cognitive style in bipolar disorder sub-types

Psychiatry Research 206 (2013) 232–239

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Psychiatry Research

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Cognitive style in bipolar disorder sub-types

Kathryn Fletcher n, Gordon Parker, Vijaya Manicavasagar

School of Psychiatry, University of New South Wales, and Black Dog Institute, Hospital Road, Randwick, NSW 2031, Australia

a r t i c l e i n f o

Article history:

Received 9 July 2012

Received in revised form

11 October 2012

Accepted 28 November 2012

Keywords:

Bipolar disorder

Unipolar depression

Cognitive style

Stress appraisal

Anxiety

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esponding author. Tel.: þ61 29 3823 708; fax

ail address: [email protected] (K. Fletch

a b s t r a c t

Clearer understanding of psychological processes and mechanisms such as cognitive style inform more

targeted psychological treatments for mood disorders. Studies to date have focused on bipolar I

disorder or combined bipolar sub-types, precluding identification of any distinctive cognitive style

profiles. We examined cognitive style separately in the bipolar sub-types, contrasted with unipolar and

non-clinical controls. A total of 417 participants (94 bipolar I, 114 bipolar II, 109 unipolar, 100 healthy

controls) completed cognitive style measures including the Rosenberg Self-Esteem Scale, Dysfunctional

Attitudes Scale, Inferential Styles Questionnaire, Stress Appraisal Measure and the Behavioural

Inhibition System/Behavioural Activation System Scale. Overall, cognitive styles were similar in

unipolar and bipolar participants, but with styles relevant to the Behavioral Activation System

differentiating bipolar I disorder in particular. State anxiety influenced negative inferential style in

unipolar participants and appraisal of stress in bipolar II participants. Analyses restricted to bipolar I vs.

II comparisons revealed subtle differences in terms of dispositional stress appraisal, with higher scores

on two stress appraisal sub-scales in the bipolar I group. Further exploration of cognitive style in

bipolar sub-types is indicated in order to determine whether there are specific psychological

vulnerabilities that would benefit from more targeted psychological interventions.

& 2012 Elsevier Ireland Ltd. All rights reserved.

1. Introduction

In order to develop more effective and targeted psychologicaltreatments for mood disorders, a clearer understanding of theprocesses and mechanisms underpinning specific disorders isrequired (Kuyken et al., 2007). According to cognitive behaviouraltheory for bipolar disorder, cognitive style (e.g. self-esteem,dysfunctional attitudes, attributional styles, etc.) and behaviouralcoping responses to stressors mediate the extent to which theunderlying biological vulnerability is expressed in terms of mooddysregulation (Kuyken et al., 2007). Exploration of cognitive stylein those with bipolar disorder is therefore of key theoretical andclinical relevance, and has the capacity to inform psychologicalmodels and treatment.

There is a growing appreciation of the role of cognitive style inbipolar disorder. Whilst a number of studies have examinedcognitive style in those with a bipolar condition (Pardoen et al.,1993; Alloy et al., 1999, 2009; Scott et al., 2000; Scott and Pope,2003; Lam et al., 2004; Jones et al., 2005; Goldberg et al., 2008;Lex et al., 2008; Van der Gucht et al., 2009; Alatiq et al., 2010;Reilly-Harrington et al., 2010; Mansell et al., 2011; Perich et al.,2011), such studies have produced inconsistent results, reflecting

d Ltd. All rights reserved.

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er).

limited sample sizes, non-clinical samples, differing definitions ofremission or euthymia, and inadequately controlling for currentpsychopathology. Nonetheless, the evidence to date suggeststhat those with bipolar disorder exhibit cognitive styles akin tothose with unipolar depression, but with some unique featuresincluding increased goal striving, perfectionism, self-criticism,autonomy, and Behavioural Activation System (BAS) sensitivity(Alloy et al., 2005).

Whilst current mood state and co-morbid anxiety both havean impact on self-reported measures of cognitive style (Joneset al., 2005; Perich et al., 2011), some studies have found thatnegative cognitive styles are still evident in euthymic bipolarsamples, even after controlling for current symptomatology (e.g.Van der Gucht et al., 2009). In addition to reports of cognitivestyle (both alone and in combination with activating life events)prospectively predicting the course of illness (e.g. Alloy et al.,2009; Scott and Pope, 2003; Reilly-Harrington et al., 1999;Johnson and Fingerhut, 2004; Francis-Raniere et al., 2006), suchfindings argue for negative cognitive style nuances acting as avulnerability factor for those with bipolar conditions.

