Center for Anxiety and Related Disorders, Department of Psychology, Boston University, Boston, Massachusetts
Departments of Psychology and Psychiatry and Behavioral Science, Emory University, Atlanta, Georgia, USA
Correspondence: Dr Heather Thompson-Brenner, Center for Anxiety and Related Disorders, Department of Psychology, Boston University, 648 Beacon Street, Boston, MA 02215, USA. Tel: +1 617 353 9236; fax: +1 617 353 9609; e-mail: ht141{at}hotmail.com
Declaration of interest None. Funding detailed in Acknowledgements.
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ABSTRACT |
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Aims To see whether the subtypes are distinguished in ways indicative of valid classification, notably in patterns of adaptive functioning, comorbidity, treatment response and therapeutic interventions.
Method A random sample of experienced clinicians provided data on 145 patients with bulimic symptoms, including data on eating disorder symptoms, DSM-IV comorbidity, personality pathology, treatment response and treatment interventions.
Results Patients categorised as dysregulated had the poorest functioning, most comorbidity and worst outcome, followed by patients in the constricted and high-functioning groups. The three subtypes elicited different therapeutic interventions and accounted for substantial incremental variance in outcome, holding constant the severity of eating disorder symptoms and presence of other Axis I disorders.
Conclusions The data provide accumulating evidence for the validity of three personality subtypes in patients with eating disorders.
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INTRODUCTION |
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METHOD |
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Participants
We contacted a random national sample of doctoral-level (MD and PhD)
members of the American Psychiatric Association and American Psychological
Association, with a minimum of 5 years' experience post-residency or
licensure, and asked them if they would participate (uncompensated) in a study
of the treatment of bulimia nervosa.
Measures
We asked participating clinicians to select their most recently terminated
course of psychotherapy of three sessions or more with a female patient who
had clinically significant symptoms of bulimia and was binge
eating and purging at the time she began treatment. We chose to include
sub-threshold cases to maximise generalisability to patients treated in the
community. We explicitly instructed clinicians not to choose a case based on
outcome, to sample both successful and unsuccessful cases (as seen below, this
goal was successful). The questionnaire required 20-30 min to complete and
included five sections.
Demographics
The first section of the questionnaire assessed clinician and patient
demographic characteristics. Because prior studies using similar methods (e.g.
Westen & Shedler, 1999)
have found that the majority of clinicians report a cognitive-behavioural,
psychodynamic or eclectic theoretical orientation, we measured self-reported
treatment orientation by asking clinicians to tick one of four boxes:
CBT (cognitive-behavioural therapy),
psychodynamic, eclectic or other.
Those who ticked eclectic were asked to describe the primary
orientation that informed their work. For data-analytic purposes we created a
dichotomous variable, coded 1 for psychodynamic spectrum
(including psychodynamic and eclectic - primarily
psychodynamic) and 2 for CBT spectrum (including CBT and
eclectic - primarily CBT); this variable included 71% of the
sample (n=103). For validity of these ratings, see Thompson-Brenner
& Westen (2005).
Diagnostic information
The second section addressed patient diagnosis and adaptive functioning.
Clinicians rated individual criteria for each of the DSM-IV eating disorders
(American Psychiatric Association,
1994), which allowed us to apply DSM-IV diagnostic algorithms to
make structured diagnoses, rather than relying on potentially unsystematic
clinician use of diagnostic categories. Clinicians also rated history of
eating disorder symptoms and adaptive functioning variables, such as history
of psychiatric hospital admissions and ratings of Global Assessment of
Functioning (GAF; American Psychiatric
Association, 1994). In addition, clinicians indicated the presence
or absence of DSM-IV Axis I disorders commonly comorbid with eating disorders
and the ten DSM-IV Axis II diagnoses in checklist form. Respondents also rated
a list of 17 sub-threshold personality problems used in prior
research (Westen, 1997;
Westen & Arkowitz-Westen,
1998) that have been strongly associated with treatment outcome in
naturalistic samples of patients with mood and anxiety disorders
(Novotny et al,
2005).
