Which of the following is a diagnostic symptom associated with social phobias?

Current Comorbidity Rates

Table 9.3 summarizes rates of current DSM-IV Axis I comorbidity in social anxiety disorder from five studies, three of which were conducted with community samples. Lampe, Slade, Issakisis, and Andrews (2003) examined rates of comorbidity in 10,641 people aged 18 and older who participated in the Australian National Survey of Mental Health and Well-Being. Diagnoses were assigned through a computerized version of the CIDI when participants met criteria for a particular disorder in the previous 12 months. Grant et al. (2005) calculated 12-month comorbidity rates in the National Epidemiologic Survey on Alcohol and Related Conditions using the Alcohol Use and Associated Disabilities Interview Schedule – DSM-IV Edition, which was described in the previous section. Fehm, Beesdo, Jacobi, and Fiedler (2008) examined rates of comorbidity in 4,174 people aged 18–65 who participated in the German National Health Interview and Examination Survey. Diagnoses were assigned through a computerized version of the Munich Composite Diagnostic Interview (M-CIDI), which is a modified version of the CIDI that accounts for a wider range of DSM-IV diagnoses than previous versions of the CIDI. Participants were regarded as having current diagnoses of psychiatric disorders if they met diagnostic criteria during the previous 12 months.

Two additional studies in Table 9.3 reported rates of current Axis I comorbidity in clinical samples. Brown et al. (2001) used the Anxiety Disorders Interview Schedule for DSM-IV to assess current psychiatric disorders in their sample of patients seeking treatment at their anxiety disorders clinic, which were assigned when participants met diagnostic criteria for the disorders at the time of the assessment. Kashdan, Frueh, Knapp, Herbert, and Magruder (2006) examined comorbidity in 733 veterans who were seen in four Veterans’ Affairs primary care clinics, with 26 of these veterans being diagnosed at the time of the assessment. Comorbid psychiatric disorders were assigned during a telephone interview conducted by master’s level clinicians, who used the Clinician-Administered PTSD Scale (Blake et al., 1990) to assess PTSD and the Mini International Neuropsychiatric Interview (Sheehan et al., 1997) to assess all other Axis I conditions.

Results from the three studies that used community samples indicate that between 50% and 70% of people with a current diagnosis of social anxiety disorder were diagnosed with another current anxiety disorder, and that between 30% and 60% were diagnosed with a current mood disorder. These rates are not appreciably different from the lifetime comorbidity rates described in the previous section. The range of comorbidity rates of specific anxiety and mood disorders was somewhat smaller than the ranges of lifetime comorbidity rates; for example, rates of comorbid current GAD ranged from 17.3% to 33.9%. In general, rates of current comorbidity ranged from approximately 5% to 20% for agoraphobia, OCD, and panic disorder; comorbidity rates extended to 25–30% for panic disorder and GAD; and one community study suggested that the rate of current comorbid specific phobia was as high as 37%. Between 38% and 65% of people with current social anxiety disorder endorsed current major depressive disorder; between 6% and 38% of people with current social anxiety disorder endorsed current dysthymic disorder; and 5–16% of people with current social anxiety disorder endorsed bipolar I or bipolar II disorder. As with lifetime comorbidity rates, the odds of having a current comorbid alcohol or substance use disorder were lower than most odds of having a comorbid anxiety or mood disorder, although Lampe et al.’s (2003) study suggests that over 20% of people with current social anxiety disorder are diagnosed with some form of current alcohol or drug use disorder.

The two studies that calculated current rates of comorbidity in clinical samples yielded discrepant results. The current comorbidity rates reported by Brown et al. (2001) were similar to, and at times lower than, current comorbidity rates reported by studies that used community samples. In contrast, Kashdan et al. (2006) identified high rates of comorbidity in their sample of veterans diagnosed with social anxiety disorder who were seeking treatment at Veterans’ Affairs primary care clinics. For example, over 70% of the veterans diagnosed with social anxiety disorder also carried diagnoses of PTSD. The only diagnosis that was not elevated in this sample was alcohol abuse. It is likely that the high rate of comorbidity can be accounted for by the unique nature of the veteran sample, as less than a third of the respondents in the sample were employed, and nearly half of them had been in a war zone at some point in their lives.

