Evaluation of the social phobia scale and social interaction anxiety scale as assessments of performance and interaction anxiety

Factor structure of the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS) in a clinical sample recruited from the community

BACKGROUND

The Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS) are widely used self-report questionnaires to assess symptoms of social anxiety. While SPS measures social performance anxiety, SIAS measures social interaction anxiety. They are mostly reported simultaneously, but there have not been consistent results of the joint factor structure and therefore no consistent recommendations on how to use and evaluate the questionnaires. This study aimed (1) to evaluate the underlying joint factor structure of the SPS and SIAS and (2) to test whether SPS and SIAS are reliable scales to assess two different aspects of social anxiety.

METHODS

The one-factor, two-factor, and bifactor models were tested in a clinical sample recruited from the community and diagnosed with a social anxiety disorder. Exploratory and confirmatory factor analyses were conducted, bifactor-specific indices were calculated, and the content of the less fitting items was examined.

RESULTS

Confirmatory factor analyses showed that the best-fitting model was the bifactor model with a reduced set of items. The bifactor-specific indices showed that the factor structure cannot be considered unidimensional and that SPS and SIAS are reliable subscales. A closer examination of the less fitting item content and implications for future studies are discussed.

CONCLUSIONS

In conclusion, SPS and SIAS can be reported together as an overall score of social anxiety and are separately reliable measures to assess different aspects of social anxiety.

TRIAL REGISTRATION

This is a secondary analysis of data from two trials registered under ISRCTN75894275 and ISRCTN10627379.

Virtual reality exposure therapy for reducing social anxiety in stuttering: A randomized controlled pilot trial

We report on findings from the first randomized controlled pilot trial of virtual reality exposure therapy (VRET) developed specifically for reducing social anxiety associated with stuttering. People who stutter with heightened social anxiety were recruited from online adverts and randomly allocated to receive VRET (n = 13) or be put on a waitlist (n = 12). Treatment was delivered remotely using a smartphone-based VR headset. It consisted of three weekly sessions, each comprising both performative and interactive exposure exercises, and was guided by a virtual therapist. Multilevel model analyses failed to demonstrate the effectiveness of VRET at reducing social anxiety between pre- and post-treatment. We found similar results for fear of negative evaluation, negative thoughts associated with stuttering, and stuttering characteristics. However, VRET was associated with reduced social anxiety between post-treatment and one-month follow-up. These pilot findings suggest that our current VRET protocol may not be effective at reducing social anxiety amongst people who stutter, though might be capable of supporting longer-term change. Future VRET protocols targeting stuttering-related social anxiety should be explored with larger samples. The results from this pilot trial provide a solid basis for further design improvements and for future research to explore appropriate techniques for widening access to social anxiety treatments in stuttering.

Detecting Subclinical Social Anxiety Using Physiological Data From a Wrist-Worn Wearable: Small-Scale Feasibility Study

BACKGROUND

Subclinical (ie, threshold) social anxiety can greatly affect young people's lives, but existing solutions appear inadequate considering its rising prevalence. Wearable sensors may provide a novel way to detect social anxiety and result in new opportunities for monitoring and treatment, which would be greatly beneficial for persons with social anxiety, society, and health care services. Nevertheless, indicators such as skin temperature measured by wrist-worn sensors have not been used in prior work on physiological social anxiety detection.

OBJECTIVE

This study aimed to investigate whether subclinical social anxiety in young adults can be detected using physiological data obtained from wearable sensors, including heart rate, skin temperature, and electrodermal activity (EDA).

METHODS

Young adults (N=12) with self-reported subclinical social anxiety (measured using the widely used self-reported version of the Liebowitz Social Anxiety Scale) participated in an impromptu speech task. Physiological data were collected using an E4 Empatica wearable device. Using the preprocessed data and following a supervised machine learning approach, various classification algorithms such as Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbours (KNN) were used to develop models for 3 different contexts. Models were trained to differentiate (1) between baseline and socially anxious states, (2) among baseline, anticipation anxiety, and reactive anxiety states, and (3) social anxiety among individuals with social anxiety of differing severity. The predictive capability of the singular modalities was also explored in each of the 3 supervised learning experiments. The generalizability of the developed models was evaluated using 10-fold cross-validation as a performance index.

RESULTS

With modalities combined, the developed models yielded accuracies between 97.54% and 99.48% when differentiating between baseline and socially anxious states. Models trained to differentiate among baseline, anticipation anxiety, and reactive anxiety states yielded accuracies between 95.18% and 98.10%. Furthermore, the models developed to differentiate between social anxiety experienced by individuals with anxiety of differing severity scores successfully classified with accuracies between 98.86% and 99.52%. Surprisingly, EDA was identified as the most effective singular modality when differentiating between baseline and social anxiety states, whereas ST was the most effective modality when differentiating anxiety among individuals with social anxiety of differing severity.

