I have attached the Final assignment since this assignment is building off of it this will be a big deal. I will let you pick the four domains that you are comfortable with and if you want I will help

Behavioural Risk of Bipolar Disorder in an Analogue Population: The Role of Cognitive, Developmental and Interpersonal Factors Matthias Schwannauer, 1,2 * Abbi Noble 1,2 and Gillian Fraser 1 1School of Health in Social Science,The University of Edinburgh, Edinburgh, UK2CAMHS, NHS Lothian, Edinburgh, UK Research to date has identiﬁed the contribution of a number of cognitive, developmental and interpersonal risk factors in the development of bipolar affective disorder. However, further work is needed to understand the mechanisms and interactions between these risk factors in relation to bipolar mood instability. The aim of this study is to explore the possibility of identifying high risk of bipolar disorder through cognitive and interpersonal factors and to further expand our knowledge regarding the relationship between such factors.

Theﬁndings from this work demonstrate that when both cognitive and interpersonal variables are entered into one model to predict bipolar high risk, direct effects are observed for the interpersonal factors, which then have a fully mediational effect on the cognitive factors. This work proposes that interpersonal factors develop and maintain cognitive risk factors and that future formulations and treatment of bipolar disorder need to focus on addressing such interpersonal issues. Copyright © 2011 John Wiley & Sons, Ltd.

Key Practitioner Message:

This study highlights the importance of the interpersonal context of mood dysregulation and the inter- action of cognitive and interpersonal aspects of affect regulation.

The interpersonal context needs to be fully considered when investigating and working with indivi- duals at risk of bipolar disorder.

Keywords:Bipolar Disorder, Cognitive and Interpersonal Factors, Behavioural High Risk INTRODUCTION Bipolar disorder (BD) is a mood disorder characterized by episodes of mania and depression, which can be widely diverse from mild hypomania and mild depres- sion to severe mania and severe and recurrent depres- sion with chronic psychosis (Muller-Oerlinghausen, Berghofer, & Bauer, 2002). The impact of BD on indivi- duals and their family is psychological, social andﬁnan- cial (Cooke & Jones, 2009), and the prevalence of suicide in individuals with BD is signiﬁcantly higher compared with that in the population as a whole (Dutta et al., 2007).

High-risk paradigm studies tend to focus on the identi- ﬁcation of genetic or behavioural factors that contribute to the risk of developing BD. Work with a behavioural focus has primarily investigated the impact of cognitions in predicting the risk of developing BD, e.g., studies have illustrated how dysfunctional cognitive styles mayincrease vulnerability to the development of BD (Alloy et al., 2005; Reilly-Harrington, Alloy, Fresco, & Whitehouse, 1999). It has been established that dysfunctional negative thinking by individuals with BD increases their vulner- ability to experiencing a bipolar episode when exposed to a negative stressful situation (Reilly-Harrington et al., 1999; Scott, Stanton, Garland, & Ferrier, 2000).

Further work into dysfunctional cognitions has focused on the association between the regulation systems of anxiety and impulsivity with BD. Responses to positive reward and positive feelings are regulated by the Behav- ioural Activation System (BAS); a plethora of research supports the premise that an increased BAS sensitivity is predictive of manic episodes (Alloy et al., 2008; Eisner, Johnson, & Carver, 2008; Meyer & Baur, 2009; Meyer, Johnson, & Carver, 1999; Van der Gucht, Morriss, Lancaster, Kidderman, & Bentall, 2009). The Behavioural Inhibition System (BIS), which regulates responses to anxiety and inhibits behaviour that would result in negative out- comes, is thought to be associated with depressive epi- sodes (Alloy et al., 2008; Eisner et al., 2008; Jones, Mansell, & Waller, 2006; Meyer et al., 1999; Van der Gucht et al., 2009). Although work has demonstrated an associ- ation between both BIS and BAS with BD, some *Correspondence to: Matthias Schwannauer, Section of Clinical Psychology, The University of Edinburgh, Teviot Place, Edinburgh, UK EH8 9AG.

E-mail: [email protected] Clinical Psychology and Psychotherapy Clin. Psychol. Psychother.18,411–417 (2011) Published online 30 August 2011 in Wiley Online Library (wileyonlinelibrary.com).DOI:10.1002/cpp.781 Copyright © 2011 John Wiley & Sons, Ltd. researchers believe that BD is more a reaction to positive affect and behavioural activation (BAS) (Depue & Iacono, 1989; Meyer & Baur, 2009).

