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Current Research and Viewpoints on Internet Addiction in Adolescents

• Adolescent Medicine (M Goldstein, Section Editor)
• Published: 09 January 2021
• Volume 9 , pages 1–10, ( 2021 )

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• David S. Bickham   ORCID: orcid.org/0000-0002-2139-6804 1  

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Purpose of Review

This review describes recent research findings and contemporary viewpoints regarding internet addiction in adolescents including its nomenclature, prevalence, potential determinants, comorbid disorders, and treatment.

Recent Findings

Prevalence studies show findings that are disparate by location and vary widely by definitions being used. Impulsivity, aggression, and neuroticism potentially predispose youth to internet addiction. Cognitive behavioral therapy and medications that treat commonly co-occurring mental health problems including depression and ADHD hold considerable clinical promise for internet addiction.

The inclusion of internet gaming disorder in the DSM-5 and the ICD-11 has prompted considerable work demonstrating the validity of these diagnostic approaches. However, there is also a movement for a conceptualization of the disorder that captures a broader range of media-use behaviors beyond only gaming. Efforts to resolve these approaches are necessary in order to standardize definitions and clinical approaches. Future work should focus on clinical investigations of treatments, especially in the USA, and longitudinal studies of the disorder’s etiology.

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Introduction

Every day we carry with us a tool that provides unlimited social, creative, and entertainment possibilities. Activities facilitated by our smartphones have always been central to the developmental goals of adolescents—as young people move toward their peers as their primary social support system, their phones provide constant connection to their friends as well as access to the popular media that often defines and shapes youth culture. Considering young people’s continued use of more venerable forms of entertainment screen media (e.g., television, video games, computers), it is not surprising that adolescents spend more time using media than they do sleeping or in school—an average of 7 h 22 min a day [ 1 ]. While the majority of young media users adequately integrate it into their otherwise rich lives, an undeniable subset suffers from what has been termed by some as internet addiction [ 2 ] but, as discussed below, has been referred to by many different names. While overuse of technology and its impact has been of concern since the days of television, the constantly changing media landscape as well as advances in our understanding of the issue requires regular updates of what is known. The purpose of this review is to provide an understanding of this issue grounded in the established evidence of the field but primarily informed by work published between 2015 and 2020 and, in doing so, address the following questions: What is internet addiction and is this the best term for the problem? What is its prevalence among adolescents around the world? What individual characteristics predispose young people to internet addiction and what are the common comorbidities? And, finally, what treatment strategies are being use and which have been found to be effective?

Defining the Issue

To answer any of these questions, first we must define the problem at hand. Unfortunately, this is a difficult task as recent publications use a wide variety of terms to reference this problem. Video game addiction, problematic internet use, problematic internet gaming, internet addiction, problematic video gaming, and numerous other terms have been used to identify this problem in the last 5 years. Such terms all have limitations. Focusing on a specific behavior, such as internet gaming, does not capture the variety of media use problems experienced by young people. Even the term “internet” may not be especially precise or consistent in meaning as online functionality is now seamless and permeates all activities on a phone, computer, tablet, game system, or television. In order to focus the nomenclature on the variety of behaviors that cross devices and avoid the term addiction which may unnecessarily stigmatize game players and impede their seeking help, my colleagues and I have suggested the use of the term problematic interactive media use (PIMU) [ 3 , 4 , 5 , 6 ]. The term PIMU attempts to capture the broad spectrum of potential media use behaviors seen in clinical settings including gaming, information seeking, pornography use, and social media use without naming a specific behavior or type of media which could position the term for obsolescence [ 3 •].

A Focus on Gaming

Another approach to defining this issue has been to focus on internet games as they are seen as having unique features and elevated harm through excessive use [ 7 ]. In 2013 the American Psychiatric Association described internet gaming disorder (IGD) in its updated Diagnostic and Statistical Manual of Mental Disorders (DSM-5) as a condition needing further research in order to classify as a unique mental disorder [ 8 ]. The proposed clinical diagnosis of IGD includes persistent use of the internet to play games with associated distress or life impairment as well as endorsement of at least 5 of 9 symptoms including preoccupation with games, increased need to spend more time gaming, inability to reduce game time, lying to others about the amount of gaming, and using gaming to reduce negative mood [ 8 ]. Following suit, the World Health Organization included gaming disorder (GD) in its 11th revision of the International Classification of Diseases (ICD-11) [ 9 ]. These two diagnostic approaches both characterize problematic gaming as repetitive, persistent, lasting at least a year, and resulting in significant impairments of daily life [ 10 ]. While there is considerable overlap in the identified clinical symptoms (e.g., loss of control over gaming and continued use of gaming even when after negative consequences), the GD diagnosis does seem to focus on more severe levels of problematic use and worse functional impairment [ 10 ]. The inclusion of IGD and GD in these major diagnostic manuals have been seen as an opportunity for unification in the field around the conceptualization, and measurement of problematic gaming and resulting discussions have, to some extent, indicated increasing agreement [ 7 ].

However, in the years following the definition of IGD, numerous authors took umbrage with these diagnostic criteria pointing out limitations of the defined symptoms and calling into question the idea that there is consensus in the field around this diagnosis [ 11 ••]. For example, preoccupation with gaming, they argue, could represent a form of engagement similar to other types of engrossing activities rather than something pathological [ 11 ••]. Similarly, using gaming to avoid adverse moods is unlikely to differentiate problematic from casual gamers. The use of the term “internet” in the name of the condition was also met with resistance considering that it assumes that video games accessed through the internet are different from other video games in terms of their addictive qualities [ 11 ••]. Some argue that the field is lacking the unified definitions and extensive, foundational research necessary that must precede a diagnosis [ 12 ]. Finally, by focusing on gaming, IGD does not account for other potentially addictive online behaviors. There appears, however, not to be an easy solution to this concern. A broader conceptualization of the disorder has been seen as too general by some, but it seems untenable to create new diagnostic criteria for each specific online behavior. This complexity is evident even within the APA’s description of IGD when the manual states that “Internet gaming disorder” is “also commonly referred to as Internet use disorder, Internet addiction, or gaming addiction [ 8 ].”

Scales and Assessment

Building effective igd scales.

As evidence that much of the field is accepting IGD as a unifying conceptualization of problematic media use, numerous clinicians and scientist have investigated the DSM-5 criteria by designing and testing new scales or applying existing scales to this new framework. Some early testing utilized an interview procedure to confirm a 5-symptom cutoff for IGD, although a cutoff of 4 was adequate for differentiating between those suffering from IGD and healthy controls [ 13 ]. Scales such as the Internet Gaming Disorder Scale and its short form as well as the Internet Gaming Disorder Test (IGDT-10) have been designed and tested demonstrating that fairly short (e.g., 9 or 10 items) assessments can demonstrate strong psychometric properties, support the defined cutoff of 5 symptoms, and successfully measure a single construct [ 14 , 15 , 16 , 17 ]. Testing has been done on other assessment tools that are aligned with the IGD criteria including the Clinical Video Game Addiction Test which provided further support for the 5-item cutoff diagnosis [ 18 ] and the Chen Internet Addiction Scale—Gaming Version which identified its own cutoff [ 19 ]. This abundance of screeners and other instruments demonstrates how, as a result of the inclusion of IGD in the DSM-5, researchers and clinicians have access to numerous well-designed and tested assessments for problematic game play. On the other hand, the profusion of scales may also indicate that the field is still far from one regularly stated goal: a universal and standardized measurement tool.

Internet Addiction Scales

To further expand the assessment landscape, researchers and clinicians who prefer a broader conceptualization of this disorder, one more aligned with internet addiction rather than gaming disorder, have also created scales for research and clinical settings. The Chen Internet Addiction Scale is one of the earliest and most utilized scales [ 20 ]. Developed by applying established concepts from substance abuse and impulse control, it and its revised form have established internal reliability and criterion validity [ 21 ]. The designers of the 20-item Internet Addiction Test (IAT) used the criteria for pathological gambling as the basis of the test and designed it specifically to differentiate between casual and compulsive internet users [ 2 ]. The IAT has high internal reliability [ 22 ], a consistent factor structure across age categories [ 23 ], and is associated with expected comorbidities including depression [ 22 ] and attention-deficit disorders [ 24 ]. The 18-item Problematic and Risky Internet Use Screening Scale (PRIUSS) has three subscales—social consequences, emotional consequences, and risky/impulsive internet use—and a 3-item version was created that used one question from each subscale [ 25 , 26 ]. The strong psychometric properties of both versions of this scale are indicative of their value as tools for identifying adolescents and young adults struggling with their technology use.

Much like the measures of IGD, these internet addiction scales are more similar than dissimilar. They all assess a diverse array of experiences and consequences related to PIMU including its impact on social relationships, sleep, and aspects of mental health. In fact, some items from the different scales are almost identical. For example, the IAT asks, “Do you choose to spend more time online over going out with others?” the PRIUSS asks, “Do you choose to socialize online instead of in person?” and the CIAS asks how much this statement matches your experiences: “I find myself going online instead of spending time with friends.” The scales share an overall approach of asking about internet use in general rather than about specific online activities. While this allows the instruments to focus on the impulsive and risky aspects of internet use in general, it requires young people to differentiate between online and offline activities, a distinction that may no longer be relevant. Scales using this approach should continually be tested and revised as technology develops.

Considering the similarities of the scales, a researcher or clinician would likely be well served by any of them. However, even though the IAT and the CIAS both have identified diagnostic cutoffs, the availability of a 3-item pre-screener for the PRIUSS makes this instrument especially useful for inclusion in a battery of in-office measures. The PRIUSS does, however, require the adolescent or young adult patient to endorse behaviors that are worded in such a way that might activate feelings of judgment or reactance. For example, the question “Do you neglect your responsibilities because of the internet?” puts the onus directly on the user with little room for rationalizing an external cause. That said, the consistently high performance of this scale indicates the set of questions as a whole are successful at classifying problematic internet users.

Because the field lacks standardized language, reporting on the current prevalence of this issue requires the use of work that employs different definitions. However, the similarities across measures likely result in reasonably comparable prevalence rates. In a systematic review focusing on problematic gaming, reported rates varied from 0.6 (in Norway) to 50% (in Korea) with a median prevalence rate of 5.5% across all included studies and 2.0% for population-based studies [ 27 ]. A meta-analyses using data across multiple decades found a pooled prevalence of 4.6% with a range of .6 to 19.9% with higher frequencies in studies performed in the 1990s (12.1%), those with samples under 1000 (8.6%), those that utilized concepts based of psychological gambling (9.5%), and those performed in Asia (9.9%) and North America (9.4%) [ 28 ••].

