social anxiety disorder case study

“I Don’t Want to Bother You” – A Case Study in Social Anxiety Disorder

• First Online: 29 March 2023

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• Katharine E. Daniel 3 &
• Bethany A. Teachman 3  

Part of the book series: CBT: Science Into Practice ((CBT))

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Gi, a 34-year-old second-generation Korean American man, presented to treatment with pronounced and longstanding anxiety in many social situations, which significantly impaired his functioning (e.g., his perceived ability to run errands in crowded stores and care for his ill father). Gi engaged in cognitive behavior therapy (CBT) via telehealth during the COVID-19 pandemic. Key cognitions and biased cognitive processes that were maintaining his anxiety included a judgment that others frequently reject him, an assumption that if he expressed his own needs, then he would be unreasonably burdening others, and a core belief that he was incompetent, along with a pervasive tendency to make negative interpretations about his abilities in most social situations. He experienced marked functional improvements and reduced anxiety throughout his 17-session course of treatment. Gi’s case and treatment are detailed throughout this chapter to illustrate how individual CBT for social anxiety disorder can be implemented. Special discussion of how the clinician continuously and collaboratively modified her case conceptualization and intervention approaches with reference to aspects of Gi’s identities and in response to her own missteps are offered throughout.

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Daniel, K.E., Teachman, B.A. (2023). “I Don’t Want to Bother You” – A Case Study in Social Anxiety Disorder. In: Woud, M.L. (eds) Interpretational Processing Biases in Emotional Psychopathology . CBT: Science Into Practice. Springer, Cham. https://doi.org/10.1007/978-3-031-23650-1_16

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8 Treatment of Social Anxiety Disorder: A Case Complicated by Panic Disorder

• Published: February 2013
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Chapter 8 covers the treatment of Social Anxiety Disorder (SAD), and includes information about the condition, epidemiological considerations, the case study, assessment strategy and case formulation, intervention model and course of treatment, strategies for handling homework non-compliance, handling poor attendance and relapse, relapse prevention, post-treatment assessment, basic science for this case/condition, alternative strategies to consider, ethical consideration, cultural factors, clinical implications, and avoiding common mistakes during treatment.

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• Test for Panic Disorder
• Online Therapy for Panic and Anxiety
• Test for Social Anxiety Disorder
• Online Therapy for Social Anxiety
• Generalized Anxiety
• Post-Traumatic Stress Disorder
• Selective Mutism
• Obsessive-Compulsive Disorder
• Help & Support
• Test for Social Anxiety

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• Social Phobia/Anxiety Case Study: Jim

Jim was a nice looking man in his mid-30’s.  He could trace his shyness to boyhood and his social anxiety to his teenage years.  He had married a girl he knew well from high school and had almost no other dating history.  He and his wife, Lesley, had three children, two girls and a boy.

At our first meeting, Jim was very shy and averted his eyes from me, but he did shake hands, respond, and smile a genuine smile.  A few minutes into our session and Jim was noticeably more relaxed.  "I’ve suffered with this anxiety for as long as I can remember", he said.  "Even in school, I was backward and didn’t know what to say.  After I got married, my wife started taking over all of the daily, family responsibilities and I was more than glad to let her."

If there was an appointment to be made, Lesley made it.  If there was a parent-teacher conference to go to, Lesley went to it.  If Jim had something coming up, Lesley would make all the social arrangements.  Even when the family ordered takeout food, it was Lesley who made the call.  Jim was simply too afraid and shy.

Indeed, because of his wife, Jim was able to avoid almost all social responsibility -- except at his job.  It was his job and its responsibilities that brought Jim into treatment.

Years earlier, Jim had worked at a small, locally-owned record and tape store, where he knew the owner and felt a part of the family.  The business was slow and manageable and he never found himself on display in front of lines of people.  Several years previously, however, the owner had sold his business to a national record chain, and Jim found himself a lower mid-range manager in a national corporation, a position he did not enjoy.

"When I have to call people up to tell them that their order is in," he said, "I know my voice is going to be weak and break, and I will be unable to get my words out.  I’ll stumble around and choke up....then I’ll blurt out the rest of my message so fast I’m afraid they won’t understand me.  Sometimes I have to repeat myself and that is excruciatingly embarrassing........"

Jim felt great humiliation and embarrassment about this afterwards: he couldn’t even make a telephone call to a stranger without getting extremely anxious and giving himself away.  That was pretty bad!  Then he would beat himself up.  What was wrong with him?  Why was he so timid and scared?  No one else seemed to be like he was.  He simply must be crazy!  After a day full of this pressure, anxiety and negative thinking, Jim would leave work feeling fatigued, tired, and defeated.

Meanwhile, his wife, being naturally sociable and vocal, continually enabled Jim not to have to deal with any social situations.  In restaurants, his wife always ordered.  At home, she answered the telephone and made all the calls out.  He would tell her things that needed to be done and she would do them.

He had no friends of his own, except for the couples his wife knew from her work.  At times when he felt he simply had to go to these social events, Jim was very ill-at-ease, never knew what to say, and felt the silences that occurred in conversation were his fault for being so backward.  He knew he made everyone else uncomfortable and ill-at-ease.

Of course, the worst part of all was the anticipatory anxiety Jim felt ahead of time – when he knew he had to perform, do something in public, or even make phone calls from work.  The more time he had to worry and stew about these situations, the more anxious, fearful and uncomfortable he felt.

REMARKS: Jim presented a very typical case of generalized social phobia/social anxiety.  His strong anticipation and belief that he wouldn’t do well at social interactions and in social events became a self-fulfilling prophecy, and his belief came true: he didn’t do well.  The more nervous and anxious he got over a situation, and the more attention he paid to it, the more he could not perform well.  This was a very negative paradox or "vicious cycle" that all people with social anxiety get stuck in.  If your beliefs are strong that you will NOT do well, then it is likely you will not do well.  Therefore, thoughts, beliefs, and emotions need to be changed.

The depression (technically "dysthymia") that comes about after the anxious event continued to fuel the fire.  "I’ll never be able to deal with this," Jim would tell himself, thus constantly reinforcing the fact that he saw himself as a failure and a loser.

Unusual in this situation is that Jim’s wife remained loyal to him, understood his problem to some extent, and even seemed to enjoy her role as the family’s "social director".  The more and more she did for Jim, the more and more he could avoid.  It got so bad that Jim, who loved to listen to new albums and read new books -- could not even go to stores or to the library.  He would tell his wife what to buy and she would buy it.  She even kept track of when the library books were due and made sure she took them back on time.

This family situation is unusual because most people with social anxiety/social phobia have an extremely difficult time making and continuing personal relationships -- because of self-consciousness and the need for more privacy than most other people.  In fact, social phobia ranks among one of the highest psychological disorders when it comes to failed relationships, divorce, and living alone.

TREATMENT for Jim consisted of the normal course of cognitive strategies so that he would relearn and rethink what he was doing to himself.  He was cooperative from the beginning, and progressed nicely doing therapy.  He took each of the practice handouts and spent time each day practicing.  He made a "special time" for himself that his family respected and he used this place and time to practice the cognitive strategies his mind had to learn.

His biggest real-life fear, speaking to another person in public, was not really a speaking problem; it was an anxiety problem.  There was nothing wrong with Jim’s voice, his reading ability, or his speaking ability.  Jim was a bright man who had associated great anxiety around these social events in public situations.

