what process works for whom: individual differences and
TRANSCRIPT
University of Pennsylvania University of Pennsylvania
ScholarlyCommons ScholarlyCommons
Publicly Accessible Penn Dissertations
2018
What Process Works For Whom: Individual Differences And The What Process Works For Whom: Individual Differences And The
Impact Of Therapy Techniques And Treatment Mechanisms Impact Of Therapy Techniques And Treatment Mechanisms
John Raymond Keefe University of Pennsylvania, [email protected]
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Part of the Clinical Psychology Commons
Recommended Citation Recommended Citation Keefe, John Raymond, "What Process Works For Whom: Individual Differences And The Impact Of Therapy Techniques And Treatment Mechanisms" (2018). Publicly Accessible Penn Dissertations. 3432. https://repository.upenn.edu/edissertations/3432
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What Process Works For Whom: Individual Differences And The Impact Of What Process Works For Whom: Individual Differences And The Impact Of Therapy Techniques And Treatment Mechanisms Therapy Techniques And Treatment Mechanisms
Abstract Abstract While psychotherapy treatment manuals define the broad structure and targets of a therapy, therapists must decide how to implement treatments with a specific patient. Yet, patients are heterogeneous even within a disorder class, and there is little systematic research to guide a therapist to make principled adaptations. We examined the question of whether individual differences moderate the treatment effects of therapist interventions and both in-session and between-session processes of change, using data from a randomized controlled trial for panic disorder comparing panic-focused psychodynamic psychotherapy (PFPP) to cognitive-behavioral therapy. In Chapter 1, adherence to PFPP (n = 65) was observer rated in Sessions 2 and 10 to predict panic change after the rated session. Panic-specific interpretations predicted improvements, while non-panic-focused interventions did not. Concordant with dynamic theory, patients with more interpersonal problems benefitted especially from heightened focus on the interplay between interpersonal-emotional conflict and panic. In Chapter 2, we examined whether higher levels of observer-rated emotional expression—a marker of therapeutic engagement—across early PFPP sessions predicted subsequent panic improvements. We hypothesized that this relationship would be moderated by certain personality disorder traits related to emotionality: (1) borderline traits, which denote heightened, labile, dysregulated emotionality; and (2) obsessive-compulsive traits, related to muted, constrained emotionality. As predicted, borderline traits attenuated the otherwise positive relationship between emotional expression and symptom improvement, but obsessive-compulsive traits had inconsistent relationships between trial sites. Finally, in Chapter 3, we built on mediational analyses for both CBT and PFPP (n = 138), examining whether the presence of different psychological vulnerabilities moderated the symptomatic impact of improvement in two mediators of panic change: catastrophic, body-focused interpretation style, and panic-specific reflective functioning (PSRF). Patients beginning treatment with a more catastrophic style benefitted more from improvements in either mediator. Personality disorder traits blunted the impact of improvements in PSRF, but patients with no personality disorder evidenced high benefit from PSRF improvements. This set of findings suggests that personalization of psychotherapy can be empirically grounded. Clinical characteristics may inform how a therapist chooses to intervene, what in-session processes they focus on, and what types of psychological changes they aim to encourage.
Degree Type Degree Type Dissertation
Degree Name Degree Name Doctor of Philosophy (PhD)
Graduate Group Graduate Group Psychology
First Advisor First Advisor Robert J. DeRubeis
Second Advisor Second Advisor Dianne L. Chambless
Keywords Keywords mediation, personalized medicine, psychotherapy, psychotherapy process
Subject Categories Subject Categories Clinical Psychology
This dissertation is available at ScholarlyCommons: https://repository.upenn.edu/edissertations/3432
WHAT PROCESS WORKS FOR WHOM: INDIVIDUAL DIFFERENCES AND THE
IMPACT OF THERAPY TECHNIQUES AND TREATMENT MECHANISMS
John Raymond Keefe
A DISSERTATION
in
Psychology
Presented to the Faculties of the University of Pennsylvania
in
Partial Fulfillment of the Requirements for the
Degree of Doctor of Philosophy
2019
Supervisor of Dissertation
_________________________________
Robert J. DeRubeis, PhD, Samuel H. Preston Term Professor in the Social Sciences
Graduate Group Chairperson
__________________________________
John Trueswell, PhD, Professor of Psychology
Dissertation Committee
Dianne L. Chambless, PhD, Professor of Psychology
Sara R. Jaffee, PhD, Professor of Psychology
ii
ACKNOWLEDGMENT
I must begin my acknowledgments by recognizing the incomparable contribution
and influence of my dissertation advisor Robert DeRubeis on my career and my person.
Like a securely attached parent, Rob steadfastly encouraged me to go out on my own
intellectually and academically, but he was always there with Dutch M&Ms, espresso,
and counsel when I got lost along the way. I greatly admire Rob’s understated
inquisitiveness and flexibility, and his commitment to long-spanning, ambling intellectual
dialogue, which deepened my thinking and work. If I ever have students of my own, I
hope to emulate Rob.
I owe a special debt to Jacques Barber, who by recommendation of my
undergraduate abnormal psychology professor Michelle Reimer adopted a young post-
bac neuroscientist and retrained him as a clinical psychologist at the Center for
Psychotherapy Research. Jacques, along with his then-postdoctoral fellow Kevin
McCarthy, gave generous, warm, sagacious mentorship and great opportunities that made
it even possible for me to apply to graduate school.
My dissertation chair Dianne Chambless modeled for me impeccable grace as a
person and precision in thinking and research design, and I am a better person and
researcher from knowing her. I also must thank Barbara Milrod for bringing me into the
fold of her research team in New York, for downloading into me her gifted clinical
acumen, for her sharp, direct, but always on point critique of my writing and ideas, and
for her almost unbelievable faith in me. I also thank the great professors I have learned
from at Penn, especially Sara Jaffee and Geoff Goodwin.
iii
The extended research families of Rob and Jacques have served as amazing
intellectual interlocutors and social raconteurs during my degree. I am particularly
grateful for my fellow graduate students in the DeRubeis lab—Lorenzo Lorenzo-Luaces,
Zachary Cohen, Colin Xu, and Thomas Kim. Ramaris German should be particularly
thanked as a surrogate parent on behalf of Rob when he was out of the country my first
semester at Penn. I have sincerely appreciated how Jacques has kept me connected to his
active lab group, and particularly for the strong personal and professional relationships I
formed with Kevin McCarthy, Sigal Zilcha-Mano, Ulrike Dinger, and Nili Solomonov.
I also must give unique thanks to my clinical supervisors at Penn, whose insights
into my patients and my clinical work shaped not only my clinical skills but also my
research: Sydney Pulver, Alan Goldstein, Rob DeRubeis (a twofer), and Melissa Hunt. I
also have to thank the support staff at Penn, who have organized me and tolerated my
bizarre, last-minute requests and queries with warmth, competence, and humor: Yuni
Thornton, Joan Massimo, Pat Spann, Claire Ingulli, and Paul Newlon.
I also wish to express gratitude to past teachers who have shaped me: Allen
Schneider, my undergraduate mentor in psychobiology; Sara Hiebert-Burch, who
unwittingly talked me out of quitting the Honors Program at Swarthmore; and Margie
Craig, my high-school journalism mentor.
To get through a doctorate a whole person, it is important to have a “life worth
living” (as Marsha Linehan emphasized), and for making that possible I am in profound
gratitude for the many people who fill my life with joy: my erstwhile friends back home
in Virginia (Justin Belcher, Jessica Johnson, Caroline Mayen, Kelsey Sowers, Michael
Terlecki); my friends in Philly (Hannah Blake, Jen Brown, Celia Caust-Ellenbogen, Lisa
iv
Drago, Lori Felipe-Barkin, Lindsay Friedman, Elizabeth Jenkins, Elizabeth
Laudenslager, Christiane Moore, Aaron Stock, Jessica Swaysland, Joshua Warner,
Zachary Wiener); my Dungeons and Dragons group (!) (Mark Lewis, Lauren Lewis,
Jasmine Erdener, Danielle Hanley); my friends in the diaspora (Toby Altman, Aaron
Brecher, Suzanne van Bronswijk, Chelsea Davis, Jessa Deutsch, Jess Engebretson, Alex
Ho, Lotte Lemmens, John Maltempo, Charles Rabideau, James Robinson, Melvin Pelaez,
Smith Koy Smith, Lauren Stokes, Kate Swisher, Laura Wang, Julia Wittes); and my
friends here in the program (especially Kelly Allred, Corey Cusimano, Eliora Porter, and
Rachel Schwartz).
I also must thank my loving family for everything they have done: my mother
Joan Pease, who instilled in me great determination and whose optimism and pride
sustain me, and my father Richard Keefe, who taught me to be gentle, curious, and odd;
my Italian family, my dear cousins Chiara and Stephanie Ferraro and my aunt Eva Anne
Ferraro who taught me kindness; my endlessly supportive maternal grandmother
Madeline Pease; and my paternal grandmother Eloise Keefe, who did not live to see me
enter graduate school but who is single-handedly the person most responsible for making
me the psychologist I am today.
Finally, I would also like to thank the many, many cats who provided me
unconditional positive regard during my doctoral process: Lenny & Belli at Fitzwater;
and the full rescue litter my parents took care of—Momma, Willie, Rosie, Thelma,
Louise, Right-Eye, Scooter, Blue, Bear, Princess, Abe, Mr. Two, Lily, and Bo.
v
ABSTRACT
WHAT PROCESS WORKS FOR WHOM: INDIVIDUAL DIFFERENCES AND THE
IMPACT OF THERAPY TECHNIQUES AND TREATMENT MECHANISMS
John Raymond Keefe
Robert J. DeRubeis
While psychotherapy treatment manuals define the broad structure and targets of a
therapy, therapists must decide how to implement treatments with a specific patient. Yet,
patients are heterogeneous even within a disorder class, and there is little systematic
research to guide a therapist to make principled adaptations. We examined the question of
whether individual differences moderate the treatment effects of therapist interventions
and both in-session and between-session processes of change, using data from a
randomized controlled trial for panic disorder comparing panic-focused psychodynamic
psychotherapy (PFPP) to cognitive-behavioral therapy. In Chapter 1, adherence to PFPP
(n = 65) was observer rated in Sessions 2 and 10 to predict panic change after the rated
session. Panic-specific interpretations predicted improvements, while non-panic-focused
interventions did not. Concordant with dynamic theory, patients with more interpersonal
problems benefitted especially from heightened focus on the interplay between
interpersonal-emotional conflict and panic. In Chapter 2, we examined whether higher
levels of observer-rated emotional expression—a marker of therapeutic engagement—
across early PFPP sessions predicted subsequent panic improvements. We hypothesized
that this relationship would be moderated by certain personality disorder traits related to
emotionality: (1) borderline traits, which denote heightened, labile, dysregulated
emotionality; and (2) obsessive-compulsive traits, related to muted, constrained
vi
emotionality. As predicted, borderline traits attenuated the otherwise positive relationship
between emotional expression and symptom improvement, but obsessive-compulsive
traits had inconsistent relationships between trial sites. Finally, in Chapter 3, we built on
mediational analyses for both CBT and PFPP (n = 138), examining whether the presence
of different psychological vulnerabilities moderated the symptomatic impact of
improvement in two mediators of panic change: catastrophic, body-focused interpretation
style, and panic-specific reflective functioning (PSRF). Patients beginning treatment with
a more catastrophic style benefitted more from improvements in either mediator.
Personality disorder traits blunted the impact of improvements in PSRF, but patients with
no personality disorder evidenced high benefit from PSRF improvements. This set of
findings suggests that personalization of psychotherapy can be empirically grounded.
Clinical characteristics may inform how a therapist chooses to intervene, what in-session
processes they focus on, and what types of psychological changes they aim to encourage.
vii
TABLE OF CONTENTS
ACKNOWLEDGMENT ........................................................................................ IV
ABSTRACT ...................................................................................................... VIIII
LIST OF TABLES ................................................................................................ X
LIST OF ILLUSTRATIONS ................................................................................. XI
SPECIFIC PSYCHODYNAMIC TECHNIQUES IN PANIC TREATMENT ............ 1
PATIENT PERSONALITY AND EMOTIONAL PROCESS ................................ 34
WHAT MECHANISM WORKS FOR WHOM IN PANIC ..................................... 79
APPENDICES .................................................................................................. 109
viii
LIST OF TABLES
Table Title Page
1.1 Descriptive data for baseline characteristics and for symptom change,
in Study 1.
32
1.2 Descriptive statistics for technique process measurements (average 15
minutes per session), in Study 1.
33
1.3 Correlations between technique process measurements and baseline
characteristics, in Study 1.
33
2.1 Descriptive data for baseline characteristics and for symptom change,
in Study 2.
76
2.2 Example transcripts from sessions rated high/low in emotional
expression, in Study 2.
77
3.1 Descriptive data for baseline characteristics and for symptom change,
in Study 3.
107
S1 Examples of panic-focused clarification and interpretation, for Study
1 in Appendix C.
117
S2 Correlations between emotional expression ratings within the same 5-
minute segment, for Study 2 in Appendix D.
118
S3 Average levels of emotional expression per 5-minute segment across
sampled sessions, for Study 2 in Appendix D.
118
S4 Sources of variance in emotional expression per 5-minute segment,
for Study 2 in Appendix D.
119
ix
LIST OF ILLUSTRATIONS
Figure Title Page
1.1 Estimated effect sizes for the relationship between psychodynamic
technique use at a given session and subsequent improvement in
panic symptoms, in Study 1.
34
1.2 Estimated change in panic symptoms between Weeks 5 to 9, as a
function of degree of interpersonal problems and their interaction
with use of panic-focused interpretations at session 10, in Study 1.
35
2.1 Estimated effect sizes for the relationship between the average level
of emotional expression and subsequent improvements in panic
symptoms, in Study 2.
79
2.2 Estimated change in panic symptoms as a function of overall
emotional expression in interaction with borderline personality
disorder criteria, in Study 2.
80
3.1 Predicted degree of later symptom change attributable to early
improvements in PSRF, as a function of baseline severity of
catastrophic interpretive style, in Study 3.
109
3.2 Predicted degree of later symptom change attributable to early
improvements in PSRF as a function of baseline personality disorder
pathology, in Study 3.
110
1
Specific Psychodynamic Techniques in Panic Treatment
Panic-focused psychodynamic psychotherapy (PFPP; Busch, Milrod, Singer, &
Aronson, 2012) is a 24-session, twice weekly brief psychodynamic psychotherapy (PDT)
formulated for the treatment of panic disorder (PD) with and without agoraphobia. In
several randomized controlled trials (RCTs), PFPP has been shown to be equivalent in
efficacy to various forms of cognitive-behavioral psychotherapy (CBT; Beutel et al.,
2013; Keefe, McCarthy, Dinger, Zilcha-Mano, & Barber, 2014; Milrod et al., 2016;
Milrod et al., 2007). The exception comes from one site of a two-site study at which
PFPP was inferior to CBT (Milrod et al., 2016). Further trials are underway (Sandell et
al., 2015).
The PFPP treatment model is based on the assumption that the acute emergence
of panic and the developmental vulnerability toward PD has underlying psychological
meanings related to emotional-interpersonal conflicts and attachment dysregulation
(Busch, Milrod, & Singer, 1999; Busch et al., 2012). For example, a patient who
experiences ambivalence regarding a life-long romantic commitment to his partner may
develop a PD shortly after announcing their engagement. The patient’s panic attacks may
be triggered by anxious moments of unacknowledged intolerable ambivalence toward his
partner. He may either be unaware of or perhaps frightened to recognize this ambivalence
and its potentially frightening consequences, such as breaking up with or losing the
support of his partner, which could result in a vicious cycle of unrecognized conflicted
feelings and anxious arousal leading to panic. Consistent with PFPP’s etiological
hypothesis, patients with PD report higher rates of alexithymia (i.e., difficulty verbalizing
one’s emotions and motives), experiential avoidance, and lack of emotional acceptance,
2
compared to psychiatrically healthy controls and persons with simple phobias (Galderisi
et al., 2008; Izci et al., 2014; Parker, Taylor, Bagby, & Acklin, 1993; Tull & Roemer,
2007).
One important intervention in PFPP, clarification, is the attempt by the therapist
to help the patient become aware of the avoided and unconscious intrapsychic conflicts
that give rise to panic (Busch et al., 2012). This process entails gathering information
concerning the potential meanings of symptoms and actively helping the patient
recognize, verbalize, and reflect on those meanings; the context of symptoms also helps
to identify meanings. Successful clarifications along these lines lay the groundwork for
accurate interpretations of conflicts. For example, clarification could help the
aforementioned patient become aware that he habitually assigns frightful somatic sources
(e.g., heart disease) to anxiety that emerges when he became engaged and works on
wedding details, triggering intensely mixed feelings about his fiancée. This would enable
the therapist to help the patient focus on specific causes for these feelings.