Like the studies summarized in Table 9.2, all of the comorbidity rates in Table 9.3 pertain to diagnoses of social anxiety disorder in general, which includes both the generalized and non-generalized subtypes. No epidemiological studies have broken down current comorbidity rates as a function of subtype. However, Fehm et al. (2008) identified rates of comorbidity among people who met full criteria for social anxiety disorder, people who were subthreshold for social anxiety disorder (i.e., those who met Criterion A for a diagnosis of social anxiety disorder but were missing one criterion), and those who were symptomatic for social anxiety disorder (i.e., those who reported social fears, but who were missing two or more criteria). They identified a dose-response relation between the severity of social anxiety disorder and the amount of comorbidity, such that the odds of being diagnosed with all of the other disorders considered were substantially elevated in people with social anxiety disorder, whereas the odds were much lower (but in most cases, still elevated) in those who were only symptomatic. This study was the first to demonstrate that subthreshold levels of social anxiety are associated with significant life interference and distress, in the form of comorbid psychiatric disorders.

6.04.1 Overview

In the early 1980s, anxiety was subdivided into distinct entities including social phobia (aka social anxiety disorder, SAD), posttraumatic stress disorder (PTSD), obsessive compulsive disorder (OCD), generalized anxiety disorder (GAD), and panic in the Diagnostic and Statistical Manual of Mental Disorders IV/IV-TR (DSM IV/IV-TR).1,2 Anxiety is usually associated with fear, nervousness, apprehension, and panic.

6.21.2 Definition and Overview of Social Anxiety Disorder

Causal Role of Attentional Biases and their Modification

Although the above studies indicate that social anxiety is associated with biased attention, they do not speak to the issue of causality. Indirect evidence for the causal role of attentional bias to threat in social phobia has been evaluated in the context of treatment outcome studies. That is, if attentional bias to threat is a necessary condition for social phobia, then amelioration of the disorder should be associated with a reduction of attentional bias to threat. Empirical investigations of this question have generally supported this hypothesis in socially anxious individuals using both the emotional Stroop paradigm (Lundh & Öst, 2001; Mattia et al., 1993) and the dot-probe paradigm (Pishyar, Harris, & Menzies, 2008). Thus, there is evidence that successful treatment for social phobia is associated with a normalization of attentional bias for threat. However, such results do not rule out the possibility that attentional bias is simply a correlate of anxiety, rather than being a cause of anxiety.

Researchers have also examined the causal relationship between attentional bias toward threat and social anxiety using experimental methods that manipulate attention to examine the effect on anxiety. For example, Amir and colleagues (2009) administered an attentional training program to individuals diagnosed with SAD. The authors modified the dot-probe procedures used by Mathews and MacLeod (2002) to encourage SAIs to disengage their attention from threat stimuli. Specifically, the paradigm created a contingency between the location of the non-threat stimuli (i.e., neutral face) and the probe in one group (attentional bias modification, or ABM) but not in the other (attention control condition, or ACC). Anxiety and attentional bias were assessed before and after training. Results revealed that the procedure effectively modified attentional disengagement. After eight sessions, the ABM group experienced significantly greater reductions in social anxiety symptoms compared to the control group. Moreover, 50% of the active condition, compared to 14% of the control condition, lost their diagnosis of SAD.

Schmidt et al. (2009) reported similar results with an identical protocol, finding that 72% of participants in the active condition, compared to 11% of participants in the control condition, did not meet diagnostic criteria for SAD after eight sessions of attentional training. Heeren et al. (2012) extended the examination of ABM beyond SAD symptoms, and also examined social anxiety-related behavior and physiological response to a social stressor. These investigators found that after four sessions of attentional training, both the ABM and the ACC groups displayed significant reductions in social anxiety symptoms from pre- to post-treatment. However, these results were only maintained at follow-up for the ABM group. Moreover, the ABM group, but not ACC group, demonstrated behavioral improvements in social anxiety symptoms and reduced physiological response associated with a speech performance task. These studies indicate that attention modification procedures may have clinical utility.

Several meta-analyses now support the efficacy of ABM as an effective intervention for anxiety (Beard, Sawyer, & Hofmann, 2012; Hakamata et al., 2010; Hallion & Ruscio, 2011). However despite these initial promising results several recent attempts at replicating these findings in SAD have failed to find expected group differences between the ABM and the ACC groups (Boettcher, Berger, & Renneberg, 2012; Carlbring et al., 2012; Neubauer et al., 2012), thus calling into question the efficacy of ABM (Emmelkamp, 2012).