It is unclear however whether dysfunctional cognitions result from negative situations and experiences or vice versa. Some researchers, such as Alloy, Abramson, Walshaw, Keyser, and Gerstein (2006) and also Lovejoy and Steuerwald (1997), argue that negative experiences within our interpersonal environment can lead to the devel- opment and maintenance of dysfunctional cognitions. For example, the lack of a responsive caregiver in early life can resultinthedevelopmentofadysfunctionalattitude style, which is known to increase the risk of developing BD (Morriss, van der Gucht, Lancaster, & Bentall, 2009).

A negative interpersonal environment may also impact on an individual’s ability to regulate their emotions, with individuals adopting dysfunctional strategies being more at risk of developing BD (Cooke & Jones, 2009).

The presence or lack of social support is another envir- onmental factor that has been shown to impact on BD. Re- ceiving low social support has been found to result in a longer recovery from depressive episodes and an increase in the likelihood of relapse and recurrence of episodes (Cohen, Hammer, Henry, & Daley, 2004; Johnson, Lundstrom, Aberg-Wistedt, & Mathe, 2003; Johnson, Meyer, Winett, & Small, 2000).

Previous work demonstrates evidence of both cognitive and interpersonal factors impacting on the development and course of BD. However, less is known about the inter- action of these factors in identifying at-risk individuals.

This study of an analogue sample will attempt to under- stand such interactions when predicting the risk of devel- oping BD. It is suggested that the role cognitive factors take in predicating this risk will be mediated by interper- sonal factors.

METHOD Participants In total, 725 students, aged between 16 and 63 years old, from across the Lothian region in Scotland participated in the research. As the onset of bipolar typically occurs prior to the early thirties, those participants over 35 were excluded. The participants were also required to complete at least all but one of the measures in the survey. Thus, the core sample was made up from 549 students (158 men, 391 women) aged between 16 and 35 years old with a mean age of 22.89 (SD = 4.31).

Materials Internal State Scale (Bauer et al., 1991) This 17-item questionnaire is a self-report scale for manic symptoms. It assesses the current mood state ofthe individual. Each item is scored on a 100-mm visual analogue line with reference to their experience in the pre- vious 24 h. The scale has four subscales: activation, per- ceived conﬁdence, well-being and depression index. The internal consistency for these subscales is between 0.81 and 0.92 (Bauer et al., 1991). Inventory of Interpersonal Problems (Horowitz, Rosenberg, Baer, Ureno, & Villasenor, 1988) This questionnaire was designed to assist in the identiﬁ- cation of interpersonal sources of distress. The Inventory of Interpersonal Problems (IIP) is made up of 78 items associated with‘it’s hard for me’and 49 items of‘these are things I do too much’. These items are scored from 0 (‘not at all’)to4(‘extremely’). The IIP provides a total score, which is made up from the six subscale scores:

domineering/controlling, vindictive/self-centred, cold/ distant, socially inhibited, non-assertive, overly accommo- dating, self-sacriﬁcing and intrusive/needy. A very good internal consistency (a= 0.82 to 0.94) and test–retest reli- ability (r= 0.80 to 0.90) have been reported by the original authors.

Behavioural Inhibition System/Behavioural Activation System (Caver & White, 1994) This scale aims to assess the behavioural systems under- lying anxiety and impulsivity, the BIS and the BAS, respectively. It has 24 items, which are rated on a 4-point Likert Scale from‘very false for me’(1) to‘very true for me’(4). The BIS/BAS has four factors: behavioural inhib- ition (BIS), BAS drive, BAS reward response and BAS fun seeking. A test–retest reliability score of between 0.59 and 0.69 has been reported for these subscales (Caver & White, 1994).

Hypomanic Personality Scale (Eckblad & Chapman, 1986) This scale was designed to identify individuals predis- posed to hypomanic episodes and BD. Respondents pro- vide true/false responses to 48 items associated with stable characteristics and recurrent experiences. Aﬁnal total score is derived for each participant. Eckblad and Chapman (1986) reported a test–retest reliability of 0.81 and an internal consistency score of 0.87 for this scale.

Mood Disorder Questionnaire (Hirschfeld et al., 2000) The Mood Disorder Questionnaire (MDQ) assesses the lifetime history of manic/hypomanic symptoms. It is made up of 13 yes/no items, where participants indicate whether they experience them or not and whether they currently have them. There then follows an evaluation by the participants as to whether the 13 items have caused them any problems (‘no problems’to‘serious problems’).