Recent studies reinforce the variability of prevalence in different regions of the world. In a study of 7 European countries with a representative sample of 12,938, the prevalence of IGD was 1.6% with 5.1% being considered “at-risk” for IGD with little variation among countries [ 29 ]. In studies of individual countries, prevalence of IGD in Germany ranged from 1.16 [ 30 ] to 3.5% [ 31 ]. In Italy, 12.1% were classified as having problematic use and .4% as having internet addiction [ 32 ].

Countries in Asia showed similar disparities. In a review of 38 studies from countries defined by the authors as Southeast Asia (with most being from India), prevalence of internet addiction ranged from 0 to 47.4% [ 33 ]. Among middle and high school students in Japan, prevalence was 7.9% for problematic internet use and 15.9% for adaptive internet use, a lower cutoff of the diagnostic questionnaire [ 34 ]. In rural Thailand, 5.4% reached the cutoff for IGD [ 35 ], and in Taiwan 3.1% met that threshold [ 17 ]. Among 2666 urban middle school children in China, prevalence of IGD was 13.0% [ 36 ]. Finally, in rural South Korea, the prevalence of PIU was 21.6% among a sample of 1168 13- to 18-year-olds [ 37 ].

With such disparate findings from around the world, it seems that PIMU prevalence varies considerably from county to country and region to region. While this may be the case, summary findings from two large reviews do have similar final estimates—5.5% [ 27 ] and 4.6% [ 28 •• ]. This rate is also similar to the prevalence of youth “at-risk” for IGD across Europe (5.1%) [ 29 ] and for full IGD in rural Thailand (5.4%) [ 35 ]. While far from definitive, 5% might be our strongest general prevalence estimate given the evidence. There are some sample and study characteristics that seem to result in a higher prevalence. Unsurprisingly, rates are higher when less restrictive definitions of the disorder are used. There is also some evidence that rates are lower in Europe and higher in North America and Asia, but these results were not universal. If we accept a prevalence of approximately 5% in the USA, that would translate to approximately 1.5 million adolescents experiencing significant life consequences as a result of their struggles with digital technology. Understanding who is most at risk and how best to treat this problem is essential for comprehensive, contemporary adolescent medicine.

Potential Determinants of PIMU

Individual characteristics, demographic features, and psychosocial traits have all been identified as possible determinants of PIMU. Perhaps the most widely documented risk factor is being male. Prevalence among boys and young men has been found to be 2 [ 38 ], 3 [ 28 ••], or even 5 [ 27 ] times higher than among girls and young women. Throughout early adolescence PIMU increases with age, but peaks around 15–16 [ 39 ]. Indicators of lower socioeconomic status including less maternal education and a single parent household have been shown to increase the risk for PIMU [ 36 ].

Family Functioning

Young people’s family functioning also seems to play a role in their development of PIMU. Risk factors seem to include lower levels of family cohesion, more family conflict, and poorer family relationships [ 40 ]. The most frequent finding in a recent systematic review was that a worse parent-child relationship was associated with more problematic gaming [ 41 ]. Less time with parents, less affection from parents, more hostility from parents, and lower quality parenting were all family characteristics potentially indicated in the development of gaming problems [ 41 ]. Game play and other online social activities may serve as solace from difficult family lives as adolescents seeking treatment for gaming addiction report that they are motived to play in part by escapism and the draw of virtual friendships [ 42 ]. At the other end of the spectrum, positive parent-child relationships may be protective against the development of problematic gaming [ 41 ]. Additionally, parental monitoring of adolescents’ internet use can also reduce PIMU which, in turn, improves parent-child relationships [ 43 ]. Parents, it seems, have some prevention tools available to them which could improve their family functioning overall. Fathers appear to have a particularly influential role as their relationships with adolescents has been shown to be especially protective [ 41 , 43 ].

Personality Traits

Certain individual personality traits appear to be common among adolescents with media use issues potentially indicating that young people with these traits are predisposed to develop PIMU. PIMU sufferers regularly demonstrate limitations in areas related to self-control including higher levels of impulsivity. In two studies examining problematic smartphone use, one identified dysfunctional impulsivity and low self-control as two key risk factors [ 44 ] and the other found impulsivity to predict this behavior in their female participants [ 45 ]. Patients diagnosed with IGD also demonstrated higher levels of impulsivity than healthy controls [ 46 ]. A systematic review of research examining the personality traits predictive of IGD concludes that impulsivity plays a role in IGD and that certain aspects of this trait, such as high levels of urgency, are especially potent risk factors. [ 47 •].

In addition to impulsivity, behavior traits related to aggression and hostility are common among adolescents with media use problems. Aggressive tendencies were identified as a predictor of IGD by multiple studies in a recent review of the research [ 47 •]. In a large European survey study, adolescents who reported IGD had higher scores on rule-breaking and aggressive behaviors scales [ 29 ]. While it may seem that aggression findings are simply indicative of the observed gender differences, models that include gender as well as other traits that predict PIMU found that hostility was independently associated with problematic smartphone use [ 48 ] and conduct problems were predictive of problematic internet use [ 49 ].

Neuroticism, the tendency to feel nervous and to worry, has been identified as a potential predisposing factor for PIMU. Using the Big Five model of personality to investigate commonalities among young people with IGD, the authors of a recent review highlighted multiple studies linking neuroticism with PIMU and concluded that this work demonstrates a clear and consistent link [ 47 •]. Some of the strongest evidence comes from clinical samples in which young people seeking care for IGD showed higher levels of neuroticism than healthy controls [ 50 ]. Additionally, neuroticism may be an important trait that differentiates game players who have problematic use versus those who are simply heavily engaged with the games [ 51 ] perhaps in part because the control provided by video games is especially appealing to those with neurotic tendencies [ 50 ]. Neuroticism is a common element of internalizing mood disorders including anxiety and depression [ 52 ], which, as described below, are frequently comorbid with PIMU.

While it is clear that some traits are common among PIMU sufferers (and there are others not covered above), we must stop short of claiming a defining personality profile. Young people experiencing PIMU are likely to have as much diversity as they do similarity in their psychological and personality characteristics. Some of the most conclusive findings originate from clinical samples, but, because of limited specialized care opportunities, this work has been almost entirely conducted outside of the USA. Seeing as culture plays an important role in the development of personality, investigations are necessary to determine if our current knowledge is generalizable to the USA.

Neurobiology and Brain Function

Apart from individual characteristics and family functioning, there appear to be some neurobiological dysfunction that may characterize PIMU sufferers. Working from models based on the brain functioning in gambling and substance use addicts, researchers have looked for similarities with these disorders. Sussman and colleagues call attention to the viewpoint that people are not actually addicted to a substance or a behavior itself but rather to the brain’s response to the drug or activity [ 53 ••]. This perspective opens the door for digital entertainment obsession to be compared to substance use and gambling disorder. Video games and certain types of internet use have been shown to release dopamine at a rapid rate leading to immediate gratification and the potential for a repetitive response that can include compulsive behaviors and increased tolerance [ 53 ••]. In a simultaneous test of reward processing and inhibitory control, both behavioral and electroencephalography findings indicate adolescents with IGD demonstrate irregularities in both systems [ 54 • ]. Additionally, fMRI studies have documented neurobiological explanations for dysregulated reward processing, diminished impulse control, and other behavioral and cognitive patterns in IGD sufferers that are similar to those from people with gambling disorders [ 55 ]. Imaging studies have demonstrated that the brains of adolescents with internet addiction share at least one structural abnormality with brains of those with substance use disorder, namely, reduced thickness in the orbitofrontal cortex [ 56 ]. The evidence at hand seems to indicate that PIMU shares similarities in neural functioning and potentially some brain structures with other compulsive behaviors as well as substance use. However, there are still many fewer neuroimaging studies of PIMU sufferers than of substance users, and many of the existing studies are hindered by small, heterogeneous samples and lack of attention to comorbid conditions [ 55 ].

The observed similarities between PIMU and substance use disorder do not necessarily signify that compulsive technology use should be characterized as a behavioral addiction. In fact, there are strong reasons to consider other conceptualizations for this set of behaviors. Excessive use may be indicative of maladaptive coping [ 57 ] or the manifestation of existing self-regulatory problems [ 58 •]. Rather than being a novel disorder, PIMU behaviors may be symptoms of existing psychiatric problems being expressed within the digital environment [ 3 •]. If these underlying disorders are appropriate explanations for these behaviors, then, some argue, we should not classify the set of symptoms as a behavioral addiction [ 59 ]. Furthermore, there is limited evidence that stopping use results in serious withdrawal symptoms which is a key factor in some diagnostic tools [ 60 ].The term addiction may also convey a sense of stigma and potentially interfere with one’s likelihood for seeking help or leading to incorrect treatment [ 3 , 61 ]. A consistent set of observed, troublesome, comorbid disorders may support the possibility that existing problems drive problematic media use rather than the behavior indicating a uniquely diagnosable behavioral addiction.

Comorbidities

A core set of mental health problems comorbid with PIMU have been identified and include depression, attention deficit hyperactivity disorder (ADHD), anxiety, and autism [ 62 •]. As most of the research in this area is cross-sectional, the exact explanation for the association between PIMU and these other disorders is unknown and could include a one directional relationship (in either direction), a bi-directional relationship, or a common factor causing both issues [ 62 •]. Bearing in mind the complex etiology of these severe mental health issues, PIMU may very well arise from pre-existing mental health problems. The behaviors and environment afforded by excessive game play and internet use may also exacerbate certain symptoms of these disorders. The associations likely differ by unique co-occurring disorder as well as by the specific behaviors evident in an individual’s experience of PIMU. Longitudinal representative research along with additional clinical investigations examining different presentations of PIMU (especially using samples from the USA) is needed to fully understand this relationship.

Depression and Anxiety

Regardless of the specifics of the relationships, identifying the most common mental health issues that are comorbid with PIMU can help illuminate the disorder. Depression is consistently found to be predictive of problematic video game, internet, and smartphone use [ 63 , 64 , 65 ]. In a study comparing multiple predictors of the Internet Addiction Scale, level of depression had the strongest association even when considering demographics, personality traits, and future time perspective (i.e., the ability to envision and pursue future goals) [ 22 ]. Considering anxiety is closely related to depression, it is not surprising that it too has been shown to be linked to PIMU. Young people’s use of technology to cope with depression and anxiety likely explains at least some of these observed relationships, but a reciprocal relationship between PIMU and depression or anxiety is likely most realistic [ 64 , 66 ].

Seeing as impulsivity is a common trait of adolescents suffering from PIMU, it follows that ADHD is one of its most common comorbidities. In a recent review, 87% of the included studies found significant relationships between ADHD symptoms and PIMU [ 62 •]. Findings from a meta-analysis align with these results with studies consistently showing that PIMU is present at higher rates among those with ADHD from those without [ 67 ]. Furthermore, adolescents with ADHD show more severe symptoms of PIMU and are less likely to respond to treatment [ 67 , 68 ]. Ease of boredom, poor self-control, and other typical symptoms of ADHD are likely driving this association [ 67 ].

