Social Anxiety - Psychology
What is social anxiety?
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Instructions:
APA Format
Please read carefully!!!!!!
Cognitive Behavioral therapy and social anxiety? Who does it affect most? What are some cultural differences?
Must demonstrate an excellent understanding of all of the concepts and key points presented in the text(s) and Learning Resources. Paper provides significant detail including multiple relevant examples, evidence from the readings and other sources, and discerning ideas.
Must be well organized, uses scholarly tone, follows APA Style, uses original writing and proper paraphrasing, contains very few or no writing and/or spelling errors, and is fully consistent with graduate-level writing style. Must contain multiple, appropriate and exemplary sources expected/required for the Assignment.
The first section is the Introduction (1 paragraph). Create an introduction, explains what the paper will be about.
-The second section is entitled Topic (Social Anxiety)(2 pgs).
*Examine topic and decide on an aspect of the topic
*The topic should be narrow enough to be able to research it in the scholarly literature.
*Support your description with the information cited from two peer-reviewed articles.
- The third section is Literature Review (8 pgs). ALL LITERATURE and REFERENCES are ATTACHED
*peer-reviewed journal articles
*Brief introduction explaining the topic
*Provide an integrated synthesis of your resources related to your topic
*Conclude with a summary paragraph
- The fourth section is Research Gap and Problem Statement (1pg).
*A problem statement is a concise description of the specific focus for a study. You have reviewed the research literature about your topic/problem and identified a gap in the research that needs further study
*Describe the gap and complete a concise problem statement
-The fifth section is Positive Social Change (1pg).
*Write a statement describing the implications for positive social change that could result from research
*Support ideas with information from scholarly resources.
-The final section is a Conclusion (2 paragraphs).
Use section headings
Introduction, Topic, Literature Review, Research Gap and Problem Statement, Positive Social Change, Conclusion and References.
References
Anderson, K. N., Jeon, A. B., Blenner, J. A., Wiener, R. L., & Hope, D. A. (2015). How people evaluate others with social anxiety disorder: A comparison to depression and general mental illness stigma. American Journal Of Orthopsychiatry, 85(2), 131-138.
Boukhechba, M., Chow, P., Fua, K., Teachman, B. A., & Barnes, L. E. (2018). Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study. JMIR mental health, 5(3), e10101. https://doi.org/10.2196/10101
Cao, J., Yang, J., Zhou, Y., Chu, F., Zhao, X., Wang, W., Wang, Y., & Peng, T. (2016). The effect of Interaction Anxiousness Scale and Brief Social Phobia Scale for screening social anxiety disorder in college students: a study on discriminative validity. Journal of Mental Health, 25(6), 500–505. https://doi-org.ezp.waldenulibrary.org/10.3109/09638237.2015.1124391doi:10.1037/ort0000046
Dryman, M. T., McTeague, L. M., Olino, T. M., & Heimberg, R. G. (2017). Evaluation of an open-access CBT-based Internet program for social anxiety: Patterns of use, retention, and outcomes. Journal of Consulting and Clinical Psychology, 85(10), 988-999. doi:10.1037/ccp0000232
Grumet, R., & Fitzpatrick, M. (2016). A case for integrating values clarification work into cognitive behavioral therapy for social anxiety disorder. Journal Of Psychotherapy Integration, 26(1), 11-21. doi:10.1037/a0039633
Hajure, M., & Abdu, Z. (2020). Social Phobia and Its Impact on Quality of Life Among Regular Undergraduate Students of Mettu University, Mettu, Ethiopia. Adolescent health, medicine and therapeutics, 11, 79–87. https://doi.org/10.2147/AHMT.S254002
Hakami, R. M., Mahfouz, M. S., Adawi, A. M., Mahha, A. J., Athathi, A. J., Daghreeri, H. H., Najmi, H. H., & Areeshi, N. A. (2018). Social anxiety disorder and its impact in undergraduate students at Jazan University, Saudi Arabia. Mental illness, 9(2), 7274. https://doi.org/10.4081/mi.2017.7274
Kampmann, I. L., Emmelkamp, P. M. G., Hartanto, D., Brinkman, W.-P., Zijlstra, B. J. H., & Morina, N. (2016). Exposure to virtual social interactions in the treatment of social anxiety disorder: A randomized controlled trial. Behaviour Research & Therapy, 77, 147–156. https://doi-org.ezp.waldenulibrary.org/10.1016/j.brat.2015.12.016
Kivity, Y., Cohen, L., Weiss, M., Elizur, J., & Huppert, J. D. (2021). The role of expressive suppression and cognitive reappraisal in cognitive behavioral therapy for social anxiety disorder: A study of self-report, subjective, and electrocortical measures. Journal of Affective Disorders, 279, 334–342. https://doi-org.ezp.waldenulibrary.org/10.1016/j.jad.2020.10.021
Mesri, B., Niles, A. N., Pittig, A., LeBeau, R. T., Haik, E., & Craske, M. G. (2017). Public speaking avoidance as a treatment moderator for social anxiety disorder. Journal of behavior therapy and experimental psychiatry, 55, 66–72. https://doi.org/10.1016/j.jbtep.2016.11.010
Pickard, H., Rijsdijk, F., Happé, F., & Mandy, W. (2017). Are Social and Communication Difficulties a Risk Factor for the Development of Social Anxiety?. Journal of the American Academy of Child and Adolescent Psychiatry, 56(4), 344–351.e3. https://doi.org/10.1016/j.jaac.2017.01.007
NIMH » Social Anxiety Disorder: More Than Just Shyness. (2021). Retrieved 27 August 2021, from https://www.nimh.nih.gov/health/publications/social-anxiety-disorder-more-than-just-shyness
Rosen, A. (2021). How Social Anxiety Impacts Higher Education and Career Choices - The Center for Treatment of Anxiety and Mood Disorders. Retrieved 27 August 2021, from https://centerforanxietydisorders.com/social-anxiety-impacts-higher-education-career-choices/
Wedding, D., & Corsini, R. J. (Eds.). (2014). Current psychotherapies (10th ed.). Belmont, CA: Brooks/Cole, Cengage Learning
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A case for integrating values clarification work into cognitive
behavioral therapy for social anxiety disorder
Article in Journal of Psychotherapy Integration · March 2016
DOI: 10.1037/a0039633
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A Case for Integrating Values Clarification Work Into Cognitive
Behavioral Therapy for Social Anxiety Disorder
Robin Grumet and Marilyn Fitzpatrick
McGill University
Social anxiety disorder (SAD) is a common and often debilitating psychological
disorder. Cognitive behavioral therapy (CBT) has been found to be efficacious in
treating SAD, however, barriers to successful treatment still remain. In particular, given
the difficulty of engaging clients in anxiety-inducing exposure interventions, it is
important to address issues of client motivation in treatment. The current article
provides a rationale for incorporating values clarification work from an acceptance and
commitment therapy perspective into CBT for SAD. More specifically, it proposes
helping clients in CBT for SAD to clarify their values and commit to behaving in ways
consistent with their values. The rationale is that values work could enhance treatment
motivation and adherence by providing motivation to engage in the difficult work of
exposure. Values work also contributes to a sense of meaning and purpose that can
enhance positive well-being and quality of life. Finally, values work might be beneficial
in maintaining gains following the termination of treatment. Suggestions for application
strategies of incorporating values work in to CBT for SAD are provided, in addition to
recommendations for research.
Keywords: cognitive behavioral therapy, social anxiety disorder, acceptance and com-
mitment therapy, values clarification
Social anxiety disorder (SAD) is a debilitat-
ing psychological disorder characterized by an
intense fear of social situations, particularly
those in which there is the possibility of scrutiny
or negative evaluation from others (American
Psychiatric Association, 2013). SAD is the third
most common psychiatric disorder, with a life-
time prevalence estimated at 13% in the Uni-
ted States (Kessler, Petukhova, Sampson,
Zaslavsky, & Wittchen, 2012), and is associated
with significant functional impairment in social,
occupational, and other areas of functioning
(American Psychiatric Association, 2013). SAD
is also associated with a markedly reduced qual-
ity of life (QOL; Hambrick, Turk, Heimberg,
Schneier, & Liebowitz, 2003; Kessler, 2003;
Stein & Kean, 2000), diminished positive affect
(Kashdan, 2007; Kashdan & McKnight, 2013),
and fewer positive psychological experiences,
such as curiosity (Kashdan, 2007; Weeks &
Heimberg, 2012), as well as physical health
problems (Sareen, Cox, Clara, & Asmundson,
2005) and suicidal ideation and attempts
(Cougle, Keough, Riccardi, & Sachs-Ericsson,
2009). Given the prevalence of SAD and its
debilitating impact, it is important to extend the
impact of validated treatments to address SAD
symptoms and to improve QOL and foster pos-
itive functioning among sufferers.
CBT for SAD: The Need for Values
There is ample evidence that cognitive be-
havioral therapy (CBT) is efficacious in treating
SAD in both individual (Clark et al., 2003;
Lincoln et al., 2003; Stangier, Heidenreich,
Peitz, Lauterbach, & Clark, 2003) and group
formats (Heimberg & Becker, 2002; Hope,
Heimberg, & Bruch, 1995; McEvoy, Nathan,
Rapee, & Campbell, 2012; Mörtberg, Clark, &
Bejerot, 2011). The fundamental aim of CBT
for SAD is to modify the socially relevant dys-
functional cognitions that maintain avoidance
Robin Grumet and Marilyn Fitzpatrick, Department of
Educational and Counselling Psychology, McGill Univer-
sity.
Correspondence concerning this article should be ad-
dressed to Robin Grumet, Department of Educational and
Counselling Psychology, McGill University, 3700 McTav-
ish Street, Montreal, Quebec, H3A 1Y2. E-mail: robin
[email protected]
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Journal of Psychotherapy Integration © 2016 American Psychological Association
2016, Vol. 26, No. 1, 11–21 1053-0479/16/$12.00 http://dx.doi.org/10.1037/a0039633
11
mailto:[email protected]
mailto:[email protected]
http://dx.doi.org/10.1037/a0039633
behavior and the symptoms of the disorder
(Hope, Burns, Hayes, Herbert, & Warner,
2010). This is achieved through two types of
(related) interventions. First, cognitive restruc-
turing is used to help clients identify, reexam-
ine, and modify distorted cognitions (i.e.,
thoughts, core beliefs; Hope et al., 2010). Cli-
ents then complete exposure tasks that put them
in situations that they fear and would typically
avoid (e.g., assigning a client homework to go
to a party and initiate a conversation with a
stranger) until they habituate to the feared stim-
uli (Hope et al., 2010).
In addition to cognitive restructuring, expo-
sure is also meant to restructure cognitions and
disconfirm false and maladaptive beliefs by pro-
viding an experience that is contrary to the
distorted cognition (Heimberg, 2002). Exposure
also improves SAD symptomatology through a
behavioral route. Fear extinction occurs when
the individual faces a feared situation with high
anxiety; the anxiety is reduced over time until it
ultimately abates (Heimberg, 2002). Emotional
processing theory posits that extinction occurs
through exposure when new incompatible
learning takes place and replaces the old faulty
association (Foa & Kozak, 1986; Foa & Mc-
Nally, 1996). The individual habituates to the
feared stimulus, producing a corrective learning
experience (Foa & Kozak, 1986; Foa & Mc-
Nally, 1996). The newer inhibitory learning
model of extinction postulates that a new asso-
ciation is developed while the original fear as-
sociation remains intact (Craske et al., 2008;
Craske, Treanor, Conway, Zbozinek, & Verv-
liet, 2014). Both associations remain accessible,
indicating that the original fear association can
arise in some contexts. Working to enhance the
retrieval of inhibitory learning is essential
(Craske et al., 2008, 2014). From this perspec-
tive, habituation per se is not the central focus,
but rather the importance of distress tolerance is
highlighted.
Exposure interventions have received an
abundance of empirical attention, as they are
considered powerful and efficacious interven-
tions (Dalrymple & Herbert, 2007). Though re-
sults have been mixed, there is some evidence
to suggest that exposure treatments are at least
as effective as full CBT treatments that include
both cognitive restructuring and exposure
(Feske & Chambless, 1995; Gould, Buckmin-
ster, Pollack, Otto, & Massachusetts, 1997;
Hope et al., 1995). For instance, there is re-
search to support the efficacy of exposure inter-
ventions (used independently) in producing
cognitive changes similar to the effect produced
by cognitive restructuring techniques on their
own (Newman, Hofmann, Trabert, Roth, &
Taylor, 1994).
Despite the well-documented effectiveness of
CBT for SAD, it is not always successful. Some
clients do not make a full recovery or improve
at all; they even occasionally deteriorate
(McAleavey, Castonguay, & Goldfried, 2014).
Additionally, of those individuals that do re-
spond to treatment, many still experience resid-
ual symptoms (Dalrymple & Herbert, 2007).
Furthermore, a number of clients (10% to
20%) also terminate treatment prematurely (Es-
kildsen, Hougaard, & Rosenberg, 2010). Even
more concerning, when dropout rates are in-
cluded, 40% to 50% of individuals with SAD
show little or no improvement following CBT
treatment (Eskildsen et al., 2010); clearly, this
group needs something else. Exposure interven-
tions, in particular, have been associated with an
increased risk for dropout (McAleavey et al.,
2014). In addition, clients sometimes do not
adhere to treatment protocols and neglect to do
homework. Furthermore, though some evidence
indicates an improvement in QOL following
participation in CBT for SAD, findings suggest
that treated individuals still do not achieve the
same levels of QOL as nonclinical populations
(Dalrymple & Herbert, 2007). Thus, barriers to
successfully treating this population using CBT
still remain.
McAleavey and colleagues (2014) investi-
gated clinician’s perceptions of barriers to treat-
ment in CBT for SAD and found a myriad of
issues that interfere with treatment and contrib-
ute to less optimal outcomes. For instance, cli-
ent motivation was a prominent barrier; 60.5%
of clinicians reported that they believed that
when client motivation was minimal at the out-
set of treatment, CBT would be less successful,
and that CBT would be more successful if mo-
tivation improved. Relative to client motivation,
57.2% of clinicians were concerned about pre-
mature termination and found that motivation
decreased as patients attributed gains to medi-
cations (26.1%), as improvement occurred
(16.3%), or as an understanding of social phobia
developed (9.1%). Notably, 55.4% of practitio-
ners reported clients not working independently
12 GRUMET AND FITZPATRICK
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between sessions as a barrier. Relatedly, 27.5%
also endorsed fear of exposure and associated
emotional reactions as a barrier to successful
treatment. Taken together, these findings indi-
cate that clinicians saw client motivation and
commitment to the therapeutic tasks as impor-
tant obstacles to progress. In addition to thera-
pists’ perspectives on client motivation, client
self-reports have been related to outcomes in the
treatment of anxiety disorders (de Haan et al.,
1997; Keijsers, Hoogduin, & Schaap, 1994a,
1994b). However, findings in studies using self-
report to assess motivation have been mixed.
This inconsistency has been attributed to mea-
surement issues with self-report (Lombardi,
Button, & Westra, 2014). In a recent study
using observational coding, Lombardi et al.
(2014) found that client motivational language
early in therapy was a strong predictor of treat-
ment outcome in CBT for generalized anxiety
disorder (GAD).
Given the perceived importance of motiva-
tion to treatment outcome in the context of CBT
for SAD, McAleavey et al. (2014) suggest that
clinicians might find it helpful to complement
their CBT for SAD treatment plan using other
techniques to foster client motivation. As an
example, they discuss motivational interview-
ing (MI) to uncover clients’ intrinsic motivation
to change. MI is predicated on the notion that in
order to effectively foster readiness and com-
mitment to change, the motivation for change
should not be imposed from the outside (e.g.,
the therapist), but rather should be elicited from
within the client (Hettema, Steele, & Miller,
2005). Given that personal values are an inher-
ently motivational, direct human behavior
(Bardi & Schwartz, 2003), and are intrinsic to
the individual (Plumb, Stewart, Dahl, & Lund-
gren, 2009), integrating values clarification
work into CBT for SAD could enhance motiva-
tion for SAD treatment.
Another strong argument for including values
work in CBT treatment of SAD is to foster
positive psychological functioning and augment
QOL. Research indicates that values-behavior
congruence (i.e., acting in a manner consistent
with personal values) is associated with several
wellness outcomes, including increased life sat-
isfaction (Lundgren, Dahl, & Hayes, 2008) and
QOL (Michelson, Lee, Orsillo, & Roemer,
2011). Relative to anxiety disorders, Michelson
et al. (2011) found that values– behavior con-
gruence was associated with increased self-
reports of QOL in a population of individuals
with GAD. In terms of SAD, following an ex-
posure-based treatment for SAD, including val-
ues work in a full acceptance and commitment
therapy (ACT) model, individuals reported
greater functioning and QOL, in addition to
greater values-behavior congruence (Dalrymple
& Herbert, 2007). These authors noted that “the
focus on experiential acceptance in the context
of behavior change consistent with personal
values may hold the potential to result in greater
functional improvement and quality of life”
(Dalrymple & Herbert, 2007, p. 546).
As the research suggests, individuals suffer-
ing from SAD not only experience negative
affect but also lack elements of positive well-
being (Kashdan, 2007; Kashdan & McKnight,
2013; Weeks & Heimberg, 2012). Interventions
fostering positive psychological functioning are
important to SAD sufferers. Integrating values
interventions in CBT treatment plans for SAD
may facilitate this endeavor. Recently, similar
integration approaches have been suggested.
For instance, Macarthur (2013) proposes an as-
similative integration approach to treating SAD,
particularly with regard to addressing the core
beliefs characteristic of this disorder. In a sim-
ilar vein, Cameron, Reed, and Gaudiano (2014)
proposed a rationale for incorporating values-
based exercises into dialectical behavior ther-
apy to treat borderline personality disorder.
Similar to the current rationale, they reason that
values integration could help to promote moti-
vation to engage in treatment and adhere to
treatment protocol (Cameron et al., 2014).
The current article discusses the integration
of values clarification work from an ACT
framework into CBT treatment for SAD. Many
of the suggestions in this article have applica-
tion to CBT in general; however, there are sev-
eral compelling reasons for focusing on SAD.
The threats associated with many anxiety dis-
orders and phobias tend to be circumscribed to
specific stimuli (e.g., fear of elevators), whereas
in SAD, the nature of the threat is social and
pervasive. SAD can interfere with vocational
success and the quality of romantic relation-
ships and life satisfaction; individuals with a
diagnosis of SAD are less likely to be married
or in a romantic relationship (Teo, Lerrigo, &
Rogers, 2013), and are more likely to experi-
ence social issues and impairments in education
13VALUES CLARIFICATION WORK
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(Van Ameringen, Mancini, & Farvolden, 2003)
and employment (Bruch, Fallon, & Heimberg,
2003; Stein & Kean, 2000). Because of the
pervasiveness and importance of values in all
life domains, values work in SAD has particular
salience.
Research indicates that although all anxiety
disorders are associated with negative affect,
SAD is unique in its association with low levels
of positive affect (Kashdan et al., 2013) and
high levels of experiential avoidance (Kashdan
et al., 2013). In avoiding negative internal ex-
periences, individuals with SAD often avoid
social interactions. Kashdan et al. (2013) re-
cently suggested that when working with indi-
viduals with SAD, clinicians should incorporate
more strategies targeting experiential avoidance
and enhancing positive experience. The current
article argues that values in the context of CBT
treatment for SAD can support this strategy.
Prior to elaborating the rationale for incorpo-
rating ACT values work in to CBT for SAD, a
brief description of ACT as well as the defini-
tion of values from this framework are pro-
vided. Next, the rationale for incorporating
ACT values work in to CBT for SAD is dis-
cussed. Finally, suggestions for implementing
this work, as well as directions for future re-
search, are offered.
ACT Values Work: Fitting into
CBT for SAD
ACT is a third-wave behavioral therapy,
which aims to teach individuals to accept and
embrace difficult psychological experience in
order to live a life in service of their personal
values (Hayes, Luoma, Bond, Masuda, & Lillis,
2006). ACT is composed of six components
(acceptance, mindfulness, cognitive defusion,
self-as context, values clarification, and com-
mitted action). A full description of ACT is
beyond the scope of this article (see Hayes et
al., 2006, for a comprehensive presentation).
Instead, we will focus on values and committed
action as the components that have most to
complement existing exposure-based methods
of SAD treatment. These components were cho-
sen because they give meaning to, and are the
reason for the other ACT treatment processes,
which “help clear the path for a more vital,
values-consistent life. Values dignify these
other processes and make them meaningful”
(Hayes, Levin, Plumb-Vilardaga, Villatte, & Pi-
storello, 2013, p. 186). A focus on values in
CBT can infuse meaning and motivation into
the CBT treatment process. In addition, values
and committed action work involves setting
short-, medium-, and long-term behavioral
change goals that support valued living (Hayes
et al., 2013) and is highly congruent with tradi-
tional CBT protocols (Hayes et al., 2013).
In ACT, values are defined as “chosen qual-
ities of purposive action that can never be ob-
tained as an object but can be instantiated mo-
ment by moment” (Hayes et al., 2006, p. 9).
Unlike goals, values can never be achieved but
can be continually expressed in moment-to-
moment behavior. A commonly used metaphor
in ACT is that values are more like a direction
on a compass, as opposed to a destination or
goal (Yadavaia & Hayes, 2009). For example, if
the value is “living generously,” it is possible to
achieve a goal of raising $2,000 for a local
charity or of giving an hour of your time weekly
to visit your elderly grandmother; however, the
quality of generosity can never actually be
achieved. Values also cross contexts; one can be
generous in a variety of times and situations. I
can be generous with time; generous with mon-
ey; generous in my compassion for friends,
family, or coworkers; or even generous with
myself, by making time for self-care. Helping
clients with SAD understand the way that val-
ues cross contexts can help them to apply new
learning from CBT more broadly. This is par-
ticularly important in SAD, given its pervasive
impairments to daily living.
According to the ACT conceptualization, val-
ues are freely chosen; they are not needed to
please others or to avoid negative consequences
(Ciarrochi, Fisher, & Lane, 2011; Plumb et al.,
2009; Wilson, Sandoz, Kitchens, & Roberts,
2010). As such, they should be intrinsically
motivating, inherently rewarding, and satisfy-
ing. For example, one should value generosity
for its own sake and not because one gets rec-
ognition or praise from others. Although valued
living is rewarding, it is not always easy and can
be painful (Hayes et al., 2013). Accordingly if
the difficulties of exposure in SAD treatment
can be related to goals that are values consis-
tent, motivation even in the face of psycholog-
ical barriers may increase.
This is particularly relevant with SAD, in
which anxiety, fear, and negative emotions pre-
14 GRUMET AND FITZPATRICK
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
vent individuals from engaging in activities that
create a sense of worth. For example, it is well-
documented that anxiety about social perfor-
mance often promotes avoidant behavior in in-
dividuals with SAD (Dalrymple & Herbert,
2007). Research has shown that helping indi-
viduals to focus on what is deeply important to
them (i.e., what they value) can help them to
persist in valued directions even in the face of
hardship. For instance, values interventions
have been found to increase persistence in pain-
tolerance tasks (Páez-Blarrina et al., 2008).
There is also evidence to indicate that including
values work in therapy can enhance treatment
adherence (Forman, Butryn, Hoffman, & Her-
bert, 2009; Woods, Wetterneck, & Flessner,
2006). Helping individuals to clarify their per-
sonal values and commit to values-congruent
action is a promising way of increasing motiva-
tion for exposure.
To do such work, clients need to be helped to
connect their personal value to the treatment
process and understand its relevance to the chal-
lenges of exposure (e.g., negative internal ex-
perience). Individuals with SAD can then be
helped to use their values as a guide to decision
making and behavior, replacing the use of neg-
ative internal experience as a guide and facili-
tating the use of approach, rather than avoid-
ance-based, behaviors (Kashdan & McKnight,
2013). For example, if an individual values liv-
ing vitally but is reluctant to engage in some
difficult aspects of treatment, it might be worth-
while to connect the hard work in treatment to
the experience of vitality in the long term. Us-
ing the values example of living generously, the
act of attending a friend’s birthday dinner can
be understood as generosity to a friend and
might improve client motivation to tolerate the
difficulty of the event. Different values can
serve the same goal. For example, a socially
anxious person who values authentic connec-
tion but is afraid of social rejection might be
motivated to attend the birthday dinner to create
an opportunity for strengthening her connection
with the friend. A SAD sufferer who has clari-
fied the value of promoting harmony in the
world might see the dinner as an opportunity to
interact harmoniously. The value may serve as
the basis for increasing motivation in the con-
text of the difficult exposure task. As described
above, challenge of exposure tasks in the treat-
ment of SAD can be mitigated if clients can
clarify personal values and translate them to
committed action in service of a value. The
process of values clarification and fostering val-
ues-congruent behavior is elaborated in greater
detail below.
In addition to supporting motivation for ex-
posure, the addition of values work in CBT for
SAD may also serve to foster positive function-
ing (e.g., enhancing meaning in life) and well-
being. Efficacy of CBT for SAD is often gauged
in terms of symptom reduction. Although this is
an important goal, well-being is more than the
absence of psychological distress (Duckworth,
Steen, & Seligman, 2005). Subjective well-
being (SWB), or “happiness,” a widely used
measure of well-being, is comprised of two
components: life satisfaction, and the relative
presence of positive affect and absence of neg-
ative affect (Diener, 2000). Furthermore, from a
positive psychology perspective, experiencing
positive emotions, having a sense of meaning in
life, and engaging in rewarding activities all
contribute to SWB (Duckworth et al., 2005).
However, research indicates that individuals
with SAD have impaired positive functioning
(Kashdan, 2007; Weeks & Heimberg, 2012),
including less positive emotions, less meaning
in life, as well as lower self-esteem when com-
pared with their nonclinical counterparts (Kash-
dan & McKnight, 2013). Effort toward living
with a greater purpose in life has been associ-
ated with greater positive emotions, increased
self-esteem, and greater meaning in life in indi-
viduals with SAD (Kashdan & McKnight,
2013). Though values and purpose are not syn-
onymous, the constructs are closely related:
“Purpose can be viewed as a subcategory of
values, reflecting the most important or central.
As a self-organizing system, purpose provides a
framework for people to create goals and then
specific behaviors that, if pursued, reflect com-
mitted action” (Kashdan & McKnight, 2013, p.
1150). However, individuals with SAD report
greater obstacles and failures and less intrinsic
motivation in working toward their purpose
(Kashdan & McKnight, 2013). Methods to ef-
fectively address values are potentially useful in
this work. Using values to foster purpose and
meaning in life throughout the therapeutic pro-
cess in CBT for SAD may result in enhanced
well-being for clients.
Given the pervasive nature of values (across
both time and contexts), values work in the
15VALUES CLARIFICATION WORK
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
context of CBT for SAD might also be a useful
tool for maintaining gains and even continuing
to achieve new gains following termination of
treatment. For instance, a client might be helped
and supported by a therapist to drop several
safety behaviors (e.g., sitting at a far distance
from others in a social situation) over the course
of treatment. With a clear sense of values as
well as an understanding of how this behavior
interferes with values-congruent goals (e.g.,
meeting new people for a client who values
novel experience and authentic connection), the
likelihood of maintaining these treatment gains
and reducing safety behaviors might be en-
hanced. Furthermore, with a commitment to
working toward a values-congruent lifestyle,
the client might also drop other …
[page 42] [Mental Illness 2017; 9:7274]
Social anxiety disorder and its
impact in undergraduate stu-
dents at Jazan University,
Saudi Arabia
Ramzi M. Hakami,1
Mohamed S. Mahfouz,2
Abdulrahman M. Adawi,1
Adeebah J. Mahha,1 Alaa J. Athathi,1
Hadi H. Daghreeri,1 Hatim H. Najmi,1
Nuha A. Areeshi1
1Faculty of Medicine, Jazan University,
Jazan; 2Department of Family and
Community Medicine, Faculty of
Medicine, Jazan University, Jazan,
Saudi Arabia
Abstract
Although social anxiety disorder
(SAD) is a common mental disorder, it is
often under diagnosed and under treated.
The aim of this study is to assess the preva-
lence, severity, disability, and quality of life
towards SAD among students of Jazan
University, Saudi Arabia. A cross-sectional
study was conducted among a stratified
sample of 500 undergraduate students to
identify the prevalence of SAD, its corre-
lates, related disability, and its impact on the
quality life. All participants completed the
Social Phobia Inventory, Leibowitz Social
Anxiety Scale, Sheehan Disability Scale,
and the WHO Quality of Life – BREF ques-
tionnaire. Of 476 students, 25.8% were
screened positive for SAD. About 47.2% of
the students had mild symptoms, 42.3% had
moderate to marked symptoms, and 10.5%
had severe to very severe symptoms of
SAD. Students who resulted positive for
SAD reported significant disabilities in
work, social, and family areas, and this has
adversely affected their quality of life as
compared to those who screened negative
for SAD. Students reported several clinical
manifestations that affected their function-
ing and social life. Acting, performing or
giving a talk in front of an audience was the
most commonly feared situation. Blushing
in front of people was the most commonly
avoided situation. Since the present study
showed a marked prevalence of SAD
among students, increased disability, and
impaired quality of life, rigorous efforts are
needed for early recognition and treatment
of SAD.
Introduction
While most of us experience some level
of social unease when we feel scrutinized
by others, such as while speaking in public
or presenting at meetings, social anxiety
disorder (SAD) is defined as an excessive
and persistent fear of acting in a way that
will be embarrassing and humiliating. This
fear is almost invariably provoked by the
feared situations, which are avoided or
endured with severe distress, and interferes
significantly with personal, occupational,
and social functioning.1
Social anxiety disorder commonly
appears in the teenage years,2 and usually
affects 3 to 5% of youths.3 It is an extraor-
dinarily persistent condition if left untreated
and it may lead to a variety of comorbidi-
ties, such as other anxiety disorders, affec-
tive disorders, nicotine dependence, and
substance-use disorder,4-6 predicting poorer
treatment outcomes.7 Most of patients with
SAD have been reported to have at least
moderate impairment at some point in their
lives. Education, employment, family,
romantic relationships, friendships, social
networks, quality of life, and other areas of
life have been reported to be liable to
impairment in patients with SAD.8-12
Unfortunately, although it is the third most
common mental disorder in adults world-
wide,13 SAD is often under diagnosed and
undertreated.14 Furthermore, it has received
little attention by both clinicians and
researchers.8
In general, there is a lack of data on the
prevalence of SAD and the reported rates
vary widely between studies, with much of
the variability possibly being due to differ-
ent instruments used to determine diagno-
sis.10 However, SAD is obviously one of the
most common of all anxiety disorders.10 For
instance, Kesseler and colleagues (2005)
interviewed 9282 English-speaking partici-
pants aged 18 years and older and found
that SAD was the most common anxiety
disorder, with a lifetime prevalence of up to
12%15 and a 12-month prevalence of
6.8%.16
Studies looking at country-specific pop-
ulations of university students have pro-
duced quite variable results when it comes
to the prevalence of SAD. Many studies
have indicated that social anxiety is a preva-
lent disorder among university stu-
dents.11,12,17-20 For example, studies from
Sweden and India have reported the preva-
lence of SAD among university students to
be 16.1% and 19.5%, respectively.11,12 In
the Kingdome of Saudi Arabia, less is
known about SAD in general and among
undergraduate students. However, high
prevalence rates have been reported among
Saudis, especially adolescents and young
adults.21-25 Elhadad and colleagues (2017)
have carried out a study on 380 medical stu-
dents and found that as high as 59.5% of
them were screened positive for SAD. In
the same study, SAD was associated with
decreased academic achievement, weak
clinical exam performance, and avoidance
of oral presentation.22
The present study aims to investigate
SAD prevalence, severity, related disabili-
ties, and its impact in students from five
faculties at Jazan University, Saudi Arabia.
We expect that this study would be helpful
in bridging the gap in the local research of
SAD, and will be useful to the future studies
attempting to reduce the high prevalence of
this disorder and to prevent its long-term
consequences.
Materials and Methods
Study place, design and participants
Jazan University is situated in Jazan
region, southwest of the kingdom of Saudi
Arabia. It is the leading higher educational
institution in Jazan province. This is an
Mental Illness 2017; volume 9:7274
Correspondence: Ramzi Mohammed Hakami,
Faculty of Medicine, Jazan University, Jazan,
Saudi Arabia.
E-mail: [email protected]
Key words: Mental disorder; social phobia;
social anxiety disorder; Saudi Arabia; Social
Phobia Inventory.
Acknowledgements: the authors thank Dr.
Rashad Alsanosy (Substance Abuse Research
Center (SARC), Jazan University and the
Department of Family and Community
Medicine) for his assistance with the research
project.
Contributions: the authors contributed equally.
Conflict of interest: the authors declare no
potential conflict of interest.
Received for publication: 20 June 2017.
Revision received: 7 August 2017.
Accepted for publication: 8 August 2017.
This work is licensed under a Creative
Commons Attribution-NonCommercial 4.0
International License (CC BY-NC 4.0).
©Copyright R.M.Hakami et al., 2017
Licensee PAGEPress, Italy
Mental Illness 2017; 9:7274
doi:10.4081/mi.2017.7274
[Mental Illness 2017; 9:7274] [page 43]
observational cross-sectional survey target-
ing Jazan University students who are over
18 years and registered for the academic
year 2016/2017. The target colleges were
Applied Medical Sciences, Pharmacy,
Sciences, Computer sciences and Business
administration.
Sample size and sample design
A sample of 400 participants was esti-
mated for the purpose of this study. The
sample size was calculated using the formu-
la for a cross-sectional study, n=[(z2 * p *
q)]/d2. Sample size was calculated using the
following parameters: p=prevalence of
Knowledge 50%, Z=95% confidence inter-
val, d=error ≤5%, and a 25% non-response
rate. Probability proportional to size sam-
pling (PPS) was used to adjust the number
of students in each faculty.
Data collection
The structured questionnaire was writ-
ten in Arabic and distributed by six medical
students to the study population. After
explaining the purpose of the study and
obtaining verbal consents, data collectors
waited somewhere near for the completion
of the questionnaire to give the respondents
the opportunity to ask clarifying questions
regarding the interpretation of terms or
items in the questionnaire. All respondents
were asked to fill out the survey separately
to make sure that they do not duplicate each
other’s answers. The data collection process
took place in the period from November
2016 to January 2017.
Instruments
The questionnaire consisted of demo-
graphic information such as age, sex, facul-
ty type, family size, birth order, perceived
family income, marital status, and housing
type. Rating instruments included the Social
Phobia Inventory (SPIN) to detect social
anxiety disorder, the Leibowitz Social
Anxiety Scale (LSAS) to evaluate social
anxiety disorder severity, the Sheehan
Disability Scale (SDS) to assess disability
due to social anxiety disorder, and the WHO
Quality of Life – BREF questionnaire to
assess the quality of life. All study tools
were translated to simple Arabic by the
study authors. The questionnaire took about
15 to 20 minutes to complete.
Social Phobia Inventory
The SPIN is a short, self-rating scale
developed by Dr. K.M. Connor to capture
the social phobia symptoms.26 It consists of
17 items and each item is rated from 0 (not
at all) to 4 (extremely). The scale ranges
from 0-68. A score ≥19 suggests social anx-
iety disorder. It has good test-retest reliabil-
ity, internal consistency, convergent and
divergent validity and can be used for
screening of and detecting treatment
response to social anxiety disorder.
Regarding diagnosis of social anxiety disor-
der, it has a sensitivity of 73-85% and a
specificity of 69-84%. Although Shah and
Kataria12 used a cut-off point of 19 on this
scale in a similar study, Dogaheh27 reported
that the cut-off point of 29 resulted in bal-
anced sensitivity (0.96) and 1-specificity
(0.87), and it was more appropriate for this
study (a cut-off point of 19 resulted in an
oddly very high prevalence).
Liebowitz Social Anxiety Scale
The LSAS is self-rating scale developed
by Dr. Michael Liebowitz to rate fear/anxi-
ety and avoidance regarding 24 commonly
feared performance or social situations.28 It
consists of 13 performance-related items
and 11 social-related items which are rated
from 0 (none/never) to 3 (severe/usually). It
has a good internal consistency and evalu-
ates the severity of fear and avoidance in
common social situations. A score of <55
suggests mild social anxiety disorder, 55-64
suggests moderate social anxiety disorder,
65-79 suggests marked social anxiety disor-
der, 80-94 suggests severe social anxiety
disorder, and >95 suggests very severe
social anxiety disorder.
Sheehan Disability Scale
The SDS is a simple and commonly
used scale developed by David V.
Sheehan29 to evaluate functional impair-
ments/disabilities in the domains of work,
social life/leisure and family life/home
responsibility due to an anxiety disorder.
Each domain is rated on an 11-point, where
0=no impairment, 10=most severe, 1-
3=mild, 4-6=moderate, and 7-9=marked.
WHO Quality of Life – Bref
The WHOQOL-BREF is an abbreviated
version of the WHOQOL-100 developed by
the WHOQOL Group30 to assess the quality
of life in multiple dimensions, and it is
applicable cross-culturally. It consists of 26
items based on a four-domain structure:
Physical health (7 items), Psychological
health (6 items), Social relationships (3
items) and Environment (8 items), along
with a self-rating of general quality of life
Article
Table 1. Socio-demographic characteristics of participants.
