increases in objectively measured physical activity predict theory of planned behaviour cognitions

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Do increases in physical activity encourage positive beliefs about furtherchange in the ProActive cohort?Wendy Hardemana; Susan Michieb; Ann Louise Kinmontha; Stephen Suttona; on behalf of the ProActiveproject teama Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge,Cambridge CB2 0SR, UK b Department of Clinical, Educational and Health Psychology, UniversityCollege London, 1-19 Torrington Place, London WC1E 7HB, UK

First published on: 15 April 2011

To cite this Article Hardeman, Wendy , Michie, Susan , Kinmonth, Ann Louise , Sutton, Stephen and on behalf of theProActive project team(2011) 'Do increases in physical activity encourage positive beliefs about further change in theProActive cohort?', Psychology & Health,, First published on: 15 April 2011 (iFirst)To link to this Article: DOI: 10.1080/08870446.2010.512662URL: http://dx.doi.org/10.1080/08870446.2010.512662

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Psychology and Health2011, 1–16, iFirst

Do increases in physical activity encourage positive beliefs about

further change in the ProActive cohort?

Wendy Hardemana*, Susan Michieb, Ann Louise Kinmontha, Stephen Suttona

on behalf of the ProActive project team

aDepartment of Public Health and Primary Care, Institute of Public Health, University ofCambridge, Cambridge CB2 0SR, UK; bDepartment of Clinical, Educational andHealth Psychology, University College London, 1-19 Torrington Place, London

WC1E 7HB, UK

(Received 6 October 2009; final version received 23 July 2010)

Effects of behaviour change on cognitions are rarely examined within theTheory of Planned Behaviour. We tested whether increases in physicalactivity resulted in more positive beliefs about further change among acohort of sedentary adults participating in a behavioural intervention trial(ProActive). At baseline, 6 and 12 months, 365 adults completedquestionnaires assessing physical activity and cognitions about becomingmore active over the coming year. Objective activity was assessed atbaseline and 12 months. Participants reporting larger increases in activitywere no more positive about making further increases than those reportingless behaviour change ( p-values4 0.05). Participants with larger increasesin objective activity reported weaker perceived control (�¼�0.342;p¼ 0.001) and more negative instrumental attitudes (�¼�0.230;p¼ 0.017) at 12 months. Participants may have felt that they had changedenough or measures of perceived success may be more sensitive tobehaviour change. Alternatively, long measurement intervals may havemissed immediate cognitive and affective consequences of behaviourchange, or such effects may require participants to consistently self-monitor or receive feedback on performance. Future studies could test theeffect of such techniques on physical activity and a wider range ofcognitive, affective and physiological consequences, using more frequentmeasurement intervals.

Keywords: behaviour; psychological feedback; cognition; randomisedcontrolled trial; theory of planned behaviour

Introduction

A large body of literature has tested the utility of social cognition models in thecontext of health and disease, e.g. the Theory of Planned Behaviour (TPB) (Ajzen,1991) and Social Cognitive Theory (Bandura, 1986). Reviews and meta-analyseshave shown that theory-based cognitions predict behaviour (Armitage & Conner,

*Corresponding author. Email: wh207@medschl.cam.ac.uk

ISSN 0887–0446 print/ISSN 1476–8321 online

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2001; Floyd, Prentice-Dunn, & Rogers, 2007; Godin & Kok, 1996; Hagger,

Chatzisarantis, & Biddle, 2002; Janz & Becker, 1984). Of equal practical and

theoretical interest is whether behavioural performance, in turn, affects cognitions.

Behavioural interventions often assume that success with behaviour change will

increase motivation and perceptions of self-efficacy, thereby facilitating further

behaviour change. Many psychological theories assume a feedback loop from

behaviour to cognitions. However, this is not very well researched and cohort studies

are inconsistent, showing both positive and negative feedback loops from

behavioural performance to cognitions.In a study among 94 volunteers, higher attendance at gym classes over 12 weeks

was associated with stronger perceived control and intentions with respect to

participating in regular physical activity at 12-weeks follow-up (Armitage, 2005). In a

study among 150 participants involved in rehabilitation for dizziness, beliefs about

carrying out balance retraining exercise every day for up to 3 months became less

positive from baseline to 3-months follow-up in the intervention group. Adherent

participants reported more positive beliefs at follow-up than non-adherent

participants (Yardley & Donovan-Hall, 2007). In a cross-sectional study among

409 university employees, type of self-reported activity and to a lesser degree length

