Implicit motives and cognitive variables: Specific links to vulnerability for unipolar or bipolar disorder

Psychiatry Research 215 (2014) 61–68

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Implicit motives and cognitive variables: Specific links to vulnerability for unipolar or bipolar disorder Kristina Fuhr a,n, Martin Hautzinger a, Thomas Daniel Meyer b a b

Clinical Psychology and Psychotherapy, Department of Psychology, University of Tübingen, Schleichstraße 4, D-72076 Tübingen, Germany Institute of Neuroscience, Newcastle University, Newcastle Upon Tyne, United Kingdom

art ic l e i nf o

a b s t r a c t

Article history: Received 5 October 2012 Received in revised form 29 August 2013 Accepted 10 October 2013 Available online 18 October 2013

Cognitive variables contribute to the etiology of affective disorders. With the differentiation between explicit and implicit measures some studies have indicated underlying depressogenic schemata even in bipolar disorders. We tested for differences in implicit motives and cognitive variables between patients with remitted unipolar and bipolar disorder compared to controls and in a high-risk sample. Additionally we investigated whether affective symptoms relate to those variables. We cross-sectionally examined N¼ 164 participants (53 with bipolar disorder, 58 with major depression, and 53 without affective disorders) and a high-risk sample (N ¼49) of adolescent children of either parents with unipolar or bipolar disorder or of healthy parents. The Multi-Motive-Grid was used to measure the implicit motives achievement, affiliation, and power, in addition to the cognitive measures of self-esteem, dysfunctional attitudes, and perfectionism. Unipolar and bipolar groups did not differ from healthy controls in implicit motives but showed higher scores in the cognitive factors. Adolescents at high risk for unipolar disorder showed lower scores in the power and achievement motives compared to adolescents at low risk. Subsyndromal depressive symptoms were related to the cognitive variables in both samples. Our results underline the importance of cognitive-behavioral treatment for both unipolar and bipolar disorder. & 2013 Elsevier Ireland Ltd. All rights reserved.

Keywords: Cognitive vulnerability High risk Dysfunctional attitudes

1. Introduction Cognitive vulnerability factors like dysfunctional attitudes or negative self-esteem are considered to be highly relevant for unipolar and bipolar disorders (Alloy et al., 2006). For example, currently depressed patients with bipolar disorder show similar automatic negative thoughts, dysfunctional attitudes (Hollon et al., 1986), and attributional style as currently depressed patients with unipolar disorder (Reilly-Harrington et al., 1999). Even in manic or euthymic episodes of bipolar illness, patients display a more pronounced negative cognitive style or lower self-esteem compared to healthy controls (Scott et al., 2000; Van der Gucht et al., 2009; Lex et al., 2011). A negative cognitive style found during remission might reflect residual depression (Lex et al., 2008). Nonetheless, differences in cognition have been observed after controlling for residual symptoms (Van der Gucht et al., 2009). In conclusion, individuals with bipolar and unipolar disorder seem to show more similarities than differences in their self-esteem and cognitive style, however, both differ from healthy controls. When looking at personality factors, studies have found that individuals with unipolar and bipolar disorders differ from healthy

n

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0165-1781/$ - see front matter & 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.psychres.2013.10.001

participants. For instance, participants with unipolar disorder were more perfectionistic compared to controls (Hewitt and Flett, 1991). Patients with bipolar disorder showed higher scores in goal-attainment compared to subjects with unipolar depression (Lam et al., 2004) and were high academic achievers (Johnson, 2005). Furthermore, different dimensions of perfectionism (the need to be perfect and the need to meet expectations of other people) predicted unipolar symptoms while other dimensions (the need to meet expectations of other people and the need for others to be perfect) predicted bipolar symptoms (Hewitt et al., 1998). However, overall few studies have directly compared patients with unipolar versus bipolar depression with respect to personality variables such as perfectionism, achievement or affiliation motivation. Furthermore, most studies have relied on explicit measures in which the participants were asked to directly rate their own experiences and behaviors. Implicit measures allow for indirect assessment of personal attitudes and processes so that there is less conscious awareness than with individual self-evaluation. The results indicate that implicit and explicit cognitive processes seem to be two different aspects of cognitive vulnerability (Haeffel et al., 2007). When looking at implicit motives, no strong associations with explicit cognitions have been found (McClelland et al., 1989). Using implicit measures, individuals with remitted depression showed an attention bias towards negative stimuli compared to controls

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(Ingram and Ritter, 2000; Gemar et al., 2001). Concerning bipolar disorders, some studies have also only found differences between patients with remitted bipolar disorder and controls in the implicit assessment of self-esteem (e.g. in the Pragmatic Inference Task, where subjects with bipolar disorder made more internal attributions for negative than for positive events like subjects with unipolar disorder), but not in explicit ones (Pardoen et al., 1993; Knowles et al., 2007). Nevertheless, some studies have reported finding no differences between patients with bipolar disorder and controls in implicit measures (Lex et al., 2008; Van der Gucht et al., 2009). In summary, the results for unipolar and bipolar disorders are inconsistent. However, some authors suggest similar depressogenic processes in both disorders (Neale, 1988). Furthermore, implicit motives are of clinical relevance as they are associated with clinical impairment (Michalak et al., 2006). But, to our knowledge, no one has ever studied implicit motives as potential vulnerability factors for affective disorders. On one hand, cross-sectional studies about cognitive factors in remitted patients provide some evidence of potential vulnerability factors for affective disorders (Ingram and Siegle, 2009). On the other hand, any observed differences from cross-sectional studies could just be a consequence of or a residual symptom from the illness (Lewinsohn et al., 1981; Just et al., 2001). Thus, results from prospective longitudinal studies are important to ascertain if risk factors cause the development of affective disorders. However, if such differences are observed additionally before the onset of a disorder, they can be considered etiologically relevant risk factors (Kraemer et al., 1997; Ingram and Price, 2001). Therefore, unaffected offspring of affected parents are a suitable high-risk sample in whom to study vulnerability factors (Garber and Flynn, 2001). Our study combines both the remitted and the high-risk approaches. The primary goal of the study was to test whether both patients with remitted bipolar and unipolar disorder show heightened levels of implicit motives that are different from healthy controls. Secondary goals were (a) to replicate and extend prior results with respect to other explicit cognitive factors such as self-esteem and perfectionism, (b) to examine the association of residual depressive symptoms with cognitive and personality variables, and (c) to explore whether the results extend to a high-risk sample of nonaffected offspring.