Research on psychological processes in bipolar II disorder islacking, as most studies have focussed on bipolar I or on acombined sample of bipolar I and II subjects. Whilst bipolarspectrum samples (combining bipolar II and cyclothymicsamples) have been examined (e.g. Alloy et al., 2009), no studyto date has specifically examined cognitive style in bipolar II

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K. Fletcher et al. / Psychiatry Research 206 (2013) 232–239 233

disorder, nor compared this sub-type to other mood disordergroups and non-clinical controls. As reviewed previously (Fletcheret al., 2012), those with bipolar II have a set of characteristics thatdifferentiate them from those with a bipolar I condition, includinga more chronic course (Vieta et al., 1997; Judd et al., 2005), morefrequent depressive episodes (Vieta et al., 1997; Mantere et al.,2008; Baek et al., 2011), shorter periods of wellness (Judd et al.,2003), and possibly a higher suicide risk than that occurs inunipolar and bipolar I conditions (Rihmer, 2005). Two studieshave reported higher levels of self-reported hopelessness inbipolar II participants compared to those with a bipolar I condi-tion (Valtonen, 2007; Pompili et al., 2012).

The current study explores cognitive style in a clearly definedbipolar II sample contrasted with bipolar I, recurrent unipolarmajor depression and healthy control participants, seekingto clarify any differentiating features which may advancecausal hypotheses and contribute to more targeted psychologicalmanagement strategies.

2. Method

2.1. Participants

Participants were recruited via three Black Dog Institute sources—its website

(www.blackdog.org.au), Volunteer Research Register and Depression Clinic. Exclu-

sion criteria were: under 18 or over 65 years of age, inability to provide informed

consent, poor English comprehension, current psychosis, or a diagnosis of schizo-

phrenia or schizoaffective disorder. Participants required a prior clinical diagnosis

of a mood disorder (bipolar I, II or unipolar recurrent major depression), which

was subsequently confirmed via the MINI International Neuropsychiatric Inter-

view (MINI; Sheehan et al., 1998) assessing formal DSM-IV criteria. Healthy

control participants had no current or past mood disorder history, as assessed

by the MINI. Diagnostic groupings for analyses were derived on the basis of

agreement between clinician diagnosis and MINI diagnosis. The final sample

comprised 417 participants (114 bipolar II, 94 bipolar I, 109 unipolar recurrent

major depression and 100 controls).

2.2. Procedure

The study was approved by the University of New South Wales Human

Research Ethics Committee. After providing a description of the study, written

informed consent was obtained from all participants. Following confirmation of

diagnosis with the MINI, participants completed a battery of questionnaires

online, including demographic information, mood disorder and treatment history,

and measures of cognitive style. Current mood state severity was assessed via the

Internal State Scale (ISS; Bauer et al., 1991), Quick Inventory of Depressive

Symptomatology-Self-Report (QIDS-SR; Rush et al., 2003) and the Altman Self-

Rating Mania Scale (ASRM; Altman et al., 1997). State anxiety was assessed via the

Spielberger State-Trait Anxiety Inventory-State version (STAI-S; Spielberger et al.,

1983).

2.3. Cognitive style measures

2.3.1. Rosenberg Self-Esteem scale (RSE; Rosenberg, 1965)

The RSE is the most widely-used measure of global self-esteem, containing 10

items rated on a seven-point scale (1¼ I agree a lot; 7¼I disagree a lot). The

measure has high internal consistency, validity and test re-test reliability (Torrey

et al., 2000; Sinclair et al., 2010).

2.3.2. Dysfunctional Attitudes Scale (DAS-24; Power et al., 1994; Weissman, 1979)

The DAS-24 assesses dysfunctional beliefs about achievement, dependency

and self-control. Participants rate their level of agreement with 24 statements on

seven-point scales. The measure comprises three sub-scales (Goal Attainment,

Dependency and Achievement) identified via component analysis of data from a

remitted bipolar sample (Lam et al., 2004). Each sub-scale has high internal

consistency (Lam et al., 2004; Power et al., 1994).

2.3.3. Inferential Styles Questionnaire (ISQ; Rose et al., 1994)

The ISQ is a modified version of the Attributional Style Questionnaire (ASQ;

Seligman et al., 1979), developed for use with non-college adult groups, psychiatric

patients and community volunteers (Rose et al., 1994). The ISQ measures attribu-

tional style (inferences about cause, consequences and self) as outlined in hope-

lessness theory (Abramson et al., 1989). Hypothetical positive and negative events

from interpersonal and achievement-related domains are presented to participants,

requiring them to nominate a ‘cause’ for the event, and provide ratings on a scale of

1–7 for each of three dimensions—internal/external (e.g. ‘the event was caused by

me’ vs. ‘caused by others’), stable/unstable (e.g. ‘the cause of the event will or will not

lead to similar problems in the future’), and global/specific (e.g. ‘the cause of the

event does or does not affect other areas of my life’). Negative inferential style

(labelled here as NEG-ISQ) is calculated by summing scores for the nine negative

events, yielding a score range of 35–252 (Rose et al., 1994). Alternatively, a mean

negative generality score (NEG-Stable-Global) can be calculated by summing scores

based solely on the stability and global dimensions of the ISQ (Quiggle et al., 1992),

allowing comparisons to other studies that have used the ASQ. Sub-scales of inferred

negative consequences (NEG-Consequences) and inferred negative self-characteris-

tics/self-worth implications (NEG-Self) can also be calculated (Rose et al., 1994). We

examined all scoring approaches for comparative purposes.