Personality prototype ratings
The third section directed clinicians to rate the patient on the three
personality subtypes - dysregulated, constricted and
high-functioning/perfectionistic. For this study we developed paragraph-long
descriptions of the prototype for each category, using the 15 items most
descriptive of each from the original derivation study. To obtain both
dimensional and categorical measures of these variables, we asked respondents
first to rate the degree to which their patient's personality matched each
prototype using a scale of 1-5 and then to indicate which prototype best
matched the patient's personality. Although similar single-item prototype
ratings have yielded valid data in other domains, such as attachment and
personality disorders (Mickelson et
al, 1997; Westen &
Bradley, 2005), we applied principal components analysis to
personality data in this study to maximise reliability of dimensional
assessment.
Treatment outcome
The fourth section requested clinicians to describe the length and outcome
of the treatment. It included both inferential ratings (e.g. degree of
improvement in eating symptoms and degree of global improvement, rated on a
scale of 1-5) and relatively objective assessments (e.g. complete remission of
binge eating and purging, rated no/yes). We relied primarily on two composite
scores: eating disorder outcome (two items, coefficient =0.89) and
global outcome (six items, coefficient
=0.88).
Therapeutic interventions
The final section directed clinicians to describe the characteristic
interventions used in the treatment. We devised an adaptation for eating
disorders of the Comparative Psychotherapy Process Scale (CPPS;
Blagys & Hilsenroth, 2000;
Hilsenroth et al,
2003), a 20-item measure designed to assess therapy practices that
have empirically differentiated psychodynamic and cognitive-behavioural
treatments in controlled trials. Factor analysis of the CPPS yields a
cognitive-behavioural factor and a psychodynamic factor. To maximise relevance
to bulimia nervosa, we modified the CPPS to assess interventions from the CBT
manual for this disorder (Fairburn et
al, 1993), relevant psychodynamic interventions not addressed
in the original item set, and interventions commonly employed for particular
personality problems of relevance to patients with eating disorders (e.g.
addressing emotional dysregulation; see
Linehan, 1993). The adapted
questionnaire, the CPPS-BN, has 41 items, each rated on a five-point
scale.
Factor analysis of the CPPS-BN identified three factors that were robust
across different factor solutions and estimation procedures, showed minimal
cross-factor loadings, and accounted for 5% of the variance:
psychodynamic, cognitive-behavioural and
adjunctive treatments
(Thompson-Brenner & Westen,
2005). The psychodynamic factor included seven interventions
identified by Blagys & Hilsenroth
(2000) as characteristic of
psychodynamic therapies (e.g. addressing the patient's avoidance of important
topics and shifts in mood), as well as several items we had added to reflect
the broad spectrum of psychodynamic interventions used in the community (e.g.
use of the therapeutic relationship for a corrective emotional experience).
The cognitive-behavioural factor included seven items identified by Hilsenroth
et al (2003) as
characteristic of this form of therapy (e.g. teaching the patient specific
techniques for coping with her symptoms) and four items we added based on the
manual by Fairburn et al,
(1993) (e.g. prescribing
regular eating patterns). The adjunctive treatments factor included
interventions such as psychiatric hospital admission not specific to any
single theoretical approach. Reliabilities (coefficient ) were 0.91 (15
items), 0.86 (11 items) and 0.67 (5 items), respectively. Supporting
convergent and discriminant validity, self-reported CBT-spectrum and
psychodynamic-spectrum clinicians significantly differed as expected on the
first two factors but not on the third.
Data analysis
Because we assessed many variables with measures adapted from other
studies, where possible we performed multiple validity checks and aggregated
items to maximise reliability. We first used analysis of variance (ANOVA) to
compare patients assigned categorically to the three prototypes on three sets
of variables: adaptive functioning, aetiology and comorbidity. To compare
rates of comorbid Axis I and Axis II diagnoses, we made a priori
predictions using contrast analysis regarding the relative frequency of each
diagnosis for patients assigned to each prototype (see
Rosnow et al, 2000).
