research hypothesis about internet addiction

• Open access
• Published: 19 March 2024

Relationship between loneliness and internet addiction: a meta-analysis

• Yue Wang 1 &
• Youlai Zeng 1  

BMC Public Health volume  24 , Article number:  858 ( 2024 ) Cite this article

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In the digital age, the Internet has become integrated into all aspects of people’s work, study, entertainment, and other activities, leading to a dramatic increase in the frequency of Internet use. However, excessive Internet use has negative effects on the body, psychology, and many other aspects. This study aims to systematically analyze the research findings on the relationship between loneliness and Internet addiction to obtain a more objective, comprehensive effect size.

This study employed a comprehensive meta-analysis of empirical research conducted over the past two decades to investigate the relationship between loneliness and Internet addiction, with a focus on the moderating variables influencing this relationship. This meta-analysis adopted a unique approach by categorizing moderating variables into two distinct groups: the objective characteristics of research subjects and the subjective characteristics of researchers. It sheds light on the multifaceted factors that influence the relationship between loneliness and Internet addiction.

A literature search in web of science yielded 32 independent effect sizes involving 35,623 subjects. Heterogeneity testing indicated that a random effects model was appropriate. A funnel plot and Begg and Mazumdar’s rank correlation test revealed no publication bias in this meta-analysis. Following the effect size test, it was evident that loneliness was significantly and positively correlated with Internet addiction ( r  = 0.291, p  < 0.001). The moderating effect analysis showed that objective characteristics significantly affected the relationship. However, subjective characteristics did not affect the relationship.

Conclusions

The study revealed a moderately positive correlation between loneliness and Internet addiction. Moreover, this correlation’s strength was found to be influenced by various factors, including gender, age, grade, and the region of the subjects. However, it was not affected by variables such as the measurement tool, research design, or research year (whether before or after COVID-19).

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Introduction

In the digital age, the Internet has become integrated into all aspects of people’s work, study, entertainment, and other activities, leading to a dramatic increase in the frequency of Internet use. However, excessive Internet use has negative effects on the body (vision, sleep, obesity, sedentary lifestyle, and musculoskeletal disorders) [ 1 ], psychology (depression, anxiety, and loneliness), academic performance [ 2 ], cognitive ability [ 3 ], interpersonal relationships [ 4 ], and many other aspects. Kraut, R. et al., were the first to investigate the effects of Internet use on individual social participation and psychological health [ 5 ], and since then, the exploration of the relationship between Internet addiction and loneliness has garnered significant attention from scholars.

The concept of loneliness

In his seminal work, Robert S. stated that loneliness is a subjective psychological feeling or experience in which an individual lacks satisfactory interpersonal relationships due to a gap between their desired social interaction and the actual level [ 6 ]. Subsequent research has presented varying definitions of loneliness by different psychologists. Behaviorists believe that loneliness arises from a response to inadequate social reinforcement. Cognitive theorists emphasize that loneliness is a perception resulting from an inconsistency between desired and actual social interactions. Psychoanalytic schools posit that loneliness is related to unfulfilled individual social interaction needs [ 7 ].

The concept of internet addiction

Internet Addiction Disorder (IAD), also known as Internet addiction, was first proposed by Goldberg in 1995. He argued that Internet addiction, as a coping mechanism, is a way of relieving stress and is characterized by excessive Internet use [ 8 ]. This concept gained prominence through Young’s pioneering study in 1996. Internet addiction is a problematic behavior defined as an impulse control disorder that does not involve substance addiction. It can have negative effects on academics, relationships, finances, careers, and physical well-being [ 9 ].

Scholars have used different theoretical models and terminology to describe excessive Internet use behavior, with the most commonly used terms being “Internet addiction” and “pathological Internet use”. Davis developed a cognitive-behavioral model to explain the causes of pathological Internet use (PIU), emphasizing that individual thoughts play a crucial role in abnormal behavior. Individuals with negative self-perceptions and views of the world receive positive reinforcement through Internet use, which leads to continued and increasingly frequent Internet use. Davis categorized pathological Internet use into two types: specific pathological Internet use, which involves the overuse or misuse of specific Internet functions, and generalized pathological Internet use, which is characterized by pervasive and excessive Internet use, particularly for online socialization [ 10 ].

This paper uses the term “Internet addiction” to define excessive Internet use behavior. First, the term “specific pathological Internet use” refers to the overuse of specific online activities, while “generalized pathological Internet use” emphasizes the social function of Internet use. Internet addiction encompasses a wide range of addictive activities and Internet functions, with addiction measured by Internet addiction scales fully reflecting the severity of the issue. Second, the severity of Internet addiction can be expressed on a continuum of problem severity. The term “pathological Internet use” falls in the middle range of problem severity, producing a more benign negative impact. However, “Internet addiction” lies at the top of the continuum and is characterized by more severe consequences [ 11 ]. This paper underscores the negative effects of excessive Internet use by using the term “Internet addiction”.

The relationship between loneliness and internet addiction

In the academic community, three primary research conclusions have emerged regarding the relationship between loneliness and Internet addiction:

Loneliness leading to internet addiction

Research indicates that loneliness serves as a predictive factor for Internet addiction [ 12 , 13 ]. Studies, including one conducted during the COVID−19 pandemic, have consistently shown that loneliness significantly predicts Internet addiction [ 14 ]. It is suggested that lonely individuals may resort to excessive Internet use as a coping mechanism to seek emotional support and social interaction [ 15 ].

Internet addiction leading to loneliness

Another perspective posits that Internet addiction contributes to feelings of loneliness. Research has demonstrated a positive correlation between Internet addiction and loneliness, indicating that individuals with higher levels of Internet addiction tend to experience a stronger sense of loneliness [ 16 ]. This is often attributed to the isolation resulting from excessive online engagement, leading to reduced social and family interactions [ 17 ].

A vicious cycle of loneliness and internet addiction

The third perspective suggests that loneliness and Internet addiction interact in a reinforcing cycle. Studies have shown that lonely individuals are more likely to exhibit Internet addiction behaviors, which, in turn, exacerbate their loneliness [ 18 ]. Conversely, excessive Internet use can intensify feelings of loneliness, creating a vicious cycle [ 19 ]. Scholars have confirmed the existence of a clear and strong bidirectional relationship between Internet addiction and loneliness [ 20 ]. However, this bidirectional relationship is complexity; using the Internet to replace offline social interaction can increase loneliness, while using it to enhance or expand social connections may reduce loneliness [ 21 ].

These three perspectives provide valuable insights into the intricate relationship between loneliness and Internet addiction, shedding light on the various pathways through which these phenomena interact.

The moderating variables of the relationship between loneliness and internet addiction

Research findings on the gender effects of Internet addiction vary widely. Some studies confirm that the prevalence of Internet addiction is significantly higher in women than in men (male = 24%, female = 48%) [ 22 ]. Conversely, there are contrary conclusions suggesting that Internet addiction is more common among men [ 23 , 24 , 25 ]. However, some studies have shown that there is no significant gender difference in Internet addiction [ 26 ].

Similarly, there is no consensus on the gender effect of loneliness in research. Women have higher rates of loneliness than men (male = 23.3%, female = 28.3%) and are more likely to feel a lack of companionship [ 27 ]. On the other hand, some studies have shown that loneliness is more common in males than in females [ 28 ].

Research on the relationship between loneliness and Internet addiction found no gender differences [ 29 , 30 ]. However, the results of another meta-analysis showed that, as a moderating variable, the association between Internet addiction and loneliness among females was weak [ 31 ]. Therefore, we propose the first hypothesis that there may be a moderating effect of gender (male and female) on the relationship between loneliness and Internet addiction.

Current research on the age effect of Internet addiction has not yielded consistent conclusions. Numerous studies have shown that younger Internet users are more prone to Internet addiction than older users [ 32 , 33 ]. Teenagers who feel lonely are more likely to alleviate their depression and stress through the Internet, leading to Internet addiction [ 34 ]. There are also studies showing that both middle-aged and elderly people are inclined to excessive Internet use [ 35 ].

Similarly, studies on the age effect of loneliness have not been consistent. Loneliness is not only common phenomenon among adults, with a high prevalence among those aged 60 and above (20–30%) [ 36 ], but also among adolescents under 25 (5–10%) [ 37 , 38 ].

Research has shown that there is no statistically significant difference between adolescents and adults in the effect sizes of the relationship between loneliness and Internet addiction [ 39 ]. Similar studies have found no differences in the relationship among children, adolescents, college students, adults, and the elderly [ 30 ]. To further investigate whether age has a moderating effect on the relationship, this study proposes the second hypothesis that there is a moderating effect of age (adolescent and adult) on the relationship between loneliness and Internet addiction.

Current research on the grade effect of Internet addiction has not yielded consistent conclusions. Few studies have examined the relationship across different grades, including primary schools, secondary schools, and universities. Some studies found no significant difference in the severity of Internet addiction among these grades [ 40 ]. In contrast, other studies have reported significant differences in Internet addiction rates across different grades [ 23 ]. Research conducted in middle schools suggests that as grades increase, the rate of Internet addiction gradually rises [ 41 ]. For instance, eighth-grade students have been found to be more addicted to the Internet than sixth-grade students (6th graders = 36.7%, 8th graders = 24%) [ 42 ]. Furthermore, students in secondary schools tend to show higher levels of Internet addiction than those in middle schools [ 43 ]. Among college students, Internet addiction tends to increase with the progression of the school year (1st graders = 8.4%, 2nd graders = 11.5%, 3rd graders = 11.1%, 4th or 5th graders = 12.9%) [ 23 ]. Some studies have reported similar conclusions, with a higher prevalence rate of Internet addiction as grade level increases [ 44 ]. However, there are also studies that have reached opposite conclusions [ 45 ].

Currently, research on the role of grade in regulating loneliness has not reached a consensus. Changes in the level of loneliness among middle school students have not been statistically significant [ 46 , 47 ]. However, in college, the level of loneliness in freshmen is significantly higher than that in other grades [ 48 ].

Research on the relationship between loneliness and Internet addiction has shown a statistically significant and highly positive correlation among middle school students of different grades [ 49 ]. Nevertheless, some scholars have found that there is no difference in the relationship between the two regarding grades [ 31 ]. In light of these varying findings, this study proposes the third research hypothesis, suggesting that grade (primary schools, secondary schools, and university) has a moderating effect on the relationship between loneliness and Internet addiction.

Current research on the regional effects of Internet addiction has not reached a consistent conclusion. Studies have shown that in comparison to Asia and Europe, the severity of Internet addiction in Oceania (Australia and New Zealand) is lower [ 50 ]. However, one study found that the Italian sample had the highest mean value of Internet addiction, while the Chinese sample had the lowest mean value of Internet addiction [ 51 ].

Similarly, research on the regional effects of loneliness has failed to yield consistent conclusions. The loneliness of teenagers is lowest in Southeast Asia and highest in the eastern Mediterranean region. Among adults, middle-aged individuals, and elderly individuals, the sense of loneliness is lowest in Northern countries and highest in Eastern European countries (Northern European countries = 2.9%, 1.8–4.5%, Eastern European countries = 7.5%, 5.9–9.4% ) [ 52 ].

Research has shown that regions have a moderating effect on the relationship between loneliness and Internet addiction, with the correlation between loneliness and Internet addiction in non-Chinese cultures being significantly higher than that in Chinese backgrounds [ 39 ]. Therefore, to further explore regional differences, we propose the fourth research hypothesis that region [East Asia (China), West Asia (Turkey, Kuwait, and Saudi Arabia), South Asia (India, Bangladesh), Southeast Asia (Thailand, Malaysia), and Europe (Greece)] has a moderating effect on the relationship between loneliness and Internet addiction.

Measurement tool

Russell, an early advocate of the one-dimensional structure of loneliness, argued that there is no difference in the core nature of loneliness, and all lonely individuals understand and experience loneliness in the same way. Consequently, he developed the first edition (1978) of the UCLA (University of California at Los Angeles) Loneliness Scale, which comprised 20 items and had a reliability coefficient of 0.96 [ 53 ]. However, because all the items pointed to loneliness, respondents may provide a single response, potentially leading to result deviation. The second edition (1980) of the UCLA Loneliness Scale addressed this issue by including 10 positive and 10 negative items, with the negatively scored items converted to calculate the total score alongside the other items. A higher total score indicates a stronger sense of loneliness, and the reliability coefficient of the scale is 0.94 [ 54 ]. Early studies primarily focused on college students with high reading ability. As research deepened, Russell’s third edition (1996) of the UCLA Loneliness Scale underwent simplification and became applicable to various groups. The scale now includes 11 positive items and 9 negative items, rated using a 4-point Likert scale. Its reliability coefficient ranges from 0.89 to 0.94 [ 55 ]. The UCLA Loneliness Scale has been adapted into Chinese by Wang, D [ 56 ]., Turkish by Demir, A. G [ 57 ]., Thai by Wongpakaran, T. et al. [ 58 ], and various other versions. Additionally, the Children’s Loneliness Scale, developed by Asher, S. R. et al. is a multidimensional scale containing 24 items designed to measure children’s subjective feelings of loneliness in grades 3–6. Sixteen main items assess loneliness, while eight supplemental items inquire about children’s hobbies and activity preferences, allowing children to answer more honestly and relaxedly. The scale is rated on a 5-point Likert scale with a reliability coefficient of 0.90 for the main items [ 59 ]. The Chinese Children’s Loneliness Scale was translated by Wang and other scholars [ 60 ] and adapted by Li, X. et al. for middle school students [ 61 ].

Young (1996) developed the first Internet addiction screening tool, Young’s Diagnostic Questionnaire for Internet addiction (YDQ), based on the diagnostic criteria for pathological gambling in the Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition (DSM-IV). YDQ is a self-report checklist consisting of 8 yes/no screening criteria, with a diagnosis of Internet addiction requiring the satisfaction of five criteria [ 62 ]. In subsequent studies, Young (1998) expanded the scale to 12 items and renamed it the Internet Addiction Test (IAT), which uses a Likert-5 scale with 20 items to measure the presence and severity of Internet addiction [ 63 ]. Respondents can be classified as normal, mild, moderate, or severe Internet addicts based on their scores [ 64 ]. The IAT is the most widely used scale to measure Internet addiction, gaining international recognition for its reliability and consistency [ 65 ]. It has been translated into multiple national versions, including Chinese [ 66 ], French [ 67 ], Italian [ 68 ], Turkish [ 69 ], Greek [ 70 ], Thai [ 71 ], Finnish [ 72 ], Korean [ 73 ], and Malay [ 74 ]. Additionally, the Chinese scholars Chen, S.H. et al. developed the Revised Chen Internet Addiction Scale (CIAS-R), which includes 26 items rated on a Likert-4 scale to assess Internet addiction [ 75 ]. It covers core symptoms and related problems of Internet addiction, with dimensions consistent with Block’s proposal of four dimensions involved in Internet addiction [ 76 ]. The CIAS-R has been validated by a large number of studies in Taiwan and mainland China and has been adapted into a Turkish version [ 77 ].

Differences exist in the dimensions, diagnostic criteria, and focus of measurement tools established on the basis of various theoretical models [ 78 ]. Meta-analysis has revealed significant variations in the measurement of Internet addiction when different tools are employed [ 79 ]. Studies have shown that the prevalence rates of Internet addiction measured by different measurement tools, were YDQ-8, YDQ-10, IAT and CIAS in increasing order (8.4%, 9.3%, 11.2%, 14.0%, respectively) [ 23 ]. It has also been observed that scores measured by the IAT have the highest correlation with loneliness. This may be because the IAT places greater emphasis on evaluating the symptoms [ 80 ].

Furthermore, another study confirmed the moderating effect of the Internet addiction measurement tool on the relationship between loneliness and Internet addiction [ 39 ]. In light of these findings, this study proposes the fifth research hypothesis that the measurement tools (YDQ, IAT, and CIAS) have a moderating effect on the relationship between loneliness and Internet addiction.

Research design

In a cross-sectional study design, data collection occurs at a specific point in time. In contrast, a longitudinal study design involves data collection at predetermined time intervals or fixed events, with subjects continuously tracked over time. Research has demonstrated that compared to cross-sectional studies, longitudinal designs offer a unique perspective on preventing loneliness [ 81 ].

Therefore, this meta-analysis introduces the sixth research hypothesis: the study design (cross-sectional study and longitudinal study) has a moderating effect on the relationship between loneliness and Internet addiction.

Research year

Research has revealed that with the increase in Internet usage time, Internet addiction has become a prominent issue during the COVID-19 [ 82 ]. Scholars have compared people’s levels of loneliness before and after the pandemic. Longitudinal studies have shown that loneliness levels increased after the pandemic [ 83 ]. As most reports have noted, people often feel lonely during COVID-19 [ 84 ]. However, there are also studies that have reached the opposite conclusion [ 85 ].

Statistical analysis indicates that before COVID-19, during the early stage and the recovery stage of the pandemic, the level of Internet addiction among groups with more severe Internet addiction has declined [ 86 ]. This meta-analysis proposes the seventh research hypothesis: that the research year (before and after COVID-19) has a moderating effect on the relationship between loneliness and Internet addiction.

Due to differences in research subjects, research tools [ 49 ] and measurement methods, there are inconsistencies and even contradictions in research conclusions. For example, scholars point out that the two variables are positively correlated ( r  = 0.43) [ 87 ], while Turan, N. et al. have concluded that there is a negative correlation between them ( r =-0.154) [ 88 ]. Using meta-analysis, this study aims to systematically analyze the research findings on the relationship between loneliness and Internet addiction to obtain a more objective, comprehensive effect size. Simultaneously, it seeks to investigate the moderating effects of the objective characteristics of research subjects (gender, age, grade, and region) and the subjective characteristics of researchers (measurement tools, research design, and research year whether before or after COVID-19) on the relationship between loneliness and Internet addiction, with the intention of providing references for subsequent studies.

Eligibility criteria

Population, Intervention, Comparison(s) and Outcome (PICO) is usually used for systematic review and meta-analysis of clinical trial study. For the study without Intervention or Comparison(s), it is enough to use P (Population) and O (Outcome) only to formulate a research question [ 89 ]. A well-formulated question creates the structure and delineates the approach to defining research objectives [ 90 ].

Studies involved both Internet addictive and non-Internet addictive samples. Research is only limited to Internet addiction, not to social media addiction, digital game addiction or smartphone addiction. We did not have any exclusion criteria regarding demographic (gender, age, grade, region) or the research design and research year of the study.

The outcome was the correlation coefficient of relationship between loneliness and Internet addiction. Regarding the measurement of variables, the inclusive articles use the generally recognized and report the adequate information on reliability and consistency of measurement tools. We include articles using Children’s Loneliness Scale, UCLA Loneliness Scale to measure the level of loneliness and YDQ, IAT, or CIAS to measure Internet addiction.

Literature selection criteria

First, we collected empirical studies on the relationship between loneliness and Internet addiction, excluding theoretical studies or review articles. Second, we selected studies that employed quantitative empirical research methods with complete and explicit data. These studies reported correlation coefficients or statistics (e.g., F values, t values, or χ2 values) that could be transformed into correlation coefficients. Third, the literature had to explicitly report the measurement tools used for assessing loneliness and Internet addiction. Fourth, we excluded duplicate publications and included only one instance of repeated data.

Search strategy

The literature search was divided into three steps. In the first step, we initiated the retrieval process. Internet addiction was formally proposed in 1996, and the literature search included articles published from 1996. The search was conducted in Web of Science using the keywords “Internet addiction” and “loneliness”. The deadline for the literature search was June 25, 2023. Based on our research topic, we initially collected 591 articles. In the second step, we conducted screening and removed an additional 157 articles that did not meet the screening criteria. In the third step, we confirmed the inclusion of 32 articles for meta-analysis after reading the full texts again. In total, the final set of literature included in the meta-analysis consisted of 32 articles, encompassing 32 effect sizes. The flow chart of the literature selection process is depicted in Fig.  1 .

The PRISMA flow chart used to identify studies for detailed analysis of loneliness and Internet addiction

Document coding

The articles included in the meta-analysis were coded using the following categories: (a) references (independent or first author, and year), (b) sample, (c) correlation coefficient, (d) gender (percentage of males), (e) age (adolescent and adult), (f) grade (primary schools, secondary schools, and university), (g) region [East Asia (China), West Asia (Turkey, Kuwait, Saudi Arabia), South Asia (India, Bangladesh), Southeast Asia (Thailand, Malaysia), and Europe (Greece)], (h) measurement tool (YDQ, IAT-12, IAT-20, and CIAS), (i) research design (cross-sectional study and longitudinal study) and (j) research year (before and after the COVID-19 pandemic). The final coding results of 32 target articles were shown in Table  1 .

Data analysis

In this study, we employed Comprehensive Meta Analysis 3.0 (CMA 3.0) for our meta-analysis. The effect size used for analysis was the correlation coefficient. To combine the effect sizes from the included studies, we chose the random effects model for statistical models that account for the potential variability between studies.

The random effects model assumes that each study is drawn from different aggregates, leading to significant variability among studies. As we aimed to investigate the moderating effects of various variables, these differences among studies could influence the final results. Therefore, the use of the random effects model was appropriate for evaluating the effect sizes. The results are measured by the effect sizes. Below 0.2 is low level effect, 0.2–0.5 is moderate low level, 0.5–0.8 is upper medium level, and above 0.8 is high effect level [ 117 ]. The heterogeneity between studies was tested with Higgins’ criteria for I 2 , values of 25%, 50%, and 75% correspond to low, moderate, and high degrees of heterogeneity, respectively [ 118 ].

Sample characteristics

This meta-analysis incorporated data from 32 independent samples, encompassing a total of 35,623 subjects. The age coverage of the study population is wide, the grades are concentrated in senior grades, like secondary schools and university. Subjects on the relationship between Internet addiction and loneliness are mostly located in Asian countries. IAT-20 is the most used questionnaire to measure Internet addiction, and the CIAS is mostly used by Chinese scholars. The research design was mostly cross-sectional study, and the research year were evenly distributed in the period of 2013–2023.

