24, 580–594.

Eid, M., Schneider, C., & Schwenkmezger, P. (1999). Doyou feel better or worse? The validity of perceiveddeviations of mood states from mood traits. EuropeanJournal of Personality, 13, 283–306.

Gollwitzer, P. M., & Sheeran, P. (2006). Implementationintentions and goal achievement: A meta-analysis ofeffects and processes. Advances in Experimental SocialPsychology, 38, 249–268.

Gutiérrez-Doña, B., Lippke, S., Renner, B., Kwon, S., &Schwarzer, R. (2009). How self-efficacy and planningpredict dietary behaviors in Costa Rican and South

Korean women: A moderated mediation analysis.Applied Psychology: Health & Well-Being, 1, 91–104.

Heckhausen, H., & Gollwitzer, P. M. (1987). Thoughtcontents and cognitive functioning in motivational vs.volitional states of mind. Motivation & Emotion, 11,101–120.

Lippke, S., Wiedemann, A. U., Ziegelmann, J. P., Reuter,T., & Schwarzer, R. (2009). Self-efficacy moderates themediation of intentions into behavior via plans.American Journal of Health Behavior, 33, 521–529.

Lippke, S., & Ziegelmann, J. P. (2008). Theory-basedhealth behavior change: Developing, testing, and apply-ing theories for evidence-based interventions. AppliedPsychology: An International Review, 57, 689–716.

Luszczynska, A., & Schwarzer, R. (2003). Planning andself-efficacy in the adoption and maintenance of breastself-examination: A longitudinal study on self-regula-tory cognitions. Psychology & Health, 18, 93–108.

MacKinnon, D. P., Lockwood, C. M., & Williams, J.(2004). Confidence limits for the indirect effect:Distribution of the product and resampling methods.Multivariate Behavioral Research, 39, 99–128.

Meredith, W. (1993). Measurement invariance, factoranalysis and factorial invariance. Psychometrika, 58,525–543.

Muthén, L. K., & Muthén, B. O. (2005). MPlus user’sguide, 3rd edn. Los Angeles, CA: Muthén & Muthén.

Nesselroade, J. R., & Ghisletta, P. (2000). Beyond staticconcepts in modeling behavior. In L. R. Bergman &R. B. Cairns (Eds.), Developmental science and theholistic approach (pp. 121–135). Mahwah, NJ: Erlbaum.

Rademacher, J. D. M., & Lippke, S. (2007). Dynamiconline surveys and experiments with the free opensource software dynQuest. Behavior Research Methods,39, 415–426.

Reuter, T., Ziegelmann, J. P., Lippke, S., & Schwarzer, R.(2009). Long-term relations between intentions, planningand exercise: A three-year longitudinal study in orthopedicrehabilitation. Rehabilitation Psychology, 54, 363–371.

Reuter, T., Ziegelmann, J. P., Wiedemann, A. U., & Lippke, S.(2008). Dietary planning as a mediator of the intention–behavior relation: An experimental-causal-chain design.Special Issue on Health and Well-Being of AppliedPsychology: An International Review, 57, 194–297.

Schafer, J. L., & Graham, J. W. (2002). Missing data: Ourview of the state of the art. Psychological Methods, 7,147–177.

Schermelleh-Engel, K., Moosbrugger, H., & Müller, H.(2003). Evaluating the fit of structural equation models:Test of significance and descriptive goodness-of-fitmeasures. Methods of Psychological Research—Online,8, 23–74.

Scholz, U., Nagy, G., Göhner, W., Luszczynska, A., &Kliegel, M. (2009). Changes in self-regulatory cognitionsas predictors of changes in smoking and nutritionbehaviour. Psychology & Health, 24, 545–561.

Scholz, U., Sniehotta, F. F., & Schwarzer, R. (2005).Predicting physical exercise in cardiac rehabilitation:The role of phase-specific self-efficacy beliefs. Journalof Sport & Exercise Psychology, 27, 135–151.

Schüz, B., Sniehotta, F. F., & Schwarzer, R. (2007). Stage-specific effects of an action control intervention on dentalflossing. Health Education Research, 22, 332–341.

Schwarzer, R. (2008). Modeling health behavior change:How to predict and modify the adoption andmaintenance of health behaviors. Applied Psychology:An International Review, 57(1), 1–29.

Schwarzer, R., Luszczynska, A., Ziegelmann, J. P., Scholz,U., & Lippke, S. (2008). Social-cognitive predictors ofphysical exercise adherence: Three longitudinal studiesin rehabilitation. Health Psychology, 27(1, Suppl.),S54–S63.

