research topics in internet addiction

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How has Internet Addiction been Tracked Over the Last Decade? A Literature Review and 3C Paradigm for Future Research

Xuan-lam duong.

1 Faculty of Economics and Rural Development, Thai Nguyen University of Agriculture and Forestry, Thai Nguyen Province, Vietnam

2 Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Pingtung City, Taiwan

Shu-Yi Liaw

3 Management College, Computer Centre, National Pingtung University of Science and Technology, Pingtung City, Taiwan

Jean-Luc Pradel Mathurin Augustin

Background:.

The popularity of the internet aggravated by its excessive and uncontrolled use has resulted in psychological impairment or addiction. Internet addiction is hypothesized as an impulse-control disorder of internet use having detrimental impacts on daily life functions, family relationships, and emotional stability. The goal of this review is to provide an exhaustive overview of the empirical evidence on internet addiction and draw attention to future research themes.

We performed a literature search on ScienceDirect and PubMed to review original research articles with empirical evidence published on peer-reviewed international journals from 2010 to 2019. Eight hundred and 26 articles were eligible for analysis. Frequency and descriptive statistics were calculated by Microsoft Excel.

A substantial contribution has been coming from researchers from China, Turkey, Korea, Germany, and Taiwan respectively. Despite controversies regarding its definition and diagnostic procedures, internet addiction has become the focal point of a myriad of studies that investigated this particular phenomenon from different exposures. Given observed literature review data regarding research design, data acquisition, and data analysis strategies, we proposed the 3C paradigm which emphasizes the necessity of research incorporating cross-disciplinary investigation conducted on cross-cultural settings with conscientious cross-validation considerations to gain a better comprehension of internet addiction.

Conclusions:

The findings of the present literature review will serve both academics and practitioners to develop new solutions for better characterize internet addiction.

Introduction

The internet has become an indispensable part of modern society and its use has grown exponentially, causing internet addiction to become a growing concern across all age groups and countries.[ 1 ] Uncontrolled use of the internet significantly affects not only individuals' quality of life and social functioning but impacts their physical and psychological health.[ 2 , 3 ] Despite its ongoing controversy and debate concerning its conceptualization and classification among the scientific community,[ 4 ] internet addiction has received increasing attention over the past decades. Researchers initially considered internet addiction as part of the impulse-control disorder and/or obsessive-compulsive disorder models[ 5 ] or belonged to behavioural addiction spectrum,[ 6 ] because it exhibits the features of excessive use despite adverse consequences, withdrawal phenomena, and tolerance that typify many substance use disorders.[ 7 ] Neither a conclusive nor an agreed-upon definition for this disorder has been reached, making it difficult to establish a coherent picture of the phenomenon. Moreover, central discussions around internet addiction are further complicated by serious flaws in research designs as most studies are reliant on self-reported data recruited via multiple channels particularly prone to selection bias. The present study strives to highlight some key elements believed to cover all critical aspects of internet addiction. We also attempted to single out some key elements in the articles such as: research design, data collection, and data analysis strategies. Critical research priorities are provided to establish a concrete set of research preferences for better-managed prospective investigations. It is expected that such initiatives will, at least in part, orientate current and future research agendas to accommodate research trends across a broader spectrum with better knowledge of internet addiction.

The following section dedicates to outlining the prevalence and evolution of internet addiction, providing a brief overview of internet-related activities that can be engaged online. This is followed by characterizing associated risk factors and psychiatric comorbidity of internet addiction. Subsequently, the current literature on existing instruments that have been using to measure internet addiction is discussed. Finally, contemporary intervention and treatment are going to be delineated.

Epidemiology

Generally, prevalence estimates are essential to evaluate the demand for consulting, treatment offers and preventive strategies.[ 8 ] Yet, epidemiological studies have reported a significant variance in the prevalence rates among adolescents and young people from 6.3 to 37.9% in Asia.[ 4 , 9 ] In the United States, it ranges from 0.3 to 8.2%[ 7 , 10 ] while in Europe, it has been reported to be between 1% and 21.3%.[ 11 , 12 ] The global pooled prevalence in the general population was estimated to be 6.0%,[ 13 ] indicating that internet addiction has been an increasingly alarming issue worldwide. People in Asia, particularly males, have been reported to have a relatively higher likelihood of getting addicted in comparison with their counterparts in non-Asian and female populations.[ 14 , 15 ] Internet addiction is more prevalent among younger population[ 16 ] and this disorder may be more common among lesbian, gay and bisexual individuals than in the heterosexual population.[ 17 ] People living in urban areas are more likely to get addicted than their counterparts residing in non-urban regions.[ 18 , 19 , 20 ] Nonetheless, caution is needed when drawing conclusions or comparisons among different countries due to the discrepancy in internet access in the populations studied, differences in recruitment of respondents, age-groups included, and dissimilar set of criteria used.

There have been several different proposals about internet addiction classifications. For instance, Young and colleagues[ 21 ] perceived internet addiction as an umbrella term for a wide variety of behaviors that divided into five different forms of addictive behavior (i.e., the computer itself, the search for information, cyber sexuality, cyber contracts, and net compulsions including contact with the web through online games, shopping, etc., Davis[ 6 ] asserts that pathological internet use consists of two distinct forms: general and specific. While the former refers to a broader set of behaviors, the latter involves engagement with either specific internet functions or applications. Given the ever-increasing ubiquity of internet technology, smartphone use, and web-based application, individuals are susceptible to develop potentially addictive online behaviors. Internet gaming disorder (IGD) was the only internet-related condition officially recognized in the diagnostic manual as a legitimate disorder. IGD is reported to be more frequent in males than in females and tended to be higher among younger rather than older people,[ 22 ] yet its prevalence is still inconclusive. The ever-growing prevalence of using social networking sites (SNSs) predominantly among the tech-savvy has raised concerns over its addictive usage. Andreassen and Pallesen[ 23 ] defined SNSs addiction as being overly concerned about SNSs due to an uncontrollable urge in which excessive use leads to negative consequences in real-life areas. Yet, little insight into the behavioral characteristics of those who lose control over their SNS use and develop problematic SNS use has led to prevalence rates that varied significantly across studies [ Appendix 1 ]. Cybersex addicts were portrayed as one who uses the internet for sexual purposes for more than 11 hours per week.[ 24 ] Afterward, it was defined as any use of internet pornography that creates interpersonal, vocational, or personal difficulties.[ 25 ] Although excessive use of the internet for sexual purposes may have positive experiences for individuals, it can either be disordered or addictive.[ 26 ] Online shopping addiction refers to a tendency of excessive, compulsive and problematic shopping behavior via the internet that results in consequences associated with economic, social, and emotional problems.[ 27 ] The two best distinctions between normal urges to buy and shopping addiction are the negative consequences of the behavior and the fact that items purchased compulsively will not be used as much as expected. Gambling disorder, on the other hand, is fully recognized as a behavioral addiction, characterized by persistent and recurrent maladaptive patterns of gambling behavior, leading to impaired functioning.[ 28 ] The online form of gambling consists of wagering and gambling through internet-integrated devices enables bet anonymously and provides continuous instant feedback.[ 29 ] These conveniences raise concerns that online gambling could become a contributing factor to the development of gambling disorder and bring about individuals who would otherwise not regularly gamble, to develop a pathological use of internet gambling platforms.[ 30 ]

Risk factors for internet addiction

Exploring the patterns of internet addiction and associated factors are necessary to develop preventive measures and treatment protocols. Numerous studies have identified risk factors associated with internet addiction, generally categorized into individual and contextual factors. Specifically, the relationship between personality traits of internet addicts and psychosocial factors has been investigated and reported to have a positive association with neuroticism, extraversion, and openness but a negative relationship with agreeableness and conscientiousness.[ 31 , 32 ] Poor academic performance[ 33 ] and insecure attachment styles[ 34 ] were also found to have an association with internet addiction. Rather, family-related factors such as low family functioning,[ 35 , 36 ] poor parent-adolescent relationships,[ 37 ] low parental monitoring,[ 33 ] and parent marital conflict[ 38 , 39 ] have been intensively discussed in previous studies. Referring to cultural and economic attributes, internet addiction was found to be positively related to economic well-being, social progress, and human development, whilst negatively related to human well-being, health, safety and security.[ 40 ]

Psychiatric comorbidity of internet addiction

The co-occurrence of internet addiction and psychiatric symptoms have been reported in the literature, including, but not limited to personality disorders,[ 41 , 42 ] attention deficit and hyperactivity disorder (ADHD),[ 43 , 44 ] hostility,[ 45 ] anxiety,[ 46 , 47 ] loneliness,[ 48 , 49 ] low self-esteem,[ 46 , 50 , 51 ] poor self-control,[ 51 ] impulsivity,[ 52 ]depression,[ 46 , 53 , 54 ]alexithymia,[ 55 ] and sensation-seeking.[ 56 ] Cross-sectional studies on samples of patients reported high comorbidity with psychiatric disorders such as anxiety disorder,[ 46 ] problem gambling,[ 57 , 58 ] suicidal ideation,[ 59 , 60 ] self-injurious and risk-taking behaviour,[ 61 , 62 ] eating disorders,[ 63 ] and obesity-related problems.[ 64 ] Adolescents with internet addiction are more likely to have limited extracurricular activities, and may engage in high-risk behaviours.[ 65 , 66 ] Other severe consequences of internet addiction have also been reported such as sleep deprivation,[ 59 , 67 ] deficient working memory and execution dysfunction.[ 68 ] The picture may be more complex, requiring practical responses from supporting agencies such as nursing, psychology, counselling, and social workers.