Homogeneity test

In the heterogeneity test, the results in Table  2 indicated significant heterogeneity (Q = 395.797, I 2  = 92.168, p  < 0.001). This finding suggests that a substantial proportion, 92.168%, of the observed variance in the relationship between loneliness and Internet addiction is attributed to real differences in this relationship. Additionally, the Tau-squared value was 0.013, indicating that 1.3% of the variation between studies could be considered for the calculation of the weights.

Given the high heterogeneity observed, a random effects model was appropriately employed for the meta-analysis. This aligns with the inference that the relationship between loneliness and Internet addiction is influenced by certain moderating variables.

Assessment of publication bias

As evident from Fig.  2 , the literature included in the meta-analysis was distributed on both sides of the center line. Notably, there are relatively few points on the bottom-right side of the funnel plot, indicating a small number of studies with large effect sizes and potentially low accuracy. Conversely, the majority of points cluster at the top of the funnel plot, suggesting small errors and large sample sizes.

These observations collectively indicate that meta-analysis is minimally affected by publication bias. The distribution of studies and the symmetry of the funnel plot suggest that the included literature provides a balanced representation of the relationship between loneliness and Internet addiction.

Funnel plot of effect sizes of the correlation between loneliness and Internet addiction

To further objectively evaluate publication bias, we conducted Begg and Mazumdar’s rank correlation test. The results showed that Kendall’s Tau was 0.06855 ( p  > 0.05), indicating that there was no evidence of publication bias in the meta-analysis. These findings align with the observations from the funnel plot, reaffirming the absence of publication bias in the study.

Main effect test

We employed a random effects model to assess the main effects of the eligible literature, the results were shown in Fig.  3 . The results from the random effects model revealed a correlation coefficient of 0.291 (95% CI = 0.251–0.331, Z = 13.436, p  < 0.001). This finding suggests a moderately positive correlation between loneliness and Internet addiction.

Forest plot of the comprehensive effects of loneliness and Internet addiction

Moderating effect test

This study investigated the moderating impact of both objective characteristics of subjects and subjective characteristics of researchers on the relationship between loneliness and Internet addiction, and the findings are summarized in Table  3 . The results revealed that several subject characteristics—gender (Qb = 4.159, p  < 0.05), age (Qb = 5.879, p  < 0.05), grade (Qb = 9.281, p  < 0.05), and region (Qb = 9.787, p  < 0.05)—influenced the association between loneliness and Internet addiction. Specifically, as the proportion of males increased, the correlation coefficient between Internet addiction and loneliness was significantly lower than that observed among females. Moreover, the correlation between loneliness and Internet addiction was notably lower in adolescents than that in adults. Furthermore, the strength of the relationship was significantly lower among primary and secondary school students than that among university students. Additionally, region-specific variations emerged, indicating that the correlation between loneliness and Internet addiction increased sequentially in Europe, South Asia, East Asia, Southeast Asia, and West Asia.

However, we found no significant moderating effects related to the measurement tool (Qb = 6.573, P  > 0.05), research design (Qb = 0.672, P  > 0.05), or research year relative to COVID-19 (Qb = 0.633, P  > 0.05) on the relationship between loneliness and Internet addiction.

Relationship between loneliness and internet addiction

This study conducted a comprehensive meta-analysis of empirical research conducted over the past two decades to examine the relationship between loneliness and Internet addiction. It incorporated data from 32 studies involving a total of 35,623 subjects. The findings confirmed a significant positive correlation between loneliness and Internet addiction ( r  = 0.291, p  < 0.001), underscoring a moderate relationship between two variables. These results align with the conclusions of previous study [ 119 ]. According to problem-behavior theory, problem behavior is defined as behavior that is socially disapproved by the institutions of authority. Problem behavior may be an instrumental effort to attain goals that are blocked or that seem otherwise unattainable [ 120 ]. Unmet needs such as loneliness lead them to seek solace in the online world and perpetuating a cycle of loneliness.

Notably, this meta-analysis adopted a unique approach by categorizing moderating variables into two distinct groups: the objective characteristics of research subjects and the subjective characteristics of researchers. It sheds light on the multifaceted factors that influence the relationship between loneliness and Internet addiction. Furthermore, it explored the impact of research design on these findings, providing novel insights into this relationship.

In addition to these contributions, this study also considered global COVID-19, incorporating literature published after the outbreak. This allowed for an investigation into the influence of the pandemic on the relationship between loneliness and Internet addiction. This meta-analysis thus provides a comprehensive understanding of the evolving dynamics between loneliness and Internet addiction.

Moderating effect of the relationship between loneliness and internet addiction

The moderating role of gender.

This study categorized the proportion of male participants into two groups and found that as the proportion of male participants increased, the correlation between loneliness and Internet addiction gradually decreased, with statistically significant differences between the groups. These results, contrary to previous findings [ 31 ], warrant further investigation.

Analyzing the reasons behind this, it is worth noting that men and women often differ in the functions of Internet use. Women tend to use it for socializing and meeting interpersonal needs, while men are more inclined to spend time on online games to fulfill self-actualization and personal needs [ 121 ]. Studies have also shown that women exhibit a stronger correlation between social use of the Internet and loneliness, while men display a stronger correlation between leisure use and loneliness compared to women [ 122 ]. Additionally, women may be more vulnerable to Internet addiction [ 123 ].

The moderating role of age

The study confirmed that loneliness is significantly less associated with Internet addiction in adolescents than in adults. Loneliness is with a high prevalence among adults [ 124 ], and the incidence of Internet addiction in adults is also high [ 50 ]. Adolescents, who often study and live in collective environments with peer support and parental supervision, are less likely to feel lonely and become addicted to the Internet. In contrast, adults may use the Internet as a means to escape life pressures, leading to increased loneliness due to excessive online engagement.

The moderating role of grade

The findings indicated that the correlation between loneliness and Internet addiction is significantly lower among primary and secondary school students than among university students. The results are consistent with the conclusions of the existing studies [ 45 ]. Primary school students’ immaturity, limited self-control, and susceptibility to Internet addiction contribute to this pattern. Secondary school students, focused on academic pressures, tend to have the lowest correlation between loneliness and Internet addiction. Conversely, in addition to academic pressure, there are two important tasks for university students: forming identity and building meaningful and intimate relationships. Many people have not achieved an independent identity and remain overly attached to their families. This may cause the sense of loneliness, Internet addiction as one of the coping mechanisms to alleviate psychological problems [ 125 ].

The moderating role of region

The correlation coefficients between loneliness and Internet addiction varied across regions, with Europe exhibiting a lower correlation compared to Asian regions. The result support a previous cross-national meta-analysis study [ 126 ]. Some European countries have implemented policies and regulations to curb Internet addiction, which has had a controlling effect [ 127 ]. However, it is essential to note that the European and South Asian subgroups included only one study, potentially affecting the findings.

The moderating role of measurement tool

The results suggested that the measurement tool used did not significantly moderate the relationship between loneliness and Internet addiction. This is consistent with the conclusions of the existing studies that even different instruments give comparable results [ 128 ]. This underscores the consistency and scientific validity of the measurement tools. However, it is worth exploring the impact of different thresholds within the IAT-20 scale on the relationship between loneliness and Internet addiction in future studies, as there have been discrepancies in threshold selections [ 129 ].

The moderating role of research design

Interestingly, the research design was found to have no significant moderating effect on the relationship between loneliness and Internet addiction. This suggests that research results are robust across different research designs, even though cross-sectional research designs have been subject to credibility concerns in social science research.

The moderating role of research year

The analysis revealed that the research year did not moderate the relationship between loneliness and Internet addiction. This underscores the stability and resilience of this relationship, which is unaffected by external events such as the COVID-19.

Limitations

In the analysis of moderating effects, the sample distribution of certain moderating variables was not adequately balanced, and the sample sizes for specific subgroups were relatively small. For instance, variables such as grade (primary school) and region (Europe and South Asia) which had only one data point is also included, in order to ensure the integrity and authenticity of the data. This could impact the accuracy of the moderating effects analysis.

This study employed a meta-analysis methodology and CMA 3.0 (Comprehensive Meta-analysis 3.0) to quantitatively analyze 32 foreign literature sources examining the relationship between loneliness and Internet addiction. The primary objectives were to objectively estimate the overall effect size of loneliness and Internet addiction and to investigate how research characteristics might moderate this effect.

The study’s findings revealed a moderately positive correlation between loneliness and Internet addiction. Moreover, this correlation’s strength was found to be influenced by various factors, including gender, age, grade, and the region of the subjects. However, it was not affected by variables such as the measurement tool, research design, or research year (whether before or after COVID-19).

In summary, this meta-analysis suggests a noticeable link between loneliness and Internet addiction, with specific demographic and contextual factors impacting the strength of this relationship.

Data availability

Data can be requested from the corresponding author.

Abbreviations

Revised Chen Internet Addiction Scale

Diagnostic and Statistical Manual of Mental Disorders—Fourth Edition

Internet Addiction Disorder

Internet Addiction Test

Population, Intervention, Comparison(s) and Outcome

Pathological Internet Use

Young’s Diagnostic Questionnaire for Internet addiction

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The mediating effect of internet addiction and the moderating effect of physical activity on the relationship between alexithymia and depression

• Yang Liu 1   na1 ,
• Liangfan Duan 1   na1 ,
• Qingxin Shen 1   na1 ,
• Yuanyuan Ma 1 ,
• Yiyi Chen 1 ,
• Lei Xu 1 , 2 ,
• Yawen Wu 1 &
• Tiancheng Zhang 1  

Scientific Reports volume  14 , Article number:  9781 ( 2024 ) Cite this article

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• Epidemiology
• Psychology and behaviour

There is a certain relationship between alexithymia and depression, but further investigation is needed to explore their underlying mechanisms. The aims of this study was to explore the mediating role of internet addiction between alexithymia and depression and the moderating role of physical activity. A total of 594 valid responses were included in the analysis, with a mean age of 18.72 years (SD = 1.09). The sample comprised 250 males (42.09%) and 344 females (57.91%). These responses were utilized for descriptive analysis, correlation analysis, regression analysis, and the development of mediation and moderation models. Alexithymia showed positive correlations with depression and internet addiction, and physical activity was negatively correlated with internet addiction and depression. Internet addiction partially mediated the relationship between alexithymia and depression, while physical activity weakened the association between internet addiction and depression, acting as a moderator. Our findings suggest that excessive Internet engagement may mediate the relationship between alexithymia and depression as an emotional regulatory coping strategy, and that physical activity attenuates the predictive effect of Internet addiction on depression.

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Internet addiction and residual depressive symptoms among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic: a network analysis perspective

Introduction.

Alexithymia, often manifested as a multidimensional impairment in recognizing, understanding, and describing emotions 1 , 2 , is a stable personality trait. Individuals with higher levels of alexithymia experience increasing difficulties in establishing and maintaining interpersonal relationships, perceive less social support, and exhibit lower levels of social skills 3 . Upon transitioning to university, individuals confront a plethora of unknowns and challenges 4 . Particularly within the context of China, the transition to university presents them with more flexible schedules and expanded social circles, with "adaptation" emerging as one of their foremost hurdles due to the characteristic of alexithymia 5 . Consequently, due to impairments in recognizing and responding to emotions, they struggle to develop healthy or intimate social relationships 6 , 7 , leading to emotional distress and discomfort 8 , 9 . Individuals with alexithymia inevitably face difficulties in social interactions and maintaining emotional connections due to its inherent characteristics, and it is often associated with other psychological disorders 10 , such as social anxiety 11 , substance abuse 12 , depression 13 , 14 , eating disorders 15 , and non-suicidal self-injury 16 , 17 . Hence, comprehending the relationship between alexithymia in university students and other psychological disorders, along with their underlying physiological and psychological mechanisms, is crucial for promoting the healthy development of individuals with alexithymia or predisposition to alexithymia.

Previous studies have found a high correlation between alexithymia and depression in individuals 14 , 18 , 19 , 20 . This high correlation between alexithymia and depression is not only present in clinical depression populations 21 , but also in non-clinical populations 13 , and similar results have been obtained in studies of populations with other diseases or psychological disorders 14 , 22 , 23 . Evidence of a high correlation between alexithymia and depression exists in various populations 20 . Furthermore, depression mediates the relationship between alexithymia and other risk behaviors in individuals 24 , even completely mediating it 25 . Therefore, based on the above review, we hypothesize that there is a positive correlation between alexithymia and depression in college students.

Interpersonal relationships are challenged due to impaired recognition and response to emotions 6 , 7 and often fall into emotional distress 8 , 9 . Hence, individuals with alexithymia attempt to regulate their emotions through compulsive behaviors 26 , 27 . The internet provides them with an ideal avenue due to its anonymity, convenience of remote interaction 28 , and absence of face-to-face observation 29 , which may mitigate the deficits in understanding and identifying others' emotions associated with alexithymia while fulfilling the need for social interaction. Consequently, individuals with alexithymia may exhibit excessive dependence on and usage of the internet, leading to the development of internet addiction. Internet addiction refers to the pathological use of the Internet by individuals, leading to adverse consequences in personal, social, and occupational life 30 . Based on detection rates of internet addiction among young people in China, it has been found that internet addiction gradually increases with age and grade, reaching 7.7% among high school students 31 , and even exceeding half among college students 32 . Research has found a significant positive correlation between alexithymia and internet addiction in college students 33 , 34 , and alexithymia significantly predicts internet addiction in college students 35 , 36 , 37 . This predictive effect also exists in studies focusing on a single gender 38 . Additionally, in a large sample study of Chinese college students, a significant relationship between internet addiction and depression was found 39 , and internet addiction significantly predicted subsequent levels of depression in college students 40 . This relationship also exists in other countries and regions 41 , 42 , 43 , 44 . Based on the above review, we can establish the second hypothesis of this study, which is that internet addiction plays a mediating role in the relationship between alexithymia and depression in college students.

Research has indicated that dysfunction of the hypothalamic–pituitary–adrenal (HPA) axis is prevalent among individuals with internet addiction 45 , 46 . Dysfunction of the HPA axis serves as a significant indicator of depression 47 and is also a crucial predictive factor for depression 48 . Therefore, regulating the functionality of the HPA axis naturally emerges as a potential pathway for alleviating depression 49 . Studies have shown that physical activity can reduce cortisol levels and promote the development of HPA axis function 50 , 51 . Hence, physical activity may decrease the strong correlation between internet addiction and depression. Research has found that compared to non-internet addicted college students, internet addicted college students have higher levels of depression and lower levels of physical activity 52 . Physical activity can mitigate internet addiction by modulating the neurobiology of the central and autonomic nervous systems 53 , thereby significantly reducing depression levels among internet-addicted college students 54 , and this evidence is also supported by retrospective studies 55 . Therefore, we make the final hypothesis of this study that physical activity can regulate the relationship between internet addiction and depression in college students.

In conclusion, the relationship between alexithymia and depression in college students has been widely reported, but little is known about other psychological factors between the two, including the mediating role of internet addiction and the moderating role of physical activity. This study will use internet addiction as a mediating factor between alexithymia and depression, and physical activity to moderate the relationship between internet addiction and depression, further enriching the underlying psychological mechanisms between alexithymia and depression. A theoretical model diagram is constructed (see Fig.  1 ).

Hypothesized model.

Participants

The present study was conducted in October 2023 at two universities in the western part of Hunan Province, China. Prior to the commencement of the research, approval was obtained from the Biomedical Ethics Committee of Jishou University (Grant number: JSDX-2023-0034). During the survey, our staff first communicated with the leading teachers of the college students and obtained approval. Subsequently, on a class-by-class basis, the investigators delivered presentations to all participants, informing them of the main content of the survey, the anonymity and confidentiality of the data, their right to freely withdraw, and the disposition of the results. After obtaining informed consent from all individuals, electronic questionnaires were distributed, and participants could complete the questionnaire in its entirety within 20 min. Ultimately, a total of 676 college students completed the survey. We confirm that all the experiment is in accordance with the relevant guidelines and regulations such as the declaration of Helsinki. After screening for insufficient response times (instances where participants completed the questionnaire in an unreasonably short amount of time, suggesting potential rushed or careless responses) and regular patterned responses (responses that exhibit a consistent and predictable pattern, possibly indicating a lack of genuine engagement or random guessing), valid data from 594 participants (250 male, 344 female; 225 non-left-behind, 369 left-behind) were obtained, with an average age of 18.72 years (SD = 1.09).

• Alexithymia

The Toronto Alexithymia Scale (TAS-20) was used to assess the level of alexithymia 2 , 56 . The scale consists of 20 items, and a Likert 5-point scoring system is used to evaluate the level of alexithymia, ranging from 1 (completely inconsistent) to 5 (completely consistent). Except for the five reverse-scored items (4, 5, 10, 18, and 19), which are scored in reverse, all other items are scored between 1 and 5 points. The sum of all item scores represents the total score of alexithymia, with higher scores indicating more severe levels of alexithymia. In this study, Cronbach's α for the sample was 0.832.

The Chinese version of the Depression Anxiety Stress Scale (DASS-21) 57 , 58 was utilized to measure depression. This study employed the depression subscale of the DASS-21, which comprises 7 items and utilizes a Likert 4-point scoring system, ranging from 1 (strongly disagree) to 4 (strongly agree), to assess depression. Each item is scored between 1 and 5 points, and the sum of all item scores represents the total depression score, with higher scores indicating more severe levels of depression. In this study, the Cronbach's α for the sample was 0.899.

• Internet addiction

The internet addiction level was measured using the Internet Addiction Test (IAT) developed by Wei 59 , 60 . The questionnaire consists of 8 items and utilizes a Likert 5-point scoring system, ranging from 1 (strongly disagree) to 5 (strongly agree), to evaluate internet addiction. Each item is scored between 1 and 5 points, and the sum of all item scores represents the total internet addiction score, with higher scores indicating more severe levels of internet addiction. In this study, the Cronbach’s α for the sample was 0.876.

• Physical activity

The physical activity level was measured using the Physical Activity Scale developed by Liang Deqing 61 , 62 . The scale consists of 3 items, including exercise intensity, duration, and frequency. Each item has 5 different levels, with scores ranging from 1 to 5 for intensity and frequency, and scores ranging from 0 to 4 for duration. The physical activity score is derived by multiplying the scores of the three items, with higher scores indicating higher levels of physical activity. The current sample's Cronbach's alpha for the scale is 0.654.

Taking into account the influence of demographic variables on the outcome analysis, such as gender and age 35 , 63 , we controlled for these variables during the analysis. The gender was coded as 1 for male and 2 for female.

Statistical analyses

In our study, all statistical analyses were conducted using SPSS 26.0 software. Firstly, a method bias test was performed to explore potential biases associated with the use of self-report questionnaires. Subsequently, descriptive statistics and correlation analysis were conducted to describe the demographic characteristics of the participants and the main variables of interest. Prior to further analysis, standardization was applied to the data of the main variables. Finally, to test our hypotheses, we employed the PROCESS macro plugin in SPSS (Model 14) to examine the relationships among alexithymia, depression in college students, the mediating role of internet addiction, and the moderating effect of physical activity 64 . In this process, we utilized 5000 bootstrap resampling iterations to assess model fit and estimate 95% confidence intervals, considering a relationship as significant when the 95% confidence interval did not include zero. Throughout the analysis, gender and age were included as covariates for control analysis.

Ethics approval and consent to participate

The study was approved by the Biomedicine Ethics Committee of Jishou University before the initiation of the project (Grant number: JSDX-2023-0034). And informed consent was obtained from the participants before starting the program.

Harman’s single factor test

The common method bias was examined using Harman's single factor test. The analysis results indicated that among the factors with eigenvalues greater than 1, only two factors met this criterion. Without conducting principal component factor rotation, the first factor accounted for 30.20% of the variance, which was lower than the recommended threshold of 40% 65 . Therefore, based on the analysis results, there was no significant evidence of common method bias in this study.

Descriptive data and Correlational analyses

Table  1 presents the Pearson correlation data among the variables. Alexithymia is significantly positively correlated with internet addiction (r = 0.413, p  < 0.001) and depression (r = 0.523, p  < 0.001) in college students, and significantly negatively correlated with physical activity (r = − 0.255, p  < 0.001). Internet addiction is significantly positively correlated with depression (r = 0.384, p  < 0.001) and significantly negatively correlated with physical activity (r = − 0.087, p  < 0.05) in college students. Physical activity is significantly negatively correlated with depression in college students (r = − 0.269, p  < 0.001).

Moderated and mediation analysis

After including mediating and moderating variables, as well as controlling for covariates, alexithymia still significantly and positively predicts the level of depression in college students (β = 0.391, SE = 0.038, p  < 0.001). Furthermore, in the mediation analysis, alexithymia significantly and positively predicts internet addiction in college students (β = 0.407, SE = 0.037, p  < 0.001), and internet addiction acts as a mediator between alexithymia and the level of depression in college students (β = 0.228, SE = 0.037, p  < 0.001). In the moderation analysis, physical activity negatively predicts depression in college students (β = − 0.223, SE = 0.038, p  < 0.001), and the interaction term between internet addiction and physical activity also significantly and negatively predicts depression in college students (β = − 0.067, SE = 0.031, p  < 0.05). Please refer to Table  2 , Figs.  2 and 3 for more details.

Hypothesized model, * p  < 0.05; *** p  < 0.001.

Moderating effect of physical activity on Internet addiction and depression in college students.

This study discusses the interrelationships between alexithymia, internet addiction, depression, and physical activity among college students. The results revealed positive correlations between alexithymia, internet addiction, and depression, as well as negative correlations between these variables and physical activity, all of which reached significance levels. After controlling for demographic variables, internet addiction was found to mediate the relationship between alexithymia and depression among college students, while physical activity played a moderating role in the relationship between internet addiction and depression.