PIMU was shown to be prevalence in 45.5% of a small clinical sample of youth with Autism Spectrum Disorder (ASD) [ 69 ]. Youth with ASD have higher levels of compulsive internet use and video game play compared to healthy peers [ 70 ]. Online communication platforms especially those that occur within the well-defined ruleset of multiplayer games may be seen as less threatening and thereby particularly attractive to youth with ASD who desire connection but tend to lack well-developed social skills [ 4 ]. The coexistence of ADHD and ASD is an especially predictive combination with PIMU observed in 12.5% of patients with ADHD, 10.8% of those with ASD, and 20.0% of those with both disorders [ 71 ].

For clinicians hoping to better discriminate between adolescents who are heavily engaged with screen media and those who are experiencing problematic use, it is likely effective to attend carefully to young people with mental health issues commonly comorbid to PIMU. To inform on this effort, my colleagues and I have proposed the acronym A-SAD (ADHD, social anxiety, ASD,depression) to remember these key disorders [ 5 •]. While this suggestion is consistent with current evidence, research testing this approach is still necessary in order to understand its overall effectiveness in clinical settings.

Even though there is continued debate about the nomenclature around this issue and the appropriateness of labeling the problem an addiction or its own mental health diagnosis, adolescents around the world are seeking treatment to overcome their disordered media use and its consequences. As of yet, there is not an agreed upon approach for treating PIMU resulting in resourceful and skilled clinicians applying and adapting multiple approaches known to be effective to similar issues to this newer problem. For many years, there were few systematic investigations of these treatments, but recently the number of clinical trials has increased.

Cognitive Behavioral Therapy

With rigorous research in this field becoming more common, a recent review was able to rely more heavily on randomized clinical trials in reaching its conclusions [ 72 •]. This work identified 3 treatment possibilities as most heavily researched—cognitive behavioral therapy (CBT), pharmacological, and group/family therapies—however, approaches in all three were only classified as experimental [ 72 •]. CBT seeks to change problematic thought patterns and their resulting behaviors especially in terms of coping with psychological problems in healthy, direct ways. The approach of using CBT to address the cognitions of problematic users was proposed almost two decades ago and has been applied and adjusted to numerous populations and settings [ 73 ]. In a prototypical study, patients identified as having internet addiction and a comorbid disorder received CBT for 10 sessions and showed improvement in both internet use and anxiety [ 74 •]. Pooled effect sizes from studies of this treatment have demonstrated that overall, CBT is successful at reducing symptoms of depression and of IGD and slightly less so for anxiety [ 75 ••]. Although there is less evidence for CBT’s effectiveness at reducing game play, such a goal is less central as gaming is not inherently problematic [ 75 ••]. Dialectical behavior therapy, which is based on CBT but addresses emotions along with thoughts and behaviors, has also been applied to PIMU and seems to offer promise for future treatment [ 6 ].

Pharmacological Treatment

Other treatments including pharmacological and group and family therapies have not been the subject of as many research investigations as CBT, but findings from these areas do show encouraging effects. The general approach of pharmacological treatment has been to use medications to treat comorbid conditions or underlying pathologies of PIMU including depression [ 76 ], ADHD [ 77 ], obsessive-compulsive disorder (OCD) [ 78 ], and others. In an exemplar RCT of 114 adolescents and adults with IGD, the effectiveness of two antidepressants (escitalopram and bupropion) were investigated [ 79 ••]. Both were effective at reducing IGD, but bupropion also improved impulsivity, inattention, and mood problems which is consistent with its reported use as a treatment for ADHD [ 79 ••]. Following a similar protocol, researchers compared the effectiveness of two ADHD medications, a stimulant (methylphenidate) and non-stimulant (atomoxetine), on symptoms of both ADHD and IGD [ 80 ]. Both medications successfully reduced symptoms of IGD seemingly through their ability to regulate impulsivity [ 80 ]. Other studies reveal similar effects resulting in an overall conclusion that a pharmacological approach can be successful in reducing symptoms of both PIMU and comorbid disorders [ 81 ].

Group and Family Therapies

Group and family therapies are also being used to address PIMU. While group-based interventions that are 8-weeks or longer and include 9–12 people appear most effective [ 82 ], these approaches vary greatly making it difficult to determine which other aspects of the approach contribute to any observed successes. A systematic review describes four studies using single-family groups, multi-family groups, and school-based groups and implementing CBT-based approaches, novel psychotherapy approaches designed specifically for PIMU sufferers, and traditional family therapy approaches [ 81 ]. Group interventions have also been designed to prevent PIMU among adolescents although the effectiveness of this approach is still unknown [ 83 ]. Investigations of these treatments do show some promise. For example, a study of using multi-family group therapy found 20 out of 21 adolescent participants were no longer considered addicted to the internet following the six, 2-h sessions [ 84 ]. While the approach as a whole is based on strategies known to be effective in substance use and other adolescent problems, the heterogeneity of the therapies makes it difficult to draw any final conclusions.

There has been much advancement in identifying and treating PIMU over the last 5 years. The inclusion of IGD in the DSM-5 and of GD in the WHO’s ICD-11 has been the impetus for a growing consensus around terminology and approach. Considerable research has demonstrated that IGD can be assessed reliably and that the defined cutoffs effectively differentiate between those with and without the disorder. However, a large debate continues about whether the terminology and subsequent conceptual and clinical approaches should be based on a specific activity or broader set of behaviors. A framework that describes and addresses a multitude of behaviors that share certain determinants, comorbidities, and expressions can avoid the unsustainable situation of developing a new term and tactic for every problematic media behavior.

Additional research is necessary to more fully develop our clinical understanding and treatment approach to PIMU. Foundational, longitudinal work would help disentangle the direction of association between mental health problems and PIMU, and clinical investigations could continue to determine how therapy and medication can most effectively treat the condition. Clinical work investigating patient samples from the USA are very rare and are necessary to build awareness and increase resources available to treat the problem. Additionally, new research should explore the impact of the COVID-19 pandemic on PIMU. As screens have been relied upon for essential purposes including education, communication, and social connectedness, use has inevitably risen, and youth previously balancing media use and other activities may find themselves struggling. While our knowledge has grown substantially in this area, there are still questions that need to be answered before we can effectively treat this modern facet of adolescent health.

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The author would like to thank Jill Kavanaugh, MLIS for her assistance with the literature searches for this review.

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Relationship between loneliness and internet addiction: a meta-analysis

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• 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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ORIGINAL RESEARCH article

Internet addiction and related clinical problems: a study on italian young adults.

• 1 Department of Neurosciences, University of Verona, Verona, Italy
• 2 Unit of Addiction Medicine, Department of Internal Medicine, Integrated University Hospital of Verona, Policlinico “G.B. Rossi”, Verona, Italy
• 3 Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
• 4 Amici C.A.S.A. San Simone No Profit Association, Mantova, Italy

The considerable prominence of internet addiction (IA) in adolescence is at least partly explained by the limited knowledge thus far available on this complex phenomenon. In discussing IA, it is necessary to be aware that this is a construct for which there is still no clear definition in the literature. Nonetheless, its important clinical implications, as emerging in recent years, justify the lively interest of researchers in this new form of behavioral addiction. Over the years, studies have associated IA with numerous clinical problems. However, fewer studies have investigated what factors might mediate the relationship between IA and the different problems associated with it. Ours is one such study. The Italian version of the SCL-90 and the IAT were administered to a sample of almost 800 adolescents aged between 16 and 22 years. We found the presence of a significant association between IA and two variables: somatization (β = 7.80; p < 0.001) and obsessive-compulsive symptoms (β = 2.18; p < 0.05). In line with our hypothesis, the results showed that somatization predicted the relationship between obsessive-compulsive symptoms and IA (β = −2.75; t = −3.55; p < 0.001), explaining 24.5% of its variance (Δ R 2 = 1.2%; F = 12.78; p < 0.01). In addition, simple slopes analyses revealed that, on reaching clinical significance (+1 SD), somatization showed higher moderation effects in the relationship between obsessive-compulsive symptoms and IA (β = 6.13; t = 7.83; p < 0.001). These results appear to be of great interest due to the absence of similar evidence in the literature, and may open the way for further research in the IA field. Although the absence of studies in the literature does not allow us to offer an exhaustive explanation of these results, our study supports current addiction theories which emphasize the important function performed by the enteroceptive system, alongside the more cited reflexive and impulsive systems.

Introduction

Internet addiction (IA), also referred to as problematic , pathological , or compulsive Internet use , is a controversial concept in the research field. The frequent use of different terms to describe this new phenomenon, linked to the advent and growth of the Internet, leads to confusion over what it really consists of Tereshchenko and Kasparov (2019) .

Although researchers have yet to find a common definition of IA, it can be considered a “ non-chemical, behavioral addiction, which involves human-machine interaction ” ( Griffiths, 2000 ). Useful clinical criteria were proposed by Block (2008) , who associates IA with (a) increased feelings of anger, anxiety or sadness when the Internet is not accessible (craving); (b) the need to spend more hours on Internet devices in order to feel pleasure or cope with dysregulation of mood (tolerance); (c) poor school performance or vocational achievement; and (d) isolation or social withdrawal.

One aspect that researchers agree on is the importance of IA prevention in children and adolescents ( Lan and Lee, 2013 ). As with other forms of addiction, younger people are at greater risk of the negative effects of out-of-control Internet use ( Ko et al., 2008 ). In adolescence, distress is expressed in the form of behavioral agitation, somatic symptoms, boredom and an inclination to act ( Carlson, 2000 ), all modalities that facilitate the development of a coping strategy based on compulsive Internet use. This is a problem, given that 80% of adolescents use tablets or smartphones ( Fox and Duggan, 2013 ), whereas general population prevalence rates range from 0.8% (Italy) to 26.5% (Hong Kong) ( Kuss et al., 2014 ).

We currently know that IA is associated with symptoms of ADHD in teens ( Yoo et al., 2004 ), pathological gambling ( Phillips et al., 2012 ), depression ( Andreou and Svoli, 2013 ; Ho et al., 2014 ), anxiety ( Griffiths and Meredith, 2009 ; Zboralski et al., 2009 ), social phobia ( Carli et al., 2013 ; Gonzalez-Bueso et al., 2018 ), experiential avoidance ( Hayes et al., 1996 ; García-Oliva and Piqueras, 2016 ), obsessive-compulsive disorder (OCD) ( Jang et al., 2008 ; Cecilia et al., 2013 ), eating disorders ( Shapira et al., 2003 ; Bernardi and Pallanti, 2009 ), and sleep disorders ( Nuutinen et al., 2014 ; Tamura et al., 2017 ), as well as with relational conflicts ( Gundogar et al., 2012 ), aggression ( Cecilia et al., 2013 ), self-destructive behaviors ( Sasmaz et al., 2014 ), suicidal behaviors ( Durkee et al., 2016 ), physical health problems ( Sung et al., 2013 ), and chronic pain syndrome ( Wei et al., 2012 ). However, little is known about the factors potentially implicated in the etiopathogenesis of IA ( Tereshchenko and Kasparov, 2019 ).