A high-risk individual would score 7 or more on theﬁrst 412M. Schwannaueret al.

Copyright © 2011 John Wiley & Sons, Ltd.Clin. Psychol. Psychother.18,411–417 (2011) part, answer yes to currently experiencing the symptoms and answer moderate or serious to the problems caused by these symptoms. A high internal consistency (a= 0.90) has been reported by the original authors for the MDQ.

Relationship Scale Questionnaire (Grifﬁn & Bartholomew, 1994) The Relationship Scale Questionnaire assesses individ- ual attachment styles in close relationships. Items are scored on a 5-point Likert scale as to whether the state- ment is‘not at all like me’(1) to‘very much like me’(5).

The scale has four subscales: fearful, dismissing, secure and preoccupied. The internal consistency for this scale is modest and is reported as between 0.41 and 0.71 by Grifﬁn and Bartholomew (1994).

Regulation of Emotion Questionnaire (Phillips & Power, 2007) The Regulation of Emotion Questionnaire (ERQ) is a self-report instrument that measures both internal and ex- ternal functional and dysfunctional emotion regulation strategies. It is a 21-item scale rated on a 5-point measure from‘never’to‘always’. The ERQ has four subscales: in- ternal dysfunctional, internal functional, external dysfunc- tional and external functional. A Cronbach alpha score of 0.66–0.76 was found for the subscales of the ERQ by the original authors.

Social Support Questionnaire (Sommer & Fydrich, 1991) The Social Support Questionnaire (SSQ) has 32 items and was designed to measure an individual’s perception of the social support available to them and that they re- ceive. Each item is scored on a 5-point Likert scale from 0 (not at all) to 4 (exactly right). Fydrich, Geyer, Hessel, Sommer, and Brahler (1999) reported an internal consistency score of between 0.81 and 0.93 for the sub- scales of the SSQ.

Procedure Students in nine further and higher educational institutes within the Lothian area were invited to participate in the project. Students completed the measures through a Web-based platform, which they accessed through a link sent to them via e-mail or posted on their institution’s intranet page.

Analyses An initial regression analysis was followed up with struc- tural equation modelling (SEM) to understand the path- ways to developing BD. For the regression analysis, arepresentation of risk of developing BD was derived through creating an Internal State Scale (ISS) total score through the sum of the four subscales.

There was no major concern over multicollinearity due to all variables correlating at less than 0.65 with one another, tolerances of>0.4 (apart from SSQ social integration with a tolerance of 0.33) and Variance Inﬂation Factors (VIFs) of <2.5 (excluding SSQ social integration with a VIF of 3.17).

Structural equation modelling simultaneously estimates the relationship between observed and latent variables (the measurement model) and among latent variables themselves (the construct model), providing estimates of both direct and indirect or mediating effects. Since a pre- liminary data analysis revealed that all variables in the model satisﬁed the assumptions of normality and multi- collinearity and there were no signiﬁcant outliers, a max- imum likelihood was used for the data analysis. No differences were found between the bootstrapped esti- mates of the standard errors and those obtained in the maximum-likelihood model, indicating that the model did not violate the assumption of a normal distribution.

MPlus offered the additional beneﬁt of formally testing the signiﬁcance of direct and indirect effects.

For the SEM, the modelﬁt will be measured by the com- parativeﬁt index (CFI), which provides aﬁt index be- tween 0 and 1, with values greater than 0.95 indicating a goodﬁt (Hu & Bentler, 1999). The standardized root mean square residual (SRMR) will also be reported, which mea- sures the difference between the observed and predicted covariance. Zero indicates a perfectﬁt, whereas values of less than 0.08 are considered a goodﬁt (Hu & Bentler, 1999). The root mean square error of approximation is also commonly reported along with the CFI. However, in this work, it will not be used as it can often be misleading with a small sample size and degrees of freedom.

PASW version 18 (SPSS, Inc., 2009, Chicago, Il, US) was used for the regression analysis and MPlus version 6 (MPlus, Muthén & Muthén, 2010) for the SEM analysis. RESULTS The descriptive characteristics for the whole sample on each measure used in this study are shown in Table 1. In line with the cut-off scores for the ISS as suggested by Bauer et al. (2005), the ISS scores indicative of mania (ISS-A) or depression (ISS-WB) are well below the cut-off points for clinical scores but comparable with similar non-clinical samples investigating predictors of bipolar personality (e.g., Jones & Day, 2008; Cooke & Jones, 2009).