Characteristics Male, n (%) Female, n (%) Total, n (%)
N=243 N=233 N=476
Age in years*
19 – 21 78 (32.1) 161 (70.9) 239 (50.8)
22 – 24 152 (62.6) 64 (28.2) 216 (45.9)
25 – 27 13 (5.3) 2 (0.9) 15 (3.2)
College
Applied Medical Sciences 44 (18.1) 41 (17.6) 85 (17.9)
Pharmacy 14 (5.8) 5 (2.1) 19 (4.0)
Business Administration 70 (28.8) 86 (36.9) 156 (32.7)
Computer Sciences 59 (24.3) 39 (16.7) 98 (20.6)
Sciences 56 (23) 62 (26.6) 118 (24.8)
Marital status*
Single 232 (95.9) 192 (83.8) 424 (90.0)
Married 8 (3.3) 31 (13.5) 39 (8.3)
Divorced 2 (0.8) 6 (2.6) 8 (1.7)
Family size*
<6 42 (17.3) 33 (14.4) 75 (15.9)
06-10 135 (55.6) 162 (70.7) 297 (62.9)
>10 66 (27.2) 34 (14.8) 100 (21.2)
Birth order*
First or only child 46 (18.9) 47 (20.5) 93 (19.2)
In the middle 159 (65.4) 144 (62.9) 303 (64.1)
Last baby 38 (15.6) 38 (16.6) 76 (16.1)
Perceived family income (SR/month)*
Very good 49 (20.3) 38 (17.4) 87 (19.0)
Good 117 (48.5) 98 (45.0) 215 (46.8)
Bad 75 (31.1) 82 (37.6) 157 (34.2)
Housing type*
Owning housing 191 (78.9) 207 (90.0) 398 (84.3)
Rent housing 51 (21.1) 23 (10.0) 74 (15.7)
*Because of missing responses, the total percentages do not add up to 100%.
(1 item) and general satisfaction with health
(1 item). It is self-administered and each
item is scaled from 1-5 in a positive direc-
tion, which means that higher scores indi-
cate a higher quality of life. Each domain
score (mean score of items within that
domain) is converted to a scale of 0-100 and
indicates an individual’s perception of qual-
ity of life in that domain. In the absence of
clear cut-off point for such study, a cut-off
point of 88.22 (70% of the total scores) was
used as suggested by Al-Fayez and Ohaeri31
and Xia et al.32
Statistical analysis
The data was analysed using SPSS ver-
sion 20. Descriptive (frequency and per-
centage) and inferential statistics (chi-
square test) were used to interpret the data.
An independent samples t-test was used to
analyse the difference between the two
groups (students with/without social anxi-
ety disorder). Pearson correlation coeffi-
cient was used for correlation analysis.
Ethical consideration
All participants were informed of their
rights to participate and that their informa-
tion would be kept anonymous and only
used for the purpose of this study. Ethical
approval was obtained from the University
Ethical Committee.
Results
Of 500 questionnaires, students com-
pleted 476 questionnaires giving a response
rate of 95.2%. Table 1 details the sociode-
mographic distribution of the study popula-
tion. The results show that 243 (51.1%) of
respondents were males and 233 (48.9%)
were females. The respondents’ age ranged
from 19 to 27 years. The mean, median, and
mode of students’ age were 21.49, 21, and
22 years, respectively (SD=1.57), which
indicates a fairly even distribution of partic-
ipants’ ages. The sample consisted of differ-
ent faculties with the highest number from
Business administration (156, 32.7%) and
the lowest number from Pharmacy (19,
4.0%). Most of the respondents (90%) were
single (N=424), 8.3% were married (N=39),
and 1.7% were divorced (N=8). Those who
lived in families consisted of 6-10 members
comprised the majority of the study popula-
tion (62.9%). Regarding birth order, a high
frequency of respondents (303, 64.1%)
reported that they were in the middle of
their families. Most of the study population
perceived their family income as very good
(19.0%) and good (46.8%), and lived in
their own household (84.3%).
Using a cut-off score of 29, participants
were screened positive for social anxiety
disorder if they scored 29 or higher on the
SPIN scale. Table 2 shows that 123 (25.8%)
students were screened positive for SAD,
71 of them (51.1%) were males and 52 were
females (42.3%). There was a statistically
significant difference in the prevalence of
SAD regarding the birth order. Being a first-
born child (or the only child) was associated
with least prevalence of SAD (15.6%) and
being a middle born child was associated
with higher prevalence of SAD (61.5%)
(X2=6.407, P<0.05). However, with respect
to gender, faculty type, family size, per-
ceived family income, and housing type,
there was no statistically significant differ-
ence in the prevalence of SAD (all P values
>0.05). In addition, as the range of age
groups was narrow, (i.e. most of students
were young adults, who are the target popu-
lation of this study) and as most of the stu-
dents were single, these two parameters
(age and marital status) were not signifi-
cantly associated (P=0.777 and P=0.511,
respectively) with the prevalence of SAD.
The Cronbach’s alpha for SPIN scale
obtained in this study sample was 0.85.
Using the LSAS scale to detect the
severity of SAD, 47.2% (N=58) had mild
symptoms, 42.3%, (N=52) had moderate to
marked symptoms, and 10.5% (N=13) had
severe to very severe symptoms. As shown
in Table 3, the descending ranking of com-
monly feared/avoided situations (LSAS
scale) was obtained. The most commonly
feared situations reported by students were
acting, performing or giving a talk in front
of an audience (75.0%, N=357), followed
by taking a test (74.0%, N=352). The most
commonly avoided situations reported by
students were blushing in front of people
(79.4%, N=377), followed by having to give
speeches (76.7%, N=365). The majority of
students (76.5%, N=364) reported that
being embarrassed or looking stupid is
among their worst fears. The Cronbach’s
alpha for LSAS scale obtained in this sam-
ple was (0.87) and (0.85) for the fear/anxi-
ety and avoidance domains, respectively.
An independent samples t-test was
employed to compare between students
with SAD and students without SAD in
their scores on the SDS and QOL scales. As
Table 4 shows, the difference between the
two groups was statistically significant.
Students who screened positive for SAD
reported significantly more disabilities in
the work (t(474)=6.596, P<0.01), social life
(t(473)=6.941, P<0.01), and home areas
Article
Table 2. Comparing social phobia with demographic variables of the participants.
Demographic variables SPIN score <29 SPIN score ≥29 X2 P value
n (%) n (%)
Study population 353 (74.2) 123 (25.8)
Gender 2.956 0.090
Male 172 (48.7) 71 (57.7)
Female 181 (51.3) 52 (42.3)
Age* 0.504 0.777
19 – 21 179 (51.1) 60 (50.0)
22 – 24 161 (46.0) 55 (45.8)
25 – 27 10 (2.9) 5 (4.2)
Faculty type 0.225 0.705
Health faculties 79 (22.4) 25 (20.3)
Others 274 (77.6) 98 (79.7)
Family size* 0.611 0.737
<6 53 (15.1) 22 (18.0)
06-10 223 (63.7) 74 (60.7)
>10 74 (21.1) 26 (21.3)
Birth order 6.407 0.041
First or only child 74 (21.1) 19 (15.6)
In the middle 228 (65.1) 75 (61.5)
Last baby 48 (13.9) 28 (23.0)
Perceived family income (SR/month)* 0.480 0.787
Very good 31 (9.2) 10 (8.3)
Good 104 (30.8) 34 (28.1)
Bad 203 (60.1) 77 (63.6)
Housing type* 1.985 0.192
Owning housing 300 (85.7) 98 (80.3)
Rent housing 50 (14.3) 24 (19.7)
SPIN, Social Phobia Inventory. *Because of missing responses, total percentages do not add up to 100%.
[page 44] [Mental Illness 2017; 9:7274]
[Mental Illness 2017; 9:7274] [page 45]
(t(474)=4.375, P<0.01). As well, students
who screened positive for SAD reported
significantly worse quality of life, that is,
they scored lower than students who
screened negative for SAD on the physical
health domain (t(473)=4.220, P<0.01), psy-
chological health domain (t(459)=3.970,
P<0.01), social relationship domain
(t(472)=1.999, P<0.05), and environment
domain (t(474)=2.297, P<0.05). The
Cronbach’s alpha for SDS scale obtained in
this sample was (0.74), and for QOL scale,
the Cronbach’s alpha for the respective
domains were 0.64 (physical health), 0.64
(psychological health), 0.55 (social rela-
tionships), and 0.72 (environment).
As shown in Table 5, both SPIN and
LSAS scores were positively correlated
with SDS scores. Thus, SAD and its severi-
ty were significantly associated with report-
ed disabilities in the areas of work, social
life, and home life. In contrast, both SPIN
and LSAS scores were negatively correlat-
ed with QOL score. This means that SAD
and its severity were significantly associat-
ed with deterioration in all domains of qual-
ity of life. In general, these results suggest
that students who screened positive for
SAD suffered more than students who
screened negative from deteriorated func-
tioning and quality of life.
Discussion
The main purpose of the present study
was to investigate SAD prevalence, severi-
ty, related disabilities, and its impact in
undergraduate students at Jazan University.
SAD symptoms may overlap with other dis-
eases making it challenging to recognize
and separate SAD from shyness or poor
social skills. Many studies of SAD from dif-
ferent countries and cultures reported wide-
ly varied estimates of the prevalence rang-
ing from 1.9% and 20.4% among the gener-
al population and depending on the diag-
nostic threshold.33 In the present study,
SAD was as high as 25.8% among the study
population, much higher than many other
studies among undergraduate stu-
dents.11,12,17,18,34 However, as SPIN, the
screening scale used in this study, has a
specificity of 0.84-0.94 and the analysis
using LSAS shows that 47.2% of those with
SAD have a mild degree of SAD, it can be
inferred that the prevalence might be lower
than identified. However, the prevalence
looks quite high even after this considera-
tion. Within the Saudi context, a few studies
have investigated SAD among university
students and most of them have been con-
ducted on medical students, making it diffi-
cult to compare our findings with a similar
study. However, consistently with the pres-
ent study, social anxiety have been revealed
to be a highly prevalent disorder in Saudi
Article
Table 3. Rank ordering of most commonly feared/avoided situations.
Rank Situation N (%)
Feared situations
1 Acting, performing or giving a talk in front of an audience 357 (75.0)
2 Taking a test 352 (74.0)
3 Speaking up at a meeting 326 (68.5)
4 Talking to people in authority 299 (62.8)
5 Meeting strangers 289 (60.7)
6 Working while being observed 289 …
Contents lists available at ScienceDirect
Clinical Psychology Review
journal homepage: www.elsevier.com/locate/clinpsychrev
Review
Gender differences in social anxiety disorder: A review
Maya Ashera, Anu Asnaanib, Idan M. Aderkaa,⁎
a Department of Psychology, University of Haifa, Israel
b Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
A R T I C L E I N F O
Keywords:
Social anxiety disorder
Gender differences
Review
Prevalence
Impairment
A B S T R A C T
Gender differences in social anxiety disorder (SAD) have not received much empirical attention despite the large
body of research on the disorder, and in contrast to significant literature about gender differences in other
disorders such as depression or posttraumatic stress disorder. To address this gap, we comprehensively reviewed
the literature regarding gender differences in eight domains of SAD: prevalence, clinical presentation,
functioning and impairment, comorbidity, course, treatment seeking, physiological arousal, and the oxytocin
system. Findings from the present review indicate that women are more likely to have SAD and report greater
clinical severity. Notwithstanding, men with the disorder may seek treatment to a greater extent. According to
the present review, the course of SAD seems to be similar for men and women, and findings regarding gender
differences in functional impairment and comorbidity are inconclusive. We highlight areas requiring future
research and discuss the findings in the context of a number of theoretical perspectives. We believe that further
research and integration of scientific findings with existing theories is essential in order to increase our
understanding and awareness of gender differences in SAD, thus facilitating gender-sensitive and specifically-
tailored interventions for both men and women with the disorder.
1. Introduction
Social anxiety disorder (SAD) is a common and debilitating
psychiatric disorder with an estimated lifetime prevalence rate of
12.1% (Kessler et al., 2005). It is characterized by a marked and
persistent fear of one or more social situations (e.g., talking to a
stranger or peer, going to a party) or performance activities (e.g., giving
a speech) in which the person is exposed to unfamiliar people, or where
they may face possible scrutiny by others (American Psychiatric
Association, 2013). Individuals with SAD fear they will act in a way
(or show anxiety symptoms) that will be embarrassing and may lead to
a negative evaluation by others (Alden & Taylor, 2010). As a result, they
tend to avoid social situations, or endure them with significant distress.
The difficulties in interpersonal interactions described above result
in significant impairment in almost all facets of daily life, including
relationships, work, and studies (e.g., Aderka et al., 2012;
Alden & Taylor, 2004). Compared to individuals without SAD, those
with the disorder are more likely to drop out of school prematurely
(Stein & Kean, 2000), to have lower educational attainment
(Katzelnick & Greist, 2001; Wittchen, Stein, & Kessler, 1999), to hold
jobs below their level of qualification (Katzelnick & Greist, 2001), to
have lower income and to be unemployed (Lecrubier et al., 2000), and
even when employed, tend to miss 8 times more work days (Wittchen,
Fuetsch, Sonntag, Müller, & Liebowitz, 2000). Individuals with SAD
report poor quality of life (Alonso et al., 2004), are more likely to
attempt suicide (Wunderlich, Bronisch, & Wittchen, 1998), and are
more likely to have alcohol and nicotine dependence (Wittchen et al.,
1999). Thus, SAD results in significant negative health, economic and
functional consequences.
Considering the large body of research on SAD, and despite
accumulating data about gender differences in other disorders (e.g.,
agoraphobia: Bekker, 1996; specific phobias: Fredrikson, Annas,
Fischer, & Wik, 1996; obsessive-compulsive disorder: Bogetto,
Venturello, Albert, Maina, & Ravizza, 1999; panic disorder: Barzega,
Maina, Venturello, & Bogetto, 2001; generalized anxiety disorder:
Vesga-López et al., 2008; posttraumatic stress disorder: Tolin & Foa,
2006; depression: Parker & Brotchie, 2010) there is a paucity of
research directly examining gender differences in SAD. This is particu-
larly surprising because several older epidemiological studies have
found that SAD is more frequent in women compared to men (e.g.,
Kessler et al., 1994). Although the gender literature for SAD is limited,
it can offer meaningful information for both researchers and clinicians
(Schneier & Goldmark, 2015). The goal of the present review is to
systematically review the literature, identify studies reporting on
gender differences in SAD, structure and integrate the findings, present
the findings clearly, and interpret the findings within the context of
http://dx.doi.org/10.1016/j.cpr.2017.05.004
Received 25 December 2016; Received in revised form 24 May 2017; Accepted 29 May 2017
⁎ Corresponding author at: Department of Psychology, Mount Carmel, Haifa 31905, Israel.
E-mail address: [email protected] (I.M. Aderka).
Clinical Psychology Review 56 (2017) 1–12
Available online 30 May 2017
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extant theories of both SAD and gender differences. Specifically, this
paper will review gender differences in eight domains of SAD:
prevalence, clinical presentation, functioning and impairment, comor-
bidity, course, treatment seeking, physiological arousal, and the
oxytocin system. Finally, an additional goal of this paper is to map
different areas requiring further research.
Understanding gender differences in SAD can have implications for
clinical assessment and diagnosis, as well as for treatment delivery. For
instance, information regarding gender differences in types of feared
situations can guide and inform clinical assessment, as well as choice of
exposure exercises for men and women. We believe that this review can
contribute to a more refined and gender-sensitive understanding of the
disorder and can ultimately facilitate clinical work specifically tailored
to men and women.
2. Literature search
The literature search for the present review was conducted in a
number of stages. First, we searched the PubMed, PsycINFO, and the
Cochrane Library databases using a number of keywords to identify
relevant studies. Key words included: social anxiety, gender, differ-
ences, men, women, male, female, boys, girls. In the second stage, we
reviewed the reference lists of relevant papers to identify additional
sources that may have been missed in our database search. In addition
to peer reviewed publications, we reviewed book chapters on gender
differences in SAD (and their reference lists) to reduce the risk of biases
in the peer review process. In the fourth and final stage, we sent an e-
mail to researchers in the field of social anxiety disorder, requesting
additional unpublished data on gender differences in order to reduce
the risk of publication bias.
3. Gender differences in prevalence
According to the DSM-5, prevalence of SAD is higher in women and
this difference is more pronounced among adolescents (American
Psychiatric Association, 2013). This statement is based on a number
of epidemiological studies which have demonstrated that women are
more likely than men to meet diagnostic criteria for SAD. For example,
the Epidemiologic Catchment Area study (ECA; Schneier, Johnson,
Hornig, Liebowitz, & Weissman, 1992) is an older epidemiologic study
which examined approximately 13,000 young adults aged 18–29 which
found that compared to men, women are 1.5 times more likely to meet
diagnostic criteria for SAD; lifetime prevalence rates reported in that
study were 3.1% in women, compared to 2.0% in men. Data from the
National Comorbidity Survey (NCS), with a sample of over 8000
individuals aged 15–54, also indicated a higher lifetime prevalence
rate of 15.5% for women, compared to 11.1% for men (Kessler et al.,
1994). Recently, a study based on data from the National Epidemiologic
Survey on Alcohol and Related Conditions (NESARC) with a sample of
43,093 adults found similar results, indicating that significantly more
women suffer from SAD, with a lifetime prevalence of 4.2% for men,
compared to 5.7% for women (Xu et al., 2012). In sum, despite
differences in overall lifetime prevalence rates between epidemiological
studies, a number of epidemiological studies have shown that women
are consistently found to have higher rates of SAD compared to men.
It is important to note that whereas the studies mentioned above
demonstrated significant gender differences in SAD prevalence, a study
by McLean, Asnaani, Litz, and Hofmann (2011) reported divergent
findings. These authors examined data from the Collaborative Psychia-
try Epidemiology Studies (CPES), which is an integration of three
national surveys of mental health conducted among an overall of
20,013 adult (aged 18 and older) residents of the United States
(U.S.). The pattern of gender differences found was in contrast to those
reported in previous studies; in fact, SAD was the only anxiety disorder
that did not evidence significant gender differences in current or
lifetime rates. However, it is important to note that these null findings
were found using a Bonferroni correction accounting for 20 compar-
isons (i.e., critical level for significance = 0.0025) and controlling for
SES, education level, age, and race. Both Bonferroni correction and the
practice of including multiple covariates in regression analyses have
been criticized for significantly reducing statistical power (e.g.,
Perneger, 1998; Tabachnick & Fidell, 2013) suggesting that interpreta-
tion of these null findings should be done with caution. Moreover,
descriptive statistics were in the direction indicating greater prevalence
among women compared to men (lifetime prevalence for wo-
men = 10.3%, lifetime prevalence for men = 8.7%).
Studies conducted outside the United States have demonstrated
similar gender differences in SAD prevalence as found in the majority of
epidemiological findings within the U.S. For example, results from a
European study with a sample of 18,980 individuals (aged 15 or older)
from the United Kingdom, Germany, Italy, Spain, and Portugal indi-
cated that prevalence rates were found to be higher in women
compared to men, with an odds ratio of 1.6 (Ohayon & Schatzberg,
2010). Similar findings were reported in a prospective longitudinal
study, which followed 591 young adults in Switzerland from the age of
18 to the age of 35 (Merikangas, Avenevoli, Acharyya, Zhang, & Angst,
2002). In this study, women exhibited higher lifetime prevalence rates
of SAD, as well as higher sub-clinical levels of social anxiety compared
to men. A community study conducted in France (Lépine & Lellouch,
1995), with a sample of 1787 participants aged 18 and above also found
higher lifetime prevalence rates of SAD in women compared to men
(5.4% and 2.1% respectively). Results from the Canadian Community
Health Survey Cycle 1.2. (MacKenzie & Fowler, 2013) with a sample of
36,984 Canadians aged 15 or older indicated that women were 1.5
times more likely to meet diagnostic criteria for SAD. Another study
conducted in Russia has also demonstrated a higher lifetime prevalence
of SAD in women compared to men (Pakriev, Vasar, Aluoja, & Shlik,
2000).
Studies conducted in East Asia have revealed similar patterns of
gender differences in SAD prevalence. A study conducted in Korea (Cho
et al., 2007), with a sample of 6275 adults aged 18–64, demonstrated
higher descriptive 12-month and lifetime prevalence rates in women
compared to men (no inferential tests were reported). However, it is
important to note that prevalence in this sample was distinctly lower
than that observed in Western samples (e.g., 0.4% lifetime prevalence
for women vs. 0.1% for men). This may be due to differences between
individualistic and collectivistic cultures both in general
(Oyserman & Lee, 2008) and in SAD specifically (Chang, 1997;
Hofmann, Asnaani, & Hinton, 2010; Schreier et al., 2010). In addition,
differences in stigma of mental disorders between countries and
cultures can also contribute to the observed differences in prevalence
(e.g., Griffiths et al., 2006; Ryder et al., 2008). Importantly, these
explanations are by no means exhaustive, and many other potential
explanations can contribute to these findings.
In an older large epidemiologic study conducted in Taiwan (The
Taiwan Psychiatric Epidemiological Project; TPEP) gender differences
in SAD prevalence were found (Hwu, Yeh, & Chang, 1989). This study
was based on three samples of 5005, 3004 and 2995 participants aged
18 and above, selected from metropolitan Taipei, 2 small towns and 6
rural villages in Taiwan, respectively. It was found that women in
metropolitan Taipei had a higher lifetime prevalence of SAD compared
to men in the same area (9.5% and 2.4% respectively). Interestingly,
this difference in prevalence was found only in the metropolitan area
but not in small towns and rural villages. In addition, it is important to
note that the study was conducted over 25 years ago and was based on
DSM-III criteria. Additional studies in East Asian countries are needed
to draw firm conclusions regarding gender differences in SAD. Finally,
it is important to note that a review of 43 epidemiological studies from
all around the world (Furmark, 2002), and a review of 21 epidemio-
logical studies conducted in European countries (Fehm, Pelissolo,
Furmark, & Wittchen, 2005) both concluded that women are more
likely to have SAD compared to men.
M. Asher et al. Clinical Psychology Review 56 (2017) 1–12
2
Studies on adolescents have also revealed similar gender differences
in prevalence of SAD. For example, data from the Early Developmental
Stages of Psychopathology Study (EDSP), with a sample of 3021
German adolescents aged 14–25 years indicated a higher lifetime
prevalence rate of SAD in girls and women compared to boys and
men (9.5% and 4.9% respectively; Wittchen et al., 1999). In addition,
according to data from the National Comorbidity Survey Replicatio-
n–Adolescent Supplement (NCS-A), SAD was more prevalent in girls
compared to boys, with lifetime prevalence rates of 11.2% and 7%
respectively (Merikangas et al., 2010). Another study (Essau,
Conradt, & Petermann, 1999) with a sample of 1035 German adoles-
cents aged 12–17 years indicated that girls were twice as likely to meet
lifetime diagnostic criteria for SAD compared to boys (2.1% and 1%
respectively). Although the higher rates of SAD found in girls compared
to boys are consistent with previous findings, it is worth noting that the
overall lifetime prevalence of SAD in this study was distinctly lower
than those observed in other samples of adolescents. Finally, results
from studies conducted in non-clinical samples of adolescents (e.g., La
Greca & Lopez, 1998; Ranta et al., 2007) indicated that girls reported
higher levels of social anxiety compared to boys.
In sum, the literature consistently points to a higher prevalence rate
of SAD in women compared to men, and this difference may be greater
among adolescents (see Table 1 for a summary of findings on
prevalence). Findings on gender differences in prevalence rates have
been replicated in studies conducted around the world (U.S., Europe,
East Asia), and using different designs (e.g., epidemiological studies,
prospective longitudinal studies) indicating that the difference is
robust.
4. Gender differences in clinical presentation
In this section we will review findings regarding gender differences
in (1) clinical severity, (2) types of social situations feared, and (3)
subjective distress.
4.1. Clinical severity
Turk et al. (1998) found that women who sought treatment for SAD
reported greater clinical severity compared to men on a number of
symptoms measures (the Social Interaction Anxiety Scale, Social Phobia
Scale, the Fear Questionnaire – Social Phobia subscale and the
Liebowitz Social Anxiety Scale – Performance Fear subscale). Moreover,
in that study women reported greater fear and avoidance compared to
men when constructing an individualized hierarchy of social anxiety-
provoking situations. Finally, women reported greater anxiety com-
pared to men both in anticipation of and during a brief exposure.
Another study based on data from the Australian National Survey of
Mental Health and Well-being (NSMHWB; Crome, Baillie, & Taylor,
2012), with a sample of 1755 adults reporting at least one social fear,
demonstrated that women tended to report higher levels of social fear,
compared to men.
Similarly, a number of studies have demonstrated that women with
SAD endorse a greater number of social fears compared to men with
SAD (Turk et al., 1998; Xu et al., 2012). For example, data from the
National Comorbidity Survey Replication (NCS-R), demonstrated that
SAD involving 1–4 social fears is more common among men, whereas
SAD involving a larger number of fears is more common in women
(Ruscio et al., 2008).
Finally, women with SAD were more likely to endorse a desire to die
and a desire to commit suicide compared to men with SAD. This finding
was above and beyond the contribution of comorbid depression
indicating that the difference cannot be attributed women's greater
likelihood to receive a diagnosis of major depressive disorder
(Lépine & Lellouch, 1995). In sum, women with SAD report more severe
symptoms, a greater number of social fears, as well as a greater desire to
die and commit suicide, compared to men.
Importantly, all the findings described in this section are based on
self-report methodology. Thus it remains unclear if women actually
experience social anxiety more than men or simply report more social
anxiety compared to men. Although a comprehensive and definitive
answer to this question is beyond the scope of the present review, we
discuss gender differences in physiological arousal (in Section 9) as well
Table 1
Gender differences in social anxiety disorder (SAD) prevalence among males and females with SAD.
Study Sample type Location Type of diagnosis Prevalence Significance
Males Females
Schneier, Johnson, Hornig, Liebowitz
and Weissman, 1992
> 13,000 young adults (18–29) USA Lifetime SAD 2% 3.1% Yes
Kessler et al., 1994 > 8000 adults (15–54) USA Lifetime SAD 11.1% 15.5% Yes
Xu et al., 2012 43,093 adults USA Lifetime SAD 4.2% 5.7% Yes
McLean, Asnaani, Litz and Hofmann,
2011
20,013 adults (18+) USA Lifetime SAD 8.7% 10.3% No
Ohayon & Schatzberg, 2010 18, 980 individuals (15+) U.K., Germany, Italy,
Spain, and Portugal
Current SAD 3.4% 5.4% Yes
Merikangas, Avenevoli, Acharyya,
Zhang and Angst, 2002
Longitudinal study (following
591 young adults from the age
of 18 to 35)
Switzerland Cumulative 12-months
prevalence across the years
of the study
3.7% 7.3% Yes
Lépine & Lellouch, 1995 1787 adults
(18+)
France Lifetime SAD 2.1% 5.4% Yes
MacKenzie & Fowler, 2013 36,984 individuals (15+) Canada Lifetime SAD 3.3 5% Yes
Pakriev, Vasar, Aluoja and Shlik,
2000
855 adults
(18–65)
Russia Lifetime SAD 37.5% 51.8% Yes
Cho et al., 2007 6275 adults (18–64) Korea Lifetime SAD 0.1 0.4 No inferential tests
were reported
Hwu, Yeh and Chang, 1989 5005, 3004 and 2995 adults
(18+)
Taiwan (Taipei, 2 small
towns and 6 rural
villages)
Lifetime SAD 2.4%
(Taipei)
9.5%
(Taipei)
Yes-only in Taipei
Wittchen et al., 1999 3021 adolescents
(14–25)
Germany Lifetime SAD 4.9% 9.5% Yes
Merikangas et al., 2010 10,123 adolescents (13–18) USA Lifetime SAD 7% 11.2% Yes
Essau, Conradt and Petermann, 1999 1035 adolescents (12–17) Germany Lifetime SAD 1% 2.1% Yes
M. Asher et al. Clinical Psychology Review 56 (2017) 1–12
3
as studies of reporting biases in anxiety (in Section 12) which converge
to suggest that women may experience more anxiety above and beyond
the possible effect of biased reporting. This topic is discussed in more
detail in the Discussion section.
4.2. Types of feared situations
Differences have been found in the types of anxiety-provoking
situations feared by men and women with SAD. Specifically, women
with SAD reported significantly greater fear compared to men with SAD
when interacting with authority figures, giving a talk in front of an
audience, working while being observed, entering a room when others
are already seated, being the center of attention, expressing disagree-
ment or disapproval, giving a report to a group, and having a party.
Men reported more fear compared to women when urinating in a public
restroom and returning goods to a store (Turk et al., 1998). It is
important to note that men and women with SAD were found to
experience similar fears in two domains: informal social interactions
(e.g., participating in small groups, going to a party) and being
observed (e.g., telephoning in public, eating in public). Interestingly,
in contrast to the null findings regarding gender differences in being
observed (Turk et al., 1998), a large community study in Germany
found that women with SAD were more likely to report fear of eating
and drinking in public compared to men with SAD (Wittchen et al.,
1999).
An additional gender difference was reported by Flynn, Markway,
and Pollard (1992) who asked individuals with SAD to rate their fear
that other people would describe them using 26 negative adjectives
(e.g., weak, crazy). Compared to men with SAD, significantly more
women with SAD feared other people would describe them as crazy,
making no sense, being a bad parent, and being too fat or too tall.
Recent data from the epidemiologic sample of alcohol and related
conditions (NESARC) demonstrated that compared to men with SAD,
women with SAD were more likely to fear professional situations such
as being interviewed, speaking to an authority figure, and speaking up
in a meeting (Xu et al., 2012). They were also more likely to fear taking
an important exam and eating and drinking in front of others. Men with
SAD, however, were more likely to fear dating.
Taken together, the data suggest that women fear a wider range of
social situations compared to men; however, it is important to note that
the studies documenting such differences are about two decades old
and no recent examinations have been made of such gender differences.
Given the significant changes observed in gender roles across the world
in the past decade (e.g., changes in employment, education patterns,
and assumed family roles for women and men; Cotter,
England, & Hermsen, 2008; England et al., 2004; Cotter,
Hermsen, & Vanneman, 2011; Bolzendahl & Myers, 2004), it is possible
that the types of social situations that are feared by women versus men
with social anxiety have also changed. Future studies in the current
cultural context of gender roles are needed in order to draw firm
conclusions regarding differences in the types of social situations feared
by men and women.
4.3. Subjective distress
Whereas previous data suggest that women have been found to
report more fear compared to men in a number of social situations and
to have a greater number of social fears compared to men, there are
some findings suggesting that men may experience more distress as a
result of their social anxiety compared to women. For instance, in a
longitudinal community study, men with sub-clinical SAD symptoms
were found to report greater subjective distress compared to women
with sub-clinical SAD symptoms, suggesting that men experienced
substantial distress even at a low level of symptomatology
(Merikangas et al., 2002).
Along these lines, despite the higher prevalence rate of SAD among
women in the community (see Section 3 above), it has been observed
that men with SAD are as likely or even more likely to seek treatment
compared to women with the disorder (Weinstock, 1999), suggesting
that distress or impairment for men may be greater. Patterns of
treatment seeking among men and women with SAD will be discussed
in Section 8.
5. Gender differences in functioning and impairment
Men and women with SAD may have different patterns of impair-
ment at work, and in their social life. Regarding employment, studies
have shown that compared to men with SAD, fewer women with the
disorder are employed (MacKenzie & Fowler, 2013) and among those
employed, men are more likely to be employed on a full time basis
compared to women (Turk et al., 1998). Considering these findings it is
not surprising that women with SAD report having lower personal
income compared to men with the disorder (MacKenzie & Fowler,
2013).
These gender differences in employment may be related to the types
of fears endorsed by men and women (see Section 4 above). Specifi-
cally, women have been shown to have greater fear of interacting with
authority figures, giving a talk in front of an audience, working while
being observed, entering a room when others are already seated, and
giving a report to a group – all of which are common situations in work
settings (Turk et al., 1998). This has led some researchers to suggest
that men may have more exposure to work settings and may thus
develop greater comfort on the job compared to women (Turk et al.,
1998). Alternatively, women may be less inclined to work or to work
full time because of their work-related fears compared to men (Turk
et al., 1998). As we noted previously, however, employment patterns
and exposure to work settings for women have significantly changed
over the past decades (e.g., Cotter et al., 2008). Thus, it is important to
conduct current examinations on this topic before firm conclusions can
be drawn.
In contrast to the findings described above, some studies have found
that men have greater work impairment compared to women. For
instance, an epidemiological study found greater occupational impair-
ment in men with SAD compared to women with the disorder (Lampel,
Slade, Issakidis, & Andrews, 2003). However, other studies have found
no gender differences in work impairment. For instance, Merikangas
et al. (2002) found that along the course of their 15-year longitudinal
study, occupational impairment was similar for men and for women.
Thus, findings on gender differences in work impairment remain
inconclusive at this point.
Some studies have found that functioning and impairment in social
life may differ between men and women with SAD. Merikangas et al.
(2002) found that men had greater social impairment compared to
women in their longitudinal study. Along these lines, gender differences
in relationship status and living arrangements among individuals with
SAD have been reported (MacKenzie & Fowler, 2013). Specifically, it
was found that men with SAD were more likely to report being single
and living alone compared to women with SAD, whereas women with
SAD were more likely to report being widowed, being separated, or
divorced.
In contrast to findings suggesting that men may have greater
impairment in social life, other studies have found the opposite pattern
in which socially anxious women may be more socially impaired
compared to socially anxious men. For instance, in a community
survey, Cuming and Rapee (2010) reported that social anxiety was
associated with diminished levels of disclosure and openness in
romantic relations and close friendships among women but not among
men. A similar pattern of results was also previously found in
adolescents. For example, an older study with a sample of 250 high
school students ranging from 10th to 12th grade (La Greca & Lopez,
1998) demonstrated that social anxiety (SA) was more strongly linked
to girls' social impairment compared to boys, such that girls with higher
M. Asher et al. Clinical Psychology Review 56 (2017) 1–12
4
levels of SA reported fewer friendships and less intimacy, companion-
ship, and support in their close relationships.
Other studies have found no differences between men and women
with SAD in their satisfaction of their relationships with their spouse,
children, or friends (Yonkers, Dyck, & Keller, 2001). Similarly,
Sparrevohn and Rapee (2009) examined individuals with SAD and
found no gender differences in quality of romantic relationships, self-
disclosure, emotional expression, and intimacy in romantic relation-
ships. In a study based on data from the National Comorbidity Survey
(NCS; Rodebaugh, Fernandez, & Levinson, 2012), it was found that SAD
had a negative effect on friendship quality in both men and women.
Whereas in men this negative effect was exacerbated when comorbid
with generalized anxiety disorder, in women it was exacerbated when
comorbid with MDD.
In sum, research on gender differences in functioning and impair-
ment has yielded equivocal findings. Currently, opposing findings
preclude us from making reliable inferences on gender differences in
functioning and impairment and future research is needed to shed light
on this issue.
6. Gender differences in comorbidity
Data from the national epidemiologic sample on alcohol and related
conditions (NESARC) has indicated that whereas men with SAD are
more likely to suffer from a comorbid externalizing disorder, women
with SAD are more likely to suffer from comorbid …
S O C I A L A N X I E T Y D I S O R D E R :
More Than Just Shyness
Are you extremely afraid of being judged by others?
Are you very self-conscious in everyday social situations?
Do you avoid meeting new people?
If you have been feeling this way for at least six months and
these feelings make it hard for you to do everyday tasks—
such as talking to people at work or school—you may have a
social anxiety disorder.
Social anxiety disorder (also called social phobia) is a
mental health condition. It is an intense, persistent fear
of being watched and judged by others. This fear can
affect work, school, and your other day-to-day activities.
It can even make it hard to make and keep friends.
But social anxiety disorder doesn’t have to stop you
from reaching your potential. Treatment can help you
overcome your symptoms.
What is it like having social anxiety disorder?
In school, I was always afraid of being called on, even when I
knew the answers. I didn’t want people to think I was stupid
or boring. My heart would pound and I would feel dizzy and
sick. When I got a job, I hated to meet with my boss or talk
in a meeting. I couldn’t attend my best friend’s wedding
reception because I was afraid of having to meet new people.
I tried to calm myself by drinking several glasses of wine
before an event and then I started drinking every day to try to
face what I had to do.
I finally talked to my doctor because I was tired of feeling this
way and I was worried that I would lose my job. I now take
medicine and meet with a counselor to talk about ways to
cope with my fears. I refuse to use alcohol to escape my fears
and I’m on my way to feeling better.
What is social anxiety disorder?
Social anxiety disorder is a common type of anxiety disorder. A person with
social anxiety disorder feels symptoms of anxiety or fear in certain or all
social situations, such as meeting new people, dating, being on a job
interview, answering a question in class, or having to talk to a cashier in a
store. Doing everyday things in front of people—such as eating or drinking
in front of others or using a public restroom—also causes anxiety or fear.
The person is afraid that he or she will be humiliated, judged, and rejected.
The fear that people with social anxiety disorder have in social situations is
so strong that they feel it is beyond their ability to control. As a result, it gets
in the way of going to work, attending school, or doing everyday things.
People with social anxiety disorder may worry about these and other things
for weeks before they happen. Sometimes, they end up staying away from
places or events where they think they might have to do something that will
embarrass them.
Some people with the disorder do not have anxiety in social situations but
have performance anxiety instead. They feel physical symptoms of anxiety
in situations such as giving a speech, playing a sports game, or dancing or
playing a musical instrument on stage.
Social anxiety disorder usually starts during youth in people who are
extremely shy. Social anxiety disorder is not uncommon; research suggests
that about 7 percent of Americans are affected. Without treatment, social
anxiety disorder can last for many years or a lifetime and prevent a person
from reaching his or her full potential.
What are the signs and symptoms of social anxiety
disorder?
When having to perform in front of or be around others, people with social
anxiety disorder tend to:
Ê Blush, sweat, tremble, feel a rapid heart rate, or feel their “mind
going blank”
Ê Feel nauseous or sick to their stomach
Ê Show a rigid body posture, make little eye contact, or speak with an
overly soft voice
Ê Find it scary and difficult to be with other people, especially those they
don’t already know, and have a hard time talking to them even though
they wish they could
Ê Be very self-conscious in front of other people and feel embarrassed
and awkward
Ê Be very afraid that other people will judge them
Ê Stay away from places where there are other people
What causes social anxiety disorder?