The course of treatment here is NOT to practice!  In fact, practicing would just draw attention to what Jim perceived was the problem: his voice, his awkwardness, his perceived inability to speak to others.  Thus, it would reinforce the very behaviors we do not want to reinforce.

Instead, Jim worked on paradoxes.  We deliberately goofed-up.  We tried to make as many mistakes as possible.  We injected humor into the situation and found that when he exaggerated his fears, he thought this was funny.  Although more is involved than just this, the concept here is to de-stress the situation and enable the person to see it for what it is: NO BIG DEAL!  If you make a mistake, SO WHAT?  Everyone else does too!

Over the weeks, before group therapy began, Jim did a number of interesting things in public that began proving to him that he was NOT the center of attention, and it just didn’t matter if he made a mistake or two.  After all, he was human just like everyone else.  It’s this idea of perfectionism, of always having to "do your best" that must be broken down.  Jim was human; humans make mistakes; so what?  It was certainly nothing to get upset about.  In fact, as time went by, it become even more funny and humorous, rather than humiliating or embarrassing.

After completion of the behavioral group therapy, Jim had an opportunity for advancement in his company, which he now felt comfortable to take.  The promotion entailed holding weekly meetings in which he was in charge.  He would have to do some public speaking and respond to his employees’ questions.  By this time, Jim was feeling much more comfortable and much less anxious about the whole situation.  "I think I’ll deliberately goof up," he joked to me before the start of his new job.  "It would be interesting to see how everyone else responds."

To say that Jim did not have any anticipatory anxiety before taking this position or before making his weekly presentations would be inaccurate.  The difference was now they were manageable.  They were simply minor roadblocks that could be overcome.  Jim’s thinking about social events and activities had changed a great deal since the first day I saw him in therapy.

I talked to Jim a few months ago and everything was going well.  His responsibilities at work had increased slightly, but Jim now had the ability and beliefs to deal with them.  He was much more confident and had a feeling of being in control.  He was doing more around the house and his wife was a little surprised at his metamorphosis.  Luckily, this did not change the marriage dynamics adversely, and the last time I talked with him, Jim had become a father again: another little boy.

"He’s the last," Jim said, laughing over the phone, "I can’t get too distracted.  I’ve got too many speeches to give now."

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Look closer ... I am not "just shy": recognizing social anxiety disorder: a case study

• PMID: 25417333
• DOI: 10.1177/1942602x14545481

Early recognition, assessment, and treatment of social anxiety disorder are criteria to prevent persistent functional impairment in educational and occupational settings and in relationships. Individuals who avoid social settings due to the fear of embarrassment miss out on activities, learning opportunities, and interactions with others. Those who work with children in schools or health care settings are in an ideal position to help those who often don't have a voice. The 2013 updated NICE guideline, Social Anxiety Disorder: Recognition, Assessment and Treatment, has been critically reviewed and applied to a case study. The guideline is intended to provide evidence-based best practice advice for providers on how to recognize, complete assessments of and treat social anxiety disorder.

Publication types

• Case Reports
• Anxiety Disorders / diagnosis*
• Anxiety Disorders / nursing*
• Evidence-Based Nursing / methods
• Practice Guidelines as Topic
• School Nursing / methods*
• United States

• Systematic Review
• Open access
• Published: 12 May 2024

Association between problematic social networking use and anxiety symptoms: a systematic review and meta-analysis

• Mingxuan Du 1 ,
• Chengjia Zhao 2 ,
• Haiyan Hu 1 ,
• Ningning Ding 1 ,
• Jiankang He 1 ,
• Wenwen Tian 1 ,
• Wenqian Zhao 1 ,
• Xiujian Lin 1 ,
• Gaoyang Liu 1 ,
• Wendan Chen 1 ,
• ShuangLiu Wang 1 ,
• Pengcheng Wang 3 ,
• Dongwu Xu 1 ,
• Xinhua Shen 4 &
• Guohua Zhang 1  

BMC Psychology volume  12 , Article number:  263 ( 2024 ) Cite this article

186 Accesses

Metrics details

A growing number of studies have reported that problematic social networking use (PSNU) is strongly associated with anxiety symptoms. However, due to the presence of multiple anxiety subtypes, existing research findings on the extent of this association vary widely, leading to a lack of consensus. The current meta-analysis aimed to summarize studies exploring the relationship between PSNU levels and anxiety symptoms, including generalized anxiety, social anxiety, attachment anxiety, and fear of missing out. 209 studies with a total of 172 articles were included in the meta-analysis, involving 252,337 participants from 28 countries. The results showed a moderately positive association between PSNU and generalized anxiety (GA), social anxiety (SA), attachment anxiety (AA), and fear of missing out (FoMO) respectively (GA: r  = 0.388, 95% CI [0.362, 0.413]; SA: r  = 0.437, 95% CI [0.395, 0.478]; AA: r  = 0.345, 95% CI [0.286, 0.402]; FoMO: r  = 0.496, 95% CI [0.461, 0.529]), and there were different regulatory factors between PSNU and different anxiety subtypes. This study provides the first comprehensive estimate of the association of PSNU with multiple anxiety subtypes, which vary by time of measurement, region, gender, and measurement tool.

Peer Review reports

Introduction

Social network refers to online platforms that allow users to create, share, and exchange information, encompassing text, images, audio, and video [ 1 ]. The use of social network, a term encompassing various activities on these platforms, has been measured from angles such as frequency, duration, intensity, and addictive behavior, all indicative of the extent of social networking usage [ 2 ]. As of April 2023, there are 4.8 billion social network users globally, representing 59.9% of the world’s population [ 3 ]. The usage of social network is considered a normal behavior and a part of everyday life [ 4 , 5 ]. Although social network offers convenience in daily life, excessive use can lead to PSNU [ 6 , 7 ], posing potential threats to mental health, particularly anxiety symptoms (Rasmussen et al., 2020). Empirical research has shown that anxiety symptoms, including generalized anxiety (GA), social anxiety (SA), attachment anxiety (AA), and fear of missing out (FoMO), are closely related to PSNU [ 8 , 9 , 10 , 11 , 12 ]. While some empirical studies have explored the relationship between PSNU and anxiety symptoms, their conclusions are not consistent. Some studies have found a significant positive correlation [ 13 , 14 , 15 ], while others have found no significant correlation [ 16 , 17 , 18 , 19 ]. Furthermore, the degree of correlation varies widely in existing research, with reported r-values ranging from 0.12 to 0.80 [ 20 , 21 ]. Therefore, a systematic meta-analysis is necessary to clarify the impact of PSNU on individual anxiety symptoms.

Previous research lacks a unified concept of PSNU, primarily due to differing theoretical interpretations by various authors, and the use of varied standards and diagnostic tools. Currently, this phenomenon is referred to by several terms, including compulsive social networking use, problematic social networking use, excessive social networking use, social networking dependency, and social networking addiction [ 22 , 23 , 24 , 25 , 26 ]. These conceptual differences hinder the development of a cohesive and systematic research framework, as it remains unclear whether these definitions and tools capture the same underlying construct [ 27 ]. To address this lack of uniformity, this paper will use the term “problematic use” to encompass all the aforementioned nomenclatures (i.e., compulsive, excessive, dependent, and addictive use).