CONCLUSIONS

The results indicate that it is possible to accurately detect social anxiety as well as distinguish between levels of severity in young adults by leveraging physiological data collected from wearable sensors.

Analysis of Default Mode Network in Social Anxiety Disorder: EEG Resting-State Effective Connectivity Study

Recent brain imaging findings by using different methods (e.g., fMRI and PET) have suggested that social anxiety disorder (SAD) is correlated with alterations in regional or network-level brain function. However, due to many limitations associated with these methods, such as poor temporal resolution and limited number of samples per second, neuroscientists could not quantify the fast dynamic connectivity of causal information networks in SAD. In this study, SAD-related changes in brain connections within the default mode network (DMN) were investigated using eight electroencephalographic (EEG) regions of interest. Partial directed coherence (PDC) was used to assess the causal influences of DMN regions on each other and indicate the changes in the DMN effective network related to SAD severity. The DMN is a large-scale brain network basically composed of the mesial prefrontal cortex (mPFC), posterior cingulate cortex (PCC)/precuneus, and lateral parietal cortex (LPC). The EEG data were collected from 88 subjects (22 control, 22 mild, 22 moderate, 22 severe) and used to estimate the effective connectivity between DMN regions at different frequency bands: delta (1-3 Hz), theta (4-8 Hz), alpha (8-12 Hz), low beta (13-21 Hz), and high beta (22-30 Hz). Among the healthy control (HC) and the three considered levels of severity of SAD, the results indicated a higher level of causal interactions for the mild and moderate SAD groups than for the severe and HC groups. Between the control and the severe SAD groups, the results indicated a higher level of causal connections for the control throughout all the DMN regions. We found significant increases in the mean PDC in the delta (p = 0.009) and alpha (p = 0.001) bands between the SAD groups. Among the DMN regions, the precuneus exhibited a higher level of causal influence than other regions. Therefore, it was suggested to be a major source hub that contributes to the mental exploration and emotional content of SAD. In contrast to the severe group, HC exhibited higher resting-state connectivity at the mPFC, providing evidence for mPFC dysfunction in the severe SAD group. Furthermore, the total Social Interaction Anxiety Scale (SIAS) was positively correlated with the mean values of the PDC of the severe SAD group, r (22) = 0.576, p = 0.006 and negatively correlated with those of the HC group, r (22) = -0.689, p = 0.001. The reported results may facilitate greater comprehension of the underlying potential SAD neural biomarkers and can be used to characterize possible targets for further medication.

Social anxiety is associated with poor quality of life in adults with epilepsy in Northeast China: A cross-sectional study

OBJECTIVE

This study aimed to investigate the factors influencing the level of social anxiety in patients with epilepsy (PWE) in Northeast China. We also identified the effect of social anxiety on the quality of life in these patients.

METHODS

A consecutive cohort of 148 adult PWE from The First Hospital of Jilin University were recruited. In this sample, 116 patients had focal epilepsy, 20 had generalized epilepsy, and 12 had unclassified epilepsy. Depressive symptoms, social anxiety, and quality of life were evaluated using the Chinese version of the Patient Health Questionnaire 9 (PHQ-9), 20-item Social Phobia Scale (SPS), 20-item Social Interaction Anxiety Scale (SIAS), and Quality-of-Life Inventory in Epilepsy-31 (QOLIE-31), respectively. Multivariate linear regression analyses were employed to identify independent factors influencing SPS scores and SIAS scores.

RESULTS

Correlation analysis suggested that sex, age at onset, seizure frequency over the last year, AED treatment model, >50% nocturnal seizures, PHQ-9 score, and QOLIE-31 score had a significant correlation with the SPS score. The age at onset, seizure frequency over the last year, AED treatment model, PHQ-9 score, and QOLIE-31 score correlated with the SIAS score. Multiple linear regression analysis showed that the total QOLIE-31 score (β = - 0.481; p = 0.001) was inversely associated with the SPS score in PWE. Additionally, earlier age of onset (β = -0.156; p = 0.022) and low total QOLIE-31 score (β = -0.457; p = 0.001) were risk factors for high SIAS scores.

CONCLUSION

We found that social anxiety was independently associated with poor quality of life. Earlier age of onset was also a risk factor for social anxiety. Future studies with large sample sizes are required to confirm our findings.