2. Methods 2.1. Study 1 We used a cross-sectional design with three groups matched for age and gender.

2.1.1. Participants Participants were recruited through the outpatient clinic at the psychology department, through self-help groups, by an email newsletter to all members of the university, and via a newspaper article. Inclusion criteria were: (a) a DSM-IV (APA, 1994) lifetime diagnosis of major depression or bipolar-I or -II-disorder; (b) Patients were required to be in stable remission for at least Z 2 months. To be included into the healthy control group, absence of a lifetime history of any affective disorder was requested. Furthermore, participants (c) were between 18 and 65 years old and (d) provided informed consent and (e) agreed to be recorded for the structured interview. Exclusion criteria were (a) current scores from the Young Mania Rating Scale4 5 (YMRS, Young et al., 1978), Hamilton Rating Scale for Depression47 (HRSD, Hamilton, 1960), and Beck Depression Inventory II 414 (BDI-II, Beck et al., 1996) in order to ensure asymptomatic status at testing. Other exclusion criteria were (b) substanceinduced affective disorder or due to a general medical factor; (c) psychotic disorder; (d) current substance dependency. In total, 311 people were interested to participate in the study. As 94 people cancelled their participation due to lack of time or for other personal reasons, 217 attended the first diagnostic session. Afterwards, 53 participants were excluded because they fulfilled criteria for current hypo/mania or depression (n ¼14), had a non-affective psychotic disorder (n¼10), exceeded the pre-defined levels of

depressive or hypomanic symptoms on the YMRS or HRSD (n¼ 8), or on the BDIII (n¼ 17), or did not meet other criteria for participating in the study (n ¼4). As interviewer-based ratings and self-ratings of depression do not necessarily correspond (Enns et al., 2000), we excluded participants who reported high self-rated depression even if they had been classified as remitted via clinician-rating. The final sample consisted of 164 participants: Fifty-three patients met DSM-IV lifetime criteria for bipolar-I or -II-disorder, 58 patients met DSM-IV lifetime criteria for major depressive disorder, and 53 individuals were without any lifetime history of an affective disorder. The average age was M¼ 42.77 (S.D.¼ 12.54). The sample consisted of 66 men (40.2%) and 98 women (59.8%). Age and gender were equally distributed among the three groups, age: F (2,161)¼ 0.34, n.s., ω2 o 0.001; gender: Χ2(2) ¼0.64, n.s., V ¼0.06. Most of the participants (n¼ 121, 73.8%) had a high school degree. Individuals with unipolar disorder had experienced about 6 depressive episodes (M ¼ 5.78, S.D.¼ 6.97), while patients with bipolar disorder had 11 depressive episodes (M ¼11.12, S.D.¼ 15.43). Individuals with bipolar disorder reported further either 7 manic episodes associated with bipolar-I-disorder (M ¼ 6.57, S.D. ¼6.19) or 4 hypomanic episodes associated with bipolar-II-disorder (M¼ 3.83, S.D. ¼ 3.31). Forty-six (86.8%) patients with bipolar disorder and 26 (44.8%) patients with unipolar depression received psychopharmacological treatment. The medication dose was required to have been constant over the last 2 months so that potential differences between groups could not be attributed to changes in medication.

