Table of Contents  
Year : 2020  |  Volume : 3  |  Issue : 3  |  Page : 93-102

The effectiveness of group cognitive-behavioral therapy on general self-efficacy, self-control, and internet addiction prevalence among medical university students

1 Department of Health Education, School of Health, Qazvin University of Medical Sciences, Qazvin, Iran
2 Student Research Committee, Qazvin University of Medical Sciences, Qazvin, Iran
3 Trauma and Injury Research Center, Iran University of Medical Sciences, Tehran, Iran

Date of Submission27-Apr-2020
Date of Decision30-May-2020
Date of Acceptance08-Jul-2020
Date of Web Publication27-Jul-2020

Correspondence Address:
Isa Mohammadi Zeidi
Social Determinants of Health Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/SHB.SHB_20_20

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Introduction: Various studies have highlighted the high prevalence of psychological and psychiatric problems among students with Internet addiction (IA). This study aimed to determine the effect of GCBT on self-control, self-efficacy as well as the prevalence of IA amongst students of Qazvin University of Medical Sciences (QUMS). Methods: This randomized controlled trial was performed on 80 students addicted to the Internet. Participants were randomly divided into control (without intervention) and treatment group (GCBT). The experimental group participated in a GCBT program consisted of 10 two2-hour sessions based on psychosocial training, cognitive reconstruction, behavior modification, and improving emotion regulation. Data were collected using demographic information, Yang IA test, brief self-control scale, and compulsive iInternet usage scale before and 3 months after GCBT. Results: The Rfindings demonstrated significant improvements in general self-efficacy (21.90 ± 5.1-–27.31 ± 3.9, F = 46.131, df = 1, P < 0.001) and self-control (33.03 ± 4.7-–44.78 ± 6.1, F = 59.252, df = 1, P < 0.001), while compulsive Internet usage (41.41 ± 6.35-–25.13 ± 3.97, F = 163.359, df = 1, P < 0.001) and IA (60.83 ± 9.95-–36.10 ± 5.16, F = 183.302, df = 1, P < 0.001) were remarkably reduced in the experimental group after GCBT. Conclusion: This study suggests that GCBT can be an effective treatment for those college students struggling with IA, with improving the psychological variables affecting IA.

Keywords: Cognitive-behavior therapy, gambling disorder, internet addiction, self-control, self-efficacy

How to cite this article:
Zeidi IM, Divsalar S, Morshedi H, Alizadeh H. The effectiveness of group cognitive-behavioral therapy on general self-efficacy, self-control, and internet addiction prevalence among medical university students. Soc Health Behav 2020;3:93-102

How to cite this URL:
Zeidi IM, Divsalar S, Morshedi H, Alizadeh H. The effectiveness of group cognitive-behavioral therapy on general self-efficacy, self-control, and internet addiction prevalence among medical university students. Soc Health Behav [serial online] 2020 [cited 2024 Feb 25];3:93-102. Available from:

  Introduction Top

Research has displayed that the number of Internet users is currently more than 3 billion people worldwide, which is a remarkable increase compared to 360 million Internet users in 2000. Moreover, the number of such users in Iran is estimated to be around 46 million, which accounts for 57.2% of all the Middle East users. In addition, the Internet penetration rate with the growth of 80% has raised in Iran from 11.7% in 2006 to 16.1% in 2018.[1]

The Internet application as the basis of planning and development in cultural, social, economic, and scientific programs in the present age is irrefutable. Despite the potential benefits, excessive and uncontrolled usage of the Internet can lead to numerous problems such as exposure to inappropriate images and content, lack of privacy, addictive behaviors, and ultimately Internet addiction (IA).[2]

In fact, IA is a type of impulse control disorder associated with immoderate and unrestrained utilization of the Internet to the extent that could dominates the rest of one's social activities and would result in performance decline in occupational, social, family, economic, and psychological domains. Furthermore, from the perspective of classical psychology and psychiatric, IA is a relatively new phenomenon, and literatures use a variety of terms to name it, including “compulsory internet use,” “problematic internet use,” “pathologic internet use,” and “internet addiction.” [3] In general, the consequences of IA include weakness in emotional and social skills, loneliness, depression, anxiety disorders, substance abuse, sleep disturbance, impaired physical activity, decreased family relationships, neglect of social duties, lack of work efficiency, and academic performance decline.[4]

As a pandemic in the 21st century, the prevalence of IA varies from 0.3% in the US to 18.3% in the UK.[5] Whereas the currency of IA in China and South Korea has been reported to be 8.8% and 9%, respectively, review studies suggest the 20% prevalence of IA in Iran.[1]