Measurement of internet addiction

A growing body of research has examined the validity of different measurement scales in different populations, particularly focusing on their psychometric properties and measuring the invariance of these assessment tools to identify internet addicts. Self-reported questionnaires on addictive disorders are often used to assess internet addiction at the general population level. Yet, their reliability and validity have not been adequately determined in terms of having clear diagnostic criteria. To date, multidimensional instruments such as the Internet Addiction Test (IAT), the Chen Internet Addiction Scale (CIAS), and uni-dimensional instruments such as the Compulsive Internet Use Scale (CIUS) have been widely adopted to measure the internet addiction.[ 69 ] While the IAT has received overwhelming support for its validity and reliability,[ 70 ] yet its accuracy is lower in comparison with CIUS in general population.[ 71 ] Differences in the underlying psychometric constructs must be taken into consideration when administering the IAT in different cultural contexts. Consequently, most of the existing scales for internet addiction require further validation.

Intervention and treatment

The response is more effective if the addiction is detected and properly diagnosed as early as possible. However, the evidence-based interventions for internet addiction are sparse, mainly based on strategies previously used in the treatment of substance use disorders. Cognitive-behavioural therapy is currently the most common psychological intervention tested, together with family-based intervention and counselling programs.[ 72 ] Further research is required to better clarify formal diagnosis and treatment for internet addiction.

Methodology

This literature review sought to map contemporary research patterns and provide recommendations for future investigation on internet addiction over the last decade. However, attention was paid only to empirical studies conducted using international or national community or clinical samples. In November 2019, a literature search was performed using two scientific databases: PubMed and ScienceDirect. These two databases had also been used in a prior study[ 73 ] for their systematic review of longitudinal research trends in adolescence and emergent adulthood.

The following terms were entered to perform a search through titles and abstracts in the respective databases: “internet AND (addiction* OR ((problematic OR excessive OR pathological AND use)) and “disorder OR compulsive*”. All searches were confined to full-text English papers published between January 2010 and December 2019. The year 2010 was selected as the earliest date for studies, as we firmly believe an emphasis on the last ten years would be the most illustrative and informative to understand existing patterns of internet addiction. Furthermore, given the release of Diagnostic and Statistical Manual of Mental Disorders (DSM-5)[ 28 ] and the inclusion of gaming disorder in the 11 th revision of the International Classification of Diseases (ICD-11) as a clinical illness,[ 74 ] research trends and patterns discovered within this period are especially important. Publications that were not obtained in the initial search were added after reviewing the reference lists of all retrieved articles.

Once duplicates were removed, the remaining articles were then screened out based on following criteria: (1) contain quantitative, qualitative or mixed approaches; (2) published between January 2010 and December 2019; (3) include general or clinical samples; (4) provide a full-text article and (5) published in English. Articles in languages other than English and those that did not assess internet addiction empirically were not considered. Publications such as theoretical papers, opinion, comments, perspectives, letters to the editor, short communications, conference proceedings, dissertations, and any content derived from sources other than peer-reviewed journals without a clear relationship to internet addiction were also excluded. The extracted data for each publication included: (i) study location, or, in case of not being clearly stated, the country of the first and/or co-author (s), (ii) publication types, (iii) type of study design, (iv) research methods employed and (v) journals that published those manuscripts. Frequency and descriptive statistics were then calculated to derive tables and figures. The whole process for selecting appropriate research articles is presented in Figure 1 as follows.

Flowchart of article selection

Findings and Discussion

Number of scholarly articles published.

Once duplicates and non-relevant articles were eliminated, 826 valid articles remained for further analysis, including 318 indexed in Science Direct and 508 in PubMed [ Figure 2 ].

Number of scholarly articles on internet addiction

There was a steady increase in the number of published articles on internet addiction over the last decade. The year with the highest number of publications was 2018 ( n = 131), followed by the year 2014 ( n = 111). A tentative explanation could be due to research interest stimulated by the inclusion of IGD in Section 3 of the DSM-5. This pattern then recurred in 2018, just one year before gaming disorder is formally recognized as a mental health disorder in the ICD-11. The increase in the number of articles highlights the awareness and importance of this area among the scientific community, clinics, and international bodies worldwide. However, a vast majority of the current literature is primarily centered on adolescents or young students, arguing that they are the most vulnerable groups to potentially develop problematic internet use due to their ever-growing internet use. Yet, their left-behind counterparts (i.e., emerging adults and the elderly), deserve to be assessed thoughtfully as well. Therefore, studies on widening samples across different age groups are necessary to examine whether the association between internet addiction and certain factors is consistent across the general population.

Number of regions (geospatial coverage)

A total of 826 articles were published by authors from 54 different countries [ Figure 3 ]. We took into account the geographical location of the first author or co-author(s) to avoid the misrepresentation that each paper was single-authored.

Top 5 countries with highest number of publications

The most productive countries were China ( n = 174, c = 21.06%), Turkey ( n = 83, c = 10.05%), Korea ( n = 64, c = 7.45%), Taiwan ( n = 58, c = 7.02%), Germany ( n = 56, c = 6.78%), and the United States ( n = 46, c = 5.57%). China has published more than one-fifth of the total articles during the studied timespan, presumably indicating the current situation of internet addiction in this country. In contrast, 36 nations that each published fewer than ten articles ( n = 110, c = 13.32%) exhibit a huge disparity in the number of publications compared to the countries in the top five. The previous finding raises a pivotal research agenda for internet addiction experts and practitioners to explore. Therefore, it seems important and beneficial to investigate internet addiction in regions that have not been studied or have been insufficiently studied and then compare the results with previous studies conducted worldwide. Such collaboration is essential to facilitate cross-national and cross-cultural studies employing interdisciplinary approaches to improve the understanding of internet addiction. The European Cooperation in Science and Technology Action Program (COST) under Horizon 2020 can be singled out as a vivid example of its strong commitment to developing fruitful collaborations among researchers, experts, and different stakeholders regarding internet addiction.[ 75 ] Likely, the number of publications from this continent would increase considerably in the future.

Common research methods employed

Regarding the research design, the vast majority of studies on internet addiction are cross-sectional ( n = 709; c = 85.84%), mostly gathering samples among individuals who may or may not fully represent attributes of the general population. Cross-sectional studies are primarily performed to estimate the prevalence and examine relevant risk factors for internet addiction. This type of study does not have an inherent temporal dimension as it only evaluates subjects at one point in time. By contrast, cohort and case-control studies ( n = 51, c = 6.17%) enable researchers to assess the history of the treatment-seekers and endeavour to examine the causal relationship between risk factors and internet addiction. Although cohort and case-control studies have inherent limitations in showing the correlations among different variables and more susceptible to recall and selection bias, they can provide valid results to address important clinical research questions. Yet, interpretation drawn from case-control research should be thoughtfully verified by replication in other designs such as prospective cohort studies. Furthermore, cross-sectional, in comparison with longitudinal studies ( n = 66; c = 7.99%) have inherent limitations in determining cause and effect's relationship as they cannot fully ascertain whether a factor was either presented before or after the onset of internet addiction. Therefore, future works would be better suited using alternative methodological approaches to enhance the robustness of the findings and conduct more longitudinal studies to provide valuable insight into the predictors and outcomes.

Concerning data acquisition methods, a substantial amount of research administered surveys ( n = 649; c = 78.57%) by applying validated instruments to design self-reported questionnaires either used in the classroom or online environment through crowd-sourcing platforms or some cloud-based survey services to gather the data. Data collected in experimental settings have been used in 92 articles (c = 11.14%), particularly prevalent among brain imaging and neuroimaging studies. Interviews, either face-to-face or in diagnostic form, have been detected in 25 papers (c = 3.02%), showed great promise for the detection of psychiatric comorbidities as they provide greater diagnostic accuracy and contribute to a more exhaustive evaluation. Research that analysed secondary data were reported in 34 studies (c = 4.11%), while the mixed method was employed in 22 studies (c = 2.66%), and only 4 papers (c = 0.48%) applied focus group discussion, a qualitative approach to collecting data.