Many of the most common symptoms of addiction and OCD are similar to each other, to the point that some authors define IA as compulsive computer use ( Kuss et al., 2014 ). However, there are also significant differences between the two sets of psychopathological symptoms. The obsessive-compulsive symptoms that characterize OCD can be described as recurring and persistent inappropriate thoughts (obsessions) that lead the individual to implement behaviors (compulsions) aimed at reducing the intensity of the distress deriving from these obsessive thoughts ( American Psychiatric Association [APA], 2013 ). Instead, the obsessive-compulsive symptoms reported in the context of addiction can also derive from positive thoughts about the object of the addiction, which drive the individual to seek and, in this case, engage in the activity in order to obtain gratification ( Robbins and Clark, 2015 ).

In this framework, the obsessive-compulsive component of IA can be considered in terms of (a) recurrent positive and negative thoughts (obsessions), associated, respectively, with the memory of the enjoyable experience of using the Internet, and with craving or withdrawal syndrome; and (b) instrumental behaviors (compulsion) geared toward seeking the former (positive reward) or reducing the discomfort associated with the latter (negative reward).

Adolescents with IA can be expected to display: (a) a lower ability to use reflexivity to manage their internal states; and (b) a greater propensity for impulsive behaviors to manage these states. This is the hypothesis recently proposed by Wei et al. (2017) to explain internet gaming disorder (IGD), a form of IA. However, alongside the presence of a hypoactive reflective system and an overactive impulsive system, these authors also hypothesize a dysregulation of the interoceptive awareness system, and suggest that this dysregulation increases the incentive salience of Internet use, as well as the feeling of craving deriving from its compulsive use ( Wei et al., 2017 ). This thesis could explain the relationship commonly observed between compulsive use of the Internet and somatization ( Yang et al., 2005 ).

Somatization is defined as the “ unconscious process of expressing psychological distress in the form of physical symptoms ” ( Nakkas et al., 2019 ), and it is commonly found among adolescents with IA. It is estimated that 9% of Internet-addicted adolescents display somatization ( Yang, 2001 ), reported in the literature to consist of somatic symptoms ( Potembska et al., 2019 ), chronic pain ( Wei et al., 2012 ; Fava et al., 2019 ), physical health problems ( Sung et al., 2013 ), and sleep disorders ( Tamura et al., 2017 ). Moreover, in late adolescence, the presence of somatization has been positively associated with the intensity of specific forms of IA, such as IGD ( Cerniglia et al., 2019 ). One study showed that higher somatization and interpersonal sensitivity scores predict problematic smartphone use ( Fırat et al., 2018 ). Ballespi et al. (2019) , illustrate that inability to mentalize is associated with a higher frequency of somatic complaints.

Although the involvement of somatization in the etiopathogenesis of IA is not yet clear, models recently advanced to explain the development of addiction assign it a primary role. In the triadic neurocognitive model of addiction ( Noël et al., 2013 ), for example, perception of the somatic state of the organism, governed by the insular cortex, is considered a factor that mediates the development of addiction. In fact, in the absence of cognitive processing of the bottom-up somatic signals mediated by this cerebral structure, the main symptoms of addiction suddenly disappear.

These data were recently confirmed by Naqvi et al. (2007) , who showed that absence of the somatic symptoms typical of craving and physical abstinence, induced by ischemic damage to the insula, allowed heavy smokers to give up smoking.

Somatization has been reported in association with IA in a college student population ( Alavi et al., 2011 ), and it has also been identified among the causal factors and predictors of IA among first-year college students ( Yao et al., 2013 ). Indeed, this latter study confirmed that students with somatization seem to have a greater tendency to develop IA. In addition, a study by Biby (1998) showed that higher somatization scores are linked to higher obsessive-compulsive tendency scores. Therefore, if a key role of somatic symptoms in modulating the activity of the reflexive and impulsive systems can be taken to explain the development of IA, it seems possible to hypothesize that the presence of obsessive-compulsive symptoms, commonly found in adolescents with IA ( Yen et al., 2008 ), may also be linked to the presence of somatization. In this sense, an additional hypothesis is that higher somatization in adolescents might exacerbate the effect of obsessive-compulsive symptoms on IA. Surprisingly, this hypothesis has not been investigated in the literature to date, although contemporary etiopathogenetic models suggest the importance of bottom-up somatic signals in addiction disorders ( Verdejo-García and Bechara, 2009 ). In fact, somatic symptoms may be linked to the presence of the same top-down processing of body signals related to craving or abstinence. According to the above hypothesis, these symptoms may upset the activity of the cognitive system, shifting it away from inhibitory control of Internet use, implemented by the reflexive system, toward compulsive behaviors, driven by the impulsive system ( Wei et al., 2017 ). In this way, high levels of somatization could both promote the development of IA and reinforce the relationship between obsessive-compulsive symptoms and IA.

In conclusion, our hypothesis is that somatization moderates the positive relationship between OC symptoms and IA. Specifically, the higher the level of somatization, the stronger the relationship.

Materials and Methods

Participants.

Participants were recruited from schools in the north of Italy. The study was presented during the participants’ classes. Students were invited to take part in a research study that aimed to investigate: drug use/abuse, gambling problems, alcohol use/abuse, mood. Students who provided informed consent were given two self-report instruments. All the participants were free to stop filling in the questionnaires at any time. Underage subjects needed parental permission to participate in this study.

The participants (57.7% females) ranged in age from 16 to 22 years (mean age 17.52 ± 1.15). All were third (35%), fourth (37%), or fifth grade (28%) Italian secondary school students.

Instruments

Symptom checklist 90—revised (scl-90-r; derogatis, 1994 ).

The Somatization (SOM;12 items) and Obsessive-Compulsive (OC; 10 items) subscales of the Italian version of the SCL-90-R were used. The participants used a five-point Likert scale, ranging from 0 (Not at all) to 4 (Extremely), to rate the extent to which they had experienced the listed symptoms during the past week. Cronbach’s alpha was 0.86 for SOM, and 0.82 for OC.

Internet Addiction Test (IAT; Young, 1998 )

This is a 20-item questionnaire on which respondents are asked to rate, on a five-point Likert scale, items investigating the degree to which their Internet use affects their daily routine, social life, productivity, sleeping patterns, and feelings. The minimum score is 20, and the maximum is 100; the higher the score, the greater the problems caused by Internet use. Young suggests that a score of 20–39 points is that of an average on-line user who has complete control over his/her Internet use; a score of 40–69 indicates frequent problems due to Internet use; and a score of 70–100 means that the individual’s Internet use is causing significant problems. Cronbach’s alpha was 0.88.

Socio-Demographics

The participants reported their age, gender, school and grade. In order to maintain privacy, no other personal information was requested.

Control Variables

The use of illicit drugs and gambling behavior were introduced as control variables. Specifically, the participants answered questions on their habits regarding any use of illicit drugs (cannabis, cocaine, heroin), alcohol consumption, and gambling activities, such as scratch cards, lottery tickets, football pools, new slot machines (VLTs) and video poker, betting on sporting or other events, poker and other card games.

Statistical Analyses

All the analyses were carried out using IBM SPSS Statistics 26.0 and AMOS ( Arbuckle, 2012 ). A series of confirmatory factor analyses (CFAs) was conducted to establish the discriminant validity of the scales. A full measurement model was initially tested, comparing it to a one-factor structure (in which all the items loaded into a common factor). The model fit was tested by using the comparative fit index (CFI), the incremental fit index (IFI), and the root-mean-square error of approximation (RMSEA). According to Kline (2008) and Byrne (2016) , the CFI and IFI values should have a cutoff value of ≥0.90, and the RMSEA a value of ≤ 0.08 to indicate a good fit of the model. Internal consistency of the constructs was evaluated using Cronbach’s alpha (α).

We tested the effects of somatization symptoms, obsessive-compulsive symptoms, and their interaction on IA by using the SPSS version of Hayes’s (2017) bootstrap-based PROCESS macro ( Hayes, 2012 , 2013 ; Model 1). All predictors were mean-centered prior to computing the interaction term and simple slopes were calculated at ± 1 SD. Age, sex, type of school, grade, use of illicit drugs, and gambling behaviors were included as covariates. To account for non-normality, analyses were performed with bootstrapping with 5,000 resamples.

Preliminary Analyses

Table 1 shows the means, standard deviations and internal consistencies obtained for each scale, and the correlations between the measures used in the current study.

Table 1. Descriptives of study variables ( n = 796).

Measurement Model

Prior to testing our hypothesis, we used CFAs to examine the convergent and discriminant validity of our study variables. The data were found to fit the measurement model: χ 2 (811) = 1150.99, p < 0.001, CFI = 0.90, TLI = 0.90, RMSEA = 0.035. All items loaded significantly on the intended latent factors.

Moderation Analysis

It was hypothesized that obsessive-compulsive symptoms would predict IA, depending on the somatization symptoms (moderation hypothesis). Regression analyses ( Table 2 ) conducted with the PROCESS macro (Model 1; Hayes, 2012 ) showed that obsessive-compulsive symptoms (β = 7.80, p < 0.001) and somatization symptoms (β = 2.18, p < 0.05) were related to IA after controlling for age, sex, grade, school, illicit drug use, and gambling behaviors. The moderation effect was significant t (788) = −3.55; p < 0.001 (β = −2.75, SE = 0.77, CI −4.27 to −1.2) and accounted for a significant portion of variance of IA [Δ R 2 = 1.2%; F ( 788 ) = 12.78 p < 0.01]. In this sense, increasing obsessive-compulsive symptoms predicted increased IA, but this effect was greatest at higher levels of somatization symptoms. The final model accounted for a total of 24.5% of the variance in IA.

Table 2. Results of the moderation analysis.

Simple slopes analyses revealed that when somatization symptoms were low (−1 SD), there was a statistically significant effect of obsessive-compulsive symptoms on increased IA (β = 9.48, SE = 0.88, t = 10.80, p < 0.01). Furthermore, also when somatization symptoms were high (+1 SD), there was a significant effect of obsessive-compulsive symptoms on IA (β = 6.13, SE = 0.79, t = 7.83, p < 0.001). Simple slopes analyses ( Figure 1 ) revealed that when somatization symptoms were low.