The nature of scholarly activities along with student-specific lifestyles has resulted in an increase in the Internet utilization time by students. Thereupon, students' vulnerability toward IA, as well as the prevalence of IA among them, is increasingly expanding. In this regard, the findings of a meta-analysis study by Modara et al. indicated the IA prevalence of 52% among medical students.[1]

There is no clear definition of IA as a psychopathological condition. However, according to Griffiths, “Internet addiction is a nonchemical behavioral addiction that involves machine-human interaction.” [5] Moreover, despite disagreeing with the precise definition of IA and its diagnostic criteria, psychologists and psychiatrists agree with four essential characteristics for diagnosing IA: excessive Internet use (especially when combined with neglect of social functions and lack of sufficient time), withdrawal symptoms (such as anxiety, anger, and depression), tolerance and, finally, negative consequences.[6] According to IA definition, problematic use of the Internet is a behavioral addiction with evident symptoms such as “tolerance” and “abandonment,” which has pathological and neuropsychological mechanisms similar to substance abuse or gambling disorders. Hence, there are several models for explaining IA, such as: the access, affordability, anonymity (Triple-A) engine, the anonymity, convenience and escape model, cognitive-behavioral model of problematic Internet use, comprehensive model of the development and maintenance of IA by Winkler and Dörsing, and a phases model of pathological Internet use by Grohol.[7] The models have emphasized the role of sociocultural factors (e.g., demographic factors, Internet access, and acceptance, etc.), biological vulnerability (e.g., genetic factors and abnormalities in neurological processes), and physiological predispositions (e.g., personality traits and negative emotions) in excessive use of the Internet.[7] Due to the pathophysiological mechanism affecting the development of IA-hyperfunction in certain brain areas such as the limbic or subthalamic region or excessive dopamine secretion in the ventral striatum – in addition to the role of psychological and behavioral factors, it is essential to apply comprehensive strategies that can simultaneously change these variables.[6],[7]

Moreover, the same as mentioned disorders, IA treatment programs are largely based on cognitive-behavioral models.[8] The cognitive-behavioral therapy (CBT) is a collaborative approach between a client and a therapist, and the main components used in group CBT (GCBT) include psychoeducation, cognitive reconstruction, exposure, behavioral activation, social skills training, and problem solving. During CBT, a patient becomes aware of the addictive behaviors, feelings, and thoughts caused them and understands that these variables replicate addictive behaviors with particular dynamics. Research has shown that CBT is preferred over other treatment strategies for users utilizing the Internet excessively.[9]

CBT assumes that thoughts are determinants of emotions, and Internet dependency can be reduced by controlling dysfunctional thoughts as well as behavior management. By applying CBT, patients would learn ways to control and identify addictive thoughts, temptation overcoming skills, and recurrence prevention methods. GCBT interventions are useful in providing alternative learning opportunities and feedback, especially for people with relatively homogeneous characteristics. By CBT, addicted patients' pessimism could be controlled, with diminishing their feeling of worthlessness. Moreover, the potential causes of users' tendency toward extreme usage of the Internet can be impressively identified through CBT application.[10]

The results of review studies imply the effectiveness of CBT-based interventions on IA reduction, life quality improvement, depression prevalence depletion, and advancement of psychological variables affecting IA such as self-efficacy and self-control.[11] Furthermore, considering the benefits of group therapies comprising saving cost and time and subsequently the efficacy of group therapy compared to individual therapies, the necessity to the implementation of collaborative CBT is felt.[8],[9],[10],[11]

Many studies have recognized unfavorable and dysfunctional beliefs such as self-awareness, self-esteem, self-confidence, self-efficacy, negative self-evaluation, and self-control as underlying, predisposing, and reinforcing factors of IA. Among these factors, the role of self-efficacy and self-control in creating and exacerbating IA has been well confirmed.[12]

Studies have reported that the lack of control over habitual and addictive behaviors is the pivot of addiction formation process. In other words, people with low self-control do not possess the ability of managing emotional reactions to situations, objects, events, and other individuals during interactions. Moreover, inappropriate social behaviors and the level of rebellion were reported to be significantly higher in Internet-addicted users with low self-control. Inordinate usage of the Internet could lead to the feeling of loneliness, which would subsequently be accompanied by lessening of the level of social interaction, social skills, self-esteem, and mental health.[13]

Self-efficacy refers to one's belief in their ability to organize and execute the actions needed to accomplish expected situations. The association between low self-efficacy, depression, and the probability of a person' vulnerability to certain disorders like IA has been identified in studies. People with low self-efficacy are more likely to get involved in IA; yet, self-efficacy is regarded as a protective factor against IA.[14]

Overall, with regard to the reasons comprising (a) the increasing number of Internet users in the Iran; (b) the central role of the Internet in academic activities and its contribution to medical students' lifestyle; (c) the increasing prevalence of IA and its physical, psychological, and social consequences; (d) the importance of controlling and preventing the consequences of IA; and (e) effectiveness of interventions based on GCBT approach, the current investigation was designed to determine the effect of GCBT on self-control, self-efficacy, and prevalence of IA among students of Qazvin University of Medical Sciences (QUMS).