2.1.2. Materials and procedure All participants were individually tested. In the first session, the Structured Clinical Interview for DSM-IV (SCID-I; First et al., 1995) was used to confirm the presence or absence of a lifetime history of mood disorder and any other disorders. Diagnostic interviews were completed by clinical psychologists who had been trained in order to assure reliable assessment. Training included a 2-day workshop plus a minimum of 5 supervised diagnostic interviews. Of the recorded diagnostic interviews, 25% was used as a random sample to be rated by clinical psychologists who were blind according to the diagnosis. The interrater-reliability coefficients for the presence of a mood disorder was excellent (κ¼ 0.97). The Kappa was κ ¼ 1.00 for bipolar and κ¼ 0.92 for unipolar disorder. The presence and severity of current symptoms were assessed by the interviewer-based YMRS (Young et al., 1978) and the HRSD (Hamilton, 1960) for the last week. The YMRS cut-off for remission, defined at 5 points, is commonly used for defining remitted state (Bauer et al., 1991; Masand et al., 2008), Intra-Class-Correlation was acceptable (ICC¼ 0.65). For the HRSD, the cut-off for remission was defined at 7 points which has been previously used in studies with unipolar and bipolar disorders (Zimmerman et al., 2006; Lojko and Rybakowski, 2007). The intra-class-correlation for the HRSD was good (ICC¼ 0.85). The second session, in which implicit motives and cognitive variables were assessed, took place within the next 7 days. However to ensure that patients would continue to be in remission, the BDI-II self-rating scale was again used (Beck et al., 1996). The cut-off for remission at a score of 14 is commonly used in research with participants with unipolar (Beck et al., 1996; Viinamäki et al., 2002) and bipolar disorder (Wright et al., 2005). The internal consistency of the BDI-II in the present sample was 0.78. After completing the BDI-II, the participants completed a battery of different instruments. The Multi-Motive-Grid (MMG, Schmalt et al., 2000) was used to assess the implicit motives of power, affiliation, and achievement. It is a ‘semi-projective’ measure which includes ambiguous pictures in which the participant has to choose whether a set of statements worded in the third person characterizes the displayed situation. Thus, it reduces self-awareness and therefore also the effect of social desirability. The MMG has good internal consistency, test-retest reliability, and external validity (Schmalt et al., 2000; Sokolowski et al., 2000). The two avoidance and approach components of each motive were analyzed: Fear of failure (FF) and hope of success (HS), fear of rejection (FR) and hope of affiliation (HA), fear of power (FP) and hope of power (HP). The internal consistencies for the six components in the sample of the present study were between 0.71 and 0.79. For measuring explicit cognitive variables we used the Dysfunctional Attitudes Scale (DAS; Weissman, 1979) which is based on the cognitive triad for depression by Beck. The DAS consists of two subscales, the achievement (DAS-A) and the dependency (DAS-D) scale which were used in our study. The internal consistencies in the present study were good (Cronbach's alpha: 0.98 for the total DAS score, 0.92 for DAS-A, and 0.76 for DAS-D). Furthermore, the Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1965) was used. This self-report measure of global self-esteem has been internationally used in a wide range of studies focused on affective disorders (e.g. Schmitz et al., 2003; Blairy et al., 2004). The internal consistency in our study was 0.86. Participants also completed the Multidimensional Perfectionism Scale (MPS, Hewitt and Flett, 1989; Hewitt et al., 1991) which is used to measure specific dimensions of perfectionism. We used self-oriented perfectionism (SOP) and socially prescribed perfectionism (SPP) in a short form with eight and ten items respectively (Stöber, 2000, 2002). The internal consistencies in our sample were 0.87 (SOP) and 0.82 (SPP). It was ensured that the experimenter at the second session was kept blind to diagnostic status. Participants received financial compensation (40 €) for their participation.

K. Fuhr et al. / Psychiatry Research 215 (2014) 61–68

2.1.3. Statistical analysis Analyses were done with SPSS 15 for Windows. One-way ANOVAS were calculated to analyze group differences. Post-hoc Bonferroni tests followed if a significant effect was detected. ANCOVAS were calculated if covariates were significantly correlated with the dependent variables, and if there were no a priori group differences in the covariate (Miller and Chapman, 2001). Effect sizes were computed as follows (Field, 2005): ω2 ¼

SSM  ðdf M ÞMSR SST þ MSR

The symbols indicate the following measures: SSM ¼ between-group effect; SST ¼ total amount of variance; MSR ¼ mean square; d.f.M ¼degree of freedom for the effect of the model. For the interpretation of effect sizes, ω2 ¼ .01 indicates a small effect, ω2 ¼ .06 an average effect, and ω2 ¼.14 a great effect (Cohen, 1988). The testing was two-tailed using p o 0.05 to indicate significance. All p-values below p ¼0.10 are reported. All others are indicated as n.s.

2.1.4. Power analysis According to the GnPower program (Faul et al., 2007), a minimum total sample size of N ¼ 154 was needed to find a medium effect with 80%. Sample 1 was therefore large enough.

2.2. Study 2 As outlined, we extended the study by using the same methodology for a highrisk sample which was matched to a control group, as well being matched according to age and gender. The materials and procedure, as well as statistical analyses, were the same for this study as described in the main study above.

2.2.1. Participants The high-risk sample consisted of unaffected biological children of a parent having the diagnosis of either unipolar or bipolar disorder or of healthy parents. The participants were recruited by asking participants in Study 1 if they had children in the relevant age range and if they would agree that we invite them to participate in the study. The inclusion criteria were: (a) a parent with a DSM-IV diagnosis of major depressive or bipolar disorder (high-risk) or parents without a lifetime history of any affective disorder (low-risk); (b) age 14–25 years; (c) informed consent and (e) agreement to be recorded for the structured interview; (d) children under the age of 18 needed the agreement of a caregiver to participate in the study. As in the patient sample, offspring participants were excluded if their scores were as follows: (a) YMRS4 5, HRSD 47, or BDI-II 4 14. Further exclusion criteria were: (b) no biological first-degree relative available to ascertain diagnostic status; (c) psychotic disorder (schizophrenia or schizoaffective disorder) in parents or offspring; (d) substance dependency in the offspring. Offspring were excluded if they had a lifetime history of any affective disorder. In total, 77 people were interested to participate in the study. As seven of them cancelled their participation due to lack of time or for other personal reasons, 70 attended the first diagnostic session. Afterwards, 18 participants were excluded for the following reasons: fulfilling criteria for a lifetime history of an affective disorder (n ¼8); fulfilling criteria for a psychotic disorder (n¼ 1) or substance dependency (n¼ 1); additionally we excluded participants who originally were supposed to be in the low-risk group (with unaffected parents) but who reported having a first-degree relative with a lifetime history of an affective disorder without us being able to verify this diagnosis (n¼8). After the second session, three further participants were excluded because they had BDI-II scores414. In total, 49 participants took part in the study: Sixteen individuals had a parent with bipolar disorder, 15 individuals had a parent with unipolar disorder, and 18 were children of affectively healthy parents. The average age was M ¼ 18.59 (S.D.¼3.51). The sample consisted of 23 males (46.9%) and 26 females (53.1%). Age and gender were equally distributed among the three groups, age: F (2,46) ¼ 0.28, n.s. ω2 o0.001; gender: Χ2 (2) ¼1.65, n.s., V ¼ 0.18. Most of the participants (n¼34, 69.4%) had a high school degree or were currently in high school.