In terms of analytical techniques, a wide variety of data analysis strategies have been performed to evaluate internet addiction, depending on the type of data collected that allegedly supported researchers dealing with missing data issues and testing the proposed hypotheses or coming into further multivariate analyses [ Table 1 ]. Apart from using traditional approaches to examine the association of possible factors with internet addiction, there is a need for analysis strategies that integrate quantitative and qualitative or mixed approaches and make it possible to identify solve complicate methodological settings. Methods dealing with massive datasets are also required as modern research is increasingly familiar with panel or longitudinal data collecting and processing a large sample of respondents. A prior study[ 76 ] provides an approach to examine the severity of internet addiction among college students by using their behaviour data on campus, which can easily be collected through handheld and smart devices.

Top 10 most popular techniques for data analysis on internet addiction

We observe a growing interest in utilizing structural equation modelling or moderation and mediation analyses to evaluate the mediating role of associated variables. Likewise, latent profile analysis has sufficient flexible capabilities compared to cluster analysis to capture complex contextual effects that are difficult to assess using classical techniques, as it explores patterns of multiple variables rather than the relationship between two variables.[ 77 ] Given the drawback of statistical methods, a recent study[ 78 ] has employed a machine learning approach with a relatively larger dataset, which subsequently yielded its efficiency and provided a new view for researchers in this area.

A plethora of articles have been published in flagship journals of psychiatry, psychology and human-computer behaviour [ Table 2 ], with only one exception from PLoS One, an interdisciplinary journal that covers primary research from any discipline within science and medicine.

Top 10 journals publishing the most articles on internet addiction

There would be room for future studies to combine expertise across different fields and use a much more integrative and inclusive approach to investigate internet addiction. Therefore, multidisciplinary and interdisciplinary journals hold great promise for further examination.

Common research topics on internet addiction

In accordance with present research strategies, 826 articles were deliberately classified and then assigned into six categories, namely epidemiological studies - the most commonly investigated topic ( n = 384; c = 46.60%), comorbidity studies ( n = 283; c = 34.34%), scale measurement studies ( n = 80; c = 9.71%), neuroimaging or brain imaging studies ( n = 50; c = 6.07%), intervention and treatment studies ( n = 23; c = 2.79%), and gene studies ( n = 4; c = 0.48%).

The overrepresentation of epidemiological and comorbidity studies reflects the proliferation of internet addiction among psychiatry and psychology disciplines, where so much effort has been dedicated to shedding light on different perspectives on this fairly new topic of interest. Moreover, given that internet addiction is yet to be formally included in any of the official diseases' classifications, it is not surprising that extensive research on its comorbidity and epidemiology dimensions have significantly outweigh other research topics. Likewise, there have been some initial efforts into the heritability of internet addiction by employing a gene approach to evaluate the molecular genetics of this particular behaviour.[ 79 , 80 ] It is recommended that these topics should be supported with a more detailed analysis in prospective studies.

Additionally, the geographic information system is employed to report maps of internet addiction and then can inform researchers, community organizers, and policymakers on the status quo of internet addiction.[ 81 , 82 ] An effort has also been paid to apply behavioural economic framework into internet addiction to examine whether the relationship between internet addiction and behavioural economic indicators is similar to other addictive behaviours.[ 83 ] These pilot results are expected to support future research that applies behavioral economic models to understand the etiology, developmental course, and to guide prevention and treatment approaches of internet addiction. Furthermore, a scant amount of research has underlined the importance of internet addiction to consumer behavior,[ 84 ] particularly paying attention to estimating the association between internet addiction and customers' electronic word-of-mouth behavior in the context of the hotel and restaurant industry.[ 84 , 85 ] Also, an ecological model called Process-Person-Context-Time has been proposed to examine online activities and internet addiction.[ 86 ] Moreover, the application of big data approaches to addiction research for cognition, neuroimaging, and genetics has been introduced. Big data can afford greater replicability of findings, especially in conjunction with the application of artificial intelligence. The advent of machine learning may improve the diagnosis and classification of individual patients based on data patterns that were not consciously considered by clinical in the past.[ 87 ] This advanced technology has been used to detect internet addiction[ 78 ] by combining grid search and support vector machines to improve detection capabilities.

Future research direction

Although there are major questions remain unanswered regarding the inconclusiveness of internet addiction's definitions as well as the dearth of globally accepted measurements and the variations in prevalence estimates, the association between internet addiction and various cyber-psychosocial-related problems such as cyber-crime, cyber-harassment, cybersecurity,[ 88 ] and cyber-bullying[ 89 ] is tentative and requires further investigation [ Figure 4 ]. The 15 th ed.ition of the World Economic Forum's Global Risks Report[ 90 ] contemplates that technological risks can generate a broad-based movement for various collaborators, including scientists and mental health experts, to address. Furthermore, the role of science has evolved to address multifaceted issues, become more interconnected, interdisciplinary, collaborative and data-intensive. As such, collaboration among scholars and experts play a significant role in determining research preferences and allocation of funds and investment for internet addiction research.

The trend of research on human addiction

Currently, there is neither a single term to unify the concept nor an agreed-upon consensus on diagnostic procedures and definition, making it troublesome to early diagnose and propose sound treatment and intervention arrangements for treatment-seekers. Furthermore, there is a paucity of assessment tools to screen, diagnose and measure internet addiction cross-culturally. Consequently, research is needed to fully describe, from different perspectives, the spectrum of disorders and clinical courses that comprise internet addiction across genders, age groups, and cultures, to attain consensus on the diagnostic thresholds and criteria. Nevertheless, it is ambiguous how internet addiction performs over time. There is an imperative need for longitudinal population-based analysis of incidence, comorbidity, and remission, using extended cohorts (e.g., older adults). Such investigations would accommodate new data about the crucial developments over a lifespan and may introduce novel theories bonding these behaviors and disorders. Furthermore, effective intervention and treatment therapy have not been proved. Early detection of susceptible individuals, aiming at early intervention strategies, could diminish the burden of diseases and help to deter improper functional consequences. To date, cognitive behavioral therapy seems to yield the best results. However, due to study limitations, clear evidence needs to be revealed by further testing.[ 91 ]

The 3C paradigm for future research

The aforementioned justifications allow us to formulate the 3C paradigm for future research which accentuates the significance of incorporating a wide range of researchers from multiple disciplines.

• Cross-disciplinary collaborations between scientists from different disciplines have become increasingly important,[ 92 ] being a way to learn about cutting-edge knowledge directly from experts and to work towards more integrative and inclusive approaches. Researchers are suggested to cooperate and establish an agreement regarding diagnostic criteria and measures to improve the reliability across studies and to develop effective and efficient treatment approaches for treatment seekers.[ 93 ] For research on internet addiction, it is suggested to involve not only academic institutes and research centers but also nursing agencies and public health institutions, particularly where there is a call for projects centered on clinical assessment, intervention, and treatment. While such practices enable the synthesis of ideas and knowledge from many expertise, still, when conducting cross-disciplinary research, institutional or funding-related factors, as well as the conceptual and methodological differences between knowledge domains, must be taken into account
• The cross-cultural study is mainly concerned with looking at how our knowledge about people from one particular culture, and their behavior may or may not be the same as people from another culture. Examining internet addiction at a global scale is valuable in the era of globalization and corporate multi-nationalism.[ 70 ] Similarly, studies that assess the commonalities and differences between collectivist and individualist cultures are required as prior studies[ 94 , 95 ] have shown that countries in Asia with collectivist cultures are more likely to report higher levels of internet addiction. Finally, researching countries or territories with common cultural determinants such as China, Hong Kong, Taiwan and Macau or conducting research among Spanish or Portuguese communities would also be beneficial and helpful to observe different patterns of internet addiction
• Cross-validation refers to the methods and procedures used to validate results so that they can be generalized. It is useful to check whether a proposed research can generate similar results with the same variables in different samples. Presently, significant efforts have been devoted to examine the reliability and validity of the existing diagnostic instruments and to validate the conceptual model of internet addiction in different populations. Prospective studies should investigate the types of cyberspace activities as previous studies demonstrated that men and women often engage in different types of online activities, i.e., men are more likely to use the internet for playing games, while women mostly use it for social networking and shopping purposes.[ 96 , 97 , 98 ] Further in-depth investigations are also required into the validation of clinical instruments, prevalence estimates, and brain-based biology mechanisms to establish a proper conceptualization and more concrete operationalization.

The goal of this review is to provide an exhaustive overview of the empirical evidence on internet addiction and draw attention on future research topics. We found that the number of journal articles on internet addiction has steadily increased with a substantial contribution from China, Turkey, Korea, Germany, and Taiwan respectively. Internet addiction has predominantly been scrutinized from a psychological, psychiatric and behavioral addiction point of view with considerable amount of research exploring epidemiological, neurobiological, comorbidity, measurement scales to intervention and treatment. Nonetheless, research on internet addiction has been impeded by the use of inconsistent and non-standardized criteria to assess and identify internet addicts or their addictive behavior. Currently, the diagnostic and research landscape appears particularly broad, and diagnostic criteria used to identify internet addiction are not globally agreed upon. Future investigation is prescribed to collaborate cross-disciplines research into cross-cultural studies employing cross-validation methods to allow better generalization of the findings and to gain a deeper insight into the concept of internet addiction.