Regarding the relationship between PSNU and anxiety symptoms, two main perspectives exist: the first suggests a positive correlation, while the second proposes a U-shaped relationship. The former perspective, advocating a positive correlation, aligns with the social cognitive theory of mass communication. It posits that PSNU can reinforce certain cognitions, emotions, attitudes, and behaviors [ 28 , 29 ], potentially elevating individuals’ anxiety levels [ 30 ]. Additionally, the cognitive-behavioral model of pathological use, a primary framework for explaining factors related to internet-based addictions, indicates that psychiatric symptoms like depression or anxiety may precede internet addiction, implying that individuals experiencing anxiety may turn to social networking platforms as a coping mechanism [ 31 ]. Empirical research also suggests that highly anxious individuals prefer computer-mediated communication due to the control and social liberation it offers and are more likely to have maladaptive emotional regulation, potentially leading to problematic social network service use [ 32 ]. Turning to the alternate perspective, it proposes a U-shaped relationship as per the digital Goldilocks hypothesis. In this view, moderate social networking usage is considered beneficial for psychosocial adaptation, providing individuals with opportunities for social connection and support. Conversely, both excessive use and abstinence can negatively impact psychosocial adaptation [ 33 ]. In summary, both perspectives offer plausible explanations.

Incorporating findings from previous meta-analyses, we identified seven systematic reviews and two meta-analyses that investigated the association between PSNU and anxiety. The results of these meta-analyses indicated a significant positive correlation between PSNU and anxiety (ranging from 0.33 to 0.38). However, it is evident that these previous meta-analyses had certain limitations. Firstly, they focused only on specific subtypes of anxiety; secondly, they were limited to adolescents and emerging adults in terms of age. In summary, this systematic review aims to ascertain which theoretical perspective more effectively explains the relationship between PSNU and anxiety, addressing the gaps in previous meta-analyses. Additionally, the association between PSNU and anxiety could be moderated by various factors. Drawing from a broad research perspective, any individual study is influenced by researcher-specific designs and associated sample estimates. These may lead to bias compared to the broader population. Considering the selection criteria for moderating variables in empirical studies and meta-analyses [ 34 , 35 ], the heterogeneity of findings on problematic social network usage and anxiety symptoms could be driven by divergence in sample characteristics (e.g., gender, age, region) and research characteristics (measurement instrument of study variables). Since the 2019 coronavirus pandemic, heightened public anxiety may be attributed to the fear of the virus or heightened real life stress. The increased use of electronic devices, particularly smartphones during the pandemic, also instigates the prevalence of problematic social networking. Thus, our analysis focuses on three moderators: sample characteristics (participants’ gender, age, region), measurement tools (for PSNU and anxiety symptoms) and the time of measurement (before COVID-19 vs. during COVID-19).

The present study was conducted in accordance with the 2020 statement on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 36 ]. To facilitate transparency and to avoid unnecessary duplication of research, this study was registered on PROSPERO, and the number is CRD42022350902.

Literature search

Studies on the relationship between the PSNU and anxiety symptoms from 2000 to 2023 were retrieved from seven databases. These databases included China National Knowledge Infrastructure (CNKI), Wanfang Data, Chongqing VIP Information Co. Ltd. (VIP), Web of Science, ScienceDirect, PubMed, and PsycARTICLES. The search strings consisted of (a) anxiety symptoms, (b) social network, and (c) Problematic use. As shown in Table  1 , the keywords for anxiety are as follows: anxiety, generalized anxiety, social anxiety, attachment anxiety, fear of missing out, and FoMO. The keywords for social network are as follows: social network, social media, social networking site, Instagram, and Facebook. The keywords for addiction are as follows: addiction, dependence, problem/problematic use, excessive use. The search deadline was March 19, 2023. A total of 2078 studies were initially retrieved and all were identified ultimately.

Inclusion and exclusion criteria

Retrieved studies were eligible for the present meta-analysis if they met the following inclusion criteria: (a) the study provided Pearson correlation coefficients used to measure the relationship between PSNU and anxiety symptoms; (b) the study reported the sample size and the measurement instruments for the variables; (c) the study was written in English and Chinese; (d) the study provided sufficient statistics to calculate the effect sizes; (e) effect sizes were extracted from independent samples. If multiple independent samples were investigated in the same study, they were coded separately; if the study was a longitudinal study, they were coded by the first measurement. In addition, studies were excluded if they: (a) examined non-problematic social network use; (b) had an abnormal sample population; (c) the results of the same sample were included in another study and (d) were case reports or review articles. Two evaluators with master’s degrees independently assessed the eligibility of the articles. A third evaluator with a PhD examined the results and resolved dissenting views.

Data extraction and quality assessment

Two evaluators independently coded the selected articles according to the following characteristics: literature information, time of measurement (before the COVID-19 vs. during the COVID-19), sample source (developed country vs. developing country), sample size, proportion of males, mean age, type of anxiety, and measurement instruments for PSNU and anxiety symptoms. The following principles needed to be adhered to in the coding process: (a) effect sizes were extracted from independent samples. If multiple independent samples were investigated in the same study, they were coded separately; if the study was a longitudinal study, it was coded by the first measurement; (b) if multiple studies used the same data, the one with the most complete information was selected; (c) If studies reported t or F values rather than r , the following formula \( r=\sqrt{\frac{{t}^{2}}{{t}^{2}+df}}\) ; \( r=\sqrt{\frac{F}{F+d{f}_{e}}}\) was used to convert them into r values [ 37 , 38 ]. Additionally, if some studies only reported the correlation matrix between each dimension of PSNU and anxiety symptoms, the following formula \( {r}_{xy}=\frac{\sum {r}_{xi}{r}_{yj}}{\sqrt{n+n(n-1){r}_{xixj}}\sqrt{m+m(m-1){r}_{yiyj}}}\) was used to synthesize the r values [ 39 ], where n or m is the number of dimensions of variable x or variable y, respectively, and \( {r}_{xixj} \) or \( {r}_{yiyj}\) represents the mean of the correlation coefficients between the dimensions of variable x or variable y, respectively.

Literature quality was determined according to the meta-analysis quality evaluation scale developed [ 40 ]. The quality of the post-screening studies was assessed by five dimensions: sampling method, efficiency of sample collection, level of publication, and reliability of PSNU and anxiety symptom measurement instruments. The total score of the scale ranged from 0 to 10; higher scores indicated better quality of the literature.

Data analysis

All data were performed using Comprehensive Meta Analysis 3.3 (CMA 3.3). Pearson’s product-moment coefficient r was selected as the effect size index in this meta-analysis. Firstly, \( {\text{F}\text{i}\text{s}\text{h}\text{e}\text{r}}^{{\prime }}\text{s} Z=\frac{1}{2}\times \text{ln}\left(\frac{1+r}{1-r}\right)\) was used to convert the correlation coefficient to Fisher Z . Then the formula \( SE=\sqrt{\frac{1}{n-3}}\) was used to calculate the standard error ( SE ). Finally, the summary of r was obtained from the formula \( r=\frac{{e}^{2z}-1}{{e}^{2z}+1}\) for a comprehensive measure of the relationship between PSNU and anxiety symptoms [ 37 , 41 ].

Although the effect sizes estimated by the included studies may be similar, considering the actual differences between studies (e.g., region and gender), the random effects model was a better choice for data analysis for the current meta-analysis. The heterogeneity of the included study effect sizes was measured for significance by Cochran’s Q test and estimated quantitatively by the I 2 statistic [ 42 ]. If the results indicate there is a significant heterogeneity (the Q test: p -value < 0.05, I 2  > 75) and the results of different studies are significantly different from the overall effect size. Conversely, it indicates there are no differences between the studies and the overall effect size. And significant heterogeneity tends to indicate the possible presence of potential moderating variables. Subgroup analysis and meta-regression analysis were used to examine the moderating effect of categorical and continuous variables, respectively.