While a number of factors could account for these discrepant results, one explanation may be that in these latter studies, the active dose of the mechanism of change for attentional training (i.e., attentional disengagement from threat) was delivered equally to both groups. At a fundamental level ABM is predicated on the idea that attentional bias is a malleable construct and that change in attentional bias is causally related to change in anxiety. Surprisingly in their enthusiasm to develop and deliver effective treatments, many of these basic questions have not been addressed in many of the extant studies. To formally test the relationship between change in attentional bias and change in anxiety, Amir and colleagues (2009) conducted formal mediational analyses showing that change in attentional bias mediated the relationship between treatment condition (ABM versus ACC) and reduction in social anxiety. Similarly, Heeren et al. (2012) found that change in attentional bias mediated the relationship between treatment condition and change in physiological reactivity from pre- to post-treatment, as well as fear of negative evaluation from post-treatment to follow-up. Thus, change in attentional bias appears to be an essential ingredient of ABM, and as such, studies that fail to demonstrate this change in bias would not be expected to find changes in symptoms (Clarke, Notebaert, & MacLeod, 2014). Consistent with this hypothesis, the studies that failed to find significant group effects of ABM on symptoms also failed to show an effect of training on attentional bias (Boettcher et al., 2012; Carlbring et al., 2012; Neubauer et al., 2013).

To address some of these inconsistencies, Kuckertz et al. (2014) compared two groups of participants completing ABM. One group of participants completed traditional ABM via the internet. The second ABM condition was identical with the addition that participants were asked to activate their social anxiety fears immediately prior to each session. The first groups did not demonstrate a decrease in attentional bias, and did not experience a decrease in symptoms (data from Carlbring et al., 2012). In the second group there was both a decrease in attentional bias as well as significantly reduced symptoms. Moreover, Kuckertz et al. showed that change in attentional bias mediated the difference between these two active ABM groups and decrease in social anxiety symptoms, suggesting that specific protocol manipulations may be critical to optimizing the active ingredient hypothesized to underlie ABM treatments.

Epidemiology

Anxiety disorders are very frequent. In the US National Comorbidity Survey, the lifetime prevalence for any anxiety disorder was 28.8%, and for the 12-month prevalence 18.1%. GAD has a 12-month prevalence of 3.1% in the United States and a lifetime prevalence of 5.7–6.4% in the United States and Europe. Women have a two- to threefold increased risk for GAD. Lifetime and 12-month prevalence rates for agoraphobia without panic are 1.4 and 0.8%, respectively; for panic disorder, 4.7% and 2.7%; for specific phobia, 12% and 6.8%; and for the somewhat controversial diagnosis of SAD, 12.1% and 6.8%.

Table 52.1 gives an overview of the prevalence of dual diagnoses of various psychiatric disorders and substance use disorders (SUDs). Generally, epidemiologic studies indicate a two- to threefold increased risk for alcohol use disorders in patients with anxiety disorders. Grant et al. (2005) reported data on GAD from the National Epidemiologic Survey on Alcohol and Related Conditions, which studied a large (N = 43 093) representative sample of the adult US population; prevalence rates for 12-month and lifetime GAD were 2.1 and 4.1%, respectively. Higher rates were found for males. GAD was highly comorbid with substance use in general and other anxiety, mood, and personality disorders. Specifically, the odds ratios for any alcohol use disorder were 2.0 for 12-month prevalence and 2.2 for lifetime prevalence. While the comorbidity with alcohol abuse was not increased (respective odds ratios 1.0 and 1.1), the odds ratios for alcohol dependence were significant (3.1 and 2.8). Similar results – i.e. an association with dependence but not abuse – were found for nicotine and drug dependence, which had the strongest relationship (odds ratios 9.9 and 5.2, respectively) In addition, as shown in previous studies, there was also a strong association of GAD with other mood and anxiety disorders. Further analysis of this database showed that men with GAD had significantly higher rates of comorbid alcohol and drug use disorders and reported greater use of alcohol and drugs to help relieve GAD symptoms.