3. Results 3.1. Study 1 The means and standard deviations for all symptom measures, implicit measures, and cognitive variables are displayed in Table 1 (including the relevant information on one-way ANOVAs). In the text, we report the results of the ANCOVAs.

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3.1.1. Affective symptoms To check the symptomatic status, we first tested for potential group differences in affective symptoms. Despite the rigorous definition of remission, significant differences were still found between the groups for residual depressive symptoms (Table 1). Even on this low level the unipolar and the bipolar groups still reported more depressive symptoms in the BDI-II than the control group (unipolar group vs. control group: po0.001, bipolar group vs. control group: p¼0.009). No significant differences were observed between the unipolar and bipolar groups. Similar results were obtained concerning the HRSD; the unipolar group had higher scores compared to both the control group (po0.001), and the bipolar group (p¼0.008). The bipolar group also had higher scores than the control group (p¼0.040). Concerning residual hypomanic symptoms (YMRS), no differences were observed.

3.1.2. Implicit motives In line with our primary goal we next examined group differences with respect to the implicit motives for power, affiliation, and achievement. First we looked at the positive ‘hope’ components, and then at the ‘fear’ motives. As age correlated with hope of success (HS), r ¼0.22, p ¼0.005, hope of affiliation (HA), r ¼0.23, p ¼0.003, and hope of power (HP), r ¼0.20, p ¼0.012, we conducted ANCOVAS. For HS, age had an effect, F (1,160)¼ 8.12, p¼ 0.005, ω2 ¼ 0.042. After controlling for age, the groups did not significantly differ in HS, F (2,160) ¼0.64, n.s., ω2 o 0.001. This was also the case for HA, in which age had an effect, F (1,160) ¼9.15, p¼ 0.003, ω2 ¼0.047, but after controlling for age, there was no main effect of group, F (2,160) ¼2.38, p ¼0.096, ω2 ¼0.016. Age again showed an effect on HP, F (1,160)¼ 6.50, p¼ 0.012, ω2 ¼0.033. As with the other components, after controlling for age, the groups did not significantly differ, F (2,160) ¼ 0.61, n.s., ω2 o0.001. For fear of power (FP), fear of failure (FF), and fear of rejection (FR) no correlates were identified, therefore ANOVAS were conducted. However, the groups did not differ in any of the three implicit fear motives (Table 1).

3.1.3. Cognitive variables Differences were found in the total DAS score, the DAS-A (achievement), and the DAS-D (dependency). As age correlated with the DAS, r ¼  0.19, p ¼0.013, ANCOVAS were conducted. Age had an effect on the DAS, F (1,160) ¼7.14, p¼ 0.008, ω2 ¼ 0.034, but groups still differed after controlling for age, F (2,160) ¼ 5.30, p¼ 0.006, ω2 ¼0.048. Patients with unipolar disorder showed higher DAS scores than the controls (p ¼0.011); individuals with bipolar disorder however did not significantly differ from participants with unipolar disorder (n.s.) or healthy controls (p ¼0.056). Age also correlated with the DAS-A, r ¼  0.16, p¼ 0.040. Again, we observed an effect of age on the DAS-A in the ANCOVA, F (1,160) ¼4.90, p ¼0.028, ω2 ¼0.022, and an effect of the group after controlling for age, F (2,160) ¼ 4.32, p ¼0.015, ω2 ¼ 0.038. Posthoc analyses showed no differences between the unipolar and bipolar groups; the unipolar group scored higher than the control group (p¼ 0.028). Bipolar and control groups did not significantly differ from each other (p ¼0.079). As both age (r ¼  0.24, p ¼0.002) and YMRS scores (r ¼0.17, p¼ 0.026) significantly correlated with the DAS-D, an ANCOVA was conducted. We found an effect of age on the DAS-D, F (1,159) ¼ 10.76, p ¼0.001, ω2 ¼0.053, however, there was no significant effect of the covariate YMRS scores on the DAS-D, F (1,159) ¼ 3.03, p¼ 0.083, ω2 ¼0.011. After controlling for the covariates, we also found a group effect, F (2,159) ¼4.66, p ¼0.011, ω2 ¼0.040. The unipolar and bipolar groups did not differ significantly from each

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K. Fuhr et al. / Psychiatry Research 215 (2014) 61–68

Table 1 Means and standard deviations of affective symptoms, implicit motives, and cognitive variables in the patient sample.