Social anxiety disorder sometimes runs in families, but no one knows for
sure why some family members have it while others don’t. Researchers have
found that several parts of the brain are involved in fear and anxiety. Some
researchers think that misreading of others’ behavior may play a role in
causing or worsening social anxiety. For example, you may think that people
are staring or frowning at you when they truly are not. Underdeveloped
social skills are another possible contributor to social anxiety. For example, if
you have underdeveloped social skills, you may feel discouraged after
talking with people and may worry about doing it in the future. By learning
more about fear and anxiety in the brain, scientists may be able to create
better treatments. Researchers are also looking for ways in which stress and
environmental factors may play a role.
How is social anxiety disorder treated?
First, talk to your doctor or health care professional about your symptoms.
Your doctor should do an exam and ask you about your health history to
make sure that an unrelated physical problem is not causing your symptoms.
Your doctor may refer you to a mental health specialist, such as a
psychiatrist, psychologist, clinical social worker, or counselor. The first step
to effective treatment is to have a diagnosis made, usually by a mental
health specialist.
Social anxiety disorder is generally treated with psychotherapy (sometimes
called “talk” therapy), medication, or both. Speak with your doctor or health
care provider about the best treatment for you. If your health care provider
cannot provide a referral, visit the NIMH Help for Mental Illnesses web page
at www.nimh.nih.gov/findhelp for resources you may find helpful.
http://www.nimh.nih.gov/findhelp
Psychotherapy
A type of psychotherapy called cognitive behavioral therapy (CBT) is
especially useful for treating social anxiety disorder. CBT teaches you
different ways of thinking, behaving, and reacting to situations that help you
feel less anxious and fearful. It can also help you learn and practice social
skills. CBT delivered in a group format can be especially helpful. For more
information on psychotherapy, please visit www.nimh.nih.gov/
psychotherapies.
Support Groups
Many people with social anxiety also find support groups helpful. In a group
of people who all have social anxiety disorder, you can receive unbiased,
honest feedback about how others in the group see you. This way, you can
learn that your thoughts about judgment and rejection are not true or are
distorted. You can also learn how others with social anxiety disorder
approach and overcome the fear of social situations.
Medication
There are three types of medications used to help treat social
anxiety disorder:
Ê Anti-anxiety medications
Ê Antidepressants
Ê Beta-blockers
Anti-anxiety medications are powerful and begin working right away to
reduce anxious feelings; however, these medications are usually not taken
for long periods of time. People can build up a tolerance if they are taken
over a long period of time and may need higher and higher doses to get the
same effect. Some people may even become dependent on them. To avoid
these problems, doctors usually prescribe anti-anxiety medications for short
periods, a practice that is especially helpful for older adults.
Antidepressants are mainly used to treat depression, but are also helpful for
the symptoms of social anxiety disorder. In contrast to anti-anxiety
medications, they may take several weeks to start working. Antidepressants
may also cause side effects, such as headaches, nausea, or difficulty
https://www.nimh.nih.gov/psychotherapies
https://www.nimh.nih.gov/psychotherapies
sleeping. These side effects are usually not severe for most people,
especially if the dose starts off low and is increased slowly over time.
Talk to your doctor about any side effects that you have.
Beta-blockers are medicines that can help block some of the physical
symptoms of anxiety on the body, such as an increased heart rate, sweating,
or tremors. Beta-blockers are commonly the medications of choice for the
“performance anxiety” type of social anxiety.
Your doctor will work with you to find the best medication, dose, and duration
of treatment. Many people with social anxiety disorder obtain the best
results with a combination of medication and CBT or other psychotherapies.
Don’t give up on treatment too quickly. Both psychotherapy and medication
can take some time to work. A healthy lifestyle can also help combat
anxiety. Make sure to get enough sleep and exercise, eat a healthy diet, and
turn to family and friends who you trust for support.
For basic information about these and other mental health medications, visit
www.nimh.nih.gov/medications.
Visit the Food and Drug Administration’s website (www.fda.gov/) for the
latest information on warnings, patient medication guides, or newly
approved medications.
For More Information
To learn more about social anxiety disorder, visit:
MedlinePlus (National Library of Medicine)
http://medlineplus.gov
(En Español: http://medlineplus.gov/spanish)
For information on clinical trials, visit:
ClinicalTrials.gov: http://www.clinicaltrials.gov
(En Español: http://salud.nih.gov/investigacion-clinica/)
For more information on conditions that affect mental health, resources, and
research, visit the NIMH website http://www.nimh.nih.gov
https://www.nimh.nih.gov/medications
http://www.fda.gov/
http://medlineplus.gov/spanish
http://www.clinicaltrials.gov
http://www.nimh.nih.gov
https://medlineplus.gov/
http://salud.nih.gov/investigacion-clinica/
Finding Help
Mental Health Treatment Program Locator
The Substance Abuse and Mental Health Services Administration (SAMHSA)
provides this online resource for locating mental health treatment facilities and
programs. The Mental Health Treatment Locator section of the Behavioral Health
Treatment Services Locator lists facilities providing mental health services to persons
with mental illness. Find a facility in your state at www.findtreatment.samhsa.gov/.
For additional resources, visit www.nimh.nih.gov/findhelp.
Reprints
This publication is in the public domain and may be reproduced or copied without
permission from NIMH. Citation of NIMH as a source is appreciated. We encourage
you to reproduce it and use it in your efforts to improve public health. However,
using government materials inappropriately can raise legal or ethical concerns, so
we ask you to use these guidelines:
Ê NIMH does not endorse or recommend any commercial products, processes, or
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If you have questions regarding these guidelines and use of NIMH publications,
please contact the NIMH Information Resource Center at 1-866-615-6464 or e-mail
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mailto:[email protected]
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Office of Science Policy, Planning, and Communications
Science Writing, Press, and Dissemination Branch
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Room 6200, MSC 9663
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NIH Publication No. 19-MH-8083
Revised 2016
mailto:[email protected]
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SOCIAL ANXIETY DISORDER: More Than Just Shyness
What is it like having social anxiety disorder?
What is social anxiety disorder?
What are the signs and symptoms of social anxiety disorder?
What causes social anxiety disorder?
How is social anxiety disorder treated?
Psychotherapy
Support Groups
Medication
For More Information
Finding Help
Mental Health Treatment Program Locator
Reprints
National Institute of Mental Health
34
EW RESEARCH
N
4
Are Social and Communication Difficulties a Risk
Factor for the Development of Social Anxiety?
Hannah Pickard, MSc, Fruhling Rijsdijk, PhD, Francesca Happ�e, PhD, William Mandy, PhD
Objective: Social anxiety (SA) is a common condition
associated with social and communication (SC) difficulties
in typically developing young people, as well as those
with autism spectrum disorder (ASD). Whether SC diffi-
culties place children at risk for developing SA is unclear.
Using a longitudinal design, the present study aimed to
disentangle the relationship between SA symptoms and
SC difficulties using a population-based sample of 9,491
children from the Avon Longitudinal Study of Parents and
Children (ALSPAC).
Method: Parent-reported data on SC difficulties and SA
symptoms were collected at ages 7, 10, and 13 years. A
cross-lagged panel model was used to investigate the
longitudinal stability and directional relationship between
latent SC difficulties and SA constructs over time.
Results: More SC difficulties were associated with greater
SA symptoms at all ages. Earlier SC difficulties predicted a
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small but significant amount of variance in later SA
symptoms. The reverse relationship from SA to SC diffi-
culties was not observed. The relationship from SC diffi-
culties to SA was strongest from age 7 to 10 years. No sex
differences were observed.
Conclusion: The evidence suggests that SC difficulties
may be an important risk factor for the development of
SA. These findings suggest the potential usefulness of
incorporating social skills training alongside effective in-
terventions to prevent or alleviate symptoms of SA in
childhood.
Key words: ALSPAC, social anxiety, social and
communication difficulties, autism spectrum disorders,
longitudinal
J Am Acad Child Adolesc Psychiatry 2017;56(4):344–351.
ocial anxiety (SA) disorder is characterized by an
intense fear of social situations, which is often
S accompanied by the fear of being scrutinized by
others.1 SA is often experienced during several social situa-
tions, including interacting with others, eating in public, or
giving speeches. Anxiety-related fears are commonly driven
by negative self-perceptions and a fear of being ridiculed by
others, which can lead to increased social withdrawal and
avoidance.2 SA is the third most common psychiatric dis-
order, with epidemiological research showing prevalence
rates between 3% to 4% in childhood and 9% in adoles-
cence.3,4 The onset of SA is usually between late childhood
and adolescence5; however, SA disorder can manifest in
children at 7 or 8 years of age.6
SA is a dimensional trait that is continuously distributed
throughout the general population. Subthreshold symp-
toms of SA are associated with adverse outcomes and an
increased risk of developing SA disorder and additional
comorbid disorders.7 Given the burden that subthreshold
SA can have on an individual’s well-being, it is important
that research investigates potential risk factors under-
pinning dimensionally measured SA traits in the general
population.
SA and Social Communication Difficulties in Childhood
Etiological models of SA in childhood have implicated the
role of several development risk factors, including behav-
ioral inhibition, parent–child interactions, and peer re-
lationships.8 In addition, social and communication (SC)
difficulties, including problems in social behaviors, social
cognition, and reciprocal social communication, are common
among children with SA and have also been proposed as a
risk factor. SC ability is a continuously distributed trait that
extends throughout the general population,9 with those who
experience severe difficulties often receiving a diagnosis of
autism spectrum disorder (ASD), a neurodevelopmental
condition characterized by SC difficulties and restricted in-
terests and repetitive behaviors. SA co-occurs highly in
children with ASD (4.5�9.5 years) and high subthreshold
ASD traits (10�15 years),10,11 suggesting that those with
greater SC difficulties may have a heightened risk of
developing SA disorder. However, the developmental rela-
tionship between SC difficulties and SA is unclear. The
present study aims to address whether an individual’s po-
sition on the continuum of SC traits influences their risk of
later developing SA.
Cross-sectional research has supported the association
between SC difficulties and SA. For example, typically
developing children with SA disorder exhibited lower self
and peer ratings of social competence during both labora-
tory and school-based social interaction tasks, compared to
peers without anxiety.4,12 Furthermore, using parent-report
questionnaires, research has found that SC difficulties are
higher among children with SA disorder compared to those
JOURNAL OF THE AMERICAN ACADEMY OF CHILD & ADOLESCENT PSYCHIATRY
VOLUME 56 NUMBER 4 APRIL 2017
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SOCIAL AND COMMUNICATION DIFFICULTIES AS RISK FACTOR FOR SOCIAL ANXIETY
with other anxiety disorders,13 suggesting a specific rela-
tionship between SC difficulties and SA. Although SC dif-
ficulties may not be universal in SA,14 the evidence suggests
that for a significant subgroup in the general population, SC
difficulties may underlie the development of SA.15
Intervention studies in children have informed our un-
derstanding of the relationship between SC difficulties and
SA. Research has shown that children (age 8�12 years) with
SA disorder who completed a Social Effectiveness Therapy
(SET) program to enhance social skills and peer relationships
showed increases in social skills and decreases in SA at
posttreatment and after 6 months, compared to the control
participants in a nonspecific intervention.16 It is evident that
a relationship between SA and SC difficulties exists and that
social skills training effectively reduces SA; however, we do
not fully understand whether SC difficulties contribute to
the development of SA. This research is important for
identifying early warning signs on the developmental
trajectory of SA.
In the ASD literature, cross-sectional studies have shown
that social skill deficits and greater physiological arousal
contributed toward elevated SA symptoms in adolescents
with ASD.17 Contradictory research in children with ASD
revealed that higher levels of SA predicted lower respon-
sible and assertive social skills.18 Inconclusive findings
regarding the directional relationship between SA and SC
difficulties have led researchers to postulate a bidirectional
relationship in ASD. It is suggested that SC difficulties
may hinder social experiences, contributing to increased SA
and social withdrawal, which subsequently impedes an in-
dividual’s SC ability.19 However, this relationship remains
to be explored.
Research using population-based samples has supported
the relationship between SA symptoms and SC difficulties.
Population-based research allows the use of large samples to
examine associations across the trait distribution. These
findings can inform research in clinical populations. Using a
population-based twin sample of children with ASD and
their affected and unaffected cotwins, Hallett et al.11 found
that increased SC difficulties and higher IQ were associated
with greater parent-reported SA, supporting clinical find-
ings. To date, no longitudinal work using a population-
based sample has specifically assessed whether SC
difficulties are a risk factor for SA, or whether SA reduces an
individual’s SC ability.
TABLE 1 Demographic Information for the Sample at 7, 10, and 1
Demographics Age 7 (n ¼ 7,90
Female % 49
Parental HE% (age 18þ) 42
Owned/mortgaged home % 82
Ethnicity, white % 96
Full scale IQ, mean (SD) (Range: 45�151)a 105.30 (16.32
Verbal IQ 108.14 (16.68
Performance IQ 100.57 (16.94
Note: IQ age 7 (n ¼ 5,829), 10 (n ¼ 5,761), and 13 (n ¼ 5,307) years. HE ¼ h
aFull range of scores at all ages.
JOURNAL OF THE AMERICAN ACADEMY OF CHILD & ADOLESCENT PSYCHIATRY
VOLUME 56 NUMBER 4 APRIL 2017
We aim to examine the directional relationship between
parent-reported SA symptoms and SC difficulties in a
population-based sample of children at ages 7, 10, and
13 years. Sex differences will also be explored. Furthermore,
the relationship between SA and SC difficulties will be
examined while controlling for generalized anxiety, to test
whether SC difficulties are related to SA-specific symptoms,
compared to generalised anxiety. Based on previous
research, we predict a directional and specific relationship
between SC difficulties and SA, with early SC difficulties
contributing to later SA symptoms.
METHOD
Sample
All participants were from the Avon Longitudinal Study of Parents
and Children (ALSPAC) cohort, a population-based sample of
children born in Bristol between 1991 and 1992. A total of 14,541
children were recruited into the original cohort, with 14,062 live
births and 13,988 alive at 12 months.20 (The study website contains
details of all of the data available through a fully searchable data
dictionary at http://www.bris.ac.uk/alspac/researchers/data-
access/data-dictionary/.) Ethical approval for this study was ob-
tained from the ALSPAC Law and Ethics Committee and local
research ethics committees. A total of 9,597 children had available
data to test the study hypotheses at ages 7 (n ¼ 8,148), 10 (n ¼ 7,723),
and 13 (n ¼ 7,008) years. Following ALSPACs exclusion criteria for
prorated scores, only children with 50% or more complete data on
all measures of interest at all ages were included in the present
study. Based on these exclusion criteria, 248 children (3%), 204
children (2.6%), and 226 children (3.2%) were excluded at ages 7, 10,
and 13 years, respectively. Merging the three samples at ages 7
(n ¼ 7,900), 10 (n ¼ 7,519), and 13 (n ¼ 6,782) years, the final sample
included 9,491 children (4,654 female) with data at one, two, or three
time points. This final sample was used in all further analyses
(Table 1). Compared to the original ALSPAC cohort not included in
the current analyses, young people in our final sample were more
likely to have a mother who was a homeowner (odds ratio
[OR] ¼ 2.94, 95% CI ¼ 2.72, 3.19) and had completed higher edu-
cation (OR ¼ 2.33, 95% CI ¼ 2.13, 2.55).
Measures
Socioeconomic Status. Socioeconomic status (SES) was captured using
parental maternal education. Previous research in ALSPAC has re-
ported that maternal education is a valid indicator of SES.21 At
32 weeks of gestation, mothers reported their current highest level of
educational achievement from six possible responses: “none,” “CSE”
(basic UK qualification), “vocational,” “O-level” (a prerequisite to
3 Years of Age
0) Age 10 (n ¼ 7,519) Age 13 (n ¼ 6,782)
50 50
43 44
83 84
96 96
) 105.24 (16.39) 105.70 (16.31)
) 108.08 (16.69) 108.51 (16.63)
) 100.59 (17.03) 100.98 (16.93)
igher education; SD ¼ standard deviation.
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PICKARD et al.
further education), “A-levels,” and “degree or above.” Higher scores
are indicative of better maternal education and thus higher SES.
Wechsler Intelligence Scale for Children�Third Edition. The
Wechsler Intelligence Scale for Children�Third Edition (WISC-III)22
is a measure of child IQ. In the present study, the abbreviated
version of the WISC, including random items from 10 subtests, was
administered during the clinical data collection wave at age 8 years.
A total of 6,726 children (70.9%) from the final sample had a com-
plete IQ measure at age 8 years.
Social and Communication Disorders Checklist. The Social and
Communication Disorders Checklist (SCDC)23,24 is a parent-
reported questionnaire that measures social and communication
(SC) difficulties related to ASD. The questionnaire consists of 12
items with a response scale ranging from 0 to 2 (“not true,” “quite or
sometimes true,” “very often true”), which was designed to capture
a child’s social behavior and functioning over the previous
6 months. A total score ranges from 0 to 24, with higher scores
indicating greater SC difficulties. The SCDC shows high internal
consistency (0.93), as well as good specificity (0.91) and sensitivity
(0.88) when discriminating between individuals with and without
ASD.23 Furthermore, research conducted in the ALSPAC cohort
supports both the construct validity and reliability of the SCDC at
measuring SC traits in the general population.24 In the ALSPAC
sample, research has shown that the SCDC measures SC trait vari-
ability in the general population that overlaps with ASD in terms of
genetic effects,25 supporting the SCDC’s validity as a measure of
ASD-specific SC difficulties. The SCDC had excellent internal reli-
ability (a ¼ 0.81�0.89).
Development and Wellbeing Assessment. The Development and
Wellbeing Assessment (DAWBA)26 questionnaire was administered
as a parent-report questionnaire to capture child and adolescent
psychopathology that corresponds with the International Classifica-
tion of Diseases–10th Revision (ICD-10) and Diagnostic and Statistical
Manual of Mental Disorders—4th Edition (DSM-IV) criteria. The
DAWBA has been tested and validated in large population sam-
ples.26 In the present research, SA symptoms were measured using
the social fears (SF) subscale, and generalized anxiety was measured
using the general anxiety (GA) subscale. The DAWBA-SF has six
items in which parents report whether their child had experienced
any specific SA symptoms over the last month: “no,” “a little,”
“a lot,” and “hasn’t done this in last month.” Any parent responses
FIGURE 1 Cross-lagged panel model of social and communicatio
Note: A ¼ autoregressive paths, b ¼ cross-lagged paths; c ¼ cova
346 www.jaacap.com
of “hasn’t done this in last month” were excluded, as this response is
not present in the original online DAWBA and is ambiguous in its
answer to the six SF items. An SF total score (range 0�12) can be
created by summing the responses over the six SA items, which was
used in the present study. Higher scores on the DAWBA-SF indicate
more severe SA symptoms. The DAWBA-SF showed good internal
reliability (a ¼ 0.79�0.81). The DAWBA-GA subscale consists of
seven items in which parents report the frequency of their child
worrying over the past 6 months: “no,” “sometimes,” and “often.” A
GA total score (range 0�14) is computed by summing responses on
all items, with higher scores indicating more generalized anxiety
symptoms. The DAWBA-GA showed acceptable internal reliability
(a ¼ 0.53�0.72).
Data Analyses
Analyses were conducted in R, using the Lavaan package for
structural equation modeling (SEM).27 The present study used a
three-wave (time), two-level cross-lagged panel model to estimate
relationships between SC difficulties and SA symptoms. The cross-
lagged panel model incorporates the inherent time nature of longi-
tudinal data and is frequently used to assess causal relationships in
nonexperimental studies using panel data.28,29
Confirmatory Factor Analyses. Three confirmatory factor ana-
lyses were conducted to assess the construct validity of the
DAWBA-SF and SCDC at all ages. A two-factor structure was
specified with a single factor for each scale: SC difficulties
(SCDC) with 12 indicators, and SA (DAWBA-SF) with 6 in-
dicators. Measures recommended for large datasets were used.30
Absolute fit measures included the standardized root mean
square residual (SRMR) and root mean square error of approxi-
mation (RMSEA). For the SRMR, a value less than 0.08 indicates
a good model fit, and for RMSEA, a value below 0.08 indicates an
acceptable model fit, with values less than 0.05 indicating good
model fit.31,32 The comparative fit index (CFI) was also used,
with values above 0.90 and 0.95 indicating acceptable to good
model fit, respectively.32
Cross-Lagged Panel Model. The predicted relationships between
SC difficulties and SA symptoms at ages 7, 10, and 13 years are
depicted in Figure 1. The simultaneously solved paths are re-
ported as partial regression coefficients: autoregressive paths
n difficulties (SC Diff) and social anxiety (SA) latent factors.
riance paths.
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SOCIAL AND COMMUNICATION DIFFICULTIES AS RISK FACTOR FOR SOCIAL ANXIETY
estimate the stability of one trait over time; covariance paths es-
timate the correlation between two traits at each time point; cross-
lagged paths estimate the predictive relationship of one variable
on another at a later time point, independent of the stability and
covariance paths.
The relationships were tested between latent factors to capture
more robust constructs free of measurement error. Each latent factor
was specified within the model, with the 6 items from the DAWBA-
SF loading on to the SA construct and the 12 items from the SCDC
loading on to the SC difficulties construct (excluded from Figure 1
for simplicity). In the full model, latent factors were free to covary
within time points. Latent factor item residuals were specified to
covary between time points.
Model fit was tested with the Satorra–Bentler Scaled c2
statistic,33 to compare c2 when data are nonnormal. To test the
study’s first hypothesis, model fit across 8 nested models were
examined to assess the following: longitudinal stability of each latent
variable; the relationship between the latent variables within time;
and the stability of cross-lagged paths and difference in the cross-
lagged paths at each time point (7/10, 10/13). Model fit was
determined by the difference in fit statistics of the full model and a
nested model in which equality constraints are applied to path
estimates (e.g., a1 ¼ a2 or a3 ¼ a4 to assess stability of the auto-
regressive paths). To assess sex differences, likelihood ratio testing
was conducted between a full model in which all paths were freely
estimated across sex and one in which either all cross-lagged paths
(b12, b21, b23, b32) or autoregressive paths (a1, a2, a3, a4) were equated
across sex. A Bonferroni correction was applied to assess the
significance of all path coefficients (p < .003). c2 for model-fit
differences were considered to be statistically significant at p < .006.
Specificity. To explore the specificity of the relationship between
SC difficulties and SA, scores on the DAWBA-GA subscale (where
available) were regressed out of the SA latent variable traits at ages 7,
10, and 13 years to create a more specific SA-related latent construct.
RESULTS
All questionnaire data were cleaned using ALSPAC guide-
lines for data preparation. Tests of selective attrition for SC
difficulties and SA symptoms were conducted, and accept-
able results were observed (see Supplements 1 and 2,
available online). Mean scores on the SCDC and DAWBA-SF
are reported in Table 2. The full distribution of scores on the
SCDC and DAWBA-SF scales are available online
(see Table S1, available online).
Confirmatory Factor Analysis
Three two-factor models were specified to test the construct
validity of the SCDC scale and DAWBA-SF subscale at ages 7,
TABLE 2 Parent-Reported Child Characteristics on Questionnaire D
Questionnaires
Age 7 y (n ¼ 7,900)
Mean (SD)
[CI]
SCDC 2.8 (3.66)
(Range: 0�24)a [2.72, 2.88]
DAWBA-SF 0.88 (1.6)
(Range: 0�12)a [0.85, 0.92]
Note: DAWBA-SF ¼ Development and Wellbeing AssessmenteSocial Fears; SCDC ¼
aFull range of scores at all ages.
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10, and 13 years. Two out of three of the fit indices for the two-
factor models at 7 years (RMSEA ¼ 0.067 [0.065, 0.068],
SRMR ¼ 0.06, CFI ¼ 0.83), 10 years (RMSEA ¼ 0.067 [0.066,
0.069], SRMR ¼ 0.06, CFI ¼ 0.84), and 13 years
(RMSEA ¼ 0.073 [0.072, 0.074], SRMR ¼ 0.06, CFI ¼ 0.82) were
indicative of a good/acceptable model fit. The results imply
that the SCDC and DAWBA-SF are two distinct and separate
constructs measuring SC difficulties and SA symptoms.
Latent Variable Correlations
A saturated model with no cross-lagged or stability paths
was fitted to examine the correlations among all latent fac-
tors. Significant associations among all latent factors were
observed (Table 3).
Cross-Lagged Panel Model Path Estimates
In the cross-lagged panel model, effects of IQ and SES were
regressed out of the SA latent variable at ages 7 years (IQ:
b ¼ �0.18, SES: b ¼ �0.02) 10 years (IQ: b ¼ �0.11, SES:
b ¼ 0.01), and 13 years (IQ: b ¼ �0.04, SES: b ¼ 0.00). For the
SC difficulties latent variable, the effects were at ages 7 years
(IQ: b ¼ �0.17, SES: b ¼ 0.00), 10 years (IQ: b ¼ �0.11, SES:
b ¼ �0.00), and 13 years (IQ: b ¼ �0.04, SES: b ¼ �0.00).
Covariance Paths
The covariance path estimates between SC difficulties and
SA were significant at all ages (Figure 2). The covariance
path weights steadily decreased over time; however, no
significant decrease in model fit was observed when the
covariance paths at ages 7 and 10 years (Dc2[df] ¼ 4.92[1],
p ¼ .03) and ages 10 and 13 years (Dc2[df] ¼ 0.12[1], p ¼ .73)
were constrained to be equal (see Table S2, available online).
Stability Paths
The autoregressive paths for SC difficulties (a1 and a2) and
SA (a3 and a4) were significantly stable over time. However,
the longitudinal stability of both SC difficulties and
SA significantly decreased over time: SC difficulties
(Dc2[df] ¼ 53.87[1], p ¼ 2.15e-13) and SA symptoms
(Dc2[df] ¼ 12.16[1], p ¼ 4.89e-04).
Cross-Lagged Paths
The cross-lagged paths from SC difficulties to SA (b12 and b23)
were both significant, but not significantly different in size
ata
Age 10 y (n ¼ 7,519) Age 13 y (n ¼ 6,782)
Mean (SD)
[CI]
Mean (SD)
[CI]
2.37 (3.58) 2.52 (3.60)
[2.29, 2.45] [2.44, 2.61]
0.98 (1.7) 1.26 (1.91)
[0.94, 1.02] [1.21, 1.30]
Social Communication Disorders Checklist; SD ¼ standard deviation.
www.jaacap.com 347
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TABLE 3 Correlation Coefficients Among All Latent Factors in the Saturated Model
R [CI]
1 2 3 4 5 6
1. SA7 1
2. SC Diff7 0.20*** [0.17, 0.24] 1
3. SA10 0.54*** [0.51, 0.58] 0.21*** [0.18, 0.25] 1
4. SC Diff10 0.16*** [0.12, 0.19] 0.74*** [0.72, 0.77] 0.23*** [0.19, 0.26] 1
5. SA13 0.40*** [0.36, 0.44] 0.17*** [0.14, 0.21] 0.52*** [0.49, 0.56] 0.20*** [0.16, 0.23] 1
6. SC Diff13 0.14*** [0.11, 0.18] 0.61*** [0.58, 0.65] 0.19*** [0.16, 0.23] 0.71*** [0.68, 0.74] 0.22*** [0.19, 0.26] 1
Note: Subscript numbers show the age at assessment. SA ¼ social anxiety symptoms; SC Diff ¼ social and communicative difficulties.
***p < .001.
PICKARD et al.
(Dc2[df] ¼ 4.52[1], p ¼ .03). The reverse cross-lagged paths
from SA to SC difficulties (b21 and b32) were not significant.
Subsequent analyses explored the difference in cross-lagged
path weights at ages 7/10 years and 10/13 years. A sig-
nificant difference in the cross-lagged paths from age 7/10
years was observed (Dc2[df] ¼ 13.04[1], p ¼ 3.06e-04), with the
path from SC difficulties to SA having a significantly greater
contribution compared to the reverse cross-lagged path. No
significant difference was seen for the cross-lagged paths from
age 10/13 years (Dc2[df] ¼ 1.06[1], p ¼ .30).
Sex Differences
No significant decrease in model fit was observed for a
nested model constraining all cross-lagged paths to be equal
across male and female participants (Dc2[df] ¼ 1.59[4],
p ¼.81), compared to a full model, indicating no sex differ-
ences in the predictive relationship between SA and SC
difficulties constructs at all ages. Analyses investigating sex
differences in the longitudinal stability showed a significant
difference in the autoregressive pathways for SC difficulties
(Dc2[df] ¼ 22.68[2], p ¼ 1.19e-05), with females showing less
stability in SC difficulties compared to males. No sex dif-
ferences were observed for the SA autoregressive paths
(Dc2[df] ¼ 4.61[2], p ¼ .10).
Specificity Analyses
Specificity analyses tested the relationship between SC dif-
ficulties and SA, while controlling for generalized anxiety
(see Table S3, available online). The analyses revealed a
pattern of results identical to that of the full cross-lagged
panel model, showing both significant autoregressive paths
and significant cross-lagged paths from SC difficulties to SA
at ages 7/10 and 10/13 years. The reverse cross-lagged
paths from SA to SC difficulties were not significant.
DISCUSSION
We used a longitudinal design to investigate the relationship
between SC difficulties and SA symptoms in a population-
based cohort of children at ages 7, 10, and 13 years. We
predicted that SC difficulties would contribute specifically to
the development of SA symptoms in later childhood. We
found that, first, more parent-reported SC difficulties were
348 www.jaacap.com
associated with heightened SA symptoms across all ages.
Second, the data supported the construct validity of the
SCDC and DAWBA-SF, suggesting that SA and SC diffi-
culties are distinct domains across childhood. Third,
extending previous research and supporting our predictions,
we found a directional and asymmetrical relationship be-
tween SC difficulties and SA symptoms; earlier SC diffi-
culties contributed toward the development of later SA
symptoms, but not vice versa. In terms of this directional
relationship, sex differences were not observed. Finally, SC
difficulties predicted later SA symptoms while controlling
for generalized anxiety, emphasizing that SC difficulties are
a specific risk factor for SA. The interpretation of these re-
sults, clinical implications, limitations, and conclusions are
discussed below.
In typically developing children, associations between
clinical SA symptoms and poorer social skills have been
reported.4,12 Our results both support and extend previous
findings by illustrating the stability of these relationships
throughout childhood. In accordance with research report-
ing more SC difficulties and greater SA symptoms in in-
dividuals with ASD,11,13 we found similar associations in a
population-based sample of children. The magnitude of
these associations, although only modest compared to re-
sults in clinical samples,11 mimic the findings from previous
traitwise research examining parent-reported SC difficulties
and SA symptoms.34 Our results may be indicative of the
low levels of SA and SC difficulty scores in the present
sample.
Previous intervention studies have supported the effi-
cacy of social skills therapies for improving SC ability and
having downstream benefits on SA.16 Building on this
work, our study demonstrates that these SC difficulties
not only co-occur with SA, but also appear to play a role in
the development of SA across childhood. In addition, our
novel longitudinal findings in a population-based sample
suggest that SC difficulties are a risk factor for the
development of SA across the trait distribution. These
findings emphasize a potential marker for the develop-
ment of SA that could be targeted with early prevention
approaches.
Furthermore, our results are consistent with etiological
theories proposing that SC difficulties may provoke negative
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VOLUME 56 NUMBER 4 APRIL 2017
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FIGURE 2 Cross-lagged panel model showing the relationship between social and communication difficulties (SC Diff) and social
anxiety (SA) latent factors at 7, 10, and 13 years of age. Note: Standardized b coefficients with standard errors. All analyses
controlled for IQ and socioeconomic status (SES). Significant paths are shown in bold. *p < .001.
SOCIAL AND COMMUNICATION DIFFICULTIES AS RISK FACTOR FOR SOCIAL ANXIETY
reactions from others, which, through repeated experience,
may result in increased SA.15 This is one possible mechanism
through which SC difficulties may predispose to greater SA
symptoms in childhood; however, there may be several
alternative mechanisms, for example peer victimization,
bullying, or social insight,35 that may contribute to the
development of SA in those who exhibit severe SC diffi-
culties. For example, in adolescents with ASD, self-reported
peer victimization and bullying are associated with
increased internalizing problems.36 It is possible that SC
difficulties predispose to these additional risk factors or that
they develop independent of social ability. Further research
exploring the mediating mechanisms on the developmental
pathway from SC difficulties to SA in childhood is
warranted.
Interestingly, SC difficulties in earlier childhood made a
greater contribution to SA symptoms, compared to the
alternative cross-lagged path from age 7 to 10 years, sug-
gesting that earlier SC problems heighten a child’s risk of
developing …
Available online at www.sciencedirect.com
ScienceDirect
Behavior Therapy 45 (2014) 530–540
www.elsevier.com/locate/bt
Theory of Mind Impairments in Social Anxiety Disorder
Dianne M. Hezel
Richard J. McNally
Harvard University
Social anxiety disorder (SAD) is a common psychiatric
disorder characterized by a persistent, excessive fear and
avoidance of social and performance situations. Research on
cognitive biases indicates individuals with SAD may lack an
accurate view of how they are perceived by others, especially
in social situations when they allocate important attentional
resources to monitoring their own actions as well as external
threat. In the present study, we explored whether socially
anxious individuals also have impairments in theory of mind
(ToM), or the ability to comprehend others’ mental states,
including emotions, beliefs, and intentions. Forty socially
anxious and 40 non-socially-anxious comparison participants
completed two ToM tasks: the Reading the Mind in the Eyes
and the Movie for the Assessment of Social Cognition.
Participants with SAD performed worse on ToM tasks than
did non-socially-anxious participants. Relative to comparison
participants, those with SAD were more likely to attribute
more intense emotions and greater meaning to what others
were thinking and feeling. These group differences were not
due to interpretation bias. The ToM impairments in people
with SAD are in the opposite direction of those in people with
autism spectrum conditions whose inferences about the
mental states of other people are absent or very limited.
This association between SAD and ToM may have important
implications for our understanding of both the maintenance
and treatment of social anxiety disorder.
The authors thank Christine Hooker for her valuable feedback
on this study and David Dodell-Feder for his help with program-
ming the tasks and measures used in this study. They also thank
members of the McNally Lab who assisted in the MIE valence
ratings.
Address correspondence to Dianne M. Hezel, Department of
Psychology, Harvard University, 33 Kirkland Street, Cambridge,
MA 02138; e-mail: [email protected]
0005-7894/45/530-540/$1.00/0
© 2014 Association for Behavioral and Cognitive Therapies. Published by
Elsevier Ltd. All rights reserved.
Keywords: social anxiety disorder; theory of mind; social cognition;
anxiety disorders
SOCIAL ANXIETY DISORDER (SAD) affects 6.8% of
American adults annually, and its lifetime prevalence
is 12.1% (Kessler, Berglund, et al., 2005; Kessler,
Chiu, Demler, & Walters, 2005). SAD is character-
ized by a marked, persistent fear of scrutiny and
humiliation and by avoidance of social and perfor-
mance situations (American Psychiatric Association,
2000). Cognitive-processing biases figure in the onset
and maintenance of SAD (Constans, Penn, Ihen, &
Hope, 1999). Attentional (Amir, Foa, & Coles, 1998;
Hope, Rapee, Heimberg, & Dombeck, 1990), mem-
ory (Foa, Gilboa-Schechtman, Amir, & Freshman,
2000; Kim, 2004), imagery (Hirsch, Meynen, &
Clark, 2004; Makkar & Grisham, 2011; Wells &
Papageorgiou, 1999), and interpretive biases (Amir
et al., 1998; Constans et al., 1999; Stopa & Clark,
2000; Voncken, Bogels, & de Vries, 2003) for
threat-relevant information are evident in SAD.
Studies on interpretive bias suggest that people
with SAD construe neutral and ambiguous stimuli as
more threatening than do non-socially-anxious
individuals. For example, Niels-Christensen, Stein,
and Means-Christensen (2003) found that socially
anxious individuals evaluated themselves negatively,
and erroneously believed that others judged them
negatively in a social interaction, implying they
seemingly lack an accurate view of how others view
them, consistent with an interpretation bias. Other
research has suggested that socially anxious people
possess abnormal processing of positive stimuli,
including fearful responses to favorable feedback
(for a review, see Kashdan, Weeks, & Savostyanova,
2011) and the absence of a positive interpretation
bias that nonanxious individuals possess (Hirsch
http://dx.doi.org/
http://dx.doi.org/
http://dx.doi.org/
mailto:[email protected]
531the ory of mi nd i n sad
& Mathews, 1997, 2000). It is also possible, how-
ever, that socially anxious individuals have difficulty
comprehending the mental states of others irrespec-
tive of the valence or ambiguity of the stimulus. That
is, they may be impaired in inferring and reasoning
about others’ beliefs, emotions, and intentions, and
hence in predicting their thoughts and actions,
especially in social situations. The cognitive capacity
to identify and reason about mental states in other
people is called theory of mind (Premack &
Woodruff, 1978).
A term coined by researchers studying the cog-
nitive abilities of chimpanzees, theory of mind
(ToM) is both a critical adaptation for social
functioning and an important developmental mile-
stone in humans. Sabbagh (2004) delineates two
component processes of ToM: “(1) detecting or
decoding others’ mental states based on immedi-
ately available observation information and (2)
reasoning about those mental states in the service of
explaining or predicting others’ actions” (p. 210).
Decoding abilities refer to basic skills, such as
identifying facial expressions or following eye gaze,
whereas reasoning abilities require higher-order
skills such as detecting sarcasm or inferring that
someone is upset because they did poorly on a job
review (Sabbagh, 2004; Washburn, 2012).
People with ToM deficits have difficulty evaluat-
ing others’ thoughts, and thereby experience social
impairment that may contribute to functional
impairment seen in autism (Baron-Cohen, 1995,
2005; Baron-Cohen, Leslie, & Frith, 1985; Frith,
1989) and schizophrenia (Brune, 2005; Corcoran,
2000; Couture, Penn, & Roberts, 2006). Likewise,
it is possible that if individuals have trouble
identifying and reasoning about others’ emotions
and intentions that they may experience anxiety
when in social situations. Social anxiety and ToM
ability correlate inversely among people with
schizophrenia spectrum disorders; one interpreta-
tion of this finding is that “… fully intact ToM
capacities have a protective effect against paranoia
or that high levels of social anxiety have a negative
impact on ToM” (p. 84; Lysaker et al., 2010).