Funnel plots, fail-safe number (Nfs) and Egger linear regression were utilized to evaluate the publication bias [ 43 , 44 , 45 ]. The likelihood of publication bias was considered low if the intercept obtained from Egger linear regression was not significant. A larger Nfs indicated a lower risk of publication bias, and if Nfs < 5k + 10 (k representing the original number of studies), publication bias should be a concern [ 46 ]. When Egger’s linear regression was significant, the Duval and Tweedie’s trim-and-fill was performed to correct the effect size. If there was no significant change in the effect size, it was assumed that there was no serious publication bias [ 47 ].

A significance level of P  < 0.05 was deemed applicable in this study.

Sample characteristics

The PRISMA search process is depicted in Fig.  1 . The database search yielded 2078 records. After removing duplicate records and screening the title and abstract, the full text was subject to further evaluation. Ultimately, 172 records fit the inclusion criteria, including 209 independent effect sizes. The present meta-analysis included 68 studies on generalized anxiety, 44 on social anxiety, 22 on attachment anxiety, and 75 on fear of missing out. The characteristics of the selected studies are summarized in Table  2 . The majority of the sample group were adults. Quality scores for selected studies ranged from 0 to 10, with only 34 effect sizes below the theoretical mean, indicating high quality for the included studies. The literature included utilized BSMAS as the primary tool to measure PSNU, DASS-21-A to measure GA, IAS to measure SA, ECR to measure AA, and FoMOS to measure FoMO.

Flow chart of the search and selection strategy

Overall analysis, homogeneity tests and publication bias

As shown in Table  3 , there was significant heterogeneity between PSNU and all four anxiety symptoms (GA: Q  = 1623.090, I 2  = 95.872%; SA: Q  = 1396.828, I 2  = 96.922%; AA: Q  = 264.899, I 2  = 92.072%; FoMO: Q  = 1847.110, I 2  = 95.994%), so a random effects model was chosen. The results of the random effects model indicate a moderate positive correlation between PSNU and anxiety symptoms (GA: r  = 0.350, 95% CI [0.323, 0.378]; SA: r  = 0.390, 95% CI [0.347, 0.431]; AA: r  = 0.345, 95% CI [0.286, 0.402]; FoMO: r  = 0.496, 95% CI [0.461, 0.529]).

Figure  2 shows the funnel plot of the relationship between PSNU and anxiety symptoms. No significant symmetry was seen in the funnel plot of the relationship between PSNU and GA and between PSNU and SA. And the Egger’s regression results also indicated that there might be publication bias ( t  = 3.775, p  < 0.001; t  = 2.309, p  < 0.05). Therefore, it was necessary to use fail-safe number (Nfs) and the trim and fill method for further examination and correction. The Nfs for PSNU and GA as well as PSNU and SA are 4591 and 7568, respectively. Both Nfs were much larger than the standard 5 k  + 10. After performing the trim and fill method, 14 effect sizes were added to the right side of the funnel plat (Fig.  2 .a), the correlation coefficient between PSNU and GA changed to ( r  = 0.388, 95% CI [0.362, 0.413]); 10 effect sizes were added to the right side of the funnel plat (Fig.  2 .b), the correlation coefficient between PSNU and SA changed to ( r  = 0.437, 95% CI [0.395, 0.478]). The correlation coefficients did not change significantly, indicating that there was no significant publication bias associated with the relationship between PSNU and these two anxiety symptoms (GA and SA).

Funnel plot of the relationship between PSNU and anxiety symptoms. Note: Black dots indicated additional studies after using trim and fill method; ( a ) = Funnel plot of the PSNU and GA; ( b ) = Funnel plot of the PSNU and SA; ( c ) = Funnel plot of the PSNU and AA; ( d ) = Funnel plot of the PSNU and FoMO

Sensitivity analyses

Initially, the findings obtained through the one-study-removed approach indicated that the heterogeneities in the relationship between PSNU and anxiety symptoms were not attributed to any individual study. Nevertheless, it is important to note that sensitivity analysis should be performed based on literature quality [ 223 ] since low-quality literature could potentially impact result stability. In the relationship between PSNU and GA, the 10 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.402, 95% CI [0.375, 0.428]); In the relationship between PSNU and SA, the 8 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.431, 95% CI [0.387, 0.472]); In the relationship between PSNU and AA, the 5 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.367, 95% CI [0.298, 0.433]); In the relationship between PSNU and FoMO, the 11 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.508, 95% CI [0.470, 0.544]). The revised estimates indicate that meta-analysis results were stable.

Moderator analysis

The impact of moderator variables on the relation between psnu and ga.

The results of subgroup analysis and meta-regression are shown in Table  4 , the time of measurement significantly moderated the correlation between PSNU and GA ( Q between = 19.268, df  = 2, p  < 0.001). The relation between the two variables was significantly higher during the COVID-19 ( r  = 0.392, 95% CI [0.357, 0.425]) than before the COVID-19 ( r  = 0.270, 95% CI [0.227, 0.313]) or measurement time uncertain ( r  = 0.352, 95% CI [0.285, 0.415]).

The moderating effect of the PSNU measurement was significant ( Q between = 6.852, df  = 1, p  = 0.009). The relation was significantly higher when PSNU was measured with the BSMAS ( r  = 0.373, 95% CI [0.341, 0.404]) compared to others ( r  = 0.301, 95% CI [0.256, 0.344]).

The moderating effect of the GA measurement was significant ( Q between = 60.061, df  = 5, p  < 0.001). Specifically, when GA measured by the GAD ( r  = 0.398, 95% CI [0.356, 0.438]) and the DASS-21-A ( r  = 0.433, 95% CI [0.389, 0.475]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the STAI ( r  = 0.232, 95% CI [0.187, 0.276]).

For the relation between PSNU and GA, the moderating effect of region, gender and age were not significant.

The impact of moderator variables on the relation between PSNU and SA

The effects of the moderating variables in the relation between PSNU and SA were shown in Table  5 . The results revealed a gender-moderated variances between the two variables (b = 0.601, 95% CI [ 0.041, 1.161], Q model (1, k = 41) = 4.705, p  = 0.036).

For the relation between PSNU and SA, the moderating effects of time of measurement, region, measurement of PSNU and SA, and age were not significant.

The impact of moderator variables on the relation between PSNU and AA

The effects of the moderating variables in the relation between PSNU and AA were shown in Table  6 , region significantly moderated the correlation between PSNU and AA ( Q between = 6.410, df  = 2, p  = 0.041). The correlation between the two variables was significantly higher in developing country ( r  = 0.378, 95% CI [0.304, 0.448]) than in developed country ( r  = 0.242, 95% CI [0.162, 0.319]).

The moderating effect of the PSNU measurement was significant ( Q between = 6.852, df  = 1, p  = 0.009). Specifically, when AA was measured by the GPIUS-2 ( r  = 0.484, 95% CI [0.200, 0.692]) and the PMSMUAQ ( r  = 0.443, 95% CI [0.381, 0.501]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the BSMAS ( r  = 0.248, 95% CI [0.161, 0.331]) and others ( r  = 0.313, 95% CI [0.250, 0.372]).