  Methods Top

Participants and sampling process

The present study was a randomized controlled trial conducted in QUMS from June 2018 to February 2019. The population of this investigation consisted of all the students of QUMS.

Of the total number of students who completed the screening test (n = 342), 118 received a score >20 and were invited to participate in the study. Furthermore, 16 students were barred from studying because of mental disorders and 22 were reluctant to take part in the trial. Finally, 80 students participated in the study in control (n = 40) and experimental groups (n = 40).

Then, participants were selected using proportional stratified sampling based on the number of students in each field. The involved fields were composed of medicine (n = 10), dentistry (n = 8), health (public health (n = 6), environmental health (n = 6), occupational health (n = 6), health care management (n = 4), paramedical (operating room (n = 8)), anesthesia (n = 6), medical emergency (n = 6), laboratory sciences (n = 6), nursing (n = 8), and midwifery (n = 6). Ultimately, after preparing a list of students who met the inclusion criteria, they were assigned to experimental (n = 40) and control (n = 40) groups using purposeful sampling method. Sampling process and assigning students to control and experimental groups are shown in [Figure 1].
Figure 1: Planned sampling process

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The inclusion criteria were as follows: voluntary and informed participation, no psychiatric disorders such as anxiety and depression (based on neurologist's diagnosis and approval), Internet usage history for at least 3 h a day, and being in high-risk group IA based on Compulsive Internet Use Scale (CIUS) score. Using the CIUS scale, students who scored ≥20 were identified as high risk for obsessive-compulsive disorder identified as high risk for obsessive-compulsive disorder. Given that the purpose of the current study was treating students with IA, all the attendees with a CIUS score of ≤20 were excluded from the study. The similar exclusion was also applied to the students with depression or anxiety disorders.

Data gathering process

At the beginning of the research and after coordinating with the Vice Chancellor for Research and Education of QUMS, the researcher introduced himself to the students on the site of each college, with providing a description about the aims, significance, and stages of the investigation. To comply with ethical considerations, written consent was obtained from all the students and it was assured that their information would remain confidential. Students could drop out of the study whenever they wanted. Meanwhile, during completing the questionnaires, one member of the research team was present at the faculty site with the aim of explaining how the questions be answered, ensuring the precise completion of questionnaire, responding to potential questions, and resolving ambiguities raised by students (compliance with Codes 1, 5, 8, 10, and 17 of the National Committee on Ethics in Medical Sciences). Data were collected using self-report scales at baseline and 3 months after GCBT sessions.

Instrument and psychometric properties

The data collection tools in the current study were as follows:

Demographic information includes age, gender, field of study, economic status, marital status, monthly fee allocating for the Internet, hours of work with the Internet per day/week, ownership of a laptop or PC, kind of Internet service, and reasons for working with the Internet.

Yang Internet addiction test

Designed by Yang (1998) with the aim of assessing IA, this questionnaire was comprised 20 items. Subjects were asked for such items using a 5-point Likert scale, ranging from rarely (score 1) to always (score 5). According to total score gained from the Yang Internet Addiction Test, the participants were categorized into one of the following three groups: the first group with the score of 20–39 as natural users with complete control over the usage of the Internet; the second category with the score of 40–69 as users with mild addiction experiencing problems due to excessive usage of the Internet; and the third group with the score of 40–00 as users with severe addiction where intemperate usage of the Internet is causing significant problems. The validity and reliability of the scale have been well established in various studies.[15]

Brief self-control scale

A self-report questionnaire developed by Tangney et al. consisting of 13 items was used to appraise how individuals could control their motivation, change their moods or feelings, limit bad habits, maintain their discipline, and manage performance, i.e., “I'm good at resisting temptation.” In addition, this scale was composed of two subscales: (1) inhibitory self-control and (2) primary self-control. Attendees were asked to indicate their answers to these items on 5-point Likert scale (1 = not at all, 2 = to some extent, 3 = so so, 4 = alike, and 5 = most likely). The score ranged between 13 and 65 where higher scores indicated greater self-control. The psychometric properties of this scale have been confirmed in previous studies.[16]