Similarly, others (Samson, Lackner, Weiss, &
Papousek, 2012) found that people with high levels
of social anxiety rated cartoons requiring an
understanding of others’ mental states (“ToM
cartoons”), but not other cartoons, as less humor-
ous than did people without social anxiety.
However, only one study has examined ToM in
individuals with diagnosed SAD (Washburn,
2012). This study found that nondepressed, socially
anxious participants performed worse than non-
anxious participants on one decoding measure of
ToM, whereas individuals with comorbid depres-
sion and anxiety performed better on the task (one
interpretation of enhanced decoding abilities in
individuals with depression is that these individuals
may be especially attentive to subtle social cues).
However, the nondepressed, socially anxious
group, which consisted of only nine participants,
was rather small and hence perhaps underpowered
to reveal group differences in the two other
reasoning ToM tasks used in the study.
In the present study, we compared socially
anxious and non-socially-anxious participants’
performance on socially relevant ToM tasks that
require participants to both decode others’ emo-
tions and reason about their mental states. We
attempted to extend the findings of Washburn
(2012) with the addition of a cognitive load
condition. According to cognitive-behavioral
models, people with SAD disproportionately allo-
cate attentional resources to monitoring self-image,
external threat, and personal expectations of how
others will react to them in social situations (Rapee
& Heimberg, 1997). This increased cognitive load,
in turn, impairs performance on unrelated, complex
cognitive tasks. Hope, Heimberg, and Klein (1990)
found that socially anxious participants reported
increased self-focused attention and performed less
accurately than did nonanxious participants on a
recall task following a social interaction. Hence,
increased allocation of attentional resources may
impair the processing of social information.
If self-preoccupation impairs the ability to make
accurate inferences about the mental states of other
people, we would expect cognitive load to impair
ToM ability in both non-socially-anxious compar-
ison and socially anxious participants. To test this
hypothesis, we gave half of the participants a memory
task prior to completing the ToM measures. If, on
the other hand, socially anxious participants have
impairments in ToM irrespective of cognitive load,
we would expect them to perform worse than non-
socially-anxious participants in the no-load condition
when they are presumably not self-monitoring.
Unlike interpretation bias paradigms, the tasks
used in this study require that participants identify
not only the emotions, but also the thoughts and
intentions of others, irrespective of valence. By
analyzing the errors that people make on these
ToM tasks, one can discern whether participants’
errors are due to an interpretation bias (in which
case we would expect that they would choose
answers more negative in valence than the correct
answer) as well as the extent to which they are
taking the perspective of others. These ToM tasks
are ecologically valid in that they require that
participants make real-time assessments of what
other people are thinking and feeling and why they
532 hezel & mcnally
are acting a certain way. If individuals with social
anxiety disorder do possess impairments in ToM,
clinicians could potentially target these weaknesses
in treatment. Research indicates that people with
schizophrenia have benefited greatly from cognitive
remediation, which aims to improve cognition and
social cognition through different computerized
exercises (McGurk, Twamley, Sitzer, McHugo, &
Mueser, 2007; Wykes, Huddy, Cellard, McGurk,
& Czobor, 2011).
Lastly, we examined the relation of IQ to ToM
performance to ensure that differences in cognitive
ability did not account for differential performance
on the ToM tasks. Similarly, we measured depressive
symptoms since some studies have identified less
accurate ToM performance in depressed individuals
(Lee, Harkness, Sabbagh, & Jacobson, 2005)
whereas other studies have found enhanced ToM
performance in dysthymic individuals (Harkness,
Sabbagh, Jacobson, Chowdrey, & Chen, 2005).
Method
participants
Participants were recruited from the Harvard Uni-
versity study pool, which consists of Harvard
undergraduate students and adults living in metro-
politan Boston, and from a Boston University job/
volunteer site. The online postings included a brief
description of the study and instructions that anyone,
socially anxious or not, between the ages of 18 and
65 were welcome to participate. Individuals who
signed up for the Harvard study pool have the option
of completing a prescreen questionnaire. Three of the
questions on the prescreen were specific to the
current study and included items from the Mini
Social Phobia Inventory, a self-report measure that
assesses the extent to which a person fears and avoids
social situations (Seeley-Wait, Abbott, & Rapee,
2009). In order to recruit additional socially anxious
participants, we emailed individuals who scored six
or higher on the scale, which is indicative of social
anxiety, to invite them to take part in the study. All
participants, irrespective of the results of the
prescreen questionnaire, were assessed with the
Mini International Neuropsychiatric Interview
(Sheehan et al., 1998). The first author classified all
participants into one of two groups: (1) those
meeting DSM-IV criteria for SAD and (2) those
without SAD (see Table 3 for information on the
groups’ additional Axis I diagnoses). The SAD group
comprised 40 participants (27 women) with a mean
age of 26.5 years (SD = 11.9), and the non-SAD
comparison group comprised 40 participants (34
women) with a mean age of 20.1 years (SD = 2.2).
We excluded the data of 10 additional participants
for various reasons, including suspected malingering
and inability to complete the cognitive load task.
With the remaining 80 participants, we had .94
power to detect large effects (Faul, Erdfelder, Lang,
& Buchner, 2007). Harvard students received study
pool credit, whereas others received $10 per hour for
their participation. Data on race and ethnicity were
not collected.
materials
The Mini International Neuropsychiatric Interview
(MINI) is a structured interview used to diagnose a
range of current and lifetime Axis I disorders
according to DSM-IV criteria (Sheehan et al., 1998).
Administration takes approximately 15 minutes, and
the MINI has good to very good concordance with
the International Classification of Diseases and the
Structured Clinical Interview for DSM-IV Diagnoses
(SCID). Interrater reliability is excellent, with the
majority of the scales having a kappa of .9 or higher;
and test-retest reliability isvery good, with most scales
having a kappa of .75.
The Liebowitz Social Anxiety Scale Self-Report
(LSAS) is a 24-item scale that accurately identifies
the presence and severity of SAD (Fresco et al.,
2001; Rytwinski et al., 2009). Participants indicate
on a Likert Scale of zero to three the extent to
which they fear and avoid 24 different social and
performance situations (e.g., eating in public,
speaking to an authority figure, etc.). Scores
range from 0 to 144, with higher scores signifying
greater social anxiety; a score of 60 indicates
generalized SAD (Mennin et al., 2002; Rytwinski
et al., 2009). The LSAS has high internal consis-
tency (α = .95), strong convergent and discrimi-
nant validity, and good test-retest reliability (r = .83,
p b .01) (Baker, Heinrichs, Kim, & Hofmann,
2002).
The Center for Epidemiologic Studies Depres-
sion Scale, Revised (CESD) scale consists of 20
items that assess the frequency at which individ-
uals have experienced symptoms of depression
over the prior week (Eaton, Smith, Ybarra,
Muntaner, & Tien, 2004). Scores ranging from 0
to 60 are calculated by adding item responses (four
items are reversed scored), and 16 is the suggested
clinical cutoff score for depression. The CESD has
high internal consistency (α = .90), acceptable
test-retest reliability (r = .57), and good discrimi-
nant and concurrent validity as measured by
correlations with self-report measures (r = .74)
and clinical interviews of depression (r = .46;
Radloff, 1977).
The American National Adult Reading Test
(NART) requires participants to read aloud a list
of 50 short words of irregular pronunciation. The
number of pronunciation errors are tallied and
Table 1
Reading the Mind in the Eyes: Valence of All Answer Choices
on the Task
Word t(11) p Word t(11) p
Negative Valenced Words
Upset -16.58 b.001 Doubtful -5.00 b.001
Insisting -3.02 .01 Tentative -2.35 .04
Worried -11.00 b.001 Defiant -2.87 .02
Uneasy -5.93 b.001 Hostile -16.58 b.001
Despondent -9.57 b.001 Cautious -2.57 .03
Preoccupied -4.18 .002 Serious -2.80 .02
Cautious -2.57 .03 Distrustful -17.23 b.001
Regretful -11.00 b.001 Nervous -7.34 b.001
Skeptical -3.55 .005 Suspicious -11.00 b.001
Accusing -10.34 b.001
Neutral Valenced Words
Anticipating 1.82 .10 Concerned -1.48 .17
Pensive 0 1.00
Positive Valenced Words
Playful 12.54 b.001 Friendly 10.58 b.001
Desire 5.20 b.001 Interested 4.73 .001
Fantasizing 4.53 .001 Reflective 3.92 .002
Contemplative 2.35 .04 Flirtatious 6.28 b.001
Thoughtful 8.86 b.001 Confident 12.54 b.001
Decisive 2.97 .01
533the ory of mi nd i n sad
used to estimate aspects of IQ. The NART validly
estimates general IQ, verbal IQ, and performance
IQ (Crawford, Parker, Stewart, Besson, & De
Lacey, 1989), and has very high test-retest reliability
(r = .98), very high interrater reliability (ranging from
r = .96 to r = .98), and high split-half reliability (r =
.90 to .93; Crawford et al., 1989). Administration
takes approximately 2 to 3 minutes. NART scores
were not used for eight participants (five SAD
and three non-SAD) who were not native English
speakers.
The Wechsler Adult Intelligence Scale–Fourth
Edition (WAIS) measures the cognitive ability, or
IQ, of adults. In the present study, we used
Similarities, a verbal comprehension task, and
Matrix Reasoning, a perceptual organization task
(Wechsler, 2008). Both Similarities and Matrix
Reasoning have good to excellent internal consisten-
cy (r = .87 and .90, respectively), good test-retest
stability (r = .83 and r = .76, respectively), excellent
interrater reliability (r = .93 and .98, respectively),
and high convergent and discriminant validity.
The Reading the Mind in the Eyes (MIE) is a
decoding theory of mind task that consists of
photographs of the eye-regions of actors and
actresses (Baron-Cohen, Wheelwright, Hill, Raste,
& Plumb, 2001). Individuals are asked to choose
which of four words best describes what the person
in the picture is thinking or feeling, as determined
by the test-maker, earning one point per correct
answer. This ToM test comprises two 18-question
parts (Part A and Part B) and scores range from 0 to
18 (on each part), with low scores indicating
impaired decoding abilities. The Mind in the Eyes
is an advanced ToM task as individuals must
identify complex emotions by relying on a limited
amount of information (from the eyes only). The
Mind in the Eyes has good test-retest reliability with
an intraclass correlation coefficient of .83 and good
internal consistency with a Cronbach’s alpha of .61
(Vallante et al., 2013), and can reliably detect subtle
differences in social cognitive abilities (Baron-
Cohen et al., 2001). Following others (Harkness
et al., 2005), we classified all possible answers (both
correct and incorrect) on this task in terms of
accuracy and valence. Specifically, 12 members of
our lab rated each emotion on a 1 (negative) to 7
(positive) scale, yielding a mean intraclass reliability
correlation coefficient of .98. We then conducted
one-sample t-tests to determine what words were
significantly different from neutral (a score of 4).
Words that had a significantly higher mean than 4
were classified as positive, words that had a mean
significantly lower than 4 were classified as
negative, and words that did not differ from 4
were classified as neutral. We determined that 20 of
the eyes depict a negative valence, 13 a positive
valence, and 3 a neutral valence (see Table 1).
The Movie for the Assessment of Social Cognition
(MASC) is a reasoning ToM task that consists of a
15-minute video depicting four actors and actresses
interacting (Dziobek et al., 2006). Throughout the
task, the film is paused and participants are asked to
answer a total of 45 questions requiring them to
identify the characters’ feelings, thoughts, and
intentions (e.g., “What is Sandra feeling?”). Correct
answers receive 1 point and incorrect answers are
scored in one of three ways: (1) no ToM, meaning the
answer was based on some physical aspect (e.g., “her
hair does not look that nice”); (2) less ToM signifying
an insufficient response that misses a crucial aspect of
the social situation (i.e., a “half-right” answer [e.g.,
“she is pleased about his compliment”]); and (3)
excessive ToM, signifying reading too much into the
character’s state of mind (e.g., “she is exasperated
about Michael coming on too strong”). Identifying
the types of errors people make on this task enables
one to evaluate the extent to which participants are
taking the perspective of the characters in the film. The
task also includes six additional control questions
assessing understanding of non-socially-relevant as-
pects of the plot and characters; these questions are
totaled separately from the overall MASC score. The
MASC has high internal consistency (α = .84) and
strong test-retest reliability (ICC = .97) and high
534 hezel & mcnally
convergent validity with other measures of social
cognition (Dziobek et al., 2006).
procedure
Participants provided written informed consent as
approved by Harvard’s Committee on the Use of
Human Subjects and in compliance with the
Helsinki Declaration of 1975. After being catego-
rized as either socially anxious or not, participants
were randomly assigned to either a cognitive load
or no cognitive load condition. We achieved
random assignment by using an Excel sheet that
was programmed to randomly assign group mem-
bership; at no time were the investigators able to
predict what the next assignment would be.
Participants first completed the CESD scale and
the LSAS, followed by Part A of Mind in the Eyes,
which was used as a baseline measure of participants’
performance. Participants completed these tasks,
which took approximately 15 minutes combined,
on a computer that was facing away from the
examiner (as to reduce any anxiety participants may
feel when completing the measures). The first author
then administered the Similarities and Matrix
Reasoning subscales of the WAIS-IV and the
NART. Administration of these cognitive ability
assessments took approximately 20 minutes. Finally,
participants completed Part B of the MIE and the
MASC on the same computer as the other measures.
Participants in the load condition received a
memory task before completing Part B of the MIE
and the MASC. Specifically, before the MIE task,
they had 20 seconds to memorize a sequence of
eight symbols (e.g., * ? = \ N & ! #) and were
instructed to rehearse them aloud while completing
the task (Gilbert & Osborne, 1989; van den Bos,
Peters, Bobocel, & Ybema, 2006). When they
finished the test, participants repeated the symbols
back to the first author and then rated how difficult
they found the memory task on a scale of 1 to 10.
Table 2
Group Characteristics and Cognitive Ability
SAD
M (SD)
Non-
M (S
LSAS (anxiety severity) 72.48 (22.35) 26.
CESD (depression severity) 20.70 (13.88) 6.
NART: full-scale IQ 118.23 (5.10) 118.
NART: verbal IQ 118.63 (5.35) 118.
NART: performance IQ 113.31 (3.7) 113.
WAIS-IV: Similarities 1 10.73 (2.21) 11.
WAIS-IV: Matrix Reasoning 1 10.75 (2.73) 11.
Note. * = p ≤ .05 criteria; 1 = scaled scores.
LSAS = Liebowitz Social Anxiety Scale; CESD = Center for Epidemiolo
WAIS-IV = Weschler Adult Intelligence Scale, Fourth Edition.
Participants received a second sequence of symbols
before the MASC, whereas those in the no cognitive
load condition performed a filler task for about
20 seconds before completing the ToM tasks. To
reduce any anxiety experienced during the cognitive
load task, the experimenter told participants in this
condition that some people find the memory task
challenging and that they should try to do the best
they could. The entire study took a maximum of
2 hours to complete, and participants were permit-
ted to take a break if necessary.
Results
preliminary results
The groups did not differ significantly in number of
men and women, χ(1) = 3.38, p = .07, or in cog-
nitive ability (Table 2). Relative to non-socially-
anxious comparison participants, those with SAD
were older, t(78) = 3.34, p = .001, r = .35, and had
higher levels of social anxiety and depression (see
Table 2). The SAD group had an average LSAS
score of 72.48 (SD = 22.35), thereby scoring above
the clinical threshold (Rytwinski et al., 2009).
Fourteen of the 40 nonanxious comparison partic-
ipants and 32 of the 40 participants with SAD met
criteria for Axis I disorders on the MINI (Table 3).
The socially anxious group had more Axis I
diagnoses (other than SAD; M = 1.55, SD = 1.13)
than did the non-socially-anxious group (M = .48,
SD = .75), t(78) = 1.08, p b .001.
theory of mind
To test the hypothesis that individuals with social
anxiety have ToM impairments that appear either
independent of or only when under cognitive load,
we conducted a 2 (group: SAD vs. non-SAD) × 2
(cognitive load: high vs. low) Analysis of Variance
(ANOVA) for each ToM task. The dependent
variable for each ANOVA was the number of
correctly answered questions on the MIE and the
SAD
D)
t(df) p r
03 (16.37) t(78) = 10.60 b.001* .77
35 (6.63) t(78) = 5.90 b.001* .56
38 (3.66) t(70) = .14 .89 .02
97 (4.02) t(70) = .31 .76 .04
51 (2.71) t(70) = .26 .79 .03
33 (1.79) t(78) = 1.34 .19 .15
58 (2.76) t(78) = 1.34 .90 .15
gic Studies – Depression; NART = National Adult Reading Test;
Table 3
Axis I Diagnoses Present in the Study Groups
Diagnosis SAD
n (%)
Non-SAD
n (%)
Major Depressive Disorder 27 (67.5%) 10 (25%)
Current 0 1 (2.5%)
Lifetime 26 (67.5%) ⁎ 9 (22.5%) ⁎⁎
Generalized Anxiety Disorder 13 (32.5%) 1 (2.5%)
Panic Disorder (with and
without Agoraphobia)
12 (30%) 1 (2.5%)
Substance Abuse/Dependence 6 (15%) 1 (2.5%)
Bipolar Disorder (I or II) 3 (7.5%) 2 (5%)
Post Traumatic Stress Disorder 2 (5%) 0
Agoraphobia (without
Panic Disorder)
1 (2.5%) 3 (7.5%)
Obsessive Compulsive
Disorder
1 (2.5%) 0
Bulimia 0 1 (2.5%)
⁎ Six of the 26 individuals in the SAD group met criteria for a
single past major depressive episode; the remaining 20 individuals
met criteria for recurrent MDD.
⁎⁎ Five of the nine individuals in the non-socially anxious group
met criteria for a single past major depressive episode; the
remaining four individuals met criteria for recurrent MDD.
535the ory of mi nd i n sad
MASC, respectively. Because participants received
the MIE twice (the first as a baseline measure, the
second either under cognitive load or not), we
conducted a repeated measures ANOVA for this
task. The socially anxious group performed worse
than the comparison group did on the MIE, F(1,
76) = 6.73, p = .01, r = .29, and participants in the
load condition performed worse than those in the
no-load condition, F(1, 76) = 5.10, p = .03, r =
.22, whereas the Group × Cognitive Load inter-
action fell short of significance, F(1, 76) = 2.67,
p = .11, r = .18 (see Figure 1). Likewise, there were
no significant main effects or interactions for the
0
6
12
18
24
30
36
SAD
No Load Load No Load Load
Non-SAD
#
C
o
rr
e
ct
FIGURE 1 Performance on Reading the Mind in the Eyes
Revised. Note. Dotted line represents mean score of subjects with
Asperger Syndrome or High Functioning Autism (Baron-Cohen
et al., 2001).
repeated measure (MIE at time one versus MIE at
time two).
To evaluate the type of errors participants made
on the Mind in the Eyes task, we used our classi-
fication of the MIE items to determine if valence
was related to group performance. A repeated mea-
sure ANOVA showed an interaction effect between
group and the valence of the eyes in question. Using
follow-up t-tests, we found that participants with
SAD made significantly more errors than did
comparison participants on questions about nega-
tive, t(78) = 3.40, p = .001, r = .36, valenced sets
of eyes, and this difference remained significant
after we applied a Bonferroni correction for
multiple comparisons (p b .02). The groups did
not differ, however, in the number of errors they
made in response to positive, t(78) = .24, p = .81,
r = .03, or neutral, t(78) = .72, p = .47, r = .08,
valenced expressions. We categorized each incor-
rect answer on the MIE as more negatively
valenced, more positively valenced, or the same
valence as the correct answer. For example, if the
correct answer for a particular item was positive in
valence, but the participant chose an incorrect
answer that was either neutral or negative in
valence, this would be coded as a “more negative
error.” Alternatively, if a participant chose an
incorrect answer that was more positive in valence
than the correct answer, this was coded as a “more
positive error.” Finally, if someone chose an
incorrect answer that was the same valence as the
correct answer (e.g., the correct answer is negative
in valence and the person chose an incorrect answer
that was also negative in valence), this was coded as
a “same valence error.” After a repeated-measures
ANOVA indicated a significant interaction between
group and error type, we performed follow-up
t-tests, which revealed no group differences in the
number of more positive, t(78) = .39, p = .70, r =
.04, or more negative answers, t(78) = .61, p = .54,
r = .07. However, socially anxious participants
chose significantly more incorrect answers that
were the same valence as the correct answer,
t(78) = 3.5, p = .001, r = .37. This difference
remained significant after we corrected for multiple
comparisons (Bonferroni corrected p b .02).
Analysis of MASC scores (with a second 2 × 2
ANOVA) showed a similar pattern of findings to
the MIE, though performance on the two tasks was
uncorrelated when we controlled for the presence
of SAD (partial r = .17, p = .13). Participants with
SAD were less accurate on the task than were
non-socially-anxious participants, F(1, 76) = 9.37,
p = .003, r = .33. Moreover, participants in the
cognitive load condition performed worse overall
on the MASC than did participants under no load,
0
5
10
15
20
25
30
35
40
45
SAD
No Load Load No Load Load
Non-SAD
#
C
o
rr
e
ct
FIGURE 2 Performance on the Movie for the Assessment of
Social Cognition. Note. Dotted line represents mean score of
subjects with Asperger Syndrome (Dziobek et al., 2006).
536 hezel & mcnally
F(1, 76) = 4.02, p = .05, r = .22. There was no
significant interaction between group …
O R I G I N A L R E S E A R C H
Social Phobia and Its Impact on Quality of Life
Among Regular Undergraduate Students of Mettu
University, Mettu, Ethiopia
This article was published in the following Dove Press journal:
Adolescent Health, Medicine and Therapeutics
Mohammedamin Hajure
Zakir Abdu
Department of Psychiatry, Mettu
University, Mettu, Oromia, Ethiopia
Video abstract
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code reader the video abstract will appear. Or use:
https://youtu.be/ggViE65C2Fo
Background: Social anxiety disorder is a serious and disabling mental health problem that
begins before or during adolescence, with the potential to significantly interfere with an
individual’s daily functioning and overall quality of life.
Objective: The aims of this study were to assess the prevalence, severity, and quality of life
towards social anxiety disorder among students of Mettu University, Ethiopia.
Subjects and Methods: A cross-sectional study was conducted among a stratified sample
of 523 undergraduate students to identify the prevalence, correlates of social anxiety dis-
order, and impacts on quality life. All participants completed the Social Phobia Inventory,
Liebowitz Social Anxiety Scale, and World Health Organization Quality of Life-Brief Form,
Turkish Version (WHOQOL-BREF-TR). Of 523 students, 26% were screened positive for
social anxiety disorder. About 69.4% and 17.4% of the students had mild and moderate
symptoms of social anxiety disorder, respectively. WHOQOL BREF-TR scores showed that
students with social phobia had significantly lower quality of life quality than those without
social phobia. Being criticized by others or fear of parties was the most commonly feared
situations. Talking to strangers was the most commonly avoided situations. Being females,
current tobacco use, and family history of psychiatric illness were factors significantly
associated with social phobia symptoms using logistic regression analysis.
Conclusion: The current study shows high prevalence of social phobia among the university
students and its significant negative effects on quality of life which require prompt identi-
fication and treatment.
Keywords: social anxiety, university, quality of life
Background
Social phobia or social anxiety disorder is a serious and disabling mental health
problem that begins before or during adolescence, has a chronic course, is asso-
ciated with significant impairment in social functioning and work, and reduced
quality of life.1 Among university, social phobia symptoms arise in a great number
of students or existing symptoms increase.2 During this period, students go into the
effort of having himself or herself accepted by others as a self-governing person
and showing himself or herself. Performing or giving a talk in front of an audience
was the most commonly feared situations and also showed an association with
increased disability, and impaired quality of life.3,4
It is generally estimated that 13% of the population will meet the diagnostic criteria
for lifetime social phobia with onset typically occurring in adolescence or early
Correspondence: Mohammedamin
Hajure
Email [email protected]
Adolescent Health, Medicine and Therapeutics Dovepress
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adulthood.5 However, recent studies suggest that lifetime
prevalence rates may be much higher.6,7 After major depres-
sion disorder and alcohol dependence, social anxiety disorder
is the third most common disorder in the general population
and it is also the most prevalent anxiety disorder.8
A cross-sectional study conducted in Swedish, Jordan
University, Saudi Arabia (2014), and University of
Parakuo students showed that about 9–16.1% of partici-
pants were positive of social phobia.9–12 Research con-
ducted in Nigeria shows SAD in about 9.4% which states
that there will be a significant difference in the phobic
health of adolescents in the selected private and public
Nigerian universities.13
In Ethiopia, research conducted on prevalence of social
phobia among high school students in Woldia, Gondar and
Hawassa was 27.5%, 31.2%, 32.8%.14–16 Factors have shown
to have an association include being the first or only child,17
medical science faculties,18,19 being female, younger age, reli-
gion, marital status (unmarried), the presence psychiatric ill-
ness, having a positive family history of mental disorder had
a significant role for development of social phobia.16,20
Evidence showed that social phobia was associated with sub-
stance use,21 low socioeconomic status, unemployment, low
level of education, and social support.22 Also decreased aca-
demic achievement, poor clinical exam performance,11 and
impaired quality of life23 also shown to have associated with
social phobia.
Despite the high worldwide burden of social phobia,
like shy, withdraw, unfriendly, and disinterested in social
activity and limited evidence is available, particularly in
developing countries. To the best of the author’s knowl-
edge, no study has investigated the effects of social phobia
on quality of life in Ethiopia. The present study aimed to
determine the prevalence of social phobia among univer-
sity students, its correlate, and impacts on quality of life.
Subjects and Methods
An institution based cross-sectional study conducted at Mettu
University from May to April, 2019. Mettu University is one
of the higher institutions in Ethiopia, which is a third
Generation University. It is located at 600 km southwest of
Addis Ababa. Mettu University established in 2011. Mettu
University currently has two campuses. Main campus was
study area containing 7 faculties. Those are faculty of health
science, faculty of natural and computational science, faculty
of social science and Humanity, school of law. The campus has
a total of 43 departments and 8290 regular undergraduate
students.
This study included 8290 undergraduate students at
Mettu University during the 2019–2020 academic years.
Using a confidence interval of 95%, 5% margin of error,
design effect of 2, the prevalence of social phobia of
19.9%16 and adding 10% non-response rate of 10%, mak-
ing a final number of participants to be 523. As such, 523
students were selected as the study group.
A study is part of a mega project undertaken in among
regular undergraduate students of Mettu University and
previously published study assessing suicidal behavior
among this population were also part of the project.24
Multistage stratified sampling technique was used to
select the study participants. Stratification was first done
on the faculty/college level, then by department and by
the year of study. Finally, taking students from registration
as a sampling frame a random selection was done. All
regular undergraduate students aged 18yrs old and above
were included, while critically ill students were excluded.
A self-administered structured questionnaire was used
to collect information. Questionnaires about demographic,
family and campus related and clinical factors were devel-
oped after an extensive review of literatures and similar
study tools. Rating instruments included the Social Phobia
Inventory to detect Social Phobia, the Liebowitz Social
Anxiety Scale to measure the severity of Social Phobia
and the WHO Quality of Life – BREF questionnaire to
assess Quality of Life. The questionnaire was translated to
Amharic and Afaan Oromo language, and then retranslated
back to English so as to see and keep the consistency.
Pretest was done on 26 students in Bedele agricultural
campus whose completed the questionnaires beforehand
and the questions were evaluated and re-arranged accord-
ingly before actual data collection.
Ethical clearance was obtained from the ethical review
board of Mettu University and permission was obtained
from the concerned body.
Rating Instruments
Social Phobia Inventory (SPIN, a 17-item self-rating scale
developed to measure social phobia). It shows the symp-
tom domains of social phobia (fear, avoidance, and phy-
siological arousal) and has reliable and valid psychometric
properties in screening social phobia in adolescents and
other populations. The Cronbach’s α in this study was
0.87. Subjects are asked to rate symptoms occurrences as
0 (not at all), 1 (a little bit), 2 (somewhat), 3 (very much),
or 4 (extremely during the past week) and the sum score
ranged from 0 to 68. A student with a score of 20 and
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above on SPIN will be considered as having social
phobia.25
Liebowitz Social Anxiety Scale is a self-rating scale
used to rate fear/anxiety and avoidance regarding com-
monly feared performance or social situations. The scale
includes 24 items and 2 subscales. The first subscale has
11 items and investigates social relationships. The second
subscale has 13 items and investigates performance. The
4-point Likert-type scale measures the intensity of fear and
avoidance behaviour during the previous week. It has
a good internal consistency and evaluates the severity of
fear and avoidance in common social situations. A score of
<55 suggests mild social anxiety disorder, 55–64 suggests
the moderate social anxiety disorder, 65–79 suggests
marked social anxiety disorder, 80–94 suggests severe
social anxiety disorder, and >95 suggests very severe
social anxiety disorder. It is validated in and reliable for
measuring the severity of social phobia26. The Cronbach’s
α in this study was 0.98.
World Health Organization Quality of Life Scale –
Brief version (WHOQOL – BREF) which is a 26-item self-
administered generic questionnaire. It produces a profile
with four domain scores: physical health (7 items), psy-
chological health (6 items), social relationships (3 items),
environmental domain (8 items) as well as two separately
scored items about the individuals‟ perception of their
quality of life (QI) and health (Q2). Each item was scored
in a Likert format from 1 (very dissatisfied) to 5 (very
satisfied) in a positive direction, which means that higher
scores indicate a higher quality of life. The scores of ques-
tions 3, 4 and 26 are reversed, so as to transform these
negatively framed questions to positively frame. The
Turkish version of the form had an internal validity score
of 0.83 (Cronbach’s alpha) in physical terms, 0.66 in men-
tal terms, 0.53 in social terms, and 0.73 in both environ-
mental and environment-national terms27. The Cronbach’s
α in this study was 0.82
Statistical Analysis
The data were analyzed using SPSS version 21. Descriptive
(frequency and percentage) and inferential statistics (chi
square test was used for categorical variables, and ANOVA
(analysis of variance) were used to compare groups in terms
of SPIN and LSAS scores).
An independent samples t-test was used to analyze the
difference between the two groups (students with/without
social anxiety disorder). Logistical regression analysis was
used to evaluate the significance of the relationship between
two dependent and independent variables. The Pearson cor-
relation coefficient was used for correlation analysis.
Result
Socio-Demographic Characteristics of
the Study Participants
A total of 523 participants were recruited for the study,
which makes the response rate 100%. The results show
that 270 (51.6%) of respondents were males and 253
(48.6%) were females. The mean age of students was
22.07 (SD = 2.36), with ages ranging from 18 to 32
years and the majority of them (61.0%) were at the age
of 22 years or below. The sample consisted of different
faculties with the highest number of engineering faculty
(110, 21.0%) and the lowest number from Institute of
education (42, 8.0%) which was proportionally recruited
from each stratum. Also the study has revealed that 351
(67.1%) of the participants had one of two siblings and
most perceived that their family income as bad (58.9%).
The majority of the participants 319 (61.0%) were from
rural backgrounds and first-year students comprises the
majority of participants (222, 42.4%) (Table 1).
Social Phobia
The regarding students’ reports of their social phobia
symptoms, the analysis (Table 2) showed that the mean
score for students in general was 13.08 (SD = 9.24), with
scores ranging from 0 to 43. About, 70% (n = 361) had
a score of 16 or less.
Further analysis using LSAS score, for the levels of
social anxiety symptoms showed that the majority of uni-
versity students had mild symptoms, 69.4% (n = 363)
followed by moderate symptoms 91 (17.4%), and those
with marked to severe represented about 13.2% (n = 69).
The Cronbach’s alpha for LSAS scale obtained in this
sample was 0.976.
As shown in Table 2, 25.8% of the subjects had a SPIN
score of 19 (Connor et al, 2000), and more which accounted
for about one-fourth of participants, ie, There was
a statistically significant difference in the prevalence of
SAD regarding the age category, birth order, faculties, family
history of mental illness. Being a younger age18–20 group was
associated with higher prevalence of SAD (26.7%) and being
in the age group of 21–23years was associated with lower
prevalence of SAD (9.6%) (X2= 0.24, P<0.05). There is
a higher prevalence of SAD among students in Engineering
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faculties, while lower prevalence is seen in the faculty of
social science and humanities (X2=0.163, P<0.05).
The results of the present study show that significantly
more of the students without social phobia have a family
history of psychiatric illness than those with social phobia.
Table 1 The Basic Sociodemographic, Clinical and Substance
Use Characteristics of the Participants (n= 523)
Variables Categories Frequency Percentage
Sex Male 270 51.6
Female 253 48.6
Age 18–20 148 28.3
21–23 87 16.6
24–26 149 28.5
≥27 139 26.6
Ethnicity Oromo 321 61.4
Amhara 117 22.4
Gurage 41 7.8
Tigre 25 4.8
Others* 19 3.6
Faculty Engineering 110 21.0
Health sciences 97 18.5
Social science and
humanities
85 16.3
Natural and
computational
89 17.0
Business and
economics
52 9.9
School of Law 48 9.2
Institute of
education
42 8.0
Residence
before campus
Urban 204 39.0
Rural 319 61.0
Birth order Frist or only child 123 23.5
Middle 320 61.2
Last 80 15.3
Year of study First 130 24.9
Second 133 25.4
Third 107 20.5
Fourth 77 14.7
Fifth 76 14.5
Alcohol use Yes 299 57.4
No 127 42.5
Cigarette use Yes 78 14.9
No 53 67.9
Khat use Yes 98 18.3
No 67 68.4
Note: *Wolayta, Dawuro, Kefa, Sidama, Gurage, Silte.
Table 2 Comparing Social Phobia with Demographic and Clinical
Variables
Variables Categories Frequency Percentage
Sex Male 270 51.6
Female 253 48.6
Age 18–20 148 28.3
21–23 87 16.6
24–26 149 28.5
≥27 139 26.6
Ethnicity Oromo 321 61.4
Amhara 117 22.4
Gurage 41 7.8
Tigre 25 4.8
Others* 19 3.6
Faculty Engineering 110 21.0
Health sciences 97 18.5
Social science
and humanities
85 16.3
Natural and
computational
89 17.0
Business and
economics
52 9.9
School of Law 48 9.2
Institute of
education
42 8.0
Residence before
campus
Urban 204 39.0
Rural 319 61.0
No. of siblings Mean ±SD (1.98±1.16)
Birth order Frist or only
child
123 23.5
Middle 320 61.2
Last 80 15.3
Fathers education No formal
education
50 9.6
Primary school 217 41.5
Secondary
school
137 26.2
Above
secondary
119 22.8
Mothers’
education
No formal
education
62 11.9
Primary school 187 35.8
Secondary
school
235 44.9
Above
secondary
39 7.5
Perceived family
monthly income
Bad 308 58.9
Moderate 127 24.3
(Continued)
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However, with respect to gender, ethnicity, year of study,
family educational status, perceived family income, and
residency, there was no statistically significant difference
in the prevalence of SAD (all P values >0.05).
Using logistical regression analysis, three independent
variables that were significantly shown to have association
in the final model. The risk of social phobia was 1. Ninety-
five-fold higher among female students than male students, 1.
Eighty-four-fold higher among those with a family history of
psychiatric illness than those without, and 2. Ninety-five-fold
higher among students who smoked cigarettes in the past
3months compared to those who did not (Table 3).
Using item analysis to examine the items that had the
highest and lowest scores (Table 4), the analysis showed
that the mean items ranged from 0.56 (SD = 0.81) (item
13: Heart palpitations bother me when I am around peo-
ple) to 0.99 (SD = 1.07) (item 2: I am bothered by blush-
ing in front of people).
The highest three items in addition to item 2 were item
10 (M = 0.96, SD = 1.10: Talking to strangers scares me)
and item 7 (M = 0.85, SD = 1.009: Sweating in front of
people causes me distress). This also goes for the highest
three items that students reported being very much to
extremely experiencing social phobia symptoms over the
past week as items 2, 10, and 7 had the highest percentage
among all other items.
Table 2 (Continued).
Variables Categories Frequency Percentage
Good 88 16.8
Year of study First 130 24.9
Second 133 25.4
Third 107 20.5
Fourth 77 14.7
Fifth 76 14.5
Alcohol use Yes 299 57.4
No 127 42.5
Cigarette use Yes 78 14.9
No 53 67.9
Khat use Yes 98 18.3
No 67 68.4
Note: *Wolayta, Dawuro, Kefa, Sidama, Gurage, Silte.
Table 3 Logistical Regression Analysis Showing Factors
Associated with Social Phobia Among Students in Mettu Health
Science Students, Mettu, Ethiopia, 2019 (n=523)
Variables Category COR (95% CI) AOR (95% CI)
Sex Male
®
Female
1.78 (1.20–2.64) 2.04 (1.26–3.28)*
Previous history of
chronic physical
illness
No
®
Yes
1.83 (1.02–3.30) 1.84 (1.01–3.35)*
Current tobacco
use
No
®
Yes
1.27 (.74–2.2) 2.95 (1.36–6.40)**
Lifetime khat use No
®
Yes
1.99 (1.00–3.99) 1.52 (0.68–3.37)
Residence Urban
®
Rural
4.52 (2.69–7.7) 1.24 (.494–3.12)
Notes: *P value < 0.05, **P value < 0.01, VIF 1.06–2.10. Hosmer–Lemeshow -
goodness of fit test corresponding, P value = 0.77, Reference =
®
.
Abbreviations: COR, crude odds ratio; AOR, adjusted odds ratio.