The moderating effect of the AA measurement was significant ( Q between = 17.283, df  = 2, p  < 0.001). The correlation was significantly higher when measured using the ECR ( r  = 0.386, 95% CI [0.338, 0.432]) compared to the RQ ( r  = 0.200, 95% CI [0.123, 0.275]).

For the relation between PSNU and AA, the moderating effects of time of measurement, region, gender, and age were not significant.

The impact of moderator variables on the relation between PSNU and FoMO

The effects of the moderating variables in the relation between PSNU and FoMO were shown in Table  7 , the moderating effect of the PSNU measurement was significant ( Q between = 8.170, df  = 2, p  = 0.017). Among the sub-dimensions, the others was excluded because there was only one sample. Specifically, when measured using the FoMOS-MSME ( r  = 0.630, 95% CI [0.513, 0.725]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the FoMOS ( r  = 0.472, 95% CI [0.432, 0.509]) and the T-S FoMOS ( r  = 0.557, 95% CI [0.463, 0.639]).

For the relationship between PSNU and FoMO, the moderating effects of time of measurement, region, measurement of PSNU, gender and age were not significant.

Through systematic review and meta-analysis, this study established a positive correlation between PSNU and anxiety symptoms (i.e., generalized anxiety, social anxiety, attachment anxiety, and fear of missing out), confirming a linear relationship and partially supporting the Social Cognitive Theory of Mass Communication [ 28 ] and the Cognitive Behavioral Model of Pathological Use [ 31 ]. Specifically, a significant positive correlation between PSNU and GA was observed, implying that GA sufferers might resort to social network for validation or as an escape from reality, potentially alleviating their anxiety. Similarly, the meta-analysis demonstrated a strong positive correlation between PSNU and SA, suggesting a preference for computer-mediated communication among those with high social anxiety due to perceived control and liberation offered by social network. This preference is often accompanied by maladaptive emotional regulation, predisposing them to problematic use. In AA, a robust positive correlation was found with PSNU, indicating a higher propensity for such use among individuals with attachment anxiety. Notably, the study identified the strongest correlation in the context of FoMO. FoMO’s significant association with PSNU is multifaceted, stemming from the real-time nature of social networks that engenders a continuous concern about missing crucial updates or events. This drives frequent engagement with social network, thereby establishing a direct link to problematic usage patterns. Additionally, social network’s feedback loops amplify this effect, intensifying FoMO. The culture of social comparison on these platforms further exacerbates FoMO, as users frequently compare their lives with others’ selectively curated portrayals, enhancing both their social networking usage frequency and the pursuit for social validation. Furthermore, the integral role of social network in modern life broadens FoMO’s scope, encompassing anxieties about staying informed and connected.

The notable correlation between FoMO and PSNU can be comprehensively understood through various perspectives. FoMO is inherently linked to the real-time nature of social networks, which cultivates an ongoing concern about missing significant updates or events in one’s social circle [ 221 ]. This anxiety prompts frequent engagement with social network, leading to patterns of problematic use. Moreover, the feedback loops in social network algorithms, designed to enhance user engagement, further intensify this fear [ 224 ]. Additionally, social comparison, a common phenomenon on these platforms, exacerbates FoMO as users continuously compare their lives with the idealized representations of others, amplifying feelings of missing out on key social experiences [ 225 ]. This behavior not only increases social networking usage but also is closely linked to the quest for social validation and identity construction on these platforms. The extensive role of social network in modern life further amplifies FoMO, as these platforms are crucial for information exchange and maintaining social ties. FoMO thus encompasses more than social concerns, extending to anxieties about staying informed with trends and dynamics within social networks [ 226 ]. The multifaceted nature of FoMO in relation to social network underscores its pronounced correlation with problematic social networking usage. In essence, the combination of social network’s intrinsic characteristics, psychological drivers of user behavior, the culture of social comparison, and the pervasiveness of social network in everyday life collectively make FoMO the most pronouncedly correlated anxiety type with PSNU.

Additionally, we conducted subgroup analyses on the timing of measurement (before COVID-19 vs. during COVID-19), measurement tools (for PSNU and anxiety symptoms), sample characteristics (participants’ region), and performed a meta-regression analysis on gender and age in the context of PSNU and anxiety symptoms. It was found that the timing of measurement, tools used for assessing PSNU and anxiety, region, and gender had a moderating effect, whereas age did not show a significant moderating impact.

Firstly, the relationship between PSNU and anxiety symptoms was significantly higher during the COVID-19 period than before, especially between PSNU and GA. However, the moderating effect of measurement timing was not significant in the relationship between PSNU and other types of anxiety. This could be attributed to the increased uncertainty and stress during the pandemic, leading to heightened levels of general anxiety [ 227 ]. The overuse of social network for information seeking and anxiety alleviation might have paradoxically exacerbated anxiety symptoms, particularly among individuals with broad future-related worries [ 228 ]. While the COVID-19 pandemic altered the relationship between PSNU and GA, its impact on other types of anxiety (such as SA and AA) may not have been significant, likely due to these anxiety types being more influenced by other factors like social skills and attachment styles, which were minimally impacted by the epidemic.

Secondly, the observed variance in the relationship between PSNU and AA across different economic contexts, notably between developing and developed countries, underscores the multifaceted influence of socio-economic, cultural, and technological factors on this dynamic. The amplified connection in developing countries may be attributed to greater socio-economic challenges, distinct cultural norms regarding social support and interaction, rising social network penetration, especially among younger demographics, and technological disparities influencing accessibility and user experience [ 229 , 230 ]. Moreover, the role of social network as a coping mechanism for emotional distress, potentially fostering insecure attachment patterns, is more pronounced in these settings [ 231 ]. These findings highlight the necessity of considering contextual variations in assessing the psychological impacts of social network, advocating for a nuanced understanding of how socio-economic and cultural backgrounds mediate the relationship between PSNU and mental health outcomes [ 232 ]. Additionally, the relationship between PSNU and other types of anxiety (such as GA and SA) presents uniform characteristics across different economic contexts.

Thirdly, the significant moderating effects of measurement tools in the context of PSNU and its correlation with various forms of anxiety, including GA, and AA, are crucial in interpreting the research findings. Specifically, the study reveals that the Bergen Social Media Addiction Scale (BSMAS) demonstrates a stronger correlation between PSNU and GA, compared to other tools. Similarly, for AA, the Griffiths’ Problematic Internet Use Scale 2 (GPIUS2) and the Problematic Media Social Media Use Assessment Questionnaire (PMSMUAQ) show a more pronounced correlation with AA than the BSMAS or other instruments, but for SA and FoMO, the PSNU instrument doesn’t significantly moderate the correlation. The PSNU measurement tool typically contains an emotional change dimension. SA and FoMO, due to their specific conditional stimuli triggers and correlation with social networks [ 233 , 234 ], are likely to yield more consistent scores in this dimension, while GA and AA may be less reliable due to their lesser sensitivity to specific conditional stimuli. Consequently, the adjustment effects of PSNU measurements vary across anxiety symptoms. Regarding the measurement tools for anxiety, different scales exhibit varying degrees of sensitivity in detecting the relationship with PSNU. The Generalized Anxiety Disorder Scale (GAD) and the Depression Anxiety Stress Scales 21 (DASS-21) are more effective in illustrating a strong relationship between GA and PSNU than the State-Trait Anxiety Inventory (STAI). In the case of AA, the Experiences in Close Relationships-21 (ECR-21) provides a more substantial correlation than the Relationship Questionnaire (RQ). Furthermore, for FoMO, the Fear of Missing Out Scale - Multi-Social Media Environment (FoMOS-MSME) is more indicative of a strong relationship with PSNU compared to the standard FoMOS or the T-S FoMOS. These findings underscore the importance of the selection of appropriate measurement tools in research. Different tools, due to their unique design, focus, and sensitivity, can reveal varying degrees of correlation between PSNU and anxiety disorders. This highlights the need for careful consideration of tool characteristics and their potential impact on research outcomes. It also cautions against drawing direct comparisons between studies without acknowledging the possible variances introduced by the use of different measurement instruments.