Compulsive Internet usage scale

The 14-item scale designed by Meerkerk et al. was used to measure the risk of obsessive-compulsive usage of the Internet.[17] This scale measures factors such as tolerance for the Internet usage, desire toward it, withdrawal symptoms of the Internet, and negative effects of Internet utilization. The CIUS was developed according to diagnostic criteria for dependency and obsessive-compulsive disorders. The participants were queried to reply items using a 5-point Likert scale (0 = never, 1 = rarely, 2 = sometimes, 3 = often, and 4 = always). The total score ranged between 0 and 56 which was able to identify individuals with IA with a sensitivity of 70% and a score of ≥24. Moreover, previous studies with the aim of achieving high sensitivity yet limiting the total number of people in need of treatment have used a cutoff score of 20 to select high-risk subjects. The validity and reliability of this scale have been well established in previous research. For example, Alavi et al. confirmed the psychometric properties of the given scale using confirmatory factor analysis, Cronbach's alpha, and test coefficient.[18]

General self-efficacy scale

The General Self-Efficacy Scale designed by Schwarzer and Jerusalem (1995) was applied to measure students' self-efficacy beliefs. The scale consisted of 10 items in which the participants were asked to respond questions with a 4-point Likert scale (1 = not at all true, 2 = hardly true 3 = moderately true, and 4 = exactly true). Minimum and maximum scores on this scale were between 10 and 40, with higher scores indicating stronger self-efficacy. Previous research has completely confirmed the validity and reliability of the given scale.[19],[20]

Structure and content of intervention

The experimental group participated in an intervention program based on GCBT consisting of 10 2-h sessions. Prior to attending GCBT treatment, four time schedules were offered to the experimental group and students chose one of the programs with considering their class schedule, leisure time, and other limitations. A reminder was sent to each subject a day prior to each session and 2 h before it. The intervention program was, generally, rely on the following four axioms, drawing from empirical findings and previous research on IA risk factors: (A) time management, fatigue, and motivational problems, (2) procrastination and performance anxiety, (3) social interaction, and (4) emotion regulation. Besides, the overall focus of the sessions with regard to GCBT was as follows: (1) psychosocial training, (2) cognitive reconstruction (identifying and correcting dysfunctional beliefs), (3) behavior modification (improving problem-solving skills, operational behavior training, and managing unforeseen events), as well as (4) improving emotion regulation (including teaching sensory techniques, imagination, and mindfulness). Emotional–rational stories were applied to illustrate the paths that lead to IA. Thereupon, dysfunctional beliefs, behaviors, and affective disorders were discussed and resolved by cognitive techniques using the cognitive-behavioral techniques cited above. Finally, according to the study of Beheshtiyan[21] and the protocol proposed by Lindenberg et al.,[22] the content and purpose of GCBT sessions were implemented as follows:

The first session

This session consisted of introductions and welcoming, description of duties of members of the group, and purposes of the sessions and provided theoretical explanations for the IA. The origin of individual's emotions was also discussed using the A-B-C technique, and it was emphasized that the determinant of emotions is not external events but individual's thought and belief about events.

The second session

This session aimed at analyzing and evaluating stimulating factors and motivations affecting excessive Internet usage and assessing the role of factors such as place, time, influential persons, and emotional modes including anxiety, anger, happiness, and boredom.

The third session

This session aimed at identifying craving, its role in excessive usage of the Internet, and presenting different strategies for its control. Examples of cognitive strategies were composed of visualizing the negative consequences of IA as well as recalling reasons for decision-making to control Internet usage. Behavioral strategies included delaying half an hour working with the Internet or focusing on other tasks, while daily strategies consisted of muscle relaxation, daily exercise, and rewards for controlling and limiting Internet use time.

The fourth session

This session included training Jacobson's progressive muscle relaxation technique and its repetition by providing feedback to correct the technique.

The fifth session

This session consisted of cognitive strategies utilization for analyzing thoughts such as registering daily thoughts, assessing the pros and cons of thoughts, mental imagery, flashcards, paying attention to doubts, and increasing motivations.

The sixth session

This session cocsisted of training of 6-step problem-solving skills including accepting problem, identifying them, providing different solutions, evaluating solutions, and implementing the best ones, ultimately appraising the outcome of performing the solution and if necessary, considering other options.

The seventh session

This session aimed at identifying cognitive errors, evaluating their impact on the excessive Internet usage, and presenting methods to deal with these errors.

The eighth session

This session consisted of time management.

The ninth session

This session consisted of methods for identifying and coping with anxiety and depression.

The tenth session

It consisted of an overview of the trained techniques and giving feedback.