Figure 1. Interaction term for two levels of somatization: low (−1 SD) and high (+1 SD). Using the Johnson-Neyman technique ( Bauer and Curran, 2005 ), we identified the region where the effect of somatization symptoms on the relationship between OC and IA ceased to be statistically significant. Application of the Johnson-Neyman technique gave cutoff scores for somatization symptoms of below 1.81 and above 1.63.

The aim of this study was to increase current knowledge about the relationship between somatization symptoms, obsessive-compulsive symptoms, and IA in adolescents. Specifically, we hypothesized that the relationship between obsessive-compulsive symptoms and IA would be stronger at higher levels of somatization symptoms.

First, findings from our study suggest that obsessive-compulsive symptoms are associated with IA. These results are in line with prior research, which found that high levels of obsessive-compulsive symptoms are linked to higher IA risk ( Jang et al., 2008 ; Dong et al., 2011 ; Ko et al., 2012 ). Furthermore, IA has typically been described as a secondary condition resulting from various primary disorders, although findings in young adult samples have suggested that, within a range of psychopathologies, only obsessive-compulsive symptoms preceded IA ( Dong et al., 2011 ; Ko et al., 2012 ). The obsessive-compulsive symptoms observed in association with IA are similar to those of OCD, so much so that many researchers define IA as compulsive computer use ( Kuss et al., 2014 ). However, the obsessive-compulsive symptoms of OCD have been described as more ego-dystonic than those of IA ( Shapira et al., 2000 ). In general, the obsessive-compulsive symptoms of IA stem from recurring or persistent positive or negative thoughts (obsessions) that motivate the individual to implement behaviors (compulsions) intended to allow him/her to experience the hedonic satisfaction deriving from obtaining a positive reinforcement ( Robbins and Clark, 2015 ), or to reduce the distress typically associated with craving and abstinence states. IA may thus serve as a strategy for relieving pre-existing obsessive-compulsive psychopathology, a mechanism that, in turn, could actually reinforce the symptoms ( Ko et al., 2012 ). Similarly, this association could be further reinforced by underlying mechanisms shared by OC and IA behaviors ( Ko et al., 2012 ). Repetitive behavioral manifestations aimed at achieving immediate gratification or de-escalating the distress triggered by obsessive thoughts in order to improve one’s feelings are typical of addictions and compulsive behaviors ( Robbins and Clark, 2015 ). In the present study, the main effect of somatization symptoms on IA was in line with the findings of previous research ( Yang et al., 2005 ; Yen et al., 2008 ; Alavi et al., 2011 ; Yao et al., 2013 ). Somatization is conceptualized as a process that leads to translation of psycho-emotional distress into bodily discomfort ( Nakkas et al., 2019 ). Subjects with somatization disorders requiring inpatient treatment manifest deficits in both emotional awareness and Theory of Mind functioning. These deficits may underlie the phenomenon of somatization ( Subic-Wrana et al., 2010 ).

As regards our moderation hypothesis, we found that the relationship between obsessive-compulsive symptoms and IA was greatest at higher levels of somatization symptoms. Our results showed that in adolescents with higher somatization (+1 SD), the relationship between obsessive-compulsive symptoms and IA was stronger. To our knowledge, this is the first study that has investigated this relationship. Our results are in line with the triadic theory of addiction ( Noël et al., 2013 ), where somatization, as a major expression of the enteroceptive system, could hinder the management of normal emotional distress through problem-focused coping strategies based on reflexive system mentalization skills. This apparent partial impairment of the reflexive system’s capacity to regulate emotional distress could therefore lead adolescents to adopt emotion-focused coping strategies, such as ones related to implementation of the same obsessive-compulsive behaviors promoted by the impulsive system. Somatization could therefore impair the mentalization skills used by the reflexive system to inhibit compulsive behaviors driven by the impulsive system, predisposing the adolescent to develop IA. This could explain why obsessive-compulsive symptoms are often found in the literature as prodromes of IA development ( Dong et al., 2011 ; Ko et al., 2012 ), as well as why IA has typically been described as a secondary disorder resulting from a primary one, like obsessive-compulsive symptomatology ( Dong et al., 2011 ; Ko et al., 2012 ), a relationship that is confirmed in our study.

Our analyses were performed controlling for gender, age, grade, and school. Specifically, a significant gender difference emerged, as showed in previous studies ( Cao et al., 2011 ; Barke et al., 2012 ; Kuss et al., 2013 ). As showed in a study by Feng et al. (2019) , we have found a significant grade difference.

The present study has several limitations. First, the cross-sectional design used does not allow the identification of causal relationships among variables. We cannot definitively conclude that obsessive-compulsive symptoms cause IA and that this relationship depends on levels of somatization. Future studies should consider longitudinal data to overcome the cross-sectional limitations. A second, potential, limitation concerns the reliance on self-reported data, which might have caused common method bias. However, we ran the Harman’s single factor test, which suggested that common method bias did not affect the results of this study. A third limitation concerns mentalization ability. Good mentalization could be protective against somatization, but we did not measure it. Future research could explore this aspect through specific questionnaires.

Adolescence is an important period of physical and psychological development. From a clinical perspective, the results of this study show that somatization is an important moderation factor in adolescence. The incapacity to use coping strategies and mentalization strategies to counter negative emotions could increase the somatization effect. In adolescents, obsessive-compulsive symptoms can be moderated by somatization. In this period of development, it is very important to pay attention to bodily signals, as they can mask psychological problems. Obsessive-compulsive symptoms can be very invalidating, and they can be exacerbated by somatization. Teenagers seeking a coping response in technological devices are at considerable risk of developing pathological use of these devices.

In conclusion, somatization is an important aspect to consider when dealing with adolescent patients. It could be a moderation factor capable of exacerbating obsessive-compulsive symptoms or IA. This particular aspect needs more studies in the future.

Data Availability Statement

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

Ethics Statement

The studies involving human participants were reviewed and approved by the CARU-Comitato di Approvazione per la Ricerca sull’Uomo, Università di Verona. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

FL, AR, and LZ were responsible for the study concept and design. SC, FC, and RM contributed to the data acquisition. IP assisted with the data analysis and interpretation of findings. AF, LZ, IP, and AC drafted the manuscript. All authors critically reviewed the content and approved the final version of the manuscript for publication.

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.

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Keywords : somatization, internet addiction, adolescent, moderation, obssessive-compulsive disorder

Citation: Zamboni L, Portoghese I, Congiu A, Carli S, Munari R, Federico A, Centoni F, Rizzini AL and Lugoboni F (2020) Internet Addiction and Related Clinical Problems: A Study on Italian Young Adults. Front. Psychol. 11:571638. doi: 10.3389/fpsyg.2020.571638

Received: 11 June 2020; Accepted: 21 October 2020; Published: 10 November 2020.

Reviewed by:

Copyright © 2020 Zamboni, Portoghese, Congiu, Carli, Munari, Federico, Centoni, Rizzini and Lugoboni. 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: Lorenzo Zamboni, [email protected]

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A study of internet addiction and its effects on mental health: A study based on Iranian University Students

Affiliations.

• 1 Health Education and Health Promotion, Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran.
• 2 Social Determinants in Health Promotion Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
• 3 Antai College of Economics and Management/School of Media and Communication, Shanghai Jiao Tong University, Shanghai-China.
• 4 Department of Health Care Services and Health Education, School of Health, Ardabil University of Medical Science, Ardabil, Iran.
• 5 Department of Anatomical Sciences, Medical School, Kermanshah University of Medical Sciences, Kermanshah, Iran.
• 6 Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran.
• 7 Students Research Committee, Kermanshah University of Medical Sciences, Kermanshah, Iran.
• PMID: 33062738
• PMCID: PMC7530416
• DOI: 10.4103/jehp.jehp_148_20

Introduction: The Internet has drastically affected human behavior, and it has positive and negative effects; however, its excessive usage exposes users to internet addiction. The diagnosis of students' mental dysfunction is vital to monitor their academic progress and success by preventing this technology through proper handling of the usage addiction.

Materials and methods: This descriptive-analytical study selected 447 students (232 females and 215 males) of the first and second semesters enrolled at Kermanshah University of Medical Sciences, Iran, in 2018 by using Cochrane's sample size formula and stratified random sampling. The study applied Young's Internet Addiction Test and Goldberg General Health Questionnaire 28 for data collection. The study screened the data received and analyzed valid data set through the t -test and Pearson's correlation coefficient by incorporating SPSS Statistics software version 23.0.

Results: The results of the current study specified that the total mean score of the students for internet addiction and mental health was 3.81 ± 0.88 and 2.56 ± 0.33, correspondingly. The results revealed that internet addiction positively correlated with depression and mental health, which indicated a negative relationship ( P > 0.001). The multiple regression analysis results showed students' five significant vulnerability predictors toward internet addiction, such as the critical reason for using the Internet, faculty, depression, the central place for using the Internet, and somatic symptoms.

Conclusions: The study findings specified that students' excessive internet usage leads to anxiety, depression, and adverse mental health, which affect their academic performance. Monitoring and controlling students' internet addiction through informative sessions on how to use the Internet adequately is useful.

Keywords: Internet addiction; medical sciences; mental health; students; technology advancement.

Copyright: © 2020 Journal of Education and Health Promotion.

• 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.

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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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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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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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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
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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.

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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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Article contents

Development and effects of internet addiction in china.

• Qiaolei Jiang Qiaolei Jiang School of Journalism and Communication, Tsinghua University
• https://doi.org/10.1093/acrefore/9780190228613.013.1142
• Published online: 15 September 2022

Internet addiction is a growing social issue in many societies worldwide. With the largest number of Internet users worldwide, China has witnessed the growth of the Internet along with the development and effects of Internet addiction, especially among the young. Originally reported anecdotally in mass media, Internet addiction has become an issue of great public concern after more than 20 years. The process of Internet addiction as an emerging risk in the Chinese context can be a showcase for risks related to information and communication technologies (ICTs), health, and everyday life. The term Internet addiction was first coined in the Western context and has since been recognized as a technology-driven social problem in China. Plenty of anecdotes, increasing academic research, and public awareness and concerns have put the threat of Internet addiction firmly on the policy agenda. Therefore, for prevention and intervention, research projects, rehab facilities, welfare services, and self-help programs have spread all over the country, and related regulations, policies, and laws have changed accordingly. Although controversies remain, through the staging of, and coping with, Internet addiction, people can better understand China’s digital natives and contemporary life.