2.3.4. Stress Appraisal Measure (SAM-19) (Roesch and Rowley, 2005)

The SAM-19 (originally based on the SAM; Peacock and Wong, 1990) is a

revised dispositional measure of appraisal, based on the premise that individuals

possess trait-like qualities to person–environment interactions the predispose

them to habitually appraise stressors as challenging or threatening (Roesch and

Rowley, 2005). It assesses the multidimensional structure of the appraisal

construct, corresponding to the transactional model of stress and coping

(Lazarus and Folkman, 1984). The measure possesses high convergent and

discriminant validity (Roesch and Rowley, 2005; Peacock and Wong, 1990) and

comprises four sub-scales (appraisal dimensions): challenge, threat, centrality and

resources. Participants respond to items based on how they generally think and

feel when faced with a stressful events, on a five-point scale (0¼not at all; 4¼a

great amount).

2.3.5. Behavioural Inhibition System/Behavioural Activation System Scale (BIS/BAS)

(Carver and White, 1994)

Based on behavioural and psychopharmacological studies, Gray (1982) described

two psychobiological systems—the Behavioural Activation System (BAS) and Beha-

vioural Inhibition System (BIS). The BAS is viewed as regulating approach behaviour

and motivation in relation to goals and rewards, whilst the BIS is considered to be

activated by negative (punishment or non-reward), novel and innate fear stimuli,

serving to inhibit ongoing motor behaviour increasing arousal levels and attention,

and mediating anxiety. The BIS/BAS measure comprises four sub-scales: BAS-Drive,

BAS-Fun Seeking, BAS-Reward Responsiveness, and Behavioural Inhibition. Sub-scale

internal consistencies range from 0.59 to 0.74 (Carver and White, 1994), demonstrat-

ing acceptable test–retest reliability in bipolar spectrum and normal samples

(Urosevic et al., 2008).

2.4. Data analyses

Groups were compared using one-way between-groups analysis of variance

for continuous dependent variables, with Bonferroni-corrected post-hoc compar-

isons. The Chi square statistic was used for categorical dependent variables, with

loglinear analyses used to determine significant differences between diagnostic

groups. One-way between-groups analysis of covariance (ANCOVA) was con-

ducted to quantify mean score differences between groups on each cognitive style

measure, with diagnosis as the independent variable, and mean scale score as the

dependent variable. Age, gender, employment status, psychological treatment status,

state depression (QIDS score, ISS-Depression Index), hypomanic severity (ASRM,

ISS-Activation) and state anxiety (STAI-S) severity, were used as covariates.

3. Results

3.1. Sample characteristics

Characteristics of the 417 participants are outlined in Table 1.The mean age of participants differed significantly betweengroups, with post-hoc comparisons quantifying controls as sig-nificantly younger than all patient groups (po0.01), but withcomparable mean ages for the unipolar and bipolar (I and II)groups. Females were slightly over-represented overall, but withnon-significant differences between groups. All groups werecomparable in terms of marital status, age of onset of depressiveor hypo/manic episodes, current medication and psychologicaltreatment status, however differences were observed in terms ofemployment status, with greater levels of unemployment in thepatient groups relative to controls.

In terms of current depressive symptomatology, the patientgroups had significantly higher mean QIDS-SR scores than con-trols but did not differ from each other. Similarly, significantly

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K. Fletcher et al. / Psychiatry Research 206 (2013) 232–239234

higher ISS-Depression Index scores were quantified in all patientgroups relative to controls, but with the bipolar II participantsreporting significantly higher levels of depression relative to bipolarI (p¼0.03) and unipolar (p¼0.01) participants. Current hypomanicsymptom levels did not differ between groups according to ASRMscores, however ISS-Activation scores indicated that both bipolargroups returned higher hypo/manic scores than unipolar partici-pants (po0.01), and with bipolar I participants scoring significantlyhigher than controls (po0.01). Overall, groups differed in terms ofstate anxiety, with significantly higher anxiety in patient groupsrelative to controls (po0.01) and a non-significant trend (p¼0.05)for higher anxiety in those with bipolar II relative to bipolar Iparticipants.

3.2. Cognitive style

Relationships between cognitive style measures and currentsymptomatology (hypomania, depression, and anxiety) wereexamined in the total sample (see Table 2). Cognitive stylemeasures were significantly correlated with current mood andanxiety symptom severity, with the exception of BAS-RewardResponsiveness.

After controlling for age, gender, employment status andcurrent mood severity, a number of differences were foundbetween the four groups on differing measures of cognitive style(see Table 3).

3.2.1. Self-esteem (RSE)

RSE scores were significantly lower for all patient groupsrelative to controls.