Several limitations should be addressed in this review. First, the present study considered solely scientific articles confined by a specific interval. Therefore, a more extended timespan could result in meaningful contributions from articles published outside of the range considered in the present study. Second, the search was performed using general and more frequent terms reported in the titles and abstracts of journal articles. Future searches using other specific terms may result in obtaining additional papers on internet addiction. Specific terms relating to internet gaming disorder, shopping addiction, and social networking addiction or any other internet-specific problematic use can also be utilized to generate meaningful information. Finally, the selected databases, although they are the principal bibliography recognized by the global scholarly community, are not the only ones to address these issues. ProQuest, Embase, Medline, Scopus, and PsycINFO are also a great source of literature. Given the present limitations, the findings of this literature review will serve both internet addiction academics and practitioners to develop new solutions based on the challenges identified.[99-109].

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Prevalence estimates on different types of internet addiction

• Open access
• Published: 19 March 2024

Relationship between loneliness and internet addiction: a meta-analysis

• Yue Wang 1 &
• Youlai Zeng 1  

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

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

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

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

Conclusions

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

Peer Review reports

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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Wang, Y., Zeng, Y. Relationship between loneliness and internet addiction: a meta-analysis. BMC Public Health 24 , 858 (2024). https://doi.org/10.1186/s12889-024-18366-4

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Internet Addiction

Dear Colleagues,

The extensive availability of the Internet has led to the recognition of problematic internet use (so-called internet addiction, IA). Problematic internet use is usually defined as a problematic, compulsive use of the internet, resulting in significant impairment in an individual's function in various life domains over a prolonged period of time. The disorder is increasingly prevalent; about 5% of the adolescent population are supposed to be sufferers. The difficulty of recognition is that internet-based technology has improved many aspects of our lives and it is now an essential part of our everyday routine, including work, private life and social functioning, therefore many individuals are not aware of the misuse or problematic use.

Problematic internet use seems to be associated with medical conditions such as anxiety, depression, drug abuse and malnutrition; furthermore, recent studies have demonstrated a possible association of burnout and internet addiction, as they are considered to be serious mental health problems based on symptomatology that is related to chronic stress, and are mostly conjoined among adolescents. Several MRI studies suggest the breakdown of functional brain networks, especially the involvement of the prefrontal cortex, which may play a role in the behavioral and mental consequences of addiction. However, these and other relationships between digital media use and mental health have been under considerable research, and have generated controversy, debate and quarreling among expert researchers, healthcare and non-healthcare professionals, due to insufficient data, poor quality research and a lack of randomized studies.

The aim of our Special Topic is to focus on the complex background of internet addiction (for example, prevalence, demographic data, burnout, depression, sleep disturbance and quality of life, etc.) in different populations (for example adolescents, eSport users, adults, etc.), including fMRI studies. Original research, meta-analysis, and review articles related to this Special Topic are welcome, from preclinical research to multidisciplinary clinical management.

Potential themes relevant to this Research Topic may include, but are not limited to, the following:

• Prevalence of internet addiction
• Risk factors of internet addiction
• Burnout and internet addiction
• Brain networks in internet addiction
• Internet addiction and quality of life
• Mental and physical consequences of internet addiction
• Co-incidence of internet and other addictions
• Therapy of internet addiction

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Research Article

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

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

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

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

* E-mail: [email protected]

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

• Max L. Y. Chang, 
• Irene O. Lee

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

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

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

Editor: Kizito Omona, Uganda Martyrs University, UGANDA

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

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

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

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

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

Introduction

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

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

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

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

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

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

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

Search strategy and selection process

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

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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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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

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Smartphone Addiction and Beyond: Initial Insights on an Emerging Research Topic and Its Relationship to Internet Addiction

• First Online: 29 March 2017

Cite this chapter

• Éilish Duke 5 &
• Christian Montag 6 , 7  

Part of the book series: Studies in Neuroscience, Psychology and Behavioral Economics ((SNPBE))

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The present chapter considers early insights on some pressing issues in the investigation of smartphone (over)use. More specifically, we consider whether tendencies toward overuse of the smartphone and Internet are related. And, if so, whether the same personality structure represents a vulnerability factor for both kinds of digital addiction. This chapter also identifies some similarities and differences between Internet and smartphone overuse, beyond the findings from personality psychology. Finally, the chapter provides a short overview of the important relationship between smartphone use, flow experience at work, and productivity issues. This section is followed by a simple behaviorist model, which aims to explain the aetiogenesis of problematic smartphone use. The chapter closes with some easy to implement therapeutic interventions designed to reduce smartphone use in order to live more meaningful lives in the here and now.

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To our knowledge only one recent paper had used neuroscience methods to investigate “smartphone addiction” at time of print. This study used electroencephalography to demonstrate that excessive smartphone users were characterized by a more negative N2 amplitude when confronted with NoGo trials on a Go/NoGo task. This may be interpreted as indicative of difficulties with early stage inhibitory processing among smartphone addicts (Chen et al. 2016 ), however, more work is needed to draw any strong conclusions in this respect. Finally, we would like to point to a recent published molecular framework to study smartphone addiction, which might guide researchers to disentangle the molecular underpinnings of smartphone overuse in the future (Montag et al. in press).

In Internet addiction research it has been demonstrated that generalized and specific forms of Internet addiction such as overuse of online video games or online pornography only overlap in small proportions (Montag et al. 2015a , b ). Moreover, we would like to mention that in the following text we use the terms “smartphone addiction”, “problematic smartphone use”, etc. somewhat exchangeably, because until now this new phenomenon has been not properly defined.

Information taken from the website http://www.spiegel.de/schulspiegel/smombie-ist-jugendwort-des-jahres-a-1062671.html (website accessed in 21th July 2016).

Information taken from the website http://abcnews.go.com/blogs/headlines/2012/05/texting-while-walking-banned-in-new-jersey-town/ (website accessed in 21th July 2016).

One idea would be to restrict usage to a certain time window—such as between 17.00 and 18.00 h. The children know that everyone will be online in this time window but not around this time frame. This of course can only be achieved by a concerted action by parents. The attraction of the smartphone for most young people lies only in the access it permits to friends, e.g. online chatting. In contrast, if everyone is out playing soccer, the smartphone will be much less attractive.

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Duke, É., Montag, C. (2017). Smartphone Addiction and Beyond: Initial Insights on an Emerging Research Topic and Its Relationship to Internet Addiction. In: Montag, C., Reuter, M. (eds) Internet Addiction. Studies in Neuroscience, Psychology and Behavioral Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-46276-9_21

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Internet Addiction

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More a popular idea than a scientifically valid concept, internet addiction is the belief that people can become so dependent on using their mobile phones or other electronic devices that they lose control of their own behavior and suffer negative consequences. The harm is alleged to stem both from direct involvement with the device—something that has never been proven—and from the abandonment of other activities, such as studying, face-to-face socializing, or sleep.

• What Is Internet Addiction?
• Signs of Excessive Internet Use
• Internet Use and Mental Health
• What to Do About Internet Addiction

There is much debate in the scientific community about whether excessive internet use can be classified as a true addiction. In an addiction to substances such as drugs or alcohol , consumption ceases being pleasurable but continues and is difficult to escape even as the likelihood of harm to the body and life mounts. In the case of internet use, there is no clear point at which being online becomes non-pleasurable for most individuals. In part for this reason, behavioral "addictions," including using the internet, remain controversial: Experts debate where the line should be drawn between passionate absorption in any activity—say, devoting a lot of time to playing the cello or reading books—and being stuck in a rut of compulsivity that stops being useful and detrimentally affects other areas of life.

In preparing the current edition of the Diagnostic and Statistical Manual of Mental Disorders , psychiatrists and other experts debated whether to include internet addiction. They decided that there was not enough scientific evidence to support inclusion at this time, although the DSM-5 does recognize Internet Gaming Disorder as a condition warranting further study.

Most often, the word “addiction” is used in the colloquial sense. Common Sense Media finds that 59 percent of parents “feel” their kids are addicted to their mobile devices—just as 27 percent of the parents feel that they themselves are. Sixty-nine percent of parents say they check their own devices at least hourly, as do 78 percent of teens. Spending a lot of time on the internet is increasingly considered normal behavior, especially for adolescents. Much of their social activity has simply moved online. Like any new technology, the computer has changed the way everyone lives, learns, and communicates. It is possible to be online far too much, even though this does not constitute a true addiction in the eyes of most clinicians. 

Internet content creators leverage the ways in which the brain works to rally consumers '  attention . One simple example: A perceived threat activates your fight-or-flight response, a part of the brain known as the Reticular Activating System mobilizes the body for action. So online content exploits potential dangers—violence, natural disaster, disease, etc.—to attract and hold your attention.  

Problematic or excessive internet use can indeed pose a serious problem. It can displace such important needs as sleep, homework, and exercise, often a source of friction between parents and teens. It can have negative effects on real-life relationships. 