Table 4 Item Analysis of SPIN Among University Student in
Mettu (n= 523)
Item Mean SD
1 I am afraid of people in authority. 0.68 0.976
2 I am bothered by blushing in front of people. 0.99 1.073
3 Parties and social events scare me. 0.82 0.957
4 I avoid talking to people I do not know. 0.75 0.961
5 Being criticized scares me a lot. 0.85 0.959
6 Fear of embarrassment causes me to avoid doing
things or speaking to people.
0.80 0.921
7 Sweating in front of people causes me distress. 0.85 1.009
8 I avoid going to parties. 0.79 0.892
9 I avoid activities in which I am the center of attention. 0.79 0.993
10 Talking to strangers scares me. 0.96 1.109
11 I avoid having to give speeches. 0.83 0.990
12 I would do anything to avoid being criticized. 0.65 0.925
13 Heart palpitations bother me when I am around
people.
0.56 0.811
14 I am afraid of doing things when people might be
Watching.
0.59 0.885
15 Being embarrassed or looking stupid is my worst
fears.
0.67 0.931
16 I avoid speaking to anyone in authority. 0.72 0.888
17 Trembling or shaking in front of others is distressing
to me.
0.78 0.955
Notes: Copyright ©, Jonathan Davidson. 1995, 2008, 2015. All rights reserved.
Permission to use the SPIN must be obtained from the copyright holder at
[email protected] The SPIN may not be reproduced or transmitted in any form,
or by any means, electronic or mechanical, or by any information storage or
retrieval system without permission in writing from the copyright holder.
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The highest mean item scores varied among the two
subscales (more among physiological discomfort) and also,
two of lowest mean item scores (item 1,14) belong to fear of
the social situation subscale. The results do not strictly
support the mean scores of the subscales mentioned above
in Table 2 that physiological discomfort in social situation
was the lowest reported subscale among the three subscales.
The Cronbach’s alpha for SPIN scale obtained in this study
sample was 0.869. Generally, about one-fourth of the stu-
dents showed positive symptoms of social phobia (score of
>19) and the majority of them present with mild category.
Quality of Life of Students with and
Without Social Phobia
Study participants’ quality of life was assessed by the
world health organization quality of life brief version
scale (WHOQOL-BREF) and the mean total quality of
life score was found to be (70.87+16.22). The highest
QOL domain of the students with social phobia in the
current study was environmental health domain mean
score of (23.55 ± 3.46), followed by physical health
domain mean score of (22.34±3.76), psychological health
domain mean score of (17.67±2.62) and social relation-
ships domain mean score of (6.87±2.27). The WHOQOL-
BREF scale demonstrated a high internal consistency
reliability coefficient (Cronbach’s alpha=0.821).
WHOQOL-BREF-TR scores showed that students with-
out social phobia had significantly higher quality of life scores
in all areas than the students with social phobia (Table 5).
Correlating SPIN and LSAS with QOL
Scores
As seen in Table 6. Regarding correlation of LSAS scores
to QOL scores, they were negatively correlated with
respect to physical health, psychological health, social
relationship and environment, although not significant in
majority of the domains, except the psychological domain.
Again, SPIN scores were also negatively correlated
with QOL scores in all areas. Thus, social phobia was
associated with reported deterioration in physical, psycho-
logical health, social relationship and environmental func-
tioning. SPIN and LSAS scores were found to be more
strongly correlated with psychological domain scores and
SPIN score were more strongly correlated with physical
health domain compared to other domain (Table 6).
Discussion
This study aims on the prevalence of social phobia and its
impact on quality of life among university students in
Mettu, South western, Ethiopia. The prevalence of social
phobia varies widely among different countries. In this
study, social phobia was found in 26% of subjects, much
more than other studies among undergraduate university
students in different settings.18,28-31 Regarding the severity
of social phobia, using LSAS score, the majority of the
students have mild forms of social anxiety disorder. In
other words, levels of social anxiety symptoms show
about 17.4% of them had moderate symptoms, which is
in line with the study undertaken at the University of
Jordan (6.8%). However, the finding was lower than
study done in Woldia, Ethiopia (27.5%),15 Saudi Arabia,
Table 5 Mean Distribution of QOL of Students with and
Without Social Phobia at Mettu University, 2019
Areas Students with
Social Phobia
(χ ± SD)
Students
Without Social
Phobia (χ ± SD)
Analysis*
T P
Physical 22.34±3.76 21.55±4.49 −1.821 < 0.01
Psychological 17.67±2.62 16.03±4.03 −4.42 < 0.001
Social 6.87±2.27 5.59±2.93 −4.59 < 0.001
Environmental 23.55±3.46 22.01±5.02 −3.32 < 0.001
Notes: ANOVA, χ ± SD (arithmetic mean ± standard deviation). *For all analyses
the degree of freedom was 522.
Abbreviation: QOL, quality of life.
Table 6 Correlating SPIN and LSAS with QOL Scores
Instrument Domain of Quality of Life
Physical Health Psychological Health Social Relationship Environmental
LSAS score R −0.010 −0.168 −0.019 −0.053
P value 0.820 0.000 0.662 0.227
SPIN score r −0.199 −0.102 −0.082 −0.013
P value 0.000 0.020 0.062 0.768
Note: r = Pearson correlation coefficient.
Abbreviations: SPIN, Social Phobia Inventory; LSAS, Liebowitz Social Anxiety Scale; QOL, quality of life.
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Riyadh (24.3%).32 Different studies have shown an asso-
ciation of social phobia with gender. The results of the
current study, which showed higher social phobia scores of
female students compared to their counterparts. This was
in line with the international report of different countries
such as India,11 Turkey,33 German.3 However, in one study
social phobia prevalence is found to be higher in men in
studies of prevalence conducted with clinical samples.34
The current study shows an association of cigarette
smoking and social anxiety disorder. This finding was in
agreement with international report such as in the USA35
and Turkey.36 The reason behind might be related to
smoking, which used for its reinforcing effect, by socially
anxious people to elevated negative affect especially for
social interaction.37
In contrast to studies done in Australia18 and Swedish,9
SAD was more prevalent among students of engineering
faculties than students of social science and humanities
faculties. It may be related to the consequences of social
anxiety on academic performance during pre-engineering
years and career choices made thereafter, in addition to
a larger quota of students in the school, as this stage
greatly matters their life on the campus, particularly.
Considering birth order, SAD was more prevalent
among first or only child than being middle or last child.
Which was in agreement with study done in Egypt (birth
order).17 It was hypothesized that the first-born child will
have a higher level of social anxiety than a non-first born
child.38
In terms of age, the current study shows significant
association, with higher prevalence of SAD among stu-
dents in the age group of 18–20 years as compared to older
age groups. The finding was in accordance with many of
the prior studies, shown an early onset of social anxiety
symptoms.20
Family history of psychiatric illness was found to have
significant association with SAD. This could be explained
by studies showed association of social phobia and genetic
inheritability, although the underlying mechanisms remain
unclear.39
The most commonly reported feared social situations in
the target sample were being criticized by others or fear of
parties and social events, followed by doing things or speak-
ing to people and the most commonly avoided situations
were talking to strangers followed by being a center of
attention. These findings were consistent with result of earlier
studies.4 This is because college years are a critical period to
socialize themselves, particularly via social interaction.
Again their expectation matter the way they interact, they
may avoid such interaction because of negative evaluation.
The Effects of Social Phobia on Quality of
Life
To the best of the author’s knowledge, the present study is
the first to investigate the direct relationship between social
phobia and its impact on quality of life among university
students in Ethiopia. In the present study students with social
phobia had lower scores on all areas of life quality, including
physical and psychological health, social relationships, and
the environment than those without social phobia.
Results of an epidemiological study from report that
students with social phobia have reduced quality of life in
all domains as …
lable at ScienceDirect
Behaviour Research and Therapy 77 (2016) 147e156
Contents lists avai
Behaviour Research and Therapy
journal homepage: www.elsevier.com/locate/brat
Exposure to virtual social interactions in the treatment of social
anxiety disorder: A randomized controlled trial
Isabel L. Kampmann a, *, Paul M.G. Emmelkamp c, d, Dwi Hartanto b,
Willem-Paul Brinkman b, Bonne J.H. Zijlstra e, Nexhmedin Morina a
a Department of Clinical Psychology, University of Amsterdam, Weesperplein 4, 1018 XA, Amsterdam, The Netherlands
b Interactive Intelligence Group, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands
c Netherlands Institute for Advanced Study, Meijboomlaan 1, 2242 PR, Wassenaar, The Netherlands
d King Abdulaziz University, Abdullah Sulayman, 22254, Jeddah, Saudi Arabia
e Department of Educational Sciences, University of Amsterdam, Nieuwe Achtergracht 127, 1018 WS, Amsterdam, The Netherlands
a r t i c l e i n f o
Article history:
Received 4 September 2015
Received in revised form
19 December 2015
Accepted 23 December 2015
Available online 29 December 2015
Keywords:
Virtual reality
Exposure therapy
Social anxiety disorder
Social phobia
Social interaction
* Corresponding author.
E-mail address: [email protected] (I.L. Kampm
http://dx.doi.org/10.1016/j.brat.2015.12.016
0005-7967/© 2015 Elsevier Ltd. All rights reserved.
a b s t r a c t
This randomized controlled trial investigated the efficacy of a stand-alone virtual reality exposure
intervention comprising verbal interaction with virtual humans to target heterogeneous social fears in
participants with social anxiety disorder. Sixty participants (Mage ¼ 36.9 years; 63.3% women) diagnosed
with social anxiety disorder were randomly assigned to individual virtual reality exposure therapy
(VRET), individual in vivo exposure therapy (iVET), or waiting-list. Multilevel regression analyses
revealed that both treatment groups improved from pre-to postassessment on social anxiety symptoms,
speech duration, perceived stress, and avoidant personality disorder related beliefs when compared to
the waiting-list. Participants receiving iVET, but not VRET, improved on fear of negative evaluation,
speech performance, general anxiety, depression, and quality of life relative to those on waiting-list. The
iVET condition was further superior to the VRET condition regarding decreases in social anxiety symp-
toms at post- and follow-up assessments, and avoidant personality disorder related beliefs at follow-up.
At follow-up, all improvements were significant for iVET. For VRET, only the effect for perceived stress
was significant. VRET containing extensive verbal interaction without any cognitive components can
effectively reduce complaints of generalized social anxiety disorder. Future technological and psycho-
logical improvements of virtual social interactions might further enhance the efficacy of VRET for social
anxiety disorder.
© 2015 Elsevier Ltd. All rights reserved.
Social anxiety disorder (SAD) is defined as the fear of one or
more social situations in which one might behave embarrassingly
and be negatively evaluated by others (DSM-V; American
Psychiatric Association, 2013). SAD is one of the most common
mental disorders in the US population, with an estimated lifetime
prevalence of 12.1% (Ruscio et al., 2008). Individuals who suffer
from SAD can experience a reduced quality of life and significant
impairments in various areas of functioning, such as work and
interpersonal relationships (Wittchen, Fuetsch, Sonntag, Müller, &
Liebowitz, 2000). However, only about one third of individuals
with SAD seek treatment (Ruscio et al., 2008).
The most researched treatment for SAD is cognitive behavior
ann).
therapy (CBT). CBT aims at modifying maladaptive cognitions and
behavior using both cognitive (e.g., cognitive restructuring) and
behavioural (e.g., exposure) strategies (Hofmann & Smits, 2008;
Mayo-Wilson et al., 2014). During exposure therapy, participants
encounter feared stimuli in situations containing social interaction
until anxiety decreases and/or anxiety-related expectancies are
violated. Traditional exposure exercises are usually practiced dur-
ing therapy and as homework assignments. Interestingly, a meta-
analysis of treatment efficacy found exposure therapy alone to be
comparable to cognitive therapy and that the combination of both
was no more effective than either one delivered exclusively
(Powers, Sigmarsson, & Emmelkamp, 2008).
A relatively new form of exposure therapy is Virtual Reality
Exposure Therapy (VRET). During VRET, participants are confronted
with computer-generated stimuli (e.g. virtual social interaction)
that can elicit elevated subjective levels of social anxiety (Morina,
Delta:1_given name
Delta:1_surname
Delta:1_given name
Delta:1_surname
Delta:1_given name
Delta:1_surname
mailto:[email protected]
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www.sciencedirect.com/science/journal/00057967
http://www.elsevier.com/locate/brat
http://dx.doi.org/10.1016/j.brat.2015.12.016
http://dx.doi.org/10.1016/j.brat.2015.12.016
http://dx.doi.org/10.1016/j.brat.2015.12.016
I.L. Kampmann et al. / Behaviour Research and Therapy 77 (2016) 147e156148
Brinkman, Hartanto, & Emmelkamp, 2014; Powers et al., 2013).
Cumulative research suggests that VRET is effective in the treat-
ment of several anxiety disorders (Meyerbr€oker & Emmelkamp,
2010; Morina, Ijntema, Meyerbr€oker, & Emmelkamp, 2015; Opriş
et al., 2012; Parsons & Rizzo, 2008).
While VRET has been extensively studied in specific phobias,
research on the efficacy of VRET in the treatment of SAD is still
limited. Several studies suggest that VRET can reduce SAD symp-
toms (Anderson, Rothbaum, & Hodges, 2003; Anderson, Zimand,
Hodges, & Rothbaum, 2005; Klinger et al., 2005). However, only
three randomized controlled trials on the efficacy of VRET in SAD
have been conducted (Anderson et al., 2013; Bouchard et al., 2015;
Wallach, Safir, & Bar-Zvi, 2009). In the study by Wallach et al.
(2009), VRET for public speaking anxiety, a specific social anxiety
complaint, was combined with CBT and compared to CBT plus
imagery exposure, and waiting-list. Results revealed that VRET plus
CBT was effective in treating public speaking anxiety compared to
waiting-list and as effective as CBT plus imagery exposure. How-
ever, participants in this study were not screened for a clinical
diagnosis of SAD. Anderson et al. (2013) included participants with
a SAD diagnosis and compared the efficacy of CBT plus VRET with
CBT plus group exposure therapy. The authors reported that CBT
plus VRET was as effective as CBT plus group exposure therapy.
Nonetheless, the implications of the results of this study are rather
limited by the inclusion of participants who had reported public
speaking anxiety as their primary complaint and by the two
different formats of treatment (i.e., individual vs. group).
In both the above trials, exposure exercises solely targeted
public speaking-related anxiety and included only limited verbal
interaction (i.e., answering questions). However, although fear of
public speaking is the most common subtype of SAD, the majority
of individuals with SAD report more than one fear (Ruscio et al.,
2008), emphasizing the need for research on VRET targeting het-
erogeneous social fears. Moreover, a large number of feared social
situations reported by individuals with SAD (e.g., talking to
strangers or speaking up in a meeting) contain verbal interaction
(Ruscio et al., 2008). As a consequence, incorporating extensive
dialogues into VRET and thus going beyond answering a limited
number of questions might improve the efficacy of VRET for SAD. In
contrast to Anderson et al. (2013) and Wallach et al. (2009),
Bouchard et al. (2015) included virtual scenarios in VRET target-
ing several social fears. They found individual CBT plus VRET to be
effective compared to waiting-list and more effective than CBT plus
in vivo exposure. However, all three studies investigated VRET in
combination with CBT. Therefore, no conclusions can be drawn
regarding the efficacy of VRET as stand-alone treatment and the
possibility cannot be ruled out that the effects found were caused
by CBT rather than VRET.
In summary, previous research on VRET is limited by investi-
gating VRET only in combination with CBT, focussing mainly on fear
of public speaking and including only limited verbal interaction.
The incorporation of diverse virtual scenarios with social interac-
tion that resembles real life interaction into VRET might more
adequately target the idiosyncratic fears of participants with SAD.
The aim of the present study was to single out the effects of pure
VRET without any cognitive components and to adapt VRET to in-
dividuals with heterogeneous social fears by simulating social
verbal interaction in a variety of virtual social situations believed to
be relevant for treating individuals with SAD. In a randomized
controlled trial, we examined the efficacy of VRET and in vivo
exposure therapy (iVET) for adults with SAD and heterogeneous
social fears. These active treatments were compared to a waiting-
list control group. Both active treatments were administered in
an individual format and were exposure-based only. It was hy-
pothesized that relative to individuals in the waiting-list control
group, participants in active conditions would report fewer social
anxiety symptoms and would perform better on a behavioural
assessment task at postassessment. Treatment gains were expected
to be comparable for VRET and iVET at postassessment and 3-
month follow-up.
1. Method
1.1. Participants
Participants were recruited via online and newspaper adver-
tisements, the website of the ambulatory of the University of
Amsterdam, and the project's website. Sixty participants
(Mage ¼ 36.9 years, age range: 18e65 years) meeting the criteria for
a primary diagnosis of SAD according to the 4th edition of the
Diagnostic and Statistical Manual of Mental Disorders (American
Psychiatric Association, 2000) were included and randomly
assigned to one of three conditions (20 participants each; see Fig. 1
for an overview of the randomization procedure and Table 1 for
sample characteristics per condition). Exclusion criteria were a)
psychotherapy for SAD in the past year; b) current use of tran-
quilizers or change in dosage of antidepressants in the past 6
weeks; c) a history of psychosis, current suicidal intentions, or
current substance dependence; e) severe cognitive impairment; or
f) insufficient command of the Dutch language. The average num-
ber of completed sessions was 8.50 (SD ¼ 2.63) for VRET and 8.55
(SD ¼ 2.68) for iVET. All participants received free treatment and a
small monetary reward (22 Euro) for the completion of the follow-
up assessment.
1.2. Measures
1.2.1. Screening and diagnostic measures
The Social Interaction Anxiety Scale (SIAS; Mattick & Clarke,
1998) was used for screening purposes before the in-person
interview. The SIAS consists of 20 items assessing cognitive, affec-
tive, and behavioural responses to social interactions on a 5-point
Likert scale. The SIAS possesses a high internal consistency and
test-retest reliability (Cronbach's a ¼ .93 and r ¼ 0.92 respectively;
Mattick & Clarke,1998). Individuals scoring �29 were invited for an
in-person diagnostic interview with a psychologist. We choose a
slightly lower cut-off than reported in previous research to prevent
false-negatives in this early stage of screening where the in-person
intake was still to come (Heimberg, Mueller, Holt, Hope, &
Liebowitz, 1992).
To assess the diagnosis of SAD and potential comorbidity, the
Structured Clinical Interview for DSM-IV-TR Axis I Disorders (SCID-
I; First, Spitzer, Gibbon, & Williams, 1994) was administered prior
to inclusion. All assessors were psychologists with a master degree
in clinical psychology. These assessors were blind to treatment
condition and had received a SCID training in accordance with their
individual level of expertise. The assessor at preassessment was in
most cases a different person than the therapist (52/60). In a mi-
nority of cases (8/60), the assessor became also the patient's ther-
apist after the assessment. Note, however, that these assessors were
also blind to condition because condition allocation took place after
the preassessment. The number of administered SCID-I modules
depended on participants' responses to the SCID-I screening
questions (covering substance use disorders, anxiety disorders, and
eating disorders). The modules on social phobia, mood disorders,
psychotic disorders, post-traumatic stress disorder, and somato-
form disorders were assessed for all patients. The avoidant per-
sonality disorder section of the Structured Clinical Interview for
DSM-IV Axis II Personality Disorders (SCID-II; First, Gibbon,
Spitzer, Williams, & Benjamin, 1997) was also administered
Fig. 1. Participant flow chart. VRET ¼ Virtual Reality Exposure Therapy; iVET ¼ in Vivo Exposure Therapy.
I.L. Kampmann et al. / Behaviour Research and Therapy 77 (2016) 147e156 149
because research suggests that avoidant personality disorder and
SAD might be one disorder instead of two distinct disorders, with
avoidant personality disorder being the more severe form (Reich,
2009).
1.2.2. Primary outcome measures
Social anxiety symptoms were measured with the Liebowitz
Social Anxiety Scale-Self Report (LSAS-SR; Liebowitz, 1987). The
LSAS-SR is a 24-item questionnaire that assesses fear and avoidance
in social situations on a 4-point Likert scale. The 12-week test-
retest reliability of the LSAS-SR has been reported to be high
(r ¼ 0.83; Baker, Heinrichs, Kim, & Hofmann, 2002) and the internal
consistency in the present study was excellent (Cronbach's
a ¼ .90e0.97).
The subjective fear of being negatively evaluated by others in
social situations was assessed with the Fear of Negative Evaluation
Scale-Brief Form (FNE-B; Leary, 1983). The FNE-B is a 12-item in-
strument using a 5-point Likert scale for responses. Good psycho-
metric properties have been reported for the FNE-B in earlier
research (Weeks et al., 2005) and the internal consistency in the
present study was excellent (Cronbach's a ¼ .91e0.97).
1.2.3. Secondary outcome measures
We measured speech duration and speech performance during
a behavioural assessment task, in the form of a 5 min impromptu
speech, to evaluate levels of behavioural avoidance. The behav-
ioural assessment task was a modified version of a standardized
protocol (Beidel, Turner, & Jacob, 1989). This modified version has
been used in previous studies on social anxiety (Amir, Weber,
Beard, Bomyea, & Taylor, 2008). Although participants with
diverse social fears were included in the present study, this task
was chosen because public speaking anxiety is the most prevalent
Table 1
Demographic characteristics of participants per condition.
Characteristics VRET (n ¼ 20) iVET (n ¼ 20) WL (n ¼ 20)
Age, M (SD) 39.65 (11.77) 37.50 (11.27) 33.50 (11.44)
Gender (% female) 65 75 50
Native language, n (%)
Dutch 17 (85) 17 (85) 20 (100)
Spanish 1 (5) 0 (0) 0 (0)
Russian 1 (5) 0 (0) 0 (0)
Portuguese 0 (0) 1 (5) 0 (0)
Polish 0 (0) 1 (5) 0 (0)
Indonesian 0 (0) 1 (5) 0 (0)
Berber 1 (5) 0 (0) 0 (0)
Education, n (%)
High 8 (40) 10 (50) 11 (55)
Middle 11 (55) 8 (40) 9 (45)
Low 1 (5) 2 (10) 0 (0)
Employment status, n (%)
Paid employment 10 (50) 13 (65) 13 (65)
Trainee/student 1 (5) 1 (5) 5 (25)
Social welfare 1 (5) 1 (5) 0 (0)
Unemployed with voluntary work 1 (5) 0 (0) 0 (0)
Unemployed 7 (35) 5 (25) 2 (10)
Marital status, n (%)
Married or cohabitating 9 (45) 10 (50) 11 (55)
Long distance relationship 2 (10) 3 (15) 2 (10)
Single living with children 1 (5) 0 (0) 0 (0)
Single living without children 7 (35) 7 (35) 6 (30)
Widowed 1 (5) 0 (0) 1 (5)
Comorbidity, n (%)
Any anxiety disorder 3 (15) 4 (20) 0 (0)
Depressive disorder 4 (20) 0 (0) 2 (10)
Avoidant personality disorder 7 (35) 6 (30) 3 (15)
Session completed, n
1 20 20
2 20 20
3 19 19
4 19 19
5 18 17
6 15 17
7 15 17
8 15 16
9 15 16
10 14 14
Dropout, n (%) 5 (25) 3 (15) 4 (20)
Note. VRET ¼ Virtual Reality Exposure Therapy; iVET ¼ in Vivo Exposure Therapy; WL ¼ waiting-list; Low ¼ completed elementary school or lower vocational edu-
cation; Middle ¼ completed high school or middle-level vocational education; High ¼ completed pre-university, college, or university degree.
I.L. Kampmann et al. / Behaviour Research and Therapy 77 (2016) 147e156150
social fear. Speech duration was measured using a stop watch. To
assess speech performance, two independent judges, blind for
condition and assessment point, rated the videotaped speeches
using 17 items of a public speaking performance measure on a 5-
point Likert scale (Rapee & Lim, 1992). Higher scores on this mea-
sure indicated better speech performance. The internal consistency
of this scale was good in earlier research (r ¼ 0.84; Rapee & Lim,
1992) and the present study (r ¼ 0.81e0.87).
Symptoms of depression, general anxiety, and stress were
measured with the Depression Anxiety Stress Scale (DASS-21;
Lovibond & Lovibond, 1995). The DASS-21 is a 21-item self-report
questionnaire measuring depression, anxiety, and stress on a 4-
point Likert scale with higher scores representing higher levels of
depression, anxiety, or stress, respectively. The stress scale includes
items that measure subjective coping with stressful events, such as
tension, irritability, and a tendency to overreact to stressful events.
The DASS possesses good psychometric properties (Antony, Bieling,
Cox, Enns, & Swinson, 1998; Henry & Crawford, 2005) and in the
present study its internal consistency was excellent (Cronbach's
a ¼ .91e0.95).
Avoidant personality disorder related beliefs were assessed with
the Personality Disorder Belief Questionnaire (PDBQ; Dreessen &
Arntz, 1995). Research has shown that exposure therapy without
cognitive components can affect cognitions (Powers et al., 2008).
The avoidant subscale of the PDBQ contains 10 items to assess the
strength of beliefs assumed to be specific to avoidant personality
disorder. The internal consistency of this subscale was excellent in
the present study (Cronbach's a ¼ .90e0.97).
Subjective quality of life was measured using the Eurohis
Quality of Life Scale (EUROHIS-QOL 8-item index; Schmidt, Mühlan,
& Power, 2006). The EUROHIS-QOL 8-item index measures quality
of life on a 5-point Likert scale with higher scores indicating a
better quality of life. The psychometric properties of the EUROHIS-
QOL are reported to be satisfactory (Da Rocha, Power, Bushnell, &
Fleck, 2012; Schmidt et al., 2006) and the internal consistency in
the present study was good (Cronbach's a ¼ .83e0.93).
1.3. Procedure
The present study was approved by the Institutional Review
Board of the University of Amsterdam and registered
(NCT01746667; www.clinicaltrials.gov). Potential participants were
asked on the telephone about former SAD treatment and whether
attending treatment was logistically feasible. Afterwards, they filled
in the SIAS online. Participants who scored above the cut-off were
invited to an in-person intake (SCID), where they were screened for
http://www.clinicaltrials.gov
I.L. Kampmann et al. / Behaviour Research and Therapy 77 (2016) 147e156 151
exclusion criteria. After obtaining informed consent, eligible par-
ticipants underwent a preassessment comprising a battery of self-
report measures (LSAS-SR, FNE-B, DASS-21, PDBQ, EUROHIS-QOL)
and the behavioural assessment task. For the behavioural assess-
ment task, participants were told that they would give a 5 min
speech in front of a camera and a two-person jury rating the
speech. They were then asked to choose one out of seven topics
(nuclear power, gay marriage, euthanasia, republic or monarchy,
genetic selection, burqa ban, or mandatory organ donation) and
had 2 min to prepare the speech. Participants were allowed to make
notes during the preparation time but they could not use them
during the speech. Then, the jury entered the room and the par-
ticipants gave a speech for 5 min or until they indicated that they
wanted to stop. After the assessment, participants were random-
ized to one of the three conditions (VRET, iVET, or waiting-list)
using a computerized random number generator (http://www.
randomization.com). A person who was not involved in the pre-
sent study kept a list with the randomization sequence in a locked
office cupboard and prepared sealed envelopes containing the
condition allocation. The first author opened the envelopes after
participants were enrolled. Participants in the waiting-list condi-
tion received a second assessment after a waiting period of five
weeks (i.e. the same aimed length of time as the treatment) before
being randomized to one of the treatment conditions. After the last
treatment session, all participants completed a postassessment
identical to the preassessment. Three months after the post-
assessment, participants were invited to an in-person follow-up
assessment consisting of the battery of self-report measures (LSAS-
SR, FNE-B, DASS-21, PDBQ, EUROHIS-QOL).
1.4. Treatment
The treatment protocols for VRET and iVET were based on the
protocols of Scholing and Emmelkamp (1993) and Hofmann and
Otto (2008). Consistent with our aim of examining the potential
efficacy of exposure to virtual social interactions, only behavioural
exposure elements were used in both conditions and cognitive el-
ements were discarded. Both treatments comprised ten 90 min
sessions scheduled twice a week. In standard treatment, homework
is commonly added to therapy sessions. However, due to the
technical equipment necessary for VRET, virtual exposure could
only be implemented in the lab. Therefore, homework assignments
were not feasible in this condition. To keep the amount of exposure
equal in both conditions, no homework assignment was given in
either condition and therapists were instructed not to encourage
participants to practice exposure outside of therapy sessions.
Therapists involved in the present study were clinical psychologists
and students in their last semester of a clinical master's degree
program. They received training on VRET and iVET by the second
and last author prior to administering both treatments. To monitor
treatment adherence and competence, all therapy sessions and
exposure exercises were extensively discussed during supervision.
Furthermore, therapists were asked to complete a checklist
immediately after each session in which they indicated any possible
deviations from the protocol which were discussed during super-
vision. Moreover, therapy sessions were audio recorded (except for
in vivo exposure exercises) and parts of recordings were replayed
and discussed during supervision. Due to logistical reasons treat-
ment adherence and competence were not formally assessed.
Weekly supervision was provided to the therapists by the first,
second, and last author.
1.4.1. Virtual reality exposure therapy (VRET)
VRET took place in the virtual reality laboratory of the University
of Amsterdam. The laboratory consisted of two rooms separated by
a one-way mirror, through which the therapist could see the
participant during exposure exercises while controlling the com-
puter system, whereas the participant could not see the therapist.
The therapist and the participant had face-to-face contact before
and after exposure exercises and during exposure they communi-
cated via an intercom. The virtual situations covered one-to-one
and group situations designed to provoke anxiety in individuals
with SAD: giving a talk in front of an audience followed by ques-
tions from the audience, talking to a stranger, buying and returning
clothes, attending a job interview, being interviewed by journalists,
dining in a restaurant with a friend, and having a blind date (see
Appendix A for a detailed description of all virtual scenarios and
Figure 2 in Hartanto et al. (2014) for pictures of the virtual blind
date, virtual job interview, and neutral world).
For virtual exposure, we used the Delft Remote Virtual Reality
Exposure Therapy (DRVRET; Brinkman et al., 2012) system with
virtual worlds which were visualized using a Vizard v3.0 software
package. The setup consisted of three computers. The first com-
puter, a custom Dell T3400 workstation, was used to run the VR
server and the data logging system. The second computer, a custom
Dell T3600 workstation using Intel Quadcore E5 with NVIDIA
Quadro 5000, was used to run the VR engine and environment and
the therapist could see simultaneously what the participant could
see in the head mounted display. The video output of this computer
was split for both the head mounted display (participant) and real
time monitoring purpose (therapist). On the third computer, a
custom Dell T3400 workstation, the therapist controlled the virtual
situations. Participants wore a nVisor SX head mounted display
with 1280 � 1024 pixels, a stereographic projection, and a 60� di-
agonal field of view.
Semi-structured dialogues controlled by the therapist ensured a
certain length and difficulty level of interaction between the virtual
humans and the participant, as well as allowing for individual re-
sponses for each participant (Brinkman et al., 2012). To tailor
exposure exercises to the specific needs, anxiety level, and treat-
ment progress of the individual participant, the system allowed the
therapist to vary the following components depending on the vir-
tual situation: dialogue style (friendly or unfriendly), gender of
avatar, number of avatars present in the virtual world, dialogue
topic's degree of personal relevance, and avatar's gestures (i.e., gaze
direction and posture).
Treatment Sessions 1 and 2 focused on the conveyance of the
therapy rationale, the registration of participant's relevant social
situations, and creating a hierarchy of the available virtual social
situations according to the participants' anticipated anxiety level.
Moreover, participants were introduced to virtual reality and the
technological equipment by entering a virtual neutral situation
(Busscher, de Vliegher, Ling, & Brinkman, 2011), without any social
interaction, for a maximum of 5 min.
Sessions 3 through 9 contained two 30 min blocks of exposure
exercises separated by a 5 min break. The content of exposure ex-
ercises followed the previously made hierarchy in ascending order
with regards to individual anxiety level (i.e. gradual exposure).
Participants rated their anxiety level regarding three time points in
every exposure exercise: beginning, highest level during the exer-
cise, and end. Participants practiced every virtual world at least
once and until anxiety decreased. Yet, only a maximum of two
sessions were spent on exposure exercises focussing on presenta-
tion situations to limit the amount of practice in presentation
performance, given that the behavioural assessment task also
consisted of giving a speech. Session 10 was devoted to relapse
prevention and evaluation of the therapy.
1.4.2. In vivo exposure therapy (iVET)
The iVET consisted of gradual exposure therapy to real-life
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I.L. Kampmann et al. / Behaviour Research and Therapy 77 (2016) 147e156152
situations. Similar to VRET, iVET comprised 10 sessions with 60 min
exposure in Sessions 3 through 9. As in the VRET condition, the
therapy rationale and anxiety hierarchy were discussed in Sessions
1 and 2. The hierarchy used in iVET comprised participants’ indi-
vidual social situations which were translated to exposure exercises
that could be implemented at the ambulatory of the University of
Amsterdam or in its neighbourhood (e.g., supermarkets, subway
stations, caf�es, etc.). If relevant social situations could not be
translated into exercises at the ambulatory or its nearby sur-
roundings (e.g., work-related social situations), participants could
substitute a regular session with a session in their personal envi-
ronment. In these cases, the therapist and the participant had
contact via the telephone before and after the exposure assign-
ment. Session 10 was identical to the last session in the VRET
condition.
1.5. Statistical analyses
Multilevel regression analyses were carried out to explore
within-group (Time), between-group (Condition), and interaction
(Time � Condition) effects. Only the fixed effects of the multilevel
models were reported because they model change at the group
level (in contrast to random effects, which model at the individual
level). To investigate treatment effects from pre-to postassessment,
each active treatment group was compared to waiting-list. The
estimated model (see Table 3a) consisted of two parameters for
each group: one parameter estimating the mean level of the
outcome variable at preassessment and a second parameter esti-
mating the rate of change from pre-to postassessment. For the
active treatment groups, the second parameter described the
change from pre-to postassessment relative to the change of the
waiting-list group. To investigate long term effects of the two active
treatments, the change from pre-to postassessment and the change
from preassessment to follow-up were compared between VRET
and iVET. The estimated model (see Table 3b) consisted of three
parameters for each group: the first parameter describes the mean
level of the outcome variable at preassessment, the …
Public Speaking Avoidance as a Treatment Moderator for Social
Anxiety Disorder
Bita Mesria,1, Andrea N. Nilesa,2, Andre Pittigb,3, Richard T. LeBeaua,4, Ethan Haika,5, and
Michelle G. Craskea,6
aUniversity of California, Los Angeles, Department of Psychology, 405 Hilgard Avenue, Los
Angeles, California, United States of America, 90095-1563
bInstitute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Chemnitzer
Str. 46, Dresden, Germany, D-01187
Abstract
Background and Objectives—Cognitive behavioral therapy (CBT) and acceptance and
commitment therapy (ACT) have both garnered empirical support for the effective treatment of
social anxiety disorder. However, not every patient benefits equally from either treatment.
Identifying moderators of treatment outcome can help to better understand which treatment is best
suited for a particular patient.
Methods—Forty-nine individuals who met criteria for social anxiety disorder were assessed as
part of a randomized controlled trial comparing 12 weeks of CBT and ACT. Pre-treatment
avoidance of social situations (measured via a public speaking task and clinician rating) was
investigated as a moderator of post-treatment, 6-month follow-up, and 12-month follow-up social
anxiety symptoms, stress reactivity, and quality of life.
Results—Public speaking avoidance was found to be a robust moderator of outcome measures,
with more avoidant individuals generally benefitting more from CBT than ACT by 12-month
follow-up. In contrast, clinician-rated social avoidance was not found to be a significant moderator
of any outcome measure.
Limitations—Results were found only at 12-month follow-up. More comprehensive measures of
avoidance would be useful for the field moving forward.
Please address correspondence to Michelle Craske, Ph.D., Department of Psychology, UCLA, 1193 Franz Hall, Box 951563, Los
Angeles, CA 90095-1563. Telephone: 1-310-825-8403; fax: 1-310-825-9048; [email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our
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HHS Public Access
Author manuscript
J Behav Ther Exp Psychiatry. Author manuscript; available in PMC 2018 June 01.
Published in final edited form as:
J Behav Ther Exp Psychiatry. 2017 June ; 55: 66–72. doi:10.1016/j.jbtep.2016.11.010.
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Conclusions—Findings inform personalized medicine, suggesting that social avoidance
measured behaviorally via a public speaking task may be a more robust factor in treatment
prescription compared to clinician-rated social avoidance.
Keywords
Social anxiety; Moderator; Treatment outcome; Cognitive behavioral therapy; Acceptance and
commitment therapy
1. Introduction
Cognitive behavioral therapy (CBT) is a well-established treatment for social anxiety
disorder (Butler, Chapman, Forman, & Beck, 2006; Hofmann & Smits, 2008). Recently,
acceptance and commitment therapy (ACT), a third-wave behavioral therapy, has garnered
support as another effective treatment for social anxiety (Swain, Hancock, Hainsworth, &
Bowman, 2013; Bluett, Homan, Morrison, Levin, & Twohig, 2014) with comparable
treatment outcomes to CBT (Craske et al., 2014). Clinically significant response rates of
individual patients following these interventions are around 50–55%, ranging from 43% to
70% (for a review see Loerinc et al., 2015; Craske et al., 2014; Leichsenring et al., 2014;
Lincoln et al., 2005). Identifying treatment moderators may be a key to improving response
rates, as they clarify for whom and under which circumstances treatments have different
effects. Knowledge of such moderators can help clinicians better match patients with
existing treatments from which they are likely to glean the greatest benefit (Kraemer,
Wilson, Fairburn, & Agras, 2002).
Unfortunately, though several predictors of treatment outcome have been identified, little
research exists on treatment moderators. This is likely due to the fact that the majority of
prior studies on social anxiety disorder do not compare two active treatments, which is
required for assessing treatment moderators. To our knowledge, only a few papers have
reported moderators of psychological treatments for individuals with social anxiety disorder.