3.2.2. Dysfunctional attitudes (DAS)

All patient groups scored significantly higher on the DAS,including the DAS sub-scales of Achievement and Dependency(irrespective of the scoring method chosen). DAS-Self control andDAS-Goal Attainment scores were comparable for bipolar II and

Table 1Sample characteristics.

BP-II (n¼114) BP-I (

Age—mean (S.D.) 42.2 (11.1) 39.8

Gender—n (% female) 69 (60.5) 56

Marital status—n (%)

Married/De facto 63 (55.3) 46

Divorced/Separated 23 (20.2) 20

Never married 28 (24.5) 28

Employment status—n (%)

Full time 48 (42.1) 32

Part time 17 (14.9) 18

Unemployed 30 (26.3) 27

Othera 19 (16.7) 17

Education level—n (%)

Primary/Some secondary school 12 (10.5) 14

Secondary school completed 28 (24.6) 18

Diploma/Advanced diploma 23 (20.2) 16

Bachelor degree/Graduate diploma or certificate 36 (31.6) 38

Postgraduate degree 15 (13.1) 8

Age of first depressive episode—mean (S.D.) 19.1 (10.0) 18.3

Age of first hypo/manic episode—mean (S.D.) 19.4 (7.2) 18.3

Currently taking mood disorder medication—n (% yes) 92 (80.7) 80

Currently receiving psychological therapy—n (% yes) 62 (54.4) 55

QIDS-SR—mean score (S.D.) 12.8 (6.0) 11.0

ASRM–mean score (S.D.) 3.9 (3.8) 4.5

ISS-depression index —mean score (S.D.) 86.4 (64.2) 65.2

ISS-activation—mean score (S.D.) 113.5 (111.2) 129.9

STAI-S—mean score (S.D.) 51.1 (12.7) 46.6

a ‘Other’ category includes homemaker, student, retired.

unipolar participants relative to controls, with only bipolar Iparticipants scoring significantly higher than controls on thesesub-scales.

3.2.3. Negative inferential style (ISQ)

All patient groups returned significantly higher NEG-ISQscores relative to controls, as also quantified for the NEG-Consequences and NEG-Self sub-scales. Only those with bipolarI scored significantly higher than controls on mean negativegenerality scores (NEG-Stable-Global).

3.2.4. Stress appraisal (SAM)

Whilst groups did not differ on total scores or the Resourcesub-scale, all patient groups scored significantly lower thancontrols on the Challenge sub-scale, and significantly higher onthe Threat and Centrality sub-scales.

3.2.5. Behavioural activation/inhibition (BIS/BAS)

Bipolar groups differed significantly from unipolar participantson all three BAS sub-scales—those with bipolar I had significantlyhigher scores on Drive, Fun Seeking and Reward Responsivenesssub-scales, and with bipolar II participants scoring significantlyhigher on Fun Seeking. The patient groups did not differ fromcontrols on the three BAS sub-scales, with the exception of bipolarI participants scoring significantly higher on Reward Responsive-ness. In terms of behavioural inhibition, all patient groupsreturned significantly higher BIS scores relative to controls.

3.2.6. The impact of state anxiety on cognitive style

STAI-S scores were entered as an additional covariate to deter-mine the impact of state anxiety on cognitive style (see Table 3).When state anxiety severity was taken into account, a comparablepattern of results was found, but with two key exceptions. First,unipolar participants no longer differed from controls on NegativeInferential Style (NEG-ISQ) and the Consequences sub-scale (NEG-Consequences). Second, bipolar II participants no longer differed

n¼94) UP (n¼109) Controls (n¼100) F/w2 df p

(10.6) 41.1 (10.8) 34.2 (11.7) 10.71 3 o0.01

(59.6) 64 (58.7) 57 (57.0) 0.29 3 0.96

(48.9) 48 (44.0) 58 (58.0) 12.48 6 0.05

(21.3) 26 (23.9) 8 (8.0)

(29.8) 35 (32.1) 34 (34.0)

(34.0) 46 (42.2) 63 (63.0) 41.80 12 o0.01

(19.1) 23 (21.1) 21 (21.0)

(28.7) 23 (21.1) 2 (2.0)

(18.1) 17 (15.6) 14 (14.0)

(14.9) 9 (8.2) 6 (6.0) 19.58 12 0.07

(19.1) 22 (20.2) 14 (14.0)

(17.0) 15 (13.8) 15 (15.0)

(40.4) 44 (40.4) 40 (40.0)

(8.5) 19 (17.4) 25 (25.0)

(9.5) 21.3 (11.7) – 2.35 2 0.10

(7.3) – – 1.16 1 0.28

(85.1) 80 (73.4) – 4.41 2 0.11

(58.5) 50 (45.9) – 5.47 2 0.06

(6.6) 11.2 (5.6) 3.1 (2.1) 68.11 3 o0.01

(4.2) 3.2 (2.7) 3.8 (3.0) 2.61 3 0.05

(63.5) 63.7 (57.2) 13.4 (23.2) 33.03 3 o0.01

(138.3) 65.1 (43.1) 79.2 (77.3) 9.54 3 o0.01

(16.1) 49.1 (13.0) 29.2 (8.1) 64.15 3 o0.01

Table 2Zero-order correlations of current symptomatology with cognitive style in total sample.