Our study found a significant correlation between alexithymia and depression among college students, which is consistent with previous research 20 , 25 . Alexithymia, characterized by difficulties in recognizing and understanding emotions 66 , has been linked to impaired emotional regulation and an increased risk of depression 67 , 68 , 69 . Furthermore, a longitudinal study found fluctuating scores on the depression scale in conjunction with scores on the alexithymia scale 14 . Additionally, individuals with alexithymia may receive less support 70 , leading to reduced social support and higher levels of depression 71 . Therefore, our results confirm our first hypothesis that there is a significant positive correlation between alexithymia and depression among college students, with alexithymia significantly predicting depression.

Furthermore, this study found that internet addiction plays a mediating role between alexithymia and depression among college students. As previously mentioned, the difficulty of alexithymic individuals in recognizing and understanding emotions leads to difficulties in their interactions with others in the real world 72 . Consequently, they tend to choose to escape the real world and seek social satisfaction in the online world 10 . Compensatory Internet use theory suggests 73 that negative social relationships and emotions drive individuals to escape into the online world as a coping mechanism, satisfying not only their social needs but also addressing negative emotions. In an era where smartphones and the internet are easily accessible, they find it easier to enter the virtual world. However, this way of coping cannot replace the real world and only leads to the formation and exacerbation of internet addiction. There is also a strong relationship between internet addiction and depression 40 , and longitudinal studies have found that they can predict each other 40 , 74 . Therefore, based on the evidence mentioned above, our second hypothesis that internet addiction plays a mediating role between alexithymia and students is supported.

Moreover, this study found that physical activity has a moderating effect on the relationship between internet addiction and depression among college students. Loneliness, depression, and sensitivity to interpersonal relationships are significant characteristics of individuals with internet addiction 75 , which, combined with the characteristics of alexithymia, further increases their dependence on the internet 76 . However, previous research has found that exercise can increase levels of neurotrophic factors, cortisol, and neurotransmitters, promote the development of the nervous system, and inhibit reward impulsivity 53 . Additionally, long-term physical exercise can significantly reduce the level of internet addiction and depression among college students, improve sleep quality, and balance the sympathetic and parasympathetic nervous system functions 54 . Moreover, group sports activities are enjoyable and involve social interactions, which may further weaken the depressive emotions caused by internet addiction among college students 55 . Therefore, physical activity can regulate the relationship between internet addiction and depression among college students, confirming our last hypothesis.

This study has certain strengths. It is the first to discuss internet addiction as a mediating factor between alexithymia and depression among college students, and it demonstrates that physical activity can moderate the impact of internet addiction on depression. Hence, while examining the relationship between individual alexithymia and depression, it becomes imperative to assess the presence of internet addiction or other behavioral addictions, as they may exacerbate levels of depression. Concurrently, in light of established patterns of internet addiction behavior, encouraging and leading individuals to actively engage in physical activities is advocated. This not only fosters interaction 77 , learning, and the establishment of positive social relationships during physical activity but also promotes physical and mental well-being 78 , 79 , potentially reducing depression levels holistically. However, the study also has limitations. Firstly, it is based on cross-sectional data, which may challenge the strength of causal relationships. Future research could use longitudinal data to explain the causal relationships between variables. Secondly, all the main data are self-reported, which may have subjective biases. Future research could combine subjective and objective data to improve the credibility of the evidence. Lastly, this study was conducted based on convenience sampling, which may introduce regional differences. Future research could conduct cross-regional studies.

This study reveals the relationship between alexithymia and depression among college students, the mediating role of internet addiction between the two, and the moderating effect of physical activity on the relationship between internet addiction and depression. It further enhances our understanding of the relationships and underlying mechanisms among these variables, highlighting the potential roles of these factors in psychological interventions for college students with alexithymia.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due (our experimental team's policy) but are available from the corresponding author on reasonable request.

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Yang Liu, Liangfan Duan, Qingxin Shen, Yuanyuan Ma, Yiyi Chen, Lei Xu, Yawen Wu & Tiancheng Zhang

Institute of Physical Education, Shanxi University of Finance and Economics, Taiyuan, China

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Liu, Y., Duan, L., Shen, Q. et al. The mediating effect of internet addiction and the moderating effect of physical activity on the relationship between alexithymia and depression. Sci Rep 14 , 9781 (2024). https://doi.org/10.1038/s41598-024-60326-w

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Research Article

Functional connectivity changes in the brain of adolescents with internet addiction: A systematic literature review of imaging studies

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Child and Adolescent Mental Health, Department of Brain Sciences, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom

Roles Conceptualization, Supervision, Validation, Writing – review & editing

* E-mail: [email protected]

Affiliation Behavioural Brain Sciences Unit, Population Policy Practice Programme, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom

• Max L. Y. Chang, 
• Irene O. Lee

• Published: June 4, 2024
• https://doi.org/10.1371/journal.pmen.0000022
• Peer Review
• Reader Comments

Internet usage has seen a stark global rise over the last few decades, particularly among adolescents and young people, who have also been diagnosed increasingly with internet addiction (IA). IA impacts several neural networks that influence an adolescent’s behaviour and development. This article issued a literature review on the resting-state and task-based functional magnetic resonance imaging (fMRI) studies to inspect the consequences of IA on the functional connectivity (FC) in the adolescent brain and its subsequent effects on their behaviour and development. A systematic search was conducted from two databases, PubMed and PsycINFO, to select eligible articles according to the inclusion and exclusion criteria. Eligibility criteria was especially stringent regarding the adolescent age range (10–19) and formal diagnosis of IA. Bias and quality of individual studies were evaluated. The fMRI results from 12 articles demonstrated that the effects of IA were seen throughout multiple neural networks: a mix of increases/decreases in FC in the default mode network; an overall decrease in FC in the executive control network; and no clear increase or decrease in FC within the salience network and reward pathway. The FC changes led to addictive behaviour and tendencies in adolescents. The subsequent behavioural changes are associated with the mechanisms relating to the areas of cognitive control, reward valuation, motor coordination, and the developing adolescent brain. Our results presented the FC alterations in numerous brain regions of adolescents with IA leading to the behavioural and developmental changes. Research on this topic had a low frequency with adolescent samples and were primarily produced in Asian countries. Future research studies of comparing results from Western adolescent samples provide more insight on therapeutic intervention.

Citation: Chang MLY, Lee IO (2024) Functional connectivity changes in the brain of adolescents with internet addiction: A systematic literature review of imaging studies. PLOS Ment Health 1(1): e0000022. https://doi.org/10.1371/journal.pmen.0000022

Editor: Kizito Omona, Uganda Martyrs University, UGANDA

Received: December 29, 2023; Accepted: March 18, 2024; Published: June 4, 2024

Copyright: © 2024 Chang, Lee. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The behavioural addiction brought on by excessive internet use has become a rising source of concern [ 1 ] since the last decade. According to clinical studies, individuals with Internet Addiction (IA) or Internet Gaming Disorder (IGD) may have a range of biopsychosocial effects and is classified as an impulse-control disorder owing to its resemblance to pathological gambling and substance addiction [ 2 , 3 ]. IA has been defined by researchers as a person’s inability to resist the urge to use the internet, which has negative effects on their psychological well-being as well as their social, academic, and professional lives [ 4 ]. The symptoms can have serious physical and interpersonal repercussions and are linked to mood modification, salience, tolerance, impulsivity, and conflict [ 5 ]. In severe circumstances, people may experience severe pain in their bodies or health issues like carpal tunnel syndrome, dry eyes, irregular eating and disrupted sleep [ 6 ]. Additionally, IA is significantly linked to comorbidities with other psychiatric disorders [ 7 ].

Stevens et al (2021) reviewed 53 studies including 17 countries and reported the global prevalence of IA was 3.05% [ 8 ]. Asian countries had a higher prevalence (5.1%) than European countries (2.7%) [ 8 ]. Strikingly, adolescents and young adults had a global IGD prevalence rate of 9.9% which matches previous literature that reported historically higher prevalence among adolescent populations compared to adults [ 8 , 9 ]. Over 80% of adolescent population in the UK, the USA, and Asia have direct access to the internet [ 10 ]. Children and adolescents frequently spend more time on media (possibly 7 hours and 22 minutes per day) than at school or sleeping [ 11 ]. Developing nations have also shown a sharp rise in teenage internet usage despite having lower internet penetration rates [ 10 ]. Concerns regarding the possible harms that overt internet use could do to adolescents and their development have arisen because of this surge, especially the significant impacts by the COVID-19 pandemic [ 12 ]. The growing prevalence and neurocognitive consequences of IA among adolescents makes this population a vital area of study [ 13 ].

Adolescence is a crucial developmental stage during which people go through significant changes in their biology, cognition, and personalities [ 14 ]. Adolescents’ emotional-behavioural functioning is hyperactivated, which creates risk of psychopathological vulnerability [ 15 ]. In accordance with clinical study results [ 16 ], this emotional hyperactivity is supported by a high level of neuronal plasticity. This plasticity enables teenagers to adapt to the numerous physical and emotional changes that occur during puberty as well as develop communication techniques and gain independence [ 16 ]. However, the strong neuronal plasticity is also associated with risk-taking and sensation seeking [ 17 ] which may lead to IA.

Despite the fact that the precise neuronal mechanisms underlying IA are still largely unclear, functional magnetic resonance imaging (fMRI) method has been used by scientists as an important framework to examine the neuropathological changes occurring in IA, particularly in the form of functional connectivity (FC) [ 18 ]. fMRI research study has shown that IA alters both the functional and structural makeup of the brain [ 3 ].

We hypothesise that IA has widespread neurological alteration effects rather than being limited to a few specific brain regions. Further hypothesis holds that according to these alterations of FC between the brain regions or certain neural networks, adolescents with IA would experience behavioural changes. An investigation of these domains could be useful for creating better procedures and standards as well as minimising the negative effects of overt internet use. This literature review aims to summarise and analyse the evidence of various imaging studies that have investigated the effects of IA on the FC in adolescents. This will be addressed through two research questions:

• How does internet addiction affect the functional connectivity in the adolescent brain?
• How is adolescent behaviour and development impacted by functional connectivity changes due to internet addiction?

The review protocol was conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (see S1 Checklist ).

Search strategy and selection process

A systematic search was conducted up until April 2023 from two sources of database, PubMed and PsycINFO, using a range of terms relevant to the title and research questions (see full list of search terms in S1 Appendix ). All the searched articles can be accessed in the S1 Data . The eligible articles were selected according to the inclusion and exclusion criteria. Inclusion criteria used for the present review were: (i) participants in the studies with clinical diagnosis of IA; (ii) participants between the ages of 10 and 19; (iii) imaging research investigations; (iv) works published between January 2013 and April 2023; (v) written in English language; (vi) peer-reviewed papers and (vii) full text. The numbers of articles excluded due to not meeting the inclusion criteria are shown in Fig 1 . Each study’s title and abstract were screened for eligibility.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pmen.0000022.g001

Quality appraisal

Full texts of all potentially relevant studies were then retrieved and further appraised for eligibility. Furthermore, articles were critically appraised based on the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) framework to evaluate the individual study for both quality and bias. The subsequent quality levels were then appraised to each article and listed as either low, moderate, or high.

Data collection process

Data that satisfied the inclusion requirements was entered into an excel sheet for data extraction and further selection. An article’s author, publication year, country, age range, participant sample size, sex, area of interest, measures, outcome and article quality were all included in the data extraction spreadsheet. Studies looking at FC, for instance, were grouped, while studies looking at FC in specific area were further divided into sub-groups.

Data synthesis and analysis

Articles were classified according to their location in the brain as well as the network or pathway they were a part of to create a coherent narrative between the selected studies. Conclusions concerning various research trends relevant to particular groupings were drawn from these groupings and subgroupings. To maintain the offered information in a prominent manner, these assertions were entered into the data extraction excel spreadsheet.

With the search performed on the selected databases, 238 articles in total were identified (see Fig 1 ). 15 duplicated articles were eliminated, and another 6 items were removed for various other reasons. Title and abstract screening eliminated 184 articles because they were not in English (number of article, n, = 7), did not include imaging components (n = 47), had adult participants (n = 53), did not have a clinical diagnosis of IA (n = 19), did not address FC in the brain (n = 20), and were published outside the desired timeframe (n = 38). A further 21 papers were eliminated for failing to meet inclusion requirements after the remaining 33 articles underwent full-text eligibility screening. A total of 12 papers were deemed eligible for this review analysis.

Characteristics of the included studies, as depicted in the data extraction sheet in Table 1 provide information of the author(s), publication year, sample size, study location, age range, gender, area of interest, outcome, measures used and quality appraisal. Most of the studies in this review utilised resting state functional magnetic resonance imaging techniques (n = 7), with several studies demonstrating task-based fMRI procedures (n = 3), and the remaining studies utilising whole-brain imaging measures (n = 2). The studies were all conducted in Asiatic countries, specifically coming from China (8), Korea (3), and Indonesia (1). Sample sizes ranged from 12 to 31 participants with most of the imaging studies having comparable sample sizes. Majority of the studies included a mix of male and female participants (n = 8) with several studies having a male only participant pool (n = 3). All except one of the mixed gender studies had a majority male participant pool. One study did not disclose their data on the gender demographics of their experiment. Study years ranged from 2013–2022, with 2 studies in 2013, 3 studies in 2014, 3 studies in 2015, 1 study in 2017, 1 study in 2020, 1 study in 2021, and 1 study in 2022.

https://doi.org/10.1371/journal.pmen.0000022.t001

(1) How does internet addiction affect the functional connectivity in the adolescent brain?

The included studies were organised according to the brain region or network that they were observing. The specific networks affected by IA were the default mode network, executive control system, salience network and reward pathway. These networks are vital components of adolescent behaviour and development [ 31 ]. The studies in each section were then grouped into subsections according to their specific brain regions within their network.

Default mode network (DMN)/reward network.

Out of the 12 studies, 3 have specifically studied the default mode network (DMN), and 3 observed whole-brain FC that partially included components of the DMN. The effect of IA on the various centres of the DMN was not unilaterally the same. The findings illustrate a complex mix of increases and decreases in FC depending on the specific region in the DMN (see Table 2 and Fig 2 ). The alteration of FC in posterior cingulate cortex (PCC) in the DMN was the most frequently reported area in adolescents with IA, which involved in attentional processes [ 32 ], but Lee et al. (2020) additionally found alterations of FC in other brain regions, such as anterior insula cortex, a node in the DMN that controls the integration of motivational and cognitive processes [ 20 ].

https://doi.org/10.1371/journal.pmen.0000022.g002

The overall changes of functional connectivity in the brain network including default mode network (DMN), executive control network (ECN), salience network (SN) and reward network. IA = Internet Addiction, FC = Functional Connectivity.

https://doi.org/10.1371/journal.pmen.0000022.t002

Ding et al. (2013) revealed altered FC in the cerebellum, the middle temporal gyrus, and the medial prefrontal cortex (mPFC) [ 22 ]. They found that the bilateral inferior parietal lobule, left superior parietal lobule, and right inferior temporal gyrus had decreased FC, while the bilateral posterior lobe of the cerebellum and the medial temporal gyrus had increased FC [ 22 ]. The right middle temporal gyrus was found to have 111 cluster voxels (t = 3.52, p<0.05) and the right inferior parietal lobule was found to have 324 cluster voxels (t = -4.07, p<0.05) with an extent threshold of 54 voxels (figures above this threshold are deemed significant) [ 22 ]. Additionally, there was a negative correlation, with 95 cluster voxels (p<0.05) between the FC of the left superior parietal lobule and the PCC with the Chen Internet Addiction Scores (CIAS) which are used to determine the severity of IA [ 22 ]. On the other hand, in regions of the reward system, connection with the PCC was positively connected with CIAS scores [ 22 ]. The most significant was the right praecuneus with 219 cluster voxels (p<0.05) [ 22 ]. Wang et al. (2017) also discovered that adolescents with IA had 33% less FC in the left inferior parietal lobule and 20% less FC in the dorsal mPFC [ 24 ]. A potential connection between the effects of substance use and overt internet use is revealed by the generally decreased FC in these areas of the DMN of teenagers with drug addiction and IA [ 35 ].

The putamen was one of the main regions of reduced FC in adolescents with IA [ 19 ]. The putamen and the insula-operculum demonstrated significant group differences regarding functional connectivity with a cluster size of 251 and an extent threshold of 250 (Z = 3.40, p<0.05) [ 19 ]. The molecular mechanisms behind addiction disorders have been intimately connected to decreased striatal dopaminergic function [ 19 ], making this function crucial.

Executive Control Network (ECN).

5 studies out of 12 have specifically viewed parts of the executive control network (ECN) and 3 studies observed whole-brain FC. The effects of IA on the ECN’s constituent parts were consistent across all the studies examined for this analysis (see Table 2 and Fig 3 ). The results showed a notable decline in all the ECN’s major centres. Li et al. (2014) used fMRI imaging and a behavioural task to study response inhibition in adolescents with IA [ 25 ] and found decreased activation at the striatum and frontal gyrus, particularly a reduction in FC at inferior frontal gyrus, in the IA group compared to controls [ 25 ]. The inferior frontal gyrus showed a reduction in FC in comparison to the controls with a cluster size of 71 (t = 4.18, p<0.05) [ 25 ]. In addition, the frontal-basal ganglia pathways in the adolescents with IA showed little effective connection between areas and increased degrees of response inhibition [ 25 ].

https://doi.org/10.1371/journal.pmen.0000022.g003

Lin et al. (2015) found that adolescents with IA demonstrated disrupted corticostriatal FC compared to controls [ 33 ]. The corticostriatal circuitry experienced decreased connectivity with the caudate, bilateral anterior cingulate cortex (ACC), as well as the striatum and frontal gyrus [ 33 ]. The inferior ventral striatum showed significantly reduced FC with the subcallosal ACC and caudate head with cluster size of 101 (t = -4.64, p<0.05) [ 33 ]. Decreased FC in the caudate implies dysfunction of the corticostriatal-limbic circuitry involved in cognitive and emotional control [ 36 ]. The decrease in FC in both the striatum and frontal gyrus is related to inhibitory control, a common deficit seen with disruptions with the ECN [ 33 ].

The dorsolateral prefrontal cortex (DLPFC), ACC, and right supplementary motor area (SMA) of the prefrontal cortex were all found to have significantly decreased grey matter volume [ 29 ]. In addition, the DLPFC, insula, temporal cortices, as well as significant subcortical regions like the striatum and thalamus, showed decreased FC [ 29 ]. According to Tremblay (2009), the striatum plays a significant role in the processing of rewards, decision-making, and motivation [ 37 ]. Chen et al. (2020) reported that the IA group demonstrated increased impulsivity as well as decreased reaction inhibition using a Stroop colour-word task [ 26 ]. Furthermore, Chen et al. (2020) observed that the left DLPFC and dorsal striatum experienced a negative connection efficiency value, specifically demonstrating that the dorsal striatum activity suppressed the left DLPFC [ 27 ].

Salience network (SN).

Out of the 12 chosen studies, 3 studies specifically looked at the salience network (SN) and 3 studies have observed whole-brain FC. Relative to the DMN and ECN, the findings on the SN were slightly sparser. Despite this, adolescents with IA demonstrated a moderate decrease in FC, as well as other measures like fibre connectivity and cognitive control, when compared to healthy control (see Table 2 and Fig 4 ).

https://doi.org/10.1371/journal.pmen.0000022.g004

Xing et al. (2014) used both dorsal anterior cingulate cortex (dACC) and insula to test FC changes in the SN of adolescents with IA and found decreased structural connectivity in the SN as well as decreased fractional anisotropy (FA) that correlated to behaviour performance in the Stroop colour word-task [ 21 ]. They examined the dACC and insula to determine whether the SN’s disrupted connectivity may be linked to the SN’s disruption of regulation, which would explain the impaired cognitive control seen in adolescents with IA. However, researchers did not find significant FC differences in the SN when compared to the controls [ 21 ]. These results provided evidence for the structural changes in the interconnectivity within SN in adolescents with IA.

Wang et al. (2017) investigated network interactions between the DMN, ECN, SN and reward pathway in IA subjects [ 24 ] (see Fig 5 ), and found 40% reduction of FC between the DMN and specific regions of the SN, such as the insula, in comparison to the controls (p = 0.008) [ 24 ]. The anterior insula and dACC are two areas that are impacted by this altered FC [ 24 ]. This finding supports the idea that IA has similar neurobiological abnormalities with other addictive illnesses, which is in line with a study that discovered disruptive changes in the SN and DMN’s interaction in cocaine addiction [ 38 ]. The insula has also been linked to the intensity of symptoms and has been implicated in the development of IA [ 39 ].

“+” indicates an increase in behaivour; “-”indicates a decrease in behaviour; solid arrows indicate a direct network interaction; and the dotted arrows indicates a reduction in network interaction. This diagram depicts network interactions juxtaposed with engaging in internet related behaviours. Through the neural interactions, the diagram illustrates how the networks inhibit or amplify internet usage and vice versa. Furthermore, it demonstrates how the SN mediates both the DMN and ECN.

https://doi.org/10.1371/journal.pmen.0000022.g005

(2) How is adolescent behaviour and development impacted by functional connectivity changes due to internet addiction?

The findings that IA individuals demonstrate an overall decrease in FC in the DMN is supported by numerous research [ 24 ]. Drug addict populations also exhibited similar decline in FC in the DMN [ 40 ]. The disruption of attentional orientation and self-referential processing for both substance and behavioural addiction was then hypothesised to be caused by DMN anomalies in FC [ 41 ].

In adolescents with IA, decline of FC in the parietal lobule affects visuospatial task-related behaviour [ 22 ], short-term memory [ 42 ], and the ability of controlling attention or restraining motor responses during response inhibition tests [ 42 ]. Cue-induced gaming cravings are influenced by the DMN [ 43 ]. A visual processing area called the praecuneus links gaming cues to internal information [ 22 ]. A meta-analysis found that the posterior cingulate cortex activity of individuals with IA during cue-reactivity tasks was connected with their gaming time [ 44 ], suggesting that excessive gaming may impair DMN function and that individuals with IA exert more cognitive effort to control it. Findings for the behavioural consequences of FC changes in the DMN illustrate its underlying role in regulating impulsivity, self-monitoring, and cognitive control.