Finally, all the collected data were analyzed using SPSS Statistics for Windows, Version 23.0 (IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY: IBM Corp). After confirming data normality according to Kolmogorov–Smirnov test, they were subjected to Chi-square test, independent and paired t-tests, one-way analysis of variance, and analysis of covariance tests. The significance level in the present study was considered to be <0.05 (P < 0.05). To adhere to the research ethics, the cognitive-behavioral session for the control group students was exactly performed similar to the experimental group ones after the second stage evaluation. This study was approved by the Ethics Committee of QUMS (IR.QUMS.REC.1398.235).

  Results Top

The mean age of the students was 21.7 ± 2.21 years, and average time to use the Internet was 4.74 ± 1.81 h/day. Economically, 60% of the students were medium and half of the students allocated an average of $1.5–4 to purchase Internet packages per month. The main purposes of the Internet usage were entertainment activities, watching movies and photos as well as using Instagram, Telegram, and WhatsApp. In addition, it was found that only 2.5% of the students used the Internet for research and scientific activities. Altogether, no statistically significant difference was observed between the two groups in terms of demographic variables [Table 1].
Table 1: Comparison of demographic characteristics between experimental and control group before group cognitive-behavioral therapy

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[Table 2] compares the mean and standard deviation scores of general self-efficacy, self-control, IA, and compulsive Internet usage in the experimental and control groups before the GCBT sessions. The results of independent t-test indicated that there were no statistically significant differences between both the groups before the GCBT. However, after GCBT, the mean scores of general self-efficacy and self-control increased significantly (P < 0.001), while a significant decrease was observed in the mean scores of compulsive Internet usage and IA in experimental group (P < 0.001).
Table 2: Comparison of mean and standard deviation scores of general self-efficacy, self-control, compulsive Internet use, and Internet addiction between experimental and control groups before and 3 months after group cognitive-behavioral therapy

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As it is presented in [Table 3], by controlling the effect of the pretest, a statistically significant difference was observed between the groups in terms of general self-efficacy in posttest (F = 124.720, P < 0.001). Considering the fact that P < 0.05, it was deduced that the mean score of general self-efficacy in the experimental group was significantly higher than that in the control group. Moreover, the obtained eta squared indicated that 68.6% of the variance in general self-efficacy could be explained by GCBT treatment.
Table 3: The results of covariance analysis test to examine the effect of group cognitive-behavioral therapy on general self-efficacy

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The results of covariance test on self-control are given in [Table 4]. As it is shown, controlling the effect of pretest led to a statistically significant difference between the two groups in terms of self-control in posttest (F = 59.252, P < 0.001). With regard to the amount of P value, being less than 0.05, it was concluded that the mean score of self-control in the experimental group was significantly higher than that in the control group. Besides, the obtained eta squared demonstrated that 51.1% of the variance in self-control could be explained by GCBT treatment.
Table 4: The results of covariance analysis test to examine the effect of group cognitive-behavioral therapy on self-control

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As for compulsive Internet usage, the results in [Table 5] revealed a statistically significant difference between the two groups in this term as a result of controlling the pretest effect (F = 163.359, P < 0.001). Base on the amount of P value (>0.05), it was derived that the mean score of compulsive Internet usage in the experimental group was significantly higher than that in the control group. The acquired eta squared also indicated that 74.1% of the variance in compulsive Internet usage could be explained by the GCBT.
Table 5: The results of covariance analysis test to examine the effect of group cognitive-behavioral therapy on compulsive internet use

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According to the covariance test results on IA, controlling the effect of pretest was accompanied by a statistically significant difference between the groups in this term in the posttest (F = 183.302, P < 0.001) [Table 6]. Since P < 0.05, it was concluded that the mean score of IA in the experimental group was significantly higher than that in the control group. Furthermore, the eta squared obtained indicated that 76.3% of the variance in IA can be illustrated by GCBT treatment.
Table 6: The results covariance analysis test to examine the effect of group cognitive-behavioral therapy on Internet addiction

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  Discussion Top

The purpose of this study was to determine the effect of GCBT on general self-efficacy, self-control, and prevalence of IA in students of QUMS in 2018–2019. Overall, findings indicated a significant improvement in general self-efficacy (21.90 ± 5.1–27.31 ± 3.9, F = 46.131, df = 1, P < 0.001) and self-control (33.03 ± 4.7–44.78 ± 6.1, F = 59.252, df = 1, P < 0.001) in the experimental group after GCBT treatment. Further, compulsive Internet usage (41.41 ± 6.35-25.13 ± 3.97, F = 163.359, df = 1, P < 0.001) and IA (60.83 ± 9.95–36.10 ± 5.16, F = 183.302, df = 1, P < 0.001) were significantly reduced in the experimental group after GCBT treatment.