ISS-activation ISS-depression index QIDS ASRM STAI-S

RSE (n¼381) �0.13nn�0.56nn

�0.75nn 0.09 �0.72nn

DAS-Total (n¼417) 0.16nn 0.41nn 0.58nn�0.01 0.54nn

DAS-Achievement 0.17nn 0.42nn 0.58nn 0 0.56nn

DAS-Dependency 0.11n 0.34nn 0.46nn�0.05 0.47nn

DAS-Self Control 0.12n 0.29nn 0.45nn 0.02 0.36nn

DAS-Goal Attainment (Lam) 0.15nn 0.29nn 0.45nn 0.03 0.36nn

DAS-Dependency (Lam) 0.16nn 0.43nn 0.56nn�0.04 0.54nn

DAS-Achievement (Lam) 0.17nn 0.36nn 0.52nn 0.02 0.50nn

NEG-ISQ (n¼381) 0.20nn 0.50nn 0.67nn 0 0.61nn

NEG-Stable-Global 0.19nn 0.48nn 0.63nn�0.04 0.57nn

NEG-Consequences 0.22nn 0.49nn 0.63nn 0.05 0.58nn

NEG-Self 0.15nn 0.44nn 0.61nn 0 0.57nn

SAM-Total (n¼201) 0.08 0.18nn 0.21nn�0.04 0.24nn

SAM-Challenge �0.06 �0.30nn�0.51nn 0.08 �0.49nn

SAM-Threat 0.15n 0.38nn 0.57nn�0.08 0.58nn

SAM-Resources �0.04 �0.42nn�0.41nn 0.11 �0.42nn

SAM-Centrality 0.07 0.42nn 0.56nn�0.12 0.59nn

BAS-Drive (n¼375) 0.22nn 0 0.03 0.11n�0.01

BAS-Fun Seeking (n¼375) 0.24nn 0 �0.01 0.17nn�0.02

BAS-Reward Responsiveness (n¼375) 0.09 �0.07 �0.04 0.09 �0.05

BIS (n¼375) 0.01 0.30nn 0.44nn�0.12 0.48nn

n o0.05.nn o0.01.


from controls on the stress-appraisal SAM-Challenge and SAM-Centrality sub-scales.

3.2.7. Cognitive style differences in the bipolar sub-types and the

impact of psychological therapy

Analyses were restricted to examine cognitive style differences inthe bipolar sub-types. Covariates detailed earlier were included,along with state anxiety. The impact of psychological therapywas also examined as an additional covariate. Prior to controllingfor psychological therapy, bipolar sub-types were comparable on allbut three measures—bipolar I participants scored higher thanbipolar II participants on the SAM-Threat sub-scale (mean¼14.1[SE¼0.7] vs. 11.8 [SE¼0.6], F1,76¼5.30, p¼0.02, eta squared¼0.06),the SAM-Centrality sub-scale (mean¼11.2 [SE¼0.5] vs. 9.6[SE¼0.4], F1,76¼4.87, p¼0.03, eta squared¼0.06), and with a non-significant trend observed for higher scores on the BAS-Drive Sub-scale (mean¼11.2 [SE¼0.3] vs. 10.3 [SE¼0.3], F1,181¼3.61, p¼0.06,eta squared¼0.02). After controlling for psychological therapy,results remained unchanged, with the exception of BAS-Drive scoredifferences now reaching significance (bipolar I4bipolarII—mean¼11.2 [SE¼0.3] vs. 10.3 [SE¼0.3], F1,180¼4.15, p¼0.04,eta squared¼0.02).

4. Discussion

As cognitive behaviour therapy paradigms for unipolar depres-sion are currently being adapted for bipolar disorders, it appearsparticularly pertinent to explore whether distinctive cognitivestyles are present in and across bipolar sub-types which maybenefit from more targeted interventions. To our knowledge, thisis the first study to compare cognitive style across the bipolarsub-types, contrasted with recurrent unipolar major depressionand healthy control participants.