Siconolfi, S. F., Lasater, T. M., Snow, R. C., & Carleton,R. A. (1985). Self-reported physical activity comparedwith maximal oxygen uptake. American Journal ofEpidemiology, 122, 101–105.

Sniehotta, F. F. (2009). Towards a theory of intentionalbehaviour change: Plans, planning, and self-regulation.British Journal of Health Psychology, 14, 261–273.

Sniehotta, F. F., Nagy, G., Scholz, U., & Schwarzer, R.(2006). The role of action control in implementingintentions during the first weeks of behaviour change.British Journal of Social Psychology, 45, 87–106.

Sniehotta, F. F., Scholz, U., & Schwarzer, R. (2006).Action plans and coping plans for physical exercise:A longitudinal intervention study in cardiac rehabil-itation. British Journal of Health Psychology, 11,23–37.

Steptoe, A., Perkins-Porras, L., McKay, C., Rink, E.,Hilton, S., & Cappuccio, F. P. (2003). Psychologicalfactors associated with fruit and vegetable intake andwith biomarkers in adults from a low-income neighbor-hood. Health Psychology, 22, 148–155.

Steyer, R., Eid, M., & Schwenkmezger, P. (1997).Modeling true intraindividual change: True change as alatent variable. Methods of Psychological Research—Online, 2, 21–33.

Steyer, R., Ferring, D., & Schmitt, M. (1992). States andtraits in psychological assessment. European Journal ofPsychological Assessment, 8, 79–98.

Steyer, R., Partchev, I., & Shanahan, M. (2000). Modelingtrue intra-individual change in structural equation models:The case of poverty and children’s psychosocial adjust-ment. In T. D. Little, K. U. Schnabel, & J. Baumert (Eds.),Modeling longitudinal and multiple-group data: Practicalissues, applied approaches, and specific examples(pp. 109–126). Hillsdale, NJ: Erlbaum.

Sutton, S. (2002). Testing attitude–behaviour theoriesusing non-experimental data: An examination of somehidden assumptions. European Review of SocialPsychology, 13, 293–323.

Ziegelmann, J. P., Lippke, S., & Schwarzer, R. (2006).Adoption and maintenance of physical activity:Planning interventions in young, middle-aged, andolder adults. Psychology & Health, 21, 145–163.

Ziegelmann, J. P., Luszczynska, A., Lippke, S., &Schwarzer, R. (2007). Are goal intentions orimplementation intentions better predictors of healthbehavior? A longitudinal study in orthopedicrehabilitation. Rehabilitation Psychology, 52, 97–102.

TABEA REUTER is research scientist in HealthPsychology at the University of Konstanz,Germany. Her research interests include healthbehavior theory, health behavior changeinterventions as well as structural equationmodeling of longitudinal data. A CV can befound at http://www.uni-konstanz.de/diagnostik/team_tr.htm

JOCHEN P. ZIEGELMANN chairs the researchconsortium ‘Fostering Lifelong Autonomy andResources in Europe: Behavior and SuccessfulAging’ at Freie Universität Berlin and GermanCentre of Gerontology. He focuses on lifespanhealth behavior change interventions andsuccessful aging as well as on the interplaybetween health behaviors and inflammationbiomarkers.

AMELIE WIEDEMANN is a research associate at theCharité University Medicine Berlin, Germany.Her main research interests include self-regulationof behavior and theory-based interventions forhealth behavior change. A CV can be foundat www.charite.de/medpsych/4_mitarbeiter/wiedemann.html

CHRISTIAN GEISER is an assistant professor ofquantitative psychology at Arizona StateUniversity. His research interests include structuralequation modeling of multitrait-multimethod andlongitudinal data as well as individual differencesin spatial abilities.

SONIA LIPPKE is an assistant professor (C1) at theFreie Universität Berlin, Germany. Her mainresearch interests include theories of behaviorchange and healthy lifestyle interventions. A CVcan be found at www.fu-berlin.de/gesund/slippke

BENJAMIN SCHÜZ is research scientist in HealthPsychology at the German Centre of Gerontology.His main research interests are health behaviortheories and the design of effective theory- andevidence-based interventions for health behaviorchange.

RALF SCHWARZER is Professor of Psychology at theFreie Universität Berlin, Germany. His research ison stress, coping, social support, self-efficacy, andhealth behaviors. He has been President of theEuropean Health Psychology Society. A CV can befound at www.RalfSchwarzer.de

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