The first finding of the current research was an increase in the mean score of general self-efficacy in experimental group after GCBT, which is consistent with the results of previous studies.[6],[7],[8],[23] In addition, our result is in line with those of Wölfling et al. who demonstrated that pilot CBT therapy program can significantly increase self-efficacy in individuals with IA.[24] Furthermore, Yang and Kim reported an improvement in self-efficacy of students with IA after empowerment program focusing on improving self-regulation,[25] and Gholamian et al. displayed the effectiveness of self-efficacy in female students with IA after an educational intervention.[26] Our findings with regard to improving self-efficacy after CBT sessions could be attributed to the individuals' optimistic hope toward their ability to overcome the problems and challenges associated with excessive usage of the Internet, which reaches an acceptable level after GCBT treatment.

Lin et al. by examining the contribution of self-efficacy on the disuse of the Internet in students suffering from IA concluded that there is a significant negative relationship between Internet avoidance self-efficacy and IA.[27] Based on cognitive theories, it can be assumed that a cognitive style similar to what happens in depression (self-awareness, low self-esteem, low self-confidence, low self-efficacy, and negative self-evaluation) may be a predisposing factor for IA. Moreover, Bandura's social cognitive theory suggests that individual factors, self-efficacy, and self-regulation are key determinants of behavioral change. The implementation of target behaviors is influenced by self-efficacy and self-regulation, which are important cognitive traits of humans. Hence, considering the association between one's self-efficacy with self-regulation behaviors relating to the Internet as well as the possibility of changing self-efficacy via simple strategies such as modeling, verbal persuasion, turning macro goals into goals, and performing step-by-step behavior, it is recommended that cognitive-behavioral strategies should be applied to increase self-efficacy. This would contribute to the reduction of IA.

Another important finding in the present study was the enhancement of self-control mean score among the students of experimental group participating in GCBT. Given the results of Lee and Sun, GCBT can lessen depression and augment self-control in high school students with IA.[28] Moreover, in a project conducted by Koo et al. in South Korea called “the Jump up Internet Rescue School,” depression and self-control in students were significantly improved after educational program and substitution of recreational activities.[29] Similarly, the implementation of an eight-session intervention program by Uysal and Balci in Turkish secondary schools yielded parallel results in terms of improving self-control.[30] In general, self-control is the ability to subdue and manage one's emotions and impulses, acclimatize to the environment, and keep calm in critical and stressful situations and self-motivation. The findings of Mehroof and Griffiths research indicated that emotionality, self-control, neurotic problems, aggression, and anxiety play important roles in IA.[31] Thus, the experimental group students through CBT and self-control reinforcement would evaluate possible choices and outcomes choose the best option. In fact, in GCBT, by identifying emotions and enhancing emotion regulation capabilities, individuals are being given the opportunity to detect the causes of their anxiety, depression, and irritability to control them.

Finally, the major results of this study were the reduction of IA severity among students attending GCBT sessions, which is in accordance with most previous research.[6],[7],[8],[19],[20],[21],[22],[23] Moreover, our results compare favorably with other studies; Beheshtiyan (2013) reported a positive and significant effect of GCBT on IA up to 6 month.[22] Furthermore, Kim et al. asserted a decrease in the prevalence of depression and anxiety along with a decrease in the severity of IA in adolescents receiving CBT treatment,[32] and Odaci and Celik highlighted the impact of group counseling on students' Internet dependency and improving life satisfaction.[33] CBT helps patients to identify the thoughts and feelings influencing on behaviors. It is commonly used to treat a broad spectrum of disorders including phobia, addiction, depression, and anxiety with the aim of helping people struggling with a particular problem. Throughout the treatment, individuals learn how to identify and change destructive or distressing patterns of thought that negatively affect their behavior and emotions. In addition, along with increasing their awareness toward various problems including the inability to manage time as well as emotions, difficulty in overcoming temptation, lack of an alternative to the Internet in daily life, and the issue of coping with stress and negative emotions, individuals get familiar with coping strategies such as self-control and self-regulation.[34] Strengthening self-efficacy and reassuring people to boost driving capacity and ability to cope with challenges are also effective factors in reducing IA severity. Moreover to CBT, other cognitive therapies such as mindfulness-based cognitive therapy can be utilized in treating IA. With enhancing specific skills through mindfulness-based cognitive therapy, people would be able to be more aware of their thoughts without judgment, consider positive or neutral thoughts as reflections of reality rather than negative ones, and regard them only as transient mental events.[35]


There were limitations to this study. First, the selection of participants as experimental and control groups only from QUMS was accompanied with the possibility of overlapping bias. Therefore, it is suggested that in future studies, the experimental and control groups be selected from two different universities to increase the generalizability of results as well as to control the bias. Second, given that assessment for the results of CBT treatment was performed 3 months later, it cannot adequately reflect the durability of therapeutic effects of CBT. Measuring the severity of IA and other variables affecting it over a period of 6 months and even 1 year after treatment could therefore better reflect the impact of CBT. Third, data were gathered using self-report method in the present study; however, due to the nature of some behaviors, it is not possible to accurately measure some of them, and the results can only be analyzed through individual reporting. Finally, the results of the present study, as in previous studies, showed a decrease in students' cravings for Internet use. However, another limitation of the present study was that due to the lack of measurement of hours and times of students' use of the Internet, it is not possible to judge the effect of the intervention on the actual use of the Internet.