The findings are detailed below.
In a previously published article on the current sample, individuals with social anxiety
disorder who were rated as high in experiential avoidance (i.e., self-reported unwillingness
to accept negative emotions) measured by the Acceptance and Action Questionnaire
reported greater symptom reduction at 12-month follow-up in CBT than ACT (Craske et al.,
2014). The same pattern of moderation was found in a separate study with a mixed anxiety
sample (Wolitzky-Taylor, Arch, Rosenfield, & Craske, 2012). We speculated that individuals
with high experiential avoidance benefit more from CBT in the long-term because they are
motivated to practice skills (e.g., exposures) designed to decrease avoidance of anxious
thoughts, feelings, and sensations. Compared to CBT, ACT emphasizes acceptance rather
than reducing uncomfortable internal experiences. Conversely, in the same mixed anxiety
sample, individuals with high behavioral avoidance of negative physical sensations (i.e.,
unwillingness to continue a hyperventilation task) were more likely to benefit from ACT
than CBT (Davies, Niles, Pittig, Arch, & Craske, 2015). However, this study did not
examine moderators separately by diagnosis and thus it is possible that this finding was
Mesri et al. Page 2
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driven by patients with anxiety primarily related to bodily sensations (e.g., those with panic
disorder and health anxiety), which is a common but not essential or primary component of
social anxiety disorder.
A measure of avoidance that is more specific to social anxiety disorder would be avoidance
of social situations. Behavioral measures of social avoidance including public speaking tasks
are ecologically valid and easily implemented in research, but rarely used in clinical
assessments (Beidel, Turner, Jacob, & Cooley, 1989; Hofmann, Newman, Ehlers, & Roth,
1995; Levin et al., 1993; Moscovitch, Suvak, & Hofmann, 2010). Instead, clinicians
typically make judgments of behavioral avoidance based on patient self-report. However,
anxious patients’ estimates of their avoidance can be at odds with their actual behavior
(Rachman & Lopatka, 1986; Taylor & Rachman, 1994). To our knowledge there is no
previous study evaluating behavioral measures of social avoidance as moderators of
treatment outcome for social anxiety disorder.
Theoretically, experiential and behavioral avoidance are two separate parts of anxiety.
Whereas experiential avoidance is centered on avoidance of internal experiences such as
thoughts, feelings, and physical sensation, behavioral avoidance is centered on avoidance of
external experiences such as social events, public speaking, and meetings. It would seem
likely that individuals who are avoidant of feared internal experiences would also be
avoidant of feared external experiences. Moreover, both experiential avoidance and
behavioral avoidance are indicators of poor emotion regulation (Craske, Street, & Barlow,
1989; Hayes, Wilson, Gifford, Follette, & Strosahl, 1996). From a deficit correction model,
it is likely that those who show deficits in emotion regulation would benefit from a treatment
that is targeting said deficit (e.g., CBT) compared to a treatment that is not targeting emotion
regulation (e.g., ACT).
Given prior evidence that individuals who report high levels of experiential avoidance
(indicator of poor emotion regulation) respond more positively to CBT than ACT, we
hypothesized that those with the most overt social avoidance (another indicator of poor
emotion regulation), would similarly respond more positively to CBT than ACT. To evaluate
the effects of in vivo versus clinician-rated social avoidance, we analyzed avoidance via a
public speaking task and clinician rating prior to treatment. To isolate the effect of social
avoidance above social fear, we analyzed public speaking avoidance, clinician-rated social
avoidance, public speaking fear, and clinician-rated social fear as moderators of all
outcomes.
2. Materials and methods
2.1. Participants
Forty-nine individuals who met diagnostic criteria for principal or co-principal generalized
social anxiety disorder as diagnosed using the Anxiety Disorders Interview Schedule IV
(Brown, Di Nardo, & Barlow, 1994, see Craske et al., 2014, for more details) were included
in the current analyses. Fifty-two participants completed treatment but follow-up behavioral
and self-report data were missing for 3 individuals. A clinician severity rating of 4 or higher
on the ADIS-IV indicated clinical severity and served as the cutoff for study eligibility.
Mesri et al. Page 3
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Individuals were a subset of a larger sample that included randomization to a waitlist
condition (Craske et al., 2014). Because moderator analyses examine differential response to
two active treatments and not differential response to active treatment versus control, we did
not include participants assigned to the waitlist in these analyses. Demographics for the
current subsample are in Table 1. There were no significant group differences on any
demographic or diagnostic variable at baseline.
Exclusion criteria included active suicidal ideation, pregnancy, substance abuse or
dependence within the last 6 months, bipolar disorder, psychosis, or certain medical
diseases. Additional exclusion criteria (i.e., left handedness, metal implants, claustrophobia)
were included due to a neuroimaging component. Individuals were permitted to receive
concurrent psychotherapy or psychotropic medication if they were stabilized on
benozodiazepines and beta blockers for a minimum of 1 month; on SSRIs, SNRIs,
heterocylics, and MAO inhibitors for a minimum of 3 months; and on non-anxiety related
psychotherapy for a minimum of 6 months prior to study entrance. Individuals were
recruited through online and newspaper advertisements as well as community flyers and
referrals from the greater Los Angeles area. The study took place at the Anxiety Disorders
Research Center in the University of California, Los Angeles (UCLA).
2.2. Design
Individuals were assessed prior to treatment (i.e., pre-treatment), within 6 weeks after the
end of treatment (i.e., post-treatment), 6 months after pre-treatment (i.e., 6-month follow-
up), and 12 months after pre-treatment (i.e., 12-month follow-up)1.
2.3. Treatments
Individuals in CBT and ACT groups received 12 weekly, 1-hr individual therapy sessions
based on standard manuals2. ACT and CBT were matched on number of exposure sessions
but differed in framing of the intent of exposure. CBT and ACT were administered by
advanced clinical psychology students at UCLA (see Craske et al., 2014). Therapists
received a two-day training session in CBT and ACT by Drs. Craske and Hayes,
respectively. They received weekly group supervision by Dr. Craske and members of Dr.
Craske’s and Hayes’s teams.
CBT—The 12-session CBT protocol has been effective for social anxiety disorder (Craske
et al., 2014; Arch et al., 2012). Session 1 included assessment, psychoeducation, and self-
monitoring. Sessions 2–4 covered cognitive restructuring, hypothesis testing, and breathing
retraining. Session 5–11 included exposures to social stimuli. Session 12 focused on relapse
prevention.
ACT—Session 1 included psychoeducation and experiential exercises. Sessions 2–3 covered
creative hopelessness. Sessions 4–5 covered mindfulness, acceptance, and cognitive
16-month follow-up was approximately 3 months after treatment completion and 12-month follow-up was approximately 9 months
after treatment completion.
2See authors for a copy of the CBT treatment manual (CBT manual modified from Hope, Heimberg, Juster, & Turk, 2000); the ACT
manual is published (Eifert & Forsyth, 2005).
Mesri et al. Page 4
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defusion. Sessions 6–11 honed previous skills and introduced value exploration. Exposures
were used throughout to observe and accept anxiety as well as to engage in valued activities
despite anxiety. Session 12 created a plan for future use of skills.
2.4. Moderator Variables
2.4.1. Public Speaking Avoidance and Fear—At pre-treatment, individuals were
asked to give a 3-minute speech in front of a video camera and two confederates. Speech
topics included global warming and corporeal punishment. These topics were selected to be
moderate in terms of difficulty and controversy. Individuals were given 5 minutes to prepare
the speech on one or both topics. They were instructed to rate their fear level using a 0–100
Subjective Units of Distress Scale (SUDS; Wolpe, 1990) with 0 being no fear and 100 being
maximum fear at the start of the speech, at each 1-minute interval, and at the end of the
speech. After 3 minutes, individuals were given the opportunity to continue speaking for up
to 3 more minutes. Mean SUDS ratings were calculated for each individual and analyzed as
a measure of fear on the public speaking task. Number of minutes spoken was used as a
measure of avoidance. Individuals who refused the public speaking task altogether were
given a score of 0 minutes and SUDS rating of 100. See appendix A for the brief protocol
used to assess public speaking avoidance.
2.4.2. Clinician-Rated Social Avoidance and Fear—As part of the pre-treatment
ADIS-IV, clinicians rated individuals’ avoidance and fear (0 = none, 8 = extreme anxiety or
avoidance) of 13 social situations (e.g., dating, public speaking, speaking with unfamiliar
people). Avoidance scores for all 13 situations were averaged to create a clinician-rated
social avoidance score (α = .74). Fear scores for all 13 social situations were also averaged
to create a clinician-rated social fear score (α = .77).
2.5. Outcome Variables
2.5.1. Symptom Composite Score—The self-report version of the Liebowitz Social
Anxiety Scale (LSAS-SR; Fresco et al., 2001) is a 24-item measure of fear and avoidance of
social and performance situations. Total ratings demonstrate good test-retest reliability (r = .
83), internal consistency (α = .95), convergent validity and the scale is sensitive to change
following treatment (Baker, Heinrichs, Kim, & Hofmann, 2002). The Social Interaction
Anxiety Scale (SIAS; Mattick & Clarke, 1998) is a 20-item measure of thoughts, feelings,
and behaviors in social situations. The SIAS correlates highly with other measures of social
phobia and has good internal consistency (α = .90) (Osman, Gutierrez, Barrios, Kopper, &
Chiros, 1998). The Social Phobia Scale (SPS; Mattick & Clarke, 1998) is a 20-item measure
of being observed by others during routine activities (e.g., eating, writing). The SPS
correlates highly with other measures of social phobia and has good internal consistency (α
= .91) (Osman et al., 1998). Alphas for the LSAS-SR, SIAS, and SPS were all at or above .
90 in this sample across all time points (Niles, Mesri, Burklund, Lieberman, & Craske,
2013). To improve construct validity for the measurement of social anxiety severity, a
composite was created from the three scales. Z-scores for each measure were combined to
create a standardized measure with mean 0 and standard deviation 1. The composite score
includes averages of all three measures at pre, post, and 12-month follow-up. The LSAS-SR
was not administered at 6-month follow-up, which includes only the SPS and SIAS.
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2.5.2. State-Trait Anxiety Inventory—The State-Trait Anxiety Inventory – A State
(STAI AState; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983) is a 20-item measure
of temporary anxiety in response to a stressor. Example items include “I feel nervous” and “I
feel tense.” Each item is rated on a scale from 1 to 4, with 1 being not at all and 4 being to a
great extent. The STAI A-State demonstrates good internal consistency (α = .83 – .92)
(Spielberger et al., 1983). The STAI was administered at the start of the laboratory
assessment (which included a hyperventilation task, a public speaking task, and computer
tasks) in order to assess stress reactivity. Because the laboratory assessment was not
conducted at 6-month follow-up, STAI data were analyzed only at pre, post, and 12-month
follow-up.
2.5.3. Quality of Life Inventory—The Quality of Life Inventory (QOLI; Frisch, 1994a,
1994b) is a measure of satisfaction with regard to 16 broad life domains. Each domain is
first rated for importance on a scale from 0 to 2, with 0 being not important and 2 being
extremely important. Then, individuals rate their life satisfaction with that domain on a −3 to
+3 scale, with −3 being very dissatisfied and +3 being very satisfied. The QOLI
demonstrates good test-retest reliability (r = .80 – .91), internal consistency (α = .77 – .89)
and is sensitive to treatment change (Frisch et al., 2005).
2.6. Statistical analyses
A multi-level model with repeated measures design was used. Pre-treatment scores were
modeled as a covariate rather than a repeated measure to minimize the variance in the
outcome measures (Tabachnick & Fidell, 2006). This model has been previously used in
examining moderators of treatment outcome (Craske et al., 2014; Niles et al., 2013;
Wolitzky-Taylor et al., 2012).
Analyses were run in Stata 13 using the xtmixed command. A two level growth curve model
was used. Time (post-treatment, 6-month follow-up, 12-month follow-up) was modeled on
level 1 as a continuous linear predictor. On level 2, we included baseline levels of the
outcome measures (as a covariate), Group (CBT or ACT), status (0 = completed 12-month
measures, 1 = not completed 12-month measures) and the moderators. To test specificity of
public speaking avoidance as a moderator above fear, we included fear during the public
speaking task as a covariate. When testing public speaking fear, we included public speaking
avoidance as a covariate. Pairwise correlations between public speaking avoidance and
public speaking fear revealed only a moderate correlation, r = −.39, p < .001. However,
pairwise correlations between clinician-rated social avoidance and clinician-rated social fear
revealed a strong correlation, r = .81, p < .001. Hence, we did not include clinician-rated
social fear in the model when analyzing clinician-rated social avoidance and vice versa.
Models were fitted using maximum likelihood. Random effects of intercept and time were
included in all models.
Because moderators may interact with Group (CBT or ACT) or Time, both of these
interactions, and the three-way interaction between moderator, Group, and Time were
included in each analysis. Quadratic relationships between moderator, Group, and Time
were assessed. If there was no quadratic relationship, Time was dropped and a moderation of
Mesri et al. Page 6
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Group without Time was assessed. Tests of simple effects were used to explain moderation
effects. More specifically, 1 SD above and below the mean was used to categorize high
avoidant/ fear or low avoidant/ fear individuals. 1 SD was used in order to capture
representative avoidance or fear behavior in a social anxiety group and is typical in previous
moderation studies (Niles et al., 2013).
3. Results
As reported in Craske et al. (2014), CBT and ACT were each more effective than a waitlist
comparison control for symptoms of social anxiety, with no differences between them.
3.1. Moderator of Symptom Composite
Public speaking avoidance significantly interacted with Group and Time to moderate
symptom composite, z = −2.25, p = .045 (see Fig. 1). Tests of simple effects revealed that at
12-month follow-up, more avoidant individuals (operationally defined as 1 SD above the
mean) reported .87 SD fewer symptoms following CBT than ACT, 95% confidence interval
(CI) = .05 to 1.70, z = 2.07, p = .038. No group differences were found for low avoidant
individuals (1 SD below the mean), p > .05. Public speaking avoidance did not moderate
post-treatment or 6-month follow-up, ps > .05. Neither fear on the public speaking task nor
clinician-rated social avoidance or social fear were significant moderators of symptom
composite at any time point, ps > .05.
3.2. Moderator of Stress Reactivity
Public speaking avoidance significantly interacted with Group and Time to moderate stress
reactivity (measured by STAI A-State prior to a stressful laboratory assessment), z = −3.87,
p < .001 (see Fig. 2). Tests of simple effects revealed that at 12-month follow-up, more
avoidant individuals reported 15.77 fewer points in stress reactivity following CBT than
ACT, CI = 8.38 to 23.17, z = 4.18, p < .001. No group differences were found for low
avoidant individuals, p > .05. Public speaking avoidance did not moderate at post-treatment
or 6-month follow-up, ps > .05. Neither fear on the public speaking task nor clinician-rated
social avoidance or social fear were significant moderators of stress reactivity at any time
point, ps > .05.
3.3. Moderator of Quality of Life
Clinician-rated social fear significantly moderated quality of life, z = −2.12, p = .006 (see
Fig. 3). Tests of simple effects revealed that at 6-month follow-up, less fearful individuals
reported 1.32 fewer points in quality of life following CBT than ACT, CI = −2.33 to −.31, z
= −2.56, p = .010 and more fearful individuals reported 1.26 more points in quality of life
following CBT than ACT, CI = .003 to 2.52, z = 1.96, p = .049. There were no significant
differences between high and low clinician-rated fearful individuals in CBT and ACT at
post-treatment and 12-month follow-up, ps > .05. Therefore, this finding is no longer
discussed in this paper. Public speaking fear, public speaking avoidance, and clinician-rated
social avoidance were not significant moderators of quality of life at any time point, ps > .
05.
Mesri et al. Page 7
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4. Discussion
The current study tested social avoidance as a moderator of treatment outcome for social
anxiety disorder. Understanding moderators of treatment outcome allow us to better match
patients to a particular treatment, which has important implications for improving treatment
outcome. Our findings suggest that individuals who are more avoidant during a public
speaking task benefit more, in terms of long-term symptoms and stress reactivity, from CBT
than ACT.
Conversely, fear during the public speaking task did not moderate the treatment effects,
suggesting that the results were specific to public speaking avoidance versus fear. Moreover,
clinician-rated social avoidance did not moderate treatment effects, which could imply that
the results were specific to avoidance of public speaking in particular rather than social
avoidance in general. Alternatively, these results may suggest that clinicians may not be
particularly accurate judges of a patient’s degree of social avoidance in their daily life. Such
judgments are likely to be heavily reliant on a patient’s self-report, particularly at an initial
assessment when the clinician has limited information about the patient, and self-report of
avoidance behavior may not be an exact indicator of actual avoidance behavior in laboratory
paradigms (Gamez, Kotov, & Watson, 2010; McNeil, Ries, & Turk, 1995).
We found that more avoidance on the public speaking task predicts better long-term outcome
in CBT than ACT. One possible explanation is that CBT targets avoidance in a structured
way through creation of an exposure hierarchy followed by in-session and homework
exposure assignments. Avoidant individuals may benefit from this structure. A similar
finding has been reported in a panic disorder sample that was randomly assigned to exposure
therapy with an active therapist who guided patients through exposures or a less active
therapist who was not present during assigned exposures (Hamm, et al., 2016). Overall,
panic disorder patients benefitted from exposure therapy; however, patients with greater
public speaking avoidance benefitted even more from therapist-directed exposures than self-
directed exposures. This finding may highlight the added benefit of structure during
exposures (which may be more present in CBT than ACT) for patients with high public
speaking avoidance. Although ACT includes exposure, these exposures are less structured
and their focus is not on fear reduction. Rather, in ACT, individuals conduct exposures in
order to be present, open, mindful, and accepting of their anxious feelings with the eventual
goal of taking committed action toward their values. Thus, in contrast to CBT in which
exposures are a critical strategy for alleviating symptoms, the connection between exposures
and treatment goals is more removed in ACT and possibly simply one of many approaches
toward valued living. Indeed, there was greater adherence to behavioral exposures in CBT
than ACT in the present sample (Craske et al., 2014).
Moderation was found only at the 12-month follow-up, which replicated our prior studies in
the same and different samples (Craske et al., 2014; Niles et al., 2013; Wolitzky-Taylor et
al., 2012). In prior studies, we proposed that experiential avoidance motivated continued
exposure practice over the months following treatment, in turn leading to improved long-
term outcomes (Wolitzky-Taylor et al., 2012). Perhaps those who were most avoidant of
public speaking similarly perceived the benefits of continued exposure practice following the
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end of treatment resulting in better long-term outcome in CBT than ACT. It is also important
to note that CBT was supervised directly by Dr. Craske and her team, whereas ACT was
only supervised by Dr. Hayes’s team and not himself. It is possible that if Dr. Hayes had
supervised the therapists, outcomes from ACT may have differed. Moreover, more
comprehensive measures of avoidance would be useful for the field moving forward.
Despite limitations, this is one of few studies that investigated moderators of ACT and CBT
for social anxiety disorder. Asking patients to give a speech and identifying how long they
are willing to speak may be a simple way of assessing behavioral avoidance. It may provide
useful long-term prognostic information not gleaned by traditional methods such as rating
levels of social avoidance based largely on patient self-report. Furthermore, should these
results be replicated, they suggest that those who are more behaviorally avoidant may benefit
more from CBT than ACT.
Acknowledgments
Funding: This project was funded by the National Institutes of Mental Health 1 R21 MH081299
References
Arch JJ, Eifert GH, Davies C, Vilardaga JCP, Rose RD, Craske MG. Randomized clinical trial of
cognitive behavioral therapy (CBT) versus acceptance and commitment therapy (ACT) for mixed
anxiety disorders. Journal of Consulting and Clinical Psychology. 2012; 80(5):750–765. doi:http://
dx.doi.org/10.1037/a0028310. [PubMed: 22563639]
Baker …
RESEARCH ARTICLE
Higher- and lower-order personality traits and
cluster subtypes in social anxiety disorder
Mădălina Elena Costache1, Andreas Frick2, Kristoffer Månsson1,3,4,5, Jonas Engman1,
Vanda Faria
1,6,7
, Olof Hjorth
1
, Johanna M. Hoppe
1
, Malin Gingnell
1,8
, Örjan Frans
1
,
Johannes Björkstrand
1,9
, Jörgen Rosén
1
, Iman Alaie
1,10
, Fredrik Åhs11, Clas Linnman12,
Kurt Wahlstedt
1
, Maria Tillfors
13
, Ina Marteinsdottir
14
, Mats Fredrikson
15
,
Tomas FurmarkID
1*
1 Department of Psychology, Uppsala University, Uppsala, Sweden, 2 The Beijer Laboratory, Department of
Neuroscience, Uppsala University, Uppsala, Sweden, 3 Centre for Psychiatry Research, Department of
Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm County Council,
Stockholm, Sweden, 4 Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin,
Germany and London, United Kingdom, 5 Center for Lifespan Psychology, Max Planck Institute for Human
Development, Berlin, Germany, 6 Center for Pain and The Brain, Department of Anesthesiology, Harvard
Medical School, Boston Children’s Hospital, Perioperative and Pain Medicine, Boston, MA, United States of
America, 7 Department of Otorhinolaryngology, Smell & Taste Clinic, TU Dresden, Dresden, Germany,
8 Department of Neuroscience, Uppsala University, Uppsala, Sweden, 9 Department of Psychology, Lund
University, Lund, Sweden, 10 Department of Neuroscience, Child and Adolescent Psychiatry, Uppsala
University, Uppsala, Sweden, 11 Department of Psychology and Social Work, Mid Sweden University,
Östersund, Sweden, 12 Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA, United
States of America, 13 Department of Social and Psychological Studies, Karlstad University, Karlstad,
Sweden, 14 Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden,
15 Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
* [email protected]
Abstract
Social anxiety disorder (SAD) can come in different forms, presenting problems for diagnos-
tic classification. Here, we examined personality traits in a large sample of patients (N =
265) diagnosed with SAD in comparison to healthy controls (N = 164) by use of the Revised
NEO Personality Inventory (NEO-PI-R) and Karolinska Scales of Personality (KSP). In addi-
tion, we identified subtypes of SAD based on cluster analysis of the NEO-PI-R Big Five per-
sonality dimensions. Significant group differences in personality traits between patients and
controls were noted on all Big Five dimensions except agreeableness. Group differences
were further noted on most lower-order facets of NEO-PI-R, and nearly all KSP variables. A
logistic regression analysis showed, however, that only neuroticism and extraversion
remained significant independent predictors of patient/control group when controlling for the
effects of the other Big Five dimensions. Also, only neuroticism and extraversion yielded
large effect sizes when SAD patients were compared to Swedish normative data for the
NEO-PI-R. A two-step cluster analysis resulted in three separate clusters labelled Prototypi-
cal (33%), Introvert-Conscientious (29%), and Instable-Open (38%) SAD. Individuals in the
Prototypical cluster deviated most on the Big Five dimensions and they were at the most
severe end in profile analyses of social anxiety, self-rated fear during public speaking, trait
anxiety, and anxiety-related KSP variables. While additional studies are needed to deter-
mine if personality subtypes in SAD differ in etiological and treatment-related factors, the
PLOS ONE
PLOS ONE | https://doi.org/10.1371/journal.pone.0232187 April 29, 2020 1 / 20
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OPEN ACCESS
Citation: Costache ME, Frick A, Månsson K,
Engman J, Faria V, Hjorth O, et al. (2020) Higher-
and lower-order personality traits and cluster
subtypes in social anxiety disorder. PLoS ONE 15
(4): e0232187. https://doi.org/10.1371/journal.
pone.0232187
Editor: Frantisek Sudzina, Aalborg University,
DENMARK
Received: October 25, 2019
Accepted: April 8, 2020
Published: April 29, 2020
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0232187
Copyright: © 2020 Costache et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The data underlying
the results presented in the study are available
from https://www.psyk.uu.se/forskning/
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present results demonstrate considerable personality heterogeneity in socially anxious indi-
viduals, further underscoring that SAD is a multidimensional disorder.
Introduction
Social anxiety disorder (SAD) is one of the most common psychiatric disorders [1] character-
ized by a persistent and over-whelming fear of being negatively evaluated in one or more social
or interactional situation [2]. It is associated with considerable individual suffering [3], large
societal costs [4,5] and typically follows a chronic course if left untreated [6]. Cognitive behav-
ioral therapy (CBT), serotonin reuptake inhibitors (SSRIs) and serotonin-noradrenaline reup-
take inhibitors (SNRIs) are first-line treatment options for SAD [7,8]. Although these
treatments are helpful, as many as 40–50% of patients have been reported to be either treat-
ment resistant or not responding sufficiently [9]. Several factors, like variations in symptom
profile and comorbidity of personality disorders, may underlie this and more research is
needed to better understand the etiology and relevant treatment approaches of SAD. Social
anxiety can be studied, not only as a disorder, but also as one or more dispositional traits
involving emotional discomfort and social withdrawal [10]. Spence and Rapee suggested that
social anxiety may be a personality-like construct while SAD diagnosis reflects an interaction
between social anxiety and the degree of impairment such anxiety imposes in life [11]. Mal-
adaptive personality traits may have a large impact on psychosocial functioning and, hence,
the course and expression of psychiatric disorders. Moreover, disorders and traits may share a
common etiology [12] and personality traits could be predictive of treatment outcome [13,14].
Deciphering the complex relationships between basic personality traits and SAD is therefore
theoretically and clinically important.
The revised NEO Personality Inventory (NEO-PI-R) provides comprehensive assessment
of personality dimensions, and their underlying facets, based on the five-factor model of per-
sonality i.e., the “Big Five” neuroticism, extraversion, openness, agreeableness, and conscien-
tiousness [15]. Previous studies have reported that SAD is associated high scores of
neuroticism and low scores of extraversion [16–19]. Marteinsdottir and colleagues [20]
assessed personality traits in a sample of Swedish untreated SAD individuals by use of another
common personality inventory, the Karolinska Scales of Personality; KSP [21]. In comparison
to normative data, the SAD sample scored higher on the KSP scales related to vulnerability for
anxiety, detachment, irritability, and indirect aggression, and lower on socialization and social
desirability. SAD patients with comorbid avoidant personality disorder scored higher on inhi-
bition of aggression and psychic anxiety [20]. Personality dimensions in SAD have also been
evaluated by means of the Temperament and Character Inventory (TCI) [22]. Clinical SAD
samples have then exhibited significantly higher harm-avoidance, and significantly lower self-
directedness, persistence, cooperativeness, self-transcendence, and novelty seeking when com-
pared to healthy participants [23,24]. Notably, sample sizes in these studies have been limited,
generally not exceeding N = 60. More studies with larger samples are needed to clarify the cru-
cial personality components associated with SAD, including higher-order dimensions as well
as lower-order facets. Also, little is known regarding the impact of such personality compo-
nents on subtypes of SAD.
The heterogeneity of SAD has been widely acknowledged [25] and several subtypes have
been proposed over the years. However, empirical research into SAD subtypes has yielded
mixed findings and a resultant general lack of consensus, partly reflecting use of different
PLOS ONE Personality and subtypes in social anxiety disorder
PLOS ONE | https://doi.org/10.1371/journal.pone.0232187 April 29, 2020 2 / 20
forskargrupper/uppsala-affective-neuroscience-
group/
Funding: Supported by the Swedish Research
Council (grant 2016-0228) and Riksbankens
Jubileumsfond - the Swedish Foundation for
Research in Social Sciences and the Humanities
(grant P17-0639:1) https://www.vr.se/ https://
www.rj.se/ The funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
https://doi.org/10.1371/journal.pone.0232187
https://www.psyk.uu.se/forskning/forskargrupper/uppsala-affective-neuroscience-group/
https://www.psyk.uu.se/forskning/forskargrupper/uppsala-affective-neuroscience-group/
https://www.vr.se/
https://www.rj.se/
https://www.rj.se/
statistical methods and samples [26]. Social anxiety may extend to a broad range of situations
and the generalized subtype of SAD was introduced in DSM-III-R as a descriptor of individu-
als who fear most social situations. The residual category has often been referred to as “nonge-
neralized”. However, anxiety reactions may also be limited to one or two social situations,
typically performance situations like public speaking. Heimberg and colleagues [27] proposed
that “circumscribed” SAD should be added to the generalized and nongeneralized subtypes,
and other labels have also been suggested such as “specific”, “discrete”, and “limited interac-
tional” SAD [27,28]. Blöte and colleagues argued that public speaking anxiety is a distinct
subtype, different from other subtypes [29]. In the current version of DSM, i.e. DSM-5, gener-
alized SAD has been replaced by “performance type” as the only subtype specifier, although
this may not do justice to the complexity of the issue.
As in psychiatry in general, it has been debated whether SAD subtypes are best described as
categories or dimensions. Support for a dimensional mild-moderate-severe subtype distribu-
tion was found in a cluster analytic study of SAD in a community sample [28] and other
empirical studies have also concluded that the heterogeneity of SAD should be seen as a con-
tinuum of severity, greater number of social fears being associated with greater disability [30–
33]. On the other hand, subgrouping can also be based on the type of social anxiety. The pres-
ence of observational vs. interactional anxiety could be a putative qualitative demarcation of
SAD subtypes [34]. Using factor analysis in a clinical SAD sample, Perugi and colleagues
found support for the existence of five types of social anxiety: interpersonal anxiety, formal
speaking anxiety, stranger-authority anxiety, eating and drinking while being observed, and
anxiety of doing something while observed [35]. Moreover, studies have found evidence of
qualitatively different SAD subgroups based on Cloninger’s temperamental characteristics
[22]. By use of cluster or latent class analysis, researchers have identified not only a prototypi-
cal SAD subgroup characterized by high harm-avoidance and low novelty seeking, but also an
anxious-impulsive subtype scoring high on novelty seeking [36–39]. While individuals in the
former group show behavioral inhibition and risk aversion, individuals in the latter exhibit an
atypical pattern of risk-prone approach behaviors while still being highly anxious. From a the-
oretical perspective, Hofmann and colleagues have suggested that subtypes of SAD vary across
six dimensions: fearfulness, anxiousness, shyness, self-consciousness, submissiveness, and
anger [25]. Notably, these dimensions overlap considerably with neuroticism and extraversion
facets that can be assessed with instruments like the NEO-PI-R.
The controversies around SAD subtyping bear strong resemblance with debates in person-
ality research concerning the usefulness of qualitative types vs. quantitative traits and person-
centered vs. variable-centered approaches [40,41]. There have been attempts to quantify per-
sonality types from trait instruments like the NEO-PI-R [42], and according to a widely-cited
typology, people may fall into three distinct categories: ‘resilient’, ‘overcontrolled’ or ‘under-
controlled’, e.g. [40]. Resilients have below average scores on neuroticism and above average
or intermediate scores on the remaining four dimensions; overcontrollers score high in neu-
roticism and low in extraversion whereas undercontrollers have low scores in conscientious-
ness and agreeableness [43]. Recently Gerlach et al. [44] found evidence of four robust
personality types in a Big Five data set comprising 1.5 million individuals. These were labelled
“average”, “self-centred”, “reserved” and “role model” respectively, the latter showing resem-
blance with “resilient” [44]. It is not well understood how SAD subgroups compare with these
personality types. Presumably, prototypical SAD individuals are overcontrollers but this may
not be true for the anxious-impulsive SAD subtype [36–39]. Anyhow, studies exploring sub-
types of SAD by personality inventories are scant and, to our knowledge, no previous study
has evaluated potential subtypes of SAD derived from the widely researched Big Five personal-
ity dimensions.
PLOS ONE Personality and subtypes in social anxiety disorder
PLOS ONE | https://doi.org/10.1371/journal.pone.0232187 April 29, 2020 3 / 20
https://doi.org/10.1371/journal.pone.0232187
As social anxiety may be conceptually intertwined with several personality components, the
principal aim of the present study was to examine personality traits in a large sample of indi-
viduals diagnosed with SAD (N = 265), in comparison to healthy controls (N = 164) and Swed-
ish normative data, by use of the NEO-PI-R and KSP instruments. We expected elevated
neuroticism and lower extraversion on the NEO-PI-R, as well as higher scores on KSP items
related to anxiety and behavioral inhibition, in SAD individuals. Further aims were to explore
subtypes of SAD by use of cluster analysis of the Big Five personality dimensions, and to com-
pare the personality types with respect to other clinical variables including social anxiety symp-
tom severity, interaction anxiety, trait anxiety, KSP scales and affective ratings during a public
speaking challenge.
Methods
Participants characteristics and general study set-up
In total, 265 patients [117 men, 148 women; mean age (SD): 33.5 (10.3) years] diagnosed with
DSM-IV SAD [45] and 164 healthy controls [82 men, 82 women; mean age: 30.9 (9.9) years],
answered paper-and-pen version of the personality scales NEO-PI-R and KSP. All participants
were volunteers in neuroimaging treatment trials, data being collected from 1998 to 2018, as
described elsewhere [46–54]. NEO-PI-R data were collected from trials conducted from 2003
and onwards. All studies were approved by the Regional Ethical Review Board in Uppsala and
all participants provided written informed consent. The personality forms were filled out in
the home-environment before neuroimaging assessment and any subsequent treatment.
Patients with SAD were recruited mainly through media advertisements while healthy con-
trols answered both to public billboards at Uppsala University and newspaper advertisements.
The psychiatric status was assessed either by a clinical psychologist or a psychiatrist, who
administered the anxiety disorders section of Structured Clinical Interview for DSM-IV
(SCID-I) [55] and the Mini International Neuropsychiatric Interview [56]. The complete
SCID-I and SCID-II interviews were administered in one study [54]. Participants underwent a
medical check-up and were considered physically healthy. All patients met the criteria for a
primary SAD diagnosis according to DSM-IV [45] with marked fear of social situations
including public speaking. Forty-four (17%) presented one comorbid secondary Axis I disor-
der, 21 (8%) presented two comorbidities and 2 patients (0.8%) had three comorbidities.
Comorbid conditions included generalized anxiety disorder, specific phobia, obsessive-com-
pulsive disorder, panic disorder with or without agoraphobia, post-traumatic stress disorder
and mild major depressive disorder. None of the controls fulfilled the screening criteria for
SAD or any other psychiatric condition.
Exclusion criteria were: previous or current neurological and somatic illnesses, current pre-
dominant axis I mental disorder other than SAD (e.g. bipolar or severe major depressive disor-
der, psychosis), pregnancy, menopause, psychological or psychotropic treatment that was
ongoing or had ended within the previous three months, alcohol and narcotics addiction or
abuse, age outside the range of 18–65, or other characteristics that could be expected to inter-
fere with the original neuroimaging study such as claustrophobia or metal implants [46–54].
Personality instruments
Personality traits were measured by Swedish versions of the NEO-PI-R [15] and KSP [21]. The
NEO-PI-R consists of 240 Likert-scale items, rated from 0 (“absolutely disagree) to 4 (“abso-
lutely agree). It is a widely recognized instrument developed to improve the general compre-
hension of personality in adults by assessing five factors (neuroticism, extraversion,
agreeableness, openness to experience, and conscientiousness), and six categories (facets) of
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each one of the five higher-order traits. Cronbach’s alpha values for NEO-PI-R factors in the
present study were: neuroticism 0.92, extraversion 0.86, openness 0.75, conscientiousness 0.80,
and agreeableness 0.62.
The KSP inventory was created with the aim of quantifying imperative dimensions of per-
sonality or temperament, based on psychobiological theories and research [57–59]. The instru-
ment is composed of 135 items grouped into 15 scales: five scales assess propensity to
experience anxiety states (somatic anxiety, psychic anxiety, muscular tension, psychasthenia,
and inhibition of aggression), three dimensions are related to susceptibility for behavioral dis-
inhibition (impulsivity, monotony avoidance, and detachment), and the remaining scales are
mainly associated to hostility and aggression (indirect and verbal aggression, irritability, suspi-
cion, guilt, socialization, and social desirability). In the present study, internal consistency ran-
ged from 0.61 for hostility to 0.92 for anxiety dimensions.
Other instruments
Additional clinical measures were used to compare clusters of SAD individuals. Social anxiety
symptom severity was measured primarily by the Liebowitz Social Anxiety Scale, LSAS
[60,61]. Social interaction anxiety was measured by the Social Interaction Anxiety Scale, SIAS
[62]. Trait anxiety was assessed by Spielberger’s State-trait Anxiety Inventory, STAI-T [63].
Moreover, self-rated fear and distress were assessed with 0–100 (min-max) scales during a
public speaking behavioral test administered in conjunction with the neuroimaging trial, see
e.g., [49,50,52,54]. Because the public speaking challenge was administered within the scanner
for PET trials, but outside the scanner for fMRI trials, we used type of test as a covariate in
group comparisons. Finally, clinician-rated data on severity category (mild/moderate/severe)
were retrieved from the diagnostic interview (SCID) forms or, in case of missing information,
a severity rating was derived from the Clinical Global Impression–Severity (CGI-S) scale [64],
with scores of �5 indicating severe, 4 = moderate, and 3 = mild. Diagnostic interview data on
DSM-IV subgroup (generalized/nongeneralized SAD), and avoidant personality disorder (yes/
no) as assessed with the SCID-II [65] was obtained in a subset (n = 72) of the SAD sample.
Statistical analyses
Statistical analyses were performed using SPSS Version 25 (IBM SPSS Statistics for Windows,
Version 25.0. Armonk, NY: IBM Corp). Independent sample t-tests were run to compare the
mean scores between the two groups on both personality scales. Bonferroni adjustment for
multiple comparisons was used for Big Five dimensions whereas Holm adjusted alpha levels
were applied for NEO-PI-R facets and KSP variables due to the larger number of comparisons.
To determine the magnitude of observed significant effects, a between-group effect size was
calculated using Cohen’s d formula [66]. For informatory purposes effect sizes (d) were also
calculated for SAD vs. normative group comparisons, using Swedish norm data for NEO-PI-R
[67] and KSP [68]. Logistic regression analysis including the Big Five personality variables was
performed (with a p<.01 Bonferroni criterion) to identify independent predictors of group
(patient or control).