Another important PFPP intervention is interpretation—attempts to help the
patient identify the specific dynamics and conflicts that underlie PD (Busch et al., 2012;
Summers & Barber, 2010). Types of interpretations include: (a) defense (i.e., of a
particular manner in which the patient avoids experiencing a particular distressing feeling
or issue); (b) dynamic/conflict (i.e., of how a patient’s experiences are the result of a
conflict between unacceptable wishes and the defenses against these wishes); (c) genetic
(i.e., of how early, formative attachment relationships may have made the patient’s
approach to interpersonal situations fraught with specific, identifiable vulnerabilities);
and (d) transference (i.e., how the patient’s recurring underlying formative attachment
3
patterns and conflicts emerge in the relationship with the therapist) (Busch et al., 2012).
PFPP therapists should attempt to link, whenever possible, the specifics of the patient’s
panic symptoms to his/her specific underlying dynamics (Busch et al., 1999; Busch et al.,
2012). A PFPP therapist who treats the engaged PD patient described above might
interpret that he appears to be afraid of experiencing and expressing his anger with his
partner. The therapist might cite the observation that the patient frequently talks about
how much he loves his partner immediately after expressing his frustrations, thereby
magically “undoing” his anger. The therapist could also point out that the unresolved and
frightening feelings of anger and frustration seem to emerge as sensations of physical
discomfort that trigger panic. By doing so, the therapist attempts to help the patient own
and work through these conflicts, diminishing their power as panic triggers and helping
the patient attribute any lingering anxiety to personal psychological meanings rather than,
for example, somatic problems.
Techniques Contributing to Efficacy in PDT for Anxiety
Previous studies have demonstrated that use of specific psychodynamic
techniques is predictive of outcomes in depression and personality disorder treatment
(Barber, Crits-Christoph, & Luborsky, 1996; Barber, Muran, McCarthy, & Keefe, 2013;
Hoglend, Dahl, Hersoug, Lorentzen, & Perry, 2011; Levy et al., 2006; McCarthy, Keefe,
& Barber, 2016). However, there is a lack of empirical evidence supporting the efficacy
of specific psychodynamic therapy (PDT) techniques for anxiety disorders, with only one
small sampled study (n = 20) showing that patients whose therapists employed more
interpretations across two sessions (3 and 9 out of an average of 20 sessions) of short-
term PDT tended to have superior symptomatic outcomes at termination (Pitman, Slavin-
4
Mulford, & Hilsenroth, 2014). However, because the authors were unable to establish
temporal precedence (i.e., that technique use preceded symptom change), a plausible
account of the finding is that, for instance, patients with better prognoses pulled for more
interpretations from their therapists in this setting. Moreover, disorders were not
diagnosed with a reliable measure. Further research is clearly required to investigate the
efficacy of specific PDT techniques for anxiety.
When considering the active ingredients that characterize effective PFPP, it
should be noted PFPP is distinguished from generic short-term PDT through its explicit
emphasis on panic and its associated dynamics (Busch et al., 2012). Clinical trials in
which PDTs have been operationalized without a specific focus on the primary
symptoms, or in which such a focus was proscribed or discouraged, have generated some
of the most disappointing findings regarding the efficacy of PDTs (Durham et al., 1994;
Garner et al., 1993; Gilboa-Schechtman et al., 2010; Poulsen et al., 2014). The rationale
behind PFPP is that by focusing on experiences proximal to panic and by linking
interpretations coherently to panic vulnerability, patients gain insight as to the specific
underpinnings of their panic attacks (Rudden, Milrod, Target, Ackerman, & Graf, 2006).
In PDT and other therapies, gains in insight have been found in different investigations to
both predict further functional improvements post-treatment as well as protection against
relapse across follow-up (Barber et al., 2013; Gibbons et al., 2009; Johansson et al., 2010;
Kallestad et al., 2010). However, the specific hypothesis that psychodynamic focus on
symptoms promotes greater symptom relief (Summers & Barber, 2010) has never been
addressed directly in empirical investigations.
5
In the context of a randomized controlled trial of PD with and without
agoraphobia evaluating manualized PFPP (Milrod et al., 2016), we measured the use of
psychodynamic psychotherapy interventions in two sessions of psychotherapy taken from
the early and middle phases of a 24-session treatment protocol (Sessions 2 and 10). Based
on PFPP’s conceptual model (Busch et al., 2012), we hypothesized that therapists’ more
frequent use of psychodynamic interpretations made with connections to symptoms of
and/or vulnerabilities to panic, agoraphobia, or anxiety would predict greater subsequent
symptom improvement in PFPP. In the PFPP manualization, therapists are expected to,
on average, focus on clarification of panic meanings in the beginning of treatment, but to
become progressively more interpretive as these meanings become clearer and the patient
becomes socialized to psychodynamic treatment (Busch et al., 2012). We hypothesized
that interpretations in the session taken from the middle phase of treatment (Session 10)
would be more predictive of improvement than interpretations in the earlier session
(Session 2), as by the middle phase of treatment the therapist has sufficient information
about the patient to make meaningful and accurate interpretations (Andrusyna, Luborsky,
Pham, & Tang, 2006; Crits-Christoph, Cooper, & Luborsky, 1988). We anticipated that
interpretations made without reference to panic would not predict subsequent symptom
improvement, when accounting for panic-focused interpretations.
In addition, we hypothesized that panic-focused clarifications made early on in
therapy (Session 2) would contribute to prediction of psychotherapy outcomes, as
clarification in this phase can help the therapist gather information and can encourage
initial exploration of dynamics underlying the patient’s panic symptoms.
6
Moderators of technique-outcome relationships. Like many manualized
psychotherapies, PFPP allows for therapist flexibility in focus and application of
therapeutic techniques (Busch et al., 2012), but relatively little empirical data exist to
help guide moment-to-moment judgments of how to respond to an individual patient. As
specific therapeutic techniques can be more or less conducive of change among particular
patients (Keefe, Webb, & DeRubeis, 2016; Sasso, Strunk, Braun, DeRubeis, & Brotman,
2015), we furthermore hypothesized that specific patients would be likely to evince
apparent benefit from particular psychodynamic interventions.
Specifically, we hypothesized that panic-focused interpretations would be more
important to outcomes when patients entered the trial with more interpersonal problems
as measured by the Inventory of Interpersonal Problems (IIP; Horowitz, Alden, Wiggins,
& Pincus, 2000). Interpersonal conflicts and transitions are frequently stressors
surrounding the onset of PD (Klass et al., 2009; Scocco, Barbieri, & Frank, 2007), and
couples in which one patient has PD often exhibit relational distress and avoidant
conversational and cognitive styles (Chambless, 2010). As unresolved and/or
unconscious interpersonal conflicts such as intolerable dependency and anger can be
viewed as a trigger for panic in PFPP, we hypothesized that individuals with more
interpersonal problems might be more likely to have such conflicts or to have relatively
more pervasive conflicts, and thus would benefit relatively more from panic-focused
interpretations addressing those conflicts. In addition, the dynamics associated with
panic, including difficulties with separation, dependency and anger could contribute to
both panic and interpersonal problems. In other words, our hypothesis was based on the
notion that patients with more interpersonal problems may be more likely to have the
7
dynamic-interpersonal contributions to panic that panic-focused interpretations may
specifically help reveal and resolve.
Method
Participants
Patients. The present study is a secondary analysis of patients randomized to the
PFPP condition (N = 80) of a two-site randomized controlled trial comparing PFPP,
CBT, and applied relaxation training among patients with primary DSM-IV panic
disorder with or without agoraphobia. Treatment took place twice a week for 12 weeks.
Patients were recruited at New York Presbyterian/Weill-Cornell (hereafter, “Site A”) and
the University of Pennsylvania (hereafter, “Site B”). Participants received study treatment
gratis. Participants gave informed written consent. Both sites’ institutional review boards
approved the protocol, and the study is registered with ClinicalTrials.gov (identifier:
NCT00353470).
Patients were included in the trial if they had the spontaneous occurrence of one
or more panic attacks for the month before trial entry, and qualified for DSM-IV panic
disorder diagnosis determined as per the ADIS-IV (DiNardo, Brown, & Barlow, 1995).
Cross-site agreement on ADIS ratings for panic severity (with a “4” indicating the
diagnostic threshold) was excellent (ICC = 1.00). See Milrod et al., 2016 for further
details.
Non-study psychotherapy was prohibited. Medications were permitted if stable
for at least two months at presentation, and were recorded, held constant, and monitored
during the trial. Exclusion criteria were active substance dependence (less than 6 months’
remission), a history of psychosis or bipolar disorder, acute suicidality, and organic
8
mental syndrome. Additional details on trial design, independent evaluator training, and
therapy adherence can be consulted in the primary outcome paper (Milrod et al., 2016).
Of the 80 randomized patients, 65 (n = 30 Site A, n = 35 Site B) provided the
necessary data to be included in the present study (see section on missing data).
Descriptive data on patients’ demographics may be found in Table 1.
Therapists. Sixteen doctoral-level therapists (11 M.D., 5 Ph.D.) administered
PFPP across the two sites. Therapists had an average of 15 years of post-graduate
experience (SD = 8.2), and an average of 5 years’ experience in some form of time-
limited psychodynamic therapy (SD = 6.3). The average total caseload was four (median
= 3.5, range = 1 to 11) for therapists whose patients were included in the present
analyses. All therapists were experienced therapists who were specifically trained in
PFPP over the span of a 2-day, 10-hour course. Therapists participated in monthly group
supervision and received regular individual supervision from senior clinicians. For the
primary outcome paper, basic adherence to PFPP was established (Milrod et al., 2016).
Outcome Index
Panic Disorder Severity Scale (PDSS; Shear et al., 1997). The PDSS is a
widely used diagnosis-based, composite, global rating of panic disorder severity, with
acceptable psychometric properties. The PDSS was administered by trained, master’s-
level independent evaluators who were uninformed as to treatment condition. Interrater
reliability on the PDSS was excellent (ICC[2,1] = 0.95). The PDSS was administered five
times during treatment: at baseline (Week 0), Week 1, Week 5, Week 9, and termination
(Week 12).
Moderator Measures
9
Inventory of Interpersonal Problems—Circumplex (IIP; Horowitz et al.,
2000)). The IIP-Circumplex is a 64-item self-report measure of maladaptive interpersonal
problems that is a shorter version of the full 127-item IIP. The sum score of the IIP
reflects an individual’s degree of interpersonal distress. The IIP exhibits adequate internal
reliability and 10-week test-retest reliability (Alden, Wiggins, & Pincus, 1990), and
exhibited excellent internal reliability in this sample (alpha = 0.95).
Process Measure
Panic-Focused Psychodynamic Psychotherapy Rating Scale (PFPP-RS;
Keefe, Phillips, Busch, & Milrod, 2016). The PFPP-RS is an observer-rated scale
developed to assess the degree to which therapists used general psychodynamic
interpretive techniques, as well as more specific panic-focused psychodynamic
techniques. It was developed by the author JRK in conjunction with PFPP developers
Fred Busch and Barbara Milrod. The use of each technique was rated on a 5-point Likert
scale ranging from 0 (technique not present in section) to 2 (at least one clear example of
the technique in section) to 4 (technique applied fully and comprehensively in section).
Scores reflected the degree to which a technique was prototypic to the rated segment (i.e.,
adherence), not whether the raters believed the therapist applied the technique in a
particularly apt way (i.e., competence). For items measuring the use of interpretation, a
score of 2 would indicate that the therapist made at least one clear, identifiable
interpretation. A score of 4 could represent either multiple individual interpretations
made within a rating segment, or a single, continually developed interpretation over an
extended period of time. Sessions were divided into 15-min segments, with each segment
10
rated for each technique. A single rating per session was calculated by averaging ratings
made across the three 15-min segments.
Scores of the PFPP-RS items were summed to produce the following subscales:
(a) Psychodynamic Interpretations – the degree to which the therapist used any of the
four types of interpretations (defense, genetic, dynamic, and transference); and (b) Panic-
Focus: the degree to which the therapist focused on panic, agoraphobia, and anxiety
symptoms and made connections between these symptoms and panic dynamics, including
underlying conflicts. For Panic-Focus items, to score a 2 or higher on any given item, a
therapist had to make a clear, unambiguous reference to panic, agoraphobia, or anxiety in
relation to the intervention. This subscale includes two items assessing use of
clarification with regard to panic symptoms and their personal meaning (PF-
Clarification), and three items assessing interpretations that address the emergence of and
vulnerability to experiences of panic, agoraphobia, and anxiety (PF-Interpretation).
Examples of therapist interventions qualifying as clarifications and interpretations can be
found in Supplemental Table 1. To separate panic-focused interpretations from non-
panic-focused interpretations, the PF-Interpretation subscore was subtracted from the
total Psychodynamic Interpretations score, as PF-Interpretations reflect a subset of
Psychodynamic Interpretations. This score will be referred to as Non-Panic-Focused
Interpretations.
Video-recorded sessions were rated using the PFPP-RS by six advanced
undergraduate psychology majors at the University of Pennsylvania who each received
approximately 20 hours of training by the developers of the scale. Two authors who were
PFPP developers (FB & BR), and a graduate student author familiar with the model (NS)
11
provided additional consultation as to the validity and reliability of ratings during
training. Training included a review of the rater manual, the PFPP therapy manual (Busch
et al., 2012), and rating of several training tapes of PFPP. Training was continued until
raters consistently rated single items within a point (+/-) of their graduate student trainer.
Additionally, raters met approximately every other week with the study leader JRK to
rate a tape collectively and discuss rating challenges and questions.
All available Sessions 2 and 10 were rated for each PFPP patient. Sessions 3 and
9 were rated in cases wherein Session 2 or 10 (respectively) was not available. Sessions
were randomly assigned to raters, who were uninformed of the outcome data. Two raters
rated each tape, and ratings were averaged across raters.
Random effects ICCs were calculated using variance estimates from an REML
mixed model in the R package “lme4” (Bates et al., 2016; Shrout & Fleiss, 1979).
Interrater reliability per 15-minute segment was good for all Psychodynamic
Interpretations (ICC[2,2] = 0.80) and adequate for Panic-Focused Clarification (ICC[2,2]
= 0.71), Panic-Focused Interpretations (ICC[2,2] = 0.70), and the difference score
reflecting Non-Panic-Focused Interpretations (ICC[2,2] = 0.68).
Statistical Analyses
All analyses were conducted using the R statistical programming language (R
Core Team, 2017) and run using robust regressions as implemented in the R package
“Robustbase” (Maechler et al., 2016). Given the effective sample size (n = 65), robust
regression was selected over standard regression for its superior properties of robustness
against multivariate outliers and deviation from homoscedasticity (Huber & Ronchetti,
2009). A robust regression (a) retains full information on all observations in an initial
12
estimate of parameters; (b) iteratively determines weights for each observation based on a
particular estimator function from this initial estimate, such that points much farther from
model predictions in the previous iteration are given lower weight; and (c) recalculates
final parameter estimates based on the final weighting when the values of the coefficients
converge within a specified tolerance (Koller & Stahel, 2011). Semi-partial correlation
effect sizes (sr) were estimated for parameters of interest from linear regressions.
Missing data. In the primary trial, a not missing at random (NMAR) pattern of
treatment dropout was detected, such that patients with worse PDSS symptom trajectories
were more likely to terminate from treatment prematurely (Milrod et al., 2016). When
outcomes for treatment noncompleters are imputed in the NMAR context, imputation and
other missing data methods can lead to biased estimates and confidence intervals
(Graham, 2009). As such, only individuals who provided data up to the Week 9
assessment point were included in our analyses (n = 65; 81.3% of the intention-to-treat
sample).
Several trial completers were missing videotapes of one of the two sessions due to
technical issues or therapist/research assistant error (n = 27), but not treatment dropout.
Process ratings for completers missing a video-recording of a session can be presumed to
be missing at random in relation to panic symptom outcomes (Rubin & Little, 2002). This
degree of missingness is not considered prohibitive in the missing data literature (White,
Royston, & Wood, 2011), and process ratings have been successfully imputed in the past
(e.g., Forand et al., 2018; Lorenzo-Luaces et al., 2017). Accordingly, random forest
imputation (Stekhoven & Bühlmann, 2011) was used to impute missing data for
completers, using all baseline data, in addition to all PDSS scores and termination and
13
pre-to-post treatment change scores on the Sheehan Disability Scale, Inventory of
Interpersonal Problems, and Hamilton Rating Scale for Depression. Furthermore, all
rated technique process ratings from observed sessions (the non-missing session and
Session 5, which was rated but not used in this manuscript) were included in the
imputation model, such that all patients had observed session process during their therapy
contributing to the imputation of their missing session ratings. For this data set, a
normalized root mean square error of prediction was estimated at 0.28, indicating that
imputation accuracy was adequate (Stekhoven & Bühlmann, 2011). Analyses for patients
with complete data for a given session were also run and compared to imputed data. No
changes in patterns of statistical significance were detected.