The idea of internet addiction is a particular concern among parents, who worry about the harmful effects of screen time and often argue about device use with their children. According to a 2019 survey conducted by Common Sense Media, children aged 8 to 12 now spend 5 hours a day on digital devices, while teens clock more than 7 hours—not including schoolwork. Teen screen time is slowly ticking upward, and most teens take their phones to bed with them.

Whether classified as an addiction or not, heavy use of technology can be detrimental. It can impair focus, resulting in poor performance at school or work. Excessive internet consumption also makes it more difficult for people to communicate normally or to regulate their emotions. They spend less time on non-internet-related activities at the cost of relationships with friends, family, and significant others.

One way to assess whether you’re using the internet too much is to ask yourself if your basics needs (or your child’s, if they are the concern) are being met. Do you sleep enough, eat healthy, get enough exercise, enjoy the outdoors, and spend time socializing in-person? The real harm of screen time may lie in missed opportunities for growth and connection.

Excessive screen time can be particularly harmful to a developing brain: It decreases focus and attention span while increasing the need for more constant stimulation and instant gratification. Those who use the internet excessively may feel anxious if their access to their device gets restricted. They tend to be more impulsive and struggle to recognize facial and nonverbal cues in real life.

Internet use becomes a problem when people start substituting online connections for real, physical relationships. The effects of technology on relationships include increased isolation and loneliness . Defaulting to online communication also denies us the opportunity to hear someone’s voice and read their facial cues in-person; it can also lead to poorer outcomes and miscommunication. Experts recommend that we save the important conversations for when we can be face-to-face for just this reason.   

Online content has been designed to elicit specific “checking habits,” which can result in distraction and poor performance at school or work. Constantly checking your smartphone or another device can also lead to relationship-sabotaging behaviors, like phubbing (snubbing loved ones for the instant gratification of checking the internet on your device). As more time is spent online, less is devoted to the natural pleasures of everyday life.  

Excessive use of the internet is known to negatively impact a person’s mental health. It has been associated with mental health issues, such as loneliness, depression , anxiety , and attention-deficit/hyperactivity disorder. Research suggests that people are likely to use the internet more as an emotional crutch to cope with negative feelings instead of addressing them in proactive and healthy ways.

This is a subject of debate at present. While internet addiction is not in the DSM-V, it is clearly a behavior that negatively impacts mental health and cognition for many, and many struggle to cut back on their time online. The term "addiction" is often used as a shorthand for, “My child spends a lot of time on social media , texting friends, or playing video games, and I’m worried how it will affect his or her future development and success.” At the same time, many people label it a behavioral addiction, engaging reward circuitry seen in other problematic behaviors such as gambling.

Time online is also sometimes used as an escape from boredom or relief from loneliness or other unpleasantness. Occasionally, excessive screen time masks a state of depression or anxiety. In such cases, digital engagement becomes an attempt to remedy the feelings of distress caused by true mental health disorders that could likely benefit from professional or other attention.

Given how much people rely on technology to complete everyday tasks, from online schooling to paying bills to ordering food to keeping in touch with loved ones who are far away, it isn’t feasible to stop using the internet altogether. In most cases, the goal should be to reduce the time spent online. Many of those who’ve struggled to balance internet use with other activities recommend such simple “digital detox” measures as leaving devices in the kitchen or any other room but the bedroom at night. Cognitive behavioral therapy can also help address addiction-like behaviors, like constant checking habits. 

Amidst growing concerns about the increased amount of time people are spending online, the “digital detox” has become a popular way to cope. A digital detox involves temporarily abstaining from using devices, like computers and smartphones. Someone may go on a digital detox in order to re-engage with a passion or activity, focus more on in-person interactions, or break free of a pattern of compulsive or excessive use. Digital detoxes also allow more time for self-care that a person may have been neglecting in order to stay plugged into the internet, which can lead to lower stress levels and better sleep.

There is no one-size-fits-all answer. You may want to digitally detox if you notice that you’re experiencing sleep disruptions due to staying up late or waking up early to be on a device, if the internet is making you feel depressed, or if the constant need to be connected causes you stress. Other signs may include feeling anxious if you can’t locate your phone, having FOMO ( fear of missing out) if you’re not checking the internet or social media, struggling to focus without (or due to) constant checking behaviors, etc.

Unlike other detoxes where the goal is to abstain completely, digital detoxes are more flexible and tailored to the individual. It may not be possible due to work or personal obligations to shut your devices off entirely for long periods of time. If it’s time for a digital detox , there are some strategies you can try: Block off non-screen time during the day and/or night, set a “digital curfew” for using devices at night or on weekends, specify digital-free spaces in your home (e.g., the bedroom or dinner table), and use the additional time in fulfilling ways (e.g., socialize, rekindle old interests, volunteer, etc.).

Use the internet and social media with purpose; set time limits on your unstructured use to avoid going down long and unfulfilling rabbit holes. Take advantage of the extra free time you suddenly have. Spend more time socializing in-person and volunteer. Rekindle old interests or take up a new hobby. Go outside. Pay more attention to how you are feeling, both physically and emotionally.

Criticisms of evidence suggesting causal relationships between social media and mental health problems have some scientific merit, but clinically are wide of the mark.

Experts are warning that the problem of out-of-control technology continues to grow in alarming ways. Here are 8 ways parents can protect their children.

Anhedonia is the diminished ability to experience satisfying pleasures. The ability to experience healthy pleasures can be cultivated to restore overall well-being.

Dating apps are designed to help people find dates and develop long-term relationships. But they are also designed to keep users involved with the app.

Encouraging young adults to tell their stories may help heal the sense of disconnection technology and the pandemic have created.

Smartphones bridge global connections yet chip away at the essence, and the joy, of face-to-face interactions.

In a new study, teens report finding more benefits than harms online, yet they admit to feeling “happy” and “peaceful” when away from their smartphones.

Today, more teens are at peace and happier when they are detached from their devices. Here are 5 ways to help parents create a smartphone contract to manage screen time.

Personal Perspective: How the internet influences understanding mental illness.

Take charge of your unhealthy smartphone use.

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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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Potential risks of content, features, and functions: The science of how social media affects youth

Almost a year after APA issued its health advisory on social media use in adolescence , society continues to wrestle with ways to maximize the benefits of these platforms while protecting youth from the potential harms associated with them. 1

By early 2024, few meaningful changes to social media platforms had been enacted by industry, and no federal policies had been adopted. There remains a need for social media companies to make fundamental changes to their platforms.

Psychological science continues to reveal benefits from social media use , as well as risks and opportunities that certain content, features, and functions present to young social media users. The science discussed below highlights the need to enact new, responsible safety standards to mitigate harm. 2

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Related content

• APA report calls on social media companies to take responsibility to protect youth
• How much is too much social media use?

Elaboration of science on social media content, features, and functions

Platforms built for adults are not inherently suitable for youth. i Youth require special protection due to areas of competence or vulnerability as they progress through the childhood, teenage, and late adolescent years. ii This is especially true for youth experiencing psychological, physical, intellectual, mental health, or other developmental challenges; chronological age is not directly associated with social media readiness . iii

Hypersensitivity to social feedback

Brain development starting at ages 10–13 (i.e., the outset of puberty) until approximately the mid-twenties is linked with hypersensitivity to social feedback/stimuli. iv In other words, youth become especially invested in behaviors that will help them get personalized feedback, praise, or attention from peers.

• AI-recommended content has the potential to be especially influential and hard to resist within this age range. v It is critical that AI-recommended content be designed to prioritize youth safety and welfare over engagement. This suggests potentially restricting the use of personalized recommendations using youth data, design features that may prioritize content evoking extreme emotions, or content that may depict illegal or harmful behavior.
• Likes and follower counts activate neural regions that trigger repetitive behavior, and thus may exert greater influence on youths’ attitudes and behavior than among adults. vi Youth are especially sensitive to both positive social feedback and rejection from others. Using these metrics to maintain platform engagement capitalizes on youths’ vulnerabilities and likely leads to problematic use.
• The use of youth data for tailored ad content similarly is influential for youth who are biologically predisposed toward peer influence at this stage and sensitive to personalized content. vii

Need for relationship skill building

Adolescence is a critical period for the development of more complex relationship skills, characterized by the ability to form emotionally intimate relationships. viii The adolescent years should provide opportunities to practice these skills through one-on-one or small group interactions.

• The focus on metrics of followers, likes, and views focuses adolescents’ attention on unilateral, depersonalized interactions and may discourage them from building healthier and psychologically beneficial relationship skills. ix

Susceptibility to harmful content

Adolescence is a period of heightened susceptibility to peer influence, impressionability, and sensitivity to social rejection. x Harmful content, including cyberhate, the depiction of illegal behavior, and encouragement to engage in self-harm (e.g., cutting or eating-disordered behavior) is associated with increased mental health difficulties among both the targets and witnesses of such content. xi

• The absence of clear and transparent processes for addressing reports of harmful content makes it harder for youth to feel protected or able to get help in the face of harmful content.