(We report the results here of categorical analyses, for ease of
interpretation. For the remainder of the analyses, we relied primarily on
dimensional personality diagnosis to maximise power, although dimensional and
categorical analyses yielded comparable data in all analyses.) Next, prior to
conducting dimensional analyses of the relation between our personality and
treatment variables, we applied principal components analysis to all
personality variables included in the data-set (Axis II, sub-threshold ratings
and three prototype ratings). Our twin goals were to maximise reliability of
dimensional prototype diagnosis and to see if we could reproduce the
dysregulated and constricted personality dimensions using a different item set
and statistical procedure from that used in the original study. We then
applied correlational analyses to these dimensional data to examine the
relation between personality factors and treatment length and outcome. We
followed this with a hierarchical multiple regression predicting outcome
variables from personality factor scores, holding constant Axis I (including
eating disorder) diagnoses. Finally, reasoning that clinicians should respond
to very different kinds of patients with different interventions, we examined
the relation between personality factors and the interventions clinicians
reported using on the CPPS-BN.
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RESULTS |
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Predicting adaptive functioning, comorbidity and aetiology
When forced to make a categorical judgement about personality subtype,
clinicians classified 42% of patients as high-functioning perfectionistic, 31%
as constricted and 27% as dysregulated. Approximately 84% of the sample could
be categorised as strongly resembling one of the personality prototypes (rated
4 or 5 on a five-point scale). The three groups differed systematically in
adaptive functioning. Pre-treatment GAF scores were highest for
high-functioning patients (mean 55.9, s.d.=11.6), followed by the constricted
group (mean 52.0, s.d.=12.8) and then by the dysregulated group (mean 45.3,
s.d.=11.1); F(2,133)=8.92, P<0.001. The three
groups similarly differed in history of hospitalisations
(2(2)=10.14, P=0.006), with the highest rates in the
dysregulated group (62%), followed by the constricted (40%) and the
high-functioning (29%) groups. The three groups also differed in
clinician-reported childhood sexual abuse (
2(2)=7.08,
P=0.03; n=135), with the highest rates in patients
categorised as dysregulated (42%), compared with 20% and 19% of the
constricted and high-functioning patients, respectively. These results largely
replicated the findings of Westen & Harnden-Fischer
(2001), except that the
present study did not include patients with non-purging anorexia, who are more
likely to be categorised as constricted and to have poor
adaptive functioning.
We next examined Axis I and Axis II comorbidity, on the assumption that, other things being equal, genuinely different kinds of patients should have different patterns of comorbidity. Table 1 shows the frequencies by personality subtype of diagnoses present in at least 10% of the sample. For these analyses we dummy-coded absent/present ratings of each diagnosis as 0 or 1, so that the mean values translate to frequencies (i.e. percentage of patients diagnosed with the comorbid disorder) and can be used in contrast analyses (see Rosnow et al, 2000). We conducted one-way ANOVAs with a priori contrasts to test focal one-tailed hypotheses regarding the relative frequencies for each comorbid condition. The three groups showed distinct and predictable patterns of both Axis I and Axis II comorbidity.
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Dimensional personality analyses
For the remaining analyses we used dimensional measures of personality. To
maximise reliability, and to see if we could reproduce the personality
dimensions previously identified using a different sample and item set, we
subjected all personality variables (Axis II diagnoses, sub-threshold
personality pathology items and the three personality profile ratings) to
principal components analysis (further details available from the author upon
request). The first two components were robust across algorithms and
estimation procedures: these were dysregulation and constriction
(Table 2). (Although these are
technically principal components, for purposes of exposition we refer to them
henceforward as factors.) Dysregulation derived by principal components
analysis was strongly associated with the clinician prototype rating of the
same construct (r(142)=0.81; P<0.001); the same was true
of constriction (r(143)=0.71; P<0.001). The fact that
these two factors emerged despite differences from the original derivation
study in sample, items and aggregation procedures (factor analysis v.
Q factor analysis) provides compelling evidence for their validity.