• Internet addiction
• online game
• social media
• Internet literacy
• intervention

Introduction

As of June 2008 , China became the country with the world’s largest Internet population. According to the latest statistics from the China Internet Network Information Center (CNNIC), by the end of 2020 , Chinese Internet users had reached 989 million, and Internet penetration rate had reached 70.4% of the general population ( CNNIC, 2021a , p. 1), and as many as 94.9% of minors ( CNNIC, 2021b , p. 1). Furthermore, along with the development and proliferation of the Internet, Chinese society has witnessed the development and influence of Internet addiction.

To understand the development and effects of Internet addiction in China, the term Internet addiction needs to be analyzed. Internet addiction as a disorder was first “discovered” in the United States, and the two English words were first translated into two Chinese words/characters, “Wang Yin,” correspondingly, while in recent years the Chinese translation “Wangluo Chenmi” has been used more often.

According to DSM-5 ( Diagnostic and Statistical Manual of Mental Disorders , Fifth Edition), the gold standard of the American Psychiatric Association (APA), addiction is a severe substance use disorder that can be diagnosed based on certain criteria, or symptoms ( APA, 2013 ). In the DSM-5, symptoms of substance use disorders can be divided into four main categories: impaired control, social problems, risky use, and physical dependence. Therefore, addiction is usually substance-related, is not freely chosen, and needs to be diagnosed and treated by specialists or professionals. Gambling disorder, a new category of behavioral addiction, has been included in the DSM-5 chapter on addictive disorders, which indicates that, based on current research findings, gambling disorder is similar to substance use disorders with regard to clinical expression, brain origin, comorbidity, physiology, and treatment ( APA, 2013 ). Although Internet gaming disorder has been identified as a condition warranting further research before being considered as a formal disorder to be included in the DSM, its inclusion in DSM-5 Section III reflects the increasing scientific literature on the use and influences of Internet games ( APA, 2013 ).

Addiction translates into Mandarin Chinese as “Yin.” According to Xinhua Dictionary with English Translation , Yin means habitual craving or being particularly fond of something. In the Contemporary Chinese Dictionary , Yin means habitual craving formed as a result of the central nervous system’s repeated exposure to a stimulus or, in a broad sense, strong interest. Conversely, in Chinese cultural terms, addiction is more human-centered. Although the nervous system is mentioned, the key point is that repeated exposure forms a habit or strong interest.

Although addiction means a strong habitual behavior in both American and Chinese culture, it has different focuses. In the United States, the notion of addiction is linked tightly to chemical dependency on alcohol or drugs, and addicted individuals are considered more passive. Such a concept of addiction places more blame on the substance, which causes the habit and loss of control over life. Therefore, the addicts are seen as helpless and in need of help or treatment. Conversely, in China, the concept of addiction is more focused on individuals. People are assumed to be more active and to have free will. Thus, addiction is seen as a freely chosen behavior, so the individuals in question are regarded as excessive users or as refusing to abide by the common moral code.

Internet addiction as a disorder was originally proposed by Dr. Ivan Goldberg in a sincere-looking but satirical hoax posted on the online psychiatric bulletin board PsyCom.net (no longer available) in the United States in 1995 ( Dalal & Basu, 2016 ; Wallis, 1997 ). Pathological gambling, as diagnosed in the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), was used as the model for the spoofed description and diagnostic criteria ( Dalal & Basu, 2016 ).

Although Internet addiction was meant to be a hoax, Dr. Goldberg and the bulletin board were flooded with posts about painful stories and appeals for help, and the concept was promoted as a real condition by some scholars ( Dalal & Basu, 2016 ; Wallis, 1997 ). One of the most famous researchers was Kimberly Young, who founded the Center for Internet Addiction in 1995 and wrote a groundbreaking book that was the first to identify and treat Internet addiction disorder, as well as to consider its effects on individuals and their families ( Dalal & Basu, 2016 ; Netaddiction, 2020 ; Young, 1998a ). Since then, research on, and attention to, Internet addiction have increased continuously in many societies.

In the Chinese context, Internet addiction has been adopted, adapted, and appropriated and has come to be regarded as a technology-driven social risk or problem. Although it is still controversial, Internet addiction seems to have gained legitimacy in China and has experienced rapid development at different levels.

To address the development and effects of Internet addiction in China, the main section of this article has been divided into three parts: staging, prevalence, and coping. The first section focuses on the staging of Internet addiction as a risk in China based on the media coverage of Internet addiction. It offers information on the prominence of Internet addiction in the Chinese context. The second section, on the prevalence of Internet addiction in China, explores high-risk categories, introduces the development of the Chinese diagnostic criteria for Internet addiction, and provides information on prevalence based on multiple investigation reports. The third section, on coping with Internet addiction, discusses the treatments for, regulation of, prevention of, and intervention strategies for Internet addiction in China, thereby expanding the understanding of the influence of Internet addiction in China.

Staging of Internet Addiction as Risk and Problem

Media reportage seems to lead to the staging of Internet addiction as a health risk and social problem in China. With the rapid and great development of the Internet in China, many social problems related to Internet activities and online entertainments have been heavily covered in the Chinese media, and Internet addiction is one of the topics. With plenty of pathological behaviors and bizarre stories being reported, Internet addiction has become a prominent social problem ( Golub & Lingley, 2008 ; Jiang, 2019 ). Public attention, awareness, and concerns have been raised by the extensive media coverage of this issue ( Jiang & Leung, 2012 , 2015 ).

In China, Internet addiction first received media attention in 1998 , with one news article in both Hong Kong and Taiwan, followed by four news articles in 1999 in mainland China ( Jiang, 2019 ). Since then, media reports on Internet addiction have greatly increased each year.

Before the release of the diagnostic criteria, reports about Internet addiction were driven by a moral attitude, rather than a scientific consensus ( Cui & Wu, 2016 ). Metaphors have been widely used in Chinese media coverage as important frames for the definition, cause, evaluation, and solution of Internet addiction ( Jiang, 2019 ). Although more and more scientific studies have been conducted over the past two decades, people can still easily find metaphors, including “Internet opium” and “electronic heroin,” being used to address the issue of Internet addiction ( Bax, 2016 ; Golub & Lingley, 2008 ; Szablewicz, 2010 ). On the one hand, the metaphors may help people understand the symptoms of Internet addiction more easily by analogy to the symptoms of drug addiction. On the other hand, in the Chinese context, metaphors referring to opium or heroin can involve guilt, shame, and even traumatic collective memory, for example about the Opium Wars ( Bax, 2016 ; Golub & Lingley, 2008 ), which could stigmatize Internet games and online entertainment applications.

Young people, especially adolescents, are portrayed as particularly susceptible to the lure of the Internet, including online chat rooms, interactive games, social media, and cyber literature, and the Internet’s effects on youth are overwhelmingly described as decreased efficiency, reduced work performance, failure in school, juvenile delinquency, social alienation, and psychological disorders ( Jiang, 2019 ). People can easily come across tales of tragedies related to Internet addiction in mass media: family conflicts ( Tu & Wang, 2010 ; Yang, 2002 ), cybersex and online porn addiction ( Yang & Dang, 2003 ), school dropouts ( Zhou, 2004 ), self-harm or suicidal behaviors ( Golub & Lingley, 2008 ; Xinhua News Agency, 2006 ), deaths from online gaming marathons ( Lei et al., 2007 ; Watts, 2005 ), and crimes, including robberies and murders( Tao et al., 2009 ; Watts, 2005 ; Xinhua News Agency, 2006 ).

A breakthrough like the formulation of diagnostic criteria for Internet addiction is a legitimate topic for media interest. Societies often need to publicize health matters, and media can play a crucial role in disseminating information and persuading people to take the necessary action. At the 1996 American Psychological Association convention, Dr. Young’s research paper “Internet Addiction: The Emergence of a New Clinical Disorder” was the first to discuss the subject of Internet addiction and was approved for presentation. In her book, Young (1998b) mentioned that the media soon learned of her study, and the journalists swarmed around her, microphones were thrust in her face, and photographers snapped pictures, turning a professional presentation into an impromptu press conference. Since then, her research has been widely covered by the media, including major articles in the New York Times, The Wall Street Journal, The New York Post, USA Today, Newsweek, U.S. News & World Report, Los Angeles Times, Washington Post , and so on. She has been widely interviewed about Internet addiction on American, British, Swedish, and Japanese television programs.

A similar experience happened to Dr. Ran Tao, who proposed the Chinese diagnostic criteria for Internet addiction, wrote the first academic book on Internet addiction in Chinese, and set up China’s first Internet addiction clinic at the Military General Hospital in Beijing in 2004 . Tao’s treatment center in Beijing has been regarded as the first center specific to Internet addiction in the world, and it has gathered a sufficient sample of patients for proper scientific analysis ( Tao et al., 2010 ). Dr. Tao and the clinic have been covered widely in Chinese and foreign media, which have created both attention and heated controversy.

Crisis and breakthrough make the best kind of story. People may first hear of a new illness from the media. Furthermore, given that health problems affect many people, any development in diagnosis or treatment is indisputably a legitimate topic for media attention. However, the conventions that govern the discourse on scientific research in peer groups are different from those that obtain in wider contexts. Even extensive media coverage can deal with complex issues in only a relatively superficial way, but the media have considerable influence on public perceptions. Thus, mass media have increased public consciousness of Internet addiction as a health risk and as a social problem.

The Prevalence for the Internet Addiction in China

With ever-increasing academic research and widespread expression of public concern, certain groups of Internet users and online activities have been identified as high-risk categories for Internet addiction. The development of the Chinese diagnostic criteria for Internet addiction and multiple investigations focusing on the high-risk categories further showcased the prevalence of Internet addiction in China.

High-Risk Categories

In China, young people, especially adolescents, are described as being obsessed with the Internet ( Jiang et al., 2018 ). As a high-risk group for Internet addiction, adolescents are considered to be innocent, vulnerable, and dependent, and therefore in need of protection from possible harm on the Internet. Growing up as digital natives, young people in urban China have a longer history of Internet use than older generations have. However, the parents of these youth are digital immigrants, and usually they have limited knowledge and guidance regarding their children’s Internet use. Furthermore, Internet addiction could be part of the growing pains or developmental problems of adolescence and young adulthood in the digital era.

The Internet itself is not addictive ( Young, 1998b ), but specific applications, especially online entertainments with interactive or immersive features, including online games, social media, and smartphone apps, appear to be influential in the development of Internet addiction ( Huang, 2014 ; Jiang, 2019 ; Jiang & Huang, 2013 ; Jiang et al., 2013 ).

As a common activity among young Internet users, online gaming in particular is believed to be one of the triggers of Internet addiction ( Jiang, 2019 ). After becoming immersed in the compelling and socially rich virtual worlds of online games, players usually demonstrate stronger Internet connectedness. Some researchers point out that indulging in online gaming for long periods may gradually make young people neglect their studies and become alienated from real-life relationships ( Huang et al., 2010 ; Jiang, 2019 ). Thus, online game players became a high-risk category for Internet addiction. In China, widespread expression of public concern and heavy media reportage consistently point out the addictive potential of online gaming ( Jiang, 2014b ; Jiang & Fung, 2019 ). Excessive online gaming is found to correlate with the symptoms of addiction, including impaired control, social problems, risk behaviors, and psychological impairments. In China, many young people like playing online games in Internet cafés or on their smartphones, which may lead to more risk behaviors.