of time of adherence showed positive associations with physical activity identity and

self-efficacy to participate in physical activity (Miller, Ogletree, & Welshimer, 2002).In the context of the TPB, Ajzen states that behavioural performance produces

feedback which influences subsequent attitudes, perceptions of social norms and

perceived control (Ajzen, 1985, 1991). The circumstances under which this feedback

loop occurs are not specified. In the seminal paper about the theory (Ajzen, 1991), the

figure depicting the TPB did not show feedback effects from behaviour to cognitions

for ease of presentation and it remains a neglected part of the theory. Many TPB

studies have controlled for previous levels of behaviour, using observational designs

(Hagger et al., 2002), but they have not tested whether behavioural performance

influences subsequent cognitions. To the best of our knowledge, few studies apart

from those by Armitage (2005) and Yardley and Donovan-Hall (2007) have tested

whether behaviour change affects TPB cognitions in intervention studies where

participants were encouraged to change their behaviour.A range of psychological theories make specific assumptions about whether and

when behavioural performance influences affect and cognitions about the behaviour.

Operant learning theory assumes that behaviour leading to positive reinforcement is

more likely to be repeated because people find the reinforcement rewarding, and the

situation in which the contingency between behaviour and reinforcer occurs signals

future reinforcement (Skinner, 1965). Self-perception theory states that people infer

their attitudes from their behaviour, particularly if there is no external explanation

for the behaviour and it is freely chosen (Bem, 1972).In the context of social cognitive theory, Bandura argues that the effect of

attributions on motivation is mediated by cognitions, such as perceptions of self-

efficacy (Bandura, 1991a). Successful performance of a behaviour strengthens

self-efficacy (Bandura, 1986). Failure weakens self-efficacy, particularly if this occurs

during the initial phases of behaviour change and does not reflect lack of effort or

adverse external circumstances. Bandura argued that behavioural performance can

affect cognitions through physiological feedback, for instance, people interpret

fatigue, aches and pains as indicators of physical inability (Bandura, 1976).

2 W. Hardeman et al.

Relapse prevention theory similarly assumes that lapses in behaviour mayweaken self-efficacy (Marlatt & Gordon, 1985), which is supported by empiricalevidence. Longitudinal observational studies showed that lapses in exerciseweakened self-efficacy among men (Stetson et al., 2005) and lapses in smokingcessation weakened self-efficacy (Shiffman et al., 2007). A cross-sectional communitysurvey among 2053 participants showed that low self-efficacy among exercisers wasassociated with a history of relapse (Sallis et al., 1990).

Finally, control theory assumes that the rate of progress towards a goal andprogress towards or movement away from a goal influence affect and cognitions(Carver & Scheier, 1998). If the rate of progress towards the goal is lower than thestandard or no progress is made, negative affect and doubt may result. In thiscircumstance, the person will re-assess the likelihood of a successful outcome, andtheir confidence will influence subsequent efforts at behaviour change.

In sum, many psychological theories assume that success with behaviour changeleads to more positive cognitions and affect, and failure to more negative cognitionsand affect. The aim of this study is to test this feedback mechanism in the context ofthe TPB and change in physical activity, using a longitudinal design with a 1-yearfollow-up.

ProActive evaluated a behavioural intervention to promote everyday physicalactivity, by targeting TPB cognitions about becoming more physically active. Allparticipants received a theory-based leaflet encouraging them to increase theiractivity as much as they felt able to. Two-thirds of participants also received abehavioural intervention, delivered at their homes or by phone by family healthfacilitators (Williams et al., 2004). In the intervention, participants were encouragedto become more physically active by setting incremental goals which wereindividualised, small and achievable. We assumed that initial success with behaviourchange would increase their confidence and motivation, and facilitate further goalsetting. Goal setting and review were informed by the TPB: the facilitators askedparticipants about any benefits or disadvantages, social support and factors thatmade it easy or difficult to achieve their goals. The whole cohort on averageincreased objectively measured activity by the equivalent of 20 minutes of briskwalking per day over the year of study, with sufficient between-participant variabilityto test feedback effects from behaviour change to cognitions. The behaviouralintervention was no more effective than brief advice alone in increasing objectiveactivity (Kinmonth et al., 2008).