Analytic strategy. Two sets of analyses were performed. Within a robust linear
regression framework (Koller & Stahel, 2016), technique use at Session 2 (end of Week
1) was used to predict PDSS symptom change between Weeks 1 and 5 of treatment,
while technique use at Session 10 (end of Week 5) was used to predict symptom change
between Weeks 5 and 9 of treatment. For two reasons, we examined change in the
symptom measurement interval following the sampled session rather than the entire
remainder of the therapy: (a) This permitted establishment of closer temporal precedence
between techniques and outcomes than is often performed in “long reach” studies that
sample from an early session to predict change throughout the entire treatment (e.g.,
Keefe et al., 2016) ; and (b) as improvement in the trial was linear and technique use
following the sampled session may also influence symptom change, using too large a
prediction interval may obfuscate the signal of how technique use occurring in the
sampled session per se relates to subsequent symptom change.
14
Three process terms were included in each regression unless otherwise specified:
PF-Interpretation, PF-Clarification, and Non-PF-Interpretation. In each analysis, baseline
PDSS symptom score and PDSS change prior to the measured session were included as
covariates. Prior panic symptom levels were included as a covariate to allay the
possibility that patients who were low severity or getting better could have “pulled” for
more or fewer techniques, generating an epiphenomenal relationship. Due to a site by
treatment interaction reported in the primary outcome paper across the three tested
treatments (Milrod et al., 2016), we also examined whether any process measures
interacted with site to predict outcomes, and we planned to report any such interactions if
they were found at least at trend level (p <.10). However, no such interactions with site
were detected, suggesting that process relationships were not detectably different across
sites.
Furthermore, IIP scores were examined as a moderator of the relationship of
Session 10 panic-focused interpretations to subsequent change, tested by specifying an
interaction between the two variables. The Johnson-Neyman technique was applied to
probe the regions of significance of the interaction (Johnson & Fay, 1950).
Given that we conducted seven statistical tests, we adjusted p-values using the
Benjamini-Hochberg correction to control for the false discovery rate at an alpha of 0.05
(Benjamini & Hochberg, 1995), employing the core R function “p.adjust.” These are
reported as adjusted p-values.
In addition, we conducted two secondary, post-hoc statistical checks on the
robustness of our obtained findings. In the first, we employed a mixed model to estimate
therapist-level variance simultaneously with our model estimates, which did not result in
15
substantively different conclusions. In the second, we analyzed whether the technique
variables related to pre-to-post treatment functional and interpersonal outcomes. Both
analyses are reported in Appendix B.
Results
Descriptive Statistics
The average patient in this study had a baseline PDSS score of 13.9 (range 7 to
20), considered to be in the moderately-ill severity range for patients with comorbid
agoraphobia (Furukawa et al., 2009). More than three-quarters of patients (n = 53;
81.5%) qualified for a co-morbid DSM-IV diagnosis of agoraphobia. The average patient
reported interpersonal problems in the high-normal range of severity (approximately
+0.64 SD over the normative mean). Other baseline demographic and clinical
information can be found in Table 1.
Technique scores at Sessions 2 and 10, as well as indices of their consistency over
time, are presented in Table 2. Reliable within-patient stability between sessions was
observed only for panic-focused interpretations. Within a given case, mean levels of
panic-focused and non-panic focused interpretations increased from Session 2 to Session
10.
Prior to data analysis, the correlations between every process measurement and
baseline PDSS severity and our proposed moderator variable (IIP scores) were examined.
As displayed in Table 3, there were no significant correlations between baseline panic
and interpersonal problem severity and any of the technique variables at either time point.
Early Panic Symptom Change (Weeks 1 to 5)
16
There was no significant relationship between panic-focused clarifications at
Session 2 (end of Week 1) and symptom change between Weeks 1 to 5 (B = 1.16 [95%
CI: -0.20, 2.52], SE = 0.68, t[59] = 1.71, p = 0.092, adjusted p = 0.184, sr = 0.21).
Neither panic-focused interpretations (B = -1.34 [95% CI: -3.32, 0.64], SE = 0.99, t[59] =
-1.35, p = 0.181, adjusted p = 0.290, sr = -0.15) nor non-panic focused interpretations (B
= -0.60 [95% CI: -2.88, 1.68], SE = 1.14, t = -0.53, p = 0.599, adjusted p = 0.599, sr = -
0.09) yielded significant predictions of symptom change in the subsequent measurement
interval.
Later Panic Symptom Change (Weeks 5 to 9)
Higher levels of panic-focused interpretations at mid-treatment (Session 10, end
of Week 5) predicted greater panic symptom improvement between Weeks 5 and 9 (B =
1.79 [95% CI: 0.61, 2.97], SE = 0.59, t = 3.04, p = 0.004, adjusted p = 0.016, sr = 0.37),
subsequent to the measured session. By contrast, non-panic focused interpretations were
unrelated to subsequent outcomes (B = -0.47 [95 CI: -1.54, 0.60], SE = 0.54, t[59] = -
0.88, p = 0.382, adjusted p = 0.437, sr = -0.09). Panic-focused clarification at this session
was also unrelated to outcomes (B = -0.56 [95% CI: -1.37, 0.25], SE = 0.41, t[59] = -1.38,
p = 0.175, adjusted p = 0.351, sr = -0.20). Figure 1 summarizes all technique-outcome
relationships for early and later panic symptom change.
Interpersonal Problems as a Moderator of Technique-Outcome Relationships
The higher the score on the IIP at intake, the stronger the relation of panic-
focused interpretations at Session 10 was to subsequent symptom change (B = 3.65 [95%
CI: 1.24, 6.05], SE = 1.20, t[57] = 3.04, p = 0.004, adjusted p = 0.016, sr = 0.29). The
Johnson-Neyman technique identified an IIP score of 1.0 as the cutoff for exhibiting a
17
significant, positive relationship between panic-focused interpretations and subsequent
improvement (n of individuals with IIP > 1.0 = 38, 58.5%; see Figure 2). For patients
with scores greater than or equal to 1.0, indicating higher levels of interpersonal problems
at baseline, there was a significant relation between panic-focused interpretations and
subsequent symptom improvement (sr = 0.41, p = 0.001), whereas for patients with less
interpersonal distress the association was not significant (sr = 0.11, p = 0.255), in large
part because patients with lower interpersonal distress did well symptomatically
regardless of interpretation level (see Figure 2).
Discussion
We investigated the relation between use of specific PFPP techniques and
symptomatic outcomes in the treatment of panic disorder. Our first important finding
suggests that at mid-therapy (Session 10), panic patients receiving a high level of panic-
focused interpretations exhibited greater subsequent symptom improvement. However,
non-panic-focused interpretations did not predict subsequent symptom improvement
during either the earlier or later periods of treatment. Moreover, panic-focused
interpretations at Session 10 also predicted to pre-to-post improvements in interpersonal
functioning (see Appendix B). These findings lend support to the importance of taking a
symptom-focused approach in short-term psychodynamic therapies for anxiety (Busch et
al., 2012; Tasca, Hilsenroth, & Thompson-Brenner, 2014). Past process findings
demonstrating a positive relationship between interpretations and symptom change
(Pitman et al., 2014) could reflect that many interpretations in short-term psychotherapy
in fact are symptom-focused, even when that is not the specific intent of the study.
18
On the other hand, interpretations in an early session, whether or not they were
related to panic symptoms, did not predict subsequent panic symptom improvement over
the following four weeks. Possibly, early interpretations are not as accurate as those made
after the therapist has learned more about the patient. In previous psychodynamic process
studies, observer-rated accuracy of interpretations derived from themes coded from early
session transcripts or pre-treatment history interviews predicted greater symptom change
and likelihood of having a “sudden gain” (Andrusyna et al., 2006; Crits-Christoph et al.,
1988). Alternatively, interpretation at a very early stage may sometimes be experienced
by the patient as overwhelming (McCarthy et al., 2016). At an early stage of therapy, it is
also potentially less likely that patients have been fully socialized to the both the structure
and tasks and goals of psychodynamic therapy (Luborsky, 1984), which may make it
harder for them to build on therapists’ interpretations with further personal exploration
and development of insight.
Taken together, it is possible that early interpretations are both less accurate to the
patient’s dynamics, and less likely to be perceived by the patient as well-timed, which
may be considered matters of intervention competence. Future process studies on
interpretation accuracy and the role of supportive techniques and alliance in PFPP would
help distinguish between these and other hypotheses explaining our findings. However,
our findings do not support the conclusion that the other types of dynamic techniques
assessed are necessarily without use or are counterproductive, but rather that panic-
focused interpretations are the only statistically reliable signal of positive process for the
average patient in this sample. It is also plausible that more complex process relationships
exist (e.g., interactions between early and mid-technique use; levels of clarification and
19
interpretation), but we did not explore these possibilities due to our need to limit the
number of tests performed in this small sample.
As we hypothesized, patients with more interpersonal problems at baseline
exhibited a stronger relationship between mid-therapy panic-focused interpretations and
subsequent change. This finding is consistent with PFFP’s theoretical model, which
proposes that unconscious conflict in the context of relationships may contribute to
experiences of panic, such that patients with more interpersonal distress may need a more
intense focus on the emotions and conflicts underlying this distress (Busch et al., 2012).
Panic dynamics are typically interwoven with the interpersonal problems that these
patients struggle with. For example, many panic patients are prone to being in
relationships where they struggle to assert their own needs (Zilcha-Mano et al., 2015).
This linkage may allow for more readily identifiable dynamics and conflicts, and more
opportunities to identify them in relation to interpersonal difficulties, compared to
patients with relatively fewer interpersonal problems. Ergo, it may be the case that
therapists were more accurate in their interpretations for patients with stronger
interpersonal issues.
However, patients with lower levels of interpersonal problems had good symptom
improvement in this interval (i.e., between Weeks 5 to 9) regardless of panic-focused
interpretations, whereas increasing interpersonal distress was more predictive of poor
symptom improvement in less panic-focused interpretive therapies (see Figure 2). We
would argue that this pattern of results is concordant with the perspective that patients
with more interpersonal problems particularly need a more panic-focused, interpretive
therapy. Past psychodynamic process-outcome research has rarely sought to identify
20
beneficial matches between techniques and patient characteristics, with one exception
being the body of literature suggesting an important role for transference interpretations
in treating personality disorder (Hoglend et al., 2011; Keefe & DeRubeis, 2018). Overall,
our finding is consistent with a perspective wherein patients with more complicated or
treatment-resistant presentations may require more active or skillful approaches, and may
reveal more about process-outcome relationships than those DeRubeis and colleagues
(2014) have called “easier” (or more straightforward) patients (DeRubeis, Gelfand,
German, Fournier, & Forand, 2014; Keefe et al., 2016).
Limitations and future directions
Fifteen cases (18.8%) were unable to be used for the present analyses due to
dropout, which was nonrandom and related to poorer symptom trajectories in the parent
trial (Milrod et al., 2016). The remaining patients included in our study represent a
subsample of individuals who improved relatively more symptomatically. It is possible
that the observed technique-outcome relationships would not be obtained among the
dropout patients; alternatively, relatively less efficacious therapy process may have led to
worse symptom trajectories, promoting dropout. Examining the relationship between
technique use and treatment dropout would be an interesting way to disambiguate these
possibilities, although this effort would be poorly powered in our sample due to the low
base rate of dropout and lack of early therapy tapes due to dropout. In addition, several
sessions were not available to be rated due to protocol error. However, we employed a
standard, validated method for imputing the missing ratings (Stekhoven & Bühlmann,
2011), and results obtained with data only from cases with complete data mirrored those
obtained when the imputations were included.
21
The patterns obtained using ratings from early and middle sections of therapy
were observed using only one session from each phase. A better approach would be to
sample multiple sessions from each phase (Dennhag, Gibbons, Barber, Gallop, & Crits-
Christoph, 2012). Unfortunately, at one of the two treatment sites, only recordings of
Sessions 2, 5, and 10 were available on a systematic basis. Future studies that include
ratings of multiple recordings within the same interval would allow for the investigation
of more complex patterns of the relation between process and symptom change, such as
variability in technique use across sessions (Owen & Hilsenroth, 2014). In addition, the
average therapy was not intensely interpretive (e.g., at session 10, less than one panic-
focused interpretation per fifteen minutes), indicating that we could not meaningfully
examine hypotheses that that intensities of interpretations in between the extremes is
more effective than very low or very high intensities (see McCarthy et al., 2016).
Finally, our findings suggest but cannot confirm the presence of a causal
relationship between the intensity of panic interpretations and symptom change. A
stronger test of the causal hypothesis would require experimental manipulation of the
causal variable, in a manner like Hoglend et al.’s (2008) randomized comparison of
psychodynamic therapy with versus without transference interpretations.
Conclusions
Psychodynamic therapists implementing PFPP should focus on interpretation of
the possible conflicts underlying panic as they enter the middle phase of therapy.
Particularly tying the patient’s dynamics to experiences of panic, anxiety, and
agoraphobia—rather than making general interpretations concerning relational or
personal patterns—may be especially important for effective short-term treatment of
22
panic. For patients presenting with higher levels of interpersonal distress, an emphasis on
panic-focused interpretations may be especially important in promoting remission from
panic disorder.
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32
Table 1.1
Descriptive Data for Baseline Characteristics and for Symptom Change
Baseline Measure Mean (SD) or # (%)
Baseline PDSS 13.9 (3.2)
PDSS Change Week 1 to 5 -1.5 (3.6)
PDSS Change Week 5 to 9 -1.2 (3.4)
SDS 16.1 (6.3)
HAM-D 10.6 (4.8)
IIP 1.2 (0.5)
Agoraphobia Diagnosis 53 (81.5%)
Age 39.5 (14.0)
Gender (Female) 44 (67.7%)
Ethnicity (Hispanic) 8 (12.3%)
Race (Black, Other Non-Caucasian) 11 (16.9%), 4 (6.2%)
Concurrent Psychopharmacology 16 (24.6%)
Age of Panic Onset (years) 27.5 (11.4)
SCID-II PersD Diagnosis 32 (49.2%)
Cluster A PersD Traits 1.4 (1.9)
Cluster B PersD Traits 2.7 (3.2)
Cluster C PersD Traits 4.0 (3.1)
Total PersD Traits 8.0 (6.3)
Note. HAM-D = Hamilton Rating Scale for Depression; IIP = Inventory of Interpersonal
Problems; PDSS = Panic Disorder Severity Scale; SCID-II = Structured Clinical
Interview for the Diagnosis of Axis-II Disorders; SDS = Sheehan Disability Scale
33
Table 1.2
Descriptive Statistics for Technique Process Measurements (Average per 15-minute
Segment)
Process Measurement Session 2
M (SD)
Session 10
M (SD)
Stability
Coefficient
Change in
Technique Use
Panic-Focused
Interpretation
0.9 (0.6) 1.2 (0.7) r =
0.50***
d = 0.55***
Panic-Focused
Clarification
2.3 (1.0) 2.3 (1.1) r = 0.09 d = 0.11
Non-Panic-Focused
Interpretations
0.2 (0.4) 0.5 (0.6) r = 0.03 d = 0.53**
Note. * = p <.05; ** = p <.01; *** = p <.001; Cohen’s d for paired t test calculated using
formula tc (Dunlap, Cortina, Vaslow, & Burke, 1996)
Table 1.3
Correlations between Technique Process Measurements and Baseline Characteristics
Baseline
Characteristic
Panic Focus—
Interpretation
(S2/S10)
Panic Focus—
Clarification
(S2/S10)
Non-Panic Focused
Interpretations
(S2/S10)
PDSS r = -0.06 / -0.00 r = -0.13 / 0.08 r = -0.12 / 0.07
IIP r = 0.19 / 0.06 r = -0.07 / -0.02 r = -0.03 / 0.24
Note. All ps > .05. IIP = Inventory of Interpersonal Problems, PDSS = Panic Disorder
Severity Scale
34
34
Figure 1.1. Estimated effect sizes for the relationship between psychotherapy technique
use at a given session and subsequent improvement in panic symptoms as measured by
the PDSS. Positive semipartial correlations indicate that higher levels of the intervention
are associated with more subsequent symptom improvement. Bars are 95% confidence
intervals.
35
Figure 1.2. Estimated change in panic symptoms as measured by the PDSS between
Weeks 5 to 9, as a function of degree of interpersonal problems as measured by the IIP
and their interaction with use of panic-focused interpretations at session 10. Positive
values represent predicted symptom worsening, while negative values represent predicted
symptom improvement. All regression variables not displayed in the figure were set to
the sample means.
36
Patient Personality and Emotional Process
Panic-focused psychodynamic psychotherapy (PFPP) (Busch, Milrod, Singer, &
Aronson, 2012) is a 24-session, 12-week evidence-based treatment for panic disorder
(PD; Beutel et al., 2013; Keefe, McCarthy, Dinger, Zilcha-Mano, & Barber, 2014; Milrod
et al., 2016; Milrod et al., 2007) that focuses on understanding and working through
unrealized or disavowed meanings surrounding the onset of acute attacks and associated
anxiety in patients with panic disorder. One common meaning might be real or imagined
loss of attachment figures, as suggested by epidemiological data showing high
comorbidity or past history of separation anxiety among panic patients (Kossowsky et al.,
2013; Milrod et al., 2014), and the fact that emotional stressors, such as relationship
conflict and interpersonal loss, frequently precede the development of panic disorder
(Klass et al., 2009; Scocco, Barbieri, & Frank, 2007). Recognizing the sometimes-
conflicted emotions and fantasies connected with experiences of panic and anxiety (such
as unacknowledged rage at attachment figures) is hypothesized to improve panic-specific
reflective functioning (PSRF; Rudden, Milrod, Target, Ackerman, & Graf, 2006). PSRF
is an interview-based measured intended to tap into the degree to which patients can
identify and discuss potential psychological meanings surrounding and triggers to
experiences of panic and anxiety (Rudden et al., 2006). Early improvement in PSRF has
been shown to predict subsequent improvement in panic symptoms in both PFPP and
cognitive-behavioral therapy (CBT) (Barber et al., under review).