Affective symptoms BDI-IInnn YMRS HRSDnnn Implicit motives MMG FP MMG HP MMG FF MMG HS MMG FR MMG HAT Cognitive variables DASnn DAS-Dn DAS-An RSESnnn MPS – SOPnn MPS – SPPnn

Bipolar (n¼ 53)

Unipolar (n¼ 58)

Controls (n ¼53)

M (S.D.) 4.38 (4.17) 1.11 (1.48) 2.28 (2.01)

M (S.D.) 5.63 (3.78) 1.29 (1.35) 3.41 (2.18)

M (S.D.) 2.28 (2.65) 0.81 (1.26) 1.34 (1.56)

5.15 7.72 3.92 7.06 4.49 6.72 113.08 28.40 41.62 31.72 33.45 24.19

(2.62) (3.05) (2.72) (2.95) (2.94) (2.20) (29.72) (7.94) (16.54) (4.19) (8.84) (7.83)

5.17 7.34 3.83 6.88 4.59 5.88 115.91 28.36 42.64 31.81 33.95 23.48

F 12.09 1.75 15.90

ω2 0.120 0.009 0.154

(2.64) (2.87) (2.54) (2.66) (2.84) (2.44)

4.68 7.92 3.62 7.42 4.30 6.79

(3.01) (2.70) (3.10) (2.62) (3.34) (2.72)

0.55 0.58 0.16 0.54 0.12 2.38

o0.001 o0.001 o0.001 o0.001 o0.001 0.017

(34.55) (8.43) (17.92) (5.07) (7.55) (9.08)

99.55 24.42 34.75 34.83 28.92 19.36

(21.45) (6.70) (11.96) (3.30) (7.93) (6.99)

4.85 4.69 4.01 9.20 6.31 5.62

0.045 0.043 0.035 0.091 0.061 0.053

Bonferroni (B ¼U) 4C U 4 B4 C

B ¼ U4C (B ¼U) 4C B ¼ U4C (B ¼U) o C (B ¼U) 4C (B ¼U) 4C

Notes: M ¼ mean; S.D. ¼standard deviation; n¼ sample size; B ¼Bipolar; U¼ Unipolar; C ¼ Controls; BDI-II ¼Beck Depression Inventory II; YMRS¼ Young Mania Rating Scale; HRSD¼ Hamilton Rating Scale for Depression; DAS ¼Dysfunctional Attitude Scale; DAS-D¼ dependency; DAS-A ¼ achievement; RSES ¼ Rosenberg Self-Esteem Scale; MPS¼ Multidimensional Perfectionism Scale; SOP¼ Self-oriented Perfectionism; SPP¼ Socially Prescribed Perfectionism; MMG ¼ Multi-Motive-Grid; MMG FP ¼ fear of power; MMG HP¼ hope of power; MMG FF ¼fear of failure; MMG HS ¼hope of success; MMG FR fear of rejection; MMG HA ¼hope of affiliation; d.f.M ¼ degrees of freedom for the model; d.f.R ¼ degrees of freedom for the residual sum of squares; d.f.M, d.f.R ¼ 2,161 except for BDI-II: 2,160; Results of the one-way ANOVAs are displayed. T

p o0.10. po 0.05. nn p o0.01. nnn p o0.00.1. n

other in the DAS-D, but both showed higher scores than the control group (p¼ 0.024, p ¼ 0.027, see also Table 1). Looking at self-esteem, overall we found a significant main effect of group (Table 1). The unipolar and bipolar groups did not differ from each other but both clinical groups had lower RSES scores than the control group (p ¼0.001, p¼ 0.001). For perfectionism, we observed significant group differences in both SOP and SPP (Table 1). As the YMRS scores were significantly associated with SOP, r ¼ 0.18, p ¼0.019, we used ANCOVAS. The covariate showed no significant effect on SOP, F (1,160) ¼3.84, p ¼0.052, ω2 ¼0.016, but we decided to control for the YMRS. After controlling for the YMRS, we found significant group differences, F (2,160) ¼ 5.36, p ¼0.006, ω2 ¼ 0.049. The unipolar and bipolar groups did not differ with respect to their SOP scores but both groups showed higher SOP scores than the control group (p ¼0.004, p ¼ 0.014). We also found group differences for SPP (Table 1), in which individuals with unipolar and bipolar disorder showed higher scores than participants in the control group (unipolar group vs. control group: p¼ 0.023, bipolar group vs. control group: p¼ 0.007).

3.1.4. Residual depressive symptoms and their relation to cognitive variables As we found significant a-priori group differences for the residual depressive symptoms, assumptions for conducting ANCOVAs were not met (Miller and Chapman, 2001). Instead of conducting ANCOVAs, we used partial correlations to assess the effect of depressive symptoms while analyzing correlations between group and cognitive variables. Therefore, we recoded the group variable. We combined the unipolar and bipolar groups into one group and defined a dummy variable with the two values: ‘affective disorder’ (coded as ‘1’) and ‘control group’ (coded as ‘0’). Correlations between the BDI-II and the HRSD with the cognitive and personality variables, and the implicit motives are displayed in Table 2.

The point-biserial correlation between the BDI-II and the dummy variable, as well as between the HRSD and the dummy variable, was rpb ¼0.34, po 0.001. No correlations between subsyndromal depressive symptoms and implicit motives were found. The correlation between SOP and group (rpb ¼0.27, p ¼0.001) was only slightly reduced after controlling for the HRSD (rpb ¼0.23, p ¼0.003) but still significant. Slightly larger reductions were observed for the correlation between SPP and group (rpb ¼0.25, p¼ 0.001, after controlling for the HRSD: rpb ¼0.17, p¼ 0.029) and for the correlation between group and the RSES (rpb ¼  0.32, p o0.001, after controlling for the HRSD: rpb ¼  0.22, p ¼ 0.005). However, in both cases the association remained significant after taking into account residual depression. Looking at the self-rated depressive symptoms, the correlation between SOP and group (rpb ¼0.27, po 0.001) only marginally decreased after controlling for the BDI-II, rpb ¼0.22, p¼ 0.004. Slightly larger reductions were observed for SPP (rpb ¼0.25, p¼ 0.001, after controlling for the BDI-II: rpb ¼0.17, p ¼0.032), as well as for the RSES (rpb ¼  0.32, p o0.001, after controlling for the BDI-II: rpb ¼  0.20, p ¼0.010). In both cases however, the associations remained significant even after considering the self-reported residual depressive symptoms. When looking at the DAS, controlling for the effect of the BDI-II, the correlation between group and the DAS, (rpb ¼ 0.23, p ¼0.003) also decreased but remained significant, rpb ¼0.17, p ¼0.033. The correlation between group and the DAS-A (rpb ¼0.21, p ¼0.007) was also reduced after controlling for the BDI-II, rpb ¼ 0.15, p ¼0.054.