  Conclusion Top

This study revealed the effectiveness of GCBT sessions on general self-efficacy and self-control as psychological variables affecting IA in college students. The findings also indicated a remarkable decrease in IA severity and mean of obsessive usage of the Internet among QUMS students participating in GCB sessions. Thus, it is suggested to use GCBT for IA treatment in students, aside from recalling the high prevalence of IA in college students and its consequences on physical, mental, and social health.

Statistical analysis

Ethical consideration

Ethical approval for the study was obtained from the Ethics Committee of QUMS (IR.QUMS.REC.1398.235).


This research was funded by a grant from Vice Chancellor for Research, QUMS. Furthermore, the authors wish to thank all students of QUMS who participated in this study.

Financial support


Conflicts of interest

There are no conflicts of interest.

  References Top

Modara F, Rezaee-Nour J, Sayehmiri N, Maleki F, Aghakhani N, Sayehmiri K, et al. Prevalence of internet addiction in Iran: A systematic review and meta-analysis. Addict Health 2017;9:243-52.  Back to cited text no. 1
Mihajlov M, Vejmelka L. Internet addiction: A review of the first twenty years. Psychiatr Danub 2017;29:260-72.  Back to cited text no. 2
Vondráčková P, Gabrhelík R. Prevention of Internet addiction: A systematic review. J Behav Addict 2016;5:568-79.  Back to cited text no. 3
Van Rooij AJ, Prause N. A critical review of “Internet addiction” criteria with suggestions for the future. J Behav Addict 2014;3:203-13.  Back to cited text no. 4
Griffiths M. Does internet and computer “Addiction” Exist? Some case study evidence. Cyberpsychol Behav 2000;3:211-8.  Back to cited text no. 5
Cash H, Rae CD, Steel AH, Winkler A. Internet addiction: A brief summary of research and practice. Curr Psychiatry Rev 2012;8:292-8.  Back to cited text no. 6
Steeves TD, Miyasaki J, Zurowski M, Lang AE, Pellecchia G, Van Eimeren T, et al. Increased striatal dopamine release in Parkinsonian patients with pathological gambling: A [11C] raclopride PET study. Brain 2009;132:1376-85.  Back to cited text no. 7
Zhang J, Zhang Y, Xu F. Does cognitive-behavioral therapy reduce internet addiction? Protocol for a systematic review and meta-analysis. Medicine (Baltimore) 2019;98:e17283.  Back to cited text no. 8
Zhang YY, Chen JJ, Ye H, Volantin L. Psychological effects of cognitive behavioral therapy on internet addiction in adolescents: A systematic review protocol. Medicine (Baltimore) 2020;99:e18456.  Back to cited text no. 9
Stevens MW, King DL, Dorstyn D, Delfabbro PH. Cognitive-behavioral therapy for Internet gaming disorder: A systematic review and meta-analysis. Clin Psychol Psychother 2019;26:191-203.  Back to cited text no. 10
Liu J, Nie J, Wang Y. Effects of group counseling programs, cognitive behavioral therapy, and sports intervention on internet addiction in East Asia: A systematic review and meta-analysis. Int J Environ Res Public Health 2017;14:1470.  Back to cited text no. 11
Mellouli M, Zammit N, Limam M, Elghardallou M, Mtiraoui A, Ajmi T, et al. Prevalence and predictors of internet addiction among college students in sousse, Tunisia. J Res Health Sci 2018;18:e00403.  Back to cited text no. 12
Kim J, Hong H, Lee J, Hyun MH. Effects of time perspective and self-control on procrastination and Internet addiction. J Behav Addict 2017;6:229-36.  Back to cited text no. 13
Craparo G, Messina R, Severino S, Fasciano S, Cannella V, Gori A, et al. The relationships between self-efficacy, internet addiction and shame. Indian J Psychol Med 2014;36:304-7.  Back to cited text no. 14
[PUBMED]  [Full text]  
Mohammadsalehi N, Mohammadbeigi A, Jadidi R, Anbari Z, Ghaderi E, Akbari M. Psychometric properties of the Persian language version of yang internet addiction questionnaire: An explanatory factor analysis. Int J High Risk Behav Addict 2015;4:e21560.  Back to cited text no. 15
Tangney JP, Baumeister RF, Boone AL. High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. J Pers 2004;72:271-324.  Back to cited text no. 16