Fourthly, the significant moderating role of gender in the relationship between PSNU and SA, particularly pronounced in samples with a higher proportion of females. Women tend to engage more actively and emotionally with social network, potentially leading to an increased dependency on these platforms when confronting social anxiety [ 235 ]. This intensified use might amplify the association between PSNU and SA. Societal and cultural pressures, especially those related to appearance and social status, are known to disproportionately affect women, possibly exacerbating their experience of social anxiety and prompting a greater reliance on social network for validation and support [ 236 ]. Furthermore, women’s propensity to seek emotional support and express themselves on social network platforms [ 237 ] could strengthen this link, particularly in the context of managing social anxiety. Consequently, the observed gender differences in the relationship between PSNU and SA underscore the importance of considering gender-specific dynamics and cultural influences in psychological research related to social network use. In addition, gender consistency was observed in the association between PSNU and other types of anxiety, indicating no significant gender disparities.

Fifthly, the absence of a significant moderating effect of age on the relationship between PSNU and various forms of anxiety suggests a pervasive influence of social network across different age groups. This finding indicates that the impact of PSNU on anxiety is relatively consistent, irrespective of age, highlighting the universal nature of social network’s psychological implications [ 238 ]. Furthermore, this uniformity suggests that other factors, such as individual psychological traits or socio-cultural influences, might play a more crucial role in the development of anxiety related to social networking usage than age [ 239 ]. The non-significant role of age also points towards a potential generational overlap in social networking usage patterns and their psychological effects, challenging the notion that younger individuals are uniquely susceptible to the adverse effects of social network on mental health [ 240 ]. Therefore, this insight necessitates a broader perspective in understanding the dynamics of social network and mental health, one that transcends age-based assumptions.

Limitations

There are some limitations in this research. First, most of the studies were cross-sectional surveys, resulting in difficulties in inferring causality of variables, longitudinal study data will be needed to evaluate causal interactions in the future. Second, considerable heterogeneity was found in the estimated results, although heterogeneity can be partially explained by differences in study design (e.g., Time of measurement, region, gender, and measurement tools), but this can introduce some uncertainty in the aggregation and generalization of the estimated results. Third, most studies were based on Asian samples, which limits the generality of the results. Fourth, to minimize potential sources of heterogeneity, some less frequently used measurement tools were not included in the classification of measurement tools, which may have some impact on the results of heterogeneity interpretation. Finally, since most of the included studies used self-reported scales, it is possible to get results that deviate from the actual situation to some extent.

This meta-analysis aims to quantifies the correlations between PSNU and four specific types of anxiety symptoms (i.e., generalized anxiety, social anxiety, attachment anxiety, and fear of missing out). The results revealed a significant moderate positive association between PSNU and each of these anxiety symptoms. Furthermore, Subgroup analysis and meta-regression analysis indicated that gender, region, time of measurement, and instrument of measurement significantly influenced the relationship between PSNU and specific anxiety symptoms. Specifically, the measurement time and GA measurement tools significantly influenced the relationship between PSNU and GA. Gender significantly influenced the relationship between PSNU and SA. Region, PSNU measurement tools, and AA measurement tools all significantly influenced the relationship between PSNU and AA. The FoMO measurement tool significantly influenced the relationship between PSNU and FoMO. Regarding these findings, prevention interventions for PSNU and anxiety symptoms are important.

Data availability

The datasets are available from the corresponding author on reasonable request.

Abbreviations

• Problematic social networking use
• Generalized anxiety
• Social anxiety
• Attachment anxiety

Fear of miss out

Bergen Social Media Addiction Scale

Facebook Addiction Scale

Facebook Intrusion Questionnaire

Generalized Problematic Internet Use Scale 2

Problematic Mobile Social Media Usage Assessment Questionnaire

Social Network Addiction Tendency Scale

Brief Symptom Inventory

The anxiety subscale of the Depression Anxiety Stress Scales

Generalized Anxiety Disorder

The anxiety subscale of the Hospital Anxiety and Depression Scale

State-Trait Anxiety Inventory

Interaction Anxiousness Scale

Liebowitz Social Anxiety Scale

Social Anxiety Scale for Social Media Users

Social Anxiety for Adolescents

Social Anxiety Subscale of the Self-Consciousness Scale

Social Interaction Anxiety Scale

Experiences in Close Relationship Scale

Relationship questionnaire

Fear of Missing Out Scale

FoMO Measurement Scale in the Mobile Social Media Environment

Trait-State Fear of missing Out Scale

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This research was supported by the Social Science Foundation of China (Grant Number: 23BSH135).

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Mingxuan Du, Haiyan Hu, Ningning Ding, Jiankang He, Wenwen Tian, Wenqian Zhao, Xiujian Lin, Gaoyang Liu, Wendan Chen, ShuangLiu Wang, Dongwu Xu & Guohua Zhang

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Du, M., Zhao, C., Hu, H. et al. Association between problematic social networking use and anxiety symptoms: a systematic review and meta-analysis. BMC Psychol 12 , 263 (2024). https://doi.org/10.1186/s40359-024-01705-w

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Received : 25 January 2024

Accepted : 03 April 2024

Published : 12 May 2024

DOI : https://doi.org/10.1186/s40359-024-01705-w

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• Case Rep Psychiatry
• v.2023; 2023
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Substance Abuse, Depression, and Social Anxiety: Case Study and Application of Cognitive Psychotherapy

Feda abu al-khair.

Assistant Professor in Clinical Psychology, Al-Ahliyya Amman University, 19328, Amman, Jordan

Associated Data

The data used to support the findings of this study have not been made available because of the privacy issues.

A 20-year-old male was referred by a psychiatrist to the clinic for treatment. He was diagnosed with social anxiety disorder (SAD), depression, and substances abuse. He complained of depressive mood and severe anxiety symptoms. These symptoms are triggered in social situations, as well as when talking to others, being in public areas, and going to malls or any crowded places. Because of his symptoms, he avoided getting into the situation, which affected his daily life. The patient was diagnosed with SAD, major depression, and substance abuse and underwent 20 separate sessions of cognitive behavioral therapy (CBT). The application of CBT led to a decrease in the number of anxiety attacks and angry outbursts that the patient suffers from. It also helped him learn some techniques to use in his interactions within the society, as well as other techniques, such as cognitive reorganization of dysfunctional thoughts, and gradually exposed him to the social situations. He also learned to practice some relaxation techniques, to teach him integration in social situations and confrontation instead of avoidance.

1. Introduction

Social anxiety disorder (SAD), one of the most common anxiety disorders, is rapidly increasing and disproportionately affects young people [ 1 ]. It is the third most common mental disorder after depression and alcohol dependence. According to epidemiological reports, the current prevalence is 5%–10%, and the lifetime prevalence is 8.4%–15% [ 2 ]. However, based on prevalence studies in seven countries, researchers found that the global prevalence of social anxiety is significantly higher than previously reported, and more than one-third of respondents met the threshold for SAD Standard. If left untreated, it can cause many serious problems in people's lives [ 1 , 3 ].