Underdeveloped impulse control

Youths’ developing cortical system (particularly in the brain’s inhibitory control network) makes them less capable of resisting impulses or stopping themselves from behavior that may lead to temporary benefit despite negative longer-term consequences. xii This can lead to adolescents making decisions based on short-term gain, lower appreciation of long-term risks, and interference with focus on tasks that require concentration.

• Infinite scroll is particularly risky for youth since their ability to monitor and stop engagement on social media is more limited than among adults. xiii This contributes to youths’ difficulty disengaging from social media and may contribute to high rates of youth reporting symptoms of clinical dependency on social media. xiv
• The lack of time limits on social media use similarly is challenging for youth, particularly during the school day or at times when they should be doing homework. xv
• Push notifications capitalize on youths’ sensitivity to distraction. Task-shifting is a higher order cognitive ability not fully developed until early adulthood and may interfere with youths’ focus during class time and when they should be doing homework. xvi
• The use and retention of youths’ data without appropriate parental consent, and/or child assent in developmentally appropriate language, capitalizes on youths’ relatively poor appreciation for long-term consequences of their actions, permanence of online content, or their ability to weigh the risks of their engagement on social media. xvii

Reliance on sleep for healthy brain development

Other than the first year of life, puberty is the most important period of brain growth and reorganization in our lifetimes. xviii Sleep is essential for healthy brain development and mental health in adolescence. xix Sleep delay or disruptions have significant negative effects on youths’ attention, behavior, mood, safety, and academic performance.

• A lack of limits on the time of day when youth can use social media has been cited as the predominant reason why adolescents are getting less than the recommended amount of sleep, with significant implications for brain and mental health. xx

Vulnerability to malicious actors

Youth are easily deceived by predators and other malicious actors who may attempt to interact with them on social media channels. xxi

• Connection and direct messaging with adult strangers places youth at risk of identity theft and potentially dangerous interactions, including sexploitation.

Need for parental/caregiver partnership

Research indicates that youth benefit from parental support to guide them toward safe decisions and to help them understand and appropriately respond to complex social interactions. xxii Granting parents oversight of youths’ accounts should be offered in balance with adolescents’ needs for autonomy, privacy, and independence. However, it should be easier for parents to partner with youth online in a manner that fits their family’s needs.

• The absence of transparent and easy-to-use parental/caregiver tools increases parents’ or guardians’ difficulty in supporting youths’ experience on social media. xxiii

Health advisory on social media use in adolescence

Related topics

• Social media and the internet
• Mental health

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A path forward based on science

Change is needed soon. Solutions should reflect a greater understanding of the science in at least three ways.

First, youth vary considerably in how they use social media. Some uses may promote healthy development and others may create harm. As noted in the APA health advisory , using social media is not inherently beneficial or harmful to young people. The effects of social media depend not only on what teens can do and see online, but teens’ pre-existing strengths or vulnerabilities, and the contexts in which they grow up.

Second, science has highlighted biological and psychological abilities/vulnerabilities that interact with the content, functions, and features built into social media platforms, and it is these aspects of youths’ social media experience that must be addressed to attenuate risks. xxiv Social media use, functionality, and permissions/consenting should be tailored to youths’ developmental capabilities. Design features created for adults may not be appropriate for children.

Third, youth are adept at working around age restrictions. Substantial data reveal a remarkable number of children aged 12 years and younger routinely using social media, indicating that current policies and practices to restrict use to older youth are not working. xxv

Policies will not protect youth unless technology companies are required to reduce the risks embedded within the platforms themselves.

As policymakers at every level assess their approach to this complex issue, it is important to note the limitations of frequently proposed policies, which are often misreported and fall far short of comprehensive safety solutions that will achieve meaningful change.

Restricting downloads

Restricting application downloads at the device level does not fully restrict youths’ access and will not meaningfully improve the safety of social media platforms. Allowing platforms to delegate responsibility to app stores does not address the vulnerabilities and harms built into the platforms.

Requiring age restrictions

Focusing only on age restrictions does not improve the platforms or address the biological and psychological vulnerabilities that persist past age 18. While age restriction proposals could offer some benefits if effectively and equitably implemented, they do not represent comprehensive improvements to social media platforms, for at least four reasons:

• Creating a bright line age limit ignores individual differences in adolescents’ maturity and competency
• These proposals fail to mitigate the harms for those above the age limit and can lead to a perception that social media is safe for adolescents above the threshold age, though neurological changes continue until age 25
• Completely limiting access to social media may disadvantage those who are experiencing psychological benefits from social media platforms, such as community support and access to science-based resources, which particularly impact those in marginalized populations
• The process of age-verification requires more thoughtful consideration to ensure that the storage of official identification documents does not systematically exclude subsets of youth, create risks for leaks, or circumvent the ability of young people to maintain anonymity on social platforms.

Use of parental controls

Granting parents and caregivers greater access to their children’s social media accounts will not address risks embedded within platforms themselves. More robust and easy-to-use parental controls would help some younger age groups, but as a sole strategy, this approach ignores the complexities of adolescent development, the importance of childhood autonomy and privacy, and disparities in time or resources available for monitoring across communities. xxvi

[Related: Keeping teens safe on social media: What parents should know to protect their kids ]

Some parents might be technologically ill-equipped, lack the time or documentation to complete requirements, or simply be unavailable to complete these requirements. Disenfranchising some young people from these platforms creates inequities. xxvii

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1 These recommendations enact policies and resolutions approved by the APA Council of Representatives including the APA Resolution on Child and Adolescent Mental and Behavioral Health and the APA Resolution on Dismantling Systemic Racism in contexts including social media. These are not professional practice guidelines but are intended to provide information based on psychological science.

2 This report seeks to elaborate on extant psychological science findings, which may be particularly relevant in the creation of policy solutions that protect young people, and to inform the development of social media safety standards.

Recommendations from APA’s health advisory on social media use in adolescence

• Youth using social media should be encouraged to use functions that create opportunities for social support, online companionship, and emotional intimacy that can promote healthy socialization.
• Social media use, functionality, and permissions/consenting should be tailored to youths’ developmental capabilities; designs created for adults may not be appropriate for children.
• In early adolescence (i.e., typically 10–14 years), adult monitoring (i.e., ongoing review, discussion, and coaching around social media content) is advised for most youths’ social media use; autonomy may increase gradually as kids age and if they gain digital literacy skills. However, monitoring should be balanced with youths’ appropriate needs for privacy.
• To reduce the risks of psychological harm, adolescents’ exposure to content on social media that depicts illegal or psychologically maladaptive behavior, including content that instructs or encourages youth to engage in health-risk behaviors, such as self-harm (e.g., cutting, suicide), harm to others, or those that encourage eating-disordered behavior (e.g., restrictive eating, purging, excessive exercise) should be minimized, reported, and removed; moreover, technology should not drive users to this content.
• To minimize psychological harm, adolescents’ exposure to “cyberhate” including online discrimination, prejudice, hate, or cyberbullying especially directed toward a marginalized group (e.g., racial, ethnic, gender, sexual, religious, ability status), or toward an individual because of their identity or allyship with a marginalized group should be minimized.
• Adolescents should be routinely screened for signs of “problematic social media use” that can impair their ability to engage in daily roles and routines, and may present risk for more serious psychological harms over time.
• The use of social media should be limited so as to not interfere with adolescents’ sleep and physical activity.
• Adolescents should limit use of social media for social comparison, particularly around beauty- or appearance-related content.
• Adolescents’ social media use should be preceded by training in social media literacy to ensure that users have developed psychologically-informed competencies and skills that will maximize the chances for balanced, safe, and meaningful social media use.
• Substantial resources should be provided for continued scientific examination of the positive and negative effects of social media on adolescent development.

Acknowledgments

We wish to acknowledge the outstanding contributions to this report made by the following individuals:

Expert advisory panel

Mary Ann McCabe, PhD, ABPP, member-at-large, Board of Directors, American Psychological Association; associate clinical professor of pediatrics, The George Washington University School of Medicine and Health Sciences

Mitchell J. Prinstein, PhD, ABPP, chief science officer, American Psychological Association; John Van Seters Distinguished Professor of Psychology and Neuroscience, University of North Carolina at Chapel Hill

Mary K. Alvord, PhD, founder, Alvord, Baker & Associates; board president, Resilience Across Borders; adjunct associate professor of psychiatry and behavioral sciences, The George Washington University School of Medicine and Health Sciences

Dawn T. Bounds, PhD, PMHNP-BC, FAAN, assistant professor, Sue & Bill Gross School of Nursing, University of California, Irvine

Linda Charmaraman, PhD, senior research scientist, Wellesley Centers for Women, Wellesley College

Sophia Choukas-Bradley, PhD, assistant professor, Department of Psychology, University of Pittsburgh

Dorothy L. Espelage, PhD, William C. Friday Distinguished Professor of Education, University of North Carolina at Chapel Hill

Joshua A. Goodman, PhD, assistant professor, Department of Psychology, Southern Oregon University

Jessica L. Hamilton, PhD, assistant professor, Department of Psychology, Rutgers University

Brendesha M. Tynes, PhD, Dean’s Professor of Educational Equity, University of Southern California

L. Monique Ward, PhD, professor, Department of Psychology (Developmental), University of Michigan

Lucía Magis-Weinberg, MD, PhD, assistant professor, Department of Psychology, University of Washington

We also wish to acknowledge the contributions to this report made by Katherine B. McGuire, chief advocacy officer, and Corbin Evans, JD, senior director of congressional and federal relations, American Psychological Association.