TABLE 52.1. Frequency of Dual Diagnoses (Moggi and Donati, 2004)

Substance use disorderAlcohol abuseAlcohol dependenceDrug abuseDrug dependenceAny substance use disorderPsychiatric disorder%OR%OR%OR%OR%ORSchizophrenia ECA9.71.924.03.8∗14.66.9∗12.94.2∗47.04.6∗Affective disorder Major Depression ECA5.00.911.61.67.33.3∗10.73.7∗27.21.9∗ NCS9.11.026.42.7∗6.61.7∗15.42.8∗41.42.3∗ Dysthymia ECA4.80.818.93.9∗8.13.610.83.6∗31.42.4∗ NCS8.60.928.71.05.81.316.72.540.01.9Anxiety disorders ECA5.81.012.21.8∗5.02.3∗6.92.4∗23.71.7∗ NCS40.92.1∗44.92.2∗47.62.5∗55.43.3∗––Antisocial personality disorder ECA22.15.4∗51.514.7∗11.25.2∗30.815.6∗83.629.6∗ NCS25.78.8∗37.19.9∗33.88.3∗43.99.8∗––

ECA: Epidemiologic Catchment Area Study

NCS: National Comorbidity Study

Note: These estimates are based on the results from Moggi F, Dual diagnosis. Comorbidity of psychiatric disorders and addiction (in German). Bern: Huber publishers, 2nd edition 2007.

An association of alcohol dependence, but not abuse, with anxiety disorders (and depression) was also shown in a recent Dutch Study. This study included 2329 people with lifetime DSM-IV anxiety disorders or depressive disorders or both and 652 controls. Prevalence rates for alcohol dependence in persons with combined anxiety/depression were 20.3% and in controls, 5.5%. Prevalence of alcohol abuse was similar in all groups (about 12%). Independent risk factors for alcohol dependence included male gender, vulnerability factors such as a family history of alcohol dependence or anxiety/depression, childhood trauma, smoking, drug dependence, and early onset of anxiety/depression.

More recent data come from a cross-sectional population-based study conducted in 2004–2005 in a nationally representative sample of 34 653 US adults. Mood, anxiety, and SUDs were addressed using the Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV version. The authors defined five cluster alcoholic subtypes, some with heightened risk for GAD or social phobia, among numerous other factors (Clusters 3, 4, and especially 5). Follow-up data suggest that these empirical typologies predict differential clinical outcomes 3 years later.

For drug abuse, a large national epidemiologic survey in the United States indicated a 12-month and lifetime prevalence of 1.4 and 7.7%, respectively, which clearly exceeds the rates of drug dependence (0.6 and 2.6%, respectively). Drug use and dependence were associated with male sex, low socioeconomic status and other social variables, other SUDs, and antisocial personality. Drug dependence was associated with mood disorders and GAD. Twelve-month prevalence for drug use disorders was associated with any anxiety disorder with an odds ratio of 2.7. Among those, any panic disorder was associated with an odds ratio of 3.9 (with agoraphobia: 5.6, without: 3.1); social phobia, 2.6; specific phobia, 2.3; and GAD, 4.5. When adjusted for demographic characteristics and other psychiatric disorders, any anxiety disorder was associated with drug use disorders with an odds ratio of 2.1; drug abuse, 1.5; and drug dependence, 2.8. For drug dependence, GAD had an odds ratio of 2.5. In conclusion, these data correspond in part to studies in alcohol use disorders showing that drug dependence rather than abuse is associated with anxiety disorders, specifically GAD.

Comorbid mental and SUDs may occur concurrently and successively. The chronological relationship between the onset of the two disorders does not necessarily reflect the etiology or cause of the comorbidity. There are far fewer longitudinal studies on this issue. The temporal interrelationship between the onset of anxiety disorders and alcohol use disorders is complex. Long-term longitudinal studies in studies of patients with anxiety disorders did not indicate an association of phobias with the onset of alcohol use disorders but rather a modest association between adult subclinical specific phobias and later-onset alcohol use disorders (odds ratio 3.2), which was stronger in women than men. Other studies also suggest that specific phobias occur prior to alcohol use disorders.

Flensborg-Madsen et al. (2010) studied the potential effects of alcohol intake (not alcohol use disorders) upon the risk of psychiatric disorders. This prospective cohort study included participants of the Copenhagen City Heart Study (N = 18 146), who were followed up for up to 26 years. Alcohol intake was measured by self-report, while psychiatric diagnoses were measured through registers. For women, drinking above sensible limits increased the risk for psychiatric disorders in general and especially for anxiety disorders (risk: 2.00). For men, a weekly low to moderate alcohol intake seemed to have a protective effect against developing a psychiatric disorder. Risk for anxiety disorders was lower in men drinking more than 14 drinks per week (0.79). The authors discussed the “apparent protective effect” of alcohol among men as a sign of mental and social well-being and normal functioning.