The theory that informs CBTs for panic disorder focuses on patients’ tendencies
to catastrophically misinterpret bodily sensations (Clark et al., 1997) and thereby to fear
37
those sensations (Boswell et al., 2013). However, specific problems in emotional
recognition and acceptance have also been observed in studies of patients with panic
disorder, suggesting that these factors may also contribute to panic experiences. Patients
with PD report higher rates of alexithymia, experiential avoidance, and lack of emotional
acceptance, compared to psychiatrically healthy controls and persons with simple phobias
(Galderisi et al., 2008; Izci et al., 2014; Parker, Taylor, Bagby, & Acklin, 1993; Tull &
Roemer, 2007). Relative to non-psychiatric controls, panic patients have also been
observed to use relatively more emotional avoidance strategies in response to viewing
negatively or positively-valenced film clips (Tull & Roemer, 2007). Experimentally
instructing use of such strategies in healthy controls (relative to allowing emotional
experience) promotes subjective distress, heightened physiological arousal in the
moment, and also physiological reactivity in a subsequent stressful interpersonal task
(Tull, Jakupcak, & Roemer, 2010).
Avoidance of emotions in day-to-day life may promote development of panic
attacks if emotional contents are not addressed—for example, a person may get strongly
physiologically aroused due to unacceptable emotions, but be unable to dissipate that
arousal due to lack of emotional awareness and a consequent inability to acknowledge
what is upsetting him/her. A patient may also attribute emotional arousal to frightening
somatic causes that can psychologically stand in for conflicted feelings. In support of this
conceptualization, a recent process study of CBT for panic disorder found that reductions
in patient reports of emotional suppression preceded improvements in catastrophic, body-
focused cognitions and panic symptoms (Strauss, Kivity, & Huppert, 2018).
Emotions and Outcome in Psychodynamic Therapies
38
Psychodynamic frameworks conceptualize attempts to avoid affects and particular
affectively charged contents as contributing to the emergence of psychiatric symptoms
and psychosocial dysfunction (Subic-Wrana et al., 2016). Within these frameworks,
defense mechanisms work to maintain lack of awareness (Perry & Bond, 2012), which
occurs to protect the person from experiencing psychic danger, yet precludes the
individual’s ability to process and address the relevant conflicts or wishes. Panic patients
have been observed to have heightened use of so-called neurotic and immature defenses
relative to healthy controls (Busch, Shear, Cooper, Shapiro, & Leon, 1995; Calati, Oasi,
De Ronchi, & Serretti, 2010; Kipper et al., 2004), and to exhibit a unique defensive
profile compared to depression patients (Busch et al., 1995).
Therapeutic focus on difficult-to-express or disavowed affect has been commonly
considered to be a feature distinguishing psychodynamic therapies (PDT) from cognitive-
behavioral approaches (Blagys & Hilsenroth, 2000; McCarthy & Barber, 2009). In PDT,
such focus can be achieved via supportive interventions encouraging expression of affect,
clarification/confrontation highlighting important areas of affective exploration, or
interpretations of affectively-laden meanings. In a meta-analytic examination,
psychodynamic therapies in which therapists were coded by observers as being especially
affect-focused were more successful in symptomatic outcomes, with a medium effect size
(Diener, Hilsenroth, & Weinberger, 2007), although temporal precedence was not
established in most studies and some studies in fact involved patient expression. In two
recent studies assessing psychodynamic therapist technique specifically in anxiety
disorder therapies (Pitman, Slavin-Mulford, & Hilsenroth, 2014) and in a transdiagnostic
sample examining anxiety symptom improvement (Pitman, Hilsenroth, Weinberger,
39
Conway, & Owen, 2017), higher focus by the therapist on unexpressed/avoided affects
was related to more pre-to-post treatment symptom improvement.
In PDTs, links (often temporally sequenced) have been found between the degree
to which a patient engages in emotional experiencing or processing and positive
outcomes (Abbass, Town, Ogrodniczuk, Joffres, & Lilliengren, 2017; Fisher, Atzil-
Slonim, Bar-Kalifa, Rafaeli, & Peri, 2016; Friederich et al., 2017; Johansson, Town, &
Abbass, 2014; Kramer, Pascual-Leone, Despland, & de Roten, 2015; Town, Abbass, &
Bernier, 2013; Town, Salvadori, Falkenström, Bradley, & Hardy, 2017), convergent with
findings in humanistic-experiential therapies (Pascual-Leone & Yeryomenko, 2016). A
recent meta-analysis suggested that, across psychotherapies and psychiatric disorders,
increased expression of emotion by the patient although many of the included studies had
unclear temporal precedence between the affective measurement and outcome (Peluso &
Freund, 2018). In their transtheoretical conception of the importance of emotional
expression and experiencing in psychotherapy, Lane, Ryan, Nadel, & Greenburg (2015)
propose that emotional activation of episodic and semantic memory content facilitates the
reconsolidation of those memories into new, potentially more adaptive forms. From a
psychodynamic perspective, heightened affective experiences in therapy may indicate
that a patient is tolerating more affectively charged material, allowing for working
through of conflicts and ultimately improvements in reflective functioning. Heightened
patient emotional experiencing has been examined as a positive predictor of improvement
in a study of intensive short-term psychodynamic therapy for patients with generalized
anxiety disorder (Lilliengren, Johansson, Town, Kisely, & Abbass, 2017), but we are not
aware of any other such study in anxiety.
40
Study Hypotheses: Main Effects and Moderators
PFPP’s clinical theory predicts that patient affective engagement is important to
exploration of meanings and conflicts surrounding panic and anxiety experiences (Busch
et al., 2012). To examine the relationships between early in-session emotional
engagement and subsequent changes in symptoms and PSRF, we developed a measure of
in-session emotional expression. Emotional expression can be thought of as a broad
process marker of engaged emotions, incorporating basic aspects of both emotional
experience (i.e., does the patient exhibit non-verbal signs of emotional activation?;
McCullough et al., 2003) and processing (i.e., does the patient speak in an identifiable
manner about specific, current emotional experiences; Klein, Mathieu, Gendlin, &
Kiesler, 1969; Pascual-Leone & Greenburg, 2005)? Levels of emotional expression were
assessed in early sessions (2, 5, and 10) of a 24-session PFPP protocol from a two-site
randomized controlled trial comparing PFPP to CBT and applied relaxation training for
panic (Milrod et al., 2016). We hypothesized that patients with higher levels of emotional
expression in early PFPP sessions would experience greater symptom and PSRF
improvement subsequent to the process-measured sessions. Moreover, we examined both
overall patient emotional expression and expression of specific emotional states, to help
determine whether patient engagement with particular affects is especially important to
treatment process (e.g., grief; anger) or potentially deleterious to treatment (e.g., anxiety
in-session, as might be predicted by psychodynamic conflict models).
Moderators. Although many psychotherapy processes are thought of as
universally positive (e.g., the therapeutic alliance), it may be that some processes are
more important in some psychotherapies, relative to others. Also, the same process,
41
depending on traits that can capture an aspect of specific functioning relating to affect,
may yield strong positive effects in some patients, little effect in others, and negative
effects in still others (cf. Lorenzo-Luaces et al., 2017). We hypothesized that patient
personality disorder traits related to constrained (obsessive-compulsive personality
disorder; OCPD) versus heightened (borderline personality disorder; BPD) affect would
moderate the relationship between emotional expression and symptom improvement.
A psychodynamic conception of OCPD might focus on OCPD patients’ tendency
to intellectualize emotional experiences (Caligor, Kernberg, & Clarkin, 2007; Summers
& Barber, 2010). The typically theorized repertoire of defense for OCPD patients
emphasizes focus on cognition and circumstance over affects (e.g., intellectualization;
isolation of affect; rationalization); a strong need for control, order, and perfection
defends against the dangers of experiencing and acting on affect and wishes that feel
destabilizing. Relative to psychiatrically healthy individuals, OCPD patients report being
less accepting of their emotions, less clear about what their emotions mean, and more
distressed by feeling emotional (Steenkamp, Suvak, Dickstein, Shea, & Litz, 2015). In
treatment with OCPD patients, focusing on affect may help to counteract defenses that
represent avoidance of emotional conflicts (Barber & Muenz, 1996). We thus
hypothesized that patients with more OCPD personality traits as indicated by the SCID-II
(First, Gibbon, Spitzer, Williams, & Benjamin, 1997) would show a stronger relationship
between early levels of emotional expression and later symptomatic improvements, as for
these patients emotional expression may particularly indicate a more flexible use of
defense in-session, relative to their typical profile.
42
In contrast, we hypothesized that patients with increasingly more BPD traits
would show no or even negative relationships between early emotional expression and
later improvements. While BPD is a relatively uncommon comorbidity to PD (Friborg,
Martinussen, Kaiser, Overgard, & Rosenvinge, 2013), many patients exhibit elevated
BPD pathology. In general and clinical populations, qualifying for even one DSM-
defined BPD criterion is uniquely prognostic of significant interpersonal dysfunction and
psychosocial disability (Ellison, Rosenstein, Chelminski, Dalrymple, & Zimmerman,
2016; Zimmerman, Chelminski, Young, Dalrymple, & Martinez, 2012), indicating that
even non-diagnostic BPD may be clinically relevant. Affective dysregulation is a
common if not defining feature of BPD. BPD patients show stronger reactivity to
interpersonal events (Santangelo, Bohus, & Ebner-Priemer, 2012), as well as more labile
mood around a more negative baseline (Ebner-Priemer et al., 2015), relative to healthy
controls and MDD patients. Accordingly, the therapist’s efforts to help patients regulate,
understand, and usefully work with difficult-to-comprehend affects has been identified as
a common feature across many empirically supported psychotherapies for BPD
(Bateman, Gunderson, & Mulder, 2015). For patients with more BPD traits, relatively
lower levels of emotional expression in PFPP may reflect successful work by the
therapist and patient to contain affect in order to better work with the meanings and
circumstances surrounding panic and anxiety (Fonagy & Luyten, 2009). Relatively lower
emotional expression may also indicate defensive flexibility among BPD patients, who
typically engage in defenses focusing on prominent, moment-to-moment affects (e.g.,
splitting; acting out; Kramer, de Roten, Perry, & Despland, 2013; Perry, Presniak, &
43
Olson, 2013), such that emotional expression per se may sometimes involve typical
defensive processes and not primarily a flexible use of defense or processing of conflict.
Method
Participants
Patients. The present study is a secondary analysis of patients randomized to the
PFPP condition (N = 80) of a two-site randomized controlled trial comparing PFPP,
CBT, and applied relaxation training among patients with primary DSM-IV panic
disorder with or without agoraphobia. Patients were recruited at <site A> and <site B>.
Participants received study treatment gratis. Participants gave informed written consent.
Both sites’ institutional review boards approved the protocol, and the study is registered
with ClinicalTrials.gov (identifier: NCT00353470).
Patients were included in the trial if they had one or more weekly spontaneous
panic attacks for the month before trial entry, and qualified for DSM-IV diagnosis of
primary panic disorder with or without agoraphobia determined as per the ADIS-IV
(DiNardo, Brown, & Barlow, 1995). Cross-site agreement on ADIS ratings for panic
severity (with a “4” indicating the diagnostic threshold) was excellent (ICC = 1.00; per
the norms of Portney & Watkins, 2000). See Milrod et al., 2016 for further details.
Non-study psychotherapy was prohibited. Medications were permitted if stable
for at least two months at presentation, and were recorded, held constant, and monitored
during the trial. Exclusion criteria were: active substance dependence (less than 6
months’ remission), a history of psychosis or bipolar disorder, acute suicidality, and
organic mental syndrome. Additional details on trial design, independent evaluator
44
training, and therapy adherence can be consulted in the primary outcome paper (Milrod et
al., 2016).
Assessment of personality disorder (PersD). The Structured Clinical Interview
for Axis-II disorders (First et al., 1997) was used to assess the presence of PersD criteria
and diagnoses as defined by DSM-IV. Trained, independent masters’ level diagnosticians
uninformed to treatment condition administered the interviews. Cross-site interrater
reliability was excellent for number of OCPD criteria scored as present (ICC[2,1] =
1.00), and moderate for BPD criteria (ICC[2,1] = 0.78).
Therapists. All therapists were experienced therapists (Ph.D. or M.D.) who were
specifically trained in PFPP over the span of a 2-day, 12-hour course. Therapists had an
average of 15 years of post-graduate experience (SD = 8.2), and an average of 5 years’
experience in some form of time-limited psychodynamic therapy (SD = 6.3). Therapists
participated in monthly group supervision and received regular individual supervision
from senior clinicians. For the primary outcome paper, adherence to PFPP was
established, and additional information on the number and training of therapists can be
found there (Milrod et al., 2016).
Outcome Indices
Panic Disorder Severity Scale (PDSS; Shear et al., 1997). The PDSS is a
diagnosis-based, composite, global observer rating of panic disorder severity, with
acceptable psychometric properties. The PDSS was administered by trained, master’s-
level diagnosticians, uninformed as to treatment condition, based on an interview guide.
Interrater reliability on the PDSS was excellent (ICC[2,1] = 0.95). The PDSS was
45
administered five times during treatment: at baseline (Week 0), Week 1, Week 5, Week
9, and termination (Week 12).
Panic-Specific Reflective Functioning (PSRF; Rudden et al., 2006). PSRF is
an interview-based measure that assesses the degree to which patients can recognize the
psychological contributions to their panic symptoms. Respondents are queried as to their
understanding of panic, how it has changed over time, and whether they notice any
concordance between panic and emotional states. Each response is rated for
psychological mindedness and complexity, and an overall score is based on item scores.
An example of a more impaired PSRF answer might be: “It’s the heat, the heat brings
them on” in response to “Why do you think you have panic attacks?”; a less impaired
response to that question might be: “I notice that I get them when I am feeling a lack of
control in my personal relationships. I fear that others will leave me, or that I may want to
leave them.” PSRF narratives were reliably scored by trained raters (ICC[2,1] = 0.80).
Process Measures
Emotional Expression Rating Scale (EERS; Huque & Keefe, 20). The EERS is
an observer-rated scale developed to assess the degree to which patients are engaged in
emotional discourse. It was developed for this investigation by two study authors, with
reference to prior attempts to rate emotions in-session (e.g., Klein et al., 1969;
McCullough et al., 2003). The measure was designed to be relatively atheoretical and
broadly descriptive rather than reflecting a particular perspective on what constitutes (for
example) adaptive emotional experience or deep emotional processing. Contrasted with
other scales, it was designed for ease of use by individuals with less theoretical or clinical
46
training. To incorporate information on both verbal and non-verbal indicators of
emotional expression, only sessions with usable video recordings could be sampled.
Ratings were made every five minutes to allow for a more precise and potentially valid
assessment of patient emotion expression relative to whole-session ratings, and to collect
temporal information about development of emotional responses throughout a session.
Raters made both an omnibus rating for overall emotional expression, and ratings for 4
broad emotion categories.
The primary rating concerns a patient’s overall emotional expression, which is
rated on a 0 to 5 scale. Higher scores are dependent on the peak of emotional intensity
during the rated segment, but also on the duration of non-neutral emotional expressivity.
A score of 0 indicates no notable emotional expression, or a relatively neutral or flat
affective state. A score of 2 indicates a low-to-medium but clear presence of emotional
expression lasting more than a few seconds, up to a minute, with use of affectively
charged language or verbal statements of feeling, and non-verbal indicators such as
choking up, tearing up, emphatic gestures, muscular tension, smiling, or physical
agitation/restlessness. Scores of 4 and 5 indicate a high, unconstrained peak affective
arousal with sustained emotional expression across the majority of the rated segment.
Ratings were made for emotional expression within-session, and not when a patient
reported how they were feeling in the past, with no clear indication that they are currently
feeling that particular emotion (e.g., reporting with a neutral tone that they were sad when
a parent died). In Table 2, examples of transcripts from higher- versus lower-emotional
expression therapy sessions from the present study are given.
47
Expressions of four specific broad categories of emotions were coded, three of
which are referenced in the following analyses1:
Grief/Sadness: Grief/Sadness referred to the expression of grief, sadness,
sorrow, or regret. Raters were trained to distinguish between sadness
surrounding (for example) an interpersonal figure, and indistinct
depressive distress or self-attack, which was rated as Anxiety/Distress.