Selected references

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viii Armstrong-Carter, E., & Telzer, E. H. (2021). Advancing measurement and research on youths’ prosocial behavior in the digital age. Child Development Perspectives , 15 (1), 31–36. https://doi.org/10.1111/cdep.12396 ; Newcomb, A. F., & Bagwell, C. L. (1995). Children’s friendship relations: A meta-analytic review. Psychological Bulletin , 117 (2), 306.

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x Sherman, L. E., Payton, A. A., Hernandez, L. M., Greenfield, P. M., & Dapretto, M. (2016). The power of the Like in adolescence: Effects of peer influence on neural and behavioral responses to social media. Psychological Science , 27 (7), 1027–1035. https://doi.org/10.1177/0956797616645673

xi Susi, K., Glover-Ford, F., Stewart, A., Knowles Bevis, R., & Hawton, K. (2023). Research review: Viewing self-harm images on the internet and social media platforms: Systematic review of the impact and associated psychological mechanisms. Journal of Child Psychology and Psychiatry , 64 (8), 1115–1139.

xii Hartley, C. A., & Somerville, L. H. (2015). The neuroscience of adolescent decision-making. Current Opinion in Behavioral Sciences , 5 , 108–115. https://doi.org/10.1016/j.cobeha.2015.09.004

xiii Atherton, O. E., Lawson, K. M., & Robins, R. W. (2020). The development of effortful control from late childhood to young adulthood. Journal of Personality and Social Psychology , 119 (2), 417–456. https://doi.org/10.1037/pspp0000283

xiv Boer, M., Stevens, G. W., Finkenauer, C., & Van den Eijnden, R. J. (2022). The course of problematic social media use in young adolescents: A latent class growth analysis. Child Development , 93 (2), e168–e187.

xv Hall, A. C. G., Lineweaver, T. T., Hogan, E. E., & O’Brien, S. W. (2020). On or off task: The negative influence of laptops on neighboring students’ learning depends on how they are used. Computers & Education , 153 , 103901. https://doi.org/10.1016/j.compedu.2020.103901 ; Sana, F., Weston, T., & Cepeda, N. J. (2013). Laptop multitasking hinders classroom learning for both users and nearby peers. Computers & Education , 62 , 24–31. https://doi.org/10.1016/j.compedu.2012.10.003

xvi von Bastian, C. C., & Druey, M. D. (2017). Shifting between mental sets: An individual differences approach to commonalities and differences of task switching components. Journal of Experimental Psychology: General , 146 (9), 1266–1285. https://doi.org/10.1037/xge0000333

xvii Andrews, J. C., Walker, K. L., & Kees, J. (2020). Children and online privacy protection: Empowerment from cognitive defense strategies. Journal of Public Policy & Marketing , 39 (2), 205–219. https://doi.org/10.1177/0743915619883638 ; Romer D. (2010). Adolescent risk taking, impulsivity, and brain development: Implications for prevention. Developmental Psychobiology , 52 (3), 263–276. https://doi.org/10.1002/dev.20442

xviii Orben, A., Przybylski, A. K., Blakemore, S.-J., Kievit, R. A. (2022). Windows of developmental sensitivity to social media. Nature Communications , 13 (1649). https://doi.org/10.1038/s41467-022-29296-3

xix Paruthi, S., Brooks, L. J., D’Ambrosio, C., Hall, W. A., Kotagal, S., Lloyd, R. M., Malow, B. A., Maski, K., Nichols, C., Quan, S. F., Rosen, C. L., Troester, M. M., & Wise, M. S. (2016). Recommended amount of sleep for pediatric populations: A consensus statement of the American Academy of Sleep Medicine. Journal of Clinical Sleep Medicine , 12 (6), 785–786. https://doi.org/10.5664/jcsm.5866

xx Perrault, A. A., Bayer, L., Peuvrier, M., Afyouni, A., Ghisletta, P., Brockmann, C., Spiridon, M., Hulo Vesely, S., Haller, D. M., Pichon, S., Perrig, S., Schwartz, S., & Sterpenich, V. (2019). Reducing the use of screen electronic devices in the evening is associated with improved sleep and daytime vigilance in adolescents. Sleep , 42 (9), zsz125. https://doi.org/10.1093/sleep/zsz125 ; Telzer, E. H., Goldenberg, D., Fuligni, A. J., Lieberman, M. D., & Gálvan, A. (2015). Sleep variability in adolescence is associated with altered brain development. Developmental Cognitive Neuroscience , 14, 16–22. https://doi.org/10.1016/j.dcn.2015.05.007

xxi Livingstone, S., & Smith, P. K. (2014). Annual research review: Harms experienced by child users of online and mobile technologies: The nature, prevalence and management of sexual and aggressive risks in the digital age. Journal of Child Psychology and Psychiatry , 55 (6), 635–654. https://doi.org/10.1111/jcpp.12197 ; Wolak, J., Finkelhor, D., Mitchell, K. J., & Ybarra, M. L. (2008). Online “predators” and their victims: Myths, realities, and implications for prevention and treatment. American Psychologist , 63 (2), 111–128. https://doi.org/10.1037/0003-066X.63.2.111

xxii Wachs, S., Costello, M., Wright, M. F., Flora, K., Daskalou, V., Maziridou, E., Kwon, Y., Na, E.-Y., Sittichai, R., Biswal, R., Singh, R., Almendros, C., Gámez-Guadix, M., Görzig, A., & Hong, J. S. (2021). “DNT LET ’EM H8 U!”: Applying the routine activity framework to understand cyberhate victimization among adolescents across eight countries. Computers & Education , 160 , Article 104026. https://doi.org/10.1016/j.compedu.2020.104026 ; Padilla-Walker, L. M., Stockdale, L. A., & McLean, R. D. (2020). Associations between parental media monitoring, media use, and internalizing symptoms during adolescence. Psychology of Popular Media , 9 (4), 481. https://doi.org/10.1037/ppm0000256

xxiii Dietvorst, E., Hiemstra, M., Hillegers, M. H. J., & Keijsers, L. (2018). Adolescent perceptions of parental privacy invasion and adolescent secrecy: An illustration of Simpson’s paradox. Child Development , 89 (6), 2081–2090. https://doi.org/10.1111/cdev.13002 ; Auxier, B. (2020, July 28). Parenting Children in the Age of Screens. Pew Research Center: Internet, Science & Tech; Pew Research Center. https://www.pewresearch.org/internet/2020/07/28/parenting-children-in-the-age-of-screens/

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xxvii Charmaraman, L., Lynch, A. D., Richer, A. M., & Zhai, E. (2022). Examining early adolescent positive and negative social technology behaviors and well-being during the Covid -19 pandemic. Technology, Mind, and Behavior , 3 (1), Feb 17 2022. https://doi.org/10.1037/tmb0000062

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Myelination in the brain may be key to ‘learning’ opioid addiction

New research in mice by Stanford Medicine scientists has found that the process of adaptive myelination, which helps the brain learn new skills, can also promote addiction to opioids.

June 5, 2024 - By Nina Bai

Stanford Medicine research has found that adaptive myelination, the neuronal process by which we improve our skills, can lead to morphine addiction in mice.  Sherry Young and Alex Mit - stock.adobe.com

Our brains, even in adulthood, continually adapt to what we do, strengthening or weakening neural pathways as we practice new skills or abandon old habits. Now, research by Stanford Medicine scientists has found that a particular type of neuroplasticity, known as adaptive myelination, can also contribute to drug addiction.

In adaptive myelination, more active brain circuits gain more myelin — the fatty insulation that allows electrical signals to travel faster and more efficiently through nerve fibers. Learning to juggle or practicing the piano, for example, gradually increases myelination in the brain circuits involved, optimizing for these abilities.

But the same adaptive myelination that is essential to learning, attention and memory has a dark side. In the new study in mice, researchers found that a single dose of morphine was enough to trigger the steps leading to myelination of dopamine-producing neurons — part of the brain’s reward circuitry — spurring the mice to seek out more of the drug. When myelination was blocked, the mice made no effort to find more morphine.

The new findings , published June 5 in  Nature , show how using addictive drugs can drive maladaptive myelination of the brain’s reward circuitry, which in turn reinforces drug-seeking behavior.

Myelin matters

“Myelin development does not complete until we’re in our late 20s or early 30s, which is kind of fascinating,” said  Michelle Monje , MD, PhD, the Milan Gambhir Professor in Pediatric Neuro-Oncology and senior author of the study.