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Personality and treatment outcome
A valid psychiatric classification should ideally predict treatment
response (Robins & Guze,
1970). Thus, we examined the relation between dysregulation and
constriction and measures of treatment outcome. To avoid inflation of means,
we first examined the variables for outliers. Length of treatment had two
outliers, each more than 350 sessions more than the next longest treatment. We
therefore dropped these two outliers and the corresponding cases with the two
smallest values. Length of time to recovery had one outlier, 200 sessions
after the next data point; we therefore removed the highest and lowest values
for this variable. Length of time to improvement had no evident outliers. As
predicted, both dysregulation and constriction were positively associated with
treatment length and negatively associated with outcome
(Table 3). Secondary
categorical analyses are illustrative here: patients rated as dysregulated
attained recovery on average after 92 weeks of treatment, in comparison with
73 weeks for those rated as constricted and 51 weeks for high-functioning
patients. Only 43% of the dysregulated group recovered, compared with 50% of
constricted and 62% of high-functioning patients.
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If the classificatory distinction we are proposing is valid, it should show incremental validity in predicting treatment response, above and beyond eating disorder symptoms (and comorbid Axis I pathology). To test this, we used hierarchical multiple regression. In the first block, we entered frequency of bingeing, frequency of purging and a composite (additive) Axis I comorbidity variable that indexed the presence of the three Axis I diagnoses that consistently predicted negative outcome in zero-order correlations (major depression, panic disorder and substance use disorders). In the second block, we entered dysregulated and constricted factor scores. Table 4 reports the results for two composite criterion variables: eating disorder outcome (the extent to which the patient's symptoms improved) and global outcome. This represents a very conservative test of the validity of the personality dimensions, given that it holds constant the severity of eating pathology and Axis I diagnoses that may reflect in part personality processes.
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As the data in Table 4 illustrate, adding the two personality variables in the second step substantially improved prediction of both global outcome and eating outcome. In secondary analyses, we ran these analyses again separately by theoretical orientation of the clinician (self-reported psychodynamic spectrum or CBT spectrum). Whereas outcome was not significantly associated with personality (or any other variable) in dynamic therapies, dysregulation and constriction were strongly predictive of both global and eating disorder outcome in self-reported cognitive-behavioural therapies. For global outcome, standardised ß= -0.42 for dysregulation (P=0.004) and ß= -0.38 for constriction (P=0.008); for eating disorder outcome, standardised ß= -0.45 (P=0.003) and ß= -0.30 (P=0.03), respectively.
Personality and treatment interventions
In a final set of analyses we examined the relation between
clinician-reported interventions on the CPPS-BN and the dysregulated and
constricted personality subtypes. Because the self-reported CBT-spectrum and
psychodynamic-spectrum clinicians differed substantially in the interventions
they reported using, we conducted separate analyses for each clinical
orientation.
Table 5 sets out the correlations between dysregulation and interventions endorsed by CBT-spectrum and psychodynamic-spectrum clinicians. As might be expected, both groups of clinicians used more adjunctive interventions and tended to address traumatic experiences with more dysregulated patients (who were more likely to have trauma histories). The most striking finding, however, was the large correlation between dysregulation and the use of psychodynamic interventions by the CBT-spectrum clinicians. Put another way, the more dysregulated the patient, the more CBT-spectrum clinicians turn to techniques designed to address personality diatheses.
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Table 6 reports correlations between constriction and intervention strategies. As with dysregulation, CBT-spectrum clinicians tended to become more psychodynamic in their interventions with more constricted patients, although the effect was less pronounced, centring on use of the therapeutic relationship and encouraging the patient to experience and express feelings she is inhibiting. Psychodynamic-spectrum clinicians, in contrast, reported becoming more cognitive-behavioural with more constricted patients, becoming more didactic and directive.
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DISCUSSION |
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Personality patterns also predicted differences in treatment length and outcome. Dysregulation and constriction were both negatively associated with outcome. Patients in the constricted category attained recovery on average 5 months later than the high-functioning patients, and dysregulated patients attained recovery approximately 5 months later still. The percentage of patients who recovered during treatment was lowest among the dysregulated group, followed by the constricted group, and greatest in the high-functioning patients. Of particular importance was the incremental validity of dysregulation and constriction in predicting global and eating disorder outcome above and beyond variance accounted for by eating disorder severity variables and Axis I comorbidity. Treatment response is an essential criterion laid out by Robins & Guze (1970) for validating a diagnostic distinction, given its obvious clinical relevance.