Furthermore, Ding et al. (2013) reported an activation of components of the reward pathway, including areas like the nucleus accumbens, praecuneus, SMA, caudate, and thalamus, in connection to the DMN [ 22 ]. The increased FC of the limbic and reward networks have been confirmed to be a major biomarker for IA [ 45 , 46 ]. The increased reinforcement in these networks increases the strength of reward stimuli and makes it more difficult for other networks, namely the ECN, to down-regulate the increased attention [ 29 ] (See Fig 5 ).

Executive control network (ECN).

The numerous IA-affected components in the ECN have a role in a variety of behaviours that are connected to both response inhibition and emotional regulation [ 47 ]. For instance, brain regions like the striatum, which are linked to impulsivity and the reward system, are heavily involved in the act of playing online games [ 47 ]. Online game play activates the striatum, which suppresses the left DLPFC in ECN [ 48 ]. As a result, people with IA may find it difficult to control their want to play online games [ 48 ]. This system thus causes impulsive and protracted gaming conduct, lack of inhibitory control leading to the continued use of internet in an overt manner despite a variety of negative effects, personal distress, and signs of psychological dependence [ 33 ] (See Fig 5 ).

Wang et al. (2017) report that disruptions in cognitive control networks within the ECN are frequently linked to characteristics of substance addiction [ 24 ]. With samples that were addicted to heroin and cocaine, previous studies discovered abnormal FC in the ECN and the PFC [ 49 ]. Electronic gaming is known to promote striatal dopamine release, similar to drug addiction [ 50 ]. According to Drgonova and Walther (2016), it is hypothesised that dopamine could stimulate the reward system of the striatum in the brain, leading to a loss of impulse control and a failure of prefrontal lobe executive inhibitory control [ 51 ]. In the end, IA’s resemblance to drug use disorders may point to vital biomarkers or underlying mechanisms that explain how cognitive control and impulsive behaviour are related.

A task-related fMRI study found that the decrease in FC between the left DLPFC and dorsal striatum was congruent with an increase in impulsivity in adolescents with IA [ 26 ]. The lack of response inhibition from the ECN results in a loss of control over internet usage and a reduced capacity to display goal-directed behaviour [ 33 ]. Previous studies have linked the alteration of the ECN in IA with higher cue reactivity and impaired ability to self-regulate internet specific stimuli [ 52 ].

Salience network (SN)/ other networks.

Xing et al. (2014) investigated the significance of the SN regarding cognitive control in teenagers with IA [ 21 ]. The SN, which is composed of the ACC and insula, has been demonstrated to control dynamic changes in other networks to modify cognitive performance [ 21 ]. The ACC is engaged in conflict monitoring and cognitive control, according to previous neuroimaging research [ 53 ]. The insula is a region that integrates interoceptive states into conscious feelings [ 54 ]. The results from Xing et al. (2014) showed declines in the SN regarding its structural connectivity and fractional anisotropy, even though they did not observe any appreciable change in FC in the IA participants [ 21 ]. Due to the small sample size, the results may have indicated that FC methods are not sensitive enough to detect the significant functional changes [ 21 ]. However, task performance behaviours associated with impaired cognitive control in adolescents with IA were correlated with these findings [ 21 ]. Our comprehension of the SN’s broader function in IA can be enhanced by this relationship.

Research study supports the idea that different psychological issues are caused by the functional reorganisation of expansive brain networks, such that strong association between SN and DMN may provide neurological underpinnings at the system level for the uncontrollable character of internet-using behaviours [ 24 ]. In the study by Wang et al. (2017), the decreased interconnectivity between the SN and DMN, comprising regions such the DLPFC and the insula, suggests that adolescents with IA may struggle to effectively inhibit DMN activity during internally focused processing, leading to poorly managed desires or preoccupations to use the internet [ 24 ] (See Fig 5 ). Subsequently, this may cause a failure to inhibit DMN activity as well as a restriction of ECN functionality [ 55 ]. As a result, the adolescent experiences an increased salience and sensitivity towards internet addicting cues making it difficult to avoid these triggers [ 56 ].

The primary aim of this review was to present a summary of how internet addiction impacts on the functional connectivity of adolescent brain. Subsequently, the influence of IA on the adolescent brain was compartmentalised into three sections: alterations of FC at various brain regions, specific FC relationships, and behavioural/developmental changes. Overall, the specific effects of IA on the adolescent brain were not completely clear, given the variety of FC changes. However, there were overarching behavioural, network and developmental trends that were supported that provided insight on adolescent development.

The first hypothesis that was held about this question was that IA was widespread and would be regionally similar to substance-use and gambling addiction. After conducting a review of the information in the chosen articles, the hypothesis was predictably supported. The regions of the brain affected by IA are widespread and influence multiple networks, mainly DMN, ECN, SN and reward pathway. In the DMN, there was a complex mix of increases and decreases within the network. However, in the ECN, the alterations of FC were more unilaterally decreased, but the findings of SN and reward pathway were not quite clear. Overall, the FC changes within adolescents with IA are very much network specific and lay a solid foundation from which to understand the subsequent behaviour changes that arise from the disorder.

The second hypothesis placed emphasis on the importance of between network interactions and within network interactions in the continuation of IA and the development of its behavioural symptoms. The results from the findings involving the networks, DMN, SN, ECN and reward system, support this hypothesis (see Fig 5 ). Studies confirm the influence of all these neural networks on reward valuation, impulsivity, salience to stimuli, cue reactivity and other changes that alter behaviour towards the internet use. Many of these changes are connected to the inherent nature of the adolescent brain.

There are multiple explanations that underlie the vulnerability of the adolescent brain towards IA related urges. Several of them have to do with the inherent nature and underlying mechanisms of the adolescent brain. Children’s emotional, social, and cognitive capacities grow exponentially during childhood and adolescence [ 57 ]. Early teenagers go through a process called “social reorientation” that is characterised by heightened sensitivity to social cues and peer connections [ 58 ]. Adolescents’ improvements in their social skills coincide with changes in their brains’ anatomical and functional organisation [ 59 ]. Functional hubs exhibit growing connectivity strength [ 60 ], suggesting increased functional integration during development. During this time, the brain’s functional networks change from an anatomically dominant structure to a scattered architecture [ 60 ].

The adolescent brain is very responsive to synaptic reorganisation and experience cues [ 61 ]. As a result, one of the distinguishing traits of the maturation of adolescent brains is the variation in neural network trajectory [ 62 ]. Important weaknesses of the adolescent brain that may explain the neurobiological change brought on by external stimuli are illustrated by features like the functional gaps between networks and the inadequate segregation of networks [ 62 ].

The implications of these findings towards adolescent behaviour are significant. Although the exact changes and mechanisms are not fully clear, the observed changes in functional connectivity have the capacity of influencing several aspects of adolescent development. For example, functional connectivity has been utilised to investigate attachment styles in adolescents [ 63 ]. It was observed that adolescent attachment styles were negatively associated with caudate-prefrontal connectivity, but positively with the putamen-visual area connectivity [ 63 ]. Both named areas were also influenced by the onset of internet addiction, possibly providing a connection between the two. Another study associated neighbourhood/socioeconomic disadvantage with functional connectivity alterations in the DMN and dorsal attention network [ 64 ]. The study also found multivariate brain behaviour relationships between the altered/disadvantaged functional connectivity and mental health and cognition [ 64 ]. This conclusion supports the notion that the functional connectivity alterations observed in IA are associated with specific adolescent behaviours as well as the fact that functional connectivity can be utilised as a platform onto which to compare various neurologic conditions.

Limitations/strengths

There were several limitations that were related to the conduction of the review as well as the data extracted from the articles. Firstly, the study followed a systematic literature review design when analysing the fMRI studies. The data pulled from these imaging studies were namely qualitative and were subject to bias contrasting the quantitative nature of statistical analysis. Components of the study, such as sample sizes, effect sizes, and demographics were not weighted or controlled. The second limitation brought up by a similar review was the lack of a universal consensus of terminology given IA [ 47 ]. Globally, authors writing about this topic use an array of terminology including online gaming addiction, internet addiction, internet gaming disorder, and problematic internet use. Often, authors use multiple terms interchangeably which makes it difficult to depict the subtle similarities and differences between the terms.

Reviewing the explicit limitations in each of the included studies, two major limitations were brought up in many of the articles. One was relating to the cross-sectional nature of the included studies. Due to the inherent qualities of a cross-sectional study, the studies did not provide clear evidence that IA played a causal role towards the development of the adolescent brain. While several biopsychosocial factors mediate these interactions, task-based measures that combine executive functions with imaging results reinforce the assumed connection between the two that is utilised by the papers studying IA. Another limitation regarded the small sample size of the included studies, which averaged to around 20 participants. The small sample size can influence the generalisation of the results as well as the effectiveness of statistical analyses. Ultimately, both included study specific limitations illustrate the need for future studies to clarify the causal relationship between the alterations of FC and the development of IA.

Another vital limitation was the limited number of studies applying imaging techniques for investigations on IA in adolescents were a uniformly Far East collection of studies. The reason for this was because the studies included in this review were the only fMRI studies that were found that adhered to the strict adolescent age restriction. The adolescent age range given by the WHO (10–19 years old) [ 65 ] was strictly followed. It is important to note that a multitude of studies found in the initial search utilised an older adolescent demographic that was slightly higher than the WHO age range and had a mean age that was outside of the limitations. As a result, the results of this review are biased and based on the 12 studies that met the inclusion and exclusion criteria.

Regarding the global nature of the research, although the journals that the studies were published in were all established western journals, the collection of studies were found to all originate from Asian countries, namely China and Korea. Subsequently, it pulls into question if the results and measures from these studies are generalisable towards a western population. As stated previously, Asian countries have a higher prevalence of IA, which may be the reasoning to why the majority of studies are from there [ 8 ]. However, in an additional search including other age groups, it was found that a high majority of all FC studies on IA were done in Asian countries. Interestingly, western papers studying fMRI FC were primarily focused on gambling and substance-use addiction disorders. The western papers on IA were less focused on fMRI FC but more on other components of IA such as sleep, game-genre, and other non-imaging related factors. This demonstrated an overall lack of western fMRI studies on IA. It is important to note that both western and eastern fMRI studies on IA presented an overall lack on children and adolescents in general.

Despite the several limitations, this review provided a clear reflection on the state of the data. The strengths of the review include the strict inclusion/exclusion criteria that filtered through studies and only included ones that contained a purely adolescent sample. As a result, the information presented in this review was specific to the review’s aims. Given the sparse nature of adolescent specific fMRI studies on the FC changes in IA, this review successfully provided a much-needed niche representation of adolescent specific results. Furthermore, the review provided a thorough functional explanation of the DMN, ECN, SN and reward pathway making it accessible to readers new to the topic.

Future directions and implications

Through the search process of the review, there were more imaging studies focused on older adolescence and adulthood. Furthermore, finding a review that covered a strictly adolescent population, focused on FC changes, and was specifically depicting IA, was proven difficult. Many related reviews, such as Tereshchenko and Kasparov (2019), looked at risk factors related to the biopsychosocial model, but did not tackle specific alterations in specific structural or functional changes in the brain [ 66 ]. Weinstein (2017) found similar structural and functional results as well as the role IA has in altering response inhibition and reward valuation in adolescents with IA [ 47 ]. Overall, the accumulated findings only paint an emerging pattern which aligns with similar substance-use and gambling disorders. Future studies require more specificity in depicting the interactions between neural networks, as well as more literature on adolescent and comorbid populations. One future field of interest is the incorporation of more task-based fMRI data. Advances in resting-state fMRI methods have yet to be reflected or confirmed in task-based fMRI methods [ 62 ]. Due to the fact that network connectivity is shaped by different tasks, it is critical to confirm that the findings of the resting state fMRI studies also apply to the task based ones [ 62 ]. Subsequently, work in this area will confirm if intrinsic connectivity networks function in resting state will function similarly during goal directed behaviour [ 62 ]. An elevated focus on adolescent populations as well as task-based fMRI methodology will help uncover to what extent adolescent network connectivity maturation facilitates behavioural and cognitive development [ 62 ].

A treatment implication is the potential usage of bupropion for the treatment of IA. Bupropion has been previously used to treat patients with gambling disorder and has been effective in decreasing overall gambling behaviour as well as money spent while gambling [ 67 ]. Bae et al. (2018) found a decrease in clinical symptoms of IA in line with a 12-week bupropion treatment [ 31 ]. The study found that bupropion altered the FC of both the DMN and ECN which in turn decreased impulsivity and attentional deficits for the individuals with IA [ 31 ]. Interventions like bupropion illustrate the importance of understanding the fundamental mechanisms that underlie disorders like IA.

The goal for this review was to summarise the current literature on functional connectivity changes in adolescents with internet addiction. The findings answered the primary research questions that were directed at FC alterations within several networks of the adolescent brain and how that influenced their behaviour and development. Overall, the research demonstrated several wide-ranging effects that influenced the DMN, SN, ECN, and reward centres. Additionally, the findings gave ground to important details such as the maturation of the adolescent brain, the high prevalence of Asian originated studies, and the importance of task-based studies in this field. The process of making this review allowed for a thorough understanding IA and adolescent brain interactions.

Given the influx of technology and media in the lives and education of children and adolescents, an increase in prevalence and focus on internet related behavioural changes is imperative towards future children/adolescent mental health. Events such as COVID-19 act to expose the consequences of extended internet usage on the development and lifestyle of specifically young people. While it is important for parents and older generations to be wary of these changes, it is important for them to develop a base understanding of the issue and not dismiss it as an all-bad or all-good scenario. Future research on IA will aim to better understand the causal relationship between IA and psychological symptoms that coincide with it. The current literature regarding functional connectivity changes in adolescents is limited and requires future studies to test with larger sample sizes, comorbid populations, and populations outside Far East Asia.

This review aimed to demonstrate the inner workings of how IA alters the connection between the primary behavioural networks in the adolescent brain. Predictably, the present answers merely paint an unfinished picture that does not necessarily depict internet usage as overwhelmingly positive or negative. Alternatively, the research points towards emerging patterns that can direct individuals on the consequences of certain variables or risk factors. A clearer depiction of the mechanisms of IA would allow physicians to screen and treat the onset of IA more effectively. Clinically, this could be in the form of more streamlined and accurate sessions of CBT or family therapy, targeting key symptoms of IA. Alternatively clinicians could potentially prescribe treatment such as bupropion to target FC in certain regions of the brain. Furthermore, parental education on IA is another possible avenue of prevention from a public health standpoint. Parents who are aware of the early signs and onset of IA will more effectively handle screen time, impulsivity, and minimize the risk factors surrounding IA.

Additionally, an increased attention towards internet related fMRI research is needed in the West, as mentioned previously. Despite cultural differences, Western countries may hold similarities to the eastern countries with a high prevalence of IA, like China and Korea, regarding the implications of the internet and IA. The increasing influence of the internet on the world may contribute to an overall increase in the global prevalence of IA. Nonetheless, the high saturation of eastern studies in this field should be replicated with a Western sample to determine if the same FC alterations occur. A growing interest in internet related research and education within the West will hopefully lead to the knowledge of healthier internet habits and coping strategies among parents with children and adolescents. Furthermore, IA research has the potential to become a crucial proxy for which to study adolescent brain maturation and development.

Supporting information

S1 checklist. prisma checklist..

https://doi.org/10.1371/journal.pmen.0000022.s001

S1 Appendix. Search strategies with all the terms.

https://doi.org/10.1371/journal.pmen.0000022.s002

S1 Data. Article screening records with details of categorized content.

https://doi.org/10.1371/journal.pmen.0000022.s003

Acknowledgments

The authors thank https://www.stockio.com/free-clipart/brain-01 (with attribution to Stockio.com); and https://www.rawpixel.com/image/6442258/png-sticker-vintage for the free images used to create Figs 2 – 4 .

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ORIGINAL RESEARCH article

Anxiety predicts internet addiction, which predicts depression among male college students: a cross-lagged comparison by sex.

• School of Psychology, Chengdu Medical College, Chengdu, China

Objectives: Internet addiction has become an increasingly serious public health issue, putting young people at particular risk of psychological harm. This study aimed to analyze the interactions between college students’ depression, anxiety, and Internet addiction and explore how these interactions differ between men and women.

Methods: A 6-month follow-up study was conducted on 234 college students using the Self-Rating Depression Scale, Self-rating Anxiety Scale, and Revised Chen Internet Addiction Scale.

Results: Depression, anxiety, and Internet addiction were positively correlated ( p  < 0.01). Anxiety can predict Internet addiction and that Internet addiction can predict depression. Moreover, anxiety had a significant predictive effect on Internet addiction among men.

Conclusion: Anxiety predicts Internet addiction, and Internet addiction predicts depression among male college students. These findings may better inform future Internet addiction intervention strategies. Particularly, interventions may better address Internet addiction by focusing on the role of anxiety, especially among men.

1. Introduction

With urbanization, the Internet has become increasingly convenient, cheap, and rife with addictive content ( Ko et al., 2022 ). Moreover, as Internet use has increased rapidly worldwide, Internet addiction has become a serious public health problem for all groups ( Hassan et al., 2020 ). Internet addiction is an impulse control disorder in which excessive Internet use results in the neglect of real-life relationships, work, and normal daily life ( Young, 1998 , 2004 , 2007 ).

Internet addiction has been found to be in co-morbidity with other psychological symptoms and psychiatric disorders ( Otsuka et al., 2020 ), Internet addiction has been found to be associated with depression ( Lau et al., 2018 ) and anxiety symptoms ( Cai et al., 2021 ), insomnia ( Goel et al., 2021 ), academic failure ( Kuo et al., 2018 ), interpersonal withdrawal ( Kato et al., 2020 ), and aggressive behavior ( Zhao et al., 2022 ). Internet addiction and poor mental health status each increased the risk of onset of the other ( Otsuka et al., 2020 ).

Currently, there is a high detection rate of internet addiction among college students ( Al Shawi et al., 2021 ), and the impact of Internet addiction is particularly significant for college students ( Shen et al., 2020 ), as they are still undergoing psychosocial development. Baturay and Toker, (2019) reported that Internet addiction decreases college students’ self-esteem, self-confidence, social self-efficacy, academic self-efficacy and triggers loneliness.

Compared with other maladaptive problems, Internet addiction is strongly correlated with anxiety and depression ( Li et al., 2019 ; Andrade et al., 2020 ). Emotional problems, of which depression and anxiety are the most common ( Tsai et al., 2020 ), and the comorbidity rate is high ( Zeng et al., 2019 ), can mediate other psychological and behavioral problems ( Warren et al., 2021 ); moreover, difficulty in emotional regulation can predict subsequent Internet addiction ( Effatpanah et al., 2020 ). Among college students, emotional problems are more common, particularly depression and anxiety ( Ramón-Arbués et al., 2020 ).

The influence of depression and anxiety on Internet addiction has been demonstrated in many previous studies ( Christ et al., 2020 ; Sayed et al., 2022 ). Evren et al. (2019) found that the severity of Internet addiction relates to the levels of depression and anxiety. Depression is also more common among Internet addicts and over-users ( Kim et al., 2016 ; Tan et al., 2016 ), and depressive symptoms have the highest predictive ability for Internet addiction ( Przepiorka et al., 2019 ; Diotaiuti et al., 2022a ). Furthermore, Karaer and Akdemir (2019) found that greater anxiety is an important predictor of Internet addiction, and Internet addiction is related to an increase in anxiety ( Shen et al., 2020 ; Gao et al., 2021 ). Morita et al. (2022) conducted a three-year longitudinal study and found a two-way relationship between Internet addiction and depressive symptoms.

Internet addiction differs by sex ( Liang et al., 2016 ). Men are more prone to Internet addiction than women ( Chi et al., 2020 ). Researchers have identified that men showed higher levels of Internet addiction, this is related to men being more dependent, more impulsive and more interdependent ( Diotaiuti et al., 2022b ). A follow-up survey of 1,715 adolescents showed that depressive symptoms had a more significant predictive effect on Internet addiction among male adolescents, indicating that depression can lead to Internet addiction. Conversely, among female adolescents, Internet addiction can significantly predict subsequent depression, indicating that Internet addiction can lead to depression ( Yi and Li, 2021 ).

Some studies have found that women with Internet addiction are more likely to have depressive symptoms ( Li et al., 2019 ), whereas men with Internet addiction are more likely to have anxiety symptoms ( Shan et al., 2021 ). These results show that the relationship between Internet addiction and depression varies by sex.

Many previous studies have investigated Internet addiction, but the main focus has been the bivariate study of Internet addiction and other factors rather than the relationship between depression, anxiety, and Internet addiction. Moreover, previous studies on the relationship between depression, anxiety, and Internet addiction were mostly cross-sectional; longitudinal studies have been relatively few, and there has been a lack of research on the long-term mechanism of depression, anxiety, and Internet addiction. Furthermore, to date, few studies have focused on sex-related differences in depression, anxiety, and Internet addiction. Therefore, this study adopted a longitudinal approach to explore the mutual influence and dynamic relationship between depression, anxiety, and Internet addiction in college students. In this exploration, this study aimed to clarify the relationship between Internet addiction, depression, and anxiety as well as clarify the mechanisms of Internet addiction itself.

Therefore, the purpose of this study is to explore the mutual influence and dynamic relationship between depression, anxiety, and Internet addiction in college students. Based on the findings of our literature review, we arrived at the following hypotheses: Hypothesis 1. There was a sex difference in Internet addiction. Hypothesis 2. Depression, and anxiety were positively associated with Internet addiction. Hypothesis 3. Depression and anxiety significantly predicted subsequent Internet addiction. Hypothesis 4. Internet addiction significantly predicted subsequent depression and anxiety.