Physical activity was measured by questionnaire at baseline, 6 and 12 months andobjectively by heart-rate monitor at baseline and 12 months. Results were not sharedwith the participants. TPB cognitions about becoming more physically active in thenext 12 months were assessed at baseline, 6 and 12 months. The formulation of theseitems was based on the intervention aim of helping participants to become morephysically active, and the assumption that success with behaviour change wouldresult in positive beliefs about further change. The measures included instrumentalattitude (the extent to which performing the behaviour is good or beneficial),affective attitude (the extent to which it is enjoyable), subjective norm (perceivedpressure from important others to perform the behaviour), perceived behaviouralcontrol (perceived barriers and facilitators), as well as intention.

In particular, drawing in particular from assumptions based on social cognitivetheory, we hypothesised that participants with greater increases in self-reportedphysical activity would be more positive about making further increases in their

Psychology and Health 3

activity than those reporting less behaviour change. Tests of any effects of objectiveincreases in activity on cognitions were exploratory, assuming a physiologicalfeedback mechanism as hypothesised by Bandura. We expected any effects ofobjective behaviour change to be weaker than self-reported behaviour change, as weassumed that participants’ cognitions would be primarily influenced by theirperceptions of behaviour change.

Methods

Participants

Participants (N¼ 365) were aged between 30 and 50 years, with a mean (standarddeviation) of 40.4 (6.0) years. Sixty-two per cent were female. Participants werepredominantly white, had finished full-time education at 17.9 (3.2) years and 55.3%had a managerial or professional job. Participants were sedentary and had a parentalhistory of Type 2 diabetes but no known diabetes. For further details, see Kinmonthet al. (2008).

Measures

Behaviour change

Change in self-reported leisure and work-related activity between baseline and 6months, 6 and 12 months and baseline and 12 months were assessed by the validatedEPAQ2 questionnaire and expressed as change in MET hours per week (Wareham,Jakes, Mitchell, Hennings, & Day, 2002). Change in objective physical activitybetween baseline and 12 months was expressed as change in dayPAR, the ratio ofdaytime energy expenditure to resting energy expenditure estimated using heart ratemonitoring for 3 days with individual calibration for the relationship between heartrate and energy expenditure (Wareham, Hennings, Prentice, & Day, 1997; Williamset al., 2004). The method has been validated against the gold standard techniques ofdoubly labelled water and whole-body calorimetry (Rennie & Wareham, 1998) and isstrongly associated with cardiovascular fitness (Wareham et al., 1997) and themetabolic syndrome (Simmons et al., 2008; Wareham et al., 1998). In the mainanalyses, the two behaviour change measures were treated as continuous variables,but we also report an analysis in which participants were classified into ‘increasers’and ‘decreasers’.

Cognitive measures

At baseline, 6 and 12 months, participants completed a questionnaire (available fromthe first author) based on the TPB, developed on the basis of an elicitation study(Sutton et al., 2003). Items were measured on a Likert-type scale ranging from 1(strongly disagree) to 5 (strongly agree). All cognitions were measured with twoitems. Attitude comprised instrumental attitude (‘being more physically active in thenext 12 months would be good/harmful for me’) and affective attitude (‘for me, beingmore physically active in the next 12 months would be enjoyable/boring’). Subjectivenorm was measured with: ‘most people who are important to me would want me tobecome more physically active in the next 12 months’ and ‘most people whose views Ivalue would disapprove if I was more physically active in the next 12 months’.

The latter item was omitted from the analysis as its formulation was complex andcorrelation with intention low. Perceived behavioural control included: ‘it would bedifficult for me to be more physically active in the next 12 months even if I wanted to’and ‘I am confident that I could be more physically active in the next 12 months, if Iwanted to’. Behavioural intention included ‘I intend to be more physically active inthe next 12 months’ and ‘it is likely that I will be more physically active in the next 12months’. For each variable, the scores for negatively formulated items were reversedand a mean score calculated across items for each participant. Cronbach’s alphas atbaseline were satisfactory for affective attitude (0.70) and intention (0.77), but lowfor instrumental attitude (0.45) and perceived control (0.54). Internal consistencywas similar at 6 and 12 months.

Procedure

Ethical approval was obtained from the East of England MREC (Eastern MREC 02/5/53). Participants were recruited through their parents with Type 2 diabetes,accessed from 20 general practice diabetes registers in the East of England, ordirectly from a record of a family history of diabetes in the medical notes of sevenpractices. All participants gave written informed consent. N¼ 365 participants wererandomised, and received a leaflet encouraging them to increase their activity asmuch as they felt able to. Participants visited the measurement centre at baseline and12 months, and completed postal questionnaires at 6 months. For further details, seethe trial protocol (Williams et al., 2004).