There were three steps to the hierarchical linear regres- sion analysis. In theﬁrst stage, along with the age and gender, a set of cognitive variables was entered; in the sec- ond step, the cognitive factor of interpersonal problems was entered. Finally, in the third step, a set of interper- sonal variables was entered. 413 Behavioural Risk of Bipolar Disorder Copyright © 2011 John Wiley & Sons, Ltd.Clin. Psychol. Psychother.18,411–417 (2011) Table 2 shows the signiﬁcant predictors of ISS total for each stage of the analysis. In theﬁrst step, only BIS (b= 0.215,p<0.01) and MDQ total (b= 0.279,p<0.001) had an independent association. However, once IIP total is added in the second step, BIS no longer has an inde- pendent relationship (b= 0.137,p>0.05), whereas MDQ total (b= 0.257,p<0.001) and IIP total (b= 0.178, p<0.05) are shown to hold an independent relationship.

In the third step of the analysis, once the interper- sonal factors are added, no cognitive factor maintains their independent relationship [MDQ total (b= 0.149, p>0.05), IIP total (b= 0.017,p>0.05)]. Thirty-two per cent of the variance in ISS total is predicted by ERQinternal dysfunctional (b= 0.295,p<0.005), ERQ external dysfunctional (b= 0.147,p<0.05) and SSQ practical support (b=0.185,p<0.05). This model was signiﬁcant [F(20, 188) = 3.94,p<0.001]. Findings were conﬁrmed at the bootstrappedN= 5000 level. The suppression or re- placement of sets of signiﬁcant predictor variables by the stepwise inclusion of additional psychological factors indicates signiﬁcant interaction between these factors and possible indirect effects. To investigate these complex associations further, we used the SEM to testa priori models of indirect and mediation effects. Structural Equation Modelling: Measurement Model Conﬁrmatory factor analysis was carried out on the meas- urement model prior to testing the structural model. The conﬁrmatory factor analysis resulted in a moderate model ﬁt:w 2(4) = 32.70,p<0.001, CFI = 0.92, SRMR = 0.058. The loadings of each variable onto the relevant latent construct were all signiﬁcant atp<0.001 and ranged from 0.35 to 0.93. The correlation between the latent variables was 0.67. An inspection of the individual parameter estimates provides support for the hypothesized structure of the measurement model. The factor loadings were all statisti- cally signiﬁcant and of substantial magnitude, providing a meaningful and interpretable model. As the model pro- vided an adequateﬁt and parameters, it was used in the following structural model test. Structural Equation Modelling: Structural Model Figure 1 represents the structural construct model for risk of BD in this sample. The analyses revealed an excellent modelﬁt:w 2(18) = 38.46,p= 0.005, CFI = 0.96, SRMR = 0.052. Furthermore, the modiﬁcation indices for the model were modest and did not suggest changes to fur- ther improve the modelﬁt. Overall, the hypothesized model accounted adequately for the observed co-var- iances among the indicators, providing general support for the hypothesized associations and effects. There were no unreasonable parameter estimates, such as negative variances or correlations greater than 1, and all appeared to be in the expected range of values.

Both the direct and total indirect effects were signiﬁcant.

A direct effect between ISS and practical support, and also dysfunctional regulation of emotions, is clearly observ- able. Full mediational effects of interpersonal problems by both practical social support (b= 0.07,p<0.01) and dysfunctional regulation of emotions (b= 0.55,p<0.05) were evident. Clear mediational effects are also observ- able for a previous history of mood disorder, again by practical social support (b= 0.05,p<0.05) and dysfunc- tional regulation of emotions (b= 0.30,p<0.05).

Signiﬁcant pathways were identiﬁed between BIS and Table 1. Means and standard deviations for the measures used Mean SD ISS perceived conﬂict132.05 83.75 ISS well-being131.55 72.41 ISS activation99.96 79.95 ISS depression index54.80 57.39 ISS total score417.18 130.49 BISBAS BIS21.29 2.65 BISBAS reward responsiveness14.25 2.25 BISBAS drive12.24 2.10 BISBAS fun seeking13.04 1.79 HPS total score19.74 7.50 RSQ secure14.58 2.95 RSQ fearful12.04 3.50 RSQ preoccupied11.58 2.81 RSQ dismissing16.37 3.31 ERQ internal dysfunctional13.36 3.67 ERQ internal functional15.31 3.28 ERQ external dysfunctional7.93 2.65 ERQ external functional17.40 4.41 SSQ emotional support4.04 0.75 SSQ practical support2.34 0.86 SSQ social integration3.43 0.83 SSQ social strain2.42 0.77 ISS = Internal State Scale; BIS = Behavioural Inhibition System; BAS = Behavioural Activation System; HPS = Hypomanic Personality Scale; RSQ = Relationship Scale Questionnaire; ERQ = Regulation of Emotion Questionnaire; SSQ = Social Support Questionnaire.