2.1. Participants

This study used convenience sampling to select college students from a college in Sichuan Province for a follow-up study. In this study, half of the classes with psychological commissioners were randomly selected by using the psychological commissioners system of the college, and the sampling of this study was completed by issuing and retrieving questionnaires from psychological commissioners. Moreover, they were assured anonymity and provided their written informed consent to participate in this study. Students completed questionnaires at three time points: June, September, and December 2021. Data were collected in the classroom every 3 months through a paper and pencil test. Each participant had a unique ID and used the same ID in all three waves. There were 443 participants in the first wave, 281 in the second wave, and 243 in the third wave. The 243 students (baseline age 19.74 ± 0.94 years) who participated in all three waves were included in this study, and they are the data analysis objects of this study; The final sample included 90 men (baseline age 19.88 ± 1.90 years) and 153 women (baseline age 19.65 ± 0.83 years). There was no difference in the average age by sex ( t  = 1.69, p  = 0.09). All procedures performed in this study involving human participants were in accordance with the ethical standards of the ethics committee of research institutions and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Additionally, participants provided informed consent.

2.2. Measures

2.2.1. internet addiction.

The Revised Chen Internet Addiction Scale (CIAS-R) was used to measure Internet addiction. CIAS-R was compiled by Ko et al. (2005) , based on the DSM-IV diagnostic criteria for various addictions, clinical case observations, and interview results. Including tolerance, withdrawal symptoms, time management, compulsive Internet access and interpersonal and health, there are 26 items in total. The scale divided into two subscales: Core Symptoms of Internet Addiction and Related Problems of Internet Addiction. The symptoms of Internet addiction include Internet addiction tolerance, compulsive Internet use, and Internet implicit withdrawal reaction. The problems related to Internet addiction include time management, interpersonal, and health issues. CIAS-R is scored using a 4-point Likert scale (1 = extremely inconsistent to 4 = very consistent), with higher total scores indicating higher Internet addiction tendency. Referring to the demarcation criteria of Ko et al. (2005) and other scholars, a scale score of 64 or above was defined as Internet addiction ( Ko et al., 2005 ). The internal consistency coefficient of the entire scale was 0.93, and those of the core symptoms and related problems subscales were 0.90 and 0.88, respectively, showing good overall reliability and validity ( Lin et al., 2011 ).

2.2.2. Depression

The Self-Rating Depression Scale (SDS; Zung et al., 1965 ) was used to measure depression. The internal consistency coefficient was 0.84, Pearson correlation coefficient was 0.778, and Spearman rank correlation coefficient was 0.783 for this measure. It contains 20 items and is scored on a 4-point scale where 1 = no or little time, 2 = sometimes, 3 = most of the time, and 4 = most or all of the time. Among the 20 items, 10 items are reverse-scored. The total score is calculated by adding the scores for the 20 items. The standard score is obtained by multiplying the total score by 1.25, and an index <50 indicated no depression; 50–59 indicated mild depression; 60–69 indicated moderate to severe depression; and ≥ 70 indicated severe depression. In China, an SDS standard score ≥ 50 is regarded as having depressive symptoms.

2.2.3. Anxiety

The Self-rating Anxiety Scale ( Zung, 1971 ) was used to measure anxiety. The 20-item scale had a Cronbach’s α of 0.767, Spearman Brown split half reliability coefficient of 0.724, and Guttman split half reliability coefficient of 0.720. Overall reliability was acceptable. The scoring method is similar to that of Zung et al. (1965) SDS Scale; five of the 20 items are reverse-scored. The standard score was obtained by multiplying the total score by 1.25, with higher scores indicating higher anxiety levels. According to the standard score, anxiety level was classified as follows: < 50 points, no anxiety; 50–59 points, mild anxiety; 60–70 points, moderate anxiety; and ≥ 70 points, severe anxiety.

2.3. Statistical analysis

SPSS 22.0 was used for data entry and management, and SPSS 22.0 and Amos 22.0 were used for data entry and management and statistical analysis. Statistical significance was set at P <0.05. Analyses included descriptive statistics, t -tests, F -tests, Pearson’s correlation analyses, and cross-lagged analyses.

3.1. Characteristics of participants

Baseline descriptive statistics of demographics, depression, anxiety and internet addiction are shown in Table 1 . The baseline participants included 90 men and 153 women, and 243 in total (19.65 ± 0.83 years).

Table 1 . Baseline descriptive statistics of demographics, depression, anxiety, and internet addiction.

3.2. Analysis of sex differences by variable

The average scores of male and female students at the three time periods and the t-test results of independent samples of depression, anxiety, and Internet addiction among male and female college students are shown in Table 2 . The results showed that depression (T1, T2, and T3) and anxiety (T1, T2, and T3) differed significantly by sex, whereas Internet addiction (T1, T2, and T3) did not.

Table 2 . Analysis of sex differences in depression, anxiety, and Internet addiction.

3.3. Correlation analysis between variables

The results showed an effect of group ( Table 3 ). In the overall group, depression, anxiety, and Internet addiction were positively correlated, among which T1 anxiety, T1 depression, T2 anxiety, and T2 depression had the highest correlation coefficients. Among women, a positive correlation between depression, anxiety, and Internet addiction were found at all three time points, and the correlation coefficients between T2 Internet addiction and T1 Internet addiction, T1 anxiety, and T1 depression were higher. Among men, depression, anxiety, and Internet addiction were positively correlated at all time points, and the correlations between T1 anxiety and T1 depression, T2 anxiety, and T2 depression were higher than that among women.

Table 3 . Correlation analysis between depression, anxiety, and Internet addiction in the general, male, and female groups.

3.4. Variance analysis test of variables at different time points

Table 4 shows the results of the variance analysis of variables at different time points. The results showed an effect of group; the mean values of Internet addiction ( F  = 20.96, p  < 0.001) and anxiety ( F  = 51.29, p  < 0.001) at the three time points were significantly different; however, the mean value of depression at the three time points was not significantly different ( F  = 0.27, p  = 0.77).

Table 4 . ANOVA test of variables at different time points.

3.5. Cross-lagged analyses

The theoretical model of cross-lagged analysis was first constructed in this study in accordance with the previous literature ( Figure 1 ; Krossbakken et al., 2018 ; Teng et al., 2021 ). After running each group (i.e., total, male, and female) combined with the theoretical composition and hint of correction coefficient, the final model diagram with good fit was obtained ( Figures 2 – 4 ).

Figure 1 . Cross-lagged theoretical model of depression, anxiety, and Internet addiction.

Figure 2 . Cross-lagged analysis of depression, anxiety, and Internet addiction. T1 is the baseline data and T2 and T3 are follow-up data. The solid line indicates statistical significance.

Figure 3 . Cross-lagged analysis of depression, anxiety, and Internet addiction among men. T1 is the baseline data and T2 and T3 are follow-up data. The solid line indicates statistical significance.

Figure 4 . Cross-lagged analysis of depression, anxiety, and Internet addiction among women. T1 is the baseline data and T2 and T3 are follow-up data. The solid line indicates statistical significance.

3.5.1. Cross-lagged analysis of depression, anxiety, and internet addiction in the overall group

The cross-lagged model was used to test the relationship between depression, anxiety, and Internet addiction, measured at each time point. The model fit indices were good: CMIN = 37.692, CMIN/DF = 2.899, GFI = 0.969, AGFI = 0.891, CFI = 0.975, RMSEA = 0.089. As shown in Figure 1 , T1 depression could positively predict T2 anxiety; T1 anxiety was a positive predictor of T2 depression; T1 Internet addiction could not predict T2 depression and T2 anxiety; T2 depression could positively predict T3 anxiety and T3 Internet addiction; T2 anxiety was a predictor of T3 depression and T3 Internet addiction; and T2 Internet addiction was a predictor of T1 depression but could not predict T3 anxiety.

3.5.2. Cross-lagged analysis of depression, anxiety, and internet addiction among men

The cross-lagged model was used to test the relationship between depression, anxiety, and Internet addiction among men, measured at three time points. The model fit indices were good: CMIN = 21.530, CMIN/DF = 1.435, GFI = 0.952, AGFI = 0.855, CFI = 0.978, and RMSEA = 0.070. As shown in Figure 2 , T1 anxiety can positively predict T2 depression but cannot predict T2 Internet addiction; T1 Internet addiction could not predict T2 depression or T2 anxiety; T1 depression could not predict T2 anxiety or T2 Internet addiction; T2 anxiety was a positive predictor of T3 depression and T3 Internet addiction; T2 depression could not predict T3 anxiety or T3 Internet addiction; and T3 Internet addiction could not predict T3 depression or T3 Internet addiction.

3.5.3. Cross-lagged analysis of depression, anxiety, and internet addiction among women

The cross-lagged model was used to test the relationship between depression, anxiety, and Internet addiction among women, measured at three time points. The model fit indices were good: CMIN = 20.680, CMIN/DF = 1.477, GFI = 0.972, AGFI = 0.908, CFI = 0.990, RMSEA = 0.056. As shown in Figure 4 , T1 anxiety could positively predict T2 depression but not T2 Internet addiction; T1 Internet addiction could not predict T2 depression or T2 anxiety; T1 depression could not predict T2 anxiety or T2 Internet addiction; T2 anxiety was a positive predictor of T3 depression but could not predict T3 Internet addiction; T2 depression could not predict T3 anxiety or T3 Internet addiction; and T3 Internet addiction could not predict T3 depression or T3 Internet addiction.

4. Discussion

In recent years, Internet addiction among college students has been widely studied. Researchers have proposed a close relationship between Internet addiction and college students’ emotional problems. The descriptive statistics show the high detection rate of internet addiction among college students. This is related to that they are the main group of network users ( Nagaur, 2020 ). Because most students live in the dormitory of the school, they are far away from their families, relatives and friends, so they spend more time on online entertainment and communication ( Baturay and Toker, 2019 ). Moreover, they also need to complete most learning tasks through the network.

The correlation analysis of this study show that depression, anxiety, and Internet addiction are positively correlated, which is consistent with previous studies ( Mak et al., 2018 ; Geng et al., 2021 ). These results indicate an internal relationship among the three, providing further support for their longitudinal relationship.

Gender differences can be found in many addictive behaviors and their related factors, including Internet addiction. The present study show that men are more prone to Internet addiction than women. This difference might be owing to the different ways men and women use the Internet: men may focus more on online games, whereas women may focus more on online shopping, novel reading, and interpersonal communication ( Liang et al., 2016 ). Both men and women can experience Internet addiction, but there are differences in their manner and purpose of Internet use as well as the content of their subsequent addictions, and the way men surf the Internet contains more addictive content. In previous research by this research group, it was also found that more of men’ online behaviors may be concentrated in online games, and more of women’s online behaviors may be concentrated in online shopping, novel reading, and interpersonal communication (detailed data can be obtained from the author).

This study used a half a year longitudinal design to gain insights into the role of gender in the association between depression, anxiety and Internet addiction. We found that the causal relationship between Internet addiction and anxiety varies by sex. Moreover, the causal relationship between Internet addiction and depression also varies by sex.

For male college students, Internet addiction can significantly predict the occurrence of later depression, but depression does not significantly predict Internet addiction. These results support the Internet addiction leads to depression in males. Some evidence suggests the gender differences in depression that women are more likely to have depressive symptoms than men ( Lin et al., 2021 ). Several theorists have suggested that gendered processes of socialization affect how some boys and men express depression (Swetlitz.2021). In contrast, in this study, for males, Internet addiction is a risk factor for depression ( Diotaiuti et al., 2022a ). According to the previous studies, excessive Internet use has a negative influence on real-world social interactions, including reducing the scale of social circle ( Mak et al., 2018 ). Some studies have found that Internet addicted men have less social contact in the offline world, which usually leads to depression ( Paudel et al., 2021 ). Moreover, anxiety significantly predicts Internet addiction among male college students, this is consistent with previous follow-up research results ( Tsai et al., 2020 ). This suggests that anxiety is a risk factor for Internet addiction among male college students ( Shan et al., 2021 ). This is consistent with our research hypothesis.

For female college students, Internet addiction does not significantly predict the occurrence of later anxiety, and anxiety does not significantly predict the occurrence of later Internet addiction also. This is inconsistent with previous conclusions ( Kim et al., 2021 ). Moreover, there was no causal relationship between depression and Internet addiction, contrary to the results of a previous study ( Yi and Li, 2021 ). Depression was not a significant predictor of Internet addiction, and Internet addiction was not a predictor of depression. This is inconsistent with our research hypothesis.

The cross-lagged analysis indicated that anxiety was a predictor of depression among both men and women, but depression did not significantly predict anxiety; there was no two-way predictive relationship between anxiety and depression. According to the cross-lagged analysis, college students’ depression could predict anxiety in the overall group, indicating that individuals with high depression levels are more likely to face anxiety problems. Additionally, college students’ anxiety could predict depression, indicating that individuals with high anxiety levels are also more likely to experience subsequent depression. Depression and anxiety have a two-way predictive relationship. The results for the overall group show that anxiety can predict Internet addiction, which in turn could significantly predict depression, aligning with existing research ( Hsueh et al., 2019 ; Gao et al., 2021 ). When college students experience great psychological pressure and develop anxiety about their studies and lives, they will often use the Internet to escape pain, vent about their anxiety, and obtain temporary psychological satisfaction ( Javaeed et al., 2019 ). Internet use for short periods of time may temporarily relieve individuals’ anxiety, but long-term dependence on the Internet can cause more feelings of social disconnection and aggravate depression when facing real life ( Swetlitz, 2021 ).

Although this study has some achievements, some limitations must still be addressed. First, the sample was from a single university in Southwest China, which limits the universality of the results. Second, the span of the three time points was short; future research should use more time points over a longer period to gain detailed understanding of the interaction of variables over time. Third, this study only investigated the degree of Internet addiction and did not analyze the differences in Internet addiction behaviors, such as gaming and shopping. In the future, we will conduct a thorough analysis of different Internet addiction behaviors.

This study achieved some expected results through system tracking and cross-lagged analysis. The results indicate that to develop Internet addiction interventions, attention should be paid to anxious individuals’ frequency and duration of Internet use. Corresponding policies should be formulated to guide the use of diversified ways to alleviate anxiety. The results also suggest that prevention and intervention strategies for Internet addiction should be designed for different sexes. Special attention should be paid to men, and targeted strategies and methods should be provided to alleviate anxiety when intervening in Internet addiction.

5. Conclusion

A cross-lagged study was used to analyze the relationships among depression, anxiety, and Internet addiction at three time points. The results showed that anxiety was a predictor of Internet addiction, and that Internet addiction could significantly predict depression. The results also showed that the relationship between Internet addiction and anxiety varies by sex. Male anxiety had a significant predictive effect on Internet addiction.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

XX, HC, and ZC designed the study and drafted the manuscript. XX and HC analyzed the data and discussed the results. XX and ZC revised the manuscript. All authors contributed to the article and approved the submitted version.

Acknowledgments

We are grateful to the adolescents who participated and the research assistants who assisted with the data collection.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: depression, anxiety, internet addiction, sex, college students

Citation: Xie X, Cheng H and Chen Z (2023) Anxiety predicts internet addiction, which predicts depression among male college students: A cross-lagged comparison by sex. Front. Psychol . 13:1102066. doi: 10.3389/fpsyg.2022.1102066

Received: 18 November 2022; Accepted: 28 December 2022; Published: 16 January 2023.

Reviewed by:

Copyright © 2023 Xie, Cheng and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zi Chen, ✉ [email protected]

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• Research article
• Open access
• Published: 06 January 2021

Prevalence and associated factors of internet addiction among undergraduate university students in Ethiopia: a community university-based cross-sectional study

• Yosef Zenebe   ORCID: orcid.org/0000-0002-0138-6588 1 ,
• Kunuya Kunno 1 ,
• Meseret Mekonnen 1 ,
• Ajebush Bewuket 1 ,
• Mengesha Birkie 1 ,
• Mogesie Necho 1 ,
• Muhammed Seid 1 ,
• Million Tsegaw 1 &
• Baye Akele 2  

BMC Psychology volume  9 , Article number:  4 ( 2021 ) Cite this article

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Internet addiction is a common problem in university students and negatively affects cognitive functioning, leads to poor academic performance and engagement in hazardous activities, and may lead to anxiety and stress. Behavioral addictions operate on a modified principle of the classic addiction model. The problem is not well investigated in Ethiopia. So the present study aimed to assess the prevalence of internet addiction and associated factors among university students in Ethiopia.

Main objective of this study was to assess the prevalence and associated factors of internet addiction among University Students in Ethiopia.

A community-based cross-sectional study was conducted among Wollo University students from April 10 to May 10, 2019. A total of 603 students were participated in the study using a structured questionnaire. A multistage cluster sampling technique was used to recruit study participants. A binary logistic regression method was used to explore associated factors for internet addiction and variables with a p value < 0.25 in the bivariate analysis were fitted to the multi-variable logistic regression analysis. The strength of association between internet addiction and associated factors was assessed with odds ratio, 95% CI and p value < 0.05 in the final model was considered significant.

The prevalence of internet addiction (IA) among the current internet users was 85% (n = 466). Spending more time on the internet (adjusted odds ratio (AOR) = 10.13, 95% CI 1.33–77.00)), having mental distress (AOR = 2.69, 95% CI 1.02–7.06), playing online games (AOR = 2.40, 95% CI 1.38–4.18), current khat chewing (AOR = 3.34, 95% CI 1.14–9.83) and current alcohol use (AOR = 2.32, 95% CI 1.09–4.92) were associated with internet addiction.

Conclusions

The current study documents a high prevalence of internet addiction among Wollo University students. Factors associated with internet addiction were spending more time, having mental distress, playing online games, current khat chewing, and current alcohol use. As internet addiction becomes an evident public health problem, carrying out public awareness campaigns may be a fruitful strategy to decrease its prevalence and effect. Besides to this, a collaborative work among stakeholders is important to develop other trendy, adaptive, and sustainable countermeasures.

Peer Review reports

Globally, more than three billion people use the internet daily with young people being the most common users [ 1 ]. In the field of medicine and healthcare, it helps in the practice of evidence-based medicine, research and learning, access to medical and online databases, handling patients in remote areas, and academic and recreational purposes [ 2 , 3 ].

In terms of classical psychology and psychiatry, IA is a relatively new phenomenon. The literature uses interchangeable references such as “compulsive Internet use”, “problematic Internet use”, “pathological Internet use”, and “Internet addiction”. The Psychologist Mark Griffiths, one of the widely recognized authorities in the sphere of addictive behavior, is the author of the most frequently quoted definition: “Internet addiction is a non-chemical behavioral addiction, which involves human–machine (computer-Internet) interaction” [ 4 , 5 ]. Internet addiction is a behavioural problem that has gained increasing scientific recognition in the last decade, with some researchers claiming it is a "21st Century epidemic"[ 6 ]. The psychopathologic symptoms of internet addiction includes Salience(the respondent most likely feels preoccupied with the Internet, hides the behaviour from others, and may display a loss of interest in other activities and/or relationships only to prefer more solitary time online), Excessive Use (the respondent engages in excessive online behaviour and compulsive usage, and is intermittently unable to control time online that he or she hides from others), Neglect Work (Job or school performance and productivity are most likely compromised due to the amount of time spent online), Anticipation(the respondent most likely thinks about being online when not at the computer and feels compelled to use the Internet when offline), Lack of Control(the respondent has trouble managing his or her online time, frequently stays online longer than intended, and others may complain about the amount of time he or she spends online) and Neglect Social Life (the respondent frequently forms new relationships with fellow online users and uses the Internet to establish social connections that may be missing in his or her life) [ 7 , 8 , 9 , 10 ].

Events during the adolescence period greatly influence a person's development and can determine their attitudes and behavior in later life [ 11 ]. The teenagers are often in conflict with authority and cultural and moral norms of society, certain developmental effects can trigger a series of defense mechanisms [ 12 ]. During adolescence, there is an increased risk of emotional crises, often accompanied by mood changes and periods of anxiety and depressive behavior, which some adolescents attempt to fight through withdrawal, avoidance of any extensive social contact, aggressive reactions, and addictive behaviour [ 13 , 14 ]. Adolescents are exceptionally vulnerable and receptive during this period and can become drawn to the Internet as a form of release. Over time, this can lead to addiction [ 15 ].

Relaxed access and social networking are two of the several aspects of the Internet development of addictive behaviour [ 16 ]. Internet addiction is a newly emerged behavioral problem of adults which was reported after problem behavior theory was proposed [ 17 ]. Behavioral addictions operate on a modified principle of the classic addiction model [ 18 , 19 , 20 ]. Others have reported, that there is a tendency for individuals to be multiply ''addicted'' and to have overlapping addictions between common substances such as alcohol and cigarettes and ''addictions'' to activities such as internet use, gambling, exercising, and television [ 21 ]. A key factor to both models of substance and behavioral addictions is the concept of psychological dependence, in which no physiological exchange, such as ingestion of a substance, occurs [ 18 , 22 ]. Internet addiction in puberty and young adults can negatively impact life satisfaction and engagement [ 23 ], which may negatively affect cognitive functioning [ 24 ], lead to poor academic performance [ 25 , 26 ], and engagement in hazardous activities [ 27 ]. Internet addiction is also related to depression, somatization, and obsessive–compulsive disorder [ 28 ]. It has been found that paranoid ideation, hostility, anxiety, depression, interpersonal sensitivity, and obsessive–compulsive average scores are higher in people with high Internet Addiction scores than those without Internet addiction [ 29 , 30 ].

College students are especially susceptible to developing a dependence on the Internet, more than most other segments of society. This can be qualified to numerous factors including the following: Availability of time; ease of use; the psychological and developmental characteristics of young adulthood; limited or no parental supervision; an expectation of Internet/computer use covertly if not, as some courses are Internet-dependent, from assignments and projects to link with peers and mentors; the Internet offering a way of escape from exam anxiety [ 31 ].