Data analysis

Data were analysed in AMOS Graphics 7.0 and SPSS 15.0. Analyses were conductedon 252 out of 365 participants with complete data on the behavioural and cognitivemeasures. Participants with missing data (n¼ 113) were less likely than those withcomplete data to have a managerial or professional occupation (50.0% versus57.6%) or intermediate occupation (7.4% versus 13.5%), and more likely to be asmall employer or own-account worker (13.0% versus 4.9%) (�2(4)¼ 11.675;p¼ 0.020). They also had higher baseline levels of home activity (56.16 versus45.36 MET hours per week; p¼ 0.007). No differences were found for any otherbackground variables, baseline cognitions or behavioural measures between thosewith missing and complete data.

Distributions of the TPB measures were approximately normal. All analyses wereconducted in the full trial cohort. Multi-group analysis showed that model fit whenall parameters were constrained to be identical in the three trial arms (face-to-face,distance and brief advice) was satisfactory and only slightly worse than when allparameters were freely estimated in each trial arm.

Change in physical activity was modelled as an unobserved variable, withmeasured physical activity change as a single indicator. This takes into account thepossibility that the physical activity measures are unreliable to a certain extent.The error variance of measured physical activity change was fixed in order toestimate the model, using the observed variance and a reliability estimate of 0.5,informed by evidence (Wareham et al., 2002). The error variance of the singlesubjective norm item was fixed in a similar way, using the observed variance and a

reliability estimate of 0.6. Sensitivity analyses to test the impact of different estimatesfor the reliability (0.3 and 0.7) of physical activity produced similar model fit andpath estimates, with only one exception. The TPB measures were modelled asobserved variables, as problems with model estimation occurred when they weremodelled as unobserved variables.

Structural pathways were drawn from behaviour change to the TPB measures at6 and 12 months, respectively. The impact of objective and self-reported behaviourchange was initially tested in separate models, because of a low correlation betweenthe variables (r¼ 0.054; n¼ 297), but then combined in one model to quantify theirrelative impact. TPB cognitions assessed at an earlier time point were part of themodel, to test the impact of behaviour change on subsequent cognitions controllingfor previous levels of cognitions. The cognitions at the first time point (theindependent variables) were allowed to covary. Similarly, the error terms of thecognitions at follow-up (the dependent variables) were allowed to covary, except theerror term of intention. In accordance with the TPB, at each time point structuralpathways were drawn from instrumental attitude, affective attitude, subjective normand perceived control to intention.

Structural models were assessed by inspecting the goodness-of-fit statistics andthe size and statistical significance of estimated path coefficients. We report the chi-square test, and also the standardised root mean square residual (SRMR,recommended level50.05); goodness-of-fit index (GFI, recommended level40.95);comparative fit index (CFI, recommended level40.90) and root mean square error ofapproximation (RMSEA, recommended level50.10).

Analysis of covariance was used to investigate whether participants whoincreased or decreased their objective and self-reported activity, respectively, differedin cognitions about making further increases at 1 year. The baseline measure of therespective cognition was entered as a covariate.

Results

Change in physical activity levels and cognitions over the year of study

Both self-reported and objective physical activity increased over the year of study.Most self-reported behaviour change occurred during the first 6 months (Table 1).Despite the increase, at 12 months, four out of five participants failed to meet theChief Medical Officer guideline of at least 30 minutes a day, of at least moderateintensity physical activity on five or more days of the week (Department of Health,2004). Over the year of study, 67% of participants reported an increase in activityand 33% a decrease. Fifty-four per cent showed an increase and 46% a decrease inobjectively measured activity.

At baseline, participants’ cognitions about becoming more physically active overthe next 12 months were positive and remained stable or became slightly morenegative over the course of the year.

Impact of self-reported physical activity change on cognitions about becoming morephysically active

The structural model examined the impact of self-reported behaviour change overthe year on the TPB measures at 12 months. It showed satisfactory fit, although it

was a significant departure from the data (Table 2). Participants who reportedgreater increases in physical activity had no more positive instrumental attitudes(�¼�0.031), affective attitudes (�¼ 0.061), subjective norm (�¼�0.102), perceivedcontrol (�¼�0.020) or intention (�¼ 0.049) than participants who reported lessbehaviour change (all p-values40.05).