Meerkerk GJ, Van Den Eijnden RJ, Vermulst AA, Garretsen HF. The compulsive internet use scale (CIUS): Some psychometric properties. Cyberpsychol Behav 2009;12:1-6.  Back to cited text no. 17
Alavi SS, Jannatifard F, Eslami M, Rezapour H. Validity, reliability and factor analysis of compulsive internet use scale in students of Isfahan's universities. Health Informat Manage 2011;7:724.  Back to cited text no. 18
Schwarzer R, Bäßler J, Kwiatek P, Schröder K, Zhang JX. The assessment of optimistic self beliefs: Comparison of the German, Spanish, and Chinese versions of the general self efficacy scale. Applied Psychol 1997;46:69-88.  Back to cited text no. 19
Rajabi QR. The study of the stability and reliability of the scales of the general self-efficiency beliefs among students of psychology major at Faculty of Psychology and Education of Shahid Chamran University of Ahvaz and Islamic Azad University-Marvdasht Branch. Modern Educ Thoughts 2006;2:111-22.  Back to cited text no. 20
Beheshtiyan M. The study of effectiveness of cognitive behavior therapy on addicted women in six month follows up. Pazuhesname-ye Zanan (Womens Stud) 2015;6:5-52.  Back to cited text no. 21
Lindenberg K, Halasy K, Schoenmaekers S. A randomized efficacy trial of a cognitive-behavioral group intervention to prevent internet use disorder onset in adolescents: The PROTECT study protocol. Contemp Clin Trials Commun 2017;6:64-71.  Back to cited text no. 22
Zhang MW, Lim RB, Lee C, Ho RC. Prevalence of internet addiction in medical students: A meta-analysis. Acad Psychiatry 2018;42:88-93.  Back to cited text no. 23
Wölfling K, Beutel ME, Dreier M, Müller KW. Treatment outcomes in patients with internet addiction: A clinical pilot study on the effects of a cognitive-behavioral therapy program. Biomed Res Int 2014;2014:425924.  Back to cited text no. 24
Yang SY, Kim HS. Effects of a prevention program for internet addiction among middle school students in South Korea. Public Health Nurs 2018;35:246-55.  Back to cited text no. 25
Gholamian B, Shahnazi H, Hassanzadeh A. The effect of educational intervention based on BASNEF model for reducing internet addiction among female students: A quasi-experimental study. Ital J Pediatr 2019;45:164.  Back to cited text no. 26
Lin MP, Ko HC, Wu JY. The role of positive/negative outcome expectancy and refusal self-efficacy of Internet use on Internet addiction among college students in Taiwan. Cyberpsychol Behav 2008;11:451-7.  Back to cited text no. 27
Lee JH, Son CN. The effects of the group cognitive behavioral therapy on game addiction level, depression and self-control of the high school students with internet game addiction. Korean J Stress Res 2008;16:409-18.  Back to cited text no. 28
Koo C, Wati Y, Lee CC, Oh HY. Internet-addicted kids and South Korean government efforts: Boot-camp case. Cyberpsychol Behav Soc Netw 2011;14:391-4.  Back to cited text no. 29
Uysal G, Balci S. Evaluation of a School-Based Program for Internet Addiction of Adolescents in Turkey. J Addict Nurs 2018;29:43-9.  Back to cited text no. 30
Mehroof M, Griffiths MD. Online gaming addiction: The role of sensation seeking, self-control, neuroticism, aggression, state anxiety, and trait anxiety. Cyberpsychol Behav Soc Netw 2010;13:313-6.  Back to cited text no. 31
Kim SH, Yim HW, Jo SJ, Jung KI, Lee K, Park MH. The effects of group cognitive behavioral therapy on the improvement of depression and anxiety in adolescents with problematic internet use. Soa Chongsonyon Chongsin Uihak 2018;29:73-9.  Back to cited text no. 32
Odaci H, Celik CB. Group counseling on college students' internet dependency and life satisfaction. J Psychol Counselors Sch 2017;27:239-50.  Back to cited text no. 33
Terence Wilson G. Introduction. Manual-based cognitive behavioral therapy. Behav Res Ther 2013;51:R1.  Back to cited text no. 34
Lan Y, Ding JE, Li W, Li J, Zhang Y, Liu M, et al. A pilot study of a group mindfulness-based cognitive-behavioral intervention for smartphone addiction among university students. J Behav Addict 2018;7:1171-6.  Back to cited text no. 35


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  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]

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