Nonverbal indicators included tearing up, sad vocal tone, choking up,
quavering voice, frowning, and crying. Raters were trained to pay
attention to clear verbal expressions of sadness, regret, or loss, or
particular expressions such as feelings of closeness and tenderness while
talking about a close attachment figure who died or whom they fear may
no longer be available to them.
Anger/Assertion: Anger/Assertion made a distinction between anger
directed toward an external or interpersonal figure, and anger directed
toward the self (e.g., self-attack; self-punishment), which was rated as
Anxiety/Distress. Anger/Assertion was rated if patients indicated clear
anger, criticized someone, asserted their needs or desires to someone, or
voiced wishes for recompense for a past misdeed. Nonverbal markers
included patients’ showing angry facial expressions, angry vocal tone,
becoming more tense, clenching their fists, gesturing aggressively, or
gritting their teeth.
1 Positive Affect was rarely rated as present and had low variance, and consequently we did not analyze
these scores.
48
Anxiety/Distress: Anxiety/Distress was intended to be rated when a
patient appeared activated with clearly negative feeling and arousal, such
as negative global feelings about the self, anxious tension, or experiencing
undifferentiated aversive affect. Non-verbal Anxiety/Distress indicators
included visible discomfort, fidgeting and drawing inwards,
hyperventilating or full panic attack, and uncontrollable negative affect.
Verbal indicators included explicit references to feeling anxious or
distressed or statements of self-attack and hopelessness.
Correlations between emotion ratings on a per-5 minute basis indicated
independence of the specific emotions (see Appendix D).
A total of 15 undergraduate psychology majors were trained on this measure by
study authors JRK and ZH. Raters were trained by rating sessions not used in this
investigation until they reliably rated within one point of the trainers. Weekly-to-
biweekly anti-drift sessions were held to maintain reliability and discuss rating
challenges. Raters were uninformed to patient outcomes and other study patient data.
Videotapes were coded in a random order, and the average score between three raters was
used for all analyses.
Interrater reliability (Shrout & Fleiss, 1979) as calculated in a linear mixed model
(Bates et al., 2017) was good to moderate per-5 minutes for all emotions (ICC [2,3]
Overall = 0.74, Grief/Sadness = 0.71, Anger/Assertion = 0.76, Anxiety/Distress = 0.66).
Statistical Analyses
49
All analyses were conducted in the R statistical computing language (R Core
Team, 2017). All primary analyses were run using robust regressions as implemented in
the R package “Robustbase” (Maechler et al., 2016). Given the effective sample size (n =
44), robust regression was selected over standard regression for its superior properties of
robustness against multivariate outliers and deviation from homoscedasticity (Huber &
Ronchetti, 2009). Semi-partial correlation effect sizes (sr) were estimated for parameters
of interest.
Missing data. In the parent clinical trial, a not missing at random (NMAR)
pattern of treatment dropout was detected, such that patients with worse PDSS symptom
trajectories were more likely to terminate from treatment prematurely (Milrod et al.,
2016). When outcomes for treatment noncompleters are imputed in the NMAR context,
imputation and other missing data methods can lead to biased estimates and confidence
intervals (Graham, 2009). As such, only individuals who provided data up to Week 9 (the
4th assessment) were eligible for inclusion (n = 65; 81.3% of the intent-to-treat sample).
Moreover, due to the need for video to rate nonverbal indicators of emotional expression,
only individuals with videotaped sessions (rather than audio backups) were used. The
final sample size having videotaped sessions was n = 44.
Some trial completers were missing one out of three videotaped session due to
technical issues or therapist/research assistant error (n = 13, 29.5% of the reduced
sample). In contrast to noncompleters, process ratings for completers missing a video-
recording of a session can be presumed to be missing at random in relation to panic
symptom outcomes (Rubin & Little, 2002), and psychotherapy process ratings have been
imputed in past investigations (Keefe, Solomonov, et al., 2018; Lorenzo-Luaces et al.,
50
2017). Random forest imputation (Stekhoven & Bühlmann, 2011) was used to impute
missing data for completers. For this dataset, a normalized root mean square error of
prediction was estimated at 0.12, indicating that imputation accuracy was estimated to be
more than adequate (Stekhoven & Bühlmann, 2011).
Analytic strategy. Using robust linear regressions, the average level of overall
emotional expression between Sessions 2, 5, and 10 (i.e., three sessions between baseline
and Week 5) was used to predict subsequent change in PDSS-measured panic
symptomatology between Week 5 and treatment termination (Week 12). We also
analyzed PSRF as a secondary outcome. Because site differences were detected in the
parent trial (Milrod et al., 2016), we explored whether site moderated focal effects for
this and all subsequently described analyses at a significance of at least p <.10. Control
covariates in each regression included the baseline value for either outcome, and the
degree of change in the variable of interest that occurred between baseline and Week 5
(i.e., during the measurement of emotional expression). In addition, two planned analyses
of moderators of the relationship between average emotional expression and PDSS
symptom improvement were conducted: baseline SCID-II OCPD criteria, and SCID-II
BPD criteria.
We also performed two secondary, exploratory analyses. First, for both PDSS and
PSRF change, we examined whether any of three specific types of emotional expression
(i.e., Grief/Sadness, Anger/Assertion, Anxiety/Distress) were predictive of subsequent
change. Second, in the context of recent debates about the degree to which stable therapy
process across sessions merely reflects trait-like features of patients (Falkenström, Finkel,
Sandell, Rubel, & Holmqvist, 2017), we examined whether having emotional expression
51
at Session 10 greater than one’s average across Sessions 2, 5, and 10 predicted
subsequent symptom change (i.e., a term reflecting Session 10 minus the average of all
sessions). This analysis also simultaneously controlled for average emotional expression
within the same model.
Results
Descriptive Statistics
The average patient in this study had a baseline PDSS score of 13.8 (range 9 to
20), considered to be in the moderately-ill severity range for patients with comorbid
agoraphobia (Furukawa et al., 2009). Other patient demographics and clinical
information can be found in Table 1.
Descriptive statistics on mean levels of emotional expression across each session
can be found in Appendix D. We also examined the consistency of emotional expression
across measured sessions, using a linear mixed model framework (Bates et al., 2017;
Appendix D), which revealed that the majority of variance in emotional expression was
not trait-like across sessions.
PDSS Symptom Change
Patients who had higher levels of emotional expression across the three sampled
sessions (2, 5, and 10) experienced more panic symptom improvement subsequent to
Session 10 (B = -3.10 [95% CI: -5.27 to -0.93], SE = 1.07, t[40] = -2.88, p = 0.006, sr =
0.31), controlling for baseline panic symptoms and prior change in panic symptoms.
In a secondary analysis, having emotional expression at Session 10 that was
higher than one’s average emotional expression across Sessions 2, 5, and 10 was also
predictive of having more symptom improvement subsequent to Session 10 (B = -3.58
52
[95% CI: -5.78 to -1.37], SE = 1.09, t[39] = -3.28, p = 0.002, sr = 0.38). This was true
even though the model also controlled for a patient’s average level of emotional
expression, which remained itself a statistically significant predictor of subsequent panic
symptom improvements (B = -2.90 [95% CI: -4.70 to -1.09], SE = 0.89, t[39] = -3.24, p =
0.002, sr = 0.29).
We further explored whether any specific type of emotional expression was
particularly responsible for this relationship, employing a model simultaneously
including as predictors average expression levels of grief/sadness, anger/assertion, and
anxiety/distress. Patients who expressed more grief/sadness across the sampled sessions
had superior subsequent panic outcomes (B = -5.25 [95% CI: -9.38 to -1.14], SE = 2.18,
t[38] = -2.41, p = 0.021, sr = 0.28), while neither anger/assertion (B = 2.43 [95% CI: -
1.61 to 6.46], SE = 1.93, t[38] = 1.25, p = 0.217, sr = -0.16) nor anxiety/distress (B = 0.01
[95% CI: -5.37 to 5.40], SE = 2.68, t[38] = 0.01, p = 0.996, sr = 0.00) were significant
predictors of symptom change. Figure 1 displays these relationships for overall emotional
expression and specific emotions.
Personality Moderators of Symptom Change
Next, we examined our moderation hypotheses as to whether BPD and OCPD
personality traits would predict a smaller or greater relationship (respectively) between
emotional expression and symptom improvements. Consistent with our hypothesis, the
number of baseline SCID-II BPD criteria a patient met significantly interacted with
overall emotional expression, such that meeting more BPD criteria attenuated the
relationship between overall emotional expression and symptom improvement (B = 2.36
[95% CI: 1.09 to 3.63], SE = 0.63, t[38] = 3.76, p <.001, sr = 0.29). Unpacking this
53
continuous interaction using the Johnson-Neyman technique, we found that patients
meeting 1 or no BPD criteria evidenced a significant, positive relationship between
emotional expression and symptom improvement (sr = 0.33, p = 0.003; see Figure 2). By
contrast, there was a nonsignificant, negative relationship between emotional expression
and improvement for patients meeting 2 or more BPD criteria (sr = -0.20, p = 0.062).
Thus, patients qualifying for a relatively low number of DSM-defined BPD criteria (2 or
more) exhibited no beneficial (or potentially even a negative) relationship to degree of
emotional expression compared to those with minimal (1) or no BPD pathology, who had
a significant, positive relationship.
However, as concerned SCID-II OCPD criteria, there was a significant interaction
between OCPD criteria, emotional expression, and site (p = 0.036). At Site A, the
hypothesized interaction was obtained, whereby patients meeting more OCPD criteria at
baseline had an increasingly positive relationship between early emotional expression and
subsequent symptom improvement (B = -4.48 [95% CI: -7.72 to -1.24], SE = 1.59, t[34] =
-2.81, p = 0.008, sr = 0.34). At Site B, there was no significant interaction (B = 0.48
[95% CI: -2.11 to 3.07], SE = 1.27, t[34] = 0.38, p = 0.707, sr = -0.04). Thus, OCPD
criteria cannot be considered a clear moderator.
Panic-Specific Reflective Functioning Change
Mirroring the model analyzing symptomatic outcomes, patients with a higher
level of emotional expression across the three sessions had greater gains in PSRF
subsequent to the measured sessions (B = 0.93 [95% CI: 0.03 to 1.83], SE = 0.45, t[40] =
2.10, p = 0.042, sr = 0.23), controlling for their baseline PSRF and early PSRF changes.
Neither the number of OCPD (B = 0.42 [95% CI: -0.27 to 1.11], t[38] = 1.24, p = 0.223,
54
sr = 0.21) nor BPD criteria (B = -0.42 [95% CI: -1.12 to 0.27], t[38] = -1.23, p = 0.226, sr
= 0.20) a patient met significantly moderated this relationship.
We examined whether any specific type of emotional expression drove this
relationship. In this case, there were no specific significant relations as concerned
expression of grief/sadness (B = 1.43 [95% CI: -1.62 to 4.48], SE = 1.51, t[38] = 0.95, p =
0.350, sr = 0.15), anger/assertion (B = 1.20 [95% CI: -0.54 to 2.95], SE = 0.86, t[38] =
1.40, p = 0.170, sr = 0.17), or anxiety/distress (B = -0.15 [95% CI: -2.02 to 1.71], SE =
0.92, t[38] = -0.17, p = 0.869, sr = -0.02).
Discussion
In panic-focused psychodynamic psychotherapy, patients who engage in more
emotional expression over the course of the first five weeks of therapy have superior
symptomatic outcomes across the remainder of the treatment. For interpretive context,
just under half of the average symptomatic change occurs after the first five weeks of
treatment (see Table 1). Our results are convergent with findings from psychodynamic
psychotherapy process studies investigating other operationalizations of emotional
processing or experiencing in-session (Abbass et al., 2017; Fisher et al., 2016; Friederich
et al., 2017; Johansson et al., 2014; Kramer et al., 2015; Town et al., 2013; Town,
Salvadori et al., 2017). These findings expand the literature on emotions in
psychodynamic therapy to the context of short-term manualized panic disorder-focused
treatment, and provide further evidence that emotional expression precedes rather than
contemporaneously occurs with improvements. This study thus provides relatively
stronger evidence that emotional expression is a therapy process that may help give rise
to good outcomes, rather than being merely a product of symptomatic alleviation.
55
Notably, early emotional expression also predicted subsequent gains in PSRF,
which were found in the broader trial to predict subsequent panic improvements (Barber
et al., under review). Emotional expression may be reflective of mentalization in-session
of emotional meanings and conflicts contributing to instances of panic and anxiety. As
the patient works to tolerate and understand affectively-laden meanings and conflicts,
they are expected to be able to engage with them rather than feel them as undifferentiated
anxiety or frightening somatic fantasy (Busch et al., 2012). Emotionally working through
conflict may also serve to detoxify certain affects, meanings, or wishes for patients,
which may be typically experienced as distressing, dangerous, uncontrollable, or guilt-
provoking to the point of having a panic attack.
Clinically, our results suggest that emotionally flat or withdrawn discussions of
the contributors and contexts of anxiety are unlikely to help a patient substantively
experience and work with these meanings in psychodynamic therapy. Concordantly, past
work has found that psychodynamic interventions focused on expression of difficult-to-
express affects appear to be correlated with improvements in anxiety (Pitman et al., 2014;
Pitman et al., 2017), and that anxiety patients rate as especially helpful attempts to
explore unexpressed/avoided feelings in-session (Glock, Hilsenroth, & Curtis, 2018). The
presence versus absence of emotional expression may help a therapist distinguish
between a patient’s compliant or pseudo-insightful (intellectualized) acquiescence to the
therapist’s attempts to explore the emotional underpinnings of their anxiety, versus
productive, affective engagement in the therapeutic process.
In our exploratory analyses concerning which specific emotions contribute to the
observed relationships, only expression of grief/sadness significantly predicted
56
subsequent symptom outcomes. PFPP’s clinical theory emphasizes the degree to which
actual or prospective attachment losses can precipitate panic disorder itself, often because
patients have difficulty acknowledging the emotional importance of the loss (Busch et al.,
2012; Klass et al., 2009; Milrod, Leon, & Shear, 2004). Expression of grief/sadness may
sometimes reflect patients engaging with and becoming more tolerant of the true
emotional impact of the actual or feared loss.
Anger may not have emerged as a significant predictor despite its role in
psychodynamic models of panic (Busch et al., 1999; Busch et al., 2012), as anger may
sometimes have been directed toward figures regarding whom the patient already feels
comfortable experiencing anger. In PFPP, anger is conceptualized as being repressed
particularly when the patient is worried that his/her anger will provoke
separation/retaliation and hence loss of an ambivalently-held attachment figure (Busch et
al., 1999; Busch et al., 2012; Rudden et al., 2003). As such, access to and expression of
grief/sadness about loss that might be imagined to emerge as a result of angry feelings
might indicate a deeper processing of the underlying conflict. Expression of disavowed or
unconscious anger, for instance regarding attachment figures, may be found to have
stronger relationships to outcomes compared to general anger. Interestingly, in contrast
with our findings, in an investigation of intensive short-term dynamic psychotherapy for
treatment-resistant major depressive disorder, experiencing of anger but not grief was
related to better depression outcomes (Town, Falkenstrom, Abbass, & Stride, 2017;
Town, Salvadori et al., 2017). It may be that difficulties tolerating particular, core
emotions are more common to specific symptoms.
57
Finally, anxiety/distress in-session was nonpredictive of outcome. In PFPP,
presence of anxiety in-session is not considered negative per se, as for example the
patient may feel anxious because the therapeutic work is addressing important conflicted
material, and that anxiety can be dealt with in real-time (Busch et al., 2012). From the
perspective of PFPP, a therapy that transpires without affects sometimes considered
“negative” or “inhibitory” (McCullough et al., 2003) may reflect that particular important
dynamics are not being brought into the treatment by the patient or pursued by the
therapist. Our metric of Overall emotional expression incorporated all deviations from a
neutral emotional state—which could include so-called negative/inhibitory affects—in
part to reflect our interest in emotional engagement in treatment writ large relative to
therapies in which comparatively minimal affect was mobilized in-session. Other
investigations with different goals may choose to use a modified version of the EERS
explicitly excluding Anxiety/Distress codes from consideration in the Overall score, or
other metrics that attempt to explicitly distinguish negative/inhibitory affects, such as the
Achievement of Therapeutic Objectives Scale (McCullough et al., 2003).
We also examined whether personality disorder traits theoretically related to
constrained (OCPD) or heightened (BPD) emotional expression moderated the
relationship between emotional expression and symptom change. OCPD traits only
moderated the predictive value of emotional expression at one of the two treatment sites,
such that perhaps unmeasured differences in process or patient population further
affected this relationship—for instance, patients at Site B in the trial were much more
likely to be on psychotropic medications and had a significantly higher number of
medication classes taken (Milrod et al., 2016). On the other hand, the presence of
58
elevated BPD traits diminished the relationship between emotional expression and
symptom improvements. With patients meeting relatively more BPD criteria (>1), the
PFPP therapist may wish to focus on helping a patient slow down and contain their strong
affects to better work with the meanings giving rise to powerful emotional experiences.