3.2. Study 2 The means and standard deviations for all symptom scales, implicit measures, and cognitive variables are displayed in Table 3 (including information from the ANOVAs).

K. Fuhr et al. / Psychiatry Research 215 (2014) 61–68

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Table 2 Correlations between depressive symptoms and cognitive variables in the patient sample. Variable

1. DAS

2. DAS-A

3. DAS-D

4. RSES

5. SOP

6. SPP

7. FP

8. HP

9. FF

10. HS

11. FR

12. HA

1. BDI-II 2. HRSD

0.23nn 0.14T

0.21nn 0.11

0.14T 0.15T

 0.43nnn  0.38nnn

0.19n 0.17n

0.29nnn 0.29nnn

0.05 0.06

0.11  0.01

0.07 0.12

0.08 0.01

0.09 0.04

0.13T 0.08

Notes: BDI-II ¼ Beck Depression Inventory II; HRSD ¼Hamilton Rating Scale for Depression; DAS ¼ Dysfunctional Attitude Scale; DAS-D¼ dependency; DAS-A ¼achievement; RSES ¼Rosenberg Self-Esteem Scale; MPS¼ Multidimensional Perfectionism Scale; SOP ¼Self-oriented Perfectionism; SPP ¼ Socially Prescribed Perfectionism; MMG ¼ MultiMotive-Grid; MMG FP ¼fear of power; MMG HP ¼hope of power; MMG FF¼ fear of failure; MMG HS¼ hope of success; MMG FR fear of rejection; MMG HA¼ hope of affiliation. T

p o 0.10. po 0.05. nn p o0.01. nnn p o 0.001. n

Table 3 Means and standard deviations of affective symptoms, implicit motives, and cognitive variables in the high risk sample.

Affective symptoms BDI-II YMRS HRSD Implicit motives MMG FP MMG HPn MMG FF MMG HSn MMG FR MMG HA Cognitive variables DAS DAS-D DAS-A RSES MPS – SOPnn MPS – SPP

Bipolar offspring (n ¼16)

Unipolar offspring (n¼15)

Control offspring (n¼ 18)

M (S.D.) 4.75 (4.86) 1.25 (1.39) 1.63 (2.31)

M (S.D.) 4.67 (4.40) 1.33 (1.40) 1.07 (1.53)

M (S.D.) 5.06 (4.05) 0.89 (1.41) 1.89 (1.68)

6.25 7.94 5.25 7.63 5.75 6.25 108.56 29.06 36.75 34.44 26.38 19.06

(1.61) (2.72) (2.35) (2.00) (2.74) (1.77) (21.34) (6.56) (10.13) (3.56) (6.08) (7.19)

5.47 6.60 4.00 6.13 5.47 5.87 114.40 29.13 41.53 32.47 25.33 22.87

(2.64) (1.80) (2.93) (2.59) (3.20) (1.81) (27.73) (8.25) (13.74) (4.31) (6.81) (9.33)

6.06 8.94 3.89 8.56 5.50 6.56 114.28 27.00 43.28 33.22 33.67 23.00

F 0.04 0.48 0.81

ω2 0.041 0.022 0.008

(3.67) (2.01) (3.22) (2.31) (3.13) (2.53)

0.32 4.59 1.13 4.56 0.04 0.44

o 0.001 0.128 .005 0.127 o 0.001 o 0.001

(30.75) (6.57) (15.74) (4.99) (7.51) (7.48)

0.25 0.49 1.04 0.81 7.46 1.27

o 0.001 o 0.001 0.002 o 0.001 0.209 0.011

Bonferroni

U oC U oC

(B ¼U) o C

Notes: M ¼mean; S.D.¼ standard deviation; n¼ sample size; B¼ Bipolar offspring; U¼Unipolar offspring; C¼ Control offspring; BDI-II ¼Beck Depression Inventory II; YMRS¼ Young Mania Rating Scale; HRSD¼ Hamilton Rating Scale for Depression; DAS ¼ Dysfunctional Attitude Scale; DAS-D¼dependency; DAS-A ¼ achievement; RSES ¼Rosenberg Self-Esteem Scale; MPS¼ Multidimensional Perfectionism Scale; SOP¼ Self-oriented Perfectionism; SPP¼ Socially Prescribed Perfectionism; MMG ¼Multi-Motive-Grid; MMG FP¼ fear of power; MMG HP ¼ hope of power; MMG FF ¼fear of failure; MMG HS ¼ hope of success; MMG FR fear of rejection; MMG HA ¼hope of affiliation; d.f.M ¼ degrees of freedom for the model; d.f.R ¼ degrees of freedom for the residual sum of squares; d.f.M, d.f.R ¼ 2,46; Results of the one-way ANOVAs are displayed. n

po 0.05. p o0.01.

nn

3.2.1. Affective symptoms Firstly, it is essential to note that there were no significant differences between the three groups in the BDI-II, HRSD, and YMRS scores (Table 3).