People with SAD experience significant fear and/or anxiety, in one or more types of situations on the social level, where they think others will judge and negatively evaluate them. The danger they feel during these situations is excessive compared to the feelings of a regular person, in form and intensity, when the two are placed in the same sociocultural context [ 4 ].

Comorbidity factors: SAD commonly occurs alongside other anxiety disorders, major depressive disorder, and substance use disorders. Typically, SAD emerges before the onset of these other disorders, except for specific phobia and separation anxiety disorder. Prolonged social isolation resulting from SAD can lead to the development of major depressive disorder. Among older adults, there is a significant comorbidity between SAD and depression. Individuals may turn to substances as a form of self-medication to cope with social fears. However, the symptoms of substance intoxication or withdrawal, such as trembling, can also intensify social anxiety. Body dysmorphic disorder frequently coexists with SAD, and generalized SAD often co-occurs with avoidant personality disorder. In children, there is a high prevalence of comorbidities between SAD and high-functioning autism spectrum disorder, as well as selective mutism [ 4 ].

A review highlighted the significance of the relationship between alcohol use and individuals suffering from SAD who believe that consuming alcoholic beverages will have a positive effect on them. People suffering from disorders, such as SAD and alcohol use disorder (AUD), expect alcohol to have more positive effects in social settings (such as decreased tension and social assertiveness) than those not suffering from AUD and those with lower levels of anxiety [ 5 – 7 ].

Many studies have been conducted on cannabis, nicotine, and other dependencies; however, many others concentrate on alcohol, when investigating the co-occurrence of alcohol and substance use disorder (ASUD) and SAD [ 8 ]. It is widely acknowledged that people with SAD will more likely be the consumers of alcoholic beverages and/or abuse drugs. Comorbid SAD and ASUD are clinically significant because they are linked to higher morbidity, poorer treatment outcomes, and decreased therapy seeking.

People with anxiety conditions are typically advised to begin using drugs (self-medication) to treat their symptoms. However, due to alterations in the biology of the brain, substance use can cause or contribute to a person's susceptibility to suffer from anxiety [ 5 , 6 , 9 ].

The study aims to determine the efficacy of cognitive psychotherapy in cases of SAD, depression, and substance abuse. Cognitive behavioral therapy (CBT) should have a major impact in these cases, by reducing the patient's complaints, such as anxiety from speaking in public and from social situations, low mood, and symptoms of depression. It should also allow patients to return to their studies and social life, without having to depend on different substances.

1.1. Description of Case Study and Methods

1.1.1. case report.

The patient is a 20-year-old single man. He is unemployed and has a history of polysubstance abuse and family problems. For the past 2 years, he has used alcohol, cocaine, and medical substance (Lyrica and other medications). He has a history of social anxiety with biological symptoms, which are unresponsive to medication. He expressed low self esteem and feelings of worthlessness. He was pessimistic about the future, saying, “I don't see anything ahead for me.” He described a passive death wish, but denied having any active suicidal thoughts. He described feeling fast heartbeats, blushing, trembling, sweating, trouble catching his breath, dizziness, and panic complaints, especially when talking to people and women in specific. He also described being unable to carry out a conversation with others, feelings of being watched and laughed at, and believing that he is ugly looking. He described having these complaints since adolescence. The patient has been an addict for the past 2 years. He started with Marijuana and alcohol, and then he tried several medical drugs.

The patient talked about various upsetting memories of instances that took place during his childhood and up to his late teenage years. This included memories, such as not being given a choice or participating in decision making, his father's absence from the house due to travel, followed by the discovery of his marriage to another woman and the presence of children. His family's upbringing style is firm, authoritarian on the part of the mother, and permissive on the part of the father.

1.2. Assessment and Diagnosis

Depending on psychiatrist referral letter, Structured Clinical Interview and Several tools were used to evaluate and diagnose patient's social anxiety and depression symptoms. They varied from self to clinician administered measures, and included the following:

1.2.1. Beck Depression Inventory (BDI)

The BDI [ 10 ] is constituted of 21 items. It is self-administered and represents an assessment of the physiological, affective, and cognitive aspects of depression, and is a measure of its severity. A total score of 10 or less is considered normal. A person is considered clinically depressed, if he or she obtains a score of 20 or more, on the BDI. The BDI is characterized by its high reliability and validity. Treatment outcome research makes use of the characteristics of this scale. The scale was translated by Abdul Khaleq 1996, and it has high reliability and validity in the Arab regions, as stated in [ 11 – 14 ].

1.2.2. Liebowitz Social Anxiety Scale (LSAS)

Twenty-four items constitute the LSAS [ 15 ]. It is administered by a clinician, so that the respondent may rate his or her feelings of fear and avoidance, on a scale of 0 (none) to 4 (extreme). It was translated to Arabic by Ibrahim [ 16 ], and it has high reliability and validity in the Arab regions, as stated in [ 17 , 18 ].

To examine SAD, the LSAS, with its good psychometric properties, is often employed in treatment outcome research [ 19 ]. Cutoff scores determined by Mennin et al. [ 20 ] for social phobia are greater than 30 and greater than 60 for generalized social phobia.

1.3. Formulation

Based on the assessment of the patient's case, which was determined from the psychiatrist's letter and the information he provided during the first meeting, he was diagnosed with social anxiety and depression. The patient showed symptoms of social anxiety, which were concluded to be the signs of his low self-esteem, manifested through avoidance. Depression was also another type of manifestation he exhibited, with an experience of persistent low spirits, ruminations about the past, and feeling guilty for exhibiting angry outbursts during current times. As a result, while patient's symptoms satisfied the diagnosis for social anxiety, they seemed to result from his depression, which started during adolescence. The CBT longitudinal model [ 21 ] was thought to be the best tool to use to understand patient's depression in view of the experiences he had in earlier life. These are the core beliefs, negative in nature, he has derived in life, as well as the rigid life rules, all of which contributed to his low self-esteem and led him to demonstrate signs of depression. Social anxiety has led him to drug addiction, due to his beliefs that the drug's effects will encourage him to talk to and deal with people. It is worth noting that the patient has been undergoing pharmacological treatment with his psychiatrist since the beginning.

1.4. Therapy Program

Fennell's [ 22 ] guidelines were followed for all sessions:

• Establishing the agenda.
• Reviewing events that have taken place since the previous session, feedback on the previous session, and homework.
• Going over the agenda once more.
• Prioritizing and discussing agenda items.
• Collaboratively assigning homework.
• Checking reactions.

1.5. Techniques of Cognitive Behavioral Therapy

1.5.1. self-monitoring.

Self-monitoring refers to observing one's behaviors and experiences systematically during various occasions for a certain time.

It is used in the therapy as a method of intervention, as it helps patients examine thoughts, emotions, and behaviors. It helps them identify the situations they are afraid of and find the best course of action for dealing with them.

Drastic changes occur from self-monitoring, according to Kazdin [ 23 ]. Korotitsch and Nelson-Gray [ 24 ] concluded that an immediate change is among the therapeutic effects of self-monitoring, despite their small scale. The clinician required the patient to track his thoughts, feelings, behaviors, as well as any differences he notices in himself.