Even after such a protracted developmental period, special cells in the brain called oligodendrocytes continue to generate new myelin in some brain regions.

“What we’ve come to understand over the last decade or so is that myelin, in some parts of the nervous system, is actually plastic and adaptable to experience,” Monje said. “The activity of a neuron can regulate the extent to which its axon is myelinated.”

Michelle Monje

Research in neuroplasticity has mostly focused on changes that occur at synapses — where neurons meet and communicate with each other. Adaptive myelination adds a new layer to how our brains learn from experience.

Much of the foundational knowledge about adaptive myelination has come from Monje’s lab. In 2014, her team reported that stimulating the premotor cortex of mice increased the myelination of neurons there and improved limb movement. Subsequent studies by her lab and collaborators have found that mice need adaptive myelination for spatial learning — to navigate a maze, for example, or to remember a threatening situation.

Reward learning

In the new study, Monje’s team wondered whether adaptive myelination was involved in reward learning. The researchers generated a rewarding experience in mice by giving them cocaine or morphine, or by directly stimulating their dopamine-producing neurons using optogenetic techniques.

Within three hours of a single injection of cocaine or morphine or 30 minutes of stimulation, the researchers were surprised to see a proliferation of the specialized stem cells that are destined to become myelin-producing oligodendrocytes. The proliferation was isolated to a brain region known as the ventral tegmental area, which is involved in reward learning and addiction.

“We didn’t think one dose of morphine or cocaine would do anything,” said  Belgin Yalcin , PhD, lead author of the new study and an instructor in neurology and neurological sciences. “But within three hours there was a change. A very mild change, but still a change.”

Both the speed and specificity of the changes were unexpected, the researchers said.

When researchers repeated the drug injections or brain stimulation for several days, then examined the mice a month later, they indeed found more oligodendrocytes and more myelinated dopamine-producing cells, with thicker myelin around their axons, again only in the ventral tegmental area.

Even a slight thickening of myelin — in this case, by several hundred nanometers — can affect brain function and behavior.

“Details matter in terms of myelin plasticity,” Yalcin said. “So little can make such a big difference in conduction velocity and the synchronicity of the circuit.”

Potent rewards

To see how the myelination translated into behavior, the researchers placed each mouse in a box where it could move freely between two chambers. In one chamber, the mice received a daily injection of morphine. (The researchers decided to focus on morphine because of its relevance to the opioid epidemic.) After five days, the mice strongly preferred the chamber where they had received the drug and would linger there, hoping for another hit.

Belgin Yalcin

The morphine stimulated the mice’s reward circuitry (specifically, the dopamine-producing neurons in the ventral tegmental area), increased the myelination of these neurons and tuned their brains for further reward-seeking behavior.

Curiously, when the researchers tested a food reward instead of morphine, the mice did not develop more food-seeking behavior, perhaps because the reward was less potent, the researchers said.

“You might not want your reward circuits to be modified by everyday kinds of rewards,” Monje said.

From mice to men

“In the healthy nervous system, adaptive myelination tunes circuit dynamics in a way that supports healthy cognitive functions like learning, memory and attention,” Monje said.

But as the new study demonstrates, the process can go awry, enhancing circuits that drive unhealthy behaviors or failing to enhance circuits required for healthy brain function.

In 2022, Monje’s lab reported that adaptive myelination could explain why some epileptic seizures  worsen  over time. The experience of seizures drives more myelination of the circuits involved, allowing faster and more synchronized signaling, which become more frequent and severe seizures. Her team also has found that reduced myelin plasticity  contributes  to “chemo-fog,” the cognitive impairments that often follow cancer treatment.

In the new study, the precise biochemical steps by which a drug reward leads to myelination are not completely clear. The researchers tried bathing oligodendrocyte precursor cells in dishes of morphine or dopamine and determined that neither chemical directly causes proliferation of these cells.

“A future direction would be to understand what exactly these myelin-forming cells are responding to that comes from the activity of dopaminergic neurons,” Yalcin said.

They found that a pathway known as BDNF-TrkB signaling is part of the story. When they blocked this pathway, the mice did not generate new oligodendrocytes and did not acquire a preference for the chamber where they received the drug. 

“The mice just couldn’t learn where they received their morphine reward,” Monje said.

Ultimately, a better understanding of adaptive myelination might reveal new strategies to help people recover from opioid addiction. Perhaps the process can be reversed and an addiction unlearned.

“We don’t know whether these changes are permanent, but there’s reason to believe that they would not be,” Monje said. “We think that myelin plasticity is bidirectional — you can both increase myelination of a circuit and decrease myelination of a circuit.”

The study was supported by funding from the Gatsby Charitable Foundation, the Wu Tsai Neurosciences Institute NeuroChoice Initiative, the National Institute of Neurological Disorders and Stroke (grant R01NS092597), the NIH Director’s Pioneer Award (DP1NS111132), the National Institute for Drug Abuse (P50DA042012, T32DA035165 and K99DA056573), the National Cancer Institute (P50CA165962, R01CA258384 and U19CA264504), the Robert J. Kleberg, Jr. and Helen C. Kleberg Foundation, Cancer Grand Challenges and Cancer Research UK, a Maternal and Child Health Research Institute at Stanford University Postdoctoral Award, and a Dean’s Postdoctoral Fellowship at Stanford University.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

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LGBTQI+ People and Substance Use

• Research has found that sexual and gender minorities, including lesbian, gay, bisexual, transgender, queer, and intersex people (LGBTQI+), have higher rates of substance misuse and substance use disorders than people who identify as heterosexual. People from these groups are also more likely to enter treatment with more severe disorders.
• People in LGBTQI+ communities can face stressful situations and environments like stigma and discrimination , harassment, and traumatic experiences . Coping with these issues may raise the likelihood of a person having substance use problems.
• NIDA supports research to help identify the particular challenges that sexual and gender minority people face, to prevent or reduce substance use disorders among these groups, and to promote treatment access and better health outcomes.

Latest from NIDA

A Plan to Address Racism in Addiction Science

Find more resources on lgbtqi+ health.

• Hear the latest approaches in treatment and care from experts in the fields of HIV and SUD in this NIDA video series, “ At the Intersection .”
• See the Stigma and Discrimination Research Toolkit from the National Institute of Mental Health.

Congress Should Include Addiction Treatment Access in Telehealth Legislation

Treats act would permanently allow remote prescribing of lifesaving medication.

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The U.S. House Committee on Energy and Commerce on May 16 advanced numerous health-related bills, including legislation that would extend for two years the pandemic-era flexibilities that allow Medicare payment for telehealth services. These flexibilities, currently set to expire at the end of 2024 , have been, and will continue to be, critical for connecting Medicare beneficiaries to essential health care services.

But Congress can and should do more than ensure that health care providers are paid for telehealth services. Lawmakers also must make certain that all patients—including individuals with opioid use disorder (OUD)—have access to remote care. Enacting H.R. 5163/S. 3193, the bipartisan Telehealth Response for E-prescribing Addiction Therapy Services (TREATS) Act, would go a long way toward making sure that happens.

Over the past four years, more people than ever have accessed OUD treatment , in part because of the COVID-19 telehealth flexibilities that allowed patients across the country to remotely receive prescriptions for buprenorphine, a Food and Drug Administration-approved medication proved to reduce overdose deaths and support recovery. And for the first time, patients were able to access buprenorphine through a telephone rather than video connection, which was critical for individuals with limited access to internet service or those who struggle with technology.

Since the early months of the pandemic in 2020, health care providers have prescribed buprenorphine in thousands of audio and video calls , which no doubt has helped to save lives. For example, remote prescribing has helped to close gaps in care for communities with low treatment rates. It has expanded access for people living in remote rural areas and those lacking adequate transportation or child care. And it has increased treatment retention for veterans and reduced overdose risk and improved treatment adherence among Medicare recipients .

Health care providers have also noted that prescribing buprenorphine via telehealth has led to better patient engagement and treatment adherence and has afforded unique insights into patients’ home lives. But unless lawmakers act, this lifeline will expire at the end of this year, when the Drug Enforcement Administration and Substance Abuse and Mental Health Services Administration’s temporary rule ends.

Remote buprenorphine treatment has always been temporary, which creates uncertainty for both patients and health care providers. Patients are continually in danger of losing access to lifesaving medication , while many providers remain hesitant to invest in telehealth services. In one study, addiction providers noted that they were reluctant to conduct remote evaluations of patients because of a lack of clarity surrounding the telehealth guidelines and fears about transitioning patients back to in-person care.

The TREATS legislation would solve these problems immediately. It would guarantee remote access to buprenorphine for all patients by removing the requirement that patients see their providers in person before starting treatment.

Patients with OUD and their providers can’t afford to live with this uncertainty around remote treatment anymore. It’s time for Congress to make telehealth access to OUD treatment permanent—and help save lives.

Marcelo H. Fernández-Viña works on Pew’s substance use prevention and treatment initiative.

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