Of note, meeting relatively more subthreshold BPD criteria per se was not a negative
prognostic indicator for PFPP patients in this trial and in fact BPD symptoms improved
more in PFPP than in CBT in this study (Keefe, Milrod, Gallop, Barber, & Chambless,
2018), suggesting that these patients can benefit from PFPP, given therapeutic processes
that are tailored to their needs. However, few patients in this study met SCID II criteria
for a full comorbid BPD diagnosis, potentially because acute suicidality was an exclusion
criterion, limiting our assessments to PD patients with low levels of borderline pathology.
Limitations and future directions
Several patients had one session (of three) unavailable to be rated due to
videotape missingness (n = 13, 29.5% of the reduced sample). However, we employed a
standard, validated method for imputing missing ratings (Stekhoven & Bühlmann, 2011),
and a complete data analysis showed similar results. In addition, our effective sample size
was small (n = 44).
In our study, average levels of emotional expression on the level of the session
were relatively low (around a 1 on the 5-point scale), with the range of rated values
consisting of emotionally flat sessions (mean 0) to heightened but not (consistently)
highly activated sessions (mean 2). Our obtained relationships for emotional expression
generally do not include representation of consistently high-to-extremely emotional
sessions, and it is possible that such consistently high expression would not relate to
59
positive outcomes. There was mixed evidence whether maximum emotional expression
rated in a session (rather than average across segments) was predictive of outcomes (see
Footnote 2), with a trend level relationship for grief/sadness (cf. Kramer et al., 2015).
Emotional expression may also have a different relationship to outcome later in
treatment.
Our process measure to assess emotional expression, based on the apparent
intensity and duration of expression, consisted of a simpler operationalization of
emotional experiencing/processing than other measures used heretofore by
psychodynamic and process-experiential researchers. For example, there is compelling
evidence that in emotion-focused experiential psychotherapy (Greenberg, 2015), a
prototypical sequence of emotional processing moves from feelings of undifferentiated
global distress toward feeling self-assertive anger or adaptive, relieving experiences of
grief over past hurts and losses (Pascual-Leone, 2017). It could also be that emotional
expression at particular moments in therapy—such as discussing specific relationship
episodes—may be a stronger marker of good process.
Emotional expression as measured by the EERS also did not attempt to
distinguish between more versus less adaptive expressions. We were instead primarily
interested in how manifest emotional expression was a marker of good clinical process in
this treatment. Defining adaptive versus maladaptive/inhibitory affect in-session is a
potentially important but complex task, and different psychodynamic approaches use
varying lenses for understanding affect as a treatment mechanism (for a broad survey of
heterogeneity in defining adaptiveness of affect in different therapy schools, see the
different measures used in the Peluso & Freund et al., 2018 meta-analysis). What is
60
“adaptive” affect may also strongly differ from patient to patient: for many patients
entering PFPP, expressing anger to a romantic partner might be a novel experience contra
their typical defenses against aggression, whereas for a patient with a more borderline
personality organization this same action could more frequently reflect splitting as a
defense. To our knowledge, our study is one of the first to examine how patient context
(e.g., BPD traits) influences how emotional expression relates to treatment success; such
personalized examinations may more specifically elucidate the vagaries of emotional
process in-session, even when using scales that aim to identify only adaptive or deep
emotional expression.
However, one advantage of our method is that, by definition, for more nuanced
emotional experiencing or processing to take place, a basic level of emotional expression
must nearly always also be present. Future work on this dataset might use our assay of
emotional expression across segments of sessions to orient and focus more detailed
research.
This study does not address the question of how a therapist may best affectively
engage patients in-session. Process work in psychodynamic therapy indicates that
focused confrontation interventions (Town, Hardy, McCullough, & Stride, 2012) or
interventions attempting to orient patients to their affects (Ulvenes et al., 2014) tend to be
associated with greater emotional experiencing on the part of the patient. Preliminary
work in this sample suggests that panic-focused interpretations—found to relate to
subsequent outcomes in PFPP (Keefe, Solomonov et al., 2018)—in one segment of
treatment may predict higher emotional expression in the next segment of treatment when
61
patients were discussing an attachment relationship (Keefe, Huque et al., 2018). This
research is ongoing.
We also did not examine the extent to which patients engaged in emotional
expressions in the CBT and applied relaxation therapies in the trial. In CBT for
depression, some studies report that emotional processing in-session may predict better
post-treatment outcomes (Aafjes-van Doorn & Barber, 2017), but the role of emotional
processing and experiencing is less commonly explored in this family of treatments. In
CBT for panic, we might expect emotional expression to be unrelated or positively
related to treatment outcomes (e.g., restructuring “hot cognitions”; intensely engaging in
interoceptive exposure). For applied relaxation, emotional expression may have a
negative relationship to outcomes, given the goal of in-session progressive relaxation.
Future work could apply the EERS or other experiencing scales to less affect-focused
psychotherapies for panic.
Finally, in psychotherapy research, there has been increasing attention on
distinguishing a patient’s tendency to have a given process score across all sessions (i.e.,
the “between-patients” component) from within-patient changes in process scores,
accounting for their average levels of that process (Falkenström et al., 2017). Our
secondary analysis partially addresses this critique, suggesting that having higher than
one’s average level of emotional expression (Sessions 2, 5, 10) at Session 10 predicted
superior subsequent symptom improvement, even when simultaneously modeling the
predictive value of one’s average (i.e., “between-patients”) emotional expression. This
provides limited evidence that, even among patients who have a relatively higher level of
emotional expression across their early therapy, emotional expression over one’s typical
62
level at a later session is positively prognostic of symptom improvement. Moreover, the
majority of variance in emotional expression was not trait-like and consistent across
patient sessions, indicating that it is less likely (though not impossible) that the “between-
patients” component of emotional expression is primarily driving the observed relations.
On the other hand, consistently high levels of a particular process, such as emotional
expression, may also reflect specific work within a unique therapeutic dyad, such that the
same patient working with a different or more/less skillful therapist would not show such
a pattern.
Conclusion
Emotional expression in short-term psychodynamic treatment of panic disorder
early in the course of therapy predicts greater symptomatic improvements later in
treatment, possibly through encouraging insight into the emotional meanings and
conflicts surrounding episodes of panic and anxiety. These results require replication.
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76
Table 2.1
Descriptive data for baseline characteristics and for symptom change
Baseline Measure Mean (SD) or # (%)
Baseline PDSS 13.8 (2.9)
Baseline to Week 5 PDSS Change -3.9 (4.0)
Week 5 to Termination PDSS Change -2.9 (2.9)
Baseline PSRF 3.3 (1.1)
Baseline to Week 5 PSRF Change 0.9 (1.4)
Week 5 to Termination PSRF Change 0.0 (1.2)
SDS 16.9 (6.1)
HAM-D 10.4 (3.8)
Agoraphobia Diagnosis 35 (79.5%)
Age 37.8 (14.0)
Gender (Female) 31 (70.5%)
Concurrent Psychopharmacology 12 (27.3%)
Age of Panic Onset (years) 26.6 (11.2)
SCID-II OcPD Criteria 2.1 (1.7)
SCID-II BPD Criteria 1.0 (1.7)
Note. HAM-D = Hamilton Rating Scale for Depression; IIP = Inventory of Interpersonal
Problems; PDSS = Panic Disorder Severity Scale; SCID-II = Structured Clinical
Interview for the Diagnosis of Axis-II Disorders; SDS = Sheehan Disability Scale
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Table 2.2
Example transcripts from sessions rated high/low in emotional expression
High Emotional Expression Transcript
(Session Average 1.75) Low Emotional Expression Transcript
(Session Average 0.28)
T: Is your boyfriend being on this
nocturnal schedule and maybe not
finishing his program a worry for you? I
feel that you’re frustrated and anxious in
an immediate sense of what’s going on
right now, but there’s some concern about
getting married, proceeding with that.
P: No, I mean, the pros outweigh the cons
< quiet, restrained tone of voice>.
T: I think maybe part of what might go on
inside is that thinking about these concerns
is at times a scary, anxiety-provoking
thought… because you want to proceed
with marrying him, and the pros outweigh
the cons, but he’s not taking care of
himself.
P: Yes… yes, I guess so. <tearing up>
Maybe that’s why… I don’t know, I mean,
he’s really smart, I just hope that he’ll
soon figure what he wants to focus on… I
know that in a few years if he didn’t
he’d… <begins to fully cry, cries for a few
moments>…<therapist offers tissue>…
Thank you…
T: You’re worried about that, and what
might happen to you both if that were to
happen.
P: <teary, weepy> I don’t know. He’s such
a Type-A perfectionist, I don’t know how
he keeps spiraling. I’m so worried about
him all the time, it really gets to me. I’m
really sad... <coded elevation in
Grief/Sadness>
P: It’s awful. I hate saying goodbyes. I
hate driving my son to the station to say
goodbye. I hate driving my sister to say
goodbye. <neutral tone and facial
expression>
T: How does it make you feel when you
do that?
P: I guess, and this is just pure
speculation, but I guess…
T: What is it that you feel? / P: Um. / T:
Sorry I cut you off. / P: No, that’s fine. It
makes me feel anxious, it makes me feel
sad, um, it’s just very hard, I don’t like it
at all <laughter; smiling incongruously to
statement>.
T: Just like the way you feel here.
P: When I have tears, they could literally
be for you and what you’re going
through, they could be for us, or it could
be, I don’t know. Because, I mean, there
would have to be a thought associated
with the emotion. A thought that, I’m
assuming, triggered the emotion.
<continued neutral tone/expression> And
I don’t know what that thought is. I’d like
to know what that thought is. But, um, the
leaving I don’t like. I don’t like goodbyes.
Never liked goodbyes. Well, I don’t know
if that’s true, but I don’t like goodbyes.
<coded small elevation in
Anxiety/Distress from increase in
physical agitation and pace of speech, but
overall still relatively neutral/flat>
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Note. Both transcripts are adapted from sessions from the trial, particularly Session 10,
for a patient with one of the top 3 ratings for emotional expression at this session versus a
patient with one of the bottom 3 ratings for emotional expression. Transcripts have been
edited for clarity and to eliminate potentially identifying information.
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Figure 2.1. Estimated effect sizes for the relationship between the average level of
emotion expression across sessions 2, 5, and 10, and subsequent improvement in panic
symptoms as measured by the PDSS. Positive semipartial correlations indicate that higher
levels of expression are associated with more subsequent symptom improvement. Bars
are 95% confidence intervals.
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Figure 2.2. Estimated change in panic symptoms as measured by the PDSS between
Weeks 5 to Termination (Week 12), as a function of the average overall emotional
expression between sessions 2, 5, and 10, and their interaction with the number of SCID-
II borderline personality disorder criteria a patient met at baseline. Increasingly negative
values represent greater predicted symptom improvement. All regression variables not
displayed in the figure were set to the sample means.
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What Mechanism Works for Whom in Panic
Heterogeneity between patients within a DSM-described disorder is widely
recognized clinically and, increasingly, empirically. Personalized medicine approaches to
psychiatric treatment seek to capitalize on this heterogeneity either by matching patients
to the treatment most likely to target their primary and comorbid DSM disorders, or, for
psychotherapies, by modifying a given treatment to best address the needs of a given
patient (Cohen & DeRubeis, in press).
While some dominant models of psychiatry operate explicitly or implicitly from a
latent trait conception—that different symptoms emerge from an underlying, common
disease—an alternative conception is that different causal biopsychosocial factors
interacting with one another (e.g., in a network) may promote and maintain different
patterns of presenting symptomatology (Hofmann, Curtiss, & McNally, 2016; Kendler,
Zachar, & Craver, 2011). Treatment selection models attempting to precisely allocate
patients on the basis of their personal characteristics to the treatment most likely to
benefit them may capitalize on the fact that different treatments activate different
mechanisms of change, which might be more or less likely to help a particular patient
(DeRubeis, Cohen, et al., 2014; Wallace, Frank, & Kraemer, 2013). If linked factors may
cause symptoms to correlate and “hang together” in a disorder-like manner (Kendler et
al., 2011), there is no particular reason why two individuals must share the same pathway
to manifesting an overt, diagnosable symptom. For example, a longitudinal network
analysis of symptoms and experiences among depressed and anxious patients examined
how a patient’s reports of these factors at one time point predicted higher or lower levels
82
of the other measured variables at a subsequent timepoint (e.g., from feelings of anger at
T0 to depressed mood at T1) (Fisher, Reeves, Lawyer, Medaglia, & Rubel, 2017). While
some cross-patient commonalities could be extracted across networks of relationships
between the measured psychological variables across time, patients nevertheless
exhibited substantive heterogeneity in their networks. For patient A, sleep deprivation
could lead most to lowered mood, while for patient B, experiences of anger were most
related to later lowered mood. This suggests that patients with the same set of symptoms
could differ in the paths that led to their development.
This view has strong implications for mediation analyses that aim identify a
potential mechanism by which a treatment leads to the amelioration of symptoms of a
disorder. A mechanism can be improvement in a process that led to symptom
development (e.g., defense mechanisms; Perry & Bond, 2012) or a gain in a capacity that
counteracts a psychopathological process (e.g., cognitive therapy skill use; Strunk,
DeRubeis, Chiu, & Alvarez, 2007). If such changes are found to promote symptom relief
in some patients and not others, and insofar as this can be predicted on the basis of
measurable, individual differences, the term moderated mediation is applicable (Preacher,
Rucker, & Hayes, 2007).2 Differential responsiveness between patients to change in
2 Note that the term “moderated mediation” has been used to characterize instances in which the
symptomatic impact of a psychological change is dependent on the type of treatment a patient received
(e.g. (Zilcha-Mano et al., 2016)). The theoretical implications of this type of finding are arguably unclear.
If a particular psychological change X is helpful in treatment A but not treatment B, this would necessitate
that at least one of the following is also true: (a) there is another change encouraged by treatment A but not
B that, when combined with change X, allows for X to have a salutary effect; (b) there is another change
encouraged in treatment B but not A that attenuates the impact of change X; or (c) the treatment a patient
receives affects the meaning of scores (in one treatment or both) on the measure that assesses change X,
such that shifts in the scores that index X reflect different constructs in treatments A and B.
83
psychological mechanisms may indicate more or less precise targeting of their particular
deficits or dysfunctions that maintain their symptoms.
Mechanisms of change in therapies for panic disorder
We examined the notion of moderated mediation in the context of a 2:2:1
randomized, two-site controlled trial (Milrod et al., 2016) of cognitive-behavioral therapy
(CBT; Craske, Barlow, & Meadows, 2000) and panic-focused psychodynamic
psychotherapy (PFPP; Busch, Milrod, Singer, & Aronson, 2012) for the treatment of
panic disorder with and without agoraphobia.3 We predicted that individual patient
differences would influence the symptomatic impact of change in two putative mediators
of panic improvement, catastrophic cognitions concerning bodily sensations (Clark,
1986), and panic-specific reflective functioning (PSRF) concerning the interpersonal-
emotional contexts and triggers of panic and anxiety (Rudden, Milrod, Target, Ackerman,
& Graf, 2006).
Cognitive-behavioral therapy (CBT) and psychodynamic therapy (PDT) for the
treatment of panic are each associated with their own theory of the nature of panic attacks
and of the mechanisms that treatment will marshal to ameliorate panic disorder. There is
increasing evidence that changes in panic-related cognitions can precede and predict
improvements in panic symptoms (Lorenzo-Luaces, Keefe, & DeRubeis, 2016). In CBT
models of panic disorder, catastrophic cognitions concerning the ramifications of specific
3 The ART condition was not included in the present investigation due to the smaller group of patients
randomized to ART, in addition to significantly higher dropout that was related to being more
symptomatically and psychosocially impaired at baseline (only in ART), and having worse symptomatic
trajectories during treatment. These facts imply that the few treatment-completing ART patients were
particularly unrepresentative compared to CBT and PFPP patients.
84
frightening body sensations, found to be elevated among patients with panic disorder
compared to other anxiety disorders (Clark et al., 1997), are considered causal to the
emergence of panic attacks (Clark, 1986). Panic patients are conceptualized as frequently
interpreting normal body sensations or day-to-day anxiety as indicating somatic problems
and distress, which initiates a vicious cycle of increases in arousal and a belief that a
catastrophe is imminent, leading to a panic attack. Cognitive restructuring of these
cognitions is thought to alleviate panic as it disrupts the cycle of interpretation and
arousal. (Teachman, Marker, & Clerkin, 2010), reported that improvements on the Brief
Body Sensations Interpretation Questionnaire (BBSIQ), a measure of catastrophic
interpretation, predicted subsequent improvements in core panic and agoraphobic
symptoms in CBT. Barber and colleagues (under review) replicated this finding, and
found that this pattern held also in panic-focused psychodynamic psychotherapy (PFPP;
Busch, Milrod, Singer, & Aronson, 2012).