Studies have indicated that IA is associated with different factors. Socio-demographic factors such as age (having lower age) [ 32 ] and male gender [ 33 , 34 , 35 , 36 , 37 ]. Reason for internet use related factors such as making new friendships online [ 33 ], getting into relationships online [ 33 ], using the internet less for coursework/assignments [ 33 ], visiting pornographic sites [ 34 ] and playing online games [ 31 , 34 , 38 ]. Time related and other factors such as higher internet usage time [ 37 , 39 ],continuous availability online [ 33 , 35 , 39 ] and mode of internet access [ 35 ]. Clinical and substance related factors such as insomnia [ 40 ], attention deficient disorder and hyperactivity symptoms [ 41 ], being sexual inactive [ 32 ], low self-esteem [ 40 ], failure in academic performance [ 32 ], smoking [ 41 ], and potential addictive personal habits of, drinking alcohol or coffee, and taking drugs [ 34 ]. Besides, mental illness like depression, anxiety and psychological distress [ 35 , 36 , 37 , 39 , 40 ] are associated with internet addiction. This could be based on the application of a general strain theory framework whereby negative emotions that are secondary to depression, anxiety, and psychological distress will be associated positively with internet addiction [ 42 ].

Internet Addiction is now becoming a serious mental health problem among Chinese adolescents. The researchers identified 10.6% to 13.6% of Chinese college students as Internet addicts [ 43 , 44 ]. A study conducted among Taiwan college students reported that the prevalence of Internet Addiction was 15.3% [ 37 ].

The prevalence of Problematic Internet Use (PIU) was greater among university students. For instance, the prevalence was 36.9 to 81% in Malaysian medical students by using the internet addiction questionnaire and Internet Addiction Diagnostic Questionnaire study instrument with a cut-offs point of ≥ 43 and 31to 79 respectively [ 45 , 46 ], 25.1% in American community university students by using the YIATstudy instrument with a cut-offs point of ≥ 40 [ 47 ], 40.7% in Iranian university students by utilizing the YIAT study instrument with a cut-offs point of ≥ 40 [ 48 ], 38.2–63.5% IA in Japanese university students as measured with the YIAT study instrument with a cut-offs point of ≥ 40 and ≥ 40 respectively [ 36 , 49 ], 16.8% IA in Lebanon University students by utilizing the YIAT study instrument with a cut-offs point of ≥ 50 [ 40 ], 35.4% IA in Nepal undergraduate students as measured with the YIAT study instrument with a cut-offs point of ≥ 40 [ 32 ], 40% IA in Jordan University students by utilizing the YIAT study instrument with a cut-offs point of ≥ 50 [ 50 ],19.85% to 42.9% IA in various parts of India as measured with the YIAT study instrument with a cut-offs point of 31to79, ≥ 50 and ≥ 50 respectively [ 33 , 35 , 39 ], 12% IA to 34.7% (PIU) in Greek University students by utilizing the Problematic Internet Use Diagnostic Test study instrument with no stated cut-offs point [ 34 ], 1.6% IA in Turkey students by using the Young’s Internet Addiction Scale study instrument with a cut-offs point of 70–100 [ 41 ].

In general, the main reason why youths are at particular risk of internet addiction is that they spend most of their time on online gaming and social applications like online social networking such as Twitter, Facebook, and telegrams [ 51 ].

Even though developing countries shares for a large magnitude of internet addiction, indicating the public health impact of the problem in the region, much is not known about the occurrence rate of the problem in these regions in general and Ethiopia in particular. As a result, trustworthy assessments of internet addiction in university students in these circumstances are required for delivering a focused intervention geared towards addressing the associated factors.

Moreover, it will be a ground for the expansion of national and international plans, procedures, and policy. At last but not least, the findings from this study will provide significant implications for counsellors and policymakers to prevent students' Internet addiction. Hence, this a community university-based cross-sectional study aimed and assessed the prevalence and associated factors of internet addiction among Wollo university students.

Research questions

The purpose of this study was to measure prevalence and associated factors of IA among undergraduate university students in Ethiopia. The specific research questions that guided the present study were:

What is the prevalence of IA among undergraduate university students in Ethiopia?

What are the associated factors of IA?

Methods and materials

Study area and period.

The study was done at Wollo University, Dessie campus that is found in South Wollo Zone, Amhara Regional State which is 401 kms far from Addis Ababa, Northeastern Ethiopia. It had 5 colleges and 2 schools and a total of 62 departments. The number of regular students in 2018/2019 is 7248; among these 4009 are males and 3239 are females. The study was conducted from April 10 to May 10/ 2019. The sample size was determined using single population proportion formula, taking a 50% prevalence of Internet Addiction with the following assumption: 95% CI, 5% margin of error, 10% non-response rate, and a design effect of 1.5. So, the final sample size was 603.

Sampling technique and procedure

A multistage cluster sampling technique was used to recruit study participants. In the first stage, by the use of the lottery method, two colleges (College of medicine and health sciences, and College of natural sciences, and one school (school of law)) were selected. In the second stage, 18 departments (9 from the college of medicine and health science, 8 from the college of natural science and 1 from the school of law) were selected. Students were selected proportionally from the given departments based on the number of students of a particular.

Study design

A community university-based cross-sectional study was carried out to assess the prevalence and associated factors of Internet Addiction among undergraduate students at Wollo University, Amhara Region, Ethiopia.

Inclusion and exclusion criteria

All generic regular undergraduate adult students whose ages were 18 years and above, and who were present at the time of data collection. Students who gave consent to the study were recruited. The study participants who are blind and severely ill were excluded from the study.

Study instruments

Self-administered, well-structured, and organized English version questionnaire was disseminated to students, and data were collected from the individual student. The questionnaires comprised six parts. The first part consisted of socio-demographic details; a structured questionnaire was used to assess sociodemographic characteristics. The second part consists of Young’s Internet Addiction Test (YIAT); a structured, self-administered questionnaire was used to assess Internet Addiction. The YIAT [ 7 ] is the most commonly used measure of Internet Addiction among adults [ 52 ]. It includes 20 questions with a scoring of 1–5 for each question and a total maximum score of 100. Based on scoring subjects would be classified into normal users (0–30), mild (31–49), moderate (50–79), and severe (80–100) Internet Addiction groups. Mild Internet addiction, moderate Internet addiction, and severe Internet Addiction were considered as having an Internet Addiction [ 53 , 54 , 55 ]. YIAT-20 showed that it is more reliable in University students. The Cronbach α in the present study was 0.89. The third part time-associated factors; a self-report structured questionnaire was prepared from different kinds of literature to assess time-associated factors (such as Internet use experience in months and Internet use per day in hours). The fourth part reasons for internet use; a structured questionnaire was used to assess the reasons for internet use. The fifth part psychoactive substance use-associated factors; a self-report questionnaire was used to assess the current use of psychoactive substances (Khat, Cigarette, Alcohol, and Cannabis), and the last part mental health problem-associated factors and it was assessed by Kessler10 (K10). The K10 scale [ 56 ] is a simple measure of mental distress. The K10-item scale, which has been translated into Amharic and validated in Ethiopia [ 57 ], was used to measure mental distress (depressive, anxiety, and somatic symptoms). The internal consistency of the K10 psychological distress scale in the present study was checked with a reliability assessment and was found to be 0.86 [ 58 , 59 , 60 ]. Scores will range from 10 to 50. A score under 20 is likely to be well, a score of 20–24 is likely to have mild mental distress, a score of 25–29 is likely to have moderate mental distress and a score of 30 and over are likely to have severe mental distress. Study participants with a score of 20 or more points on the K10 Likert scale were considered as having mental distress [ 61 ].

Data quality control

A structured self-administered questionnaire was developed in English and would be translated to Amharic language and again translated back into English to ensure consistency. Data collectors and supervisors would be trained for two days on the objective of the study, the content of the questionnaire, and the data collection procedure. Data would be pilot tested on 5% of the total sample size outside the study area and based on feedback obtained from the pilot test; the necessary modification would be done. During the study period, the collected data would be checked continuously daily for completeness by principal investigator and supervisor in the respective departments.

Data processing and analysis

Quantitative data would be cleaned, coded, and entered into Epi-data 3.1 and exported to SPSS version 25 for analysis. Descriptive data would be presented by a table, graphs, charts, and means. Multicollinearity test was checked by using standard error and there was no correlation between independent variables. The association between independent variables and Internet Addiction would be made using a binary logistic regression model and all independent variables having p value ≤ 0.25 would be included in multiple logistic regression models. A p value less than 0.05 and Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) not inclusive of one would be considered as statically significant and would be used to determine predictors of Internet Addiction in the final model. Hosmer–Lemeshow test was done to check model fitness and the model was fit.

Socio-demographic characteristics of study participants

A total of 603 participants were involved with a response rate of 90.9% (n = 548). However, the rest 9.1% (n = 55) participants were excluded due to incomplete responses. The mean age of the respondents was 21.4 (SD 1.8) years, the minimum and maximum age of the participants was 18 years and 30 years respectively. More than half of, 291 (53.1%) of respondents were males. Many of the study participants had a practice of using the internet for more than twelve months, 321 (58.6%). About 501 (91.4%), 268 (48.9%), 433 (79%) were using the internet less than five hours per day, most common mode of internet access Wi-Fi, and log in and off occasionally during the day respectively. The study participants with current khat use, current cigarette smoking, current alcohol use, and current cannabis use were 19.0%, 11.3%, 25.4%; and 4.0% respectively. About 19.3% of the participants had mental distress (Table 1 ).

Prevalence of Internet addiction

The prevalence of IA was 466 (85%) of the 305(55.6%), 153(27.9%), 8(1.5%) mild, moderate, and severe Internet Addiction respectively. Nevertheless, the remaining 82 (15%) are free from Internet Addiction (Fig.  1 ). Participants who login permanently had a greater figure of IA than those who log in and off occasionally during the day (92.2% versus 83.1%). Those who used the internet for about six months had a greater prevalence of Internet Addiction than those who used greater than twelve months (91.6% versus 84.1%) (Table 2 ).

Internet Addiction by severity among undergraduate university students in Ethiopia, 2019 (n = 548)

Reasons for internet use among Wollo University students

The furthermost frequent reasons for internet use among Wollo University undergraduate students were using the internet for courses / assignments (93.6%), for social networks (Facebook, etc.) (85.6%), for reading / posting news (76.6%), for getting into relationships online (66.6%),for playing mobile games (44.5%), for downloading music or videos (65.7%), for watching videos (57.8%),for retrieving sexual information (22.8%), for chat rooms (47.6%) and for e-mail ( reading, writing) (49.8%) (Fig.  2 ).

Reasons for internet use among undergraduate university students in Ethiopia, 2019 (n = 548)

Factors associated with internet addiction in the univariate analysis

Time related factors.

Duration of using the internet was associated with Internet Addiction i.e. students who used the internet for more than a year was 51% lower risks of having internet addiction than their counterparts (OR=0.49; CI 0.24–0.96). Respondents who were spending more time on the internet were more likely to develop Internet Addiction than their counterparts (OR=8.87; CI 1.21–65.25).

Mode of internet access was related to Internet Addiction i.e. those who used mobile internet were 45% lower risks of having Internet Addiction than those who used data cards (OR = 0.55; 95% CI 0.28–1.07).Participants who were permanently online were most likely to have Internet Addiction than those who were not (OR=2.39; 95% CI 1.16–4.93).

Reasons for internet use related factors

Study participants who played mobile games online were more likely to develop Internet Addiction than those who were not played mobile games (OR = 2.67; 95% CI 1.57–4.52). Those who downloaded music or videos were higher risks of having Internet Addiction than those who didn’t (OR = 1.62; 95% CI 1.00–2.61). Study participants who watched the video online were most likely to have Internet Addiction than those who didn’t watch (OR=1.94; 95% CI 1.21–3.12).

Psychoactive substance use related factors

Those who chewed khat currently were higher odds of having Internet Addiction than those who were not (OR = 5.33; 95% CI 1.90–14.91). Respondents who smoked cigarettes currently were more likely to have Internet Addiction than their counterparts (OR = 12.20; 95% CI 1.67–89.28).

Those who used alcohol currently were greater risks of having Internet Addiction than those who hadn't (OR = 2.76; 95% CI 1.38–5.51).

Mental health problem related factors

Study participants who had mental distress were four times more likely to develop Internet Addiction than those who didn't have mental distress (OR = 4.26; 95% CI 1.68–10.81) (Table 2 ).

Factors associated with internet addiction in the multivariate analysis

In the final model, spending more time on the internet, having mental distress and playing online games were the factors associated with Internet Addiction. Moreover, current khat chewing and current alcohol use were the independent predictors for Internet Addiction. Using the internet for more than twelve months and using the internet by mobile internet were negatively associated with Internet Addiction (Table 2 ).

Discussions

The present study aims to assess the prevalence and associated factors of Internet Addiction among undergraduate university students in Ethiopia. The prevalence of IA was 85% (n = 466). In the final model; spending more time on the internet, having mental distress and playing online games were the factors associated with Internet Addiction. Moreover, current khat chewing and current alcohol use were the independent predictors for Internet Addiction. Using the internet for more than twelve months and using the internet by mobile internet were negatively associated with Internet Addiction.

The prevalence of Internet Addiction in the present study was higher than the prevalence of Internet Addiction that was done in different universities such as three medical schools across three countries ( Croatia, India, and Nigeria) 49.7% [ 55 ], Malaysian 36.9% to 81% [ 45 , 46 ], American community 25.1% [ 47 ], Iran 12.5 to 40.7% [ 48 , 62 , 63 ], Japan 38.2% to 63.5% [ 36 , 49 ], Greek 12% to 30.1% [ 54 , 64 ], Jordan 40% [ 50 ], Lebanon 16.8% [ 40 ], Nepal 35.4% [ 32 ] and in different parts of India 19.85% to 42.9% [ 33 , 35 , 39 ]. The discrepancy might be due to the cut-off point of YIAT-20, instrument difference, mental health policy, a cultural difference like time utilization, the difference in study participants such as in our study the participants were from two colleges and one school, and all participants were internet users, sample size and the time difference between the studies. The study in Malaysian University was conducted among medical students only and focusing on mild Internet Addiction and moderate Internet Addiction and not on severe Internet addiction.

In our study spending more time on the internet was 10 times more likely to develop Internet Addiction than those who are spending less time. The finding of this study is in line with similar studies done on college students in Taiwan and three medical schools across three countries (Croatia, India, and Nigeria) [ 37 , 55 ]. The possible explanation for the association between Internet usage time and Internet Addiction is that it might be as much a symptom as it is a cause. However, this study design was cross-sectional and no causal relationship can be clarified, further studies ought to examine whether Internet usage time is an essential factor for determining Internet addiction.

Likewise, students who had mental distress were 2.7 times more likely to develop Internet Addiction as compared to their counterparts. Study findings in these areas showed that students who had mental distress were related to higher levels of Internet Addiction than students who hadn’t mental distress [ 35 , 36 , 39 , 40 , 41 , 50 ]. This could be due to the Khantzian’s [ 65 ] self-medication hypothesis, indicating that mentally distressed university students might come to rely on the Internet as a method for coping with their mental distress. Hence, they will devote more and more time on the Internet and headway toward addiction if their mental distress symptoms are not cured [ 66 ].Students who had playing online games were 2.4 times higher to have Internet Addiction than their counterparts. A similar finding was also reported in Greek University and others [ 34 , 38 , 54 , 67 ].

Furthermore, students who chewed khat currently were three times most likely to develop Internet Addiction than students who reported no current khat chewing which is in line with the study finding in Greek University students [ 34 ]. In this study, students who drank alcohol currently were 2.3 times most likely to have Internet Addiction as compared with students who didn’t drink alcohol. Other studies reported a similar finding [ 17 , 34 , 68 , 69 ]. Probable reasons involve; based on the problem behavior theory, the problem behaviors (Internet Addiction and substance abuse) are inter-related.

Students who used the internet by mobile internet were 60% of lower risks of having Internet Addiction as compared to those students who used data cards. This might be due to inadequate finance to use the internet on mobile internet. So, the students may refrain from using the internet through mobile internet. Students who used the internet for more than 12 months were 52% less likely to have Internet Addiction than their counterparts. The current finding is not supported by other studies in the world. The present study has limitations such as alpha inflation from multiple testing and the analysis did not account for the complex sampling strategy in adjusting the standard errors.

The current study documents a high prevalence of Internet Addiction among Wollo University students. The factors associated with Internet Addiction were spending more time on the internet, having mental distress, playing online games, current khat chewing, and current alcohol use. As internet addiction becomes an evident public health problem, carrying out public awareness campaigns on its severity and negative consequences of excruciating agonies may be a fruitful strategy to decrease its prevalence and effect. Campaign programs may aim at informing the adults on the phenomenon of internet addiction, knowing the possible risks and symptoms. Besides to this, a collaborative work among all stakeholders is important to develop other trendy, adaptive, ethical and sustainable countermeasures.

Availability of data and materials

The datasets supporting the conclusions of this article are not publicly available due to ethics regulations but may be available from the corresponding author upon reasonable request.

Abbreviations

Adjusted odds ratio

Confidence interval

Crude odds ratio

• Internet addiction

Statistical package for social science

Young’s internet addiction test

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Acknowledgements

We thank the Department of Psychiatry, College of Medicine and Health Sciences, Wollo University for supporting the research in different ways. We extend our heartfelt thanks to the student service directorate office for providing us the necessary information. We are grateful to all the students who participated in the study.

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Yosef Zenebe, Kunuya Kunno, Meseret Mekonnen, Ajebush Bewuket, Mengesha Birkie, Mogesie Necho, Muhammed Seid & Million Tsegaw

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YZ and KK designed and supervised the study, carried out the analysis, and interpreted the data; MM, AB, MB, MNA, MS, MT, and BA assisted in the design, analysis, and interpretation of the data; and YZ wrote the manuscript. All authors contributed toward data analysis, drafting, and critically revising the paper and agree to be accountable for all aspects of the work. All authors read and approved the final manuscript.

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The study was conducted after getting ethical clearance from Wollo University College of medicine and health science institutional review board with a certificate of approval number: CMHS/508/2019. A formal letter of permission was obtained from the student service directorate of Wollo University. The respondents were informed about the aim of the study. Confidentiality was maintained by giving codes for respondents rather than recording their names. The privacy of the respondents was also assured since the anonymous data collection procedure was followed. The data collectors have informed the clients that they had the full right to discontinue or refuse to participate in the study. Written consent was obtained from each participant before administering the questionnaire.

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Zenebe, Y., Kunno, K., Mekonnen, M. et al. Prevalence and associated factors of internet addiction among undergraduate university students in Ethiopia: a community university-based cross-sectional study. BMC Psychol 9 , 4 (2021). https://doi.org/10.1186/s40359-020-00508-z

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Internet addiction affects behavior and development of adolescents, study finds

by University College London

Adolescents with an internet addiction undergo changes in the brain that could lead to additional addictive behavior and tendencies, finds a new study by UCL researchers.

The findings, published in PLOS Mental Health , reviewed 12 articles involving 237 young people aged 10–19 with a formal diagnosis of internet addiction between 2013 and 2023.

Internet addiction has been defined as a person's inability to resist the urge to use the internet, negatively impacting their psychological well-being, as well as their social, academic and professional lives.

The studies used functional magnetic resonance imaging (fMRI) to inspect the functional connectivity (how regions of the brain interact with each other) of participants with internet addiction, both while resting and completing a task.

The effects of internet addiction were seen throughout multiple neural networks in the brains of adolescents. There was a mixture of increased and decreased activity in the parts of the brain that are activated when resting (the default mode network).

Meanwhile, there was an overall decrease in the functional connectivity in the parts of the brain involved in active thinking (the executive control network).

These changes were found to lead to addictive behaviors and tendencies in adolescents, as well as behavior changes associated with intellectual ability, physical coordination, mental health and development.

Lead author, MSc student, Max Chang (UCL Great Ormond Street Institute for Child Health) said, "Adolescence is a crucial developmental stage during which people go through significant changes in their biology, cognition, and personalities. As a result, the brain is particularly vulnerable to internet addiction related urges during this time, such as compulsive internet usage, cravings towards usage of the mouse or keyboard and consuming media.

"The findings from our study show that this can lead to potentially negative behavioral and developmental changes that could impact the lives of adolescents. For example, they may struggle to maintain relationships and social activities , lie about online activity and experience irregular eating and disrupted sleep."

With smartphones and laptops being ever more accessible, internet addiction is a growing problem across the globe. Previous research has shown that people in the UK spend over 24 hours every week online and, of those surveyed, more than half self-reported being addicted to the internet.

Meanwhile, Ofcom found that of the 50 million internet users in the UK, over 60% said their internet usage had a negative effect on their lives—such as being late or neglecting chores.

Senior author, Irene Lee (UCL Great Ormond Street Institute of Child Health), said, "There is no doubt that the internet has certain advantages. However, when it begins to affect our day-to-day lives, it is a problem.

"We would advise that young people enforce sensible time limits for their daily internet usage and ensure that they are aware of the psychological and social implications of spending too much time online."

Mr. Chang added, "We hope our findings will demonstrate how internet addiction alters the connection between the brain networks in adolescence, allowing physicians to screen and treat the onset of internet addiction more effectively.

"Clinicians could potentially prescribe treatment to aim at certain brain regions or suggest psychotherapy or family therapy targeting key symptoms of internet addiction.

"Importantly, parental education on internet addiction is another possible avenue of prevention from a public health standpoint. Parents who are aware of the early signs and onset of internet addiction will more effectively handle screen time , impulsivity, and minimize the risk factors surrounding internet addiction."

Study limitations

Research into the use of fMRI scans to investigate internet addiction is currently limited and the studies had small adolescent samples. They were also primarily from Asian countries. Future research studies should compare results from Western samples to provide more insight on therapeutic intervention.