1.5.2. Cognitive Reorganization

The four stages of cognitive reorganization, according to Beck et al. [ 25 ] are: (i) identifying dysfunctional thoughts, (ii) cognitive reorganization, (iii) modifying dysfunctional thoughts, and (iv) assimilating the new functional thoughts. On the second stage, patients begin to recognize their automatic or dysfunctional thoughts, as well as the emotions associated with them. For instance, a recurring thought for the patient was that others see him as an ugly, insignificant person. This notion contributed to his feelings of anxiety and fear.

Nevertheless, another adaptive thought that he can adopt instead is: “I may not like everyone, but there are those who love me as I am”.

As a result, the patient was taught during all sessions to use adaptive thoughts instead of negative thoughts. Part of six of his sessions was dedicated to discussing a record he kept of his dysfunctional thoughts.

1.5.3. Relaxation

Relaxation techniques were used to treat a patient's symptoms, particularly those that resulted from his anxiety and depression and were physiological in nature.

According to Jacobson's technique, there are certain breathing and muscle relaxation exercises in use, which can be beneficial in a patient's case. As such, the clinician worked on teaching him these exercises over eight sessions, to help him manage the physical symptoms he suffers from. The patient was taught to use deep breathing and some short muscle relaxation techniques in his day-to-day life, particularly when confronted with an unpleasant situation [ 26 ].

1.5.4. Training in Assertiveness and Motivation

Assertiveness training can be beneficial in cases where the patients suffer from depression, anxiety in social settings, addiction, and issues related to unspoken anger.

Since it is now known that assertiveness is learned rather than an inborn trait, assertiveness training can be employed in enhancing self-esteem and ameliorating interpersonal skills. It is true that some people seem more assertive, nevertheless, assertiveness can be acquired. In the patient's case, he was assisted in determining the situations where he faces more challenges on an interpersonal level, as well as the behaviors he exhibits that he needs to concentrate on in order to improve. Furthermore, the therapist assisted him in identifying the beliefs and attitudes that he may have developed, which caused him to become too passive. As part of this technique, she used role-playing exercises.

1.5.5. Clinical Sessions

The patient had one-on-one sessions for 50 min with his therapist each to complete his therapeutic treatment, over the course of 5 months. The reasons for employing CBT were examined during the first session. Educating the patient on SAD, depression, and addiction were a point of focus during therapy. Automatic thoughts and how they affect cognition were examined, helping him in identifying these types of thoughts and feelings as he experienced them.

As homework, he was given an anxiety self-monitoring diary. The therapist emphasized the issue of establishing and maintaining a good relationship between patient and clinician during the course of therapy. The patient described certain situations, as well as important events in his life where he felt that his symptoms were worse. This took place during the second session, where he also got the diary for dysfunctional thoughts as homework, after going through the explanation about cognitive reorganization and its four stages. On the third session, he learned breathing and muscle relaxation exercises, from a specialist, to acquire tools to help him relax and effectively manage his stress. Eight 20-min sessions were followed with similar exercises. He was also asked to both practice these sessions at home and track his progress on a daily basis.

From session four to session nine, time was devoted to using adaptive responses to challenge dysfunctional thoughts. Initially, there was an attempt to identify automatic negative thoughts in certain situations, during which he was asked to keep track of his moods. After recognizing patient's negative thoughts, emotions, and behaviors, the work was done to verify the evidence that supported them.

To help the patient effectively socialize with others, sessions 10–12 were dedicated to learning assertiveness skills. The therapist talked to the patient about the meaning of assertiveness for him, the reasons that prevented him from becoming assertive, and differences in behaviors, from assertiveness to aggressiveness, passing by submissiveness. This information has proved useful to him. To practice such skills, the exercises incorporating roleplay were used.

During the following sessions (13–20), situations that induce anxiety in the patient were investigated and then ordered according to the level of anxiety he feels during each. Through exposure techniques, the patient was confronted with each level of anxiety in real time, where he got to practice each step, until he signaled his confidence to proceed to the level to follow.

The last session saw the patient discussing how he got over difficult situations, including being introduced to people for the first time, visiting friends, public speaking, and participating in presentations in class. He was able to challenge his cognition in the situations he had previously identified as difficult and then employed the techniques he learned (breathing and muscle relaxation) to manage symptoms of anxiety.

Preventing relapse was discussed during the last session, as well as methods to achieve this purpose, and other methods for surmounting the difficulties and failures of the past. Finally, there was a discussion related to how to apply change to the skills he learned and how to use the new techniques he acquired on a day-to-day basis.

2. Results and Discussion

It was clearly seen that patient's levels of anxiety in daily social situations were effectively improved through CBT, in case of SAD. According to the exposure sessions, which also included a discussion of patient's efforts outside of the clinical setting, The patient had the courage to walk through a pedestrian crossing and look at people, ask a security guard for directions, and communicate with new people in public places.

Findings show that CBT helped the patient reduce his anger, cravings, and stress (see Table 1 ). Furthermore, it improved his sleep quality and assertiveness. These findings are consistent with the previous research results. Cannabis use disorder was effectively treated with CBT, a treatment method which was rendered even more effective with the use of medication. A patient's functions (physiological, psychosocial, and social) are affected by substance use and its related disorders, but CBT significantly aids in the treatment of deficits at these levels [ 27 , 28 ].

Thoughts record.

It was found that when treating alcohol and opioid withdrawal disorders in a rehabilitation facility, CBT was effective. Patients felt more at ease staying in therapeutic sessions, after the detoxification process. A previous study found that best results could be obtained from a combination of pharmacotherapy and CBT, during the treatment of patients suffering from substance use disorder.

During the current study, CBT sessions, organized, structured, and running on an individual basis between patient and therapist were conducted. Each session had a planned objective and a set agenda. The patient's cognitive reorganization, stress management, daily living functions, and lapse relapse prevention were all prioritized. The patient's depression and social anxiety significantly improved, because the results on the scales have been decreased (see Table 2 ).

Outcome measures for social anxiety and depression.

The therapeutic process of the patient investigated in this study, suffering from substance use disorder, included “skills training”, a technique also known as “skills building”. Much focus was placed on any deficits, the patients suffer from related to emotions, cognition, behaviors, organization, problem solving, and interpersonal relations, during skill building.

The treatment made use of any approach targeting individual differences between patients. The connections the patient developed with others were a point of focus because the opposite of addiction is not sobriety, but rather connection with society. The patient's interpersonal skills were also targeted in this study. Such skills aid in the resolution of relationship complications. They help enhance effective communication and allow the patient to make use of it along with social support. Assistance received from all these tools helps individuals abstain from addictive materials and promotes the establishment of healthy relationships [ 29 ].

3. Conclusion

CBT was effective in decreasing the patient's symptoms, including his low moods, avoidance of social interactions, and anger. It also affected the patient in terms of his gradual return to work and positive relationships with his family members.

Acknowledgments

The author acknowledges the support she received in editing this paper from Dahlia Eldeeb.

Data Availability

Additional points.

Study Limitations . The patient had fluctuations in motivation. The family has gone through a lot of stressful life events. The patient's father is busy and does not frequently communicate with him. The study approach was cognitive behavioral therapy (CBT), and the case had multiple problems from childhood. Perhaps it would be more useful to use some analytical or Gestalt techniques to deal with past problems.

The patient has given his consent to having the clinical information relating to his case reported in a medical publication.

Declaration of Generative Artificial Intelligence (AI) and AI-Assisted Technologies in the Writing Process: The author did not use any generative AI or AI-assisted technologies in the writing process.

Conflicts of Interest

The author declares that there is no conflicts of interest.