Two-step cluster analysis with log-likelihood distance measures was used in SPSS for
exploratory detection of potentially similar groups of persons with relatively homogenous per-
sonality traits [69]. The 15 KSP variables were previously found to represent “lower-order
traits” for neuroticism, extraversion, agreeableness, while no representation was found for
openness or conscientiousness [68]. Because of this, the NEO-PI-R Big Five dimensions were
selected as cluster variables, and the KSP scales as profile variables, in the analysis. One-way
analyses of variance (ANOVAs) were performed to ascertain significant differentiation
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between the resultant clusters, using a standard level of significance (p<0.05) followed by Bon-
ferroni post hoc comparisons, controlling for multiple comparisons.
Results
Group differences in demographic characteristics
There were no differences between the SAD patients and healthy controls with respect to gen-
der distribution (χ2 = 1.394;p = .273). There was a group difference in age (t = 2.601;df = 427;
p = .010), but age did not correlate with the NEO-PI-R or KSP personality variables, except for
weak correlations with Neuroticism (r = −.113,p<.05), Openness (r = −.138,p<.01), Social
Desirability (r = .190,p<.01), Monotony Avoidance (r = −.137,p<.05), and Detachment (r =
.193,p<.01). Controlling for age in the subsequent statistical analyses did not alter any signifi-
cant result.
Group differences in the revised NEO personality inventory
In total, 211 SAD patients (91 men, 120 women; mean age ± SD: 32.7 ±10.6 years) and 138
healthy control participants (73 men, 65 women; 30.8 ± 9.9 years) completed the NEO-PI-R
self-report. Independent samples t-tests revealed that subjects with SAD had significantly
higher scores on neuroticism and significantly lower scores on extraversion, openness, and
conscientiousness, with large effect sizes, as compared to healthy controls (p<.001)—see
Table 1. On facets, there were statistically robust group differences on all lower-order traits of
extraversion and neuroticism (S1 Table). For openness and conscientiousness facets, between-
group effect sizes varied from moderate to large and significant differences, exceeding the Bon-
ferroni criterion, were found on openness to actions-O4, ideas-O5, and values-O6; compe-
tence-C1, dutifulness-C3, and self-discipline-C5. Despite no group difference on the full
agreeableness dimension, significant differences were found at the facet level but in mixed
directions, with lower trust-A1 and altruism-A3, but higher straightforwardness-A2 and mod-
esty-A5, in patients–see S1 Table.
When comparing SAD patients to Swedish normative data [68] large effect sizes were only
noted for neuroticism and extraversion and a moderate effect size for conscientiousness
(Table 1). Effect sizes were also large for 8 of the 12 neuroticism and extraversion facets, as well
as for self-discipline-C5 (S1 Table). On openness to ideas-O5 and values-O6, patients scored
lower than the control sample but higher than the Swedish normative group, whereas patients
were steadily lower on openness for actions-O4.
To further evaluate personality dimensions that were independent predictors of group
(SAD or control), a logistic regression analysis was conducted. Results showed that only neu-
roticism and extraversion were robust significant predictors (p�.001) when all dimensions
were included in the statistical model (Table 2). The model explained 83% of the variance,
according to Nagelkerke R Square and correctly classified 93% of cases. Hosmer and Leme-
show test indicated adequate goodness of fit (χ2 = 5.536; p = .699). Variance inflation factors
(VIF) were <2.22 indicating no serious multicollinearity. Controlling for age in the model did
not alter results, neuroticism and extraversion remaining highly significant (p < .001)
predictors.
Group differences in the Karolinska Scales of Personality
The KSP was completed by 217 patients (99 men, 118 women; mean age ± SD 34.1 ±10.6
years) and 123 healthy control subjects (64 men, 59 women; 30.4 ±10.0 years). Significantly
higher scores for the SAD sample, in comparison to controls, were noted on psychic anxiety,
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somatic anxiety, psychasthenia, inhibition of aggression, detachment, muscular tension, irrita-
bility, suspicion, and guilt. Significantly lower scores were noted for socialization, monotony
avoidance, impulsivity, social desirability and verbal aggression (p�.005)–see Table 3. Effect
sizes were generally large or very large. Only on indirect aggression, the group difference was
non-significant (p = 0.062). Comparing SAD with normative data also confirmed a largely
deviant KSP profile in the patient sample although with more conservative estimates of effect
size (Table 3). Because of the large number of scales and multicollinearity issues, logistic
regression was not used for the KSP. Correlations between KSP scales and NEO-PI-R dimen-
sions are given in S2 Table (SAD sample).
Two-step cluster analysis of personality types in social anxiety disorder
The 211 SAD patients with complete NEO-PI-R data were included in a two-step cluster analy-
sis using log-likelihood distance measures, Schwarz’s Bayesian Criterion (BIC) as validation
measure [70], and the Big Five dimensions as cluster variables. This resulted in a three-cluster
solution–see Fig 1. The five input variables yielded a silhouette coefficient of 0.3, indicative of
fair cluster homogeneity. The variable exhibiting the highest predictor importance, in the crea-
tion of the three clusters, was extraversion, followed by neuroticism, conscientiousness and
openness (Fig 1A). Based on the subsequent descriptive and profile analyses (see further
below), cluster 1 was labelled Prototypical (n = 69, 32.7%); cluster 2 Introvert-Conscientious
(n = 62; 29.4%); and cluster 3 Instable-Open (n = 80, 37.9%)–see Fig 1B.
As indicated by separate ANOVA’s, …
Original Paper
Predicting Social Anxiety From Global Positioning System Traces
of College Students: Feasibility Study
Mehdi Boukhechba1, PhD; Philip Chow2, PhD; Karl Fua2, PhD; Bethany A Teachman2, PhD; Laura E Barnes1, PhD
1Systems and Information Engineering Department, School of Engineering and Applied Science, University of Virginia, Charlottesville, VA, United
States
2Department of Psychology, University of Virginia, Charlottesville, VA, United States
Corresponding Author:
Laura E Barnes, PhD
School of Engineering and Applied Science
Systems and Information Engineering Department
University of Virginia
151 Engineer's Way, Olsson Hall 101B
Charlottesville, VA, 22904
United States
Phone: 1 (434) 924 1723
Email: [email protected]
Abstract
Background: Social anxiety is highly prevalent among college students. Current methodologies for detecting symptoms are
based on client self-report in traditional clinical settings. Self-report is subject to recall bias, while visiting a clinic requires a high
level of motivation. Assessment methods that use passively collected data hold promise for detecting social anxiety symptoms
and supplementing self-report measures. Continuously collected location data may provide a fine-grained and ecologically valid
way to assess social anxiety in situ.
Objective: The objective of our study was to examine the feasibility of leveraging noninvasive mobile sensing technology to
passively assess college students’ social anxiety levels. Specifically, we explored the different relationships between mobility
and social anxiety to build a predictive model that assessed social anxiety from passively generated Global Positioning System
(GPS) data.
Methods: We recruited 228 undergraduate participants from a Southeast American university. Social anxiety symptoms were
assessed using self-report instruments at a baseline laboratory session. An app installed on participants’ personal mobile phones
passively sensed data from the GPS sensor for 2 weeks. The proposed framework supports longitudinal, dynamic tracking of
college students to evaluate the relationship between their social anxiety and movement patterns in the college campus environment.
We first extracted the following mobility features: (1) cumulative staying time at each different location, (2) the distribution of
visits over time, (3) the entropy of locations, and (4) the frequency of transitions between locations. Next, we studied the correlation
between these features and participants’ social anxiety scores to enhance the understanding of how students’ social anxiety levels
are associated with their mobility. Finally, we used a neural network-based prediction method to predict social anxiety symptoms
from the extracted daily mobility features.
Results: Several mobility features correlated with social anxiety levels. Location entropy was negatively associated with social
anxiety (during weekdays, r=−0.67; and during weekends, r=−0.51). More (vs less) socially anxious students were found to avoid
public areas and engage in less leisure activities during evenings and weekends, choosing instead to spend more time at home
after school (4 pm-12 am). Our prediction method based on extracted mobility features from GPS trajectories successfully
classified participants as high or low socially anxious with an accuracy of 85% and predicted their social anxiety score (on a scale
of 0-80) with a root-mean-square error of 7.06.
Conclusions: Results indicate that extracting and analyzing mobility features may help to reveal how social anxiety symptoms
manifest in the daily lives of college students. Given the ubiquity of mobile phones in our society, understanding how to leverage
passively sensed data has strong potential to address the growing needs for mental health monitoring and treatment.
(JMIR Ment Health 2018;5(3):e10101) doi: 10.2196/10101
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KEYWORDS
mental health; mHealth; mobility; GPS; social anxiety disorder
Introduction
Social anxiety is marked by an extreme fear of being scrutinized
and judged by others in social or performance situations [1]. In
addition to being a widespread problem among college students,
a high social anxiety level is associated with a low quality of
life. For example, socially anxious individuals suffer from
impaired academic functioning and relationships [2]. The
American College Health Association reported that 40% of
students reported feeling “overwhelming anxiety” at least once
in the preceding year [3].
Current techniques to identify social anxiety are typically based
on self-report via questionnaires or interviews in traditional
clinical settings, where only small numbers of people can be
monitored and client motivation is required to seek an
assessment. This approach is inadequate and fails to meet the
growing needs of mental health monitoring and treatment on
college campuses. As a result, many individuals who should
seek help never receive any. For example, according to the
Anxiety and Depression Association of America, 36% people
with social anxiety disorder report having symptoms for 10 or
more years before seeking help [1].
Mental health symptoms can be indirectly assessed through
both subjective (eg, self-report surveys and interviews) and
objective (physiological variables such as heart rate) methods.
Such methods have largely been employed in clinical or
laboratory settings, which limits the ecological validity of
findings. To increase the generalizability of findings, researchers
have tried to understand mental health through noninvasive and
real-time data collected from people’s everyday lives. For
example, studies using surveys to repeatedly sample people’s
momentary affective experiences over time have found that
individuals with high (vs low) social anxiety symptoms report
more anger and fewer and less intense positive emotions [4,5].
While studies that administer repeated surveys offer a glimpse
of the socioemotional aspects of daily life, regularly prompting
individuals to answer questions also raises the issue of
participation burden.
Importantly, embedded mobile phone sensors (eg,
accelerometers, light sensors, Global Positioning System [GPS])
are now advanced enough to allow for passive and continuous
data collection [6,7] and are increasingly being used to enhance
the understanding of the relationship between objective behavior
and mental health status, such as bipolar disorder [8], anxiety
and depression [7,9-13], and Alzheimer disease and dementia
[14]. Digital phenotyping, which is a term used for describing
this new approach of measuring behavior from mobile phones
and wearable sensors, is already revealing new aspects of
behaviors that appear clinically relevant [15]. For example, Saeb
et al [16,17] provided preliminary evidence that extracting
location-based mobility features could be used to detect
depression and anxiety levels. Barnet et al [18] were able to use
passively generated mobile phone data to identify statistically
significant anomalies in patients with schizophrenic behavior
in the days prior to relapse. The above studies show the
importance of analyzing behaviors to better understand mental
states.
Because social anxiety is marked by intense fear of social
scrutiny, passively sensed location information may reveal key
markers that can be used to detect a high distress level. Semantic
locations (ie, the type of social location someone visits) might
be particularly important in the context of social anxiety. For
example, individuals with social anxiety might systematically
avoid specific places, such as those of leisure, or choose to spend
peak social hours by isolating themselves at home. Thus,
analyzing GPS data from college students and the types of places
they frequent might provide crucial information about key
mobility features associated with social anxiety levels. Some
examples of mobility features include how long students spend
at different types of locations (eg, home, leisure) and how often
they frequent those locations.
Important contributions [7,9,12,17,19] have been made to
determine how passively sensed mobile phone data relates to
users’ mental health statuses and stress levels. We followed the
key steps from these valuable early studies and extended the
scope of features and questions addressed, as outlined below:
(1) recruiting participants and deploying a mobile app for data
collection; (2) collecting data such as GPS locations, recognized
activities, or self-reported data from participants during the
study; (3) assessing participants’ mental health status using
validated clinical measures; (4) extracting meaningful features
or metrics from participants’ data (eg, time spent at each
different location); and (5) correlating these features or metrics
with participants’ mental health status (eg, Pearson correlation
between number of distinct locations and clinically validated
measures).
This study builds on prior work in several ways and improves
our understanding of the relationships between social anxiety
symptoms and daily routines. In this paper, we introduce and
analyze a set of passively extracted spatiotemporal features that
enhance our understanding of the temporal and spatial dynamics
of behavioral patterns (eg, regularly visiting a location during
specific hours) of socially anxious students, which may allow
for more precise, personalized interventions. We also propose
a hierarchical social anxiety prediction method based on neural
networks. This work may ultimately help researchers and
clinicians to passively and remotely monitor patients’ social
anxiety levels. The primary aim of this paper was not to test
specific hypotheses, but rather to explore a framework for using
passively collected GPS data to detect social anxiety levels.
Methods
Study Organization and Data Collection
Participants were undergraduate students with varying social
anxiety levels, recruited from undergraduate psychology classes
that provide course credit as a participation incentive. Because
some participants met or exceeded their course credit limit, a
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subset of participants was eligible to receive a small amount of
monetary compensation (up to a maximum of US $40). Students
were recruited through email advertisements as well as through
an undergraduate study participant pool. The decision to recruit
university students was based on two reasons: (1) there are high
social anxiety levels among young adults, and (2) recruiting
young adults in a university setting provides a relatively
homogeneous sample in terms of life phase, psychological
stressors, and life experiences, thereby eliminating a wide
variety of potential confounding factors.
After receiving approval from the university Institutional Review
Board, 228 participants were recruited. Social anxiety level was
first assessed via the Social Interaction Anxiety Scale (SIAS)
[20] in a prestudy screening battery offered to select
undergraduate psychology classes in exchange for course credit.
The SIAS contains 20 items, each rated from 0 to 4. Generally,
a higher SIAS score (specifically, higher than 34) [21] indicates
a higher risk of having social anxiety concerns; a low score
indicates a lower risk for social anxiety concerns.
Following informed consent, a custom mobile app (Sensus)
[22] was installed on participants’ personal mobile phone (either
IOS or Android device). As shown in Figure 1, participants
were informed that the app passively collected the GPS location
information every 150 seconds and uploaded it to an Amazon
Web Services S3 server. After the 2-week experiment was
completed, researchers could access all participants’ raw GPS
data for analysis.
Global Positioning System Data Preprocessing
Participants’ raw GPS data were parsed by semantic locations
(eg, restaurant, campus area, and shops) by combining a
spatiotemporal clustering algorithm and OpenStreetMap (OSM)
geodatabase [23]. Specifically, we first clustered participants’
GPS locations using time and space dimensions, and then, each
cluster was associated with a semantic location using OSM data
[24] (Figure 2).
Our clustering algorithm is inspired by the work of Kang et al
[25], and it aims to eliminate the intermediate locations between
important places and determine the number of clusters
(important places) autonomously. The core idea guiding our
approach is to cluster the locations along the time axis. As a
new location measurement is read, the new location is compared
with previous locations. If the new location is moving away
from previous locations (within a certain distance of each
other—a parameter of our algorithm), the new location is
considered to belong to a different cluster than the one for the
previous locations. Otherwise, it is considered to belong to the
previous cluster. If a cluster’s time duration is longer than the
threshold (the second parameter of our algorithm), the cluster
is considered to be a significant place (see A and B in Figure
2); otherwise, it is ignored (see i1 and i2 in Figure 2).
The algorithm is depicted in Textbox 1 (d and t are our distance
and time threshold parameters). When a new location
measurement event is generated, the cluster function is invoked.
The current cluster cl is the set of location measurements that
belong to the current cluster. The pending location pl is used to
eliminate outliers. Even if the new location is far away from
the current cluster (distance is larger than the distance threshold
d), the algorithm does not start a new cluster right away with
the new location. Instead, the algorithm waits for the next
location to determine if the user is really moving away from
the cluster or if the location reading was just a spurious outlier.
The Places contain the significant places where the user stays
longer than the time threshold t.
Figure 1. Social anxiety monitoring framework.
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Figure 2. Illustration of our time-based clustering algorithm using real GPS data retrieved from one participant in the study. In (1), GPS locations are
clustered using the algorithm described in Textbox 1. In (2), the trajectory is summarized to two places (A and B) and the transition state, which
aggregates all GPS points between the clusters A and B. Finally, in (3), the clusters A and B are labeled using OSM data. GPS: Global Positioning
System, OSM: open street map.
Textbox 1. Spatiotemporal clustering algorithm.
Input: measured location loc
Output: current cluster cl, pending location pl, significant places Places
if distance (cl, loc) < d then
add loc to cl
pl = null
else
ifpl ≠ null then
if duration(cl) > t then
if contain long gaps(cl) then
remove gaps from cl
end if
add cl to Places
end if
clear cl
add pl to cl
if distance (cl, pl) < dthen
add loc to cl
pl = null
else
pl = loc
end if
else
pl = loc
end if
end if
Our algorithm was tuned using d=60 m and t=600 seconds.
These values appeared to give the best clustering results for our
data; they have also been reported in the literature to give the
best performance for spatiotemporal clustering algorithms [25].
After detecting the significant clusters, we transformed each
GPS cluster to a meaningful semantic label using OSM data.
Each GPS cluster’s centroid is associated to a geographic entity
(eg, in Figure 2, cluster A is associated to Gilmer Hall and
cluster B to Olsson Hall, both of which are buildings on the
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university campus) using a spatial query in our geodatabase
powered by OSM. The semantic data obtained from OSM is
then classified to one of the following classes:
• Home: our algorithm has been trained to recognize “Home”
as the place having a house OSM tag (eg, apartment,
dormitory, house, etc) where a subject stays the most
between 10 pm and 9 am; see [23] for more details about
OSM tags.
• Other houses: all houses other than “Home”; in this study,
given all participants are college students, other houses
were assumed to mostly be friends’ houses.
• Education: eg, university and libraries
• Leisure: eg, sports locations, pubs, cinemas, and coffee
shops
• Food: eg, dining halls and restaurants including fast food
joints
• Supermarket: all full-service grocery stores
• Religious: all places of worship, including churches,
mosques, cathedrals, synagogues, temples, etc
• Service: eg, bank, post office, courthouse
• Out of town: locations outside of the city where the study
was conducted
• In transition: going from one place to another
Note that ideally, food places would be merged with leisure and
supermarkets with service classes. However, we decided to
separate them because we discovered a particular pattern that
high socially anxious participants (SAP) share in terms of time
spent at food places and supermarkets, which will be discussed
in the next section.
When constructing GPS clusters labeled with semantics, we
verified if the users’ data contained GPS gaps. We defined a
GPS gap gi as a minimum 10-minute time span where GPS data
were missing. Gaps could be caused by different events, such
as turning the phone off or “killing” the app. For gaps ∈ [5 min,
30 min], we compared the cluster cli and the cluster cli+1, created
before and after the gap, respectively. If the 2 clusters had the
same semantic labels, we considered that the user did not change
his or her location during gi; thus, we merged the clusters cli,
cli+1, and the gap gi. However, if the 2 clusters had different
semantic labels, we assigned the “Transition” label to gi; ie, the
user changed locations during this gap.
For gaps exceeding 30 minutes in duration, we removed the
corresponding time periods from the experiment (see Textbox
1, line 7), because it was hard to predict what the participants
did during such long gaps.
Mobility Feature Extraction
After detecting participants’ visited places and labeling them
using OSM, we used the semantic labels to identify the
following mobility features for each participant:
Cumulative Staying Time in each type of location: Given a
type of location and a specific participant, this metric
characterized the percentage of total time that the participant
spent at one type of location during a specific time window (eg,
during a day, during mornings vs afternoons).
Distribution of visits for each type of location: Given a type
of location and a specific participant, this metric calculated the
density distribution of time of visits over the time of day. For
instance, if a participant was more likely to go to leisure places
during evenings, we would find more density during the evening
periods for this type of locations. We introduced this metric
because cumulative staying time captures only time spent at
each different location without recording when these visits
happened; for instance, spending 2 hours at the university during
mornings was different than that during evenings.
Location entropy: This metric was calculated using the entropy
of Shannon [26] to measure how each participant’s time was
distributed over different location classes, where pi is the
percentage of time spent at location i and n is the total number
of visited locations:
Transition Frequency from one type of location to another:
Given a specific participant and two types of locations (eg,
“Home” and “Work”), this metric characterized the frequency
at which the participant transited from one type of location to
another. This metric was applied unidirectionally; for example,
the transition frequency of “Home” “Work” and the transition
frequency of “Work” “Home” were different.
Results
Participants
Participants comprised 228 university students (mean age 19.43
[SD 2.92] years; 141/228, 62%, females). Participants reported
their race or ethnicity as white (118/228, 52%), Asian (61/228,
27%), black (12/228, 5%), Latino (5/228, 2%), and multiracial
(32/228, 14%). Figure 3 shows the distribution of SIAS scores
among the 228 participants. The SIAS scores ranged between
11 and 54 with a mean score 29.91 (SD 9.1).
The goal of our study was: (1) to understand the relationship
between the extracted mobility features and the preassessed
SIAS score and (2) to investigate whether the extracted features
could predict SIAS scores. Consequently, in the next two
sections, we will first analyze the relationship between the
mobility features and social anxiety and then investigate whether
these features can accurately predict social anxiety.
Mobility Data Analysis
In this section, we present the results of our analysis
investigating the relationship between social anxiety levels
(using the preassessed SIAS measures) and the four extracted
mobility metrics: cumulative staying time, distribution of visits,
location entropy, and transition frequency.
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Figure 3. The distribution of Social Interaction Anxiety Scale (SIAS) scores for recruited participants. The Epanechnikov kernel function was used to
compute the density estimates presented in this figure.
Cumulative Staying Time
We calculated the Pearson correlation between each participant’s
average daily cumulative staying time at each different location
and his or her SIAS score to identify the direction (positive or
negative) and strength of each correlation. To assess the
reliability of the correlations, we also calculated significance
levels (P value).
Results presented in graph 1 of Figure 4 show that daily time
spent at some locations was associated with the SIAS score.
Indeed, time spent at food locations, such as restaurants and
dining halls, was negatively correlated with the SIAS score.
However, time spent at supermarkets was positively correlated
with the SIAS score. This suggests that high SAP are more
likely to buy food from supermarkets so they can eat at home,
perhaps to avoid social interactions at restaurants.
College students may have common mobility patterns that bias
the daily correlation analysis, such as class times at the
university following a typical schedule. In order to find the
hidden patterns between the cumulative staying time and SIAS
score, we analyzed correlations in different daily time epochs:
8 am-4 pm, 4 pm-12 am, and 12 am-8 am. Results presented in
graphs 2, 3, and 4 of Figure 4 suggest the following:
• Similar to the 24-hour analysis (Figure 4, graph 1), the time
spent at food locations was negatively correlated with the
SIAS score, while time spent at supermarkets was positively
correlated with the SIAS score in both the 12 am-8 am and
8 am-4 pm time windows.
• Time spent doing leisure activities was positively correlated
with the SIAS score between 8 am and 4 pm, while the rest
of time it was negatively correlated with the SIAS score.
This suggests that high SAP prefer to do their leisure
activities between 8 am and 4 pm, rather than during the
evenings. This may reflect the social demands typical of
different types of leisure activities done during the day
versus evening (eg, it is more normative to be alone at a
coffee shop than at a pub or bar).
• We did not find a correlation between time spent at home
and the SIAS score between 12 am and 4 pm (Figure 4,
graphs 2 and 4), which may simply indicate that no matter
how socially anxious students were, they tended to stay at
home (sleeping) between 12 am-8 am and leave home to
go to school between 8 am and 4pm. However, during the
time after typical school hours (between 4 pm and 12 am,
when students have the choice to stay at home or not), we
found a positive correlation between the SIAS score and
time spent at home (Figure 4, graph 3). This finding is
consistent with a prior work that associates time spent at
home with depressive and social isolation symptoms [27].
• Finally, we found a small correlation (0.22) between time
spent out of town and the SIAS score during the 4 pm-12
am time window, perhaps indicating that more socially
anxious students leave the university to visit familiar
individuals or family, rather than engaging in campus night
life, which may have more demands to be social with
unfamiliar individuals.
After analyzing the correlation between cumulative staying time
and the SIAS score, we studied the difference between
cumulative staying time during weekdays versus weekends for
high (SIAS score ≥ 34) versus low (SIAS < 34) SAP. A score
of 34 is an established clinical cutoff for the SIAS score to
classify a subject as high or low socially anxious [21]. The
reason for this analysis is that students’ patterns may differ
between weekdays and weekends. For instance, maybe the time
spent at the university is not a good predictor of social anxiety
during weekdays, but it is during weekends when students
presumably have more autonomy in determining their schedule
(because classes are not set).
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Boukhechba et alJMIR MENTAL HEALTH
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Figure 4. The correlations between time spent at each different type of location and Social Interaction Anxiety Scale (SIAS) scores using different
time windows. In (1), we have presented the correlations on a daily basis, while in the other figures, we have focused on specific portions of the day,
ie, between 8 am and 4 pm, between 4 pm and 12 am, and between 12 am and 8 am. The x-axis represents the correlation significance; the left y-axis
describes the type of locations, and the right y-axis represents the P value of the Pearson correlation in that specific type of location. Correlations having
a coefficient r>0.2 and a strength P<0.05 are presented in purple.
Table 1. The difference between high and low socially anxious students in terms of average daily time spent (in minutes) at each different location
during weekdays versus weekends.
WeekendsWeekdaysLocation
LowaHighaLowaHigha
10.41b16.64b1.41b6.64bSupermarket
5.739.971.836.97Service
2.7510.351.637.35Religious
27.8b88.25b22.33b38.25bOut of town
75.09b43.16b45.19b26.04bLeisure
469.02b594.74b386.94434.74Home
64.81b16.21b24.81b17.63bOther houses
32.97b9.78b12.97b3.78bFood
102.98b148.79b316.88338.44Education
aSocial anxiety levels were classified to high and low using SIAS score=34 as cutoff.
bSignificant differences (P<.05) between high and low SAP detected using unpaired two-samples t test.
Results presented in Table 1 show that high SAP spent less time
at leisure and food places and more time at home and out of
town during both weekdays and weekends. However, during
weekends, high SAP tended to spend more time at education
places (around 148 minute) compared with low SAP (around
102 minute). They also appeared to spend less time at other
houses (a difference of 48 minute), perhaps because they were
less comfortable engaging in social interactions that may happen
at friends’ houses or simply had fewer opportunities (invitations)
for these interactions.
Distribution of Visits
To better understand the relationship between students’ daily
routines and their social anxiety, we analyzed the distribution
of location visits over the day for both high and low SAP. Figure
5 illustrates the difference in the distribution of location visits
between the two populations. Note that this figure analyzes the
time of visits (the time that a participant visited a specific
location) without considering the duration of visits because
cumulative staying time has already been studied above.
The results show a difference in the pattern of visits to food
places, supermarkets, others’ houses, and leisure places. Low
SAP appeared to prefer going to friends’ houses and food and
leisure places during evenings (after 4 pm) more than high SAP.
On the other hand, high SAP preferred to stay at home or go to
the supermarket during that time period. This suggests that there
may be a difference in how students plan their daily activities
based on how socially anxious they are. Understanding these
patterns may help clinicians identify when a person is starting
JMIR Ment Health 2018 | vol. 5 | iss. 3 | e10101 | p. 7http://mental.jmir.org/2018/3/e10101/
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Contents lists available at ScienceDirect
Journal of Affective Disorders
journal homepage: www.elsevier.com/locate/jad
Research paper
The role of expressive suppression and cognitive reappraisal in cognitive
behavioral therapy for social anxiety disorder: A study of self-report,
subjective, and electrocortical measures
Yogev Kivity⁎,1, Lior Cohen, Michal Weiss, Jonathan Elizur, Jonathan D. Huppert
Department of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel
A R T I C L E I N F O
Keywords:
Social Anxiety
Emotion Regulation
Cognitive Reappraisal
Expressive Suppression
Cognitive Behavioral Therapy
A B S T R A C T
Background: Contemporary models of cognitive behavioral therapy (CBT) for social anxiety disorder (SAD)
emphasize emotion dysregulation as a core impairment whose reduction may play a causal role in psy-
chotherapy. The current study examined changes in use of emotion regulation strategies as possible mechanisms
of change in CBT for SAD. Specifically, we examined changes in expressive suppression and cognitive reappraisal
during CBT and whether these changes predict treatment outcome.
Methods: Patients (n = 34; 13 females; Mean age = 28.36 (6.97)) were allocated to 16-20 sessions of CBT. An
electrocortical measure of emotion regulation and a clinician-rated measure of SAD were administered monthly.
Self-report measures of emotion regulation and social anxiety were administered weekly. Multilevel models were
used to examine changes in emotion regulation during treatment and cross-lagged associations between emotion
regulation and anxiety.
Results: CBT led to decreased suppression frequency, increased reappraisal self-efficacy, and decreased un-
pleasantness for SAD-related pictures (ps < .05). At post-treatment, patients were equivalent to healthy controls
in terms of suppression frequency and subjective reactivity to SAD-related stimuli. Gains were maintained at 3-
months follow-up. Decreases in suppression frequency and electrocortical reactivity to SAD-related pictures
predicted lower subsequent anxiety but not the other way around (ps < .05). Lower anxiety predicted greater
subsequent increases in reappraisal self-efficacy.
Limitations: The lack of a control group precludes conclusions regarding mechanisms specificity.
Conclusions: Decreased frequency of suppression is a potential mechanism of change in CBT for SAD.
1. Introduction
Recent models of anxiety, including social anxiety disorder (SAD),
emphasize impairments in emotion regulation (Hofmann, Sawyer,
Fang, & Asnaani, 2012; Morrison & Heimberg, 2013). Two regulation
strategies, cognitive reappraisal and expressive suppression, may be
particularly relevant for SAD (Morrison & Heimberg, 2013). In the
process model of emotion regulation (Gross, 2015), cognitive re-
appraisal is generally considered an adaptive strategy that involves
cognitive change to regulate one's emotion – for example, attempts to
reinterpret emotional stimuli in less threatening ways (Gross, 2015). On
the other hand, expressive suppression is an attempt to inhibit one's
expression of emotions and is generally considered maladaptive
(Gross, 2015).
In Heimberg's updated model (Morrison & Heimberg, 2013),
emotion dysregulation in SAD includes avoidance of anxiety (e.g.
avoidance or escape from stressful situations) and expressive suppres-
sion due to believing that expressing emotions will lead to rejection or
to excessive focus on oneself. The model further proposes that in-
dividuals with SAD are less effective in implementing reappraisal. Ac-
cordingly, decreased suppression and increased effective use of re-
appraisal are hypothesized to lead to symptom reduction, for example,
by outward shifting of attention and by reducing exaggerated prob-
ability and cost of rejection.
Recently, studies have examined suppression and reappraisal in SAD
(reviewed in Dryman & Heimberg, 2018). Cross-sectional and daily
diary studies typically focus on the frequency of use of a strategy and
self-efficacy (perceived success in implementation). Individuals with
SAD report an over-reliance on suppression and lower frequency and
self-efficacy of reappraisal (e.g., Farmer & Kashdan, 2012;
https://doi.org/10.1016/j.jad.2020.10.021
Received 10 May 2020; Received in revised form 16 August 2020; Accepted 11 October 2020
⁎ Corresponding author: Yogev Kivity, Department of Psychology, Bar Ilan University, Ramat Gan 5290002, Israel, Telephone: +972-3-5318715
E-mail address: [email protected] (Y. Kivity).
1 Yogev Kivity is now in the Department of Psychology, Bar Ilan University, Israel.
Journal of Affective Disorders 279 (2021) 334–342
Available online 14 October 2020
0165-0327/ © 2020 Elsevier B.V. All rights reserved.
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Gaebler, Daniels, Lamke, Fydrich, & Walter, 2014). Impairments in
frequency seem to be larger than impairments in self-efficacy in sup-
pression while the opposite is true for reappraisal (Kivity &
Huppert, 2018, 2019), thus supporting the Heimberg model.
In addition, studies have also utilized subjective ratings during lab
tasks of emotion regulation to study reappraisal and suppression abil-
ities in SAD. These studies typically present SAD-related stimuli to
participants, such as pictures of rejecting faces (Goldin et al., 2009a),
pictures portraying scenes of rejection and embarrassment (Kivity &
Huppert, 2016, 2018, 2019) and idiographic statements of negative
self-beliefs (Goldin et al., 2009b) while asking participants to change
the way they interpret these stimuli in order to reduce the distress they
evoke in them. However, compared to questionnaires and daily diary
measures, these lab studies have shown intact emotion regulation
abilities in SAD compared to controls, even under social stress (e.g.,
Gaebler et al., 2014; Kivity & Huppert, 2016, 2018, 2019). Thus, self-
reported impairments are not reflected in lab performance. Among
other possibilities, this discrepancy may suggest a difficulty im-
plementing strategies in daily life despite an intact ability to implement
them upon instruction in controlled circumstances, low ecological va-
lidity of lab-based measures, or a bias in self-reports that does not exist
in lab-based measures. Examining the role that each of these aspects
(lab-based performance, self-reported frequency and self-reported self-
efficacy) plays in treatments for SAD may shed light on their relative
importance.
Several techniques of cognitive behavioral therapy (CBT) for SAD
seem relevant for improving emotion regulation. Psychoeducation and
exposures likely decrease suppression, as patients learn that hiding
their anxiety is futile and likely to backfire. Outward shifting of at-
tention (focusing on the task at hand instead of on how one is per-
ceived) presumably decreases suppression by decreasing patients’ pre-
occupation with their overt signs of anxiety. Furthermore, cognitive
restructuring can potentially increase the use of reappraisal by chan-
ging biased catastrophic cognitions. Finally, psychoeducation and in-
vivo exposure challenge biased cognitions and are expected to promote
reappraisal too.
Studies have shown that self-reported reappraisal (frequency and
self-efficacy) increases in CBT (Goldin et al., 2014a; Goldin, Morrison,
Jazaieri, Heimberg, & Gross, 2017; Kocovski, Fleming, Hawley, Huta, &
Antony, 2013; Moscovitch et al., 2012). However, findings regarding
self-reported suppression are inconclusive, with one study reporting a
decrease in frequency (Goldin et al., 2014a) and another reporting no
change (Moscovitch et al., 2012). Less is known regarding lab-based
measures: one study found improvements in reappraisal of negative
social evaluations and negative self-beliefs during CBT (Goldin et al.,
2013, 2014b).
Importantly, the best test of the importance of emotion regulation as
a treatment target for SAD is to examine its contribution to symptom
improvement (Nock, 2007). Changes in reappraisal and suppression
that predict treatment outcome would provide further support to
Heimberg's model. Several studies found that increases in self-reported
frequency and self-efficacy of reappraisal predicted subsequent
symptom reduction (Goldin et al., 2017; Kocovski, Fleming, Hawley,
Ho, & Antony, 2015; Moscovitch et al., 2012), although another study
found that only self-efficacy (but not frequency) of reappraisal pre-
dicted subsequent outcome (Goldin et al., 2014a). Decreases in self-
reported suppression frequency predicted contemporaneous, but not
subsequent, symptoms reduction in one study (Goldin et al., 2014a) and
did not predict outcome at all in another (Moscovitch et al., 2012). The
only examination of lab-based reappraisal (Goldin et al., 2014b) found
that greater changes in fMRI measures of reappraisal predicted greater
symptom change during CBT for SAD, although subjective task per-
formance did not. Thus, the self-report findings suggest that reappraisal
increases during CBT for SAD and may be driving symptom change,
with more consistent findings regarding self-efficacy than frequency.
Additional studies are needed regarding lab-based emotion regulation.
The present study examines changes in suppression and reappraisal
during CBT for SAD and their role in treatment outcome using data
from a previously completed study (Huppert, Kivity, Cohen, Strauss,
Elizur & Weiss, 2018). We collected weekly self-reports of the frequency
and self-efficacy of reappraisal and suppression, and monthly lab-based
measures of reappraisal and suppression in response to SAD-related
pictures. Given past findings, our primary focus was self-reported self-
efficacy of reappraisal and frequency of suppression. Frequency of re-
appraisal and self-efficacy of suppression were of secondary interest.
For the lab-based task, we focused on unpleasantness and event-
related potentials (ERP) during reappraisal and suppression of SAD-
related pictures. We focused on the late positive potential (LPP), an ERP
with a posterior midline distribution and an onset of 300 ms post-sti-
mulus (Proudfit, Dunning, Foti, & Weinberg, 2013). Larger LPP reflect
sustained attention to stimuli and elaborative engagement in order to
regulate the emotion (Proudfit et al., 2013). The LPP is sensitive to
emotional intensity and to emotion regulation, with higher amplitudes
for highly arousing stimuli that are reduced following instructions to
regulate, including reappraisal and suppression, even within several
seconds from the presentation of the stimuli (Proudfit et al., 2013). The
LPP shows less habituation over repeated exposure to stimuli compared
to other psychophysiological measures (Proudfit et al., 2013), which
allows for repetition of stimuli and attribution of changes in LPPs to the
same stimuli as due to regulation. The LPP has been utilized in SAD
(Kinney, Burkhouse, & Klump, 2019; Kivity & Huppert, 2018, 2019;
Yuan et al., 2014) but we are unaware of studies examining it during
CBT for SAD.
We examined these hypotheses: 1) CBT will result in significant
improvements in suppression and reappraisal, including reduced fre-
quency of suppression and increased self-efficacy, frequency, and suc-
cessful lab implementation of reappraisal. 2) Improvements in sup-
pression and reappraisal will play a potentially causal role in CBT:
improvements will predict subsequent improvements in anxiety and not
vice versa. 3) We examined whether patients reached an equivalent
level of emotion regulation to healthy controls (HCs) at post-treatment,
and whether gains were maintained at 3-months follow-up without an
a-priori hypothesis.