We tested the same model using 6-month intervals, but again found no feedbackeffects. Self-reported behaviour change over the first half of the year did notinfluence any TPB measures at 6 months (all p-values40.05). Furthermore, self-reported behaviour change over the second half of the year had no impact on anyTPB measures at 12 months (all p-values40.05).

Table 1. Change in objective and self-reported physical activity and levels of cognitions aboutbecoming more physically active over the next year.

Baseline to6 months 6–12 months

Baseline to12 months

Change in objective physicalactivity (dayPAR)

–a –a 0.08 (0.54)

Change in leisure and work-relatedactivity (MET hours per week)

12.0 (40.1) 5.4 (41.7) 17.4 (47.5)

Levels of cognitions Baseline 6 months 12 monthsInstrumental attitude 4.5 (0.5) 4.3 (0.5) 4.3 (0.5)Affective attitude 3.9 (0.6) 3.8 (0.6) 3.8 (0.6)Subjective norm 3.6 (0.8) 3.5 (0.8) 3.5 (0.8)Perceived control 3.9 (0.5) 3.7 (0.7) 3.6 (0.7)Intention 3.7 (0.6) 3.7 (0.7) 3.6 (0.7)

Notes: All figures are means (standard deviation); n¼ 252 and range for TPB measures, 1–5.aNot measured over this time interval.

Table 2. Goodness of model fit statistics of structural models testing effects of change inobjective and self-reported physical activity on TPB cognitions.

Model �2 df p SRMR GFI CFI RMSEA

Self-reported activity change over the year12-month TPB measures 33.970 21 0.037 0.0477 0.977 0.983 0.050

Self-reported activity change from baseline to 6 months6-month TPB measures 55.510 21 0.000 0.0681 0.963 0.956 0.081

Self-reported activity change from 6 to 12 months12-month TPB measures 38.591 21 0.011 0.0454 0.974 0.982 0.058

Objective activity change over the year12-month TPB measures 32.304 21 0.055 0.0438 0.978 0.985 0.046

Objective and self-reported activity change over the year12-month TPB measures 30.848 22 0.099 0.0403 0.980 0.988 0.040

Notes: SRMR¼ standardised root mean square residual; GFI¼ goodness-of-fit index;CFI¼ comparative fit index and RMSEA¼ root mean square error of approximation.

Impact of objective physical activity change over the year on cognitions aboutbecoming more physically active at 12 months

The exploratory model tested the impact of objective behaviour change on the TPBmeasures and showed good fit (Table 2). Participants with the largest increases inobjective activity reported less control over making further increases (�¼�0.342;p¼ 0.001) and were less likely to report that increasing their activity would be goodor beneficial (instrumental attitude; �¼�0.230; p¼ 0.017). No impact was found onaffective attitude (�¼�0.072), subjective norm (�¼ 0.079) or intention(�¼�0.066) (all p-values40.05).

Relative impact of objective and self-reported change in physical activity oncognitions at 12 months

The structural model, testing the relative impact of objective and self-reportedbehaviour change on the TPB measures at 12 months showed good fit and similarpath estimates to previous models (Figure 1). Self-reported behaviour change did nothave any impact (all p-values40.05), whereas participants with the largest objectiveincreases reported more negative instrumental attitudes (�¼�0.232; p¼ 0.022) andperceived control (�¼�0.352; p¼ 0.001) than those with smaller increases.

Differences in cognitions at 12 months according to whether participants increasedor decreased their activity in the preceding year

Beliefs about becoming more active at 1 year did not differ between participants whoreported an increase or decrease in their activity in the preceding year (Table 3).Compared to participants who decreased their objective activity, participants whoshowed an objective increase were less likely to report at 1 year that increasing theiractivity would be good or beneficial. They also reported less control over makingfurther increases and had weaker intentions to make further increases. Thedifferences between objective increasers and decreasers were small (d¼ 0.23instrumental attitude, d¼ 0.34 perceived control and d¼ 0.24 intention).

Discussion

In the context of a cohort analysis of an intervention study among sedentary adultsat risk of Type 2 diabetes, we tested whether participants with greater increases inphysical activity were more positive about making further changes than those whoshowed less behaviour change. We found no evidence that self-reported behaviourchange influenced cognitions, in contrast to theoretical assumptions. Moreover thisfinding held when comparing participants who reported an increase in activity withparticipants reporting a decrease. Exploratory analyses revealed that participantswith greater objective behaviour change perceived themselves as having less controlover further changes, and were less likely to report that further increases would begood or beneficial. However, the differences between participants who showed anobjective increase versus decrease in activity were small.