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• Open access
• Published: 30 May 2024

The relationship between childhood psychological abuse and depression in college students: a moderated mediation model

• Yang Liu 1   na1 ,
• Qingxin Shen 1   na1 ,
• Liangfan Duan 1   na1 ,
• Lei Xu 1 , 2 ,
• Yongxiang Xiao 1 &
• Tiancheng Zhang 1  

BMC Psychiatry volume  24 , Article number:  410 ( 2024 ) Cite this article

205 Accesses

Metrics details

Childhood psychological abuse (CPA) are highly correlated with depression among college students, but the underlying mechanisms between variables need further exploration. This study aims to investigate internet addiction as a mediating factor and alexithymia as a moderating factor, in order to further elucidate the potential risk factors between CPA and depression among college students.

A self-report survey was conducted among 1196 college students from four universities in three provinces in China. The survey included measures of CPA, internet addiction, alexithymia, and depression. Descriptive and correlational analyses were performed on these variables, and a moderated mediation model was constructed.

CPA was positively correlated with depression among college students, as well as internet addiction with alexithymia. Internet addiction partially mediated the relationship between CPA and depression among college students, while alexithymia strengthened the relationships among the paths in the moderated mediation model.

This study provides further insights into the psychological mechanisms underlying the relationship between CPA and depression among college students. Internet addiction serves as a mediating factor in this relationship, while alexithymia may enhance the strength of the relationships among the three variables.

Peer Review reports

Introduction

Depression is a highly prevalent psychological disorder among young people, characterized by symptoms such as sadness, lack of energy, and despair [ 1 ]. Over the past decade, the incidence of depression has been continuously increasing [ 2 , 3 ]. Studies show that the prevalence of depression among Chinese university students exceeds 25% [ 4 , 5 ], and the global incidence rate is close to 30% [ 6 ]. Individuals with depression exhibit a variety of complex negative physical and mental manifestations [ 7 ]. Feelings of worthlessness, hopelessness, and self-blame are strong emotional experiences among depressed individuals [ 8 ]. Major cognitive impairments displayed by these individuals include emotional dysregulation, cognitive biases, difficulties in attention and memory, and inhibitory dysfunction [ 9 , 10 ]. Similarly, outward behaviors manifest a range of negative patterns, such as social withdrawal [ 11 ], sleep disturbances [ 12 ], and abnormal changes in appetite [ 13 ]. These physical and mental manifestations further deepen the severity of depression [ 14 ]. Additionally, the etiology of depression is complex, with early-life stress being a significant risk factor [ 15 ], and that is often associated with adverse childhood experiences [ 16 ], such as childhood abuse. Childhood abuse has a close relationship with depression [ 17 , 18 , 19 , 20 ], and studies have found that among the subtypes of childhood abuse that childhood psychological abuse (CPA) is most closely related to depression [ 18 , 21 ]. Given the significant harm of depression, social attention, and its strong association with CPA, this study strongly needs to explore the underlying mechanisms between the two, in order to intervene and prevent timely and predict the impact of CPA on individual depression.

CPA refers to inappropriate psychological parenting behavior that guardians continuously and repeatedly adopt during childhood, which has an adverse effect on individuals' growth [ 22 ]. Due to its particular nature, the detection rate of CPA is quite high in different countries [ 23 , 24 ]. Abuse and neglect are two subtypes of CPA that are increasingly accepted by scholars in related studies. Longitudinal studies have found that parents' psychological neglect predicts future depression in adolescents [ 25 ]. In addition, research has found that among the classifications of childhood abuse, the correlation between psychological abuse and depression is the highest [ 18 ]. Based on the above review, this study hypothesized that CPA can significantly predict the occurrence of depression in college students.

Individuals who have experienced CPA often encounter emotional distress. Faced with such distress, they may be at an increased risk of engaging in hazardous behaviors, such as internet addiction, as these online activities may serve as a coping mechanism to alleviate negative emotions [ 26 ]. Without intervention, this reliance on the internet can form a cyclical pattern, potentially leading to internet addiction. Internet addiction is characterized by an excessive, problematic, and compulsive engagement in behaviors related to internet use [ 27 , 28 ]. Research indicates that among the various subtypes of childhood maltreatment, CPA has the strongest association with internet addiction [ 29 ]. CPA can significantly predict individuals internet addiction, and internet addiction has been found to mediate the relationship between CPA and suicidal internet addiction behaviors [ 30 ]. In discussions that integrate the relationship between childhood maltreatment and internet addiction, CPA is highlighted as a particularly salient predictor [ 31 ]. Consistent with the social compensation theory [ 32 ], CPA may lead individuals to seek emotional fulfillment through online interactions. Furthermore, there is a recognized association between internet addiction and depression. Research has found a strong correlation between internet addiction and depression [ 33 ], with internet addiction being a significant predictor of depression [ 34 ]. Longitudinal studies have shown a significant bidirectional relationship between internet addiction and depression among college students [ 35 ]. The displacement hypothesis [ 36 ] suggests that excessive internet use may impede real-life social interactions, reduce well-being, and deepen depression. Depression can also intensify the level of internet addiction, creating a vicious cycle and leading to a "rich get richer" scenario [ 37 ]. Based on this evidence, this study posits that CPA can significantly predict internet addiction among college students, which in turn can significantly predict depression.

However, when individuals possess certain traits, the relationships among the variables mentioned above may be strengthened, exacerbating negative behaviors or psychological outcomes. Among these variables, the level of alexithymia is one of the more important ones. Alexithymia is a stable personality trait [ 38 ], characterized by limited ability to understand one's own feelings and others' emotions, inadequate emotion regulation in interpersonal interactions [ 39 ], difficulty in recognizing emotions, describing emotions, and an externally oriented thinking style [ 40 ]. Alexithymia, due to emotional dysregulation, can lead to the intensification of negative emotions such as anxiety and depression [ 41 ], and inaccurate attention and expression of emotions may result in poor interpersonal relationships [ 42 ], thereby increasing individuals' psychological burden. To escape or alleviate such negative psychological states, the internet on mobile phones provides an easily accessible avenue [ 42 , 43 , 44 , 45 ]. According to the alexithymia stress hypothesis, individuals with high levels of alexithymia often find themselves in a state of stress due to their inadequate understanding and recognition of their own and others' emotions [ 46 ], which further predicts severe negative psychological states [ 47 ]. Therefore, based on the aforementioned review, it is evident that alexithymia may enhance the relationship between CPA, internet addiction, and depression discussed in this study, further exacerbating the degree of negative psychological and behavioral outcomes. Additionally, individuals with alexithymic characteristics not only neglect emotions [ 48 ], but they may also have a generalized impairment in perceiving internal bodily sensations (interoception) compared to individuals with lower levels of alexithymia [ 49 ], as demonstrated by various studies on the accuracy of perceiving heart rate [ 50 , 51 ], delayed healthcare seeking for illnesses [ 52 ], and unstable substance intake [ 53 ], among others. Based on these features, individuals with high levels of alexithymia tend to overlook their own discomfort symptoms even when they excessively use the internet [ 54 , 55 ] due to their lower sensory perception. Therefore, we hypothesize that alexithymia moderates the relationships among CPA, internet addiction, and depression mediated by various paths.

In summary, previous research strongly indicates the relationship and predictive role of CPA and depression, but these areas are relatively understudied among Chinese university students. To further supplement research in this field and explore underlying psychological mechanisms, this study introduces the mediating variable of internet addiction and the moderating variable of alexithymia. Therefore, this study constructs a hypothetical path model (see Fig.  1 ).

Hypothesized a moderated and mediation model

Participants

This cross-sectional survey was conducted in October 2023 among Chinese university students from four universities in Hunan Province, Hubei Province, and Guangxi Province. Prior to distribution, the researchers delivered a presentation to all participants, informing them of the main content and confidentiality of the survey data, as well as its ultimate purpose. The electronic questionnaires were distributed on a class basis, with an informed consent statement attached to the questionnaire's cover page. Participants could proceed with the survey only after choosing to agree, while those who declined would be directed to an exit page. Informed consent was obtained from all the participants. The survey was anonymous and voluntary, and it could be completed within 20 min. Prior to commencement, this study obtained approval from the Biomedicine Ethics Committee of Jishou University. We confirm that all the procedure is in accordance with the relevant guidelines and regulations such as the declaration of Helsinki. A total of 1352 students completed the survey, and after excluding respondents with excessively short response times or patterns in their answers, valid data from 1196 participants (496 males, 700 females) were ultimately obtained, with an average age of 18.69 years (SD = 1.07).

Childhood psychological abuse (CPA)

The measurement of CPA utilized the psychological abuse and neglect subscales from the Short Childhood Trauma Questionnaire (SCTQ) [ 56 ]. Each subscale included 5 items, scored on a Likert scale of 1 (never) to 5 (always), assessing the experiences of participants before the age of 17. An example item from the scale is: “Someone in my family said insulting or sad things to me”. Higher scores indicated higher levels of CPA. In this study, the Cronbach's α for the sample was 0.878.

Depression among college students was measured using the depression subscale from the Chinese version of the Depression Anxiety Stress Scale (DASS-21) [ 57 ]. The subscale comprised 7 items, scored on a Likert scale of 1 (strongly disagree) to 4 (strongly agree), assessing the level of depression experienced by participants. An example item from the scale is: “I can't be enthusiastic about anything”. Higher scores indicated more severe depression. In this study, the Cronbach's α for the sample was 0.906.

• Internet addiction

Internet addiction among college students was measured using the Problematic Social Media Use (PSMU) Scale [ 58 ]. The scale comprised 8 items, scored on a Likert scale of 1 (not at all) to 5 (completely), assessing the level of internet addiction experienced by participants. An example item from the scale is: “Using social networking sites distracts me from my studies”. Higher scores indicated more severe internet addiction. In this study, the Cronbach's α for the sample was 0.857.

• Alexithymia

The Toronto Alexithymia Scale (TAS-20) was used to assess the level of alexithymia among college students [ 59 ]. The scale comprised 20 items, scored on a Likert scale of 1 (totally disagree) to 5 (totally agree), assessing the level of alexithymia experienced by participants. An example item from the scale is: “I am often confused about what emotion I am feeling”. Higher scores indicated more severe alexithymia. In this study, the Cronbach's α for the sample was 0.804.

Considering the potential influence of demographic variables, such as gender and age [ 31 , 60 ], on the analysis results, we controlled for these variables in our analysis.

Statistical analyses

All statistical analyses were conducted using SPSS 26.0 software. Firstly, we checked for methodological biases to evaluate the potential bias resulting from self-report questionnaires. Before initiating the data analysis, we assessed the normality of our data using the Shapiro–Wilk test. According to Kim's proposal, data exhibiting an absolute skewness value below 2 and an absolute kurtosis value below 7 may be deemed to approximate a normal distribution [ 61 ]. In our study, we found that the variables CPA, depression, internet addiction, and alexithymia were normally distributed. For variables conforming to a normal distribution, descriptive analysis was conducted using the mean and standard deviation (Sd), while Pearson's correlation analysis was employed to assess the relationships among them. Then, we standardized the data of the main variables before conducting the analyses. Finally, to test our hypotheses, we used the PROCESS macro (Mode 4 and Model 59) in SPSS to analyze the relationships between variables [ 62 ]. The PROCESS macro was based on a bootstrapping method with 5000 resamples to estimate the model testing and 95% confidence intervals (95% CI), and a relationship was considered significant when the 95% CI did not include 0. Gender and age were considered as covariates in the analyses, and the significance level was set at α = 0.05.

Harman’s single factor test and normality test

Harman's single-factor test was used to examine the impact of common method bias. The analysis results showed that there were 2 factors with eigenvalues greater than 1. Without rotating the principal component factors, the explanatory rate of the first factor was 35.55%, which is lower than the recommended threshold of 40% [ 63 ]. Therefore, this study did not encounter severe common method bias. Upon assessing normality for our principal variables, all variables exhibited absolute skewness values below 2 and absolute kurtosis values below 7. Consequently, parametric tests were employed for all subsequent analyses.

Descriptive analyses

The results of Table  1 show that CPA (t = 2.62, p  < 0.001), depression (t = 2.32, p  < 0.05) and Internet addiction (t = -2.17, p  < 0.05) are different between genders and reach statistical significance.

Correlational analyses

Table 2 presents the Pearson correlation data between the variables of interest. CPA was significantly positively correlated with college students' internet addiction ( r  = 0.240, p  < 0.001), depression ( r  = 0.481, p  < 0.001), and alexithymia ( r  = 0.322, p  < 0.001). College students' internet addiction was significantly positively correlated with depression ( r  = 0.384, p  < 0.001) and alexithymia ( r  = 0.262, p  < 0.001). Depression was significantly negatively correlated with college students' alexithymia ( r  = 0.461, p  < 0.001).

Mediation analysis

Table 3 presents the results showing that, after controlling for gender and age, CPA can significantly predict depression in college students (β = 0.473, SE = 0.026, p  < 0.001). When internet addiction was included as a mediator variable, CPA continued to significantly predict depression in college students (β = 0.402, SE = 0.025, p  < 0.001). Additionally, upon testing the mediation model, it was found that CPA significantly predicts internet addiction in college students (β = 0.245, SE = 0.029, p  < 0.001), and internet addiction also significantly predicts depression (β = 0.290, SE = 0.025, p  < 0.001).

Moderated and mediation analysis

After controlling for covariates, the moderated mediation model analysis revealed that the predictive effects of all paths in the mediation model remained significantly present (CPA predicting depression:β = 0.322, SE = 0.025, p  < 0.001; CPA predicting internet addiction:β = 0.180, SE = 0.030, p  < 0.001; Internet addiction predicting depression:β = 0.223, SE = 0.024, p  < 0.001). Additionally, alexithymia significantly predicted college students' internet addiction (β = 0.201, SE = 0.029, p  < 0.001) and depression (β = 0.281, SE = 0.025, p  < 0.001). Lastly, the interaction term between alexithymia and CPA significantly predicted college students' internet addiction (β = 0.072, SE = 0.027, p  < 0.01) and depression (β = 0.071, SE = 0.023, p  < 0.01), and the interaction term between internet addiction and alexithymia significantly predicted college students' depression (β = 0.060, SE = 0.022, p  < 0.01). Refer to Table  4 , Figs.  2  and 3 for details.

Moderated and mediation model

Simple slope plot

This study examines the relationships between CPA, internet addiction, depression, and alexithymia among college students. The findings reveal positive correlations between CPA, internet addiction, depression, and alexithymia, all of which are statistically significant. After controlling for demographic variables, internet addiction is found to mediate the relationship between CPA and depression in college students, while alexithymia moderates this relationship, confirming our initial hypothesis.

Our study confirms the positive correlation between CPA and depression in college students, which is consistent with previous research [ 18 , 31 ]. Studies conducted in China have shown that childhood abuse is relatively common [ 64 ], with emotional abuse being the most prevalent type [ 65 , 66 ]. Almost all types of childhood abuse are associated with mental health problems [ 20 , 23 , 67 , 68 ], increasing the risk of various adversities in individuals' later lives [ 23 , 69 , 70 ], with psychological abuse being particularly prominent [ 31 , 68 ], even predicting somatic symptoms in patients with severe depression [ 71 ]. Furthermore, research has found that CPA becomes the sole predictor of adolescent depression when controlling for other types of abuse, and early psychological abuse has a greater impact on depression [ 72 ]. Children who receive warm, rule-following, and well-bounded care from parents perform better in various aspects, including mental health [ 73 ]. On the other hand, children who experience psychological abuse from caregivers, in a state of invisible stress similar to social isolation, are more likely to develop depression, anxiety, and even aggressive behaviors [ 74 ].

Our study supports the hypothesis that internet addiction mediates the relationship between CPA and depression in college students, which is consistent with other similar studies [ 29 ]. Previous research has found a strong association between CPA and internet addiction among young people [ 30 , 31 , 75 , 76 ]. The relationship between internet addiction and depression has also been strongly supported [ 77 ], including in studies conducted in China [ 35 ]. According to the explanations of social control theory [ 78 ] and compensatory internet use theory [ 32 ], CPA seems to indicate unfavorable family support environments for adolescents, leading them to seek support from the virtual online world and developing internet addiction. It was found that adolescents who were addicted to the internet at baseline were more likely to develop depression in the future [ 77 ], which is also true in other studies [ 79 ]. Adolescents addicted to the internet often face greater stress, making them more prone to depression [ 80 ].

As previously hypothesized, alexithymia strengthens the relationships between all pathways. Individuals with alexithymia have difficulty understanding their own and others' emotions and are unable to regulate their emotions appropriately in daily interpersonal interactions [ 39 ], leading to an exacerbation of negative emotions [ 41 ]. Additionally, based on the explanations of general strain theory [ 81 ] and compensatory internet use theory [ 82 ], individuals with alexithymia feel stressed in dealing with interpersonal relationships [ 83 ]. Under this negative emotional state, in order to meet the needs of interpersonal communication and escape from the pressure of reality, they naturally choose the online world, which further leads to the development and intensification of internet addiction. However, alexithymia's "dullness" towards one's own feelings is not limited to emotions but can also extend to the perception of internal sensations [ 49 ]. Brain regions associated with these internal sensations include the anterior insula and anterior cingulate cortex [ 84 , 85 ], which not only play a role in non-emotional interoception but also have significant implications for individuals' emotional processing [ 86 , 87 ]. Studies have found structural abnormalities in these brain regions in individuals with alexithymia [ 88 , 89 , 90 ]. It is possible that individuals with severe pain and fatigue continue to use the internet despite their condition, further exacerbating their involvement in negative psychological and behavioral patterns. Therefore, high levels of alexithymia strengthen the relationships between CPA, internet addiction, and depression, which aligns with the expectations of this study.

In conclusion, our study further contributes to understanding the relationship between CPA and depression in college students, as well as the mediating role of internet addiction and the moderating role of alexithymia. These findings are not commonly seen in previous research. However, the study has several limitations. Firstly, the accuracy of self-reported CPA data may be insufficient as it involves retrospective self-reporting. Secondly, the representativeness of the sample may be inadequate as we only selected local colleges from a few provinces in China, with most of the students being locals. Future studies could increase the diversity of the sample. Lastly, due to the cross-sectional nature of the study, the causal relationships between variables are challenged. Therefore, future research could explore causal relationships based on longitudinal tracking.

This study discusses the relationships between CPA, internet addiction, depression, and alexithymia among college students, confirming the mediating role of internet addiction and the moderating role of alexithymia between CPA and depression. Individuals, families, schools, and society should pay attention to the negative impacts caused by CPA, especially for individuals with high levels of alexithymia.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due [our experimental team's policy] but are available from the corresponding author on reasonable request.

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Yang Liu, Qingxin Shen, Liangfan Duan, Lei Xu, Yongxiang Xiao & Tiancheng Zhang

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Author statement: Yang Liu12345, Qingxin Sheng12345, Liangfan Duan12345, Lei Xu156, Yongxiang Xiao15, Tiancheng Zhang156. 1 Conceptualization; 2 Methodology; 3 Data curation; 4 Writing—Original Draft; 5 Writing—Review &; Editing; 6 Funding acquisition.

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Study Reveals Internet Addiction in Teens Affects 4 Key Brain Networks

by Staff Writer June 6, 2024 at 9:36 AM UTC

Clinical relevance: UCL researchers reviewed studies on 237 adolescents and found internet addiction significantly alters brain function, affecting behavior and mental health.

• Internet addiction disrupts neural networks, decreasing activity in the executive control network responsible for decision-making.
• Adverse effects include trouble maintaining relationships, dishonesty about online activity, and irregular eating and sleeping patterns.
• Researchers call for better screening, targeted treatments, and parental education to manage screen time and mitigate risks.

A new study sheds light on the profound impact of internet addiction on the brains of young people . And it’s as bad as we feared.

The University College London researchers  – whose paper appears in PLOS Mental Health – reviewed a dozen articles involving 237 adolescents aged between 10 and 19 diagnosed with internet addiction between 2013 and 2023. And the findings provide some new insight into how excessive internet use alters brain function and – as a result – behavior.

Defining – and Observing – Internet Addiction

Scientists classify internet addiction as an inability to control internet use, which threatens one’s psychological well-being, including their social, academic, and professional lives.

The studies that researchers reviewed for this paper all relied on magnetic resonance imaging (fMRI) to examine the functional connectivity of the brains of participants both at rest and during task performance – or, in this case, internet use.

The findings exposed notable changes in multiple neural networks within the brains of adolescents suffering from an internet addiction. The researchers observed a mix of increased and decreased activity in the default mode network, which is typically active during rest. 

Conversely, the team observed an appreciable drop in functional connectivity within the executive control network, which is responsible for active thinking and decision-making.

The researchers noted a link between these alterations and addictive behaviors and tendencies among adolescents. They also observed other changes that appeared to affect intellectual ability, physical coordination, mental health, and development. 

Max Chang, the lead author and UCL student, stressed how vulnerable adolescents are at this time.

“Adolescence is a crucial developmental stage during which people undergo significant changes in their biology, cognition, and personalities,” Chang explained. “As a result, the brain is particularly susceptible to internet addiction-related urges during this time, such as compulsive internet usage and cravings towards media consumption.”

From Bad to Worse

Chang also highlighted other potential adverse behavioral and developmental consequences stemming from internet addiction, such as:

• Trouble maintaining relationships.
• Dishonesty about online activity.
• Irregular eating and sleeping patterns.

The growing accessibility of smartphones and laptops has only made things worse. Earlier research suggested that UK citizens, for example, spend at least 24 hours online every week. And more than half of those surveyed described themselves as addicted to the internet.

Additionally, Ofcom, the United Kingdom’s communications regulator, has reported that more than 60 percent of the country’s 50 million internet users are convinced that their internet usage makes their lives worse, such as causing tardiness or neglecting chores.

Senior author Irene Lee, from UCL’s Great Ormond Street Institute of Child Health, acknowledged the benefits of the internet but warned against its detrimental effects.

“When internet use begins to affect our daily lives, it becomes a problem,” Lee said. “We advise young people to set sensible time limits for daily internet usage and be mindful of the psychological and social implications of excessive time online.”