Our study had a number of key strengths, including clearlydefined diagnostic groups, relatively large sub-sample sizes rela-tive to other studies, our statistical controlling for age, gender,employment status, current depressive and hypo/manic symptoms,state anxiety, and psychological therapy, and examination of awide range of measures assessing different aspects of cognitivestyle. Some limitations require noting. First, participants were not

excluded on the basis of previous treatment with cognitivetherapy, and while receipt of ‘psychological therapy’ was includedas a covariate, we were unable to determine any specific impactof cognitive therapy per se on cognitive style. Second, cognitivestyle was assessed exclusively via self-report, and therefore mayhave been influenced by a social desirability bias. Third, analysesdid not control for the course of the disorder—an important factorin light of evidence suggesting that the number of prior moodepisodes and time since disorder onset are related to dysfunc-tional attitudes (Scott et al., 2000). Fourth, the interviewer (KF)was not blind to clinical diagnosis when conducting the MINI,risking biases. Finally, the cross-sectional design does not allowconclusions to be drawn regarding whether any cognitive styledifferences from controls are predispositional vulnerabilities orconsequences (‘scarring’) of bipolar disorder.

As expected, cognitive style measures were highly correlatedwith current mood and anxiety symptoms in all groups, with theexception of the BAS-Reward Responsiveness sub-scale. Relativelylow correlations were observed between other BAS sub-scales andcurrent symptoms, supporting the notion that the BAS representsa trait-like characteristic that is relatively independent of currentsymptomatology.

Irrespective of state anxiety, our results indicated that anumber of aspects of cognitive style characterised all patientgroups relative to controls—with lower self-esteem (RSE);harbouring more dysfunctional attitudes (DAS) overall as wellas attitudes related to achievement and dependency; being morelikely to make negative inferences about the self (ISQ); appraisingstressful events as significantly more threatening (SAM); and withan overactive Behavioural Inhibition System (BIS). As patientgroups did not differ from each other, these aspects appear tobe non-specific cognitive vulnerability factors to depression ingeneral. Results relating to each cognitive style measures are nowdiscussed in turn.

4.1. Dysfunctional attitudes

Achievement and Dependency-related dysfunctional attitudeswere comparable in bipolar (I and II) and unipolar participantsirrespective of current mood and anxiety symptoms. These resultsare consistent with some studies (Jones et al., 2005; Lex et al.,

Table 3Cognitive style descriptives, controlling for age, gender, employment status and current mood severity.

BP-II mean

(SE)

BP–I mean

(SE)

UP mean

(SE)

Controls mean

(SE)

Group

differences

F

(df¼3)

p Effect size

(eta

squared)

RSEa 14.9 (0.4) 14.8 (0.5) 14.2 (0.4) 20.2 (0.5) BP-II, BP-I,

UP oC

24.5 o0.01 0.17

Patient groups vs. C: all

po0.01

DAS-24 (Total score)b 101.6 (2.1) 101.8 (2.2) 97.9 (2.1) 83.2 (2.5) BP-II, BP-I,

UP 4C

11.6 o0.01 0.08


po0.01

DAS-Achievement 36.2 (0.9) 35.8 (1.0) 33.7 (0.9) 26.6 (1.1) BP-II, BP-I,

UP 4C

15.3 o0.01 0.1


po0.01

DAS-Dependency 31.9 (0.8) 31.5 (0.9) 31.6 (0.8) 26.3 (1.0) BP-II, BP-I,

UP 4C

6.65 o0.01 0.05


po0.01

DAS-Self- Control 33.5 (0.7) 34.5 (0.8) 32.5 (0.7) 30.3 (0.9) BP-I 4C 3.77 o0.05 0.03

BP-I vs. C: po0.01

DAS-24 (Lam sub-scales)b

DAS-Goal attainment 26.4 (0.7) 27.2 (0.7) 25.3 (0.7) 23.8 (0.8) BP-I 4C 3.25 o0.05 0.02

BP-I vs. C: po0.05

DAS-Dependency 17.0 (0.5) 17.1 (0.5) 16.6 (0.5) 12.6 (0.6) BP-II, BP-I,

UP 4C

13 o0.01 0.09


po0.01

DAS-Achievement 22.6 (0.6) 22.1 (0.6) 20.9 (0.6) 16.8 (0.7) BP-II, BP-I,

UP4C

12.14 o0.01 0.08


po0.01

NEG-ISQa 167.9 (2.9) 170.1 (3.2) 164.4 (3.0) 148.0 (3.7) BP-II, BP-I,

UP 4Cy7.09 o0.01 0.05


po0.01

NEG-Stable-Global 87.2 (1.4) 88.4 (1.5) 85.0 (1.4) 81.2 (1.7) BP-I 4C 3.51 o0.05 0.03