There are several potential reasons for the absence of a positive feedback effectfrom self-reported behaviour change to cognitions. First, the measurement intervalsin our study may have been too long (Collins & Graham, 2002). Any effects ofchange in physical activity on cognitions and affect may be more immediate and not

sustained over time. For example, using a within-person design with 1-week

measurement intervals, successful execution of a personalised physical activity action

plan by cardiac rehabilitation patients predicted subsequent increases in self-efficacy

(Scholz, Sniehotta, Schuz, & Oeberst, 2007). High-intensity exercise has been found

to decrease participants’ pleasure only during the exercise session, but not post-

exercise. Positive affective feelings increased from pre- to post-exercise (Ekkekakis,

Hall, & Petruzzello, 2008).

0.17–0.08

0.550.28

–0.23

–0.09

–0.35

–0.12

behaviour change0–12 months–0.08

–0.05

–0.02

–0.12

–0.13

Change inDayPAR

(0–12 months)

Objectivee1

Instrumentalattitude

(0 months)

Perceivedcontrol

(0 months)

Perceivedcontrol

(12 months)

Intention(0 months)

(12 months)

Affectiveattitude

(12 months)

Affectiveattitude

(0 months)

Subjectivenorm

(12 months)

Subjectivenorm

(0 months)

Intention(12 months)

Self-reportedbehaviour change

0–12 months

Change inleisure andwork activity

(0–12 months)

Figure 1. Structural equation model estimating impact of change in self-reported andobjectively measured physical activity over the year of study on 12-month TPB cognitions(n¼ 252).Note: Rectangles are measured variables, large circles are unobserved (latent) variables andsmall circles are residual variances. Path coefficients are standardised. Significant coefficients( p50.05) are in bold. �2(22)¼ 30.848, p¼ 0.099; SRMR¼ 0.0403; GFI¼ 0.980; CFI¼ 0.988;RMSEA¼ 0.040.

3.Adjusted

difference

inmeansforcognitionsat1yearbetweenparticipantswhoincreasedordecreasedtheirself-reported

andobjectivephysical

activityover

theprecedingyear.

Cognitionsat

12months

Self-reported

increasers

n¼169

Self-reported

decreasers

Adjusted

difference

inmeans

Objective

increasers

n¼116

Objective

decreasers

n¼136

Adjusted

difference

inmeans

4.27(0.54)

4.40(0.49)

�0.10(�

0.23to

4.26(0.58)

4.38(0.45)

�0.12(�

0.24to

Affectiveattitude

3.86(0.59)

3.72(0.62)

0.12(�

0.02to

3.83(0.62)

3.80(0.58)

0.03(�

0.10to

Subjectivenorm

3.46(0.74)

3.49(0.82)

�0.02(�

0.19to

3.46(0.82)

3.47(0.70)

0.03(�

0.14to

Perceived

control

3.54(0.71)

3.62(0.71)

�0.11(�

0.28to

3.46(0.77)

3.69(0.59)

�0.23(�

0.39to�0.06)

Intention

3.60(0.67)

3.60(0.68)

�0.02(�

0.19to

3.53(0.70)

3.69(0.63)

�0.18(�

0.34to�0.03)

Notes:n¼252.Thedifference

inmeansisadjusted

forthebaselinelevel

oftherespectivecognition.

Second, participants who reported more behaviour change may have felt thatthey had made sufficient increases, and those who reported less behaviour changemay have felt unable to make changes. Thus, it may be the case that neither group ismore positive about making further increases. However, four out of five participantsfailed to meet the Chief Medical Officer guidelines (Department of Health, 2004) at 1year, suggesting that there was scope for further increases among those reportingincreased activity levels. We might have observed an effect of behaviour change oncognitions if we had measured beliefs about ‘being physically active’ instead of‘becoming more physically active’, as shown in two longitudinal studies. Thesestudies assessed beliefs about walking and exercising among participants of awalking programme and found that increases in walking were accompanied bydecreasing levels of enjoyment and self-efficacy (Castro, Sallis, Hickmann, Lee, &Chen, 1999; Rodgers et al., 2008). Participants in these studies may have foundbehaviour change more challenging than they initially thought; or more negativebeliefs might indicate that participants became more realistic with experience.Alternatively, their initial enthusiasm for taking part in the study may havedecreased. In our study, we did not find a decrease in perceived control or affectiveattitude over the year.