3.2.2. Implicit motives Differences were found for the HS and HP motive components, but not for HA, FF, FP, and FR (see Table 3). Since the groups did not differ in affective symptoms but significant correlations between mood and some implicit variables were observed (Table 4), ANCOVAs were conducted. In the ANCOVA with fear of rejection (FR), the covariate BDI-II showed an effect on FR, F (1,45) ¼4.78, p ¼0.034, ω2 ¼0.074, but no differences between groups were observed after controlling for the BDI-II, F (2,45) ¼0.06, n.s., ω2 o0.001. Age significantly correlated with fear of failure (FF), r ¼  0.29, p ¼0.040. As the HRSD also correlated with FF (Table 4), we used both covariates in the ANCOVA. Age had an effect on FF, F (1,44) ¼ 9.00, p ¼0.004, ω2 ¼0.117. The HRSD also had an effect on FF, F (1,44) ¼15.03, p o0.001, ω2 ¼ 0.206. The groups however did not differ in FF after controlling for the covariates, F (2,44) ¼ 1.18, n.s., ω2 ¼ 0.005.

As no statistically significant correlations were observed between age and HS or HP and between subsyndromal affective symptoms and HS or HP, we refer to the results of the ANOVAs in Table 3. The unipolar offspring showed lower HS scores than the control offspring (p ¼0.013) which was also the case for the HP scores (p ¼0.012). The other groups did not significantly differ from each other.

3.2.3. Cognitive variables No differences were found for any of the DAS scores or selfesteem. As significant correlations between mood and some cognitive variables were observed (Table 4), ANCOVAs were conducted. After controlling for self-rated depression using the BDI-II as a covariate, we still found no differences between groups for the total DAS score, F (2,45) ¼0.25, n.s., ω2 o0.001, but the covariate had an effect on the DAS, F (1,45) ¼4.25, p ¼0.045, ω2 ¼0.064. Looking at the subscale DAS-A, again the covariate BDI-II showed an effect, F (1,45)¼ 5.14, p ¼ 0.028, ω2 ¼0.077, but no significant group differences were observed, F (2,45) ¼1.06, n.s., ω2 ¼0.002.

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K. Fuhr et al. / Psychiatry Research 215 (2014) 61–68

Table 4 Correlations between depressive symptoms and cognitive variables in the high risk sample. Variable

1. DAS

2. DAS-A

3. DAS-D

4. RSES

5. SOP

6. SPP

7. FP

8. HP

9. FF

10. HS

11. FR

12. HA

1. BDI-II 2. HRSD

0.29n  0.02

0.32n 0.02

0.10  0.05

 0.30n  0.04

 0.01 0.20

0.31n  0.13

0.15 0.27T

 0.12  0.05

0.21 0.40nn

 0.06  0.15

0.31n 0.13

0.10  0.15

Notes: BDI-II ¼Beck Depression Inventory II; HRSD¼ Hamilton Rating Scale for Depression; DAS ¼ Dysfunctional Attitude Scale; DAS-D¼ dependency; DAS-A ¼achievement; RSES ¼Rosenberg Self-Esteem Scale; MPS¼ Multidimensional Perfectionism Scale; SOP¼ Self-oriented Perfectionism; SPP ¼ Socially Prescribed Perfectionism; MMG ¼ MultiMotive-Grid; MMG FP ¼fear of power; MMG HP¼ hope of power; MMG FF¼fear of failure; MMG HS ¼ hope of success; MMG FR fear of rejection; MMG HA¼ hope of affiliation. T

p o0.10. po 0.05. nn p o0.01. n

Since none of the symptom scales correlated with the DAS-D, ANCOVAs were not conducted for this subscale. While the BDI-II as a covariate had a significant effect on selfesteem RSES, F (1,45)¼ 4.65, p ¼0.036, ω2 ¼ 0.069, the high-risk groups did not differ in self-esteem after controlling for subsyndromal depressive symptoms, F (2,45) ¼0.89, n.s., ω2 o0.001. Concerning the perfectionism dimensions, as can be seen in Table 3, significant differences were found for SOP but not for SPP. As significant correlations between the affective symptoms and some perfectionism dimensions were observed (Table 4), ANCOVAs were again conducted. Age significantly correlated with SOP (r ¼0.31, p ¼ 0.033). We found an effect of the covariate age on SOP in the ANCOVA, F (1,45)¼ 4.91, p ¼0.032, ω2 ¼ 0.058. After controlling for age, we still found differences between the three groups, F (2,45) ¼7.47, p ¼0.002, ω2 ¼ 0.193. The unipolar and bipolar offspring both had significantly lower scores of SOP than offspring of affectively healthy parents (p¼ 0.003, p¼ 0.010). The covariate BDI-II showed an effect on socially prescribed perfectionism (SPP), F (1,45)¼4.92, p¼ 0.032, ω2 ¼0.073. No group differences were observed after controlling for the BDI-II scores, F (2,45) ¼ 1.32, n.s., ω2 ¼0.012. 3.2.4. Power analysis According to the GnPower program (Faul et al., 2007), a total sample size of N ¼49 (sample 2) has only 31% power to detect a significant effect (αo 0.05) with an effect size of 0.06.