Chang seemed confident that the study’s findings would help medical professionals step up screening efforts so that they could better treat internet addiction in adolescents. The researchers added that potential treatments could target specific brain regions, or involve psychotherapy or family therapy.

Additionally, Chang emphasized how important it is for caregivers to educate parents about internet addiction so that they can be better equipped to manage screen time at home – and reduce risk factors.

The study’s authors concluded with a call for a more proactive approach to tackle the growing issue of internet addiction among adolescents, highlighting the need for awareness, prevention, and better intervention strategies.

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The relationship between internet addiction and adolescent learning engagement: the role of future orientation and cognitive reappraisal

• Original Article
• Published: 08 May 2024

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• Xidan Xiao   ORCID: orcid.org/0000-0002-1309-7089 1 ,
• Jiahan Cai 1 &
• Qiuyu Yang 2  

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The primary objective of this study was to investigate the relationship between internet addiction and adolescents’ learning engagement, with a specific focus on the mediating sequential roles of future orientation and cognitive reappraisal. A survey encompassing 1200 high school students was conducted, utilizing instruments such as the Internet Addiction Scale, Learning Engagement Scale, Future Orientation Scale, and Cognitive Reappraisal Scale. The findings of this investigation yielded the following outcomes: (1) Internet addiction exhibited a pronounced negative correlation with high school students’ learning engagement, future orientation, and cognitive reappraisal. Furthermore, future orientation and cognitive reappraisal demonstrated mutually positive associations with learning engagement. (2) Future orientation and cognitive reappraisal jointly mediated the relationship between internet addiction and learning engagement among adolescents, forming a sequential mediation process. These findings suggest that enhancing adolescents’ future orientation and cognitive reappraisal abilities can contribute to mitigating the deleterious impact of internet addiction on academic performance.

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Xiao, X., Cai, J. & Yang, Q. The relationship between internet addiction and adolescent learning engagement: the role of future orientation and cognitive reappraisal. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-05982-x

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Internet addiction affects the behaviour and development of adolescents

5 June 2024

Adolescents with an internet addiction undergo changes in the brain that could lead to addictive behaviour and tendencies, finds a new study by UCL researchers.

The findings, published in PLOS Mental Health , reviewed 12 articles involving 237 young people aged 10-19 with a formal diagnosis of internet addiction between 2013 and 2023.

Internet addiction has been defined as a person’s inability to resist the urge to use the internet, negatively impacting their psychological wellbeing, as well as their social, academic and professional lives.

The studies used functional magnetic resonance imaging (fMRI) to inspect the functional connectivity (how regions of the brain interact with each other) of participants with internet addiction, both while resting and completing a task.

The effects of internet addiction were seen throughout multiple neural networks in the brains of adolescents. There was a mixture of increased and decreased activity in the parts of the brain that are activated when resting (the default mode network).

Meanwhile, there was an overall decrease in the functional connectivity in the parts of the brain involved in active thinking (the executive control network).

These changes were found to lead to addictive behaviours and tendencies in adolescents, as well as behaviour changes associated with intellectual ability, physical coordination, mental health and development.

Lead author, MSc student, Max Chang (UCL Great Ormond Street Institute for Child Health) said: “Adolescence is a crucial developmental stage during which people go through significant changes in their biology, cognition, and personalities. As a result, the brain is particularly vulnerable to internet addiction related urges during this time, such as compulsive internet usage, cravings towards usage of the mouse or keyboard and consuming media.

“The findings from our study show that this can lead to potentially negative behavioural and developmental changes that could impact the lives of adolescents. For example, they may struggle to maintain relationships and social activities, lie about online activity and experience irregular eating and disrupted sleep.”

With smartphones and laptops being ever more accessible, internet addiction is a growing problem across the globe. Previous research has shown that people in the UK spend over 24 hours every week online and, of those surveyed, more than half self-reported being addicted to the internet.

Meanwhile, Ofcom found that of the 50 million internet users in the UK, over 60% said their internet usage had a negative effect on their lives – such as being late or neglecting chores.

Senior author, Irene Lee (UCL Great Ormond Street Institute of Child Health), said: “There is no doubt that the internet has certain advantages. However, when it begins to affect our day-to-day lives, it is a problem.

“We would advise that young people enforce sensible time limits for their daily internet usage and ensure that they are aware of the psychological and social implications of spending too much time online.”

Mr Chang added: “We hope our findings will demonstrate how internet addiction alters the connection between the brain networks in adolescence, allowing physicians to screen and treat the onset of internet addiction more effectively.

“Clinicians could potentially prescribe treatment to aim at certain brain regions or suggest psychotherapy or family therapy targeting key symptoms of internet addiction.

“Importantly, parental education on internet addiction is another possible avenue of prevention from a public health standpoint. Parents who are aware of the early signs and onset of internet addiction will more effectively handle screen time, impulsivity, and minimise the risk factors surrounding internet addiction.”

Study limitations

Research into the use of fMRI scans to investigate internet addiction is currently limited and the studies  had small adolescent samples. They were also primarily from Asian countries. Future research studies should compare results from Western samples to provide more insight on therapeutic intervention.

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A study on Internet addiction and its relation to psychopathology and self-esteem among college students

Manish kumar.

Department of Psychiatry, Calcutta Medical College, Kolkata, West Bengal, India

Anwesha Mondal

1 Department of Clinical Psychology, Institute of Psychiatry- A Center of Excellence, Kolkata, West Bengal, India

Background:

Internet use is one of the most important tools of our present-day society whose impact is felt on college students such as increased use of Internet. It brings change in mood, an inability to control the amount of time spent with the Internet, withdrawal symptoms when not engaged, a diminishing social life, and adverse work or academic consequences, and it also affects self-esteem of the students.

The main objective of this study is to explore the Internet use and its relation to psychopathology and self-esteem among college students.

Methodology:

A total of 200 college students were selected from different colleges of Kolkata through random sampling. After selection of the sample, Young's Internet Addiction Scale, Symptom Checklist-90-Revised, and Rosenberg Self-Esteem Scale were used to assess the Internet usage, psychopathology, and self-esteem of the college students.

Depression, anxiety, and interpersonal sensitivity were found to be correlated with Internet addiction. Along with that, low self-esteem has been found in students to be associated with possible users of Internet.

Conclusion:

Internet usage has been found to have a very strong impact on college students, especially in the areas of anxiety and depression, and at times it affected their social life and their relationship with their family.

Internet is being integrated as a part of day-to-day life because the usage of the Internet has been growing explosively worldwide. It has dramatically changed the current communication scenario, and there has been a considerable increase in the number of Internet users worldwide in the last decade. With the advancement in media and technologies, Internet has emerged as an effective tool in eliminating human geographical barriers. With the availability and mobility of new media, Internet addiction (IA) has emerged as a potential problem in young people which refers to excessive computer use that interferes with their daily life. The Internet is used to facilitate research and to seek information for interpersonal communication and for business transactions. On the other hand, it can be used by some to indulge in pornography, excessive gaming, chatting for long hours, and even gambling. There have been growing concerns worldwide for what has been labeled as “Internet Addiction,” which was originally proposed as a disorder by Goldberg[ 1 ] Griffith considered it a subset of behavioral addiction that meets the six “core components” of addiction, i.e., salience, mood modification, tolerance, withdrawal, conflict, and relapse. Increasing research has been conducted on IA.[ 2 , 3 ] With regard to IA, it has been questioned whether people become addicted to the platform or to the content of the Internet.[ 4 ] A study suggested that Internet addicts become addicted to different aspects of online use where it is differentiated between three subtypes of Internet addicts: excessive gaming, online sexual preoccupation, and e-mailing/texting.[ 5 , 6 ] According to the study, various types of IA are cyber-sexual addiction, cyber-relationship addiction, net compulsions, information overload, and computer addiction.

Based on a growing research base, the American Psychiatric Association vision is to include Internet use disorder in the appendix of the fifth edition of the Diagnostic and Statistical Manual for Mental Disorders[ 7 ] for the first time, acknowledging the problems arising from this type of addictive disorder. There has been an explosive growth in the use of Internet not only in India but also worldwide. Reports reveal that there were about 137 million Internet users in India in 2013 and further suggest India as the world's second largest in Internet use after China in the near future. According to the Internet and Mobile Association of India and Indian Market Research Bureau, out of 80 million active Internet users in urban India, 72% (58 million individuals) have accessed some form of social networking in 2013,[ 8 ] which is to touch around 420 million by June 2017.

The warning signs of IA include the following:

• Preoccupation with the Internet (thoughts about previous online activity or anticipation of the next online session)
• Use of the Internet in increasing amounts of time in order to achieve satisfaction
• Repeated, unsuccessful efforts to control, cut back, or stop Internet use
• Feelings of restlessness, moodiness, depression, or irritability when attempting to cut down the use of the Internet
• Online longer than originally intended
• Jeopardized or risked loss of significant relationships, job, educational, or career opportunities because of Internet use
• Lies to family members, therapists, or others to conceal the extent of involvement with the Internet
• Use of the Internet is a way to escape from problems or to relieve a dysphoric mood (e.g., feelings of hopelessness, guilt, anxiety, and depression)
• Feeling guilty and defensive about Internet use
• Feeling of euphoria while performing Internet-based activities
• Physical symptoms of IA.

Internet or computer addiction can also cause physical discomforts such as:

• Carpal tunnel syndrome (pain and numbness in hands and wrists)
• Dry eyes or strained vision
• Backaches and neck aches; severe headaches
• Sleep disturbances
• Pronounced weight gain or weight loss.

IA results in personal, family, academic, financial, and occupational problems that are characteristic of other addictions. Impairments of real-life relationships are disrupted as a result of excessive use of the Internet. IA leads to different social, psychological, and physical disorders. The worst effects of IA are anxiety, stress, and depression. Excessive use of Internet also affects the academic achievements of students. Students addicted to Internet are more involved in it than their studies, and hence they have poor academic performance.[ 9 ] This hypothesis has been confirmed by a number of studies. Many studies examined the association between psychiatric symptoms and IA in adolescents. They found that IA is associated with psychological and psychiatric symptoms such as depression, anxiety, and low self-esteem. In addition, several studies have shown links between Internet use and personality traits. They have found loneliness, shyness, loss of control, and low self-esteem to be associated with IA.

In a study[ 10 ] on young adolescents, it was found that about 74.5% were moderate (average) users and 0.7% were found to be addicts. Those with excessive use of Internet had high scores on anxiety, depression, and anxiety depression. In another study,[ 11 ] the prevalence of IA among Greek students was 4.5% and at-risk population was 66.1%. There were significant differences between the means of psychiatric symptoms in Symptom Checklist-90-Revised (SCL-90-R) subscales among addicted and nonaddicted students. Depression and anxiety appeared to have the most consistent correlation with IA. In addition, obsessive-compulsive symptoms, hostility/aggression, time in the Internet, and quarrel with parents are associated with IA. In another study by Paul et al ., 2015, on 596 students, 246 (41.3%) were mild addicts, 91 (15.2%) were moderate addicts, and 259 (43.5%) were not addicted to Internet use. There was no pattern of severe IA among the study group. Males, students of arts and engineering stream, those staying at home, no extracurricular activity involvement, time spent on Internet per day, and mode of accessing Internet were some of the factors significantly associated with IA pattern. In another study,[ 12 ] the prevalence of IA among 1100 respondents was 10.6%. People with higher scores were characterized as male, single, students, high neuroticism, life impairment due to Internet use, time for Internet use, online gaming, presence of psychiatric morbidity, recent suicidal ideation, and past suicidal attempts. Logistic regression showed that neuroticism, life impairment, and Internet use time were the three main predictors for IA. Compared to those without IA, the Internet addicts had higher rates of psychiatric morbidity (65.0%), suicidal ideation in a week (47.0%), lifetime suicidal attempts (23.1%), and suicidal attempt in a year (5.1%). In another study,[ 13 ] a significant relationship was found between IA and general psychopathology and self-esteem. The addiction status was assessed as risk of low level in 59 (31.89%) participants, high level in 27 (14.59%) participants, and none in 99 (53.51%) participants. A high positive correlation was found between Internet Addiction Scale (IAS) and SCL-90 subscales and Rosenberg Self-Esteem Scale (RSES). In three different IA groups, it was found that all SCL-90 subscale averages increase and RSES subscale averages decrease as IA severity increases.

In India, use of Internet is enormous, especially in the young population. Hence, it was found necessary to study the pattern of Internet usage in young adults in Indian setting and its relationship with their mental and physical health and self-esteem. With this aim in mind, the present study has been undertaken to take a close look on this issue.

METHODOLOGY

• Sociodemographic data sheet: A self-made, semistructured, sociodemographic data sheet was prepared to collect the participant's details, details of any previous history of psychopathology, substance abuse, and details of the Internet use
• Internet Addiction Scale: The IAS[ 14 ] is a 20-item scale that measures the presence and severity of Internet dependency. This questionnaire is scored on a 5-point scale ranging from 1 to 5. The marking for this questionnaire ranges from 20 to 100, the higher the marks, the greater the dependence on the Internet
• Symptom Checklist-90-Revised: It is a multidimensional self-report symptom inventory[ 15 ] designed to measure psychopathology by quantifying nine dimensions as follows: somatization, obsession-compulsion, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychotism. In addition, there are three global indices of distress, the General Severity Index, representing the extent or depth of the present psychiatric disturbance; the Positive Symptom Total, representing the number of questions rated above 1 point; and the Positive Symptom Distress Index, representing the intensity of the symptoms. Higher scores on the SCL-90 indicate greater psychological distress. The SCL-90 was proven to hold excellent test–retest reliability, internal consistency, and concurrent validity
• Rosenberg Self-Esteem Scale: This scale was developed by sociologist Rosenberg[ 16 ] to measure self-esteem, which is widely used in social science research. It is a 10-item scale with items answered on a 4-point scale – from strongly agree to strongly disagree. Five of the items have positively worded statements and five have negatively worded ones. The scale measures state self-esteem by asking the respondents to reflect on their current feelings. The RSES is considered a reliable and valid quantitative tool for self-esteem assessment.

A sample of 200 students studying in various disciplines such as science, arts, and commerce were selected through random sampling from five different colleges of Kolkata.

In the initial phase of the study, a total of five colleges were selected according to the convenience of the researchers. After receiving permission from the administrative departments of respective colleges for data collection, researchers approached the participants directly during their college hours, explained the purpose and method of using the questionnaires, and also ensured the confidentiality of the data. Verbal consent was taken from the participants. Only the day scholars were included in the study. The colleges selected for collecting the data did not have free Wi-Fi services. Responses were collected from the participants having Internet connection on their android phones. First, the sociodemographic data sheet was filled up by the participants. Participants having a previous history of psychopathology and substance abuse were excluded from the study. After exclusion of the participants, the questionnaires were distributed to the included participants and after completion, they were scored and interpreted according to the tool. Confidentiality of the data has been maintained.

Sociodemographic and Internet user's characteristics

Two hundred students participated in the study. The mean age of the students was found to be 21.68 years (±2.82). Students were unmarried and were undergraduates. Majority of the students reported that they use Internet for pleasure and mainly get involved in activities of social networks and online gaming. Focusing on users' characteristics and Internet activities, it was found that the concerning age of computer use initiation was 15 years, frequency of Internet use per day in hours was 3–4 h, and frequency of Internet use per week in days was every day.

Table 1 suggests the frequency of IA on the IAS. The frequency of mild users (IAS score: 20–49) was 58 and the percentile was 29. The highest frequency and percentile found in the severe users (80–100) were 79 and 39.5, respectively. The next higher frequency found in moderate users (50–79) was 63 and the percentile was 31.5.

Frequency of Internet users

Table 2 reflects t -test results between SCL-90 and IA. The comparison of scores in all dimensions and the three global indices on SCL-90 between moderate users and severe users of Internet demonstrated that severe users of Internet had higher scores in all dimensions. Symptoms such as obsession-compulsion, interpersonal sensitivity, depression, and anxiety were associated with IA.

t -test results of psychiatric symptoms with Internet addiction

Table 3 reflects t -test results between self-esteem and IA. The comparison of scores on self-esteem between moderate users and severe users of Internet demonstrated that no significant difference was found between them.

t -test results of self-esteem with Internet addiction

Table 4 describes the regression analysis results of the association between Internet users, the ten dimensions of the SCL-90. The results indicated that students with high usage of Internet had higher level of obsession-compulsion, interpersonal sensitivity, and anxiety.

Regression analysis results: IAT score

A number of studies have been conducted across the world among adults with respect to IA. This study is a preliminary step toward understanding the extent of IA among college students in India.

The random sampling method gave the opportunity to gather information from five different colleges in Kolkata. The procedure for selecting the sample has allowed the generalization of the results to the entirety of the college population.

The Internet Addiction Test has been found to be the only validated instrument which identifies the high, low, and average users of Internet. It is found from this study that 39.5% of the students were severe users of Internet. Nearly 31.5% of the students were moderate users. A number of studies reported a higher percentage of Internet-addicted youths.[ 17 , 18 ] It is of note that 29% of the students were average users of Internet. Whether these students will actually develop an addiction is difficult to be predicted. Nevertheless, the continuous exposure to Internet and a possible susceptibility to addictive behaviors may represent a possible danger. Previous studies have found similar results concerning moderate IA.[ 19 , 20 ] Students who are found to be severe users of Internet use a maximum of 3–4 h per day and they are not able to perform their responsibilities properly such as concentration on academics and developing social isolation owing to excessive use of the Internet. Users who spend a significant amount of time online experience academic, relational, economic, and occupational problems, as well as physical disorders.

The results of the present study show that severe users of Internet have shown higher psychopathological symptoms in four dimensions such as obsessive-compulsive, interpersonal sensitivity and depression, anxiety, and global severity index than those with moderate users of Internet. This finding has been supported by other studies[ 21 ] where the association between psychiatric symptoms and IA using the SCL-90 scale had been examined and was found that there was a strong association between psychiatric symptoms and IA. Students with excessive use of Internet reported the presence of psychopathological problems such as obsessive-compulsive and depression. Anxiety and problems such as interpersonal sensitivity were supported by many studies.[ 10 , 19 , 20 ] In another study,[ 22 ] it was found that psychiatric features are associated with IA.

In the present study, no significant relationship has been found between moderate users and severe users of Internet and self-esteem. This is consistent with the result of a previous study.[ 10 ] It may be attributed to the fact which states that the participants' use of the Internet is not associated as a coping style or as a way of compensating some deficiencies, rather it makes them feel better, as it allows them to assume a different personality and social identity.

Logistic regression analysis showed that obsession -compulsion, interpersonal sensitivity, and anxiety were associated with IA. It reflects that the higher the use of Internet, the individual is more prone to develop obsessive-compulsive symptoms such as difficulty in controlling to use Internet, repetitive thoughts about using Internet, and checking the Internet repetitively. The association between obsessive-compulsive disorder and IA supports previous findings.[ 23 ] Interpersonal sensitivity and anxiety were associated with IA as well. These findings are consistent with that of other studies.[ 23 , 24 ] It indicates that individuals with high usage of Internet are prone to become more sensitive in interpersonal relationships and also become more anxious when not using the Internet. In an article, a majority of surveys conveyed the association between pathological Internet use and depression, anxiety, and obsessive-compulsive symptoms.[ 19 ]

High Internet usage leads to psychological difficulties such as anxiety, depression, and loneliness. Severe users were more likely to be anxious and depressed than moderate users and low users. This study showed that severe users of Internet use the Internet more often when they are anxious and depressed. It is clear that the relation between Internet use, anxiety, and depression is affected by many variables. Severe users of Internet have also been associated with increases in impulsivity. Severe and average Internet users displayed significant difference on interpersonal relationships. Individuals with high use of Internet experience have a sense of criticism by others, shyness, and a sense of discomfort when criticized and can be easily hurt, have perceived lower social support, and found it easier to create new social relationships online. The consequence of exploring social support online often worsens their interpersonal problems in reality, accompanied by psychological problems such as anxiety symptoms. Severe users' Internet group has obsessive-compulsive symptoms more than average users' Internet group, where severe users' Internet group was found to be preoccupied with Internet, needs longer amounts of time online, makes repeated attempts to reduce Internet use, feels withdrawal when reducing Internet use, has time management issues, has environmental distress (family, school, work, and friends), and has deception around the time spent online, thus doing mood modification through Internet use.

Students are steered toward more Internet use because of many factors such as different cheap offers of Internet recharge by different telecom companies, blocks of unstructured time, newly experienced freedom from parental intervention, no monitoring of what they express online, facing a peer pressure in showing their identity, and gaining random instant popularity on social media platform. In other words, these users derive great satisfaction from Internet use and perceive it as a way of making up for their shortcomings, which, however, turns into a dependent relationship.

Psychopathologic features increase as the severity of IA increases as found in a study.[ 22 ] A causal relationship between psychiatric and psychological problems and IA needs to be further analyzed in order to determine whether Internet use causes psychiatric problems or exacerbates symptoms that already exist.

In the last one decade, the Internet has become an integral part of our life. In this article, an attempt has been made to study the severity of Internet use and its relation to psychopathology and self-esteem in college students. Individuals having high usage showed depression and anxiety. IA is also associated with obsessive-compulsive symptoms and interpersonal sensitivity. This result highlights the need for more clinical studies focusing on psychiatric or psychological symptoms.

This study has a few limitations too. No specific tool has been used to exclude any previous psychopathology apart from the information gathered through the sociodemographic data sheet. Accurate estimates of the prevalence of IA in college students are lacking. The study did not manage to clarify the causal relationship between IA and psychiatric symptoms. IA may precipitate psychiatric symptoms which may lead to IA. Another limitation of this study is it did not take into account whether psychiatric symptoms may preexist any IA and may create a vulnerability to addiction. The study did not allow us to differentiate the essential use of the Internet from its recreational use. Future studies can be implicated to analyze the results of the students according to different streams of subjects.

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Conflicts of interest.

There are no conflicts of interest.