2. Method
2.1. Participants
Data were drawn from the CBT arm of a study of treatments for SAD
(Huppert et al., 2018)2. Patients were recruited via advertisements and
referrals. Participants were 34 patients who met DSM-IV-TR
(American Psychiatric Association, 2000) criteria for SAD and 40 HCs
with no history of psychiatric disorders, matched to patients on sex, age
and education. One patient decided not to enter treatment and 5 HCs
were removed because they did not have a continued low social anxiety
score between screening and participation. The final sample included
33 patients (13 females, Age: 18-53, M = 28.36, SD = 6.97) and 35
HCs (15 females, Age: 19-45, M = 28.49, SD = 6.28). Participants were
Hebrew speaking and family status was: single: CBT = 59%,
HC = 61%; in a relationship: CBT = 41%, HC = 35%; divorced:
CBT = 0%, HC = 4%. Education levels were: high school: CBT = 15%,
HC = 29%; post-high school: CBT = 21%, HC = 9%; undergraduate
degree/student: CBT = 42%, HC = 27%; graduate degree/student:
2 The original study also included participants receiving a computerized
treatment for SAD called Attention Bias Modification. This treatment was of
shorter duration compared to CBT, of a smaller sample size and only included
three measurements of lab-based emotion regulation. In addition, group as-
signment was random only for a subset of the CBT patients. Due to these rea-
sons, we decided not to include data from this treatment in the current study,
which a priori was designed to examine the role of ER in CBT.
Y. Kivity, et al. Journal of Affective Disorders 279 (2021) 334–342
335
CBT = 21%, HC = 26%. Groups did not differ on demographics (ps >
.05). Ten SAD participants (29.41%) had one comorbid disorder and
two (5.88%) had more than one. The most common comorbid disorders
were depression (n = 9; 26.47%) or other anxiety disorders (n = 4;
11.76%).
2.2. Measures
Liebowitz Social Anxiety Scale (LSAS; Liebowitz, 1987). A 24-
item interviewer-rated measure of fear and avoidance of social inter-
actions and social performance. The Hebrew version (Levin, Marom,
Gur, Wechter, & Hermesh, 2002) was administered by trained clinical
psychology doctoral students, blind to hypotheses. Internal consistency
in all assessments was α = .90 – .96; interrater reliability for 15 ran-
domly chosen interviews was r = .94.
Social Phobia Inventory (SPIN; Connor et al., 2000). A valid and
reliable 17-item self-report measure of social anxiety symptoms,
translated and back translated to Hebrew for previous studies. Internal
consistency in all assessments was: α = .78 – .95.
Emotion Regulation Questionnaire – Self-Efficacy and
Frequency (ERQ; Gross & John, 2003). We used the reliable and valid
Hebrew version (Carthy, Horesh, Apter, Edge, & Gross, 2010). Fol-
lowing Goldin et al. (2009b), we measured both the frequency (ERQ-F)
and self-efficacy (ERQ-SE) in social situations instead of frequency only
(internal consistency of all subscales: α = .73 – .97). Items tap into
reappraisal (e.g., “When I want to feel less negative emotion, I change
the way I'm thinking about the situation”) and suppression (e.g., “I
control my emotions by not expressing them”) which participants en-
dorse using a 1 ("Seldom"/"Ineffectively") to 7 ("Often"/"Effectively")
scale.
Emotion Regulation Task. Full details are provided in supple-
mental material, section 1. We used a task that was developed by
Hajcak and Nieuwenhuis (2006) who presented emotionally-salient
pictures to participants and instructed them to either passively view the
picture or to reappraise the emotion it evokes in them while ERP ac-
tivity was recorded and unpleasantness ratings were collected. Hajcak
and Nieuwenhuis found that the amplitude of the LPP and the level of
subjective unpleasantness were decreased during reappraisal compared
to passive viewing. Thus, the task is validated and suitable for studying
the effects of emotion regulation on electrocortical activity and sub-
jective unpleasantness. In the current study, we adapted the task to
measure suppression in addition to reappraisal and used SAD-related
pictures rather than general pictures (Kivity & Huppert, 2018, 2019). In
selecting the stimuli for the task, we chose to focus on shame, embar-
rassment and rejection because these experiences are central in SAD
(Goldin et al., 2009b; Morrison & Heimberg, 2013; Moscovitch, 2009)3.
Twenty trials of each condition were included: viewing of SAD-related
pictures, viewing of neutral pictures, reappraisal of SAD-related pic-
tures and suppression of SAD-related pictures.
When viewing SAD-related pictures, participants imagined them-
selves as the character that is the focus of shame, rejection, and em-
barrassment. When viewing neutral pictures, participants responded
naturally. When reappraising, participants first imagined themselves as
the character and then changed the way they think of the picture to
decrease their unpleasantness (e.g., "This guy is not laughing at me, but
at someone else"). When suppressing, participants first imagined
themselves as the character and then concealed any expression of
emotions. To enhance the effect of the suppression manipulation, a web
camera was placed above the computer screen and participants were
told that a member of the research team would review the recordings.
Participants were instructed to avoid any expression of their emotions
such that it would be impossible to tell whether they were viewing
neutral pictures or concealing their emotions4.
After each trial, participants rated their unpleasantness on a Self-
Assessment Manikin (SAM; Lang, Bradley, & Cuthbert, 2008) scale (1
through 9; 5 being neutral; transformed such that higher ratings express
greater unpleasantness). We focused on unpleasantness ratings in order
to complement the LPP data (which is mostly correlated with arousal)
and arrive at a more complete picture of the participants’ emotional
experience that takes into account the two basic dimensions of emo-
tions – valence and arousal. After providing unpleasantness ratings,
participants were asked to indicate the instructions they followed
during that trial. In the reappraisal condition, participants were also
asked to record the new interpretation they came up with for the pic-
ture (results not reported here).
Ratings were averaged for each condition and a regulation score
(view – regulate; calculated on the transformed scores) was calculated
to capture the amount of reduction in unpleasantness. Higher scores
indicate larger regulation effects.
SAD-related pictures were collected from the internet5, normed and
shown to evoke moderate shame, embarrassment, rejection and un-
pleasantness (Kivity & Huppert, 2018, 2019). These depicted situations
of shame, rejection, and/or embarrassment such as scenes of people
pointed and laughed at, anxious people during a public speech, and
facial expressions of contempt. Neutral pictures were taken from the
International Affective Picture System (IAPS) database (Lang et al.,
2008).
Psychophysiological Recording, Data Reduction, and Analysis.
Full details are provided in section 2 of the supplement. ERPs were
constructed by averaging trials in each condition (view, suppression,
reappraisal and view neutral). Following Moser, Hartwig, Moran,
Jendrusina, & Kross (2014), the LPP was quantified as the average
voltage in 5 parietal electrodes (CPz, P1, Pz, P2, POz) in the entire
segment (400-2000 ms). A regulation change score (view – regulate)
was calculated to capture the amount of reduction in the LPP. Higher
scores indicate larger regulation effects. Trials in which participants
failed to use the instructed strategy were excluded (4.57% on average,
no group differences). Studies have shown that the LPP can be reliably
measured with as little as 8 trials and that it varies little beyond 12
trials (Moran, Jendrusina, & Moser, 2013). Assessments with fewer than
12 valid trials in each condition were removed from analyses (5%
across groups, no group differences).
2.3. Treatment and therapists
Individual CBT was delivered for up to 20 sessions using a manual
by Roth-Ledley, Foa, & Huppert (2006), based on Clark's (2005) CBT for
SAD. Components such as building an idiographic model, outward
shifting of attention, safety behaviors experiment, video feedback, be-
havioral experiments and exposures, and optional use of imaginal ex-
posure, assertiveness training, or social skills training are included.
Therapists were four clinical psychology doctoral students with 2-4
years of CBT experience. Videorecordings of sessions were used in
group supervision by the last author.
3 Shame, embarrassment and rejection are likely separate, but related, ex-
periences. Similar to previous studies (e.g., Goldin et al., 2009), when designing
and validating the task (Kivity & Huppert, 2018, 2019) we were not able to
examine these experiences separately due to a small number of stimuli that
purely fall into one of these categories. It remains for future studies to examine
these experiences separately.
4 It should be noted that although the view condition is not entirely a passive
one (as it includes perspective taking), it is still possible that it requires less
cognitive effort than the reappraisal and suppression conditions. However,
studies have shown that cognitive effort alone does not explain the down-reg-
ulatory effects of reappraisal (Foti & Hajack, 2008).
5 See a sample picture at https://tinyurl.com/ShameArousingPicture.
Y. Kivity, et al. Journal of Affective Disorders 279 (2021) 334–342
336
https://tinyurl.com/ShameArousingPicture
2.4. Procedure
The institutional review board approved the study. After providing
informed consent, participants were evaluated by trained independent
evaluators (blind to hypotheses) using the Mini-International
Neuropsychiatric Interview (Sheehan et al., 1998) and the LSAS. Par-
ticipants completed a baseline assessment and entered treatment. Pa-
tients completed the ERQ before and after each session (post-session
ratings were of secondary interest and are only reported in Supple-
mental Material, Section 4). Patients also completed in-lab assessments
at pre-treatment, every four sessions during treatment, at post-treat-
ment and at 3-months follow-up which included the LSAS, SPIN, ERQ,
and the lab task. Thus, each patient had up to seven assessments (pre-
treatment, sessions 4, 8, 12 and 16, post-treatment, and follow-up). HCs
only completed a single assessment and were not followed long-
itudinally. Thus, HCs were included only in analyses of equivalency.
2.5. Data Analyses
We used intent-to-treat linear multi-level models (assessments at
level 1 repeated within patients at level 2) implemented in R package
'nlme' (Pinheiro, Bates, DebRoy, Sarkar, & R Core Team, 2016). In-
cluding therapists as a third level showed negligible and non-significant
effects (ICCs: Med = .00, range: 0 – 0.049) and therefore this level was
removed. We used restricted maximum likelihood estimation, a first-
order autoregressive level 1 covariance structure and random intercepts
and slopes at level 2. For H1, linear rates of change were examined by
including session/assessment number as a level 1 predictor (centered at
pre-treatment). Intercepts represent estimated levels of the dependent
variable at pre-treatment and slopes represent estimated changes in the
dependent variable between two assessment points (one/four sessions,
depending on the measure). To examine changes from post-treatment to
follow-up we fitted a piece-wise model that examines changes during
treatment and from post-treatment to follow-up separately. This was
done by adding the follow-up data to the abovementioned model and
adding a dummy coded variable that captures post-treatment to follow-
up changes (coded “1” for follow-up assessment and “0” for all other
assessment). The fixed effect of the dummy variable expresses the
amount and significance of the change from post-treatment to follow-
up.
H2 was examined by modeling within-patient variation in the pre-
dictor following recommended procedures (Wang & Maxwell, 2015).
Monthly scores of the predictor (patient mean-centered) served as
within-patient scores in a cross-lagged (1-month) model. Within-subject
effects represent cross-lagged associations between the predictor and
the outcome. Per Wang and Maxwell (2015) we did not control for
linear time effects as we wished to model and explain these very effects.
Following Falkenström, Finkel, Sandell, Rubel, and Holmqvist (2017),
we did not include the lagged dependent variable as a predictor because
it introduces a dependency between the dependent variable and the
error, thus violating assumptions. However, the first auto-regressive
residual structure partly accounts for the effects of prior on current
levels of the outcome. For consistency, we only analyzed monthly
scores of the SPIN and ERQ. A cross-lagged association was interpreted
as significant only if effects were significant for clinician-rated and self-
reported anxiety.
For H3, comparisons were conducted using clinical equivalency
procedures (Kendall, Marrs-Garcia, Nath, & Sheldrick, 1999) through t
tests examining non-inferiority (i.e., < 1 SD difference) of post-treat-
ment scores compared to HCs. A significant effect in noninferiority tests
suggests that patients are non-inferior to HCs.
Effect sizes were calculated as semi-partial r (rs; Jaeger, Edwards,
Das, & Sen, 2016; Nakagawa & Schielzeth, 2013) using package
'r2glmm' in R (Jaeger & R Core Team, 2016). These represent the un-
ique contribution above and beyond the contribution of other pre-
dictors in the model and are presented in absolute values.
3. Results
3.1. Changes in regulation (H1) and equivalency to HCs (H3)
3.1.1. Change in self-reports
Descriptive statistics for all study variables are presented in sup-
plemental material, section 3. Changes are shown in Fig. 1.
Suppression. Consistent with hypotheses, the frequency of sup-
pression decreased during treatment (t467 = -3.98, p < .01, rs = .22
[.14, .30]) and did not change from post-treatment to follow-up
(b = -.10, t490 = -.62, p = .53, rs = .01 [.00, .10]). In contrast to
hypotheses, self-efficacy of suppression decreased during treatment
(t467 = -2.85, p < .01, rs = .14 [.05, .22]) and did not change from
post-treatment to follow-up (b = -.09, t490 = -.64, p = .52, rs = .01
[.00, .10]).
Reappraisal. Consistent with hypotheses, self-efficacy of reappraisal
increased during treatment (t467 = 3.67, p < .01, rs = .17 [.08, .25])
and did not change from post-treatment to follow-up (b = -.02,
t490 = -.10, p = .92, rs = .00 [.00, .10]). In contrast to hypotheses, no
changes in frequency of reappraisal were observed (t467 = .04, p = .97,
rs = .00 [.00, .10]) nor did they change from post-treatment to follow-
up (b = .08, t490 = .33, p = .74, rs = .01 [.00, .10]).
3.1.2. Change in lab-based measures
Changes are shown in Fig. 2 and Fig. 3.
View SAD-related pictures. As hypothesized, unpleasantness ratings
decreased during treatment (t115 = 4.49, p < .01, rs = .28 [.13, .42])
and did not change from post-treatment to follow-up (b = .08,
t138 = .70, p = .49, rs = .03 [.00, .18]). The LPP while viewing SAD-
related pictures did not change significantly during treatment (b = -.46,
t103 = -1.80, p = .07, rs = .14 [.01, .30]) or from post-treatment to
Fig. 1. Change in frequency and self-efficacy of reappraisal (top panel) and
suppression (bottom panel) during Cognitive Behavioral Therapy (CBT). Error
bars represent estimated standard errors. Only data from sessions 1-16 are
presented because only 5 patients received more than 16 sessions. b = esti-
mated weekly change in emotion regulation. ** p < .01
Y. Kivity, et al. Journal of Affective Disorders 279 (2021) 334–342
337
follow-up (b = 1.73, t126 = 1.11, p = .27, rs = .05 [.00, .20]).
View neutral pictures. Supporting our hypotheses, we found no
change in unpleasantness ratings during CBT or from post-treatment to
follow-up (during: t115 = -.43, p = .67, rs = .04 [.00, .20]; post-
treatment to follow-up: b = -.05, t138 = -.26, p = .79, rs = .02 [.00,
.17]) and in the LPP (during: b = -.29, t103 = -1.04, p = .30, rs = .09
[.00, .25]; post-treatment to follow-up: b = 1.86, t126 = 1.09, p = .28,
rs = .05 [.00, .20]) while viewing neutral pictures.
Suppression. Examining suppression-related reductions in un-
pleasantness (compared to viewing pictures) we found that reductions
were significantly different from zero at pre-treatment (b = .25,
t115 = 3.84, p < .01), indicating that suppression was effective in
down-regulating negativity. Consistent with hypotheses, regulation
scores did not change during treatment (t115 = -1.77, p = .08, rs = .16
[.02, .31]) or from post-treatment to follow-up (b = .03, t138 = .29,
p = .78, rs = .02 [.00, .18]). Reductions in LPP via suppression were
not different from zero at pre-treatment (b = .69, t103 = 1.00, p = .32),
suggesting that suppression was not effective in down regulating the
LPP. Also consistent with hypotheses, regulation scores …
RESEARCH ARTICLE
Social anxiety in young people: A prevalence
study in seven countries
Philip JefferiesID*, Michael Ungar
Resilience Research Centre, Faculty of Health, Dalhousie University, Halifax, Nova Scotia, Canada
* [email protected]
Abstract
Social anxiety is a fast-growing phenomenon which is thought to disproportionately affect
young people. In this study, we explore the prevalence of social anxiety around the world
using a self-report survey of 6,825 individuals (male = 3,342, female = 3,428, other = 55),
aged 16–29 years (M = 22.84, SD = 3.97), from seven countries selected for their cultural
and economic diversity: Brazil, China, Indonesia, Russia, Thailand, US, and Vietnam. The
respondents completed the Social Interaction Anxiety Scale (SIAS). The global prevalence
of social anxiety was found to be significantly higher than previously reported, with more
than 1 in 3 (36%) respondents meeting the threshold criteria for having Social Anxiety Disor-
der (SAD). Prevalence and severity of social anxiety symptoms did not differ between sexes
but varied as a function of age, country, work status, level of education, and whether an indi-
vidual lived in an urban or rural location. Additionally, 1 in 6 (18%) perceived themselves as
not having social anxiety, yet still met or exceeded the threshold for SAD. The data indicate
that social anxiety is a concern for young adults around the world, many of whom do not
recognise the difficulties they may experience. A large number of young people may be
experiencing substantial disruptions in functioning and well-being which may be ameliorable
with appropriate education and intervention.
Introduction
Social anxiety occurs when individuals fear social situations in which they anticipate negative
evaluations by others or perceive that their presence will make others feel uncomfortable [1].
From an evolutionary perspective, at appropriate levels social anxiety is adaptive, prompting
greater attention to our presentation and reflection on our behaviours. This sensitivity ensures
we adjust to those around us to maintain or improve social desirability and avoid ostracism
[2]. However, when out of proportion to threats posed by a normative social situation (e.g.,
interactions with a peer group at school or in the workplace) and when impairing functioning
to a significant degree, it may be classified as a disorder (SAD; formerly ‘social phobia’; [3]).
The hallmark of social anxiety in western contexts is an extreme and persistent fear of embar-
rassment and humiliation [1, 4, 5]. Elsewhere, notably in Asian cultures, social anxiety may
also manifest as embarrassment of others, such as Taijin kyofusho in Japan and Korea [6].
Common concerns involved in social anxiety include fears of shaking, blushing, sweating,
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OPEN ACCESS
Citation: Jefferies P, Ungar M (2020) Social
anxiety in young people: A prevalence study in
seven countries. PLoS ONE 15(9): e0239133.
https://doi.org/10.1371/journal.pone.0239133
Editor: Sarah Hope Lincoln, Harvard University,
UNITED STATES
Received: March 11, 2020
Accepted: August 31, 2020
Published: September 17, 2020
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
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all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0239133
Copyright: © 2020 Jefferies, Ungar. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All data files are
available from the Open Science Framework
repository (DOI: 10.17605/OSF.IO/VCNF7).
Funding: The author(s) received no specific
funding for this work.
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appearing anxious, boring, or incompetent [7]. Individuals experiencing social anxiety visibly
struggle with social situations. They show fewer facial expressions, avert their gaze more often,
and express greater difficulty initiating and maintaining conversations, compared to individu-
als without social anxiety [8]. Recognising difficulties can lead to dread of everyday activities
such as meeting new people or speaking on the phone. In turn, this can lead to individuals
reducing their interactions or shying away from engaging with others altogether.
The impact of social anxiety is widespread, affecting functioning in various domains of life
and lowering general mood and wellbeing [9]. For instance, individuals experiencing social
anxiety are more likely to be victims of bullying [10, 11] and are at greater risk of leaving
school early and with poorer qualifications [11, 12]. They also tend to have fewer friends [13],
are less likely to marry, more likely to divorce, and less likely to have children [14]. In the
workplace, they report more days absent from work and poorer performance [15].
A lifetime prevalence of SAD of up to 12% has been reported in the US [16], and 12-month
prevalence rates of .8% have been reported across Europe [17] and .2% in China [18]. How-
ever, there is an increasing trend to consider a spectrum of social anxiety which takes account
of those experiencing subthreshold or subclinical social anxiety, as those experiencing more
moderate levels of social anxiety also experience significant impairment across different
domains of functioning [19–21]. Therefore, the proportion of individuals significantly affected
by social anxiety, which include a substantial proportion of individuals with undiagnosed SAD
[8], may be higher than current estimates suggest.
Studies also indicate younger individuals are disproportionately affected by social anxiety,
with prevalence rates at around 10% by the end of adolescence [22–24], with 90% of cases
occurring by age 23 [16]. Higher rates of social anxiety have also been observed in females and
are associated with being unemployed [25, 26], having lower educational status [27], and living
in rural areas [28, 29]. Leigh and Clark [30] have explored the higher incidence of social anxi-
ety in younger individuals, suggesting that moving from a reliance on the family unit to peer
interactions and the development of neurocognitive abilities including public self-conscious-
ness may present a period of greater vulnerability to social anxiety. While most going through
this developmentally sensitive period are expected to experience a brief increase in social fears
[31], Leigh and Clark suggest that some who may be more behaviourally inhibited by tempera-
ment are at greater risk of developing and maintaining social anxiety.
Recent accounts suggest that levels of social anxiety may be rising. Studies have indicated
that greater social media usage, increased digital connectivity and visibility, and more options
for non-face-to-face communication are associated with higher levels of social anxiety [32–
35]. The mechanism underpinning these associations remains unclear, though studies have
suggested individuals with social anxiety favour the relative ‘safety’ of online interactions [32,
36]. However, some have suggested that such distanced interactions such as via social media
may displace some face to face relationships, as individuals experience greater control and
enjoyment online, in turn disrupting social cohesion and leading to social isolation [37, 38].
For young people, at a time when the development of social relations is critical, the perceived
safety of social interactions that take place at a distance may lead some to a spiral of with-
drawal, where the prospect of normal social interactions becomes ever more challenging.
Therefore, in this study, we sought to determine the current prevalence of social anxiety in
young people from different countries around the world, in order to clarify whether rates of
social anxiety are increasing. Specifically, we used self-report measures (rather than medical
records) to discover both the frequency of the disorder, severity of symptoms, and to examine
whether differences exist between sexes and other demographic factors associated with differ-
ences in social anxiety.
PLOS ONE Social anxiety in young people: A prevalence study in seven countries
PLOS ONE | https://doi.org/10.1371/journal.pone.0239133 September 17, 2020 2 / 18
Competing interests: I have read the journal’s
policy and the authors of this manuscript have the
following competing interests: Unilever funds the
lead author’s research fellowship at Dalhousie
University’s Resilience Research Centre, though in
no way have they directed this research, its
analysis or the reporting or results.
https://doi.org/10.1371/journal.pone.0239133
Materials and methods
Design
This study is a secondary analysis of a dataset that was created by Edelman Intelligence for a
market research campaign exploring lifestyles and the use of hair care products that was
commissioned by Clear and Unilever. The original project to collect the data took place in
November 2019, where participants were invited to complete a 20-minute online question-
naire containing measures of social anxiety, resilience, social media usage, and questions
related to functioning across various life domains. Participants were randomly recruited
through the market research companies Dynata, Online Market Intelligence (OMI), and
GMO Research, who hold nationally representative research panels. All three companies are
affiliated with market research bodies that set standards for ethical practice. Dynata adheres to
the Market Research Society code of conduct; OMI and GMO adhere to the ESOMAR market
research code of conduct. The secondary analyses of the dataset were approved by Dalhousie
University’s Research Ethics Board.
Participants
There were 6,825 participants involved in the study (male = 3,342, female = 3,428, other = 55),
aged 16–29 years (M = 22.84, SD = 3.97), from seven countries selected for their social and
economic diversity (Brazil, China, Indonesia, Russia, Thailand, US, and Vietnam) (see Table 1
for full sample characteristics). Participant ages were collected in years, but some individuals
aged 16–17 were recruited through their parents and their exact age was not given. They were
assigned an age of 16.5 years in order to derive the mean age and standard deviation for the
full sample.
Procedure
Email invitations to participate were sent to 23,346 young people aged 16–29, of whom 76%
(n = 17,817) were recruited to take the survey. These were panel members who had previously
registered and given their consent to participate in surveys. Sixty-five percent of respondents
were ineligible, with 10,816 excluded because they or their close friends worked in advertising,
market research, public relations, journalism or the media, or for a manufacturer or retailer of
haircare products. A further 176 respondents were excluded for straight-lining (selecting the
same response to every item of the social anxiety measure, indicating they were not properly
engaged with the survey; [39]). The final sample comprised 6,825 participants and matched
Table 1. Sample characteristics.
Male Female Other
a
Total
Brazil 479 491 7 977
China 486 500 6 992
Indonesia 494 457 8 959
Russia 475 500 8 983
Thailand 469 487 12 968
US 452 500 10 962
Vietnam 487 493 4 984
Total 3,342 3,428 55 6,825
a
“Other” includes individuals who selected non-binary (n = 17), prefer to self-describe (n = 7), and prefer not to say
(n = 31).
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quotas for sex, region, and age, to achieve a sample with demographics representative of each
country.
Participants were compensated for their time using a points-based incentive system, where
points earned at the end of the survey could be redeemed for gift cards, vouchers, donations to
charities, and other products or services.
Measures
The survey included the 20-item self-report Social Interaction Anxiety Scale (SIAS; [40]).
Based on the DSM, the SIAS was originally developed in conjunction with the Social Phobia
Scale to determine individuals’ levels of social anxiety and how those with SAD respond to
treatment. Both the SIAS and Social Phobia Scale correlate strongly with each other [40–43],
but while the latter was developed to assess fears of being observed or scrutinised by others,
the SIAS was developed more specifically to assess fears and anxiety related to social interac-
tions with others (e.g., meeting with others, initiating and maintaining conversations). The
SIAS discriminates between clinical and non-clinical populations [40, 44, 45] and has also
been found to differentiate between those with social anxiety and those with general anxiety
[46], making it a useful clinical screening tool. Although originally developed in Australia, it
has been tested and found to work well in diverse cultures worldwide [47–50], and has strong
psychometric properties in clinical and non-clinical samples [40, 42, 43, 45–47].
For the current study, all 20 items of the SIAS were included in the survey, though we omit-
ted the three positively-worded items from analyses, as studies have demonstrated that includ-
ing them results in weaker than expected relationships between the SIAS and other measures,
that they hamper the psychometric properties of the measure, and that the SIAS performs bet-
ter without them [e.g., 51–53] (the omitted items were ‘I find it easy to make friends my own
age’, ‘I am at ease meeting people at parties, etc’, and ‘I find it easy to think of things to talk
about’.). One item of the SIAS was also modified prior to use: ‘I have difficulty talking to attrac-
tive persons of the opposite sex’ was altered to ‘I have difficulty talking to people I am attracted
to’, to make it more applicable to individuals who do not identify as heterosexual, given that
the original item was meant to measure difficulty talking to an attractive potential partner
[54].
The questionnaire also included measures of resilience, in addition to other questions con-
cerning functioning in daily life. These were included as part of a corporate social responsibil-
ity strategy to investigate the rates of social anxiety and resilience in each target market. A
translation agency (Language Connect) translated the full survey into the national languages of
the participants.
Analyses
We analysed social anxiety scores for the overall sample, as well as by country, sex, and age (for
sex, given the limited number and heterogeneity of individuals grouped into the ‘other’ cate-
gory, we only compared males and females). As social anxiety is linked to work status [25], we
also examined differences in SIAS scores between those working and those who were unem-
ployed. Urban/rural differences were also investigated as previous research has suggested anxi-
ety disorders may differ depending on where an individual lives [28]. Education level [27], too,
was included using completion of secondary education (ISCED level 3) in a subgroup of par-
ticipants aged 20 years and above to ensure all were above mandatory ages for completing high
school. Descriptive statistics are reported for each group with significant differences explored
using ANOVA (with Tukey post-hoc tests) or t-tests.
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The SIAS is said to be unidimensional when using just the 17 straightforwardly-worded
items [52], with item scores summed to give general social anxiety scores. Higher scores indi-
cate greater levels of social anxiety. Heimberg and colleagues [42] have suggested a cut-off of
34 on the 20-item SIAS to denote a clinical level of social anxiety (SAD). This level has been
adopted in other studies [e.g., 45] and found to accurately discriminate between clinical and
non-clinical participants [53]. This threshold for SAD scales to 28.9 when just the 17 items are
used, and this is slightly more conservative than others who have used 28 as an adjusted
17-item threshold [53, 55]. Therefore, in addition to analyses of raw scores to gauge the sever-
ity of social anxiety (and reflect consideration of social anxiety as a spectrum), we also report
the proportion of individuals meeting or exceeding this threshold for SAD (�29) and analyse
differences between groups using chi-square tests.
Additionally, despite the unidimensionality of the SIAS, the individual items can be inter-
preted as examples of contexts where social anxiety may be more or less acutely experienced
(e.g., social situations with authority: ‘I get nervous if I have to speak with someone in authority’,
social situations with strangers: ‘I am nervous mixing with people I don’t know well’). Therefore,
as social anxiety may be experienced differently depending on culture [6], we also sorted the
items in the measure to understand the top and least concerning contexts for each country.
Finally, we also sought to understand whether individuals perceived themselves as having
social anxiety. After completing the SIAS, participants were presented with a definition of
social anxiety and asked to reflect on whether they thought this was what they experienced.
We contrasted responses with a SIAS threshold analysis to determine discrepancies, including
assessment of the proportion of false positives (those who thought they had social anxiety but
did not exceed the threshold) and false negatives (those who thought they did not have social
anxiety but exceeded the threshold).
All analyses were conducted using SPSS v25 [56].
Results
As the survey required a response for each item, there were no missing data. The internal reli-
ability of the SIAS was found to be strong (α = .94), with the removal of any item resulting in a
reduction in consistency.
Social anxiety by sex, age, and country
In the overall sample, the distribution of social anxiety scores formed an approximately normal
distribution with a slightly positive skew, indicating that most respondents scored lower than
the midpoint on the measure (Fig 1). However, more than one in three (36%) were found to
score above the threshold for SAD. There were no significant differences in social anxiety
scores between male and female participants (t(6768) = -1.37, n.s.) and the proportion of
males and females scoring above the SAD threshold did not significantly differ either
(χ2(1,6770) = .54, n.s.).
Social anxiety scores significantly differed between countries (F(6,6818) = 74.85, p < .001,
ηp
2
= .062). Indonesia had the lowest average scores (M = 18.94, SD = 13.21) and the US had
the highest (M = 30.35, SD = 15.44). Post-hoc tests revealed significant differences (ps�.001)
between each of the countries, except between Brazil and Thailand, between China and Viet-
nam, between Russia and China, and between Russia and Indonesia (see Table 2). The propor-
tion of individuals exceeding the threshold for SAD was also found to significantly differ
between the seven countries (χ2(6,6825) = 347.57, p < .001). Like symptom severity, the US
had the highest prevalence with more than half of participants surveyed exceeding the thresh-
old (57.6%), while Indonesia had the lowest, with fewer than one in four (22.9%).
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A significant age difference was also observed (F(2,6822) = 39.74, p < .001, ηp
2
= .012),
where 18-24-year-olds scored significantly higher (M = 25.33, SD = 13.98) than both 16-
17-year-olds (M = 21.92, SD = 14.24) and 25-29-year-olds (M = 22.44, SD = 14.22). Also, 25-
29-year-olds scored significantly higher than 18-24-year-olds (ps < .001). The proportion of
individuals scoring above the threshold for SAD also significantly differed between age groups
(χ2(2,6825) = 48.62, p < .001) (Fig 2).
A three-way ANOVA confirmed significant main effect differences in social anxiety scores
between age groups (F(2,6728) = 38.93, p < .001, ηp
2
= .011) and countries (F(6,6728) = 45.37,
p < .001, ηp
2
= .039), as well as the non-significant difference between males and females
(F(1,6728) = .493, n.s.). However, of the interactions between sex, age, and country, the two-
way country�age interaction was significant (F(12,6728) = 1.89, p = .031, ηp
2
= .003), where 16-
17-year-olds in Indonesia were found to have the lowest scores (M = 15.70, SD = 13.46) and
25-29-year-olds in the US had the highest (M = 30.47, SD = 16.17) (Fig 3). There was also a sig-
nificant country�sex interaction (F(6,6728) = 2.25, p = .036, ηp
2
= .002), where female partici-
pants in Indonesia had the lowest scores (M = 18.07, SD = 13.18) and female participants in
the US had the highest (M = 30.37, SD = 15.11) (Fig 4).
Fig 1. Frequency of social anxiety scores (full sample).
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Work status
Social anxiety scores were also found to significantly differ in terms of work status (employed/
studying/unemployed; F(2,6030) = 9.48, p < .001, ηp
2
= .003), with those in employment hav-
ing the lowest scores (M = 23.28, SD = 14.32), followed by individuals who were studying
(M = 23.96, SD = 13.50). Those who were unemployed had the highest scores (M = 26.27,
SD = 14.54). Post-hoc tests indicated there were significant differences between those who
were employed and unemployed (p < .001), between those studying and unemployed (p =
.006), but not between those employed and those who were studying. The difference between
those exceeding the SAD threshold between groups was also significant (χ2(2,6033) = 7.55, p =
.023).
Table 2. Social anxiety scores.
SCORES SCORE DIFFERENCE BETWEEN
GROUPS (T / F, P)
PROPORTION WITH SAD
(SIAS�29) (%)
PROPORTION DIFFERENCE BETWEEN
GROUPS (Χ2, P)M SD
Overall sample 23.82 14.18 36.2
Sex -1.37, n.s. .54, n.s.
Male 23.53 14.12 35.6
Female 24.00 14.18 36.5
Country 74.85, < .001 347.57, < .001
Brazil 26.18 15.23 42.4
China 22.30 13.52 32.1
Indonesia 18.94 13.21 22.9
Russia 20.78 12.79 27.0
Thailand 25.57 13.92 41.4
US 30.35 15.44 57.6
Vietnam 22.68 11.77 30.7
Age 39.74, < .001 48.62, < .001
16–17 21.92 14.24 30.8
18–24 25.33 13.98 40.3
25–29 22.44 14.22 32.8
Work 9.48, < .001 7.55, .023
Employed 23.28 14.32 35.3
Studying 23.96 13.50 36.5
Unemployed 26.27 14.54 41.7
Urban/rural 9.95, < .001 35.84, < .001
Central urban 22.70 14.67 33.0
Urban area 23.62 13.77 35.3
Suburban 25.64 14.08 42.4
Semi-rural 24.53 13.74 37.9
Rural 25.37 13.91 41.9
Education 5.51, < .001 38.75, < .001
L3 unfinished 27.94 15.07 52.0
L3 finished 23.40 14.15 34.8
M = mean, SD = standard deviation, t = t-test, F = ANOVA, χ2 = chi-square, p = significance, L3 = ISCED level 3 (secondary education), SAD = Social Anxiety
Disorder.
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Urban/Rural
Social anxiety scores also significantly varied depending on an individual’s place of residence
(F(4,6820) = 9.95, p < .001, ηp
2
= .006). However, this was not a linear relationship from urban
Fig 2. Proportion of individuals meeting the threshold for Social Anxiety Disorder by age group and country.
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to rural extremes (Fig 5); instead, those living in suburban areas had the highest scores
(M = 25.64, SD = 14.08) and those in central urban areas had the lowest (M = 22.70,
SD = 14.67). This pattern was reflected in the proportions of individuals exceeding the SAD
threshold (χ2(4,6825) = 35.84, p < .001).
Education level
In the subsample of individuals aged 20 or above, level of education also resulted in a signifi-
cant differences in social anxiety scores (t(5071) = 5.51, p < .001), with individuals who com-
pleted secondary education presenting lower scores (M = 23.40, SD = 14.15) than those who
had not completed secondary education (M = 27.94, SD = 15.07). Those exceeding the thresh-
old for SAD also significantly differed (χ2(1,5073) = 38.75, p < .001), with half of those who
had not finished secondary education exceeding the cut-off (52%), compared to just over a
third of those who had (35%).
Concerns by context
Table 3 illustrates the items of the SIAS sorted by severity for each country. For East-Asian
countries, speaking with someone in authority was a top concern, but less so for Brazil, Russia,
and the US. Patterns became less discernible between countries beyond this top concern, indi-
cating heterogeneity in the specific situations related to social anxiety, although individuals in
most countries appeared to be least challenged by mixing with co-workers and chance encoun-
ters with acquaintances.
Self-perceptions of social anxiety
Just over a third of the sample perceived themselves to experience social anxiety (34%).
Although this was similar to the proportion of individuals who exceeded the threshold for
Fig 3. Levels of social anxiety by country and age.
https://doi.org/10.1371/journal.pone.0239133.g003
PLOS ONE Social anxiety in young people: A prevalence study in seven countries
PLOS ONE | https://doi.org/10.1371/journal.pone.0239133 September 17, 2020 9 / 18
https://doi.org/10.1371/journal.pone.0239133.g003
https://doi.org/10.1371/journal.pone.0239133
SAD (36%), perceptions significantly differed from threshold results (χ2(1,6825) = 468.80, p <
.001). Just fewer than half of the sample (48%) perceived themselves as not being socially anx-
ious and were also below the threshold, and a fifth (18%) perceived themselves as being socially
anxious and exceeded the threshold (Fig 6). However, 16% perceived themselves to be socially
anxious yet did not exceed the threshold (false positives) and 18% perceived themselves not to
be socially anxious yet exceeded the threshold (false negatives). This suggests a large propor-
tion of individuals do not properly recognise their level of social anxiety (over a third of the
sample), and perhaps most importantly, that more than 1 in 6 may experience SAD yet not
recognise it (Table 4).
Discussion
This study provides an estimate of the prevalence of social anxiety among young people from
seven countries around the world. We found that levels of social anxiety were significantly
higher than those previously reported, including studies using the 17-item version of the SIAS
[e.g., 55, 57, 58]. Furthermore, our findings show that over a third of participants met the
threshold for SAD (23–58% across the different countries). This far exceeds the highest of fig-
ures previously reported, such as Kessler and colleague’s [16] lifetime prevalence rate of 12%
in the US.
As this study specifically focuses on social anxiety in young people, it may be that the inclu-
sion of older participants in other …
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