By contrast, psychodynamic theories of panic emphasize how the inability to
recognize, experience, and tolerate affects and fantasies that lead to anxiety promotes
panic. From a psychodynamic perspective, these causes often derive from unconscious
conflict (e.g., repressed anger against a loved one you are fearful of losing if anger is
acknowledged). Panic patients report high alexithymia and experiential avoidance
(Galderisi et al., 2008; Izci et al., 2014; Parker, Taylor, Bagby, & Acklin, 1993; Tull &
Roemer, 2007) and, despite the fact that in the DSM, a panic attack is defined as
appearing to “come out of the blue,” emotional-interpersonal anxiety triggers can often
be identified (Busch, Shear, Cooper, Shapiro, & Leon, 1995; Chambless, 2010; Klass et
al., 2009; Scocco, Barbieri, & Frank, 2007). Moreover, panic patients exhibit
85
emotionally-avoidant strategies in response to both positively- and negatively-valenced
stimuli (Tull & Roemer, 2007), and experimentally inducing use of these strategies
promotes distress and physiological arousal (Tull, Jakupcak, & Roemer, 2010). This lack
of awareness of triggers, whether facilitated by ignorance or avoidance, can produce the
upsetting sense that arousal and consequent panic comes "out of the blue” (Busch et al.,
1995). Panic-specific reflective functioning (PSRF), which refers to the tendency of an
individual to be aware of and to understand emotional meanings surrounding panic, is
typically impaired in patients with PD (Rudden, Milrod, Target, Ackerman, & Graf,
2006). Early improvements in PSRF were found to predict subsequent symptom change
in both PFPP and CBT, controlling for early symptom change (Barber et al., under
review). These results are consistent with research findings on gains in insight in the
context of psychodynamic psychotherapy in treating other disorders, which typically
demonstrate that patients who have improved insight over the course of psychotherapy
show further or more stable improvements across post-treatment follow-up (Gibbons et
al., 2009; Johansson et al., 2010; Kallestad et al., 2010; Ulberg, Amlo, Dahl, & Høglend,
2017).
Moderation Hypotheses
For both the BBSIQ and PSRF, we hypothesized that improvements in the BBSIQ
and PSRF were expected to be more predictive of subsequent symptom improvement
when a patient’s baseline values for that measure were in the more impaired range. We
based this on the conjecture that, for patients with these clinical impairments, these core
problems likely have a greater role in sustaining the symptom disorder. In other words,
we proposed that the presence of impaired values on these measures would indicate the
86
presence of a problem that must be addressed and compensated for, rather than indicating
an area of rigid dysfunction that implies therapist should help bolster and mobilize other,
already stronger capacities (Cheavens, Strunk, Lazarus, & Goldstein, 2012). We therefore
tested baseline values of each change variable as moderators of the impact of change in
both BBSIQ and PSRF.
We also examined the role of co-morbid personality disorder (PersD) in the
change processes for these therapies. A little less than 50% of panic patients meet criteria
for a SCID-II diagnosable comorbid personality disorder (Friborg, Martinussen, Kaiser,
Øvergård, & Rosenvinge, 2013; Keefe, Milrod, Gallop, Barber, & Chambless, in press),
and anxiety patients with PersD experience worse trajectories of symptom improvements
over time generally (Ansell et al., 2011; Skodol, Geier, Grant, & Hasin, 2014) and in
CBT for panic specifically (Porter & Chambless, 2015). Studied mechanisms of change
in personality disorder are generally based on personality change (or, in the case of DBT,
acquisition of skills) rather than the change processes studied in this investigation (Keefe
& DeRubeis, 2018). Psychotherapy with PersD-comorbid patients may sometimes
require adaptations in technique (Keefe, Webb, & DeRubeis, 2016). Taken together, we
hypothesized that the personality factors giving rise to PersD may also help contribute to
panic experiences, and that thus the presence of PersD could attenuate and render less
primary the relationship between BBSIQ and PSRF improvements and symptomatic
response.
Method
Participants
87
The present study is a secondary analysis of 138 of 201 patients randomized to
three psychotherapy treatments in a two-site randomized controlled trial comparing CBT,
applied relaxation training, and PFPP among patients with primary DSM-IV panic
disorder with or without agoraphobia. Patients were recruited at Weill Cornell Medical
College and the University of Pennsylvania. Participants received study treatment gratis.
Participants gave informed written consent. Both sites’ institutional review boards
approved the protocol, and the study is registered with ClinicalTrials.gov (identifier:
NCT00353470).
Patients were included in the trial if they had the spontaneous occurrence of one
or more panic attacks for the month before trial entry, and qualified for a primary DSM-
IV panic disorder with or without agoraphobia diagnosis determined as per the ADIS-IV
version (DiNardo, Brown, & Barlow, 1995). Cross-site agreement on ADIS ratings for
panic severity (with a “4” indicating the diagnostic threshold) was excellent (ICC = 1.00).
See Milrod et al., 2016 for further details.
Additional ongoing psychotherapy was prohibited. Medications were permitted if
stable for at least two months at presentation, and were recorded, held constant, and
monitored during the trial. Exclusion criteria were active substance dependence (of less
than 6 month’s remission), a history of psychosis or bipolar disorder, acute suicidality, or
organic mental syndrome. Additional details on trial design, diagnostician training, and
therapy adherence can be consulted in the primary outcome paper (Milrod et al., 2016).
Therapies
PFPP is based on the central assumption that panic symptoms have a partly
unconscious psychological meaning. It explores feelings and subjective content of panic
88
episodes, so the patient can begin to address these meanings rather than experiencing
conflicts physically as somatic anxiety leading to panic (Busch et al., 2012; Milrod et al.,
1997). The therapy helps patients understand and alter core conflicts (e.g., regarding
attachment and dependency) to avert future panic vulnerability. CBT for PD followed a
modified version of the Panic Control Therapy protocol (Craske et al., 2000), entailing
education about panic, correction of maladaptive thoughts about anxiety and body
sensations, and both in-session and homework interoceptive exposures to bodily
sensations designed to mimic those experienced during panic (Craske et al., 2000). Both
psychotherapies comprised 24 sessions delivered twice weekly (12 weeks). Additional
information on treatments, including training, supervision, and adherence monitoring, can
be found in Milrod et al. (2016).
Outcome Index
Panic Disorder Severity Scale (PDSS; (Shear et al., 1997)). The PDSS is a
diagnosis-based, composite, global rating of panic disorder severity, with acceptable
psychometric properties. The PDSS was administered by trained, master’s-level
diagnosticians who were uninformed as to treatment condition. Interrater reliability on
the PDSS was excellent (ICC[2,1] = 0.95). The PDSS was administered five times during
treatment: at baseline (Week 0), Week 1, Week 5, Week 9, and termination (Week 12).
The mediational analyses in this manuscript concern PDSS change between Week 5 to
termination (Week 12), controlling for early PDSS change from Week 0 to Week 5.
Psychological Change and Moderator Measures
Brief Bodily Sensations Interpretation Questionnaire (BBSIQ; Clark et al.,
1997). The BBSIQ is a 7-item measure of catastrophic misinterpretation of panic-related
89
bodily sensations. Respondents read seven scenarios about bodily sensations, and rank
three provided interpretations in the order by which they would most likely come to the
respondent’s mind in that scenario. A score for each item is determined as a function of
the reported order and the level of catastrophizing of the interpretation, and the item
scores are summed. The BBSIQ exhibits psychometric consistency conforming to a one-
factor solution, in addition to strong construct validity and test-retest reliability over three
months. It showed adequate internal consistency in this trial (Cronbach’s α = 0.86).
Panic-Specific Reflective Functioning (PSRF; Rudden et al., 2006). PSRF is
an interview-based measure that assesses the degree to which a patient can recognize the
psychological contributions to panic symptoms. Respondents are queried as to their
theory of panic, how it has changed over time, and whether they notice any concordance
between panic and particular emotional or interpersonal states. Each response is rated for
its psychological mindedness and complexity, and an overall score is assigned based on
the item scores. An example of a more impaired PSRF answer might be saying “It’s the
heat, the heat brings them on” in response to “reasons for your panic attacks.” A less
impaired response to that same question might be “I notice that I get them when I am
feeling a lack of control in my personal relationships. I fear that others will leave me, or
that I may want to leave them.” PSRF narratives were reliably scored by three trained
raters at the Weill-Cornell Medical College (ICC = 0.80).
Structured Clinical Interview for Axis-II Disorders (SCID-II; First, Gibbon,
Spitzer, Williams, & Benjamin, 1997). The Structured Clinical Interview for Axis-II
disorders (First et al., 1997) was used to assess the presence of PersD criteria and
90
diagnoses as defined by DSM-IV. SCID-II criteria counts rather than diagnostic status
were employed as a moderator variable for reasons of statistical power and validity
(MacCallum, Zhang, Preacher, & Rucker, 2002), following research suggesting that
DSM-IV PersD reflects a continuum of illness rather than categorical taxonomies
(Harford, Chen, & Grant, 2014; Harford et al., 2013; Haslam, Holland, & Kuppens,
2012).
Trained, independent masters’ level diagnosticians, uninformed to treatment
condition administered the interviews. Cross-site inter-rater reliability for number of total
PersD criteria scored as present was excellent (ICC[2,1] = 0.92).
Statistical Analyses
Missing data. Due to the nonrandom nature of dropout in this trial, wherein
patients with worse symptom improvement trajectories tended to drop out significantly
more, and because early psychological changes on the BBSIQ and PSRF were the crucial
predictors in this study, we could not use early dropout patients in our investigation (n =
24 in CBT/PFPP; 14.8% dropout rate), resulting in a final sample size of n = 138 across
the two arms. Attempts to multiply impute their outcome and predictor values would
likely be statistically biased due to the nonrandom dropout (White, Royston, & Wood,
2011). As a result, 138 CBT and PFPP patients who performed assessments through at
least Week 5 (Assessment 3) were the focus of this study.
Modeling. Statistical analyses were conducted in SAS using the PROC MIXED
command. Repeated measures mixed models were employed on the three PDSS
evaluations between Week 5 and Termination (Week 12). Random slopes of PDSS
symptom change across the time unit of week of treatment during the evaluation period
91
were nested in random person-specific intercepts, with an unstructured covariance
structure. Covariance structures were allowed to vary between the two sites of the trial. In
every model, the baseline PDSS score and early PDSS change (between baseline and
Week 5) were entered as covariates.
Within a mixed model, the primary analyses predicted slopes of PDSS change
between Week 5 and termination as a function of early change in psychological change
mechanisms (i.e., on the BBSIQ or PSRF) between baseline and Week 5. Time was
modeled as linear, following the pattern of change detected in the parent trial. Moderators
were tested as an interaction between the moderator value, early psychological change,
and time (p <.05 threshold). Significance of this interaction indicates that the degree to
which early psychological change predicts subsequent symptom change is dependent on
baseline levels of the moderator variable. Covariates included baseline PDSS score and
early PDSS change occurring between baseline and Week 5 (when the mediator was
measured the second time). Separate models were run for each of the combinations of
two psychological mechanism variables (BBSIQ, PSRF) and three baseline moderator
variables (BBSIQ, PSRF, total SCID-II criteria).
We separately tested whether any of the above three potential moderators found to
be statistically significant further interacted with site or treatment at p <.10. However, no
such interactions emerged, indicating that moderation effects were not reliably different
across sites or treatments.
Results
Descriptive statistics
92
Information on patient demographics and clinical characteristics can be found in
Table 1. Patients with better PSRF were somewhat less, but not significantly, likely to
have a catastrophic, body-focused interpretation style as measured by the BBSIQ (r[134]
= -0.16, p = 0.064). Early improvements on the PSRF (Baseline to Week 5) did not
correlate with early improvements on the BBSIQ (r[70] = 0.19, p = 0.111). The
relationship between baseline values in mediator and moderator measures at baseline and
early mediator change is reported in Table 2.
Moderators of the impact of PSRF improvements
As reported in the Barber et al. (in press) paper on mediation in the trial, patients
who experienced more PSRF improvements during the first five weeks of treatment
exhibited greater panic symptom improvements over the subsequent seven weeks (B = -
0.47 [95% CI: -0.82, -0.13], t[166] = -2.69, p = 0.008).
Two of the three tested variables moderated the impact of early PSRF
improvements on PDSS change. Our primary hypothesis concerning baseline PSRF
severity was not supported, as baseline PSRF was not related to the degree to which
improvements in PSRF predicted subsequent symptom change (B = 0.10 [95 CI: -0.20,
0.39], t[159] = 0.64, p = 0.520). Contrary to our expectation, however, patients who
engaged in more severe catastrophic interpretations at baseline on the BBSIQ
experienced more subsequent symptomatic benefits from early improvements in PSRF (B
= -1.05 [95% CI: -2.06, -0.03], t[151] = -2.04, p = 0.043) (see Figure 1).
Nevertheless, as hypothesized, personality pathology was a significant moderator,
as patients with more personality pathology as measured by the SCID-II were less likely
to experience symptom improvement after gains in PSRF (B = 0.06 [0.00, 0.11], t[158] =
93
2.00, p = 0.047), whereas patients with less personality pathology burden experienced
relatively more improvement after gains in PSRF. The critical value of this interaction, at
which point early PSRF change was no longer significantly related to subsequent PDSS
improvement, was 12 SCID-II criteria, as identified with the Johnson-Neyman technique
(Johnson & Fay, 1950; see Figure 2).
Moderators of the impact of BBSIQ improvements
Again, as was found in the Barber et al. (in press) paper on overall mediation in
this trial, early improvements in the BBSIQ (between baseline and Week 5) predicted
greater subsequent symptom change on the PDSS (B = 0.13 [0.0, 0.25], t[142] = 2.05, p =
0.042), controlling for baseline PDSS symptom severity and early symptom
improvement.
In a test of our moderation hypothesis, we found that patients with higher BBSIQ
scores at baseline experienced more symptom relief following early improvements on the
BBSIQ (B = 0.27 [95% CI: 0.05, 0.49, t[140] = 2.43, p = 0.016). Similar to the pattern
shown in Figure 1 for PSRF improvements, patients with relatively lower baseline
BBSIQ exhibited no extra symptomatic improvement subsequent to early decreases on
the BBSIQ, while those with increasingly higher BBSIQ scores at baseline were
predicted to benefit more and more from BBSIQ improvements. As expected, baseline
levels of PSRF did not moderate the relationship between BBSIQ change and symptom
improvement (B = -0.06 [95% CI: -0.16, 0.03], t[131]=-1.28, p = 0.203). The test of the
hypothesis that a patient’s level of comorbid personality pathology would affect
negatively the impact of improvements in catastrophic cognitions on symptomatic benefit
was not supported (B = 0.01 [95% CI: -0.01, 0.03], t[134] = 1.17, p = 0.244).
94
Discussion
A panic patient’s degree of symptomatic improvement after experiencing a
particular psychological change in psychotherapy differs significantly as a function of
their psychological characteristics. For certain patients, focus on helping stimulate
particular psychological changes might improve the efficacy of their treatment. From a
theoretical perspective, these results align with models of psychopathology that posit that
different psychological variables may contribute to what appear to be identical symptoms
for different patients (Fisher et al., 2017). As such, ameliorating the same putatively
pathological process or helping a patient gain an apparently adaptive capacity may be
more helpful for some patients than those same changes will be for others. Moreover, our
findings align with the viewpoint that patients’ characteristics may meaningfully
contribute to which kind of treatment they should receive to optimize their chances of
success (Cohen & DeRubeis, 2018).
Possessing a more catastrophic style of interpreting bodily sensations indicated
that patients experienced greater subsequent symptom change from early improvements
in both catastrophic interpretations as measured by BBSIQ, or insight into the possible
psychological meanings surrounding panic as assessed by the PSRF. For these patients,
the cognitive model of panic may be especially applicable (i.e., that such interpretations
spark and fuel the cycle into panic attacks; Clark, 1986), such that restructuring those
interpretations more reliably helps patients thwart the panic cycle. By contrast, patients
without a strong tendency to catastrophize may not benefit as much from attempts to
reduce this tendency further.
95
We did not expect to detect the apparent moderating influence of catastrophic
cognitive style on the impact of gains in PSRF. In PFPP, tendency to somatize is
understood as a defense mechanism. From a psychodynamic perspective, focus on body
sensations can arise due to a discomfort with the underlying emotions and fantasies
contributing to physiological arousal—somatization avoids direct acknowledgement of
these unacceptable, sometimes partially unconscious contents (Busch et al., 2012; Busch
et al., 1995). Shifting from understanding anxiety and arousal as implying somatic danger
to deriving from central psychological meanings may help patients to recognize and work
with those meanings rather than to ascribe arousal to frightening somatic causes,
especially given a heightened sense of catastrophe.
By contrast, patients with lower versus higher insight about possible
psychological meanings of their panic (i.e., PSRF) evidenced equal apparent benefit from
either type of psychological change. Ergo, patients beginning psychotherapy with little or
no insight as to possible emotional or interpersonal links to their panic appear to
nonetheless benefit from work fostering early improvements in PSRF.