4. Discussion The primary goal of the current study was to examine whether implicit motives for hope, affiliation and power can be considered vulnerability factors for unipolar and bipolar disorders. The secondary goals were to see if patients with mood disorders differ from healthy controls in other cognitive variables such as selfesteem, dysfunctional attitudes, or perfectionism; whether residual affective symptoms influences this association; and if the results extend to a high risk offspring sample. With respect to implicit motives, remitted patients with mood disorders did not differ from healthy controls. This contrasts with Michalak et al. (2006) who found associations between the avoidance components and psychopathology in general. Moreover, studies using other implicit procedures have found indications for depressogenic psychological processes in patients with bipolar and unipolar disorder (Kerr et al., 2005; Knowles et al., 2007). Interestingly, in our study the offspring of patients with unipolar depression reported lower levels of “hope for success” and “hope for power”. This puts them at higher risk for experiencing negative affect (Sokolowski et al., 2000; Elliot, 2006), especially at an age range where academic achievement and social roles are highly relevant for self-concept formation. To our knowledge this is the

first time that the MMG has been used in a clinical sample, so one question is whether it was sensitive enough to detect differences in the adult groups. With regard to our secondary question, we found that participants with a lifetime diagnosis of a major depressive or bipolar disorder had similar levels of negative cognitions, self-esteem, and perfectionism even during a stably remitted euthymic state. Whereas participants with major depression showed consistently higher scores in cognitive variables compared to controls, the bipolar group did not differ from controls in total DAS scores and DAS-achievement scores, but in those for DAS-dependency, selfesteem, and perfectionism. The results concerning the similarities between unipolar and bipolar disorders are in line with previous studies looking at the dysfunctional attitudes and self-esteem which underlie mood disorders (Scott and Pope, 2003; Blairy et al., 2004; Jones et al., 2005; Serretti et al., 2005). However, we did not find this in our high risk sample. Despite the small samples in Study 2 (power of 31%, see also 3.2.4.) this suggests that levels of self-esteem or dysfunctional attitudes are not risk factors for developing mood disorders. Nevertheless, this does not rule out that instability or reactivity of self-esteem or dysfunctional attitudes increase the risk for depression or mania (Segal et al., 2006; Knowles et al., 2007). Furthermore, in line with Van der Gucht et al. (2009) such negative cognitive styles could be a vulnerability factor for potentially developing severe depression in both affective disorders. Concerning perfectionism, both self-oriented and socially prescribed perfectionism were elevated in unipolar depression and bipolar disorder. This adds to previous findings from a sample of patients with affective disorder that in combination with stress, SOP and SPP predict depressive symptoms, (Hewitt et al., 1996). While indicators of perfectionism were more pronounced in remitted patients, we observed the opposite pattern – at least for self-oriented perfectionism – in the offspring of both patient groups. When comparing the mean scores of each group (see also Tables 1 and 3), the offspring of healthy controls showed a high level of self-oriented perfectionism similar to the remitted patients. The offspring of patients with affective disorders showed similar levels of perfectionism compared to the adult healthy control group. This could either be a cohort effect or may suggest that lower levels of SOP precede onset of mood problems. Normal development would then be rather characterized by a decrease from higher levels of SOP to lower ones over time. Although we found some differences between the patients and healthy controls in cognitive and personality variables, this reflected small to middle effect sizes. A high effect size for residual depression was observed despite the very low level of symptoms and with very strict criteria for remission (Vieta et al., 2008). We also found significant correlations between residual depressive symptoms and most of the psychological measures. However, even after controlling for the relationship with symptoms, we still found significant correlations between group status and negative

K. Fuhr et al. / Psychiatry Research 215 (2014) 61–68

cognitions, self-esteem, and perfectionism. Therefore the observed differences in psychological measures cannot totally be explained by group differences in residual symptoms. This relationship between subsyndromal depression and psychological variables was also observed in the small high-risk sample in which depressive symptoms showed substantial effects on self-esteem, dysfunctional attitudes, perfectionism, and some of the implicit motives. It is to be determined whether subsyndromal depressive symptoms and other vulnerability factors such as perfectionism (Hewitt et al., 1996), cognitive style (Alloy et al., 2006), or avoidance motivation (Michalak et al., 2006) increase the risk for mood episodes additively or whether they interact. The main limitations of the study are as follows. First, because of the cross-sectional nature, the study does not allow conclusions about the causality of the implicit and explicit cognitive, and personality factors for affective disorders. Second, the high risk sample was small limiting its power to detect potential differences between the unipolar, bipolar, and control offspring. Third, we only used one implicit measure and focused solely on motives. The use of other indicators such as priming effects might be promising in the study of vulnerability (Segal and Ingram, 1994). Fourth, we do not have any information on how many of the patients have had psychological treatments in the past potentially affecting the likelihood of finding differences. However, the current study also has some strengths such as the use of well validated tools and structured clinical interviews with demonstrated reliability. Furthermore, the sample sizes of Study 1 were large enough to even detect small to medium effects, and we applied very strict criteria to determine remission in patients. In conclusion, the results of our study suggest that in line with other studies there are more similarities between the vulnerability factors for unipolar and bipolar disorders than differences. However, future research needs to clarify under which conditions psychological factors such as self-esteem, perfectionism, motives or dysfunctional attitudes rather lead to depression or mania or increase the risk for both in vulnerable people. Last but not least the similar profile of psychological vulnerabilities in remitted patients with unipolar depression and bipolar disorder underlines the relevance and value of the use of cognitive-behavior treatment for unipolar and bipolar depression (Lam et al., 2005; Scott et al., 2006).

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