By the end of 2020 , the population of smartphone users in China totaled 989 million, with mobile Internet users accounting for 99.7% of Internet users in China ( CNNIC, 2021a ). With the popularity of smartphone use among Chinese Internet users, smartphone use and various software applications (apps) on the mobile Internet became an emerging high-risk category for Internet addiction that has garnered more and more attention. In contrast to previous studies on Internet gaming, which usually identified males as the high-risk group, research on smartphone addiction has indicated that females have become the new high-risk group for mobile Internet addiction ( Jiang & Li, 2018 ).

Diagnostic Criteria for Internet Addiction

Although Internet addiction and problematic Internet use have been reported by Chinese media for years, the first Chinese diagnostic criteria for Internet addiction were released openly in 2008 ( Xinhua, 2008 ). As China’s foremost proponent of the concept of Internet addiction, Dr. Ran Tao, a military psychiatrist who proposed the Chinese diagnostic criteria for Internet addiction, is also the author of the first book systematically examining Internet addiction in Chinese, and he set up China’s first Internet addiction clinic in Beijing in 2004 ( Jiang, 2019 ).

The Chinese diagnostic criteria include four parts: symptoms, severity, course, and exclusions. In the symptoms, eight items are used to diagnose Internet addiction: salience, tolerance, withdrawal symptoms, mood alteration, exclusiveness, relapse, hiding, and conflict ( Tao et al., 2010 ). The first four symptoms are essential for Internet addiction diagnosis, along with the severity of impairment of social functions and symptoms lasting for more than 3 months. The criteria also distinguish Internet addiction from Internet infatuation and other psychological problems ( Huang et al., 2007 ; Tao et al., 2010 ).

Tao’s criteria echo Young’s (1998a) eight-item Diagnostic Questionnaire (DQ) for Internet addiction disorder, which was based on the criteria for pathological gambling in DSM-IV. Drawing on the same criteria used to diagnose pathological gambling, Young (1998b) proposed the following items for the DQ: (a) Do you feel preoccupied with the Internet (think about previous online activity or anticipate next online session)? (b) Do you feel the need to use the Internet with increasing amounts of time in order to achieve satisfaction? (c) Have you repeatedly made unsuccessful efforts to control, cut back, or stop Internet use? (d) Do you feel restless, moody, depressed, or irritable when attempting to cut down or stop Internet use? (e) Do you stay online longer than originally intended? (f) Have you jeopardized or risked the loss of a significant relationship, job, or educational or career opportunity because of the Internet? (g) Have you lied to family members, therapist, or others to conceal the extent of involvement with the Internet? (h) Do you use the Internet as a way of escaping from problems or of relieving a dysphoric mood (e.g., feelings of helplessness, guilt, anxiety, depression)? Respondents who answer “Yes” to five or more of the eight questions are classified as addicted Internet users ( Young, 1998b ).

Both Dr. Young and Dr. Tao are pioneers in Internet addiction research and are also ardent supporters of the concept of Internet addiction. They each wrote the first book on Internet addiction in their sociocultural contexts and proposed diagnostic criteria and recovery strategies. Both Young’s DQ and Tao’s diagnostic criteria have identified Internet addiction with a list of eight symptoms. Developed and proposed more than 10 years before Tao’s criteria, Young’s DQ acted as the framework and model for Tao’s criteria, which were also based on the findings from Tao’s own studies ( Bax, 2016 ). Dr. Tao has confirmed that he compared his data with data from American scholars.

The first Chinese criteria have classified Internet addiction as a clinical disorder, although it is not yet officially recognized as a disorder in the United States, its birthplace. In this way, the concept of addiction in the Chinese criteria has adopted its meaning from the American cultural background, not the Chinese one, aiming to classify Internet addiction as a disorder. Even with heavy media coverage, it still took a long time for Internet addiction to be officially regarded as a disorder in China.

Due to much controversy about diagnosis and treatment of Internet addiction, until 2018 , when the International Classification of Diseases 11th Revision (ICD-11), released on June 18, 2018 , by the World Health Organization, included gaming disorder as a kind of mental, behavioral, or neurodevelopmental disorder, the National Health Commission of China officially included Internet addiction and its definition, diagnostic criteria, subtypes, and harms in the Key Information and Interpretation of Chinese Adolescent Health Education 2018 Revision . Recognition of gaming disorder as a diagnosable condition has helped people with the disorder get the treatment and services they need, although currently the ICD-11-based criteria are limited to gaming disorder, with general use of the Internet, social media, or other online applications not included.

The Prevalence of Internet Addiction in China

Internet addiction is regarded as a serious health risk among the young; therefore, examining its prevalence is important. Due to the web of multiple regulatory regimes and overlapping networks of power at different levels, many institutions, associations, and research centers have conducted national investigations and have released multiple reports.

To examine the prevalence of Internet addiction among adolescents in China, three national investigations were conducted by the China Youth Association for Network Development (CYAND), and accordingly three national statistical reports were released in 2005 , 2007 , and 2009 . According to the reports released by CYAND, 13.2% of adolescent Internet users were suffering from Internet addiction in 2005 . Among adolescent Internet addicts, males accounted for 7% more than females. The rate of Internet addiction was 17.1% among those age 13 to 17 years and 13.7% among those age 18 to 23 years. Moreover, among middle school students, the rate of Internet addiction was even as high as 23.2%, a very high prevalence. In 2009 , the rate of Internet addiction among adolescent Internet users in urban China was 14.1%, with 5.6% more males than females. The CYAND, 2009 Internet Addiction Report also indicated that the rate of Internet addiction was higher in less-developed areas than in more developed urban areas ( CYAND, 2010 ).

The CNNIC, which releases The Statistical Survey Report on Internet Development in China every year, has also included information related to the prevalence of Internet addiction in its official annual report. For example, the largest group of Chinese Internet users was students, with the number of Internet users between 6 and 18 years old in China reaching 183 million by the end of 2020 and with 19.6% of them feeling dependent on the Internet ( CNNIC, 2021b ).

Focused on Internet usage and dependency, the Central Committee of the Communist Young League released The National Research Report of Juvenile Internet Use three years in a row, in 2018 , 2019 , and 2020 . The reports provided the results of a national investigation of adolescents’ Internet use, Internet dependency, and Internet addiction throughout China.

Since 2010 , the Institute of Journalism and Communication at the Chinese Academy of Social Sciences has released the Blue Book of Adolescents: Chinese Juvenile Internet Use Report annually. This book is helpful for public understanding of the digital living situations of digital natives, including Internet addiction among adolescents.

In recent years, more and more institutions and research centers have focused on the issue of Internet addiction among the young. Therefore, investigation and discussion regarding Internet addiction have become an integral part of research on adolescents. For example, in the Big Data Analysis Report on Internet-related Juvenile Criminal Cases released in June 2018 by the Law and Technology Institute at Renmin University of China, Internet café patronage and Internet addiction were identified as the two main causes of Internet-related juvenile criminal cases. Moreover, in the Research Report on the Risk of Youth Internet Platform Participation released in 2020 by China Youth New Media Association, online gaming was discussed as one of the triggers of Internet addiction among adolescents due to its immersive and interactive experiences, which incur various risks.

Coping with Internet Addiction in China

To cope with the plight of Internet addiction, China has developed various treatment, regulation, prevention, and intervention strategies.

Treatments for Internet Addiction

Together with the development of Internet addiction in China, for more than 20 years, research projects, rehab facilities, welfare services, and self-help programs have spread nationwide. Based on the nationwide investigation conducted by CYAND, more than 300 institutions have offered treatment for Internet addiction, including clinics, special schools, and so on, but only dozens of them have been well developed ( CYAND, 2009 ).

China was among the earliest to set up Internet addiction clinics. However, these institutions were not able to provide standardized or generally acknowledged treatments. According to an investigative report from the Chinese Youth Research Center (CYRC), Internet clinics in China have provided a variety of coping strategies, and family communication has been found to be a key element in Internet addiction treatment ( CYRC, 2010 ). For example, Dr. Tao, who first proposed the Chinese diagnostic criteria for Internet addiction, developed an interdisciplinary “Five-in-One” integrated model, incorporating medicine, psychology, education, social conditioning theory, and social experience related to military training ( Bax, 2015 , 2016 ). Although many Internet addiction clinics have emerged in urban China, they are a mixture of good and bad: treatments can be expensive, and therapeutic efficiency varies considerably ( CYRC, 2010 ).

For a long time, until the release of ICD-11, the diagnosis of Internet addiction was based on clinical observation, a counseling interview, or criteria proposed by different researchers. In clinics, Internet addiction was treated as a new disorder, while in some rehab centers or camps, Internet addiction was considered to be adolescents’ deviant behavior or a form of distraction for growing pains ( Jiang, 2019 ).

Recently, multiple intervention strategies for Internet addiction have been applied, including medication, psychological counseling, study coaching, outdoor games, pedagogic activities, physical exercises, and military training ( Jiang, 2019 ). By using various therapeutic measures, institutions aim to foster adolescents’ self-control ability and good behavioral habits, with the aim of solving their Internet addiction problem in the long run. Most institutions have adopted a closed management system ( CYAND, 2010 ; CYRC, 2010 ).

The Internet addicts treated in institutions are mainly students at the junior high school, high school, and college levels ( CYRC, 2010 ). Some of them are tricked or forced by their parents to enter treatment, and quite few go willingly. The institutions help to prevent and to treat Internet addiction, but many problems remain. The institutions are distinguished from one another by different higher authorities, and their regulation is in disarray. There have been a series of controversial circumstances, including application of electroconvulsive therapy, the death of an adolescent Internet addict in an institution, and so on.

Regulation, Prevention, and Intervention

Public concern about Internet addiction has influenced policymaking, regulations, industry management, and welfare services ( Jiang, 2019 ).

Faced with the issue of Internet addiction among the young, the Chinese government has increasingly recognized that the diffusion of technology cannot be left to market forces and that regulation and intervention are required ( Hughes & Wacker, 2003 ; Qiu, 2004 ; Sohmen, 2001 ; Suttmeier, 2005 ). Government regulation of the Internet has become an integral part of intervention for Internet addiction. In order to protect Internet users, especially the young, the relevant changes that have been made to regulations and laws include the nationwide anti-addiction system, youth mode; the regulation of Internet cafés, the online game industry, and video websites/platforms; and the amendment to the Law on the Protection of Minors.