Third, the rate of progress towards achieving goals among more successfulparticipants may have slowed down. Participants reported less behaviour changeduring the second half of the year than the first 6 months. Bandura argued thatpeople may be dissatisfied if they initially make large progress with goals, butprogress then slows down. In these instances, motivation is not strengthened andpeople may stop setting challenging new goals, particularly when a sustained effort isneeded over a long period (Bandura, 1991a). This is congruent with control theory,where the rate of progress is assumed to influence cognitions and affect (Carver &Scheier, 1998). More frequent measurement of behaviour and cognitions inProActive might have provided a more detailed insight. However, this could haveincreased non-response and acted as an intervention in itself.

Fourth, Bandura argued that perceptions of behavioural performance can onlyinfluence motivation, affect and self-efficacy if people set goals, monitor progress orreceive specific feedback about their performance (Bandura, 1991a, 1991b). InProActive, correlations between levels of, and change in self-reported and objectivephysical activity were low. This may indicate that participants had difficulty recallingtheir activity levels or did not consistently monitor their behaviour. Alternatively, itcould reflect errors and imprecision in the objective assessment of physical activity.Behaviour change did not affect cognitions among participants of the intensiveintervention. At 12 months, 84% of these participants reported that they had setgoals and 78% that they monitored progress, which suggest that a feedbackmechanism could have happened. However, a detailed analysis of 108 interventionsessions with 27 participants revealed that the intervention facilitators discussed self-monitoring and goal review in only half of the instances specified by the protocols,and most participants did not consistently monitor progress with goals (Hardemanet al., 2008).

Fifth, an absence of feedback effects of behaviour change on attitude mayindicate that behaviour change was not internalised (Kelman, 1958). Intrinsicmotivation requires internalisation (Ryan & Deci, 2000) and lack of internalisationmay decrease the likelihood of behavioural maintenance. It is also possible thatattitudes became more ambivalent over the year as a consequence of behavioural

experiences (Armitage & Conner, 2000), making them less reliable and stable andthereby weakening their associations with behaviour change.

Finally, other unmeasured cognitions may be more sensitive to behaviourchange, such as perceived progress with goals or perceived success with behaviourchange. We found that perceived rewards of being more active were the focus ofparticipants’ talk about physical activity during the intervention sessions (Michieet al., 2008). Rothman argues that satisfaction with experiences of behaviour changemay predict maintenance (Rothman, Baldwin, & Hertel, 2004). Any positive effectsof increased activity (e.g. losing weight) may be cancelled out if participants foundthe experience unrewarding or challenging.

Rather than increases in activity fostering more positive beliefs about furtherchange, we found evidence that participants with the greatest objective increases inactivity were less confident about making further increases, and more negative intheir opinion that making further increases would be good and beneficial. Theseanalyses were exploratory, because most theories make assumptions about perceivedbehavioural performance, rather than actual performance. The objective measurewas conducted during the 4 days after completion of the TPB questionnaire, and weassume that it reflected participants’ activity levels around the time of measurement.Everyday physical activity may have provided physiological feedback, such as levelsof fatigue or energy (Bandura, 1976). If goals can serve as reference points (Heath,Larrick, & Wu, 1999), prospect theory would explain this finding in terms ofdiminishing sensitivity: participants with the largest objective increases exceededtheir goals to such an extent that they became less sensitive to further increases andless willing to further exceed their goal (Tversky & Kahneman, 1991).

A strength of the study is a robust assessment of self-reported and objectivephysical activity and TPB cognitions at baseline, 6 and 12 months in a well-characterised clinical population. In particular, the objective physical activitymeasure is well-validated and its association with clinical outcomes has beenquantified. The study also has several limitations. First, we were unable to model thecognitive measures as unobserved variables, which would have taken into accountany unreliability in the measures. Problems with model estimation were probably dueto having only two indicators per TPB construct, in combination with the largenumber of parameters to be estimated. Second, change in behaviour and cognitionswere measured over the same time interval. We cannot exclude the possibility thatpathways were in the other direction, although we assessed change from baseline andthe overall absence of effects makes this less of an issue. Third, pathways involvinginstrumental attitude and perceived control may have been underestimated due tothe low reliability of the constructs. Fourth, the study had missing data, although wefound no differences between participants with complete and missing data incognitions and behaviour. Fifth, the 3 days of objective activity measurement maynot be representative of usual physical activity levels as participants may havereacted to measurement by being more physically active. This could have affectedabsolute levels, but is unlikely to have affected estimates of behaviour change.Finally, our sample was predominantly Caucasian and fairly well-educated and ourfindings may not generalise to people at risk of Type 2 diabetes from ethnic minoritygroups and with lower educational levels.