BP-I vs. C: po0.05

NEG-Consequences 40.3 (0.9) 40.6 (1.0) 39.3 (0.9) 34.8 (1.1) BP-II, BP-I,

UP 4Cy5.4 o0.01 0.04


po0.01

NEG-Self 40.3 (1.1) 41.0 (1.1) 40.1 (1.1) 31.9 (1.3) BP-II, BP-I,

UP 4C

9.93 o0.01 0.07


po0.01

SAM-Totalc 36.2 (1.0) 38.4 (1.2) 35.6 (0.8) 33.4 (1.5) N.S. 2.19 0.09 –

SAM-Challenge 11.2 (0.8) 9.2 (0.9) 10.1 (0.6) 15.0 (1.1) BP-II, BP-I,

UP oCyy6.61 o0.01 0.09


po0.05

SAM-Threat 11.2 (0.6) 13.2 (0.7) 11.6 (0.4) 6.6 (0.8) BP-II, BP-I,

UP 4C

13.48 o0.01 0.17


po0.01

SAM-Resources 7.2 (0.3) 7.8 (0.4) 7.5 (0.3) 8.6 (0.5) N.S. 1.72 0.16 –

SAM-Centrality 9.1 (0.4) 10.6 (0.5) 9.4 (0.3) 6.5 (0.6) BP-II, BP-I,

UP 4Cyy7.95 o0.01 0.11


po0.05

BAS-Drived 10.3 (0.3) 11.1 (0.3) 9.7 (0.3) 10.1 (0.4) BP-I4UP 3.76 o0.05 0.03

BP-I vs. UP: po0.01

BAS-Fun Seeking 11.2 (0.3) 11.7 (0.3) 10.0 (0.3) 11.0 (0.3) BP-II, BP-I

4UP

6.83 o0.01 0.05

BP-II vs. UP: po0.01

BP-I vs. UP: po0.01

BAS-Reward

Responsiveness

16.4 (0.2) 16.9 (0.3) 15.6 (0.2) 15.8 (0.3) BP-I 4UP, C 5.06 o0.01 0.04

BP-I vs. UP: po0.01

BP-I vs. C: po0.05

BIS 23.4 (0.4) 23.7 (0.4) 23.5 (0.4) 19.6 (0.4) BP-II, BP-I,

UP 4C

19.19 o0.01 0.14


po0.01

a Based on data for n¼109 BP-II; 85 BP-I; 97 UP; 90 C.b Based on data for n¼114 BP-II; 94 BP-I; 109 UP; 100 C.c Based on data for n¼52 BP-II; 34 BP-I; 83 UP; 32 C.d Based on data for n¼106 BP-II; 85 BP-I; 94 UP; 90 C.y UP no longer significantly different from C after controlling for state anxiety.yy BPII no longer significantly different from C after controlling for state anxiety.


2008; Alatiq et al., 2010; Mansell et al., 2011) but contrary toothers (e.g. Perich et al., 2011). Whilst DAS-Goal Attainmentscores did not differ between bipolar (I and II) and unipolarparticipants as reported previously (e.g. Lam et al., 2004), thosewith bipolar I scored significantly higher on this and the Self-Control sub-scale relative to controls. Such results suggest thatthese aspects may form a bipolar-I specific cognitive style profile.

Overall, findings are difficult to reconcile given that all salientprevious studies have either focused on bipolar I disorder orcombined bipolar sub-types in their analyses. Furthermore,inconsistencies in research contrasting unipolar and bipolarsamples on the DAS may be due to this measure beingconstructed specifically to understand dysfunctional beliefs inunipolar samples. Indeed, theory-driven approaches linked to the


disorder of interest may show clearer differences between groups(Mansell et al., 2011). Future research using bipolar-specificmeasures to contrast diagnostic groups will further clarify cogni-tive style profiles that may differentiate bipolar and unipolarconditions, as well as determine any differences between bipolarsub-types.

4.2. Negative inferential style

State anxiety appeared to mediate negative inferential style inunipolar but not bipolar disorder participants. After controllinganxiety, only bipolar (I and II) participants differed from controls interms of total Negative Inferential Style and Consequences sub-scalescores. Irrespective of state anxiety, those with bipolar I were theonly group with significantly higher mean negative generality scores(NEG-Stable-Global) than controls—a finding that is consistent withthe reported association between risk for mania and global andstable attributions for negative events (Thompson and Bentall, 1990).

4.3. Stress appraisal

To our knowledge, this is the first study to comparativelyexamine dispositional stress-appraisal in bipolar, unipolar and con-trol participants. When contrasting the four groups, our resultssuggested that state anxiety mediates dispositional appraisal ofstress in bipolar II individuals in particular. After controlling for stateanxiety, bipolar II participants were the only group that no longerdiffered from controls on the SAM Challenge and Centrality sub-scales. The Challenge sub-scale represents optimistic and self-efficacious thoughts associated with an appraisal of challenge, whilstthe Centrality sub-scale represents the perceived importance of anevent for one’s well-being—and with both representing primaryappraisal processes (Roesch and Rowley, 2005). Lifetime prevalencerates of anxiety (social phobia and simple phobia in particular) havebeen reported to be higher in bipolar II than in bipolar I disorder (e.g.Judd et al., 2003), which may provide some explanation for the roleof anxiety in bipolar II in dispositional stress appraisal.