Table 2. Results from hierarchical regression StepR 2 FSig. predictorsbt 10.18 5.64 BIS 0.22 2.72* MDQ total0.28 3.87** 20.21 5.79 MDQ total 0.26 3.59** IIP total0.18 2.41* 30.32 3.94 ERQ internal dysfunctional 0.30 3.00** ERQ external dysfunctional0.15 2.00* SSQ practical support0.19 2.12* *p<0.05, **p<0.01.

414M. Schwannaueret al.

Further pathway identiﬁcation demonstrated that BIS did not impact on internal state indirectly through these fac- tors. Therefore, BIS was identiﬁed as a moderator to both interpersonal problems and previous history of a mood disorder in predicting bipolar risk. It appears that in this sample, social support and emotion regulation variables had a direct effect on indicators of risk of BD, with the attachment, hypomanic personality and behavioural acti- vation variables either being overshadowed or left in the role of mediators and moderators.

DISCUSSION The aim of this study was to predict the risk of developing BD using cognitive, developmental and interpersonal fac- tors. One of the primary goals was to show how these fac- tors impact on each other when predicting this risk and whether speciﬁc interaction of these variables may be in- dicative of potential psychological processes associated with bipolar mood instability and dysregulation.

Theﬁndings of this study demonstrate clear direct effects of dysfunctional regulation of emotion and a lack of practical support to predicting bipolar risk. The ana- lysis also highlights that interpersonal factors act as full mediators to the effect of cognitive factors on bipolar risk.It is interesting that a previous history of mood disorder does not have a direct path to predicting future BD but is fully mediated. It would appear that current negative interpersonal experiences are more signiﬁcant in the de- velopment of the disorder compared with past experi- ences. Suchﬁndings would suggest the need for clinicians to attend to key interpersonal experiences when working with this population and to be mindful of the im- pact of cognitive and behavioural aspects of the indivi- duals functioning on their interpersonal context.

This study also supported earlier work by researchers such as Van der Gucht et al. (2009) and Alloy et al.

(2008), identifying the impact of BIS in predicting risk of BD. However, this work demonstrated that when BIS was considered along with other interpersonal variables rather than having a direct path to bipolar risk, BIS moderated the impact of interpersonal problems and previous mood disorder. The impact of moderators are often underreported in the literature; however, for a full conceptualization of the pathways to BD in young people, such factors must be accounted for.

One key limitation to this work is that all participants recruited for this study were studying at higher or further educational institutions in the Lothian area; therefore, cau- tion should be taken when attempting to generalize to other populations. Further research should also draw on a wider population in order to be able to generalize the Internal state Dysfunctional Regulation of Emotion External Internal Depression index Well being Perceived confidence Previous mood disorder BIS Interpersonal Problems Practical social support -.92 .30 .25 -.35 .17 -.21 .33 .60 .75 .40 -.76 -.89 .62 Chi2 = 38.46, p<.005 CFI= .96 SRMR= .052 Figure 1. Structural equation modelling of bipolar risk 415 Behavioural Risk of Bipolar Disorder Copyright © 2011 John Wiley & Sons, Ltd.Clin. Psychol. Psychother.18,411–417 (2011) ﬁndings. However, this non-clinical sample also has the advantage that the participants are not affected by the im- pact of a diagnosis, the effects of psychotropic medication and the use of mental health services.

This study contributes to the clariﬁcation of the rela- tionship between cognitive and interpersonal problems when attempting to predict BD. More speciﬁcally, clear mediating effects of interpersonal factors on cognitive factors have been highlighted. Suchﬁndings suggest that earlier work identifying cognitive risk factors to BD only represent a partial picture and that more work is needed to further investigate the impact of these interpersonal factors. The knowledge from this work needs to be utilized in the formulation and treatment strategies of young people at risk of developing BD.

It will further be important to move these concepts of risk and prediction of bipolar mood instability into clinical samples at ultrahigh risk of developing bipolar mood instability and groups of individuals following early bipolar episodes to further investigate the speciﬁc association of these underlying psychological factors in the context of psychological treatments and recovery from BDs.

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