The database for drug use is more limited. Liang et al. (2011) performed a retrospective cohort study using data from the 2007 National Survey of Mental Health and Well-being (MHW) in 8841 adult Australians. Previously, Teeson et al. (2009) had reported data from this study indicating that 19% of males and 8% of females with an anxiety disorder had a co-existing SUD and 26% of males and 11% of females with an affective disorder had a coexisting SUD. Overall prevalence for drug dependence was 2.56%. Individuals with an affective disorder or anxiety disorder were at higher risk of harmful use and drug dependence (males: 9.3%; females: 3.9%). Again, the self-medication theory or common genetic factors were discussed to explain these findings.

Social Anxiety

The diagnosis of social anxiety disorder has relied on observed and self-reported behaviors, examinations of psychiatric symptoms, and clinical judgment. A useful objective marker for the diagnosis of social anxiety disorder has so far remained elusive. However, research into fMR imaging and machine learning methods as biomarkers for social anxiety disorder have shown potential. Zhang and colleagues32 examined fMR imaging regional homogeneity measures from 40 patients with social anxiety disorder and matched healthy controls. SVM classification was able to achieve a diagnostic accuracy of 76.25% (sensitivity, 70%; specificity, 82.5%). Regional homogeneity alterations indicative of social anxiety disorder primarily localized to the DMN, dorsal attention network, self-referential network, and sensory network. Liu and colleagues33 used a different approach by instead focusing on whole brain functional connectivity measures for 20 patients with social anxiety disorder and matched controls. In their paper, SVM classification was able to achieve accuracy of 82.5% (sensitivity, 85%; specificity, 80%). Informative functional connectivity features primarily localized to the DMN, sensorimotor network, affective network, and visual network. In both the work of Zhang and colleagues32 and Liu and colleagues,33 the orbitofrontal cortex was found to be particularly informative area distinguishing social anxiety disorder patients from healthy controls. Although both studies were somewhat limited by relatively small cohorts, these data show the potential for fMR imaging to one day be a useful biomarker for elusive conditions like social anxiety disorder.

3.1 Comorbidity

Studies have shown that among individuals with a lifetime diagnosis of SAD (generalized or non-generalized subtypes), 52–80% have a lifetime diagnosis of at least one other psychiatric disorder (Chartier, Walker, & Stein, 2003; Grant et al., 2005; Merikangas & Angst, 1995; Ruscio et al., 2008). Approximately one-third to three-quarters of individuals with the generalized subtype of SAD meet diagnostic criteria for another lifetime disorder (Chartier et al., 2003; Kessler, Stein, & Berglund, 1998; Stein, Tancer, Gelernter, & Vittone, 1990). In fact, individuals with the generalized subtype of SAD are approximately three times more likely to have comorbid anxiety disorders, and two times more likely to have comorbid mood disorders, than individuals with the non-generalized subtype (Wittchen, Stein, & Kessler, 1999). This may suggest that individuals with a generalized subtype of SAD not only have a more severe form of SAD, but are also at increased risk for developing other mood and anxiety disorders due to a greater general vulnerability (Rosenbaum et al., 2000). Comorbidity between SAD, particularly the generalized subtype, and other disorders is associated with greater impairment of occupational and social functioning, greater likelihood of alcohol dependence, and greater likelihood of attempting suicide (Lydiard, 2001). Similarly, having BDD in addition to a comorbid Axis I disorder is associated with adverse consequences compared to having BDD only, such as a greater likelihood of not being employed, being housebound due to BDD, and having a history of suicide ideation and attempts (Coles et al., 2006; Gunstad & Phillips, 2003). Such findings emphasize the importance of identifying individuals with comorbidities, as they are at greater risk for poor outcomes. One major problem in the literature, however, is that studies examining comorbidity rates in SAD (generalized subtype and the residual category) rarely include BDD, in part due to the fact that BDD is currently classified in the DSM-IV as a somatoform disorder, and is largely under-recognized and under-studied. As a result, data on the comorbidity between SAD and BDD is limited, and may be underestimated.