Since 2007 , Internet addiction has been included as an issue in the Law on the Protection of Minors. On June 1, 2021 , the revised Law on the Protection of Minors took effect, and for the best interests of the child, a new provision, “Internet Protection,” lays out duties for governments, schools, parents, and digital service providers to protect the young against cyber-based crimes and Internet addiction. In order to protect children on the Internet, the newly revised law stipulates a unified electronic identity authentication system for minors and a gaming curfew.

In China, many young Internet users are inveterate participants in gaming sessions. At one time, Internet cafés in both large cities and remote villages were crowded with young people glued to screens as they engaged in battles or chatting. Many of these young (mostly male) users suffered from Internet addiction ( Xinhua, 2008 ). In response to increasing attention to Internet addiction, the Chinese government has addressed the social concerns and simultaneously has used its influence to shape social values and norms. The government tried to regulate related industries and peoples’ Internet use in various ways. For example, the government banned Internet cafés and game labs within 200 meters of schools; it imposed strict licensing procedures, control of business hours, and restrictions of minors’ entry into Internet cafés; and it mandated installation of the anti-addiction system and anti-fatigue software. As for the Chinese online game industry, for the government applies a number of licensing procedures, and it uses real-name registration, the anti-addiction system, and anti-fatigue software ( Jiang & Fung, 2019 ).

As more new Internet-related entertainments have become popular, the anti-addiction system has been expanded. For example, to protect adolescents from addiction to short-video apps, including TikTok, a pop-up message is required to remind adolescent users to activate “Youth Mode,” which sets restrictions on content, features, and duration of use. The restrictions can stop adolescent users from tipping, livestreaming, topping up, and cashing out and can also include an imposed curfew. Advocates and policymakers in China contend that these new regulations and guidelines, including the anti-addiction system and anti-fatigue software, can empower parents in guiding their children’s Internet use and online behaviors more effectively.

Various welfare services have also been provided for the public to cope with the risk of Internet addiction. Regarding the importance of Internet literacy in the coping strategies ( Leung & Lee, 2011 ), related training, education, and workshops have been offered at schools, community centers, and online to teach young people and their parents more knowledge, techniques, and coping strategies. For example, in 2018 , the Ministry of Education issued the Emergency Brief on Education and Guidance for the Prevention of Internet Addiction among Primary and Middle School Students . Parents, school administrators, and health professionals commonly demonstrate their support for this kind of Internet literacy education. In this way, people may pay more attention to the narcotizing dysfunction of Internet use, the dark side of cyberspace, and the obsessive use, misuse, overuse, and even abuse of the Internet.

Review of the Literature

With the development and effects of Internet addiction in China, Internet addiction has become a growing field of research in recent years. The Chinese literature on Internet addiction is interdisciplinary and examines multiple aspects of the issue. Studies on Internet addiction in China have been conducted by scholars from communication studies (e.g., Jiang, 2019 ; Jiang & Leung, 2015 ; Liang & Leung, 2018 ), psychology (e.g., Shek & Yu, 2015 ; Yu & Shek, 2018 ), clinical medicine (e.g., Huang et al., 2010 ; Tao et al., 2010 ), education (e.g., Chou et al., 2005 ), and many other disciplines.

Current Internet addiction studies are primarily concerned with the diagnostic criteria for Internet addiction disorder (see Chen & Chou, 1999 ; Tao et al., 2010 ), different types of Internet addiction, including gaming addiction (see Chiu et al., 2004 ; Chou & Ting, 2003 ), Internet café addiction ( Wu & Cheng, 2007 ), social media addiction ( Huang, 2014 ), smartphone addiction ( Jiang & Li, 2018 ; Leung, 2008 ), and antecedents of Internet addictive behaviors and characteristics that make an individual more susceptible to becoming an Internet addict (see Chak & Leung, 2004 ; Ni et al., 2009 ; Tao et al., 2009 ; Whang et al., 2003 ; Yen et al., 2007 ), as well as treatments and interventions (see Yeh et al., 2008 ). Most of the existing research has focused on the young, who are regarded as a high-risk group (see Chou, 2001 ; Chou & Hsiao, 2000 ; Jiang, 2014a ; Jiang et al., 2018 ; Shek et al., 2008 ; Tsai & Lin, 2003 ). However, research on media coverage and public concern (see Jiang, 2019 ; Jiang & Leung, 2012 ) and critical analysis of Internet addiction in China (see Bax, 2015 ; Golub & Lingley, 2008 ; Szablewicz, 2010 ) remain limited.

Primary Sources

For those seeking an in-depth and overall understanding of Internet addiction in China, Jiang’s (2019) Internet Addiction among Cyberkids in China: Risk Factors and Intervention Strategies , which includes studies on public concern and media coverage of Internet addiction in contemporary China; clinical assessment of, and risk factors for, Internet addiction in adolescents; parent-reported signs of Internet addiction in Chinese children and adolescents; and coping strategies as well as treatments for Internet addiction, can work as a good collection of primary sources. It is the first book to cover media reportage of Internet addiction in China. Valuable clinical data on Internet-addicted adolescents in China have been retrieved and examined. The book also includes valuable firsthand ethnographic data from Internet-addicted adolescents, parents, and health professionals.

As an in-depth critical examination of the moral panic and treatment models regarding Internet addiction, Bax’s (2015) Youth and Internet Addiction in China provides a critical discussion of Internet addiction in China. Based on extensive original research, including discussions with psychiatrists, parents, and Internet-addicted young people, the book explores the conflicting attitudes that Internet addiction reveals. Bax (2015) has shown that contrasting attitudes lead to battles that are often fierce and violent. On the one hand, young people in China regard Internet use, especially online gaming, as a welcome escape from the dehumanizing pressures of contemporary Chinese life. On the other hand, the parents of these young people insist that working hard for good school grades is the correct way to progress, and they medicalize Internet overuse. The problem of Internet addiction is seen by some parents as so severe that they have sought psychiatric help for their children. However, in his book, Bax (2015) has argued that the greater problem may in fact lie with parents and other authority figures, who misguidedly apply pressure to force young people to conform to the empty values of a modern, dehumanized, consumer-oriented society.

For those who want to know more about specific types of Internet addiction in China, Huang’s (2014) Social Media Generation in Urban China: A Study of Social Media Use and Addiction among Adolescents is a collection of primary sources focusing on social media addiction. This exploratory study proposes the concept of “social media addiction.” The book examines the existence of social media addiction among adolescents in urban China, as well as the disorder’s symptoms, its sociopsychological predictors, the gratifications it confers, and its influence on adolescents’ academic performance and social capital. Based on quantitative questionnaire surveys in main urban Chinese areas, the book has shown that the adolescents addicted to social media experienced four major symptoms—preoccupation, impairment, alleviation of negative emotions, and loss of interest in social activities—which had a significant negative impact on the adolescents’ academic performance and social capital. The addicted adolescents were often self-absorbed, were bored with their usual leisure activities, and were good at using manipulation of social media for social interaction as well as for obtaining social, information, and entertainment gratification.

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• Hughes, C. R. , & Wacker, G. (Eds.). (2003). China and the Internet: Politics of the digital leap forward . Routledge.
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Internet addiction affects the behavior and development of adolescents

Adolescents with an internet addiction undergo changes in the brain that could lead to additional 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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Journal Reference :

• Max L. Y. Chang, Irene O. Lee. Functional connectivity changes in the brain of adolescents with internet addiction: A systematic literature review of imaging studies . PLOS Mental Health , 2024; 1 (1): e0000022 DOI: 10.1371/journal.pmen.0000022

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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.

• Research in  Plos Mental Health
• Ms Irene Lee's academic profile
• UCL Great Ormond Street Institute of Child Health
• UCL Population Health Sciences
• Credit:  monkeybusinessimages  on iStock

Media contact 

Poppy tombs .

E: p.tombs [at] ucl.ac.uk

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Too much internet use is changing teenage brains, study finds.

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Internet addiction can cause changes in the teenage brain which can impair a range of functions, ... [+] including short-term memory (Pic: Getty Creative)

Excessive use of the internet is reshaping teenage brains, according to a new study .

Scans show that the brains of teenagers who are addicted to the internet undergo changes in the parts of the brain involved in active thinking.

These were found to lead to additional addictive behavior, as well as changes associated with intellectual ability, physical co-ordination, mental health and development, according to researchers at University College London, who carried out the study.

“Adolescence is a crucial developmental stage during which people go through significant changes in their biology, cognition, and personalities,” said Max Chang, a masters student at the UCL Great Ormond Street Institute for Child Health and lead author of the study.

“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.”

Researchers looked at 12 studies where functional magnetic resonance imaging (fMRI) scans had been carried out on the brains of a total of 237 young people aged 10 to 19 formally diagnosed with internet addiction, defined as an inability to resist the urge to use the internet to the extent it negatively impacts their wellbeing, as well as their social, academic and professional lives.

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The scans found both increased and decreased activity in parts of the brain activated when resting, and an overall decrease in functional connectivity — how regions of the brain interact with each other — in the parts involved in active thinking, the executive control network.

The impact is similar to that resulting from drug-use and gambling addiction, the researchers found.

The implications for adolescent behavior are significant, according to the study, published in the peer-reviewed journal PLOS Mental Health.

Among the functions affected by a decline in functional connectivity are physical co-ordination, short-term memory, impulse control, attention span, decision-making, motivation, response to rewards and processing information.

Changes to the brain during adolescence make it particularly vulnerable to the impact of internet addiction, researchers say.

“The findings from our study show that this can lead to potentially negative behavioral and developmental changes that could impact the lives of adolescents,” Chang said.

“For example, they may struggle to maintain relationships and social activities, lie about online activity and experience irregular eating and disrupted sleep.”

Researchers caution that the use of fMRI scans to investigate internet addiction is limited, so the number of studies involving adolescents is relatively small. Most of the studies were carried out in Asia, and future research should compare results from Western countries, they add.

Nevertheless, the findings will add to concern about the impact of the internet and smartphone use on children and young people.

Only last month, a committee of U.K. lawmakers warned that a ban on under 16s using smartphones may be the best option to limit the damage they could cause.

More than three quarters of 10-15-year-olds in England and Wales spend three hours or more online at weekends, with one in five (22%) online for seven hours or more, and around half online for three hours plus on a school day, according to one survey .

In the U.S., almost half of teens say they use the internet “almost constantly”, according to a 2022 report by the Pew Research Center.

“There is no doubt that the internet has certain advantages,” said Irene Lee, of the UCL Great Ormond Street Institute of Child Health and senior author of the study.

“However, when it begins to affect our day-to-day lives, it is a problem.”

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• Ind Psychiatry J
• v.27(1); Jan-Jun 2018

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.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.