Taken together, these findings imply that patients with a highly catastrophic,
body-focused interpretation style may need either to ameliorate this cognitive style or
gain insight into their emotional-interpersonal triggers in order to experience greater
relief from panic disorder. Both types of changes may commonly reflect patients
recognizing that bodily sensations do not indicate danger and catastrophe, but rather that
they are harmless (BBSIQ improvement) or may signify other psychological meanings
(PSRF improvement). This may also reflect that, while BBSIQ indexes severity of a
cognitive style that specifically distinguishes and potentially causes panic disorder (Clark
96
et al., 1997), low levels of PSRF may reflect a lack of understanding of symptom links
that can be gained to help with panic, rather than a deficit particular to panic patients that
must be improved. It is possible that individual differences concerning the severity of
more general emotional awareness deficits (e.g., alexithymia; experiential avoidance)
documented in panic patients may be a better moderator for panic mediational models.
Unfortunately, it was not possible to meaningfully investigate the case of simultaneous
improvement in both constructs, as the type of changes a patient experienced was
dependent on the therapy they received (CBT vs. PFPP), and simultaneous improvements
were thus uncommon.
Patients with more personality pathology burden benefitted less from
improvements in PSRF, while the converse was true for patients with few such traits.
Indeed, patients without any personality pathology whatsoever exhibited the greatest
benefit from PSRF improvements. Nevertheless, in this subsample, there was no
indication that patients meeting more PersD criteria had significantly worse symptomatic
outcomes in PFPP as compared to CBT. Furthermore, in this trial PFPP was superior to
CBT in improving comorbid severe PersD symptoms on the SCID-II (Keefe et al., 2018).
It may be that PFPP’s capacity to treat severely PersD-comorbid patients is due to other
psychological changes targeted by psychodynamic therapies, such as improvements in
defensive functioning or attachment security (Johansson et al., 2010; Keefe & DeRubeis,
2018; Perry & Bond, 2012; Rossouw & Fonagy, 2012). On the other hand, BBSIQ
improvements were predictive of symptom change regardless of patients’ personality
pathology, which is consistent with an account that PersD is not a clear contraindication
to cognitive restructuring for panic symptom relief.
97
This investigation was not designed to elucidate specific techniques or therapeutic
foci within the two treatments that best promote the psychological changes we examined.
Most psychological treatments consist of multiple interventions targeting multiple types
of changes. CBT for panic disorder includes teaching relaxation (a coping skill),
cognitive restructuring concerning catastrophic thoughts about the body, and both
interoceptive exposure to feared bodily sensations and in vivo exposures to phobic, panic-
engendering spaces (facilitating extinction and potentially fueling cognitive change).
Panic-focused psychodynamic psychotherapy also entails several foci, including the
exploration of dysregulated attachment, the interpretation of transference and of defenses,
and efforts to enhance the patient’s awareness and tolerance of emotions and fantasy life.
Different psychological interventions within a given therapy may best encourage the
types of psychological changes most likely to help a particular patient (Keefe et al., 2016;
Sasso, Strunk, Braun, DeRubeis, & Brotman, 2015). These kinds of questions require
further investigation.
Limitations and future directions
Due to the nonrandom nature of dropout in this trial such that patients with worse
trajectories of symptom change were more likely to drop out, only patients with observed
data up to at least Week 5 were analyzed in this investigation (14.8% of CBT and PFPP
patients randomized to treatment).
For this analysis, we had the option to explore either a wide range of potential
moderators of proposed change mechanisms, or to limit our analyses to a select few on
the basis of theoretical predictions. In the interest of limiting Type I error, we elected to
test a smaller set of moderators. It is possible that other individual traits exist that would
98
help envisage how each type of psychological change might predict subsequent
symptomatic improvements. In addition, only two psychological changes relevant to
panic disorder were examined in the primary mediator analyses for this trial (catastrophic
interpretations and PSRF), whereas other putative mediators may exist, such as self-
efficacy (Fentz et al., 2013) and improvements in attachment security (Milrod, 2015),
neither of which were assessed in this trial. In addition, as this is the first investigation of
its kind in panic treatment, these relationships should be explored in other treatment trials
with relevant data.
Despite our goals to identify which psychological changes might be most
important for individual patients, our study takes a nomothetic, between-patients
approach to examining “what mechanism works for whom.” An alternative, personalized
approach would be to identify these patterns for individual patients, and to examine the
differential effects of attempting to intervene upon the core problems that sustain an
individual’s panic (Fisher & Boswell, 2016). For example, if a patient’s tendency to
catastrophize bodily sensations was observed to reliably predict their experiences of
panic and anxiety across time, cognitive interventions targeting that tendency may be
found to be more efficacious in improving panic than other cognitive-behavioral
interventions (e.g., relaxation training).
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Table 3.1
Descriptive data for baseline characteristics and for symptom change
Baseline Measure Mean (SD) or # (%)
Baseline PDSS 13.8 (3.6)
Week 5 PDSS 9.6 (4.2)
Termination PDSS 6.5 (4.3)
Week 5 to Termination PDSS Change -3.4 (3.5)
Baseline PSRF 3.4 (1.4)
Baseline to Week 5 PSRF Change (CBT / PFPP) 0.0 (1.0) / 0.5 (1.3)
Baseline BBSIQ 1.9 (0.4)
Baseline to Week 5 BBSIQ Change (CBT / PFPP) -0.2 (0.4) / 0.0 (0.3)
SCID-II Criteria Count 7.5 (6.4)
Agoraphobia Diagnosis (% Yes) 112 (81.2%)
Age 40.00 (13.5)
Gender (Female) 86 (58.1%)
Ethnicity (Hispanic) 23 (16.7%)
Race (Caucasian) 101 (73.2%)
Concurrent Psychopharmacology 42 (30.4%)
Note. BBSIQ = Brief Bodily Sensation Interpretation Questionnaire; PDSS = Panic
Disorder Severity Scale; PSRF = Panic-Specific Reflective Functioning; SCID-II =
Structured Clinical Interview for the Diagnosis of Axis-II Disorders
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Table 3.2
Correlations of baseline moderator values to improvement in BBSIQ and PSRF from
Baseline (Week 0) to Week 5 of treatment.
Baseline BBSIQ Baseline PSRF Baseline SCID-II
BBSIQ
Improvement
0.49*** -0.23* -0.02
PSRF Improvement 0.06 -0.57*** -0.04
Notes. Higher scores on the BBSIQ indicate a more pronounced tendency to
catastrophically interpret body sensations. Higher PSRF scores indicate greater symptom
insight. * = p<.05; *** = p<.001
109
Figure 3.1. Predicted degree of Week 5 to Termination (Week 12) symptom change
attributable to early (Baseline to Week 5) improvements in PSRF, as a function of
baseline severity of catastrophic interpretive style as measured by the BBSIQ (interaction
p = 0.043).
110
Figure 3.2. Predicted degree of Week 5 to Termination (Week 12) symptom change
attributable to early (Baseline to Week 5) improvements in PSRF, as a function of
baseline level of personality disorder pathology as measured by number of criteria met on
the SCID-II interview (interaction p = 0.047).
111
APPENDICES
112
Appendix A
Additional notes on statistical analyses (Study 1)
Random forest imputation. Random forest imputation is a single-dataset
imputation method that can produce nominally equivalent or more accurate imputations
compared to standard multiple imputation by chained equations, and can natively handle
simultaneous continuous/categorical variable imputation and datasets with interactions
and nonlinear relationships between variables (Liao et al., 2014; Stekhoven & Bühlmann,
2011; Waljee et al., 2013). Random forest algorithms produce nested prediction or
decision trees based on splits in continuous or categorical predictor variables (King &
Resick, 2014). Each individual tree selects a bootstrapped subset of the main data set, and
at each potential split point allows a random subset of possible predictors to be used, until
there are no more possible splits to be made. This process is repeated across many
different forests of trees, wherein predictions for the missing variables are iteratively
updated and improved with successive models, until a stopping criterion is reached. A
given imputed data value is based on the average prediction made across several decision
trees built from multiple subsets of the data and potential predicting variables.
All baseline data, technique ratings from a rated session not used in this
manuscript (Session 5), and termination and pre-to-post treatment change scores on the
PDSS, Sheehan Disability Scale, Inventory of Interpersonal Problems, and Hamilton
Rating Scale for Depression were allowed to predict missing data in the set, in addition to
all rated process measures and their constituent items.
Additional references
113
King, M. W., & Resick, P. A. (2014). Data mining in psychological treatment research: A
primer on classification and regression trees. J Consult Clin Psychol, 82(5), 895-
905. doi: 10.1037/a0035886
Liao, S. G., Lin, Y., Kang, D. D., Chandra, D., Bon, J., Kaminski, N., . . . Tseng, G. C.
(2014). Missing value imputation in high-dimensional phenomic data: imputable
or not, and how? BMC Bioinformatics, 15(1), 346. doi: 10.1186/s12859-014-
0346-6
Waljee, A. K., Mukherjee, A., Singal, A. G., Zhang, Y., Warren, J., Balis, U., . . .
Higgins, P. D. (2013). Comparison of imputation methods for missing laboratory
data in medicine. BMJ Open, 3(8). doi: 10.1136/bmjopen-2013-002847
114
Appendix B
Additional checks on robustness of obtained findings (Study 1)
Controlling for therapist effects
The packages “lme4” and “lmerTest” in the R statistical language were used
(Bates et al., 2016; Kuznetsova, Brockhoff, & Christensen, 2016). Therapists were
specified as a 2nd-level random effect in which patients were nested. When therapist
effects were examined without fixed effects, there was no statistically significant random
effect of therapist on PDSS symptom change (ICCT Week 1 to 5 = 0.05, p = 0.600; ICCT
Week 5 to 9 = 0.00, p = 1.000; ICCT Total Pre-Post Change = 0.08, p = 0.300). The lack
of estimable effect or statistical significance is likely due to the low sample size and poor
patient/therapist ratio leading to issues in estimation. On the other hand, the estimate of
reliable therapist-level variance for total pre-post symptom change (8%) was comparable
to the range of 6-9% often reported in the therapist effects literature (Lutz & Barkham,
2015).
For PDSS symptom change between Weeks 1 and 5 and Weeks 5 to 9, we
conducted an exploratory analysis to examine whether our technique-outcome findings
differed notably when simultaneously modeling therapist effects, using the same model
specifications as the primary analyses (e.g., outcome indices, covariates, technique terms)
but as applied to a mixed model framework. Unremarkably, there were no p-value shifts
across the significance level of p <.05, and effect size estimates were comparable.
Functioning and interpersonal outcomes
In our primary analyses, we found that panic-focused interpretations at Session 10
predicted panic symptom improvement on the PDSS occurring subsequent to Session 10.
115
One possibility is that panic-focused interpretations are conducive to symptomatic
improvements, but that they do not help other aspects of functioning. As a secondary,
post-hoc analysis, we explored whether these findings extended to improvements on the
Sheehan Disability Scale (Leon, Shear, Portera, & Klerman, 1992), a measure of
psychosocial dysfunction due to mental illness, and the Inventory of Interpersonal
Problems. Unlike the PDSS, which was assessed several times during the trial, the SDS
and IIP were administered only pre- and post-therapy.
Our overall modeling strategy was very similar, with the following exceptions: (1)
the predicted variable was the final SDS or IIP score; and (2) baseline levels of the SDS
or IIP were included as a covariate in lieu of baseline PDSS. We retained the degree of
PDSS symptom change between baseline and Week 5 as a covariate, such that early
symptomatic progress occurring during the process measurement period could not
contribute to the apparent relationships (or lack thereof) between process variables and
non-symptomatic outcomes measured at treatment termination.
Panic-focused interpretations at Session 10 predicted significantly lower IIP (B =
-0.27 [95% CI: -0.45 to -0.09], SE = 0.09, t[58] = -2.99, p = 0.004) scores at treatment
termination, and, at the level of a statistical trend, lower SDS scores (B = -2.42 [95% CI:
-5.21 to 0.38], SE = 1.40, t[59] = -1.73, p = 0.089). By contrast, panic-focused
interpretations at Session 2 did not relate to improvements on either the IIP (B = -0.20
[95% CI: -0.47 to 0.08], SE = 0.14, t[58] = -1.44, p = 0.156) or the SDS (B = -2.64 [95%
CI: -6.29 to 1.01], SE = 1.82, t[59] = -1.45, p = 0.152).
These findings are consistent with a perspective that panic-focused interpretations
at mid-therapy may also encourage improvements in interpersonal functioning, in
116
addition to panic symptom response. However, it should be noted that these analyses are
less analytically rigorous than our primary analyses, as temporal precedence between
technique use and symptom change cannot be assured because the SDS and IIP were
measured pre- to post-treatment.
Additional References
Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R. H. B., Singmann, H., . . .
Green, P. (2016). lme4: Linear Mixed-Effects Models using 'Eigen' and S4
(Version 1.1-12). Retrieved from https://cran.r-
project.org/web/packages/lme4/index.html
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2016). lmerTest: Tests in
Linear Mixed Effects Models (Version 2.0-30). Retrieved from https://cran.r-
project.org/web/packages/lmerTest/index.html
Leon, A. C., Shear, M. K., Portera, L., & Klerman, G. L. (1992). Assessing impairment in
patients with panic disorder: the Sheehan Disability Scale. Soc Psychiatry
Psychiatr Epidemiol, 27(2), 78-82.
Lutz, W., & Barkham, M. (2015). Therapist Effects The Encyclopedia of Clinical
Psychology: John Wiley & Sons, Inc.
117
Appendix C
Supplementary tables (Study 1)
Table S1
Examples of Panic-focused Clarification and Interpretation
Technique Example
Panic-focused
Clarification
Patient: It’s just that I know I might be having a heart attack
whenever I get that anxious. Everything in my chest is so tight, I’m
so wound up, I’m so clenched, I just feel like my heart is going to
give out and die.
Therapist: You think this even though you’ve been to specialists
several times, and all tests show you as being in not only OK, but
apparently remarkably good health for your or any age.
Patient: Yes, yes, but what if they’re wrong? I’m the one who pays
the price.
Therapist: Jane, it’s interesting. Somehow, it seems as though it’s
easier for you to believe you have a hidden, undiagnosable heart
defect impenetrable to modern medical science, than think that
there may be any psychological reason you may be panicking. Part
of you would rather feel like you’re on the razor’s edge than think
about whatever it is you’re avoiding.
Panic-focused
Interpretation Therapist: When you finally saw [your boyfriend], did you feel any
of that disappointment or anger you described to me last session,
about his lack of communication?
Patient: Maybe just a little. But, I know that he’s been so busy
recently, I can’t consider myself the most important thing all the
time in his life. That would be so selfish of me.
Therapist: You’re putting in all this grand effort to welcome him,
at the same time he’s been treating you poorly. I wonder if it is
easier for you to try to feel excited, rather than experience your
disappointment, which might lead you to have to reevaluate the
relationship. I think that considering the relationship feels very
dangerous to you, you might break up. I wonder if trying to ignore
that anger and concern about separation leaves you feeling anxious
and panicky.
118
Appendix D
Supplementary tables (Study 2)
Table S2.
Correlations between emotional expression ratings within the same 5-minute segment.
Overall
Expression
Grief/Sadness Anger/Assertion Anxiety/Distress
Overall
Expression
1 0.48*** 0.38*** 0.65***
Grief/Sadness 1 0.10† 0.24***
Anger/Assertion 1 0.01
Anxiety/Distress 1
† = p <.10; *** = p <.001
Table S3.
Average levels of emotional expression per 5-minute segment across sampled sessions.
Session 2 (M / SD) Session 5 (M / SD) Session 10 (M /
SD)
Overall Expressivity 1.10 (0.43) 0.99 (0.36) 1.04 (0.39)
Grief/Sadness 0.33 (0.29) 0.19 (0.17) 0.17 (0.19)
Anger/Assertion 0.30 (0.21) 0.32 (0.26) 0.36 (0.29)
Anxiety/Distress 0.68 (0.38) 0.66 (0.32) 0.74 (0.37)
Note. As estimated in a mixed model using the R package lme4 (Bates et al., 2017),
grief/sadness expression was estimated to significantly decrease within-person as patients
progressed from sessions 2 to 5 to 10, coded as 0, 1, and 2 time indicators (B = -0.08
[95% CI: -0.13 to -0.04], SE = 0.02, t[74.8] = -3.67, r = 0.39, p <.001). There were no
other reliable patterns of change in emotional expression experienced by the average
patient.
119
Table S4.
Sources of variance in emotional expression per 5-minute segment.
Patient-Level
Variance
Session-Level
Variance (in
Patient)
Unique/Residual
Variance
Overall Expressivity 29.8% 17.9% 52.2%
Grief/Sadness 15.2% 14.7% 70.1%
Anger/Assertion 10.0% 20.6% 69.3%
Anxiety/Distress 23.6% 27.8% 48.6%
Note. Patient-level variance indicates the degree to which a given patient was reliably
more versus less expressive across all of their observed ratings and sessions. Session-
level variance indicates the degree to which patients sometimes have entire sessions that
are more versus less expressive. Unique/residual variance indicates variability in
expression that is not predicted by the fact that a rating comes from a given patient, or
which session of a given patient is being rated. Variance components were extracted from
a mixed model (Bates et al., 2017) including a random effect of session nested within a
random effect of patient.