Previous studies show that BDD is the fourth most common comorbid disorder among individuals with SAD, preceded by simple phobias, alcohol abuse, and major depressive disorder (MDD) (Hollander & Aronowitz, 1999). Among individuals suffering from BDD, as many as 12–68.8% also have SAD, and among those with SAD, 4.8–12% also have BDD (Brawman-Mintzer, Lydiard, Phillips, & Morton, 1995; Coles et al., 2006; Gunstad & Phillips, 2003; Hollander, Cohen, & Simeon, 1993; Menezes, Fontenelle, & Versiani, 2005; Phillips & Diaz, 1997; Wilhelm, Otto, Zucker, & Pollack, 1997; Zimmerman & Mattia, 1998). Variation in point prevalence of SAD among individuals with BDD is likely due to methodological differences in sample sizes, recruitment strategies, and assessment of BDD. Sample sizes of BDD patients have varied widely across studies, ranging from 16 (Zimmerman & Mattia, 1998) to 293 (Gunstad & Phillips, 2003). One study examining the prevalence of BDD in a sample of outpatients with anxiety disorders found that 6.7% of patients (11 out of 165) met criteria for a diagnosis of BDD (Wilhelm et al., 1997). Consistent with these findings, another study found that the prevalence rate of BDD was highest among those with SAD (11%) and OCD (8%) in a sample of patients with anxiety disorders and major depression (Brawman-Mintzer et al., 1995). Therefore, data suggest that SAD and BDD are highly comorbid disorders. In addition, BDD shares high rates of comorbidity with other disorders, particularly OCD and MDD (Gunstad & Phillips, 2003; Phillips, 2002a). It has been found that approximately 54–61% of BDD patients also have a current diagnosis of MDD and 25% also have current OCD (Gunstad & Phillips, 2003). Such estimates of comorbidity are supported by other studies documenting that 37.3% of BDD patients have current MDD, 32.9% have current SAD, and 26.1% have current OCD (Phillips & Stout, 2006). BDD also appears to be highly comorbid with eating disorders. For example, a study of 200 individuals with a lifetime diagnosis of BDD found that 32.5% had a lifetime eating disorder, with 15% having anorexia nervosa and/or bulimia nervosa (Ruffolo, Phillips, Menard, Fay, & Weisberg, 2006). As such, high rates of comorbidity between BDD and SAD are not unique to this relationship because comorbidity data suggest that BDD also overlaps highly with OCD, MDD, and eating disorders. It is also unclear whether high rates of comorbidity between SAD and BDD simply reflect the higher base rate of SAD (Coles et al., 2006).

Studies examining the relationship between SAD and BDD have not distinguished the diagnostic subtypes of SAD (i.e., the generalized subtype and the additional diagnosis of avoidant personality disorder). There is an overall lack of information about the number and content of social fears in patients with BDD, as studies have only reported the presence or absence of a diagnosis of SAD, or severity of self-reported SAD symptoms in BDD patients. Some evidence suggests that BDD may be more closely related to the generalized subtype of SAD. Studies have shown that BDD is highly comorbid with avoidant personality disorder (Veale, Boocock, Gournay, & Dryden, 1996), which tends to occur more commonly with generalized SAD (Holt, Heimberg, & Hope, 1992; Schneier, Spitzer, Gibbon, & Fyer, 1991). Furthermore, data on SAD symptoms in BDD patients indicate high levels of social anxiety, which is comparable to severity of social anxiety symptoms found in patients with generalized SAD (Coles et al., 2006; Pinto & Phillips, 2005; Veale, Boocock, et al., 1996). These findings suggest that there may be an effect of SAD subtype on the relationship between SAD and BDD. To further explore this possibility, future research should examine whether BDD is more highly comorbid with generalized or non-generalized SAD by delineating the number of feared social situations among BDD patients.

1.4 The present meta-analysis

The aim of the current meta-analysis was to evaluate the efficacy of technology-assisted interventions for individuals with a diagnosis of SAD. A systematic literature review on existing randomized controlled trials showed that all studies fulfilling the inclusion criteria handled within this meta-analysis could be grouped under ICBT, VRET, and CBM. Although a growing body of literature suggests that ICBT, VRET, and CBM might be effective interventions for SAD, earlier meta-analyses often included sub-clinical samples and studies with designs other than randomized controlled trials. Moreover, whereas previous meta-analyses on ICBT focused on guided ICBT only, we also investigated the efficacy of unguided ICBT. With regard to VRET, the present analysis included recent randomized controlled trials with participants with a diagnosis of SAD. Finally, we aimed at investigating CBM for both attention and interpretation bias in SAD by including recent randomized controlled trials among clinical samples. While reduction of SAD symptoms was the primary outcome in this meta-analysis, we also explored the efficacy of ICBT, VRET, and CBM on comorbid symptoms of depression as well as quality of life.

Which of the following is a diagnostic symptom associated with social phobia?

Which of the following is an example of a social phobia?

How is social phobia diagnosis?

What are the main causes of social phobia?