Although the data are correlational and hence only suggestive as regards causation, clinicians appear to adjust their treatment strategies according to the patient's personality style. Cognitive-behavioural therapists report greater use of psychodynamic interventions with more dysregulated patients and a greater likelihood of using particular psychodynamic interventions with constricted patients (perhaps because of the focus of psychodynamic interventions on personality processes). Psychodynamic therapists, in contrast, increase their use of interventions associated with cognitive-behavioural therapy with more constricted patients (perhaps because patients rated highly on constriction have trouble initiating conversations and tend to have relatively empty or barren representations of themselves and others, which can make exploratory treatment more difficult). These data are striking, given that clinicians were reporting their use of interventions associated with the other camp. Both groups of clinicians use significantly more interventions designed to help regulate emotions, contain impulsivity and resolve crises with more dysregulated patients.
Limitations
The major limitations of this study and the potential for bias that stems
from retrospective reporting and the use of only one informant per patient
(the treating clinician). These are legitimate concerns. However, several
factors limit their impact. First, we attempted to minimise the bias in
diagnosis and outcome by providing structured diagnostic anchors when
appropriate, testing for potential biases by theoretical orientation,
aggregating variables to maximise reliability, and testing hypotheses (e.g.
about composite variables such as dysregulation) unfamiliar to clinicians and
hence not readily biased by informant knowledge or expectancies. Second,
clinicians appeared to follow our instructions to select their most recently
terminated case of a patient with bulimic symptoms, rather than to cherry-pick
successful cases (half of patients did not recover); they reported using a
range of interventions that crossed theoretical party lines, and provided data
that, in aggregate, yielded meaningful correlations with other variables that
respondents could not have anticipated. However, future research should rely
on multiple informants and prospective assessment.
Implications
The data have two clear implications. First, they point to the importance
of assessing non-random heterogeneity among patients who share an eating
disorder diagnosis. Any given research sample of patients with bulimia nervosa
is likely to include subsets of patients who approximate each of the
personality subtypes and hence differ on a tremendous range of variables. This
may help to explain some of the inconsistent research findings regarding
personality and eating disorders - for example, how bulimia nervosa can be
associated with either borderline or obsessive-compulsive personality disorder
- as well as inconsistencies in reports of the psychobiology of bulimia
nervosa - for example, why some patients with the disorder show serotonin
hypoactivity whereas others show the opposite pattern
(Steiger et al,
2004).
Second, the treatment of personality may be integral to the effective treatment of bulimia nervosa. For a substantial subset of patients, bulimic symptoms need to be understood within the context of broader patterns of thinking, feeling, and regulating impulses and emotions. The tendency to restrict food in patients with anorexic features may be part and parcel of a constricted style of regulating impulses, emotions and so forth, just as the tendency to binge and purge in some patients with bulimia may be best understood as one of many strategies for regulating powerful emotions that outstrip their capacity to cope. Addressing these broader personality processes may require rethinking basic parameters of manualised treatments tested in randomised trials, such as their focus and brevity (see Thompson-Brenner et al, 2003; Westen et al, 2004). Dysregulation and constriction systematically predicted poorer outcome in treatment across theoretical orientations and this was particularly true for cognitive-behavioural therapy, probably because this treatment for bulimia was not designed to target personality variables. Integrative treatments, targeting both personality and eating disorder symptoms, seem particularly promising in this regard (see Westen, 2000; Thompson-Brenner & Westen, 2005).
More speculatively, the data may have implications for genetic and behavioural genetic research on eating disorders. As Grice et al (2002) found, genetic analyses can be obstructed when phenotypes are not well identified by DSM-IV categorical diagnoses. Grice and her colleagues had difficulty linking the anorexia diagnosis to genetic markers identified in siblings concordant for the disorder but had more success identifying genetic markers of factor-analytically derived psychological traits related to the disorder. DNA was not created by committee, and empirically identified genetic loci may correspond more closely to similarly empirically derived phenotypic indicators.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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Received for publication April 13, 2004. Revision received September 23, 2004. Accepted for publication September 30, 2004.