When analyses were restricted to compare the bipolar sub-types, key differences emerged on this measure. Bipolar I parti-cipants scored significantly higher on appraisal of stress asthreatening (SAM-Threat) and as being central to one’s well-being (SAM-Centrality), when compared to the bipolar II partici-pants, irrespective of state anxiety or receipt of psychologicaltherapy. These results are intriguing, suggesting that dispositionalstress appraisal processes play a key role in bipolar I disorder inparticular. Further exploration of primary stress appraisalprocesses in bipolar disorder is therefore warranted, as they flaga potential specific target for psychological therapy.

4.4. Behavioural activation/inhibition

BAS sub-scales differentiated bipolar from unipolar samples,supporting the BAS dyresgulation theory of bipolar disorder (seeUrosevic et al., 2008 and Alloy and Abramson, 2010 for updatedreviews of this theory) and indicating that bipolar disorder ischaracterised in terms of a hypersensitive BAS that is easily dysre-gulated. Interestingly, BAS-Reward responsiveness was the only sub-scale representing a distinct vulnerability to bipolar disorder (bipolarI disorder significantly higher and with a non-significant trend forbipolar II) relative to unipolar and control participants. This resultruns contrary to some previous reports (e.g. Van der Gucht et al.,2009; Alloy et al., 2009; Perich et al., 2011) but is supported byothers indicating that those with bipolar spectrum disorders exhibitgreater responsiveness (behaviourally, emotionally, and cognitively)to rewards and an inability to delay rewards (Eisner et al., 2008;Hayden et al., 2008; Johnson et al., 2005; Swann et al., 2009). The

predictive utility of this construct has been shown in a number ofstudies—for example, BAS-reward responsiveness has been found topredict subsequent increases in manic symptoms in bipolar samples(Meyer et al., 2001). When examining mean scores across all BASsub-scales, the bipolar II participants had elevated scores relative tounipolar and control participants (with significant differences onlyobserved on BAS-Fun-seeking), but with slightly lower scores relativeto the bipolar I group. If BAS sensitivity is considered on a continuum,a possible interpretation is that those with bipolar II disorder haveelevated BAS sensitivity but are less likely to become as severelydysregulated as those with bipolar I, contributing to hypomania(by definition, less severe) as opposed to mania. Indeed, BASactivation-relevant life events (e.g. goal attainment) have beenshown to trigger hypo/manic symptoms in those with bipolardisorder (Johnson et al., 2000; Nusslock et al., 2007), and a highlysensitive BAS has been shown to increase vulnerability to hypo/manic episodes and behaviours in bipolar spectrum samples (Alloyet al., 2006). The comparative relationship between BAS sensitivity,cognitive style, and illness course in bipolar sub-types is an area thatis yet to be examined.

Analyses contrasting the bipolar sub-types indicated that –irrespective of state anxiety and psychological therapy – bipolar Idisorder participants scored higher on the BAS-Drive sub-scalethan bipolar II participants. The sub-scale represents vigour andpersistence in pursuit of rewards. Our results suggest that thosewith a bipolar I condition show greater determination in theirpursuit of rewards than those with a bipolar II condition, perhapsleading to more severe mood dysregulation (i.e. manic rather thanhypomanic episodes) in this group. Our effect size was very smallhowever, thus results are interpreted with caution.

Overall, a lack of differentiation was found between bipolar sub-types on the majority of cognitive style measures. Our resultssuggest, however, that subtle differences may exist between bipolarI and II disorder with dispositional stress appraisal representing onesuch difference, and possibly contributing to the differing illnesscourse characteristics seen in the bipolar sub-types. Whilst cogni-tive styles were generally comparable, behavioural coping strate-gies in response to stressors may differ in the bipolar sub-types.This notion is supported by findings reported by Parikh et al. (2007),whereby differing coping behaviours were observed—those withbipolar I tended to use a wider range of coping strategies, including‘seeking professional help’, whilst those with bipolar II were morelikely to use ‘denial and blame’ and ‘problem-oriented’ coping, andless likely to use ‘seeking professional help’ and ‘stimulationreduction’ strategies. Lam et al. (1999) postulated that extremegoal attainment attitudes in bipolar disorder may comprisedysfunctional beliefs that lead to extreme striving behaviour andirregular daily routine. Studies examining links between cognitivestyle and behavioural coping in bipolar sub-types may thereforeshed further light on any differentiating factors at each of theselevels – both of which are amenable to change and comprise thekey targets for psychological therapy.

The effectiveness of therapies for specific disorders isadvanced if the specific psychological vulnerabilities underlyingthat disorder have been established (Mansell and Scott, 2006).Future studies should seek to clarify any differential cognitivestyle profiles in bipolar I and II disorders using bipolar-specificmeasures and controlling for potentially confounding variablessuch as depressive symptomatology and anxiety, to determine ifour indicative findings can be confirmed.

Acknowledgement

Funding for this study was provided by National Health andMedical Research Council (NHMRC) Programme Grant 510135.


We would like to thank Matthew Kuti for his technical expertiseand contribution to this study. We are grateful to the researchparticipants for giving their time.

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