The research implications of this study are threefold. First, future studies coulduse frequent field measurement (e.g. diaries or mobile phones) where people recordtheir experiences prior, during and directly after performing physical activity.

This would enable assessment of more immediate antecedents and consequences ofbehaviour change, and elucidate the direction of any pathways. Researchers willneed to consider measurement burden, measurement becoming an intervention initself and financial costs. Second, studies could assess a wider range of potentialconsequences of behaviour change, such as rewards and satisfaction with theexperiences of behaviour change, perceived rate of progress, awareness ofperformance in relation to goals, and attributions for success and failure. Wehypothesise that behavioural performance is likely to have an immediate effect onaffect and confidence, but effects on perceived health benefits will be delayed. Wealso hypothesise that feedback effects from behaviour to cognitions are strongerwhen participants are instructed to set specific goals and consistently monitorprogress, rather than simply being encouraged to do so as in the ProActiveintervention. It would be of interest to characterise those participants who showed apositive feedback loop from behaviour change to cognitions and affect, compared tothose who did not. Third, studies could investigate how initial behaviour change andany cognitive and affective consequences influence long-term behavioural main-tenance, and assess beliefs about maintaining increased levels of behaviour. Ifparticipants are no more motivated or confident about further behaviour changeafter initial change, this may have a detrimental effect on behavioural maintenance.Predictors of maintenance are poorly understood, and assumed to differ frompredictors of behavioural adoption (Nigg, Borelli, Maddock, & Dishman, 2008). Aspart of the 5-year follow-up of the ProActive cohort, we aim to assess to what extentchange in behaviour and cognitions during the first year influences behaviouralmaintenance at 5 years.

Finally, studies might compare the performance of participants who set modestversus more challenging physical activity goals, using prospect theory. Applyingideas from prospect theory to goal setting, setting modest goals increases thelikelihood that they are soon exceeded. Participants are then in the domain of gainswhere marginal benefits decrease quickly because of diminishing sensitivity. Incontrast, participants with challenging goals are likely to remain longer in thedomain of losses. For these participants, marginal benefits are high because of lossaversion (e.g. participants who walk 10 minutes less per day than their goal will exertmore effort than participants 10 minutes above their goal) and these benefitsincrease as participants are approaching their goal (Heath et al., 1999; Tversky &Kahneman, 1991).

In terms of practical implications, our findings suggest that the TPB may not be auseful framework for goal review in behavioural interventions. We askedparticipants about any perceived benefits and disbenefits, support and barriers inrelation to acting on their goals, but this study showed that participants’ beliefs didnot differ as a function of self-reported behaviour change. Other strategies worthconsidering include encouraging participants to consistently monitor their progressand provide specific feedback about their performance, based on the informationgathered; and asking participants about their feelings (experienced affect) duringgoal setting and review (Loewenstein & Lerner, 2003).

In conclusion, this study shows that participants who report greater increases intheir activity levels are no more positive about further behaviour change thanparticipants who report less change. Future studies could employ more frequentmeasurement intervals and examine a wider range of cognitive, affective andphysiological consequences of physical activity.

Acknowledgements

WH was funded by the Personal Awards Scheme, National Institute for Health Research(NIHR), UK Department of Health. ProActive was supported by the UK Medical ResearchCouncil (ref. no. ISRCTN 61323766), UK National Health Service R&D, and Royal Collegeof General Practitioners Scientific Foundation and the ProActive Fidelity Study by DiabetesUK (ref. no. RG35259). ALK and SS are NIHR Senior Investigators. The ProActive ProjectTeam includes, besides the authors, Nick Wareham (PI), Simon Griffin (PI), DavidSpiegelhalter (PI), Kate Williams and Julie Grant (study coordinator and recruitmentleads); Ulf Ekelund and Emanuella De Lucia-Rolfe (measurement leads); and Toby Prevostand Tom Fanshawe (statisticians). We thank the study participants, practice teams for theircollaboration and work in helping with recruitment and David French for the development ofthe TPB questionnaire. We thank two anonymous reviewers and the associate editor for theirhelpful comments on earlier versions of this manuscript.

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